Impact of domain knowledge on blinded predictions of binding energies by alchemical free energy calculations

NASA Astrophysics Data System (ADS)

Mey, Antonia S. J. S.; Jiménez, Jordi Juárez; Michel, Julien

2018-01-01

The Drug Design Data Resource (D3R) consortium organises blinded challenges to address the latest advances in computational methods for ligand pose prediction, affinity ranking, and free energy calculations. Within the context of the second D3R Grand Challenge several blinded binding free energies predictions were made for two congeneric series of Farsenoid X Receptor (FXR) inhibitors with a semi-automated alchemical free energy calculation workflow featuring FESetup and SOMD software tools. Reasonable performance was observed in retrospective analyses of literature datasets. Nevertheless, blinded predictions on the full D3R datasets were poor due to difficulties encountered with the ranking of compounds that vary in their net-charge. Performance increased for predictions that were restricted to subsets of compounds carrying the same net-charge. Disclosure of X-ray crystallography derived binding modes maintained or improved the correlation with experiment in a subsequent rounds of predictions. The best performing protocols on D3R set1 and set2 were comparable or superior to predictions made on the basis of analysis of literature structure activity relationships (SAR)s only, and comparable or slightly inferior, to the best submissions from other groups.

Automated docking of ligands to an artificial active site: augmenting crystallographic analysis with computer modeling

NASA Astrophysics Data System (ADS)

Rosenfeld, Robin J.; Goodsell, David S.; Musah, Rabi A.; Morris, Garrett M.; Goodin, David B.; Olson, Arthur J.

2003-08-01

The W191G cavity of cytochrome c peroxidase is useful as a model system for introducing small molecule oxidation in an artificially created cavity. A set of small, cyclic, organic cations was previously shown to bind in the buried, solvent-filled pocket created by the W191G mutation. We docked these ligands and a set of non-binders in the W191G cavity using AutoDock 3.0. For the ligands, we compared docking predictions with experimentally determined binding energies and X-ray crystal structure complexes. For the ligands, predicted binding energies differed from measured values by ± 0.8 kcal/mol. For most ligands, the docking simulation clearly predicted a single binding mode that matched the crystallographic binding mode within 1.0 Å RMSD. For 2 ligands, where the docking procedure yielded an ambiguous result, solutions matching the crystallographic result could be obtained by including an additional crystallographically observed water molecule in the protein model. For the remaining 2 ligands, docking indicated multiple binding modes, consistent with the original electron density, suggesting disordered binding of these ligands. Visual inspection of the atomic affinity grid maps used in docking calculations revealed two patches of high affinity for hydrogen bond donating groups. Multiple solutions are predicted as these two sites compete for polar hydrogens in the ligand during the docking simulation. Ligands could be distinguished, to some extent, from non-binders using a combination of two trends: predicted binding energy and level of clustering. In summary, AutoDock 3.0 appears to be useful in predicting key structural and energetic features of ligand binding in the W191G cavity.

Universal binding energy relations in metallic adhesion

NASA Technical Reports Server (NTRS)

Ferrante, J.; Smith, J. R.; Rose, J. J.

1984-01-01

Rose, Smith, and Ferrante have discovered scaling relations which map the adhesive binding energy calculated by Ferrante and Smith onto a single universal binding energy curve. These binding energies are calculated for all combinations of Al(111), Zn(0001), Mg(0001), and Na(110) in contact. The scaling involves normalizing the energy by the maximum binding energy and normalizing distances by a suitable combination of Thomas-Fermi screening lengths. Rose et al. have also found that the calculated cohesive energies of K, Ba, Cu, Mo, and Sm scale by similar simple relations, suggesting the universal relation may be more general than for the simple free electron metals for which it was derived. In addition, the scaling length was defined more generally in order to relate it to measurable physical properties. Further this universality can be extended to chemisorption. A simple and yet quite accurate prediction of a zero temperature equation of state (volume as a function of pressure for metals and alloys) is presented. Thermal expansion coefficients and melting temperatures are predicted by simple, analytic expressions, and results compare favorably with experiment for a broad range of metals.

Evaluation of B3LYP, X3LYP, and M06-Class Density Functionals for Predicting the Binding Energies of Neutral, Protonated, and Deprotonated Water Clusters.

PubMed

Bryantsev, Vyacheslav S; Diallo, Mamadou S; van Duin, Adri C T; Goddard, William A

2009-04-14

In this paper we assess the accuracy of the B3LYP, X3LYP, and newly developed M06-L, M06-2X, and M06 functionals to predict the binding energies of neutral and charged water clusters including (H2O)n, n = 2-8, 20), H3O(+)(H2O)n, n = 1-6, and OH(-)(H2O)n, n = 1-6. We also compare the predicted energies of two ion hydration and neutralization reactions on the basis of the calculated binding energies. In all cases, we use as benchmarks calculated binding energies of water clusters extrapolated to the complete basis set limit of the second-order Møller-Plesset perturbation theory with the effects of higher order correlation estimated at the coupled-cluster theory with single, double, and perturbative triple excitations in the aug-cc-pVDZ basis set. We rank the accuracy of the functionals on the basis of the mean unsigned error (MUE) between calculated benchmark and density functional theory energies. The corresponding MUE (kcal/mol) for each functional is listed in parentheses. We find that M06-L (0.73) and M06 (0.84) give the most accurate binding energies using very extended basis sets such as aug-cc-pV5Z. For more affordable basis sets, the best methods for predicting the binding energies of water clusters are M06-L/aug-cc-pVTZ (1.24), B3LYP/6-311++G(2d,2p) (1.29), and M06/aug-cc-PVTZ (1.33). M06-L/aug-cc-pVTZ also gives more accurate energies for the neutralization reactions (1.38), whereas B3LYP/6-311++G(2d,2p) gives more accurate energies for the ion hydration reactions (1.69).

Exploring the free-energy landscape of carbohydrate-protein complexes: development and validation of scoring functions considering the binding-site topology

NASA Astrophysics Data System (ADS)

Eid, Sameh; Saleh, Noureldin; Zalewski, Adam; Vedani, Angelo

2014-12-01

Carbohydrates play a key role in a variety of physiological and pathological processes and, hence, represent a rich source for the development of novel therapeutic agents. Being able to predict binding mode and binding affinity is an essential, yet lacking, aspect of the structure-based design of carbohydrate-based ligands. We assembled a diverse data set comprising 273 carbohydrate-protein crystal structures with known binding affinity and evaluated the prediction accuracy of a large collection of well-established scoring and free-energy functions, as well as combinations thereof. Unfortunately, the tested functions were not capable of reproducing binding affinities in the studied complexes. To simplify the complex free-energy surface of carbohydrate-protein systems, we classified the studied proteins according to the topology and solvent exposure of the carbohydrate-binding site into five distinct categories. A free-energy model based on the proposed classification scheme reproduced binding affinities in the carbohydrate data set with an r 2 of 0.71 and root-mean-squared-error of 1.25 kcal/mol ( N = 236). The improvement in model performance underlines the significance of the differences in the local micro-environments of carbohydrate-binding sites and demonstrates the usefulness of calibrating free-energy functions individually according to binding-site topology and solvent exposure.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015.

PubMed

Deng, Nanjie; Flynn, William F; Xia, Junchao; Vijayan, R S K; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M

2016-09-01

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Large scale free energy calculations for blind predictions of protein-ligand binding: the D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Deng, Nanjie; Flynn, William F.; Xia, Junchao; Vijayan, R. S. K.; Zhang, Baofeng; He, Peng; Mentes, Ahmet; Gallicchio, Emilio; Levy, Ronald M.

2016-09-01

We describe binding free energy calculations in the D3R Grand Challenge 2015 for blind prediction of the binding affinities of 180 ligands to Hsp90. The present D3R challenge was built around experimental datasets involving Heat shock protein (Hsp) 90, an ATP-dependent molecular chaperone which is an important anticancer drug target. The Hsp90 ATP binding site is known to be a challenging target for accurate calculations of ligand binding affinities because of the ligand-dependent conformational changes in the binding site, the presence of ordered waters and the broad chemical diversity of ligands that can bind at this site. Our primary focus here is to distinguish binders from nonbinders. Large scale absolute binding free energy calculations that cover over 3000 protein-ligand complexes were performed using the BEDAM method starting from docked structures generated by Glide docking. Although the ligand dataset in this study resembles an intermediate to late stage lead optimization project while the BEDAM method is mainly developed for early stage virtual screening of hit molecules, the BEDAM binding free energy scoring has resulted in a moderate enrichment of ligand screening against this challenging drug target. Results show that, using a statistical mechanics based free energy method like BEDAM starting from docked poses offers better enrichment than classical docking scoring functions and rescoring methods like Prime MM-GBSA for the Hsp90 data set in this blind challenge. Importantly, among the three methods tested here, only the mean value of the BEDAM binding free energy scores is able to separate the large group of binders from the small group of nonbinders with a gap of 2.4 kcal/mol. None of the three methods that we have tested provided accurate ranking of the affinities of the 147 active compounds. We discuss the possible sources of errors in the binding free energy calculations. The study suggests that BEDAM can be used strategically to discriminate binders from nonbinders in virtual screening and to more accurately predict the ligand binding modes prior to the more computationally expensive FEP calculations of binding affinity.

Virtual screening using molecular simulations.

PubMed

Yang, Tianyi; Wu, Johnny C; Yan, Chunli; Wang, Yuanfeng; Luo, Ray; Gonzales, Michael B; Dalby, Kevin N; Ren, Pengyu

2011-06-01

Effective virtual screening relies on our ability to make accurate prediction of protein-ligand binding, which remains a great challenge. In this work, utilizing the molecular-mechanics Poisson-Boltzmann (or Generalized Born) surface area approach, we have evaluated the binding affinity of a set of 156 ligands to seven families of proteins, trypsin β, thrombin α, cyclin-dependent kinase (CDK), cAMP-dependent kinase (PKA), urokinase-type plasminogen activator, β-glucosidase A, and coagulation factor Xa. The effect of protein dielectric constant in the implicit-solvent model on the binding free energy calculation is shown to be important. The statistical correlations between the binding energy calculated from the implicit-solvent approach and experimental free energy are in the range of 0.56-0.79 across all the families. This performance is better than that of typical docking programs especially given that the latter is directly trained using known binding data whereas the molecular mechanics is based on general physical parameters. Estimation of entropic contribution remains the barrier to accurate free energy calculation. We show that the traditional rigid rotor harmonic oscillator approximation is unable to improve the binding free energy prediction. Inclusion of conformational restriction seems to be promising but requires further investigation. On the other hand, our preliminary study suggests that implicit-solvent based alchemical perturbation, which offers explicit sampling of configuration entropy, can be a viable approach to significantly improve the prediction of binding free energy. Overall, the molecular mechanics approach has the potential for medium to high-throughput computational drug discovery. Copyright © 2011 Wiley-Liss, Inc.

Structure-based prediction of free energy changes of binding of PTP1B inhibitors

NASA Astrophysics Data System (ADS)

Wang, Jing; Ling Chan, Shek; Ramnarayan, Kal

2003-08-01

The goals were (1) to understand the driving forces in the binding of small molecule inhibitors to the active site of PTP1B and (2) to develop a molecular mechanics-based empirical free energy function for compound potency prediction. A set of compounds with known activities was docked onto the active site. The related energy components and molecular surface areas were calculated. The bridging water molecules were identified and their contributions were considered. Linear relationships were explored between the above terms and the binding free energies of compounds derived based on experimental inhibition constants. We found that minimally three terms are required to give rise to a good correlation (0.86) with predictive power in five-group cross-validation test (q2 = 0.70). The dominant terms are the electrostatic energy and non-electrostatic energy stemming from the intra- and intermolecular interactions of solutes and from those of bridging water molecules in complexes.

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase

PubMed Central

Bai, Fang; Xu, Yechun; Chen, Jing; Liu, Qiufeng; Gu, Junfeng; Wang, Xicheng; Ma, Jianpeng; Li, Honglin; Onuchic, José N.; Jiang, Hualiang

2013-01-01

Drug-target residence time (t = 1/koff, where koff is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, koff and activation free energy of dissociation (). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer’s disease drug (−)−Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (−)−Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development. PMID:23440190

Free energy landscape for the binding process of Huperzine A to acetylcholinesterase.

PubMed

Bai, Fang; Xu, Yechun; Chen, Jing; Liu, Qiufeng; Gu, Junfeng; Wang, Xicheng; Ma, Jianpeng; Li, Honglin; Onuchic, José N; Jiang, Hualiang

2013-03-12

Drug-target residence time (t = 1/k(off), where k(off) is the dissociation rate constant) has become an important index in discovering better- or best-in-class drugs. However, little effort has been dedicated to developing computational methods that can accurately predict this kinetic parameter or related parameters, k(off) and activation free energy of dissociation (ΔG(off)≠). In this paper, energy landscape theory that has been developed to understand protein folding and function is extended to develop a generally applicable computational framework that is able to construct a complete ligand-target binding free energy landscape. This enables both the binding affinity and the binding kinetics to be accurately estimated. We applied this method to simulate the binding event of the anti-Alzheimer's disease drug (-)-Huperzine A to its target acetylcholinesterase (AChE). The computational results are in excellent agreement with our concurrent experimental measurements. All of the predicted values of binding free energy and activation free energies of association and dissociation deviate from the experimental data only by less than 1 kcal/mol. The method also provides atomic resolution information for the (-)-Huperzine A binding pathway, which may be useful in designing more potent AChE inhibitors. We expect this methodology to be widely applicable to drug discovery and development.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain.

PubMed

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or "PB/LIE" free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α 2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo . The overall performance of the model should allow its use in the design of new PDZ ligands in the future.

A Simple PB/LIE Free Energy Function Accurately Predicts the Peptide Binding Specificity of the Tiam1 PDZ Domain

PubMed Central

Panel, Nicolas; Sun, Young Joo; Fuentes, Ernesto J.; Simonson, Thomas

2017-01-01

PDZ domains generally bind short amino acid sequences at the C-terminus of target proteins, and short peptides can be used as inhibitors or model ligands. Here, we used experimental binding assays and molecular dynamics simulations to characterize 51 complexes involving the Tiam1 PDZ domain and to test the performance of a semi-empirical free energy function. The free energy function combined a Poisson-Boltzmann (PB) continuum electrostatic term, a van der Waals interaction energy, and a surface area term. Each term was empirically weighted, giving a Linear Interaction Energy or “PB/LIE” free energy. The model yielded a mean unsigned deviation of 0.43 kcal/mol and a Pearson correlation of 0.64 between experimental and computed free energies, which was superior to a Null model that assumes all complexes have the same affinity. Analyses of the models support several experimental observations that indicate the orientation of the α2 helix is a critical determinant for peptide specificity. The models were also used to predict binding free energies for nine new variants, corresponding to point mutants of the Syndecan1 and Caspr4 peptides. The predictions did not reveal improved binding; however, they suggest that an unnatural amino acid could be used to increase protease resistance and peptide lifetimes in vivo. The overall performance of the model should allow its use in the design of new PDZ ligands in the future. PMID:29018806

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

2018-01-01

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

Using the Concept of Transient Complex for Affinity Predictions in CAPRI Rounds 20–27 and Beyond

PubMed Central

Qin, Sanbo; Zhou, Huan-Xiang

2013-01-01

Predictions of protein-protein binders and binding affinities have traditionally focused on features pertaining to the native complexes. In developing a computational method for predicting protein-protein association rate constants, we introduced the concept of transient complex after mapping the interaction energy surface. The transient complex is located at the outer boundary of the bound-state energy well, having near-native separation and relative orientation between the subunits but not yet formed most of the short-range native interactions. We found that the width of the binding funnel and the electrostatic interaction energy of the transient complex are among the features predictive of binders and binding affinities. These ideas were very promising for the five affinity-related targets (T43–45, 55, and 56) of CAPRI rounds 20–27. For T43, we ranked the single crystallographic complex as number 1 and were one of only two groups that clearly identified that complex as a true binder; for T44, we ranked the only design with measurable binding affinity as number 4. For the nine docking targets, continuing on our success in previous CAPRI rounds, we produced 10 medium-quality models for T47 and acceptable models for T48 and T49. We conclude that the interaction energy landscape and the transient complex in particular will complement existing features in leading to better prediction of binding affinities. PMID:23873496

Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations.

PubMed

Cournia, Zoe; Allen, Bryce; Sherman, Woody

2017-12-26

Accurate in silico prediction of protein-ligand binding affinities has been a primary objective of structure-based drug design for decades due to the putative value it would bring to the drug discovery process. However, computational methods have historically failed to deliver value in real-world drug discovery applications due to a variety of scientific, technical, and practical challenges. Recently, a family of approaches commonly referred to as relative binding free energy (RBFE) calculations, which rely on physics-based molecular simulations and statistical mechanics, have shown promise in reliably generating accurate predictions in the context of drug discovery projects. This advance arises from accumulating developments in the underlying scientific methods (decades of research on force fields and sampling algorithms) coupled with vast increases in computational resources (graphics processing units and cloud infrastructures). Mounting evidence from retrospective validation studies, blind challenge predictions, and prospective applications suggests that RBFE simulations can now predict the affinity differences for congeneric ligands with sufficient accuracy and throughput to deliver considerable value in hit-to-lead and lead optimization efforts. Here, we present an overview of current RBFE implementations, highlighting recent advances and remaining challenges, along with examples that emphasize practical considerations for obtaining reliable RBFE results. We focus specifically on relative binding free energies because the calculations are less computationally intensive than absolute binding free energy (ABFE) calculations and map directly onto the hit-to-lead and lead optimization processes, where the prediction of relative binding energies between a reference molecule and new ideas (virtual molecules) can be used to prioritize molecules for synthesis. We describe the critical aspects of running RBFE calculations, from both theoretical and applied perspectives, using a combination of retrospective literature examples and prospective studies from drug discovery projects. This work is intended to provide a contemporary overview of the scientific, technical, and practical issues associated with running relative binding free energy simulations, with a focus on real-world drug discovery applications. We offer guidelines for improving the accuracy of RBFE simulations, especially for challenging cases, and emphasize unresolved issues that could be improved by further research in the field.

Approximations to complete basis set-extrapolated, highly correlated non-covalent interaction energies.

PubMed

Mackie, Iain D; DiLabio, Gino A

2011-10-07

The first-principles calculation of non-covalent (particularly dispersion) interactions between molecules is a considerable challenge. In this work we studied the binding energies for ten small non-covalently bonded dimers with several combinations of correlation methods (MP2, coupled-cluster single double, coupled-cluster single double (triple) (CCSD(T))), correlation-consistent basis sets (aug-cc-pVXZ, X = D, T, Q), two-point complete basis set energy extrapolations, and counterpoise corrections. For this work, complete basis set results were estimated from averaged counterpoise and non-counterpoise-corrected CCSD(T) binding energies obtained from extrapolations with aug-cc-pVQZ and aug-cc-pVTZ basis sets. It is demonstrated that, in almost all cases, binding energies converge more rapidly to the basis set limit by averaging the counterpoise and non-counterpoise corrected values than by using either counterpoise or non-counterpoise methods alone. Examination of the effect of basis set size and electron correlation shows that the triples contribution to the CCSD(T) binding energies is fairly constant with the basis set size, with a slight underestimation with CCSD(T)∕aug-cc-pVDZ compared to the value at the (estimated) complete basis set limit, and that contributions to the binding energies obtained by MP2 generally overestimate the analogous CCSD(T) contributions. Taking these factors together, we conclude that the binding energies for non-covalently bonded systems can be accurately determined using a composite method that combines CCSD(T)∕aug-cc-pVDZ with energy corrections obtained using basis set extrapolated MP2 (utilizing aug-cc-pVQZ and aug-cc-pVTZ basis sets), if all of the components are obtained by averaging the counterpoise and non-counterpoise energies. With such an approach, binding energies for the set of ten dimers are predicted with a mean absolute deviation of 0.02 kcal/mol, a maximum absolute deviation of 0.05 kcal/mol, and a mean percent absolute deviation of only 1.7%, relative to the (estimated) complete basis set CCSD(T) results. Use of this composite approach to an additional set of eight dimers gave binding energies to within 1% of previously published high-level data. It is also shown that binding within parallel and parallel-crossed conformations of naphthalene dimer is predicted by the composite approach to be 9% greater than that previously reported in the literature. The ability of some recently developed dispersion-corrected density-functional theory methods to predict the binding energies of the set of ten small dimers was also examined. © 2011 American Institute of Physics

ProBiS-CHARMMing: Web Interface for Prediction and Optimization of Ligands in Protein Binding Sites.

PubMed

Konc, Janez; Miller, Benjamin T; Štular, Tanja; Lešnik, Samo; Woodcock, H Lee; Brooks, Bernard R; Janežič, Dušanka

2015-11-23

Proteins often exist only as apo structures (unligated) in the Protein Data Bank, with their corresponding holo structures (with ligands) unavailable. However, apoproteins may not represent the amino-acid residue arrangement upon ligand binding well, which is especially problematic for molecular docking. We developed the ProBiS-CHARMMing web interface by connecting the ProBiS ( http://probis.cmm.ki.si ) and CHARMMing ( http://www.charmming.org ) web servers into one functional unit that enables prediction of protein-ligand complexes and allows for their geometry optimization and interaction energy calculation. The ProBiS web server predicts ligands (small compounds, proteins, nucleic acids, and single-atom ligands) that may bind to a query protein. This is achieved by comparing its surface structure against a nonredundant database of protein structures and finding those that have binding sites similar to that of the query protein. Existing ligands found in the similar binding sites are then transposed to the query according to predictions from ProBiS. The CHARMMing web server enables, among other things, minimization and potential energy calculation for a wide variety of biomolecular systems, and it is used here to optimize the geometry of the predicted protein-ligand complex structures using the CHARMM force field and to calculate their interaction energies with the corresponding query proteins. We show how ProBiS-CHARMMing can be used to predict ligands and their poses for a particular binding site, and minimize the predicted protein-ligand complexes to obtain representations of holoproteins. The ProBiS-CHARMMing web interface is freely available for academic users at http://probis.nih.gov.

Prediction of cyclin-dependent kinase 2 inhibitor potency using the fragment molecular orbital method

PubMed Central

2011-01-01

Background The reliable and robust estimation of ligand binding affinity continues to be a challenge in drug design. Many current methods rely on molecular mechanics (MM) calculations which do not fully explain complex molecular interactions. Full quantum mechanical (QM) computation of the electronic state of protein-ligand complexes has recently become possible by the latest advances in the development of linear-scaling QM methods such as the ab initio fragment molecular orbital (FMO) method. This approximate molecular orbital method is sufficiently fast that it can be incorporated into the development cycle during structure-based drug design for the reliable estimation of ligand binding affinity. Additionally, the FMO method can be combined with approximations for entropy and solvation to make it applicable for binding affinity prediction for a broad range of target and chemotypes. Results We applied this method to examine the binding affinity for a series of published cyclin-dependent kinase 2 (CDK2) inhibitors. We calculated the binding affinity for 28 CDK2 inhibitors using the ab initio FMO method based on a number of X-ray crystal structures. The sum of the pair interaction energies (PIE) was calculated and used to explain the gas-phase enthalpic contribution to binding. The correlation of the ligand potencies to the protein-ligand interaction energies gained from FMO was examined and was seen to give a good correlation which outperformed three MM force field based scoring functions used to appoximate the free energy of binding. Although the FMO calculation allows for the enthalpic component of binding interactions to be understood at the quantum level, as it is an in vacuo single point calculation, the entropic component and solvation terms are neglected. For this reason a more accurate and predictive estimate for binding free energy was desired. Therefore, additional terms used to describe the protein-ligand interactions were then calculated to improve the correlation of the FMO derived values to experimental free energies of binding. These terms were used to account for the polar and non-polar solvation of the molecule estimated by the Poisson-Boltzmann equation and the solvent accessible surface area (SASA), respectively, as well as a correction term for ligand entropy. A quantitative structure-activity relationship (QSAR) model obtained by Partial Least Squares projection to latent structures (PLS) analysis of the ligand potencies and the calculated terms showed a strong correlation (r2 = 0.939, q2 = 0.896) for the 14 molecule test set which had a Pearson rank order correlation of 0.97. A training set of a further 14 molecules was well predicted (r2 = 0.842), and could be used to obtain meaningful estimations of the binding free energy. Conclusions Our results show that binding energies calculated with the FMO method correlate well with published data. Analysis of the terms used to derive the FMO energies adds greater understanding to the binding interactions than can be gained by MM methods. Combining this information with additional terms and creating a scaled model to describe the data results in more accurate predictions of ligand potencies than the absolute values obtained by FMO alone. PMID:21219630

Binding Free Energy Calculations for Lead Optimization: Assessment of Their Accuracy in an Industrial Drug Design Context.

PubMed

Homeyer, Nadine; Stoll, Friederike; Hillisch, Alexander; Gohlke, Holger

2014-08-12

Correctly ranking compounds according to their computed relative binding affinities will be of great value for decision making in the lead optimization phase of industrial drug discovery. However, the performance of existing computationally demanding binding free energy calculation methods in this context is largely unknown. We analyzed the performance of the molecular mechanics continuum solvent, the linear interaction energy (LIE), and the thermodynamic integration (TI) approach for three sets of compounds from industrial lead optimization projects. The data sets pose challenges typical for this early stage of drug discovery. None of the methods was sufficiently predictive when applied out of the box without considering these challenges. Detailed investigations of failures revealed critical points that are essential for good binding free energy predictions. When data set-specific features were considered accordingly, predictions valuable for lead optimization could be obtained for all approaches but LIE. Our findings lead to clear recommendations for when to use which of the above approaches. Our findings also stress the important role of expert knowledge in this process, not least for estimating the accuracy of prediction results by TI, using indicators such as the size and chemical structure of exchanged groups and the statistical error in the predictions. Such knowledge will be invaluable when it comes to the question which of the TI results can be trusted for decision making.

A Critical Review of Validation, Blind Testing, and Real- World Use of Alchemical Protein-Ligand Binding Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Mobley, David L; Friesner, Richard A

2017-01-01

Protein-ligand binding is among the most fundamental phenomena underlying all molecular biology, and a greater ability to more accurately and robustly predict the binding free energy of a small molecule ligand for its cognate protein is expected to have vast consequences for improving the efficiency of pharmaceutical drug discovery. We briefly reviewed a number of scientific and technical advances that have enabled alchemical free energy calculations to recently emerge as a preferred approach, and critically considered proper validation and effective use of these techniques. In particular, we characterized a selection bias effect which may be important in prospective free energy calculations, and introduced a strategy to improve the accuracy of the free energy predictions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Virtual screening of integrase inhibitors by large scale binding free energy calculations: the SAMPL4 challenge

PubMed Central

Gallicchio, Emilio; Deng, Nanjie; He, Peng; Wickstrom, Lauren; Perryman, Alexander L.; Santiago, Daniel N.; Forli, Stefano; Olson, Arthur J.; Levy, Ronald M.

2014-01-01

As part of the SAMPL4 blind challenge, filtered AutoDock Vina ligand docking predictions and large scale binding energy distribution analysis method binding free energy calculations have been applied to the virtual screening of a focused library of candidate binders to the LEDGF site of the HIV integrase protein. The computational protocol leveraged docking and high level atomistic models to improve enrichment. The enrichment factor of our blind predictions ranked best among all of the computational submissions, and second best overall. This work represents to our knowledge the first example of the application of an all-atom physics-based binding free energy model to large scale virtual screening. A total of 285 parallel Hamiltonian replica exchange molecular dynamics absolute protein-ligand binding free energy simulations were conducted starting from docked poses. The setup of the simulations was fully automated, calculations were distributed on multiple computing resources and were completed in a 6-weeks period. The accuracy of the docked poses and the inclusion of intramolecular strain and entropic losses in the binding free energy estimates were the major factors behind the success of the method. Lack of sufficient time and computing resources to investigate additional protonation states of the ligands was a major cause of mispredictions. The experiment demonstrated the applicability of binding free energy modeling to improve hit rates in challenging virtual screening of focused ligand libraries during lead optimization. PMID:24504704

Binding Modes of Ligands Using Enhanced Sampling (BLUES): Rapid Decorrelation of Ligand Binding Modes via Nonequilibrium Candidate Monte Carlo.

PubMed

Gill, Samuel C; Lim, Nathan M; Grinaway, Patrick B; Rustenburg, Ariën S; Fass, Josh; Ross, Gregory A; Chodera, John D; Mobley, David L

2018-05-31

Accurately predicting protein-ligand binding affinities and binding modes is a major goal in computational chemistry, but even the prediction of ligand binding modes in proteins poses major challenges. Here, we focus on solving the binding mode prediction problem for rigid fragments. That is, we focus on computing the dominant placement, conformation, and orientations of a relatively rigid, fragment-like ligand in a receptor, and the populations of the multiple binding modes which may be relevant. This problem is important in its own right, but is even more timely given the recent success of alchemical free energy calculations. Alchemical calculations are increasingly used to predict binding free energies of ligands to receptors. However, the accuracy of these calculations is dependent on proper sampling of the relevant ligand binding modes. Unfortunately, ligand binding modes may often be uncertain, hard to predict, and/or slow to interconvert on simulation time scales, so proper sampling with current techniques can require prohibitively long simulations. We need new methods which dramatically improve sampling of ligand binding modes. Here, we develop and apply a nonequilibrium candidate Monte Carlo (NCMC) method to improve sampling of ligand binding modes. In this technique, the ligand is rotated and subsequently allowed to relax in its new position through alchemical perturbation before accepting or rejecting the rotation and relaxation as a nonequilibrium Monte Carlo move. When applied to a T4 lysozyme model binding system, this NCMC method shows over 2 orders of magnitude improvement in binding mode sampling efficiency compared to a brute force molecular dynamics simulation. This is a first step toward applying this methodology to pharmaceutically relevant binding of fragments and, eventually, drug-like molecules. We are making this approach available via our new Binding modes of ligands using enhanced sampling (BLUES) package which is freely available on GitHub.

Computational design of enzyme-ligand binding using a combined energy function and deterministic sequence optimization algorithm.

PubMed

Tian, Ye; Huang, Xiaoqiang; Zhu, Yushan

2015-08-01

Enzyme amino-acid sequences at ligand-binding interfaces are evolutionarily optimized for reactions, and the natural conformation of an enzyme-ligand complex must have a low free energy relative to alternative conformations in native-like or non-native sequences. Based on this assumption, a combined energy function was developed for enzyme design and then evaluated by recapitulating native enzyme sequences at ligand-binding interfaces for 10 enzyme-ligand complexes. In this energy function, the electrostatic interaction between polar or charged atoms at buried interfaces is described by an explicitly orientation-dependent hydrogen-bonding potential and a pairwise-decomposable generalized Born model based on the general side chain in the protein design framework. The energy function is augmented with a pairwise surface-area based hydrophobic contribution for nonpolar atom burial. Using this function, on average, 78% of the amino acids at ligand-binding sites were predicted correctly in the minimum-energy sequences, whereas 84% were predicted correctly in the most-similar sequences, which were selected from the top 20 sequences for each enzyme-ligand complex. Hydrogen bonds at the enzyme-ligand binding interfaces in the 10 complexes were usually recovered with the correct geometries. The binding energies calculated using the combined energy function helped to discriminate the active sequences from a pool of alternative sequences that were generated by repeatedly solving a series of mixed-integer linear programming problems for sequence selection with increasing integer cuts.

Binding mode prediction and MD/MMPBSA-based free energy ranking for agonists of REV-ERBα/NCoR.

PubMed

Westermaier, Yvonne; Ruiz-Carmona, Sergio; Theret, Isabelle; Perron-Sierra, Françoise; Poissonnet, Guillaume; Dacquet, Catherine; Boutin, Jean A; Ducrot, Pierre; Barril, Xavier

2017-08-01

The knowledge of the free energy of binding of small molecules to a macromolecular target is crucial in drug design as is the ability to predict the functional consequences of binding. We highlight how a molecular dynamics (MD)-based approach can be used to predict the free energy of small molecules, and to provide priorities for the synthesis and the validation via in vitro tests. Here, we study the dynamics and energetics of the nuclear receptor REV-ERBα with its co-repressor NCoR and 35 novel agonists. Our in silico approach combines molecular docking, molecular dynamics (MD), solvent-accessible surface area (SASA) and molecular mechanics poisson boltzmann surface area (MMPBSA) calculations. While docking yielded initial hints on the binding modes, their stability was assessed by MD. The SASA calculations revealed that the presence of the ligand led to a higher exposure of hydrophobic REV-ERB residues for NCoR recruitment. MMPBSA was very successful in ranking ligands by potency in a retrospective and prospective manner. Particularly, the prospective MMPBSA ranking-based validations for four compounds, three predicted to be active and one weakly active, were confirmed experimentally.

Effective Mass Theory of 2D Excitons Revisited

NASA Astrophysics Data System (ADS)

Gonzalez, Joseph; Oleynik, Ivan

Two-dimensional (2D) semiconducting materials possess an exceptionally unique set of electronic and excitonic properties due to the combined effects of quantum and dielectric confinement. Reliable determination of exciton binding energies from both first-principles many-body perturbation theory (GW/BSE) and experiment is very challenging due to the enormous computational expense as well as the tremendous technical difficulties in experiment.. Very recently, effective mass theories of 2D excitons have been developed as an attractive alternative for inexpensive and accurate evaluation of the exciton binding energies. In this presentation, we evaluate two effective mass theory approaches by Velizhanin et al and Olsen et al in predicting exciton binding energies across a wide range of 2D materials. We specifically analyze the trends related to the varying screening lengths and exciton effective masses. We also extended the effective mass theory of 2D excitons to include effects of electron and hole mass anisotropies (mx ≠ my) , the latter showing a substantial influence on exciton binding energies. The recent predictions of exciton binding energies being independent of the exciton effective mass and a linear correlation with the band gap of a specific material are also critically reexamined.

The positive binding energy envelopes of low-mass helium stars

NASA Astrophysics Data System (ADS)

Hall, Philip D.; Jeffery, C. Simon

2018-04-01

It has been hypothesized that stellar envelopes with positive binding energy may be ejected if the release of recombination energy can be triggered and the calculation of binding energy includes this contribution. The implications of this hypothesis for the evolution of normal hydrogen-rich stars have been investigated, but the implications for helium stars - which may represent mass-transfer or merger remnants in binary star systems - have not. Making a set of model helium stars, we find that those with masses between 0.9 and 2.4 M⊙ evolve to configurations with positive binding energy envelopes. We discuss consequences of the ejection hypothesis for such stars, and possible observational tests of these predictions.

Experimentally Determined Binding Energies of Astrophysically Relevant Hydrocarbons in Pure and H2O-Layered Ices

NASA Astrophysics Data System (ADS)

Behmard, Aida; Graninger, Dawn; Fayolle, Edith; Oberg, Karin I.

2017-01-01

Small hydrocarbons represent an important organic reservoir in a variety of interstellar environments. Constraints on desorption temperatures and binding energies of hydrocarbons are thus necessary for accurate predictions of where and in which phase these molecules exist. Through a series of temperature programmed desorption experiments, we determined binding energies of 1, 2, and 3-carbon interstellar hydrocarbons (CH4, C2H2, C2H4, C2H6, C3H4, C3H6, and C3H8) in pure ices and in relation to water ice, the dominant ice constituent during star and planet formation. These empirically determined values can be used to inform observations and models of the molecular spatial distribution in protoplanetary disks, thus providing insight into planetesimal composition. In addition, knowledge of hydrocarbon binding energies will refine simulations of grain surface chemistry, allowing for better predictions of the chemical conditions that lead to the production of complex organic molecules vital for life.

Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules.

PubMed

Haider, Kamran; Huggins, David J

2013-10-28

Intermolecular interactions in the aqueous phase must compete with the interactions between the two binding partners and their solvating water molecules. In biological systems, water molecules in protein binding sites cluster at well-defined hydration sites and can form strong hydrogen-bonding interactions with backbone and side-chain atoms. Displacement of such water molecules is only favorable when the ligand can form strong compensating hydrogen bonds. Conversely, water molecules in hydrophobic regions of protein binding sites make only weak interactions, and the requirements for favorable displacement are less stringent. The propensity of water molecules for displacement can be identified using inhomogeneous fluid solvation theory (IFST), a statistical mechanical method that decomposes the solvation free energy of a solute into the contributions from different spatial regions and identifies potential binding hotspots. In this study, we employed IFST to study the displacement of water molecules from the ATP binding site of Hsp90, using a test set of 103 ligands. The predicted contribution of a hydration site to the hydration free energy was found to correlate well with the observed displacement. Additionally, we investigated if this correlation could be improved by using the energetic scores of favorable probe groups binding at the location of hydration sites, derived from a multiple copy simultaneous search (MCSS) method. The probe binding scores were not highly predictive of the observed displacement and did not improve the predictivity when used in combination with IFST-based hydration free energies. The results show that IFST alone can be used to reliably predict the observed displacement of water molecules in Hsp90. However, MCSS can augment IFST calculations by suggesting which functional groups should be used to replace highly displaceable water molecules. Such an approach could be very useful in improving the hit-to-lead process for new drug targets.

Predicting bioactive conformations and binding modes of macrocycles

NASA Astrophysics Data System (ADS)

Anighoro, Andrew; de la Vega de León, Antonio; Bajorath, Jürgen

2016-10-01

Macrocyclic compounds experience increasing interest in drug discovery. It is often thought that these large and chemically complex molecules provide promising candidates to address difficult targets and interfere with protein-protein interactions. From a computational viewpoint, these molecules are difficult to treat. For example, flexible docking of macrocyclic compounds is hindered by the limited ability of current docking approaches to optimize conformations of extended ring systems for pose prediction. Herein, we report predictions of bioactive conformations of macrocycles using conformational search and binding modes using docking. Conformational ensembles generated using specialized search technique of about 70 % of the tested macrocycles contained accurate bioactive conformations. However, these conformations were difficult to identify on the basis of conformational energies. Moreover, docking calculations with limited ligand flexibility starting from individual low energy conformations rarely yielded highly accurate binding modes. In about 40 % of the test cases, binding modes were approximated with reasonable accuracy. However, when conformational ensembles were subjected to rigid body docking, an increase in meaningful binding mode predictions to more than 50 % of the test cases was observed. Electrostatic effects did not contribute to these predictions in a positive or negative manner. Rather, achieving shape complementarity at macrocycle-target interfaces was a decisive factor. In summary, a combined computational protocol using pre-computed conformational ensembles of macrocycles as a starting point for docking shows promise in modeling binding modes of macrocyclic compounds.

Fragmentation cross sections and binding energies of neutron-rich nuclei

NASA Astrophysics Data System (ADS)

Tsang, M. B.; Lynch, W. G.; Friedman, W. A.; Mocko, M.; Sun, Z. Y.; Aoi, N.; Cook, J. M.; Delaunay, F.; Famiano, M. A.; Hui, H.; Imai, N.; Iwasaki, H.; Motobayashi, T.; Niikura, M.; Onishi, T.; Rogers, A. M.; Sakurai, H.; Suzuki, H.; Takeshita, E.; Takeuchi, S.; Wallace, M. S.

2007-10-01

An exponential dependence of the fragmentation cross section on the average binding energy is observed and reproduced with a statistical model. The observed functional dependence is robust and allows the extraction of binding energies from measured cross sections. From the systematics of Cu isotope cross sections, the binding energies of Cu76,77,78,79 have been extracted. They are 636.94±0.4,647.1±0.4,651.6±0.4, and 657.8±0.5 MeV, respectively. Specifically, the uncertainty of the binding energy of Cu75 is reduced from 980 keV, as listed in the 2003 mass table of Audi, Wapstra, and Thibault to 400 keV. The predicted cross sections of two near drip-line nuclei, Na39 and Mg40 from the fragmentation of Ca48 are discussed.

Cost Function Network-based Design of Protein-Protein Interactions: predicting changes in binding affinity.

PubMed

Viricel, Clément; de Givry, Simon; Schiex, Thomas; Barbe, Sophie

2018-02-20

Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributions. Following the recent progresses of Artificial Intelligence-based algorithms for guaranteed NP-hard energy optimization and partition function computation, it becomes possible to quickly compute minimum energy conformations and to reliably estimate the entropic contribution of side-chains in the change of free energy of large protein interfaces. Using guaranteed Cost Function Network algorithms, Rosetta energy functions and Dunbrack's rotamer library, we developed and assessed EasyE and JayZ, two methods for binding affinity estimation that ignore or include conformational entropic contributions on a large benchmark of binding affinity experimental measures. If both approaches outperform most established tools, we observe that side-chain conformational entropy brings little or no improvement on most systems but becomes crucial in some rare cases. as open-source Python/C ++ code at sourcesup.renater.fr/projects/easy-jayz. thomas.schiex@inra.fr and sophie.barbe@insa-toulouse.fr. Supplementary data are available at Bioinformatics online.

Theoretical study of the rhenium–alkane interaction in transition metal–alkane σ-complexes

PubMed Central

Cobar, Erika A.; Khaliullin, Rustam Z.; Bergman, Robert G.; Head-Gordon, Martin

2007-01-01

Metal–alkane binding energies have been calculated for [CpRe(CO)2](alkane) and [(CO)2M(C5H4)CC(C5H4)M(CO)2](alkane), where M = Re or Mn. Calculated binding energies were found to increase with the number of metal–alkane interaction sites. In all cases examined, the manganese–alkane binding energies were predicted to be significantly lower than those for the analogous rhenium–alkane complexes. The metal (Mn or Re)–alkane interaction was predicted to be primarily one of charge transfer, both from the alkane to the metal complex (70–80% of total charge transfer) and from the metal complex to the alkane (20–30% of the total charge transfer). PMID:17442751

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition

PubMed Central

Kulp, John L.; Cloudsdale, Ian S.; Kulp, John L.

2017-01-01

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition. PMID:28837642

Hot-spot identification on a broad class of proteins and RNA suggest unifying principles of molecular recognition.

PubMed

Kulp, John L; Cloudsdale, Ian S; Kulp, John L; Guarnieri, Frank

2017-01-01

Chemically diverse fragments tend to collectively bind at localized sites on proteins, which is a cornerstone of fragment-based techniques. A central question is how general are these strategies for predicting a wide variety of molecular interactions such as small molecule-protein, protein-protein and protein-nucleic acid for both experimental and computational methods. To address this issue, we recently proposed three governing principles, (1) accurate prediction of fragment-macromolecule binding free energy, (2) accurate prediction of water-macromolecule binding free energy, and (3) locating sites on a macromolecule that have high affinity for a diversity of fragments and low affinity for water. To test the generality of these concepts we used the computational technique of Simulated Annealing of Chemical Potential to design one small fragment to break the RecA-RecA protein-protein interaction and three fragments that inhibit peptide-deformylase via water-mediated multi-body interactions. Experiments confirm the predictions that 6-hydroxydopamine potently inhibits RecA and that PDF inhibition quantitatively tracks the water-mediated binding predictions. Additionally, the principles correctly predict the essential bound waters in HIV Protease, the surprisingly extensive binding site of elastase, the pinpoint location of electron transfer in dihydrofolate reductase, the HIV TAT-TAR protein-RNA interactions, and the MDM2-MDM4 differential binding to p53. The experimental confirmations of highly non-obvious predictions combined with the precise characterization of a broad range of known phenomena lend strong support to the generality of fragment-based methods for characterizing molecular recognition.

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

NASA Astrophysics Data System (ADS)

Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

2013-12-01

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with R osetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several R osetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Mechanical work makes important contributions to surface chemistry at steps.

PubMed

Francis, M F; Curtin, W A

2015-02-13

The effect of mechanical strain on the binding energy of adsorbates to late transition metals is believed to be entirely controlled by electronic factors, with tensile stress inducing stronger binding. Here we show, via computation, that mechanical strain of late transition metals can modify binding at stepped surfaces opposite to well-established trends on flat surfaces. The mechanism driving the trend is mechanical, arising from the relaxation of stored mechanical energy. The mechanical energy change can be larger than, and of opposite sign than, the energy changes due to electronic effects and leads to a violation of trends predicted by the widely accepted electronic 'd-band' model. This trend has a direct impact on catalytic activity, which is demonstrated here for methanation, where biaxial tension is predicted to shift the activity of nickel significantly, reaching the peak of the volcano plot and comparable to cobalt and ruthenium.

CaFE: a tool for binding affinity prediction using end-point free energy methods.

PubMed

Liu, Hui; Hou, Tingjun

2016-07-15

Accurate prediction of binding free energy is of particular importance to computational biology and structure-based drug design. Among those methods for binding affinity predictions, the end-point approaches, such as MM/PBSA and LIE, have been widely used because they can achieve a good balance between prediction accuracy and computational cost. Here we present an easy-to-use pipeline tool named Calculation of Free Energy (CaFE) to conduct MM/PBSA and LIE calculations. Powered by the VMD and NAMD programs, CaFE is able to handle numerous static coordinate and molecular dynamics trajectory file formats generated by different molecular simulation packages and supports various force field parameters. CaFE source code and documentation are freely available under the GNU General Public License via GitHub at https://github.com/huiliucode/cafe_plugin It is a VMD plugin written in Tcl and the usage is platform-independent. tingjunhou@zju.edu.cn. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP.

PubMed

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+

NASA Astrophysics Data System (ADS)

Schindler, Christina; Rippmann, Friedrich; Kuhn, Daniel

2018-01-01

Physics-based free energy simulations have increasingly become an important tool for predicting binding affinity and the recent introduction of automated protocols has also paved the way towards a more widespread use in the pharmaceutical industry. The D3R 2016 Grand Challenge 2 provided an opportunity to blindly test the commercial free energy calculation protocol FEP+ and assess its performance relative to other affinity prediction methods. The present D3R free energy prediction challenge was built around two experimental data sets involving inhibitors of farnesoid X receptor (FXR) which is a promising anticancer drug target. The FXR binding site is predominantly hydrophobic with few conserved interaction motifs and strong induced fit effects making it a challenging target for molecular modeling and drug design. For both data sets, we achieved reasonable prediction accuracy (RMSD ≈ 1.4 kcal/mol, rank 3-4 according to RMSD out of 20 submissions) comparable to that of state-of-the-art methods in the field. Our D3R results boosted our confidence in the method and strengthen our desire to expand its applications in future in-house drug design projects.

Rapid and Reliable Binding Affinity Prediction of Bromodomain Inhibitors: A Computational Study

PubMed Central

2016-01-01

Binding free energies of bromodomain inhibitors are calculated with recently formulated approaches, namely ESMACS (enhanced sampling of molecular dynamics with approximation of continuum solvent) and TIES (thermodynamic integration with enhanced sampling). A set of compounds is provided by GlaxoSmithKline, which represents a range of chemical functionality and binding affinities. The predicted binding free energies exhibit a good Spearman correlation of 0.78 with the experimental data from the 3-trajectory ESMACS, and an excellent correlation of 0.92 from the TIES approach where applicable. Given access to suitable high end computing resources and a high degree of automation, we can compute individual binding affinities in a few hours with precisions no greater than 0.2 kcal/mol for TIES, and no larger than 0.34 and 1.71 kcal/mol for the 1- and 3-trajectory ESMACS approaches. PMID:28005370

Fragmentation cross sections and binding energies of neutron-rich nuclei

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsang, M. B.; Lynch, W. G.; Mocko, M.

An exponential dependence of the fragmentation cross section on the average binding energy is observed and reproduced with a statistical model. The observed functional dependence is robust and allows the extraction of binding energies from measured cross sections. From the systematics of Cu isotope cross sections, the binding energies of {sup 76,77,78,79}Cu have been extracted. They are 636.94{+-}0.4,647.1{+-}0.4,651.6{+-}0.4, and 657.8{+-}0.5 MeV, respectively. Specifically, the uncertainty of the binding energy of {sup 75}Cu is reduced from 980 keV, as listed in the 2003 mass table of Audi, Wapstra, and Thibault to 400 keV. The predicted cross sections of two near drip-linemore » nuclei, {sup 39}Na and {sup 40}Mg from the fragmentation of {sup 48}Ca are discussed.« less

Understanding the physical basis of the salt dependence of the electrostatic binding free energy of mutated charged ligand-nucleic acid complexes.

PubMed

Harris, Robert C; Bredenberg, Johan H; Silalahi, Alexander R J; Boschitsch, Alexander H; Fenley, Marcia O

2011-06-01

The predictions of the derivative of the electrostatic binding free energy of a biomolecular complex, ΔG(el), with respect to the logarithm of the 1:1 salt concentration, d(ΔG(el))/d(ln[NaCl]), SK, by the Poisson-Boltzmann equation, PBE, are very similar to those of the simpler Debye-Hückel equation, DHE, because the terms in the PBE's predictions of SK that depend on the details of the dielectric interface are small compared to the contributions from long-range electrostatic interactions. These facts allow one to obtain predictions of SK using a simplified charge model along with the DHE that are highly correlated with both the PBE and experimental binding data. The DHE-based model developed here, which was derived from the generalized Born model, explains the lack of correlation between SK and ΔG(el) in the presence of a dielectric discontinuity, which conflicts with the popular use of this supposed correlation to parse experimental binding free energies into electrostatic and nonelectrostatic components. Moreover, the DHE model also provides a clear justification for the correlations between SK and various empirical quantities, like the number of ion pairs, the ligand charge on the interface, the Coulomb binding free energy, and the product of the charges on the complex's components, but these correlations are weak, questioning their usefulness. Copyright © 2011 Elsevier B.V. All rights reserved.

Electrostatics, structure prediction, and the energy landscapes for protein folding and binding.

PubMed

Tsai, Min-Yeh; Zheng, Weihua; Balamurugan, D; Schafer, Nicholas P; Kim, Bobby L; Cheung, Margaret S; Wolynes, Peter G

2016-01-01

While being long in range and therefore weakly specific, electrostatic interactions are able to modulate the stability and folding landscapes of some proteins. The relevance of electrostatic forces for steering the docking of proteins to each other is widely acknowledged, however, the role of electrostatics in establishing specifically funneled landscapes and their relevance for protein structure prediction are still not clear. By introducing Debye-Hückel potentials that mimic long-range electrostatic forces into the Associative memory, Water mediated, Structure, and Energy Model (AWSEM), a transferable protein model capable of predicting tertiary structures, we assess the effects of electrostatics on the landscapes of thirteen monomeric proteins and four dimers. For the monomers, we find that adding electrostatic interactions does not improve structure prediction. Simulations of ribosomal protein S6 show, however, that folding stability depends monotonically on electrostatic strength. The trend in predicted melting temperatures of the S6 variants agrees with experimental observations. Electrostatic effects can play a range of roles in binding. The binding of the protein complex KIX-pKID is largely assisted by electrostatic interactions, which provide direct charge-charge stabilization of the native state and contribute to the funneling of the binding landscape. In contrast, for several other proteins, including the DNA-binding protein FIS, electrostatics causes frustration in the DNA-binding region, which favors its binding with DNA but not with its protein partner. This study highlights the importance of long-range electrostatics in functional responses to problems where proteins interact with their charged partners, such as DNA, RNA, as well as membranes. © 2015 The Protein Society.

Post processing of protein-compound docking for fragment-based drug discovery (FBDD): in-silico structure-based drug screening and ligand-binding pose prediction.

PubMed

Fukunishi, Yoshifumi

2010-01-01

For fragment-based drug development, both hit (active) compound prediction and docking-pose (protein-ligand complex structure) prediction of the hit compound are important, since chemical modification (fragment linking, fragment evolution) subsequent to the hit discovery must be performed based on the protein-ligand complex structure. However, the naïve protein-compound docking calculation shows poor accuracy in terms of docking-pose prediction. Thus, post-processing of the protein-compound docking is necessary. Recently, several methods for the post-processing of protein-compound docking have been proposed. In FBDD, the compounds are smaller than those for conventional drug screening. This makes it difficult to perform the protein-compound docking calculation. A method to avoid this problem has been reported. Protein-ligand binding free energy estimation is useful to reduce the procedures involved in the chemical modification of the hit fragment. Several prediction methods have been proposed for high-accuracy estimation of protein-ligand binding free energy. This paper summarizes the various computational methods proposed for docking-pose prediction and their usefulness in FBDD.

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations

NASA Astrophysics Data System (ADS)

Misini Ignjatović, Majda; Caldararu, Octav; Dong, Geng; Muñoz-Gutierrez, Camila; Adasme-Carreño, Francisco; Ryde, Ulf

2016-09-01

We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.

Binding-affinity predictions of HSP90 in the D3R Grand Challenge 2015 with docking, MM/GBSA, QM/MM, and free-energy simulations.

PubMed

Misini Ignjatović, Majda; Caldararu, Octav; Dong, Geng; Muñoz-Gutierrez, Camila; Adasme-Carreño, Francisco; Ryde, Ulf

2016-09-01

We have estimated the binding affinity of three sets of ligands of the heat-shock protein 90 in the D3R grand challenge blind test competition. We have employed four different methods, based on five different crystal structures: first, we docked the ligands to the proteins with induced-fit docking with the Glide software and calculated binding affinities with three energy functions. Second, the docked structures were minimised in a continuum solvent and binding affinities were calculated with the MM/GBSA method (molecular mechanics combined with generalised Born and solvent-accessible surface area solvation). Third, the docked structures were re-optimised by combined quantum mechanics and molecular mechanics (QM/MM) calculations. Then, interaction energies were calculated with quantum mechanical calculations employing 970-1160 atoms in a continuum solvent, combined with energy corrections for dispersion, zero-point energy and entropy, ligand distortion, ligand solvation, and an increase of the basis set to quadruple-zeta quality. Fourth, relative binding affinities were estimated by free-energy simulations, using the multi-state Bennett acceptance-ratio approach. Unfortunately, the results were varying and rather poor, with only one calculation giving a correlation to the experimental affinities larger than 0.7, and with no consistent difference in the quality of the predictions from the various methods. For one set of ligands, the results could be strongly improved (after experimental data were revealed) if it was recognised that one of the ligands displaced one or two water molecules. For the other two sets, the problem is probably that the ligands bind in different modes than in the crystal structures employed or that the conformation of the ligand-binding site or the whole protein changes.

BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

PubMed

Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

2016-10-01

Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Calculation of Host-Guest Binding Affinities Using a Quantum-Mechanical Energy Model.

PubMed

Muddana, Hari S; Gilson, Michael K

2012-06-12

The prediction of protein-ligand binding affinities is of central interest in computer-aided drug discovery, but it is still difficult to achieve a high degree of accuracy. Recent studies suggesting that available force fields may be a key source of error motivate the present study, which reports the first mining minima (M2) binding affinity calculations based on a quantum mechanical energy model, rather than an empirical force field. We apply a semi-empirical quantum-mechanical energy function, PM6-DH+, coupled with the COSMO solvation model, to 29 host-guest systems with a wide range of measured binding affinities. After correction for a systematic error, which appears to derive from the treatment of polar solvation, the computed absolute binding affinities agree well with experimental measurements, with a mean error 1.6 kcal/mol and a correlation coefficient of 0.91. These calculations also delineate the contributions of various energy components, including solute energy, configurational entropy, and solvation free energy, to the binding free energies of these host-guest complexes. Comparison with our previous calculations, which used empirical force fields, point to significant differences in both the energetic and entropic components of the binding free energy. The present study demonstrates successful combination of a quantum mechanical Hamiltonian with the M2 affinity method.

Prediction of FAD binding sites in electron transport proteins according to efficient radial basis function networks and significant amino acid pairs.

PubMed

Le, Nguyen-Quoc-Khanh; Ou, Yu-Yen

2016-07-30

Cellular respiration is a catabolic pathway for producing adenosine triphosphate (ATP) and is the most efficient process through which cells harvest energy from consumed food. When cells undergo cellular respiration, they require a pathway to keep and transfer electrons (i.e., the electron transport chain). Due to oxidation-reduction reactions, the electron transport chain produces a transmembrane proton electrochemical gradient. In case protons flow back through this membrane, this mechanical energy is converted into chemical energy by ATP synthase. The convert process is involved in producing ATP which provides energy in a lot of cellular processes. In the electron transport chain process, flavin adenine dinucleotide (FAD) is one of the most vital molecules for carrying and transferring electrons. Therefore, predicting FAD binding sites in the electron transport chain is vital for helping biologists understand the electron transport chain process and energy production in cells. We used an independent data set to evaluate the performance of the proposed method, which had an accuracy of 69.84 %. We compared the performance of the proposed method in analyzing two newly discovered electron transport protein sequences with that of the general FAD binding predictor presented by Mishra and Raghava and determined that the accuracy of the proposed method improved by 9-45 % and its Matthew's correlation coefficient was 0.14-0.5. Furthermore, the proposed method enabled reducing the number of false positives significantly and can provide useful information for biologists. We developed a method that is based on PSSM profiles and SAAPs for identifying FAD binding sites in newly discovered electron transport protein sequences. This approach achieved a significant improvement after we added SAAPs to PSSM features to analyze FAD binding proteins in the electron transport chain. The proposed method can serve as an effective tool for predicting FAD binding sites in electron transport proteins and can help biologists understand the functions of the electron transport chain, particularly those of FAD binding sites. We also developed a web server which identifies FAD binding sites in electron transporters available for academics.

Comparing alchemical and physical pathway methods for computing the absolute binding free energy of charged ligands.

PubMed

Deng, Nanjie; Cui, Di; Zhang, Bin W; Xia, Junchao; Cruz, Jeffrey; Levy, Ronald

2018-06-13

Accurately predicting absolute binding free energies of protein-ligand complexes is important as a fundamental problem in both computational biophysics and pharmaceutical discovery. Calculating binding free energies for charged ligands is generally considered to be challenging because of the strong electrostatic interactions between the ligand and its environment in aqueous solution. In this work, we compare the performance of the potential of mean force (PMF) method and the double decoupling method (DDM) for computing absolute binding free energies for charged ligands. We first clarify an unresolved issue concerning the explicit use of the binding site volume to define the complexed state in DDM together with the use of harmonic restraints. We also provide an alternative derivation for the formula for absolute binding free energy using the PMF approach. We use these formulas to compute the binding free energy of charged ligands at an allosteric site of HIV-1 integrase, which has emerged in recent years as a promising target for developing antiviral therapy. As compared with the experimental results, the absolute binding free energies obtained by using the PMF approach show unsigned errors of 1.5-3.4 kcal mol-1, which are somewhat better than the results from DDM (unsigned errors of 1.6-4.3 kcal mol-1) using the same amount of CPU time. According to the DDM decomposition of the binding free energy, the ligand binding appears to be dominated by nonpolar interactions despite the presence of very large and favorable intermolecular ligand-receptor electrostatic interactions, which are almost completely cancelled out by the equally large free energy cost of desolvation of the charged moiety of the ligands in solution. We discuss the relative strengths of computing absolute binding free energies using the alchemical and physical pathway methods.

Prediction of kinase-inhibitor binding affinity using energetic parameters

PubMed Central

Usha, Singaravelu; Selvaraj, Samuel

2016-01-01

The combination of physicochemical properties and energetic parameters derived from protein-ligand complexes play a vital role in determining the biological activity of a molecule. In the present work, protein-ligand interaction energy along with logP values was used to predict the experimental log (IC50) values of 25 different kinase-inhibitors using multiple regressions which gave a correlation coefficient of 0.93. The regression equation obtained was tested on 93 kinase-inhibitor complexes and an average deviation of 0.92 from the experimental log IC50 values was shown. The same set of descriptors was used to predict binding affinities for a test set of five individual kinase families, with correlation values > 0.9. We show that the protein-ligand interaction energies and partition coefficient values form the major deterministic factors for binding affinity of the ligand for its receptor. PMID:28149052

Comparison of the bonding between ML(+) and ML2(+) (M = metal, L = noble gas)

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.; Partridge, Harry; Langhoff, Stephen R.

1990-01-01

Ab initio calculations are reported of the spectroscopic constants for the low-lying states of the molecular ions ML2(+), where M = Li, Na, Mg, V, Fe, Co, Ni and Cu, and where L is usually Ar. Comparison with existing analogous calculations on the ML(+) ions shows how the bonding and binding energy change with the addition of a second noble gas atom. The second binding energy is predicted to be essentially the same as the first for the Li, Na, Mg, and V ions, but larger for the Fe, Co, Ni and Cu ions. The binding energies of the transition metal noble gas ions are not accurately predicted at the SCF level, because correlation is required to describe their M(0)Ln(+) character. All trends can be explained in terms of promotion and hybridization on the metal ion.

A computational approach for predicting off-target toxicity of antiviral ribonucleoside analogues to mitochondrial RNA polymerase.

PubMed

Freedman, Holly; Winter, Philip; Tuszynski, Jack; Tyrrell, D Lorne; Houghton, Michael

2018-06-22

In the development of antiviral drugs that target viral RNA-dependent RNA polymerases, off-target toxicity caused by the inhibition of the human mitochondrial RNA polymerase (POLRMT) is a major liability. Therefore, it is essential that all new ribonucleoside analogue drugs be accurately screened for POLRMT inhibition. A computational tool that can accurately predict NTP binding to POLRMT could assist in evaluating any potential toxicity and in designing possible salvaging strategies. Using the available crystal structure of POLRMT bound to an RNA transcript, here we created a model of POLRMT with an NTP molecule bound in the active site. Furthermore, we implemented a computational screening procedure that determines the relative binding free energy of an NTP analogue to POLRMT by free energy perturbation (FEP), i.e. a simulation in which the natural NTP molecule is slowly transformed into the analogue and back. In each direction, the transformation was performed over 40 ns of simulation on our IBM Blue Gene Q supercomputer. This procedure was validated across a panel of drugs for which experimental dissociation constants were available, showing that NTP relative binding free energies could be predicted to within 0.97 kcal/mol of the experimental values on average. These results demonstrate for the first time that free-energy simulation can be a useful tool for predicting binding affinities of NTP analogues to a polymerase. We expect that our model, together with similar models of viral polymerases, will be very useful in the screening and future design of NTP inhibitors of viral polymerases that have no mitochondrial toxicity. © 2018 Freedman et al.

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

PubMed Central

2010-01-01

Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/. PMID:20089173

Simulation of Reversible Protein–Protein Binding and Calculation of Binding Free Energies Using Perturbed Distance Restraints

PubMed Central

2017-01-01

Virtually all biological processes depend on the interaction between proteins at some point. The correct prediction of biomolecular binding free-energies has many interesting applications in both basic and applied pharmaceutical research. While recent advances in the field of molecular dynamics (MD) simulations have proven the feasibility of the calculation of protein–protein binding free energies, the large conformational freedom of proteins and complex free energy landscapes of binding processes make such calculations a difficult task. Moreover, convergence and reversibility of resulting free-energy values remain poorly described. In this work, an easy-to-use, yet robust approach for the calculation of standard-state protein–protein binding free energies using perturbed distance restraints is described. In the binding process the conformations of the proteins were restrained, as suggested earlier. Two approaches to avoid end-state problems upon release of the conformational restraints were compared. The method was evaluated by practical application to a small model complex of ubiquitin and the very flexible ubiquitin-binding domain of human DNA polymerase ι (UBM2). All computed free energy differences were closely monitored for convergence, and the calculated binding free energies had a mean unsigned deviation of only 1.4 or 2.5 kJ·mol–1 from experimental values. Statistical error estimates were in the order of thermal noise. We conclude that the presented method has promising potential for broad applicability to quantitatively describe protein–protein and various other kinds of complex formation. PMID:28898077

Computation of pH-Dependent Binding Free Energies

PubMed Central

Kim, M. Olivia; McCammon, J. Andrew

2015-01-01

Protein-ligand binding accompanies changes in the surrounding electrostatic environments of the two binding partners and may lead to changes in protonation upon binding. In cases where the complex formation results in a net transfer of protons, the binding process is pH-dependent. However, conventional free energy computations or molecular docking protocols typically employ fixed protonation states for the titratable groups in both binding partners set a priori, which are identical for the free and bound states. In this review, we draw attention to these important yet largely ignored binding-induced protonation changes in protein-ligand association by outlining physical origins and prevalence of the protonation changes upon binding. Following a summary of various theoretical methods for pKa prediction, we discuss the theoretical framework to examine the pH dependence of protein-ligand binding processes. PMID:26202905

RBscore&NBench: a high-level web server for nucleic acid binding residues prediction with a large-scale benchmarking database.

PubMed

Miao, Zhichao; Westhof, Eric

2016-07-08

RBscore&NBench combines a web server, RBscore and a database, NBench. RBscore predicts RNA-/DNA-binding residues in proteins and visualizes the prediction scores and features on protein structures. The scoring scheme of RBscore directly links feature values to nucleic acid binding probabilities and illustrates the nucleic acid binding energy funnel on the protein surface. To avoid dataset, binding site definition and assessment metric biases, we compared RBscore with 18 web servers and 3 stand-alone programs on 41 datasets, which demonstrated the high and stable accuracy of RBscore. A comprehensive comparison led us to develop a benchmark database named NBench. The web server is available on: http://ahsoka.u-strasbg.fr/rbscorenbench/. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

A method for predicting individual residue contributions to enzyme specificity and binding-site energies, and its application to MTH1.

PubMed

Stewart, James J P

2016-11-01

A new method for predicting the energy contributions to substrate binding and to specificity has been developed. Conventional global optimization methods do not permit the subtle effects responsible for these properties to be modeled with sufficient precision to allow confidence to be placed in the results, but by making simple alterations to the model, the precisions of the various energies involved can be improved from about ±2 kcal mol -1 to ±0.1 kcal mol -1 . This technique was applied to the oxidized nucleotide pyrophosphohydrolase enzyme MTH1. MTH1 is unusual in that the binding and reaction sites are well separated-an advantage from a computational chemistry perspective, as it allows the energetics involved in docking to be modeled without the need to consider any issues relating to reaction mechanisms. In this study, two types of energy terms were investigated: the noncovalent interactions between the binding site and the substrate, and those responsible for discriminating between the oxidized nucleotide 8-oxo-dGTP and the normal dGTP. Both of these were investigated using the semiempirical method PM7 in the program MOPAC. The contributions of the individual residues to both the binding energy and the specificity of MTH1 were calculated by simulating the effect of mutations. Where comparisons were possible, all calculated results were in agreement with experimental observations. This technique provides fresh insight into the binding mechanism that enzymes use for discriminating between possible substrates.

Improving the iterative Linear Interaction Energy approach using automated recognition of configurational transitions.

PubMed

Vosmeer, C Ruben; Kooi, Derk P; Capoferri, Luigi; Terpstra, Margreet M; Vermeulen, Nico P E; Geerke, Daan P

2016-01-01

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.

Time-dependent density-functional tight-binding method with the third-order expansion of electron density

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nishimoto, Yoshio, E-mail: nishimoto.yoshio@fukui.kyoto-u.ac.jp

2015-09-07

We develop a formalism for the calculation of excitation energies and excited state gradients for the self-consistent-charge density-functional tight-binding method with the third-order contributions of a Taylor series of the density functional theory energy with respect to the fluctuation of electron density (time-dependent density-functional tight-binding (TD-DFTB3)). The formulation of the excitation energy is based on the existing time-dependent density functional theory and the older TD-DFTB2 formulae. The analytical gradient is computed by solving Z-vector equations, and it requires one to calculate the third-order derivative of the total energy with respect to density matrix elements due to the inclusion of themore » third-order contributions. The comparison of adiabatic excitation energies for selected small and medium-size molecules using the TD-DFTB2 and TD-DFTB3 methods shows that the inclusion of the third-order contributions does not affect excitation energies significantly. A different set of parameters, which are optimized for DFTB3, slightly improves the prediction of adiabatic excitation energies statistically. The application of TD-DFTB for the prediction of absorption and fluorescence energies of cresyl violet demonstrates that TD-DFTB3 reproduced the experimental fluorescence energy quite well.« less

Time-dependent density-functional tight-binding method with the third-order expansion of electron density.

PubMed

Nishimoto, Yoshio

2015-09-07

We develop a formalism for the calculation of excitation energies and excited state gradients for the self-consistent-charge density-functional tight-binding method with the third-order contributions of a Taylor series of the density functional theory energy with respect to the fluctuation of electron density (time-dependent density-functional tight-binding (TD-DFTB3)). The formulation of the excitation energy is based on the existing time-dependent density functional theory and the older TD-DFTB2 formulae. The analytical gradient is computed by solving Z-vector equations, and it requires one to calculate the third-order derivative of the total energy with respect to density matrix elements due to the inclusion of the third-order contributions. The comparison of adiabatic excitation energies for selected small and medium-size molecules using the TD-DFTB2 and TD-DFTB3 methods shows that the inclusion of the third-order contributions does not affect excitation energies significantly. A different set of parameters, which are optimized for DFTB3, slightly improves the prediction of adiabatic excitation energies statistically. The application of TD-DFTB for the prediction of absorption and fluorescence energies of cresyl violet demonstrates that TD-DFTB3 reproduced the experimental fluorescence energy quite well.

Conformational free energy modeling of druglike molecules by metadynamics in the WHIM space.

PubMed

Spiwok, Vojtěch; Hlat-Glembová, Katarína; Tvaroška, Igor; Králová, Blanka

2012-03-26

Protein-ligand affinities can be significantly influenced not only by the interaction itself but also by conformational equilibrium of both binding partners, free ligand and free protein. Identification of important conformational families of a ligand and prediction of their thermodynamics is important for efficient ligand design. Here we report conformational free energy modeling of nine small-molecule drugs in explicitly modeled water by metadynamics with a bias potential applied in the space of weighted holistic invariant molecular (WHIM) descriptors. Application of metadynamics enhances conformational sampling compared to unbiased molecular dynamics simulation and allows to predict relative free energies of key conformations. Selected free energy minima and one example of transition state were tested by a series of unbiased molecular dynamics simulation. Comparison of free energy surfaces of free and target-bound Imatinib provides an estimate of free energy penalty of conformational change induced by its binding to the target. © 2012 American Chemical Society

Modeling side-chains using molecular dynamics improve recognition of binding region in CAPRI targets.

PubMed

Camacho, Carlos J

2005-08-01

The CAPRI-II experiment added an extra level of complexity to the problem of predicting protein-protein interactions by including 5 targets for which participants had to build or complete the 3-dimensional (3D) structure of either the receptor or ligand based on the structure of a close homolog. In this article, we describe how modeling key side-chains using molecular dynamics (MD) in explicit solvent improved the recognition of the binding region of a free energy- based computational docking method. In particular, we show that MD is able to predict with relatively high accuracy the rotamer conformation of the anchor side-chains important for molecular recognition as suggested by Rajamani et al. (Proc Natl Acad Sci USA 2004;101:11287-11292). As expected, the conformations are some of the most common rotamers for the given residue, while latch side-chains that undergo induced fit upon binding are forced into less common conformations. Using these models as starting conformations in conjunction with the rigid-body docking server ClusPro and the flexible docking algorithm SmoothDock, we produced valuable predictions for 6 of the 9 targets in CAPRI-II, missing only the 3 targets that underwent significant structural rearrangements upon binding. We also show that our free energy- based scoring function, consisting of the sum of van der Waals, Coulombic electrostatic with a distance-dependent dielectric, and desolvation free energy successfully discriminates the nativelike conformation of our submitted predictions. The latter emphasizes the critical role that thermodynamics plays on our methodology, and validates the generality of the algorithm to predict protein interactions.

Systematic optimization model and algorithm for binding sequence selection in computational enzyme design

PubMed Central

Huang, Xiaoqiang; Han, Kehang; Zhu, Yushan

2013-01-01

A systematic optimization model for binding sequence selection in computational enzyme design was developed based on the transition state theory of enzyme catalysis and graph-theoretical modeling. The saddle point on the free energy surface of the reaction system was represented by catalytic geometrical constraints, and the binding energy between the active site and transition state was minimized to reduce the activation energy barrier. The resulting hyperscale combinatorial optimization problem was tackled using a novel heuristic global optimization algorithm, which was inspired and tested by the protein core sequence selection problem. The sequence recapitulation tests on native active sites for two enzyme catalyzed hydrolytic reactions were applied to evaluate the predictive power of the design methodology. The results of the calculation show that most of the native binding sites can be successfully identified if the catalytic geometrical constraints and the structural motifs of the substrate are taken into account. Reliably predicting active site sequences may have significant implications for the creation of novel enzymes that are capable of catalyzing targeted chemical reactions. PMID:23649589

Dispersion- and Exchange-Corrected Density Functional Theory for Sodium Ion Hydration.

PubMed

Soniat, Marielle; Rogers, David M; Rempe, Susan B

2015-07-14

A challenge in density functional theory is developing functionals that simultaneously describe intermolecular electron correlation and electron delocalization. Recent exchange-correlation functionals address those two issues by adding corrections important at long ranges: an atom-centered pairwise dispersion term to account for correlation and a modified long-range component of the electron exchange term to correct for delocalization. Here we investigate how those corrections influence the accuracy of binding free energy predictions for sodium-water clusters. We find that the dual-corrected ωB97X-D functional gives cluster binding energies closest to high-level ab initio methods (CCSD(T)). Binding energy decomposition shows that the ωB97X-D functional predicts the smallest ion-water (pairwise) interaction energy and larger multibody contributions for a four-water cluster than most other functionals - a trend consistent with CCSD(T) results. Also, ωB97X-D produces the smallest amounts of charge transfer and the least polarizable waters of the density functionals studied, which mimics the lower polarizability of CCSD. When compared with experimental binding free energies, however, the exchange-corrected CAM-B3LYP functional performs best (error <1 kcal/mol), possibly because of its parametrization to experimental formation enthalpies. For clusters containing more than four waters, "split-shell" coordination must be considered to obtain accurate free energies in comparison with experiment.

Binding free energy analysis of protein-protein docking model structures by evERdock.

PubMed

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-14

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

Binding free energy analysis of protein-protein docking model structures by evERdock

NASA Astrophysics Data System (ADS)

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-01

To aid the evaluation of protein-protein complex model structures generated by protein docking prediction (decoys), we previously developed a method to calculate the binding free energies for complexes. The method combines a short (2 ns) all-atom molecular dynamics simulation with explicit solvent and solution theory in the energy representation (ER). We showed that this method successfully selected structures similar to the native complex structure (near-native decoys) as the lowest binding free energy structures. In our current work, we applied this method (evERdock) to 100 or 300 model structures of four protein-protein complexes. The crystal structures and the near-native decoys showed the lowest binding free energy of all the examined structures, indicating that evERdock can successfully evaluate decoys. Several decoys that show low interface root-mean-square distance but relatively high binding free energy were also identified. Analysis of the fraction of native contacts, hydrogen bonds, and salt bridges at the protein-protein interface indicated that these decoys were insufficiently optimized at the interface. After optimizing the interactions around the interface by including interfacial water molecules, the binding free energies of these decoys were improved. We also investigated the effect of solute entropy on binding free energy and found that consideration of the entropy term does not necessarily improve the evaluations of decoys using the normal model analysis for entropy calculation.

Large scale affinity calculations of cyclodextrin host-guest complexes: Understanding the role of reorganization in the molecular recognition process

PubMed Central

Wickstrom, Lauren; He, Peng; Gallicchio, Emilio; Levy, Ronald M.

2013-01-01

Host-guest inclusion complexes are useful models for understanding the structural and energetic aspects of molecular recognition. Due to their small size relative to much larger protein-ligand complexes, converged results can be obtained rapidly for these systems thus offering the opportunity to more reliably study fundamental aspects of the thermodynamics of binding. In this work, we have performed a large scale binding affinity survey of 57 β-cyclodextrin (CD) host guest systems using the binding energy distribution analysis method (BEDAM) with implicit solvation (OPLS-AA/AGBNP2). Converged estimates of the standard binding free energies are obtained for these systems by employing techniques such as parallel Hamitionian replica exchange molecular dynamics, conformational reservoirs and multistate free energy estimators. Good agreement with experimental measurements is obtained in terms of both numerical accuracy and affinity rankings. Overall, average effective binding energies reproduce affinity rank ordering better than the calculated binding affinities, even though calculated binding free energies, which account for effects such as conformational strain and entropy loss upon binding, provide lower root mean square errors when compared to measurements. Interestingly, we find that binding free energies are superior rank order predictors for a large subset containing the most flexible guests. The results indicate that, while challenging, accurate modeling of reorganization effects can lead to ligand design models of superior predictive power for rank ordering relative to models based only on ligand-receptor interaction energies. PMID:25147485

Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

PubMed

Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

2017-12-12

Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data.

PubMed

Barros, Rodrigo C; Winck, Ana T; Machado, Karina S; Basgalupp, Márcio P; de Carvalho, André C P L F; Ruiz, Duncan D; de Souza, Osmar Norberto

2012-11-21

This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor.

Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data

PubMed Central

2012-01-01

Background This paper addresses the prediction of the free energy of binding of a drug candidate with enzyme InhA associated with Mycobacterium tuberculosis. This problem is found within rational drug design, where interactions between drug candidates and target proteins are verified through molecular docking simulations. In this application, it is important not only to correctly predict the free energy of binding, but also to provide a comprehensible model that could be validated by a domain specialist. Decision-tree induction algorithms have been successfully used in drug-design related applications, specially considering that decision trees are simple to understand, interpret, and validate. There are several decision-tree induction algorithms available for general-use, but each one has a bias that makes it more suitable for a particular data distribution. In this article, we propose and investigate the automatic design of decision-tree induction algorithms tailored to particular drug-enzyme binding data sets. We investigate the performance of our new method for evaluating binding conformations of different drug candidates to InhA, and we analyze our findings with respect to decision tree accuracy, comprehensibility, and biological relevance. Results The empirical analysis indicates that our method is capable of automatically generating decision-tree induction algorithms that significantly outperform the traditional C4.5 algorithm with respect to both accuracy and comprehensibility. In addition, we provide the biological interpretation of the rules generated by our approach, reinforcing the importance of comprehensible predictive models in this particular bioinformatics application. Conclusions We conclude that automatically designing a decision-tree algorithm tailored to molecular docking data is a promising alternative for the prediction of the free energy from the binding of a drug candidate with a flexible-receptor. PMID:23171000

Theoretical study on the interaction of pyrrolopyrimidine derivatives as LIMK2 inhibitors: insight into structure-based inhibitor design.

PubMed

Shen, Mingyun; Zhou, Shunye; Li, Youyong; Li, Dan; Hou, Tingjun

2013-10-01

LIM kinases (LIMKs), downstream of Rho-associated protein kinases (ROCKs) and p21-activated protein kinases (PAKs), are shown to be promising targets for the treatment of cancers. In this study, the inhibition mechanism of 41 pyrrolopyrimidine derivatives as LIMK2 inhibitors was explored through a series of theoretical approaches. First, a model of LIMK2 was generated through molecular homology modeling, and the studied inhibitors were docked into the binding active site of LIMK2 by the docking protocol, taking into consideration the flexibility of the protein. The binding poses predicted by molecular docking for 17 selected inhibitors with different bioactivities complexed with LIMK2 underwent molecular dynamics (MD) simulations, and the binding free energies for the complexes were predicted by using the molecular mechanics/generalized born surface area (MM/GBSA) method. The predicted binding free energies correlated well with the experimental bioactivities (r(2) = 0.63 or 0.62). Next, the free energy decomposition analysis was utilized to highlight the following key structural features related to biological activity: (1) the important H-bond between Ile408 and pyrrolopyrimidine, (2) the H-bonds between the inhibitors and Asp469 and Gly471 which maintain the stability of the DFG-out conformation, and (3) the hydrophobic interactions between the inhibitors and several key residues (Leu337, Phe342, Ala345, Val358, Lys360, Leu389, Ile408, Leu458 and Leu472). Finally, a variety of LIMK2 inhibitors with a pyrrolopyrimidine scaffold were designed, some of which showed improved potency according to the predictions. Our studies suggest that the use of molecular docking with MD simulations and free energy calculations could be a powerful tool for understanding the binding mechanism of LIMK2 inhibitors and for the design of more potent LIMK2 inhibitors.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

Determining ERβ Binding Affinity to Singly Mutant ERE Using Dual Polarization Interferometry

NASA Astrophysics Data System (ADS)

Song, Hong Yan; Su, Xiaodi

In a classic mode of estrogen action, estrogen receptors (ERs) bind to estrogen responsive element (ERE) to activate gene transcription. A perfect ERE contains a 13-base pair sequence of a palindromic repeat separated by a three-base spacer, 5‧-GGTCAnnnTGACC-3‧. In addition to the consensus or wild-type ERE (wtERE), naturally occurring EREs often have one or two base pairs’ alternation. Based on the newly constructed Thermodynamic Modeling of ChIP-seq (TherMos) model, binding energy between ERβ and a series of 34-bp mutant EREs (mutERE) was simulated to predict the binding affinity between ERs and EREs with single base pair deviation at different sites of the 13-bp inverted sequence. Experimentally, dual polarization interferometry (DPI) method was developed to measure ERβ-mutEREs binding affinity. On a biotin-NeutrAvidin (NA)-biotin treated DPI chip, wtERE is immobilized. In a direct binding assay, ERβ-wtERE binding affinity is determined. In a competition assay, ERβ was preincubated with mutant EREs before being added for competitive binding to the immobilized wtERE. This competition strategy provided a successful platform to evaluate the binding affinity variation among large number of ERE with different base mutations. The experimental result correlates well with the mathematically predicted binding energy with a Spearman correlation coefficient of 0.97.

Role of Electrostatics in Protein-RNA Binding: The Global vs the Local Energy Landscape.

PubMed

Ghaemi, Zhaleh; Guzman, Irisbel; Gnutt, David; Luthey-Schulten, Zaida; Gruebele, Martin

2017-09-14

U1A protein-stem loop 2 RNA association is a basic step in the assembly of the spliceosomal U1 small nuclear ribonucleoprotein. Long-range electrostatic interactions due to the positive charge of U1A are thought to provide high binding affinity for the negatively charged RNA. Short range interactions, such as hydrogen bonds and contacts between RNA bases and protein side chains, favor a specific binding site. Here, we propose that electrostatic interactions are as important as local contacts in biasing the protein-RNA energy landscape toward a specific binding site. We show by using molecular dynamics simulations that deletion of two long-range electrostatic interactions (K22Q and K50Q) leads to mutant-specific alternative RNA bound states. One of these states preserves short-range interactions with aromatic residues in the original binding site, while the other one does not. We test the computational prediction with experimental temperature-jump kinetics using a tryptophan probe in the U1A-RNA binding site. The two mutants show the distinct predicted kinetic behaviors. Thus, the stem loop 2 RNA has multiple binding sites on a rough RNA-protein binding landscape. We speculate that the rough protein-RNA binding landscape, when biased to different local minima by electrostatics, could be one way that protein-RNA interactions evolve toward new binding sites and novel function.

Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach.

PubMed

Pan, Yuliang; Wang, Zixiang; Zhan, Weihua; Deng, Lei

2018-05-01

Identifying RNA-binding residues, especially energetically favored hot spots, can provide valuable clues for understanding the mechanisms and functional importance of protein-RNA interactions. Yet, limited availability of experimentally recognized energy hot spots in protein-RNA crystal structures leads to the difficulties in developing empirical identification approaches. Computational prediction of RNA-binding hot spot residues is still in its infant stage. Here, we describe a computational method, PrabHot (Prediction of protein-RNA binding hot spots), that can effectively detect hot spot residues on protein-RNA binding interfaces using an ensemble of conceptually different machine learning classifiers. Residue interaction network features and new solvent exposure characteristics are combined together and selected for classification with the Boruta algorithm. In particular, two new reference datasets (benchmark and independent) have been generated containing 107 hot spots from 47 known protein-RNA complex structures. In 10-fold cross-validation on the training dataset, PrabHot achieves promising performances with an AUC score of 0.86 and a sensitivity of 0.78, which are significantly better than that of the pioneer RNA-binding hot spot prediction method HotSPRing. We also demonstrate the capability of our proposed method on the independent test dataset and gain a competitive advantage as a result. The PrabHot webserver is freely available at http://denglab.org/PrabHot/. leideng@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Binding free energies for nicotine analogs inhibiting cytochrome P450 2A6 by a combined use of molecular dynamics simulations and QM/MM-PBSA calculations.

PubMed

Lu, Haiting; Huang, Xiaoqin; AbdulHameed, Mohamed Diwan M; Zhan, Chang-Guo

2014-04-01

Molecular dynamics (MD) simulations and hybrid quantum mechanical/molecular mechanical (QM/MM) calculations have been performed to explore the dynamic behaviors of cytochrome P450 2A6 (CYP2A6) binding with nicotine analogs (that are typical inhibitors) and to calculate their binding free energies in combination with Poisson-Boltzmann surface area (PBSA) calculations. The combined MD simulations and QM/MM-PBSA calculations reveal that the most important structural parameters affecting the CYP2A6-inhibitor binding affinity are two crucial internuclear distances, that is, the distance between the heme iron atom of CYP2A6 and the coordinating atom of the inhibitor, and the hydrogen-bonding distance between the N297 side chain of CYP2A6 and the pyridine nitrogen of the inhibitor. The combined MD simulations and QM/MM-PBSA calculations have led to dynamic CYP2A6-inhibitor binding structures that are consistent with the observed dynamic behaviors and structural features of CYP2A6-inhibitor binding, and led to the binding free energies that are in good agreement with the experimentally-derived binding free energies. The agreement between the calculated binding free energies and the experimentally-derived binding free energies suggests that the combined MD and QM/MM-PBSA approach may be used as a valuable tool to accurately predict the CYP2A6-inhibitor binding affinities in future computational design of new, potent and selective CYP2A6 inhibitors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Core Binding Site of a Thioflavin-T-Derived Imaging Probe on Amyloid β Fibrils Predicted by Computational Methods.

PubMed

Kawai, Ryoko; Araki, Mitsugu; Yoshimura, Masashi; Kamiya, Narutoshi; Ono, Masahiro; Saji, Hideo; Okuno, Yasushi

2018-05-16

Development of new diagnostic imaging probes for Alzheimer's disease, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) probes, has been strongly desired. In this study, we investigated the most accessible amyloid β (Aβ) binding site of [ 123 I]IMPY, a Thioflavin-T-derived SPECT probe, using experimental and computational methods. First, we performed a competitive inhibition assay with Orange-G, which recognizes the KLVFFA region in Aβ fibrils, suggesting that IMPY and Orange-G bind to different sites in Aβ fibrils. Next, we precisely predicted the IMPY binding site on a multiple-protofilament Aβ fibril model using computational approaches, consisting of molecular dynamics and docking simulations. We generated possible IMPY-binding structures using docking simulations to identify candidates for probe-binding sites. The binding free energy of IMPY with the Aβ fibril was calculated by a free energy simulation method, MP-CAFEE. These computational results suggest that IMPY preferentially binds to an interfacial pocket located between two protofilaments and is stabilized mainly through hydrophobic interactions. Finally, our computational approach was validated by comparing it with the experimental results. The present study demonstrates the possibility of computational approaches to screen new PET/SPECT probes for Aβ imaging.

Nonbonded interactions in membrane active cyclic biopolymers. IV - Cation dependence

NASA Technical Reports Server (NTRS)

Radhakrishnan, R.; Srinivasan, S.; Prasad, C. V.; Brinda, S. R.; Macelroy, R. D.; Sundaram, K.

1980-01-01

Interactions of valinomycin and form of its analogs in several conformations with the central ions Li(+), Na(+), K(+), Rb(+) and Cs(+) are investigated as part of a study of the specific preference of valinomycin for potassium and the mechanisms of carrier-mediated ion transport across membranes. Ion binding energies and conformational potential energies are calculated taking into account polarization energy formulas and repulsive energy between the central ion and the ligand atoms for conformations representing various stages in ion capture and release for each of the two ring chiralities of valinomycin and its analogs. Results allow the prediction of the chirality and conformation most likely to be observed for a given analog, and may be used to synthesize analogs with a desired rigidity or flexibility. The binding energies with the alkali metal cations are found to decrease with increasing ion size, and to be smaller than the corresponding ion hydration energies. It is pointed out that the observed potassium preference may be explainable in terms of differences between binding and hydration energies. Binding energies are also noted to depend on ligand conformation.

Improved version of a binding energy formula for heavy and superheavy nuclei with Z{>=}90 and N{>=}140

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dong Tiekuang; Joint Center for Particle, Nuclear Physics and Cosmology, Nanjing 210008; Ren Zhongzhou

2008-06-15

A local formula of binding energy for heavy and superheavy nuclei has very recently been proposed [Dong and Ren, Phys. Rev. C 72, 064331 (2005)]. In this paper, the limit of the predictive ability of this local formula is investigated. It is found that the neutron-proton correlations should be considered when higher precision is required. On the one hand, we introduce a new term |N-Z-50|/A, and on the other hand we consider the different strengths of proton-proton, neutron-neutron, and neutron-proton pairing correlations. For the first time, the standard deviation {radical}({sigma}{sup 2}) of the binding energies for 117 nuclei with Z{>=}90more » and N{>=}140 is reduced to 0.105 MeV. The {alpha} decay energies Q{sub {alpha}} and half-lives T{sub {alpha}} of nuclei with Z=102-118 are reproduced quite well. The proton drip line of superheavy elements from Md (Z=101) to Ds (Z=110) are predicted.« less

Exhaustive search and solvated interaction energy (SIE) for virtual screening and affinity prediction

NASA Astrophysics Data System (ADS)

Sulea, Traian; Hogues, Hervé; Purisima, Enrico O.

2012-05-01

We carried out a prospective evaluation of the utility of the SIE (solvation interaction energy) scoring function for virtual screening and binding affinity prediction. Since experimental structures of the complexes were not provided, this was an exercise in virtual docking as well. We used our exhaustive docking program, Wilma, to provide high-quality poses that were rescored using SIE to provide binding affinity predictions. We also tested the combination of SIE with our latest solvation model, first shell of hydration (FiSH), which captures some of the discrete properties of water within a continuum model. We achieved good enrichment in virtual screening of fragments against trypsin, with an area under the curve of about 0.7 for the receiver operating characteristic curve. Moreover, the early enrichment performance was quite good with 50% of true actives recovered with a 15% false positive rate in a prospective calculation and with a 3% false positive rate in a retrospective application of SIE with FiSH. Binding affinity predictions for both trypsin and host-guest complexes were generally within 2 kcal/mol of the experimental values. However, the rank ordering of affinities differing by 2 kcal/mol or less was not well predicted. On the other hand, it was encouraging that the incorporation of a more sophisticated solvation model into SIE resulted in better discrimination of true binders from binders. This suggests that the inclusion of proper Physics in our models is a fruitful strategy for improving the reliability of our binding affinity predictions.

Using DNA mechanics to predict in vitro nucleosome positions and formation energies

PubMed Central

Morozov, Alexandre V.; Fortney, Karissa; Gaykalova, Daria A.; Studitsky, Vasily M.; Widom, Jonathan; Siggia, Eric D.

2009-01-01

In eukaryotic genomes, nucleosomes function to compact DNA and to regulate access to it both by simple physical occlusion and by providing the substrate for numerous covalent epigenetic tags. While competition with other DNA-binding factors and action of chromatin remodeling enzymes significantly affect nucleosome formation in vivo, nucleosome positions in vitro are determined by steric exclusion and sequence alone. We have developed a biophysical model, DNABEND, for the sequence dependence of DNA bending energies, and validated it against a collection of in vitro free energies of nucleosome formation and a set of in vitro nucleosome positions mapped at high resolution. We have also made a first ab initio prediction of nucleosomal DNA geometries, and checked its accuracy against the nucleosome crystal structure. We have used DNABEND to design both strong and weak histone- binding sequences, and measured the corresponding free energies of nucleosome formation. We find that DNABEND can successfully predict in vitro nucleosome positions and free energies, providing a physical explanation for the intrinsic sequence dependence of histone–DNA interactions. PMID:19509309

Improved estimation of ligand macromolecule binding affinities by linear response approach using a combination of multi-mode MD simulation and QM/MM methods

NASA Astrophysics Data System (ADS)

Khandelwal, Akash; Balaz, Stefan

2007-01-01

Structure-based predictions of binding affinities of ligands binding to proteins by coordination bonds with transition metals, covalent bonds, and bonds involving charge re-distributions are hindered by the absence of proper force fields. This shortcoming affects all methods which use force-field-based molecular simulation data on complex formation for affinity predictions. One of the most frequently used methods in this category is the Linear Response (LR) approach of Åquist, correlating binding affinities with van der Waals and electrostatic energies, as extended by Jorgensen's inclusion of solvent-accessible surface areas. All these terms represent the differences, upon binding, in the ensemble averages of pertinent quantities, obtained from molecular dynamics (MD) or Monte Carlo simulations of the complex and of single components. Here we report a modification of the LR approach by: (1) the replacement of the two energy terms through the single-point QM/MM energy of the time-averaged complex structure from an MD simulation; and (2) a rigorous consideration of multiple modes (mm) of binding. The first extension alleviates the force-field related problems, while the second extension deals with the ligands exhibiting large-scale motions in the course of an MD simulation. The second modification results in the correlation equation that is nonlinear in optimized coefficients, but does not lead to an increase in the number of optimized coefficients. The application of the resulting mm QM/MM LR approach to the inhibition of zinc-dependent gelatinase B (matrix metalloproteinase 9) by 28 hydroxamate ligands indicates a significant improvement of descriptive and predictive abilities.

Comparison of binding energies of SrcSH2-phosphotyrosyl peptides with structure-based prediction using surface area based empirical parameterization.

PubMed Central

Henriques, D. A.; Ladbury, J. E.; Jackson, R. M.

2000-01-01

The prediction of binding energies from the three-dimensional (3D) structure of a protein-ligand complex is an important goal of biophysics and structural biology. Here, we critically assess the use of empirical, solvent-accessible surface area-based calculations for the prediction of the binding of Src-SH2 domain with a series of tyrosyl phosphopeptides based on the high-affinity ligand from the hamster middle T antigen (hmT), where the residue in the pY+ 3 position has been changed. Two other peptides based on the C-terminal regulatory site of the Src protein and the platelet-derived growth factor receptor (PDGFR) are also investigated. Here, we take into account the effects of proton linkage on binding, and test five different surface area-based models that include different treatments for the contributions to conformational change and protein solvation. These differences relate to the treatment of conformational flexibility in the peptide ligand and the inclusion of proximal ordered solvent molecules in the surface area calculations. This allowed the calculation of a range of thermodynamic state functions (deltaCp, deltaS, deltaH, and deltaG) directly from structure. Comparison with the experimentally derived data shows little agreement for the interaction of SrcSH2 domain and the range of tyrosyl phosphopeptides. Furthermore, the adoption of the different models to treat conformational change and solvation has a dramatic effect on the calculated thermodynamic functions, making the predicted binding energies highly model dependent. While empirical, solvent-accessible surface area based calculations are becoming widely adopted to interpret thermodynamic data, this study highlights potential problems with application and interpretation of this type of approach. There is undoubtedly some agreement between predicted and experimentally determined thermodynamic parameters: however, the tolerance of this approach is not sufficient to make it ubiquitously applicable. PMID:11106171

Predicting binding modes of reversible peptide-based inhibitors of falcipain-2 consistent with structure-activity relationships.

PubMed

Hernández González, Jorge Enrique; Hernández Alvarez, Lilian; Pascutti, Pedro Geraldo; Valiente, Pedro A

2017-09-01

Falcipain-2 (FP-2) is a major hemoglobinase of Plasmodium falciparum, considered an important drug target for the development of antimalarials. A previous study reported a novel series of 20 reversible peptide-based inhibitors of FP-2. However, the lack of tridimensional structures of the complexes hinders further optimization strategies to enhance the inhibitory activity of the compounds. Here we report the prediction of the binding modes of the aforementioned inhibitors to FP-2. A computational approach combining previous knowledge on the determinants of binding to the enzyme, docking, and postdocking refinement steps, is employed. The latter steps comprise molecular dynamics simulations and free energy calculations. Remarkably, this approach leads to the identification of near-native ligand conformations when applied to a validation set of protein-ligand structures. Overall, we proposed substrate-like binding modes of the studied compounds fulfilling the structural requirements for FP-2 binding and yielding free energy values that correlated well with the experimental data. Proteins 2017; 85:1666-1683. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Protein-Protein Interface Predictions by Data-Driven Methods: A Review

PubMed Central

Xue, Li C; Dobbs, Drena; Bonvin, Alexandre M.J.J.; Honavar, Vasant

2015-01-01

Reliably pinpointing which specific amino acid residues form the interface(s) between a protein and its binding partner(s) is critical for understanding the structural and physicochemical determinants of protein recognition and binding affinity, and has wide applications in modeling and validating protein interactions predicted by high-throughput methods, in engineering proteins, and in prioritizing drug targets. Here, we review the basic concepts, principles and recent advances in computational approaches to the analysis and prediction of protein-protein interfaces. We point out caveats for objectively evaluating interface predictors, and discuss various applications of data-driven interface predictors for improving energy model-driven protein-protein docking. Finally, we stress the importance of exploiting binding partner information in reliably predicting interfaces and highlight recent advances in this emerging direction. PMID:26460190

Prediction of potency of protease inhibitors using free energy simulations with polarizable quantum mechanics-based ligand charges and a hybrid water model.

PubMed

Das, Debananda; Koh, Yasuhiro; Tojo, Yasushi; Ghosh, Arun K; Mitsuya, Hiroaki

2009-12-01

Reliable and robust prediction of the binding affinity for drug molecules continues to be a daunting challenge. We simulated the binding interactions and free energy of binding of nine protease inhibitors (PIs) with wild-type and various mutant proteases by performing GBSA simulations in which each PI's partial charge was determined by quantum mechanics (QM) and the partial charge accounts for the polarization induced by the protease environment. We employed a hybrid solvation model that retains selected explicit water molecules in the protein with surface-generalized Born (SGB) implicit solvent. We examined the correlation of the free energy with the antiviral potency of PIs with regard to amino acid substitutions in protease. The GBSA free energy thus simulated showed strong correlations (r > 0.75) with antiviral IC(50) values of PIs when amino acid substitutions were present in the protease active site. We also simulated the binding free energy of PIs with P2-bis-tetrahydrofuranylurethane (bis-THF) or related cores, utilizing a bis-THF-containing protease crystal structure as a template. The free energy showed a strong correlation (r = 0.93) with experimentally determined anti-HIV-1 potency. The present data suggest that the presence of selected explicit water in protein and protein polarization-induced quantum charges for the inhibitor, compared to lack of explicit water and a static force-field-based charge model, can serve as an improved lead optimization tool and warrants further exploration.

Dispersion-correcting potentials can significantly improve the bond dissociation enthalpies and noncovalent binding energies predicted by density-functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

DiLabio, Gino A., E-mail: Gino.DiLabio@nrc.ca; Department of Chemistry, University of British Columbia, Okanagan, 3333 University Way, Kelowna, British Columbia V1V 1V7; Koleini, Mohammad

2014-05-14

Dispersion-correcting potentials (DCPs) are atom-centered Gaussian functions that are applied in a manner that is similar to effective core potentials. Previous work on DCPs has focussed on their use as a simple means of improving the ability of conventional density-functional theory methods to predict the binding energies of noncovalently bonded molecular dimers. We show in this work that DCPs developed for use with the LC-ωPBE functional along with 6-31+G(2d,2p) basis sets are capable of simultaneously improving predicted noncovalent binding energies of van der Waals dimer complexes and covalent bond dissociation enthalpies in molecules. Specifically, the DCPs developed herein for themore » C, H, N, and O atoms provide binding energies for a set of 66 noncovalently bonded molecular dimers (the “S66” set) with a mean absolute error (MAE) of 0.21 kcal/mol, which represents an improvement of more than a factor of 10 over unadorned LC-ωPBE/6-31+G(2d,2p) and almost a factor of two improvement over LC-ωPBE/6-31+G(2d,2p) used in conjunction with the “D3” pairwise dispersion energy corrections. In addition, the DCPs reduce the MAE of calculated X-H and X-Y (X,Y = C, H, N, O) bond dissociation enthalpies for a set of 40 species from 3.2 kcal/mol obtained with unadorned LC-ωPBE/6-31+G(2d,2p) to 1.6 kcal/mol. Our findings demonstrate that broad improvements to the performance of DFT methods may be achievable through the use of DCPs.« less

Ligand-protein docking using a quantum stochastic tunneling optimization method.

PubMed

Mancera, Ricardo L; Källblad, Per; Todorov, Nikolay P

2004-04-30

A novel hybrid optimization method called quantum stochastic tunneling has been recently introduced. Here, we report its implementation within a new docking program called EasyDock and a validation with the CCDC/Astex data set of ligand-protein complexes using the PLP score to represent the ligand-protein potential energy surface and ScreenScore to score the ligand-protein binding energies. When taking the top energy-ranked ligand binding mode pose, we were able to predict the correct crystallographic ligand binding mode in up to 75% of the cases. By using this novel optimization method run times for typical docking simulations are significantly shortened. Copyright 2004 Wiley Periodicals, Inc. J Comput Chem 25: 858-864, 2004

Proteins and Their Interacting Partners: An Introduction to Protein-Ligand Binding Site Prediction Methods.

PubMed

Roche, Daniel Barry; Brackenridge, Danielle Allison; McGuffin, Liam James

2015-12-15

Elucidating the biological and biochemical roles of proteins, and subsequently determining their interacting partners, can be difficult and time consuming using in vitro and/or in vivo methods, and consequently the majority of newly sequenced proteins will have unknown structures and functions. However, in silico methods for predicting protein-ligand binding sites and protein biochemical functions offer an alternative practical solution. The characterisation of protein-ligand binding sites is essential for investigating new functional roles, which can impact the major biological research spheres of health, food, and energy security. In this review we discuss the role in silico methods play in 3D modelling of protein-ligand binding sites, along with their role in predicting biochemical functionality. In addition, we describe in detail some of the key alternative in silico prediction approaches that are available, as well as discussing the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and the Continuous Automated Model EvaluatiOn (CAMEO) projects, and their impact on developments in the field. Furthermore, we discuss the importance of protein function prediction methods for tackling 21st century problems.

Theoretical analysis of the time-resolved binary (e, 2e) binding energy spectra on three-body photodissociation of acetone at 195 nm

NASA Astrophysics Data System (ADS)

Yamazaki, M.; Nakayama, S.; Zhu, C. Y.; Takahashi, M.

2017-11-01

We report on theoretical progress in time-resolved (e, 2e) electron momentum spectroscopy of photodissociation dynamics of the deuterated acetone molecule at 195 nm. We have examined the predicted minimum energy reaction path to investigate whether associated (e, 2e) calculations meet the experimental results. A noticeable difference between the experiment and calculations has been found at around binding energy of 10 eV, suggesting that the observed difference may originate, at least partly, in ever-unconsidered non-minimum energy paths.

Assessing the performance of MM/PBSA and MM/GBSA methods. 8. Predicting binding free energies and poses of protein-RNA complexes.

PubMed

Chen, Fu; Sun, Huiyong; Wang, Junmei; Zhu, Feng; Liu, Hui; Wang, Zhe; Lei, Tailong; Li, Youyong; Hou, Tingjun

2018-06-21

Molecular docking provides a computationally efficient way to predict the atomic structural details of protein-RNA interactions (PRI), but accurate prediction of the three-dimensional structures and binding affinities for PRI is still notoriously difficult, partly due to the unreliability of the existing scoring functions for PRI. MM/PBSA and MM/GBSA are more theoretically rigorous than most scoring functions for protein-RNA docking, but their prediction performance for protein-RNA systems remains unclear. Here, we systemically evaluated the capability of MM/PBSA and MM/GBSA to predict the binding affinities and recognize the near-native binding structures for protein-RNA systems with different solvent models and interior dielectric constants (ϵ in ). For predicting the binding affinities, the predictions given by MM/GBSA based on the minimized structures in explicit solvent and the GBGBn1 model with ϵ in = 2 yielded the highest correlation with the experimental data. Moreover, the MM/GBSA calculations based on the minimized structures in implicit solvent and the GBGBn1 model distinguished the near-native binding structures within the top 10 decoys for 118 out of the 149 protein-RNA systems (79.2%). This performance is better than all docking scoring functions studied here. Therefore, the MM/GBSA rescoring is an efficient way to improve the prediction capability of scoring functions for protein-RNA systems. Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Evaluation of Several Two-Step Scoring Functions Based on Linear Interaction Energy, Effective Ligand Size, and Empirical Pair Potentials for Prediction of Protein-Ligand Binding Geometry and Free Energy

PubMed Central

Rahaman, Obaidur; Estrada, Trilce P.; Doren, Douglas J.; Taufer, Michela; Brooks, Charles L.; Armen, Roger S.

2011-01-01

The performance of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for “step 2 discrimination” were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only “interacting” ligand atoms as the “effective size” of the ligand, and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and five-fold cross validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new dataset (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ dataset where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts. PMID:21644546

Evaluation of several two-step scoring functions based on linear interaction energy, effective ligand size, and empirical pair potentials for prediction of protein-ligand binding geometry and free energy.

PubMed

Rahaman, Obaidur; Estrada, Trilce P; Doren, Douglas J; Taufer, Michela; Brooks, Charles L; Armen, Roger S

2011-09-26

The performances of several two-step scoring approaches for molecular docking were assessed for their ability to predict binding geometries and free energies. Two new scoring functions designed for "step 2 discrimination" were proposed and compared to our CHARMM implementation of the linear interaction energy (LIE) approach using the Generalized-Born with Molecular Volume (GBMV) implicit solvation model. A scoring function S1 was proposed by considering only "interacting" ligand atoms as the "effective size" of the ligand and extended to an empirical regression-based pair potential S2. The S1 and S2 scoring schemes were trained and 5-fold cross-validated on a diverse set of 259 protein-ligand complexes from the Ligand Protein Database (LPDB). The regression-based parameters for S1 and S2 also demonstrated reasonable transferability in the CSARdock 2010 benchmark using a new data set (NRC HiQ) of diverse protein-ligand complexes. The ability of the scoring functions to accurately predict ligand geometry was evaluated by calculating the discriminative power (DP) of the scoring functions to identify native poses. The parameters for the LIE scoring function with the optimal discriminative power (DP) for geometry (step 1 discrimination) were found to be very similar to the best-fit parameters for binding free energy over a large number of protein-ligand complexes (step 2 discrimination). Reasonable performance of the scoring functions in enrichment of active compounds in four different protein target classes established that the parameters for S1 and S2 provided reasonable accuracy and transferability. Additional analysis was performed to definitively separate scoring function performance from molecular weight effects. This analysis included the prediction of ligand binding efficiencies for a subset of the CSARdock NRC HiQ data set where the number of ligand heavy atoms ranged from 17 to 35. This range of ligand heavy atoms is where improved accuracy of predicted ligand efficiencies is most relevant to real-world drug design efforts.

A density functional theory study of the correlation between analyte basicity, ZnPc adsorption strength, and sensor response.

PubMed

Tran, N L; Bohrer, F I; Trogler, W C; Kummel, A C

2009-05-28

Density functional theory (DFT) simulations were used to determine the binding strength of 12 electron-donating analytes to the zinc metal center of a zinc phthalocyanine molecule (ZnPc monomer). The analyte binding strengths were compared to the analytes' enthalpies of complex formation with boron trifluoride (BF(3)), which is a direct measure of their electron donating ability or Lewis basicity. With the exception of the most basic analyte investigated, the ZnPc binding energies were found to correlate linearly with analyte basicities. Based on natural population analysis calculations, analyte complexation to the Zn metal of the ZnPc monomer resulted in limited charge transfer from the analyte to the ZnPc molecule, which increased with analyte-ZnPc binding energy. The experimental analyte sensitivities from chemiresistor ZnPc sensor data were proportional to an exponential of the binding energies from DFT calculations consistent with sensitivity being proportional to analyte coverage and binding strength. The good correlation observed suggests DFT is a reliable method for the prediction of chemiresistor metallophthalocyanine binding strengths and response sensitivities.

Supervised machine learning techniques to predict binding affinity. A study for cyclin-dependent kinase 2.

PubMed

de Ávila, Maurício Boff; Xavier, Mariana Morrone; Pintro, Val Oliveira; de Azevedo, Walter Filgueira

2017-12-09

Here we report the development of a machine-learning model to predict binding affinity based on the crystallographic structures of protein-ligand complexes. We used an ensemble of crystallographic structures (resolution better than 1.5 Å resolution) for which half-maximal inhibitory concentration (IC 50 ) data is available. Polynomial scoring functions were built using as explanatory variables the energy terms present in the MolDock and PLANTS scoring functions. Prediction performance was tested and the supervised machine learning models showed improvement in the prediction power, when compared with PLANTS and MolDock scoring functions. In addition, the machine-learning model was applied to predict binding affinity of CDK2, which showed a better performance when compared with AutoDock4, AutoDock Vina, MolDock, and PLANTS scores. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards accurate free energy calculations in ligand protein-binding studies.

PubMed

Steinbrecher, Thomas; Labahn, Andreas

2010-01-01

Cells contain a multitude of different chemical reaction paths running simultaneously and quite independently next to each other. This amazing feat is enabled by molecular recognition, the ability of biomolecules to form stable and specific complexes with each other and with their substrates. A better understanding of this process, i.e. of the kinetics, structures and thermodynamic properties of biomolecule binding, would be invaluable in the study of biological systems. In addition, as the mode of action of many pharmaceuticals is based upon their inhibition or activation of biomolecule targets, predictive models of small molecule receptor binding are very helpful tools in rational drug design. Since the goal here is normally to design a new compound with a high inhibition strength, one of the most important thermodynamic properties is the binding free energy DeltaG(0). The prediction of binding constants has always been one of the major goals in the field of computational chemistry, because the ability to reliably assess a hypothetical compound's binding properties without having to synthesize it first would save a tremendous amount of work. The different approaches to this question range from fast and simple empirical descriptor methods to elaborate simulation protocols aimed at putting the computation of free energies onto a solid foundation of statistical thermodynamics. While the later methods are still not suited for the screenings of thousands of compounds that are routinely performed in computational drug design studies, they are increasingly put to use for the detailed study of protein ligand interactions. This review will focus on molecular mechanics force field based free energy calculations and their application to the study of protein ligand interactions. After a brief overview of other popular methods for the calculation of free energies, we will describe recent advances in methodology and a variety of exemplary studies of molecular dynamics simulation based free energy calculations.

Cloud computing approaches for prediction of ligand binding poses and pathways.

PubMed

Lawrenz, Morgan; Shukla, Diwakar; Pande, Vijay S

2015-01-22

We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.

Thermodynamic Insights into Effects of Water Displacement and Rearrangement upon Ligand Modification using Molecular Dynamics Simulations.

PubMed

Wahl, Joel; Smiesko, Martin

2018-05-04

Computational methods, namely Molecular Dynamics Simulations (MD simulations) in combination with Inhomogeneous Fluid Solvation Theory (IFST) were used to retrospectively investigate various cases of ligand structure modifications that led to the displacement of binding site water molecules. Our findings are that the water displacement per se is energetically unfavorable in the discussed examples, and that it is merely the fine balance between change in protein-ligand interaction energy, ligand solvation free energies and binding site solvation free energies that determine if water displacement is favorable or not. We furthermore evaluated if we can reproduce experimental binding affinities by a computational approach combining changes in solvation free energies with changes in protein-ligand interaction energies and entropies. In two of the seven cases, this estimation led to large errors, implying that accurate predictions of relative binding free energies based on solvent thermodynamics is challenging. Still, MD simulations can provide insights into which water molecules can be targeted for displacement. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Acceptor binding energies in GaN and AlN

NASA Astrophysics Data System (ADS)

Mireles, Francisco; Ulloa, Sergio E.

1998-08-01

We employ effective-mass theory for degenerate hole bands to calculate the acceptor binding energies for Be, Mg, Zn, Ca, C, and Si substitutional acceptors in GaN and AlN. The calculations are performed through the 6×6 Rashba-Sheka-Pikus and the Luttinger-Kohn matrix Hamiltonians for wurtzite (WZ) and zinc-blende (ZB) crystal phases, respectively. An analytic representation for the acceptor pseudopotential is used to introduce the specific nature of the impurity atoms. The energy shift due to polaron effects is also considered in this approach. The ionization energy estimates are in very good agreement with those reported experimentally in WZ GaN. The binding energies for ZB GaN acceptors are all predicted to be shallower than the corresponding impurities in the WZ phase. The binding-energy dependence upon the crystal-field splitting in WZ GaN is analyzed. Ionization levels in AlN are found to have similar ``shallow'' values to those in GaN, but with some important differences which depend on the band structure parametrizations, especially the value of the crystal-field splitting used.

Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of D-amino acid oxidase inhibitors.

PubMed

Orgován, Zoltán; Ferenczy, György G; Steinbrecher, Thomas; Szilágyi, Bence; Bajusz, Dávid; Keserű, György M

2018-02-01

Optimization of fragment size D-amino acid oxidase (DAAO) inhibitors was investigated using a combination of computational and experimental methods. Retrospective free energy perturbation (FEP) calculations were performed for benzo[d]isoxazole derivatives, a series of known inhibitors with two potential binding modes derived from X-ray structures of other DAAO inhibitors. The good agreement between experimental and computed binding free energies in only one of the hypothesized binding modes strongly support this bioactive conformation. Then, a series of 1-H-indazol-3-ol derivatives formerly not described as DAAO inhibitors was investigated. Binding geometries could be reliably identified by structural similarity to benzo[d]isoxazole and other well characterized series and FEP calculations were performed for several tautomers of the deprotonated and protonated compounds since all these forms are potentially present owing to the experimental pKa values of representative compounds in the series. Deprotonated compounds are proposed to be the most important bound species owing to the significantly better agreement between their calculated and measured affinities compared to the protonated forms. FEP calculations were also used for the prediction of the affinities of compounds not previously tested as DAAO inhibitors and for a comparative structure-activity relationship study of the benzo[d]isoxazole and indazole series. Selected indazole derivatives were synthesized and their measured binding affinity towards DAAO was in good agreement with FEP predictions.

Validation of tautomeric and protomeric binding modes by free energy calculations. A case study for the structure based optimization of d-amino acid oxidase inhibitors

NASA Astrophysics Data System (ADS)

Orgován, Zoltán; Ferenczy, György G.; Steinbrecher, Thomas; Szilágyi, Bence; Bajusz, Dávid; Keserű, György M.

2018-02-01

Optimization of fragment size d-amino acid oxidase (DAAO) inhibitors was investigated using a combination of computational and experimental methods. Retrospective free energy perturbation (FEP) calculations were performed for benzo[d]isoxazole derivatives, a series of known inhibitors with two potential binding modes derived from X-ray structures of other DAAO inhibitors. The good agreement between experimental and computed binding free energies in only one of the hypothesized binding modes strongly support this bioactive conformation. Then, a series of 1-H-indazol-3-ol derivatives formerly not described as DAAO inhibitors was investigated. Binding geometries could be reliably identified by structural similarity to benzo[d]isoxazole and other well characterized series and FEP calculations were performed for several tautomers of the deprotonated and protonated compounds since all these forms are potentially present owing to the experimental pKa values of representative compounds in the series. Deprotonated compounds are proposed to be the most important bound species owing to the significantly better agreement between their calculated and measured affinities compared to the protonated forms. FEP calculations were also used for the prediction of the affinities of compounds not previously tested as DAAO inhibitors and for a comparative structure-activity relationship study of the benzo[d]isoxazole and indazole series. Selected indazole derivatives were synthesized and their measured binding affinity towards DAAO was in good agreement with FEP predictions.

Comparison of calculation and experiment implicates significant electrostatic contributions to the binding stability of barnase and barstar.

PubMed

Dong, Feng; Vijayakumar, M; Zhou, Huan-Xiang

2003-07-01

The contributions of electrostatic interactions to the binding stability of barnase and barstar were studied by the Poisson-Boltzmann model with three different protocols: a), the dielectric boundary specified as the van der Waals (vdW) surface of the protein along with a protein dielectric constant (epsilon (p)) of 4; b), the dielectric boundary specified as the molecular (i.e., solvent-exclusion (SE)) surface along with epsilon (p) = 4; and c), "SE + epsilon (p) = 20." The "vdW + epsilon (p) = 4" and "SE + epsilon (p) = 20" protocols predicted an overall electrostatic stabilization whereas the "SE + epsilon (p) = 4" protocol predicted an overall electrostatic destabilization. The "vdW + epsilon (p) = 4" protocol was most consistent with experiment. It quantitatively reproduced the observed effects of 17 mutations neutralizing charged residues lining the binding interface and the measured coupling energies of six charge pairs across the interface and reasonably rationalized the experimental ionic strength and pH dependences of the binding constant. In contrast, the "SE + epsilon (p) = 4" protocol predicted significantly larger coupling energies of charge pairs whereas the "SE + epsilon (p) = 20" protocol did not predict any pH dependence. This study calls for further scrutiny of the different Poisson-Boltzmann protocols and demonstrates potential danger in drawing conclusions on electrostatic contributions based on a particular calculation protocol.

Impact of mutations on the allosteric conformational equilibrium

PubMed Central

Weinkam, Patrick; Chen, Yao Chi; Pons, Jaume; Sali, Andrej

2012-01-01

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and unbound protein structures. These simulations can be performed using our web server: http://salilab.org/allosmod/. We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a dataset of 10 proteins and 179 mutations, we predict both the magnitude and sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1 kBT. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction. PMID:23228330

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain.

PubMed

Hou, Tingjun; Zhang, Wei; Case, David A; Wang, Wei

2008-02-29

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.

Computational screening of functional groups for capture of toxic industrial chemicals in porous materials.

PubMed

Kim, Ki Chul; Fairen-Jimenez, David; Snurr, Randall Q

2017-12-06

A thermodynamic analysis using quantum chemical methods was carried out to identify optimal functional group candidates that can be included in metal-organic frameworks and activated carbons for the selective capture of toxic industrial chemicals (TICs) in humid air. We calculated the binding energies of 14 critical TICs plus water with a series of 10 functional groups attached to a naphthalene ring model. Using vibrational calculations, the free energies of adsorption were calculated in addition to the binding energies. Our results show that, in these systems, the binding energies and free energies follow similar trends. We identified copper(i) carboxylate as the optimal functional group (among those studied) for the selective binding of the majority of the TICs in humid air, and this functional group exhibits especially strong binding for sulfuric acid. Further thermodynamic analysis shows that the presence of water weakens the binding strength of sulfuric acid with the copper carboxylate group. Our calculations predict that functionalization of aromatic rings would be detrimental to selective capture of COCl 2 , CO 2 , and Cl 2 under humid conditions. Finally, we found that forming an ionic complex, H 3 O + HSO 4 - , between H 2 SO 4 and H 2 O via proton transfer is not favorable on copper carboxylate.

Kinetic rate constant prediction supports the conformational selection mechanism of protein binding.

PubMed

Moal, Iain H; Bates, Paul A

2012-01-01

The prediction of protein-protein kinetic rate constants provides a fundamental test of our understanding of molecular recognition, and will play an important role in the modeling of complex biological systems. In this paper, a feature selection and regression algorithm is applied to mine a large set of molecular descriptors and construct simple models for association and dissociation rate constants using empirical data. Using separate test data for validation, the predicted rate constants can be combined to calculate binding affinity with accuracy matching that of state of the art empirical free energy functions. The models show that the rate of association is linearly related to the proportion of unbound proteins in the bound conformational ensemble relative to the unbound conformational ensemble, indicating that the binding partners must adopt a geometry near to that of the bound prior to binding. Mirroring the conformational selection and population shift mechanism of protein binding, the models provide a strong separate line of evidence for the preponderance of this mechanism in protein-protein binding, complementing structural and theoretical studies.

Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model.

PubMed

Setny, Piotr

2015-12-08

Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

Free-Energy-Based Protein Design: Re-Engineering Cellular Retinoic Acid Binding Protein II Assisted by the Moveable-Type Approach.

PubMed

Zhong, Haizhen A; Santos, Elizabeth M; Vasileiou, Chrysoula; Zheng, Zheng; Geiger, James H; Borhan, Babak; Merz, Kenneth M

2018-03-14

How to fine-tune the binding free energy of a small-molecule to a receptor site by altering the amino acid residue composition is a key question in protein engineering. Indeed, the ultimate solution to this problem, to chemical accuracy (±1 kcal/mol), will result in profound and wide-ranging applications in protein design. Numerous tools have been developed to address this question using knowledge-based models to more computationally intensive molecular dynamics simulations-based free energy calculations, but while some success has been achieved there remains room for improvement in terms of overall accuracy and in the speed of the methodology. Here we report a fast, knowledge-based movable-type (MT)-based approach to estimate the absolute and relative free energy of binding as influenced by mutations in a small-molecule binding site in a protein. We retrospectively validate our approach using mutagenesis data for retinoic acid binding to the Cellular Retinoic Acid Binding Protein II (CRABPII) system and then make prospective predictions that are borne out experimentally. The overall performance of our approach is supported by its success in identifying mutants that show high or even sub-nano-molar binding affinities of retinoic acid to the CRABPII system.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

PubMed Central

2017-01-01

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein–ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol–1), with good cross-validation statistics. PMID:28776988

Development of many-body polarizable force fields for Li-battery components: 1. Ether, alkane, and carbonate-based solvents.

PubMed

Borodin, Oleg; Smith, Grant D

2006-03-30

Classical many-body polarizable force fields were developed for n-alkanes, perflouroalkanes, polyethers, ketones, and linear and cyclic carbonates on the basis of quantum chemistry dimer energies of model compounds and empirical thermodynamic liquid-state properties. The dependence of the electron correlation contribution to the dimer binding energy on basis-set size and level of theory was investigated as a function of molecular separation for a number of alkane, ether, and ketone dimers. Molecular dynamics (MD) simulations of the force fields accurately predicted structural, dynamic, and transport properties of liquids and unentangled polymer melts. On average, gas-phase dimer binding energies predicted with the force field were between those from MP2/aug-cc-pvDz and MP2/aug-cc-pvTz quantum chemistry calculations.

Recovery of known T-cell epitopes by computational scanning of a viral genome

NASA Astrophysics Data System (ADS)

Logean, Antoine; Rognan, Didier

2002-04-01

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.

An Efficient Metadynamics-Based Protocol To Model the Binding Affinity and the Transition State Ensemble of G-Protein-Coupled Receptor Ligands.

PubMed

Saleh, Noureldin; Ibrahim, Passainte; Saladino, Giorgio; Gervasio, Francesco Luigi; Clark, Timothy

2017-05-22

A generally applicable metadynamics scheme for predicting the free energy profile of ligand binding to G-protein-coupled receptors (GPCRs) is described. A common and effective collective variable (CV) has been defined using the ideally placed and highly conserved Trp6.48 as a reference point for ligand-GPCR distance measurement and the common orientation of GPCRs in the cell membrane. Using this single CV together with well-tempered multiple-walker metadynamics with a funnel-like boundary allows an efficient exploration of the entire ligand binding path from the extracellular medium to the orthosteric binding site, including vestibule and intermediate sites. The protocol can be used with X-ray structures or high-quality homology models (based on a high-quality template and after thorough refinement) for the receptor and is universally applicable to agonists, antagonists, and partial and reverse agonists. The root-mean-square error (RMSE) in predicted binding free energies for 12 diverse ligands in five receptors (a total of 23 data points) is surprisingly small (less than 1 kcal mol -1 ). The RMSEs for simulations that use receptor X-ray structures and homology models are very similar.

Computational study concerning the effect of some pesticides on the Proteus Mirabilis catalase activity

NASA Astrophysics Data System (ADS)

Isvoran, Adriana

2016-03-01

Assessment of the effects of the herbicides nicosulfuron and chlorsulfuron and the fungicides difenoconazole and drazoxlone upon catalase produced by soil microorganism Proteus mirabilis is performed using the molecular docking technique. The interactions of pesticides with the enzymes are predicted using SwissDock and PatchDock docking tools. There are correlations for predicted binding energy values for enzyme-pesticide complexes obtained using the two docking tools, all the considered pesticides revealing favorable binding to the enzyme, but only the herbicides bind to the catalytic site. These results suggest the inhibitory potential of chlorsulfuron and nicosulfuron on the catalase activity in soil.

Accurate Prediction of Inducible Transcription Factor Binding Intensities In Vivo

PubMed Central

Siepel, Adam; Lis, John T.

2012-01-01

DNA sequence and local chromatin landscape act jointly to determine transcription factor (TF) binding intensity profiles. To disentangle these influences, we developed an experimental approach, called protein/DNA binding followed by high-throughput sequencing (PB–seq), that allows the binding energy landscape to be characterized genome-wide in the absence of chromatin. We applied our methods to the Drosophila Heat Shock Factor (HSF), which inducibly binds a target DNA sequence element (HSE) following heat shock stress. PB–seq involves incubating sheared naked genomic DNA with recombinant HSF, partitioning the HSF–bound and HSF–free DNA, and then detecting HSF–bound DNA by high-throughput sequencing. We compared PB–seq binding profiles with ones observed in vivo by ChIP–seq and developed statistical models to predict the observed departures from idealized binding patterns based on covariates describing the local chromatin environment. We found that DNase I hypersensitivity and tetra-acetylation of H4 were the most influential covariates in predicting changes in HSF binding affinity. We also investigated the extent to which DNA accessibility, as measured by digital DNase I footprinting data, could be predicted from MNase–seq data and the ChIP–chip profiles for many histone modifications and TFs, and found GAGA element associated factor (GAF), tetra-acetylation of H4, and H4K16 acetylation to be the most predictive covariates. Lastly, we generated an unbiased model of HSF binding sequences, which revealed distinct biophysical properties of the HSF/HSE interaction and a previously unrecognized substructure within the HSE. These findings provide new insights into the interplay between the genomic sequence and the chromatin landscape in determining transcription factor binding intensity. PMID:22479205

Ligand deconstruction: Why some fragment binding positions are conserved and others are not.

PubMed

Kozakov, Dima; Hall, David R; Jehle, Stefan; Jehle, Sefan; Luo, Lingqi; Ochiana, Stefan O; Jones, Elizabeth V; Pollastri, Michael; Allen, Karen N; Whitty, Adrian; Vajda, Sandor

2015-05-19

Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots--regions of the protein where interactions with a ligand contribute substantial binding free energy--the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand.

A theory of adhesion at a bimetallic interface - Overlap effects.

NASA Technical Reports Server (NTRS)

Ferrante, J.; Smith, J. R.

1973-01-01

A preliminary calculation of the chemical bonding adhesive interaction between metal surfaces is provided. In this first theory the Hohenberg and Kohn formalism is used to give the bimetallic adhesive binding energy versus separation. The close-packed planes of Al, Mg, and Zn are considered. The effect of simple overlap of the metal-vacuum distributions is determined. The importance of registry between contact surfaces is ascertained. A minimum in the binding energy curve is exhibited for all combinations. The theoretical predictions agree with trends in bond strengths taken from available experimental data. An insight into the mechanisms involved in metallic transfer is given. The relationship between adhesive energies, cohesive energies, and surface energies is discussed.

A molecular modeling based method to predict elution behavior and binding patches of proteins in multimodal chromatography.

PubMed

Banerjee, Suvrajit; Parimal, Siddharth; Cramer, Steven M

2017-08-18

Multimodal (MM) chromatography provides a powerful means to enhance the selectivity of protein separations by taking advantage of multiple weak interactions that include electrostatic, hydrophobic and van der Waals interactions. In order to increase our understanding of such phenomena, a computationally efficient approach was developed that combines short molecular dynamics simulations and continuum solvent based coarse-grained free energy calculations in order to study the binding of proteins to Self Assembled Monolayers (SAM) presenting MM ligands. Using this method, the free energies of protein-MM SAM binding over a range of incident orientations of the protein can be determined. The resulting free energies were then examined to identify the more "strongly bound" orientations of different proteins with two multimodal surfaces. The overall free energy of protein-MM surface binding was then determined and correlated to retention factors from isocratic chromatography. This correlation, combined with analytical expressions from the literature, was then employed to predict protein gradient elution salt concentrations as well as selectivity reversals with different MM resin systems. Patches on protein surfaces that interacted strongly with MM surfaces were also identified by determining the frequency of heavy atom contacts with the atoms of the MM SAMs. A comparison of these patches to Electrostatic Potential and hydrophobicity maps indicated that while all of these patches contained significant positive charge, only the highest frequency sites also possessed hydrophobicity. The ability to identify key binding patches on proteins may have significant impact on process development for the separation of bioproduct related impurities. Copyright © 2017 Elsevier B.V. All rights reserved.

Adhesion of a bimetallic interface. Ph.D. Thesis - Case Western Reserve Univ.; [for Al, Mg, and Zn

NASA Technical Reports Server (NTRS)

Ferrante, J.

1978-01-01

The Hohenberg-Kohn and Kohn-Sham formalisms are used to examine binding (binding energy as a function of separation) for combinations of the simple metals Al(111), Zn(0001), Mg(0001), and Na(110) in contact. Similar metal contacts between Al, Zn, Mg, and Na are examined self-consistently in an ab initio calculation using the Kohn-Sham formalism. Crystallinity is included using the Aschroft pseudopotential via first order perturbation theory for the electron-ion interaction; and the ion-ion interaction is included exactly via a lattice sum. Binding energy was determined both in the local-density approximation and including gradient corrections to the exchange and correlation energy. Binding was found in all cases. In dissimilar metal contacts, interfacial bonding was greater than that in the weaker material predicting the possibility of metallic transfer. The nonzero position of the energy minimum in like metal contacts is explained in terms of consistency between the Ashcroft pseudopotential and the bulk charge density. Good agreement with experimental surface energies is obtained in the self-consistent calculation when nonlocal terms are included.

Evaluation of water displacement energetics in protein binding sites with grid cell theory.

PubMed

Gerogiokas, G; Southey, M W Y; Mazanetz, M P; Heifetz, A; Hefeitz, A; Bodkin, M; Law, R J; Michel, J

2015-04-07

Excess free energies, enthalpies and entropies of water in protein binding sites were computed via classical simulations and Grid Cell Theory (GCT) analyses for three pairs of congeneric ligands in complex with the proteins scytalone dehydratase, p38α MAP kinase and EGFR kinase respectively. Comparative analysis is of interest since the binding modes for each ligand pair differ in the displacement of one binding site water molecule, but significant variations in relative binding affinities are observed. Protocols that vary in their use of restraints on protein and ligand atoms were compared to determine the influence of protein-ligand flexibility on computed water structure and energetics, and to assess protocols for routine analyses of protein-ligand complexes. The GCT-derived binding affinities correctly reproduce experimental trends, but the magnitude of the predicted changes in binding affinities is exaggerated with respect to results from a previous Monte Carlo Free Energy Perturbation study. Breakdown of the GCT water free energies into enthalpic and entropic components indicates that enthalpy changes dominate the observed variations in energetics. In EGFR kinase GCT analyses revealed that replacement of a pyrimidine by a cyanopyridine perturbs water energetics up three hydration shells away from the ligand.

Frenkel and Charge-Transfer Excitations in Donor-acceptor Complexes from Many-Body Green's Functions Theory.

PubMed

Baumeier, Björn; Andrienko, Denis; Rohlfing, Michael

2012-08-14

Excited states of donor-acceptor dimers are studied using many-body Green's functions theory within the GW approximation and the Bethe-Salpeter equation. For a series of prototypical small-molecule based pairs, this method predicts energies of local Frenkel and intermolecular charge-transfer excitations with the accuracy of tens of meV. Application to larger systems is possible and allowed us to analyze energy levels and binding energies of excitons in representative dimers of dicyanovinyl-substituted quarterthiophene and fullerene, a donor-acceptor pair used in state of the art organic solar cells. In these dimers, the transition from Frenkel to charge transfer excitons is endothermic and the binding energy of charge transfer excitons is still of the order of 1.5-2 eV. Hence, even such an accurate dimer-based description does not yield internal energetics favorable for the generation of free charges either by thermal energy or an external electric field. These results confirm that, for qualitative predictions of solar cell functionality, accounting for the explicit molecular environment is as important as the accurate knowledge of internal dimer energies.

Docking analysis targeted to the whole enzyme: an application to the prediction of inhibition of PTP1B by thiomorpholine and thiazolyl derivatives.

PubMed

Ganou, C A; Eleftheriou, P Th; Theodosis-Nobelos, P; Fesatidou, M; Geronikaki, A A; Lialiaris, T; Rekka, E A

2018-02-01

PTP1b is a protein tyrosine phosphatase involved in the inactivation of insulin receptor. Since inhibition of PTP1b may prolong the action of the receptor, PTP1b has become a drug target for the treatment of type II diabetes. In the present study, prediction of inhibition using docking analysis targeted specifically to the active or allosteric site was performed on 87 compounds structurally belonging to 10 different groups. Two groups, consisting of 15 thiomorpholine and 10 thiazolyl derivatives exhibiting the best prediction results, were selected for in vitro evaluation. All thiomorpholines showed inhibitory action (with IC 50 = 4-45 μΜ, Ki = 2-23 μM), while only three thiazolyl derivatives showed low inhibition (best IC 50 = 18 μΜ, Ki = 9 μΜ). However, free binding energy (E) was in accordance with the IC 50 values only for some compounds. Docking analysis targeted to the whole enzyme revealed that the compounds exhibiting IC 50 values higher than expected could bind to other peripheral sites with lower free energy, E o , than when bound to the active/allosteric site. A prediction factor, E- (Σ Eo × 0.16), which takes into account lower energy binding to peripheral sites, was proposed and was found to correlate well with the IC 50 values following an asymmetrical sigmoidal equation with r 2 = 0.9692.

Engineering Transition-Metal-Coated Tungsten Carbides for Efficient and Selective Electrochemical Reduction of CO2 to Methane.

PubMed

Wannakao, Sippakorn; Artrith, Nongnuch; Limtrakul, Jumras; Kolpak, Alexie M

2015-08-24

The design of catalysts for CO2 reduction is challenging because of the fundamental relationships between the binding energies of the reaction intermediates. Metal carbides have shown promise for transcending these relationships and enabling low-cost alternatives. Herein, we show that directional bonding arising from the mixed covalent/metallic character plays a critical role in governing the surface chemistry. This behavior can be described by consideration of individual d-band components. We use this model to predict efficient catalysts based on tungsten carbide with a sub-monolayer of iron adatoms. Our approach can be used to predict site-preference and binding-energy trends for complex catalyst surfaces. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nature of the Active Sites for CO Reduction on Copper Nanoparticles; Suggestions for Optimizing Performance.

PubMed

Cheng, Tao; Xiao, Hai; Goddard, William A

2017-08-30

Recent experiments show that the grain boundaries (GBs) of copper nanoparticles (NPs) lead to an outstanding performance in reducing CO 2 and CO to alcohol products. We report here multiscale simulations that simulate experimental synthesis conditions to predict the structure of a 10 nm Cu NP (158 555 atoms). To identify active sites, we first predict the CO binding at a large number of sites and select four exhibiting CO binding stronger than the (211) step surface. Then, we predict the formation energy of the *OCCOH intermediate as a descriptor for C-C coupling, identifying two active sites, both of which have an under-coordinated surface square site adjacent to a subsurface stacking fault. We then propose a periodic Cu surface (4 by 4 supercell) with a similar site that substantially decreases the formation energy of *OCCOH, by 0.14 eV.

Cooperative Allosteric Ligand Binding in Calmodulin

NASA Astrophysics Data System (ADS)

Nandigrami, Prithviraj

Conformational dynamics is often essential for a protein's function. For example, proteins are able to communicate the effect of binding at one site to a distal region of the molecule through changes in its conformational dynamics. This so called allosteric coupling fine tunes the sensitivity of ligand binding to changes in concentration. A conformational change between a "closed" (apo) and an "open" (holo) conformation upon ligation often produces this coupling between binding sites. Enhanced sensitivity between the unbound and bound ensembles leads to a sharper binding curve. There are two basic conceptual frameworks that guide our visualization about ligand binding mechanisms. First, a ligand can stabilize the unstable "open" state from a dynamic ensemble of conformations within the unbound basin. This binding mechanism is called conformational selection. Second, a ligand can weakly bind to the low-affinity "closed" state followed by a conformational transition to the "open" state. In this dissertation, I focus on molecular dynamics simulations to understand microscopic origins of ligand binding cooperativity. A minimal model of allosteric binding transitions must include ligand binding/unbinding events, while capturing the transition mechanism between two distinct meta-stable free energy basins. Due in part to computational timescales limitations, work in this dissertation describes large-scale conformational transitions through a simplified, coarse-grained model based on the energy basins defined by the open and closed conformations of the protein Calmodulin (CaM). CaM is a ubiquitous calcium-binding protein consisting of two structurally similar globular domains connected by a flexible linker. The two domains of CaM, N-terminal domain (nCaM) and C-terminal domain (cCaM) consists of two helix-loop-helix motifs (the EF-hands) connected by a flexible linker. Each domain of CaM consists of two binding loops and binds 2 calcium ions each. The intact domain binds up to 4 calcium ions. The simulations use a coupled molecular dynamics/monte carlo scheme where the protein dynamics is simulated explicitly, while ligand binding/unbinding are treated implicitly. In the model, ligand binding/unbinding events coupled with a conformational change of the protein within the grand canonical ensemble. Here, ligand concentration is controlled through the chemical potential (micro). This allows us to use a simple thermodynamic model to analyze the simulated data and quantify binding cooperativity. Simulated binding titration curves are calculated through equilibrium simulations at different values of micro. First, I study domain opening transitions of isolated nCaM and cCaM in the absence of calcium. This work is motivated by results from a recent analytic variational model that predicts distinct domain opening transition mechanism for the domains of CaM. This is a surprising result because the domains have the same folded state topology. In the simulations, I find the two domains of CaM have distinct transition mechanism over a broad range of temperature, in harmony with the analytic predictions. In particular, the simulated transition mechanism of nCaM follows a two-state behavior, while domain opening in cCaM involves global unfolding and refolding of the tertiary structure. The unfolded intermediate also appears in the landscape of nCaM, but at a higher temperature than it appears in cCaM's energy landscape. This is consistent with nCaM's higher thermal stability. Under approximate physiological conditions, majority of the sampled transitions in cCaM involves unfolding and refolding during conformational change. Kinetically, the transient unfolding and refolding in cCaM significantly slows the domain opening and closing rates in cCaM. Second, I investigate the structural origins of binding affinity and allosteric cooperativity of binding 2 calcium-ions to each domain of CaM. In my work, I predict the order of binding strength of CaM's loops. I analyze simulated binding curves within the framework of the classic Monod-Wyman-Changeux (MWC) model of allostery to extract the binding free energies to the closed and open ensembles. The simulations predict that cCaM binds calcium with higher affinity and greater cooperativity than nCaM. Where it is possible to compare, these predictions are in good agreement with experimental results. The analysis of the simulations offers a rationale for why the two domains differ in cooperativity: the higher cooperativity of cCaM is due to larger difference in affinity of its binding loops. Third, I extend the work to investigate structural origins of binding cooperativity of 4 calcium-ions to intact CaM. I characterize the microscopic cooperativities of each ligation state and provide a kinetic description of the binding mechanism. Due to the heterogeneous nature of CaM's loops, as predicted in our simulations of isolated domains, I focus on investigating the influence of this heterogeneity on the kinetic flux of binding pathways as a function of concentration. The formalism developed for Network Models of protein folding kinetics, is used to evaluate the directed flux of all possible pathways between unligated and fully loaded CaM. (Abstract shortened by ProQuest.).

Comparison of Calculation and Experiment Implicates Significant Electrostatic Contributions to the Binding Stability of Barnase and Barstar

PubMed Central

Dong, Feng; Vijayakumar, M.; Zhou, Huan-Xiang

2003-01-01

The contributions of electrostatic interactions to the binding stability of barnase and barstar were studied by the Poisson-Boltzmann model with three different protocols: a), the dielectric boundary specified as the van der Waals (vdW) surface of the protein along with a protein dielectric constant (ɛp) of 4; b), the dielectric boundary specified as the molecular (i.e., solvent-exclusion (SE)) surface along with ɛp = 4; and c), “SE + ɛp = 20.” The “vdW + ɛp = 4” and “SE + ɛp = 20” protocols predicted an overall electrostatic stabilization whereas the “SE + ɛp = 4” protocol predicted an overall electrostatic destabilization. The “vdW + ɛp = 4” protocol was most consistent with experiment. It quantitatively reproduced the observed effects of 17 mutations neutralizing charged residues lining the binding interface and the measured coupling energies of six charge pairs across the interface and reasonably rationalized the experimental ionic strength and pH dependences of the binding constant. In contrast, the “SE + ɛp = 4” protocol predicted significantly larger coupling energies of charge pairs whereas the “SE + ɛp = 20” protocol did not predict any pH dependence. This study calls for further scrutiny of the different Poisson-Boltzmann protocols and demonstrates potential danger in drawing conclusions on electrostatic contributions based on a particular calculation protocol. PMID:12829463

Studies of the mechanism of selectivity of protein tyrosine phosphatase 1B (PTP1B) bidentate inhibitors using molecular dynamics simulations and free energy calculations.

PubMed

Fang, Lei; Zhang, Huai; Cui, Wei; Ji, Mingjun

2008-10-01

Bidentate inhibitors of protein tyrosine phosphatase 1B (PTP1B) are considered as a group of ideal inhibitors with high binding potential and high selectivity in treating type II diabetes. In this paper, the binding models of five bidentate inhibitors to PTP1B, TCPTP, and SHP-2 were investigated and compared by using molecular dynamics (MD) simulations and free energy calculations. The binding free energies were computed using the Molecular Mechanics/Poisson-Boltzmann Surface Area (MM/PBSA) methodology. The calculation results show that the predicted free energies of the complexes are well consistent with the experimental data. The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition analysis indicates that the residues ARG24, ARG254, and GLN262 in the second binding site of PTP1B are essential for the high selectivity of inhibitors. Furthermore, the residue PHE182 close to the active site is also important for the selectivity and the binding affinity of the inhibitors. According to our analysis, it can be concluded that in most cases the polarity of the portion of the inhibitor that binds to the second binding site of the protein is positive to the affinity of the inhibitors while negative to the selectivity of the inhibitors. We expect that the information we obtained here can help to develop potential PTP1B inhibitors with more promising specificity.

A Mixed QM/MM Scoring Function to Predict Protein-Ligand Binding Affinity

PubMed Central

Hayik, Seth A.; Dunbrack, Roland; Merz, Kenneth M.

2010-01-01

Computational methods for predicting protein-ligand binding free energy continue to be popular as a potential cost-cutting method in the drug discovery process. However, accurate predictions are often difficult to make as estimates must be made for certain electronic and entropic terms in conventional force field based scoring functions. Mixed quantum mechanics/molecular mechanics (QM/MM) methods allow electronic effects for a small region of the protein to be calculated, treating the remaining atoms as a fixed charge background for the active site. Such a semi-empirical QM/MM scoring function has been implemented in AMBER using DivCon and tested on a set of 23 metalloprotein-ligand complexes, where QM/MM methods provide a particular advantage in the modeling of the metal ion. The binding affinity of this set of proteins can be calculated with an R2 of 0.64 and a standard deviation of 1.88 kcal/mol without fitting and 0.71 and a standard deviation of 1.69 kcal/mol with fitted weighting of the individual scoring terms. In this study we explore using various methods to calculate terms in the binding free energy equation, including entropy estimates and minimization standards. From these studies we found that using the rotational bond estimate to ligand entropy results in a reasonable R2 of 0.63 without fitting. We also found that using the ESCF energy of the proteins without minimization resulted in an R2 of 0.57, when using the rotatable bond entropy estimate. PMID:21221417

Dynamics, magnetic properties, and electron binding energies of H2O2 in water.

PubMed

C Cabral, Benedito J

2017-06-21

Results for the magnetic properties and electron binding energies of H 2 O 2 in liquid water are presented. The adopted methodology relies on the combination of Born-Oppenheimer molecular dynamics and electronic structure calculations. The Keal-Tozer functional was applied for predicting magnetic shieldings and H 2 O 2 intramolecular spin-spin coupling constants. Electron binding energies were calculated with electron propagator theory. In water, H 2 O 2 is a better proton donor than proton acceptor, and the present results indicate that this feature is important for understanding magnetic properties in solution. In comparison with the gas-phase, H 2 O 2 atoms are deshielded in water. For oxygen atoms, the deshielding is mainly determined by structural/conformational changes. Hydrogen-bond interactions explain the deshielding of protons in water. The predicted chemical shift for the H 2 O 2 protons in water (δ∼11.8 ppm) is in good agreement with experimental information (δ=11.2 ppm). The two lowest electron binding energies of H 2 O 2 in water (10.7±0.5 and 11.2±0.5 eV) are in reasonable agreement with experiment. In keeping with data from photoelectron spectroscopy, an ∼1.6 eV red-shift of the two first ionisation energies relative to the gas-phase is observed in water. The strong dependence of magnetic properties on changes of the electronic density in the nuclei environment is illustrated by a correlation between the σ( 17 O) magnetic shielding constant and the energy gap between the [2a] lowest valence and [1a] core orbitals of H 2 O 2 .

Receptor-based 3D QSAR analysis of estrogen receptor ligands - merging the accuracy of receptor-based alignments with the computational efficiency of ligand-based methods

NASA Astrophysics Data System (ADS)

Sippl, Wolfgang

2000-08-01

One of the major challenges in computational approaches to drug design is the accurate prediction of binding affinity of biomolecules. In the present study several prediction methods for a published set of estrogen receptor ligands are investigated and compared. The binding modes of 30 ligands were determined using the docking program AutoDock and were compared with available X-ray structures of estrogen receptor-ligand complexes. On the basis of the docking results an interaction energy-based model, which uses the information of the whole ligand-receptor complex, was generated. Several parameters were modified in order to analyze their influence onto the correlation between binding affinities and calculated ligand-receptor interaction energies. The highest correlation coefficient ( r 2 = 0.617, q 2 LOO = 0.570) was obtained considering protein flexibility during the interaction energy evaluation. The second prediction method uses a combination of receptor-based and 3D quantitative structure-activity relationships (3D QSAR) methods. The ligand alignment obtained from the docking simulations was taken as basis for a comparative field analysis applying the GRID/GOLPE program. Using the interaction field derived with a water probe and applying the smart region definition (SRD) variable selection, a significant and robust model was obtained ( r 2 = 0.991, q 2 LOO = 0.921). The predictive ability of the established model was further evaluated by using a test set of six additional compounds. The comparison with the generated interaction energy-based model and with a traditional CoMFA model obtained using a ligand-based alignment ( r 2 = 0.951, q 2 LOO = 0.796) indicates that the combination of receptor-based and 3D QSAR methods is able to improve the quality of the underlying model.

Prediction of Binding Energy of Keap1 Interaction Motifs in the Nrf2 Antioxidant Pathway and Design of Potential High-Affinity Peptides.

PubMed

Karttunen, Mikko; Choy, Wing-Yiu; Cino, Elio A

2018-06-07

Nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor and principal regulator of the antioxidant pathway. The Kelch domain of Kelch-like ECH-associated protein 1 (Keap1) binds to motifs in the N-terminal region of Nrf2, promoting its degradation. There is interest in developing ligands that can compete with Nrf2 for binding to Kelch, thereby activating its transcriptional activities and increasing antioxidant levels. Using experimental Δ G bind values of Kelch-binding motifs determined previously, a revised hydrophobicity-based model was developed for estimating Δ G bind from amino acid sequence and applied to rank potential uncharacterized Kelch-binding motifs identified from interaction databases and BLAST searches. Model predictions and molecular dynamics (MD) simulations suggested that full-length MAD2A binds Kelch more favorably than a high-affinity 20-mer Nrf2 E78P peptide, but that the motif in isolation is not a particularly strong binder. Endeavoring to develop shorter peptides for activating Nrf2, new designs were created based on the E78P peptide, some of which showed considerable propensity to form binding-competent structures in MD, and were predicted to interact with Kelch more favorably than the E78P peptide. The peptides could be promising new ligands for enhancing the oxidative stress response.

Binding of cyclic carboxylates to octa-acid deep-cavity cavitand

NASA Astrophysics Data System (ADS)

Gibb, Corinne L. D.; Gibb, Bruce C.

2014-04-01

As part of the fourth statistical assessment of modeling of proteins and ligands (sampl.eyesopen.com) prediction challenge, the strength of association of nine guests ( 1- 9) binding to octa-acid host was determined by a combination of 1H NMR and isothermal titration calorimetry. Association constants in sodium tetraborate buffered (pH 9.2) aqueous solution ranged from 5.39 × 102 M-1 in the case of benzoate 1, up to 3.82 × 105 M-1 for trans-4-methylcyclohexanoate 7. Overall, the free energy difference between the free energies of complexation of these weakest and strongest binding guests was ΔΔG° = 3.88 kcal mol-1. Based on a multitude of previous studies, the anticipated order of strength of binding was close to that which was actually obtained. However, the binding of guest 3 (4-ethylbenzoate) was considerably stronger than initially estimated.

Seeking potential anticonvulsant agents that target GABAA receptors using experimental and theoretical procedures

NASA Astrophysics Data System (ADS)

Saavedra-Vélez, Margarita Virginia; Correa-Basurto, José; Matus, Myrna H.; Gasca-Pérez, Eloy; Bello, Martiniano; Cuevas-Hernández, Roberto; García-Rodríguez, Rosa Virginia; Trujillo-Ferrara, José; Ramos-Morales, Fernando Rafael

2014-12-01

The aim of this study was to identify compounds that possess anticonvulsant activity by using a pentylenetetrazol (PTZ)-induced seizure model. Theoretical studies of a set of ligands, explored the binding affinities of the ligands for the GABAA receptor (GABAAR), including some benzodiazepines. The ligands satisfy the Lipinski rules and contain a pharmacophore core that has been previously reported to be a GABAAR activator. To select the ligands with the best physicochemical properties, all of the compounds were analyzed by quantum mechanics and the energies of the highest occupied molecular orbital and lowest unoccupied molecular orbital were determined. Docking calculations between the ligands and the GABAAR were used to identify the complexes with the highest Gibbs binding energies. The identified compound D1 (dibenzo( b,f)(1,4)diazocine-6,11(5H,12H)-dione) was synthesized, experimentally tested, and the GABAAR-D1 complex was submitted to 12-ns-long molecular dynamics (MD) simulations to corroborate the binding conformation obtained by docking techniques. MD simulations were also used to analyze the decomposition of the Gibbs binding energy of the residues involved in the stabilization of the complex. To validate our theoretical results, molecular docking and MD simulations were also performed for three reference compounds that are currently in commercial use: clonazepam (CLZ), zolpidem and eszopiclone. The theoretical results show that the GABAAR-D1, and GABAAR-CLZ complexes bind to the benzodiazepine binding site, share a similar map of binding residues, and have similar Gibbs binding energies and entropic components. Experimental studies using a PTZ-induced seizure model showed that D1 possesses similar activity to CLZ, which corroborates the predicted binding free energy identified by theoretical calculations.

Molecular basis of endosomal-membrane association for the dengue virus envelope protein

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rogers, David M.; Kent, Michael S.; Rempe, Susan B.

Dengue virus is coated by an icosahedral shell of 90 envelope protein dimers that convert to trimers at low pH and promote fusion of its membrane with the membrane of the host endosome. We provide the first estimates for the free energy barrier and minimum for two key steps in this process: host membrane bending and protein–membrane binding. Both are studied using complementary membrane elastic, continuum electrostatics and all-atom molecular dynamics simulations. The predicted host membrane bending required to form an initial fusion stalk presents a 22–30 kcal/mol free energy barrier according to a constrained membrane elastic model. Combined continuummore » and molecular dynamics results predict a 15 kcal/mol free energy decrease on binding of each trimer of dengue envelope protein to a membrane with 30% anionic phosphatidylglycerol lipid. The bending cost depends on the preferred curvature of the lipids composing the host membrane leaflets, while the free energy gained for protein binding depends on the surface charge density of the host membrane. The fusion loop of the envelope protein inserts exactly at the level of the interface between the membrane's hydrophobic and head-group regions. As a result, the methods used in this work provide a means for further characterization of the structures and free energies of protein-assisted membrane fusion.« less

Molecular basis of endosomal-membrane association for the dengue virus envelope protein

DOE PAGES

Rogers, David M.; Kent, Michael S.; Rempe, Susan B.

2015-01-02

Dengue virus is coated by an icosahedral shell of 90 envelope protein dimers that convert to trimers at low pH and promote fusion of its membrane with the membrane of the host endosome. We provide the first estimates for the free energy barrier and minimum for two key steps in this process: host membrane bending and protein–membrane binding. Both are studied using complementary membrane elastic, continuum electrostatics and all-atom molecular dynamics simulations. The predicted host membrane bending required to form an initial fusion stalk presents a 22–30 kcal/mol free energy barrier according to a constrained membrane elastic model. Combined continuummore » and molecular dynamics results predict a 15 kcal/mol free energy decrease on binding of each trimer of dengue envelope protein to a membrane with 30% anionic phosphatidylglycerol lipid. The bending cost depends on the preferred curvature of the lipids composing the host membrane leaflets, while the free energy gained for protein binding depends on the surface charge density of the host membrane. The fusion loop of the envelope protein inserts exactly at the level of the interface between the membrane's hydrophobic and head-group regions. As a result, the methods used in this work provide a means for further characterization of the structures and free energies of protein-assisted membrane fusion.« less

Engineering Tocopherol Selectivity in α-TTP: A Combined In Vitro/In Silico Study

PubMed Central

Helbling, Rachel E.; Aeschimann, Walter; Simona, Fabio; Stocker, Achim; Cascella, Michele

2012-01-01

We present a combined in vitro/in silico study to determine the molecular origin of the selectivity of -tocopherol transfer protein (-TTP) towards -tocopherol. Molecular dynamics simulations combined to free energy perturbation calculations predict a binding free energy for -tocopherol to -TTP 8.262.13 kcal mol lower than that of -tocopherol. Our calculations show that -tocopherol binds to -TTP in a significantly distorted geometry as compared to that of the natural ligand. Variations in the hydration of the binding pocket and in the protein structure are found as well. We propose a mutation, A156L, which significantly modifies the selectivity properties of -TTP towards the two tocopherols. In particular, our simulations predict that A156L binds preferentially to -tocopherol, with striking structural similarities to the wild-type--tocopherol complex. The affinity properties are confirmed by differential scanning fluorimetry as well as in vitro competitive binding assays. Our data indicate that residue A156 is at a critical position for determination of the selectivity of -TTP. The engineering of TTP mutants with modulating binding properties can have potential impact at industrial level for easier purification of single tocopherols from vitamin E mixtures coming from natural oils or synthetic processes. Moreover, the identification of a -tocopherol selective TTP offers the possibility to challenge the hypotheses for the evolutionary development of a mechanism for -tocopherol selection in omnivorous animals. PMID:23152872

Rational design of biaryl pharmacophore inserted noscapine derivatives as potent tubulin binding anticancer agents.

PubMed

Santoshi, Seneha; Manchukonda, Naresh Kumar; Suri, Charu; Sharma, Manya; Sridhar, Balasubramanian; Joseph, Silja; Lopus, Manu; Kantevari, Srinivas; Baitharu, Iswar; Naik, Pradeep Kumar

2015-03-01

We have strategically designed a series of noscapine derivatives by inserting biaryl pharmacophore (a major structural constituent of many of the microtubule-targeting natural anticancer compounds) onto the scaffold structure of noscapine. Molecular interaction of these derivatives with α,β-tubulin heterodimer was investigated by molecular docking, molecular dynamics simulation, and binding free energy calculation. The predictive binding affinity indicates that the newly designed noscapinoids bind to tubulin with a greater affinity. The predictive binding free energy (ΔG(bind, pred)) of these derivatives (ranging from -5.568 to -5.970 kcal/mol) based on linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model showed improved binding affinity with tubulin compared to the lead compound, natural α-noscapine (-5.505 kcal/mol). Guided by the computational findings, these new biaryl type α-noscapine congeners were synthesized from 9-bromo-α-noscapine using optimized Suzuki reaction conditions for further experimental evaluation. The derivatives showed improved inhibition of the proliferation of human breast cancer cells (MCF-7), human cervical cancer cells (HeLa) and human lung adenocarcinoma cells (A549), compared to natural noscapine. The cell cycle analysis in MCF-7 further revealed that these compounds alter the cell cycle profile and cause mitotic arrest at G2/M phase more strongly than noscapine. Tubulin binding assay revealed higher binding affinity to tubulin, as suggested by dissociation constant (Kd) of 126 ± 5.0 µM for 5a, 107 ± 5.0 µM for 5c, 70 ± 4.0 µM for 5d, and 68 ± 6.0 µM for 5e compared to noscapine (Kd of 152 ± 1.0 µM). In fact, the experimentally determined value of ΔG(bind, expt) (calculated from the Kd value) are consistent with the predicted value of ΔG(bind, pred) calculated based on LIE-SGB. Based on these results, one of the derivative 5e of this series was used for further toxicological evaluation. Treatment of mice with a daily dose of 300 mg/kg and a single dose of 600 mg/kg indicates that the compound does not induce detectable pathological abnormalities in normal tissues. Also there were no significant differences in hematological parameters between the treated and untreated groups. Hence, the newly designed noscapinoid, 5e is an orally bioavailable, safe and effective anticancer agent with a potential for the treatment of cancer and might be a candidate for clinical evaluation.

Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation

NASA Astrophysics Data System (ADS)

Grudinin, Sergei; Kadukova, Maria; Eisenbarth, Andreas; Marillet, Simon; Cazals, Frédéric

2016-09-01

The 2015 D3R Grand Challenge provided an opportunity to test our new model for the binding free energy of small molecules, as well as to assess our protocol to predict binding poses for protein-ligand complexes. Our pose predictions were ranked 3-9 for the HSP90 dataset, depending on the assessment metric. For the MAP4K dataset the ranks are very dispersed and equal to 2-35, depending on the assessment metric, which does not provide any insight into the accuracy of the method. The main success of our pose prediction protocol was the re-scoring stage using the recently developed Convex-PL potential. We make a thorough analysis of our docking predictions made with AutoDock Vina and discuss the effect of the choice of rigid receptor templates, the number of flexible residues in the binding pocket, the binding pocket size, and the benefits of re-scoring. However, the main challenge was to predict experimentally determined binding affinities for two blind test sets. Our affinity prediction model consisted of two terms, a pairwise-additive enthalpy, and a non pairwise-additive entropy. We trained the free parameters of the model with a regularized regression using affinity and structural data from the PDBBind database. Our model performed very well on the training set, however, failed on the two test sets. We explain the drawback and pitfalls of our model, in particular in terms of relative coverage of the test set by the training set and missed dynamical properties from crystal structures, and discuss different routes to improve it.

Molecular models of alginic acid: Interactions with calcium ions and calcite surfaces

NASA Astrophysics Data System (ADS)

Perry, Thomas D.; Cygan, Randall T.; Mitchell, Ralph

2006-07-01

Cation binding by polysaccharides is observed in many environments and is important for predictive environmental modeling, and numerous industrial and food technology applications. The complexities of these cation-organic interactions are well suited for predictive molecular modeling and the analysis of conformation and configuration of polysaccharides and their influence on cation binding. In this study, alginic acid was chosen as a model polymer system and representative disaccharide and polysaccharide subunits were developed. Molecular dynamics simulation of the torsion angles of the ether linkage between various monomeric subunits identified local and global energy minima for selected disaccharides. The simulations indicate stable disaccharide configurations and a common global energy minimum for all disaccharide models at Φ = 274 ± 7°, Ψ = 227 ± 5°, where Φ and Ψ are the torsion angles about the ether linkage. The ability of disaccharide subunits to bind calcium ions and to associate with the (101¯4) surface of calcite was also investigated. Molecular models of disaccharide interactions with calcite provide binding energy differences for conformations that are related to the proximity and residence densities of the electron-donating moieties with calcium ions on the calcite surface, which are controlled, in part, by the torsion of the ether linkage between monosaccharide units. Dynamically optimized configurations for polymer alginate models with calcium ions were also derived.

Distinguishing Binders from False Positives by Free Energy Calculations: Fragment Screening Against the Flap Site of HIV Protease

PubMed Central

2015-01-01

Molecular docking is a powerful tool used in drug discovery and structural biology for predicting the structures of ligand–receptor complexes. However, the accuracy of docking calculations can be limited by factors such as the neglect of protein reorganization in the scoring function; as a result, ligand screening can produce a high rate of false positive hits. Although absolute binding free energy methods still have difficulty in accurately rank-ordering binders, we believe that they can be fruitfully employed to distinguish binders from nonbinders and reduce the false positive rate. Here we study a set of ligands that dock favorably to a newly discovered, potentially allosteric site on the flap of HIV-1 protease. Fragment binding to this site stabilizes a closed form of protease, which could be exploited for the design of allosteric inhibitors. Twenty-three top-ranked protein–ligand complexes from AutoDock were subject to the free energy screening using two methods, the recently developed binding energy analysis method (BEDAM) and the standard double decoupling method (DDM). Free energy calculations correctly identified most of the false positives (≥83%) and recovered all the confirmed binders. The results show a gap averaging ≥3.7 kcal/mol, separating the binders and the false positives. We present a formula that decomposes the binding free energy into contributions from the receptor conformational macrostates, which provides insights into the roles of different binding modes. Our binding free energy component analysis further suggests that improving the treatment for the desolvation penalty associated with the unfulfilled polar groups could reduce the rate of false positive hits in docking. The current study demonstrates that the combination of docking with free energy methods can be very useful for more accurate ligand screening against valuable drug targets. PMID:25189630

Improving density functional tight binding predictions of free energy surfaces for peptide condensation reactions in solution

NASA Astrophysics Data System (ADS)

Kroonblawd, Matthew; Goldman, Nir

First principles molecular dynamics using highly accurate density functional theory (DFT) is a common tool for predicting chemistry, but the accessible time and space scales are often orders of magnitude beyond the resolution of experiments. Semi-empirical methods such as density functional tight binding (DFTB) offer up to a thousand-fold reduction in required CPU hours and can approach experimental scales. However, standard DFTB parameter sets lack good transferability and calibration for a particular system is usually necessary. Force matching the pairwise repulsive energy term in DFTB to short DFT trajectories can improve the former's accuracy for chemistry that is fast relative to DFT simulation times (<10 ps), but the effects on slow chemistry and the free energy surface are not well-known. We present a force matching approach to increase the accuracy of DFTB predictions for free energy surfaces. Accelerated sampling techniques are combined with path collective variables to generate the reference DFT data set and validate fitted DFTB potentials without a priori knowledge of transition states. Accuracy of force-matched DFTB free energy surfaces is assessed for slow peptide-forming reactions by direct comparison to DFT results for particular paths. Extensions to model prebiotic chemistry under shock conditions are discussed. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Modeling Shear Induced Von Willebrand Factor Binding to Collagen

NASA Astrophysics Data System (ADS)

Dong, Chuqiao; Wei, Wei; Morabito, Michael; Webb, Edmund; Oztekin, Alparslan; Zhang, Xiaohui; Cheng, Xuanhong

2017-11-01

Von Willebrand factor (vWF) is a blood glycoprotein that binds with platelets and collagen on injured vessel surfaces to form clots. VWF bioactivity is shear flow induced: at low shear, binding between VWF and other biological entities is suppressed; for high shear rate conditions - as are found near arterial injury sites - VWF elongates, activating its binding with platelets and collagen. Based on parameters derived from single molecule force spectroscopy experiments, we developed a coarse-grain molecular model to simulate bond formation probability as a function of shear rate. By introducing a binding criterion that depends on the conformation of a sub-monomer molecular feature of our model, the model predicts shear-induced binding, even for conditions where binding is highly energetically favorable. We further investigate the influence of various model parameters on the ability to predict shear-induced binding (vWF length, collagen site density and distribution, binding energy landscape, and slip/catch bond length) and demonstrate parameter ranges where the model provides good agreement with existing experimental data. Our results may be important for understanding vWF activity and also for achieving targeted drug therapy via biomimetic synthetic molecules. National Science Foundation (NSF),Division of Mathematical Sciences (DMS).

Quantum-Mechanics Methodologies in Drug Discovery: Applications of Docking and Scoring in Lead Optimization.

PubMed

Crespo, Alejandro; Rodriguez-Granillo, Agustina; Lim, Victoria T

2017-01-01

The development and application of quantum mechanics (QM) methodologies in computer- aided drug design have flourished in the last 10 years. Despite the natural advantage of QM methods to predict binding affinities with a higher level of theory than those methods based on molecular mechanics (MM), there are only a few examples where diverse sets of protein-ligand targets have been evaluated simultaneously. In this work, we review recent advances in QM docking and scoring for those cases in which a systematic analysis has been performed. In addition, we introduce and validate a simplified QM/MM expression to compute protein-ligand binding energies. Overall, QMbased scoring functions are generally better to predict ligand affinities than those based on classical mechanics. However, the agreement between experimental activities and calculated binding energies is highly dependent on the specific chemical series considered. The advantage of more accurate QM methods is evident in cases where charge transfer and polarization effects are important, for example when metals are involved in the binding process or when dispersion forces play a significant role as in the case of hydrophobic or stacking interactions. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Toward a mechanistic understanding of patterns in biomineralization and new insights for old dogmas in geological settings (Invited)

NASA Astrophysics Data System (ADS)

Dove, P. M.; Hamm, L.; Giuffre, A. J.; Han, N.; De Yoreo, J. J.

2013-12-01

The ability of organisms to mineralize tissues into skeletons and other functional structures is a remarkable achievement of biology. Yet, the physical basis for how macromolecules regulate the placement and onset of mineral formation is not well established. Efforts to understand nucleation onto organic substrates have produced two, seemingly contradictory, lines of thought: The biomineralization community widely assumes the organic matrix promotes nucleation through stereochemical matching to guide the organization of solute ions, while materials synthesis groups use simple binding assays to correlate high binding strength with good promoters of nucleation. This study reconciles the two views and provides a mechanistic explanation for template-directed nucleation by correlating heterogeneous nucleation barriers with crystal-substrate binding free energies. Using surface assembled monolayers (SAM) as simple model systems, we first measure the kinetics of calcite nucleation onto model substrates that present different functional group chemistries (carboxyl, thiol, phosphate, hydroxyl) and conformations (C11, C16 chain lengths). We find rates are substrate-specific and obey predictions of classical nucleation theory at supersaturations that extend above the solubility of amorphous calcium carbonate (ACC). Analysis of the kinetic data shows the thermodynamic barrier to nucleation is reduced by minimizing the interfacial free energy of the system, γ. We then use dynamic force spectroscopy to independently measure calcite-substrate binding free energies, ΔGb. Moreover, we show that within the classical theory of nucleation, γ and ΔGb should be linearly related. The results bear out this prediction and demonstrate that low energy barriers to nucleation correlate with strong crystal-substrate binding. This relationship is general to all functional group chemistries and conformations. These findings reconcile the long-standing concept of templated nucleation through stereochemical matching with the conventional wisdom that ';good binders are good nucleators'. Alternative perspectives become internally consistent when viewed through the lens of crystal-substrate binding and provide a physical basis for how organic chemistry can direct temporal and spatial patterns of carbonate nucleation.

High-level ab initio calculations for the four low-lying families of minima of (H2O)(20): 1. Estimates of MP2/CBS binding energies and comparison with empirical potentials

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fanourgakis, Georgios S.; Apra, Edoardo; Xantheas, Sotiris S.

2004-08-08

We report estimates of complete basis set (CBS) limits at the second-order Møller-Plesset perturbation level of theory (MP2) for the binding energies of the lowest lying isomers within each of the four major families of minima of (H2O)20. These were obtained by performing MP2 calculations with the family of correlation-consistent basis sets up to quadruple zeta quality, augmented with additional diffuse functions (aug-cc-pVnZ, n=D, T, Q). The MP2/CBS estimates are: -200.1 kcal/mol (dodecahedron, 30 hydrogen bonds), -212.6 kcal/mol (fused cubes, 36 hydrogen bonds), -215.0 (face-sharing pentagonal prisms, 35 hydrogen bonds) and –217.9 kcal/mol (edge-sharing pentagonal prisms, 34 hydrogen bonds). Themore » energetic ordering of the various (H2O)20 isomers does not follow monotonically the number of hydrogen bonds as in the case of smaller clusters such as the different isomers of the water hexamer. The dodecahedron lies ca. 18 kcal/mol higher in energy than the most stable edge-sharing pentagonal prism isomer. The TIP4P, ASP-W4, TTM2-R, AMOEBA and TTM2-F empirical potentials also predict the energetic stabilization of the edge-sharing pentagonal prisms with respect to the dodecahedron, albeit they universally underestimate the cluster binding energies with respect to the MP2/CBS result. Among them, the TTM2-F potential was found to predict the absolute cluster binding energies to within < 1% from the corresponding MP2/CBS values, whereas the error for the rest of the potentials considered in this study ranges from 3-5%.« less

Insights into susceptibility of antiviral drugs against the E119G mutant of 2009 influenza A (H1N1) neuraminidase by molecular dynamics simulations and free energy calculations.

PubMed

Pan, Peichen; Li, Lin; Li, Youyong; Li, Dan; Hou, Tingjun

2013-11-01

Neuraminidase inhibitors (NAIs) play vital roles in controlling human influenza epidemics and pandemics. However, the emergence of new human influenza virus mutant strains resistant to existing antiviral drugs has been becoming a major challenge. Therefore, it is critical to uncover the mechanisms of drug resistance and seek alternative treatments to combat drug resistance. In this study, molecular dynamics (MD) simulations and Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) were applied to investigate the different sensitivities of oseltamivir (OTV), zanamivir (ZNV), and peramivir (PRV) against the E119G mutant of 2009 A/H1N1 neuraminidase. The predicted binding free energies indicate that the E119G mutation in NA confers resistance to all of the three studied inhibitors. The ordering of the level of drug resistance predicted by the binding free energies for the three inhibitors is ZNV>PRV>OTV, which agrees well with the experimental data. Drug resistance arises primarily from the unfavorable shifts of the polar interactions between NA and the inhibitors. It comes as a surprise that the mutation of Glu119 that can form strong H-bonds with the inhibitors in the wild-type protein does not have direct impact on the binding affinities of both OTV and PRV due to the regulation of the strong unfavorable polar desolvation energies. The indirectly conformational variations of the inhibitors, which caused by the E119G mutation, are responsible for the loss of the binding free energies. However, for ZNV, the E119G mutation has both direct and indirect influences on the drug binding. The structural and quantitative viewpoint obtained from this study provides valuable information for the rational design of novel and effective drugs to combat drug resistance. Copyright © 2013 Elsevier B.V. All rights reserved.

UO₂²⁺ uptake by proteins: understanding the binding features of the super uranyl binding protein and design of a protein with higher affinity.

PubMed

Odoh, Samuel O; Bondarevsky, Gary D; Karpus, Jason; Cui, Qiang; He, Chuan; Spezia, Riccardo; Gagliardi, Laura

2014-12-17

The capture of uranyl, UO2(2+), by a recently engineered protein (Zhou et al. Nat. Chem. 2014, 6, 236) with high selectivity and femtomolar sensitivity has been examined by a combination of density functional theory, molecular dynamics, and free-energy simulations. It was found that UO2(2+) is coordinated to five carboxylate oxygen atoms from four amino acid residues of the super uranyl binding protein (SUP). A network of hydrogen bonds between the amino acid residues coordinated to UO2(2+) and residues in its second coordination sphere also affects the protein's uranyl binding affinity. Free-energy simulations show how UO2(2+) capture is governed by the nature of the amino acid residues in the binding site, the integrity and strength of the second-sphere hydrogen bond network, and the number of water molecules in the first coordination sphere. Alteration of any of these three factors through mutations generally results in a reduction of the binding free energy of UO2(2+) to the aqueous protein as well as of the difference between the binding free energies of UO2(2+) and other ions (Ca(2+), Cu(2+), Mg(2+), and Zn(2+)), a proxy for the protein's selectivity over these ions. The results of our free-energy simulations confirmed the previously reported experimental results and allowed us to discover a mutant of SUP, specifically the GLU64ASP mutant, that not only binds UO2(2+) more strongly than SUP but that is also more selective for UO2(2+) over other ions. The predictions from the computations were confirmed experimentally.

Line Narrowing of Excited-State Transitions in Nonlinear Polarization Spectroscopy: Application to Water-Soluble Chlorophyll-Binding Protein

NASA Astrophysics Data System (ADS)

Schoth, Mario; Richter, Marten; Knorr, Andreas; Renger, Thomas

2012-04-01

The homogeneous linewidth of dye aggregates like photosynthetic light-harvesting complexes contains important information about energy transfer and relaxation times that is, however, masked by inhomogeneous broadening caused by static disorder. Whereas there exist line narrowing techniques for the study of low-energy exciton states, the homogeneous linewidth of the high-energy states is not so easy to decipher. Here we present a microscopic theory for nonlinear polarization spectroscopy in the frequency domain that contains a dynamic aggregate selection revealing the homogeneous linewidth of these states. The theory is applied to the water-soluble chlorophyll-binding protein for which the high-energy exciton state was predicted to exhibit a sub-100-fs lifetime.

Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

PubMed

Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

2017-04-24

One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

The FTMap family of web servers for determining and characterizing ligand binding hot spots of proteins

PubMed Central

Kozakov, Dima; Grove, Laurie E.; Hall, David R.; Bohnuud, Tanggis; Mottarella, Scott; Luo, Lingqi; Xia, Bing; Beglov, Dmitri; Vajda, Sandor

2016-01-01

FTMap is a computational mapping server that identifies binding hot spots of macromolecules, i.e., regions of the surface with major contributions to the ligand binding free energy. To use FTMap, users submit a protein, DNA, or RNA structure in PDB format. FTMap samples billions of positions of small organic molecules used as probes and scores the probe poses using a detailed energy expression. Regions that bind clusters of multiple probe types identify the binding hot spots, in good agreement with experimental data. FTMap serves as basis for other servers, namely FTSite to predict ligand binding sites, FTFlex to account for side chain flexibility, FTMap/param to parameterize additional probes, and FTDyn to map ensembles of protein structures. Applications include determining druggability of proteins, identifying ligand moieties that are most important for binding, finding the most bound-like conformation in ensembles of unliganded protein structures, and providing input for fragment based drug design. FTMap is more accurate than classical mapping methods such as GRID and MCSS, and is much faster than the more recent approaches to protein mapping based on mixed molecular dynamics. Using 16 probe molecules, the FTMap server finds the hot spots of an average size protein in less than an hour. Since FTFlex performs mapping for all low energy conformers of side chains in the binding site, its completion time is proportionately longer. PMID:25855957

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane

NASA Astrophysics Data System (ADS)

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S.; Gray, C. G.

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05±0.39kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Prediction of binding free energy for adsorption of antimicrobial peptide lactoferricin B on a POPC membrane.

PubMed

Vivcharuk, Victor; Tomberli, Bruno; Tolokh, Igor S; Gray, C G

2008-03-01

Molecular dynamics (MD) simulations are used to study the interaction of a zwitterionic palmitoyl-oleoyl-phosphatidylcholine (POPC) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with POPC is used as a model system for studying the details of membrane-peptide interactions, with the peptide selected because of its antimicrobial nature. Seventy-two 3 ns MD simulations, with six orientations of LFCinB at 12 different distances from a POPC membrane, are carried out to determine the potential of mean force (PMF) or free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the PMF for this relatively large system a new variant of constrained MD and thermodynamic integration is developed. A simplified method for relating the PMF to the LFCinB-membrane binding free energy is described and used to predict a free energy of adsorption (or binding) of -1.05+/-0.39 kcal/mol , and corresponding maximum binding force of about 20 pN, for LFCinB-POPC. The contributions of the ions-LFCinB and the water-LFCinB interactions to the PMF are discussed. The method developed will be a useful starting point for future work simulating peptides interacting with charged membranes and interactions involved in the penetration of membranes, features necessary to understand in order to rationally design peptides as potential alternatives to traditional antibiotics.

Is It Reliable to Take the Molecular Docking Top Scoring Position as the Best Solution without Considering Available Structural Data?

PubMed

Ramírez, David; Caballero, Julio

2018-04-28

Molecular docking is the most frequently used computational method for studying the interactions between organic molecules and biological macromolecules. In this context, docking allows predicting the preferred pose of a ligand inside a receptor binding site. However, the selection of the “best” solution is not a trivial task, despite the widely accepted selection criterion that the best pose corresponds to the best energy score. Here, several rigid-target docking methods were evaluated on the same dataset with respect to their ability to reproduce crystallographic binding orientations, to test if the best energy score is a reliable criterion for selecting the best solution. For this, two experiments were performed: (A) to reconstruct the ligand-receptor complex by performing docking of the ligand in its own crystal structure receptor (defined as self-docking), and (B) to reconstruct the ligand-receptor complex by performing docking of the ligand in a crystal structure receptor that contains other ligand (defined as cross-docking). Root-mean square deviation (RMSD) was used to evaluate how different the obtained docking orientation is from the corresponding co-crystallized pose of the same ligand molecule. We found that docking score function is capable of predicting crystallographic binding orientations, but the best ranked solution according to the docking energy is not always the pose that reproduces the experimental binding orientation. This happened when self-docking was achieved, but it was critical in cross-docking. Taking into account that docking is typically used with predictive purposes, during cross-docking experiments, our results indicate that the best energy score is not a reliable criterion to select the best solution in common docking applications. It is strongly recommended to choose the best docking solution according to the scoring function along with additional structural criteria described for analogue ligands to assure the selection of a correct docking solution.

Communication: Classical threshold law for ion-neutral-neutral three-body recombination

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pérez-Ríos, Jesús; Greene, Chris H.

2015-07-28

A very recently method for classical trajectory calculations for three-body collision [Pérez-Ríos et al., J. Chem. Phys. 140, 044307 (2014)] has been applied to describe ion-neutral-neutral ternary processes for low energy collisions: 0.1 mK–10 mK. As a result, a threshold law for the three-body recombination cross section is obtained and corroborated numerically. The derived threshold law predicts the formation of weakly bound dimers, with binding energies comparable to the collision energy of the collisional partners. In this low energy range, this analysis predicts that molecular ions should dominate over molecular neutrals as the most products formed.

Inadequate Reference Datasets Biased toward Short Non-epitopes Confound B-cell Epitope Prediction*

PubMed Central

Rahman, Kh. Shamsur; Chowdhury, Erfan Ullah; Sachse, Konrad; Kaltenboeck, Bernhard

2016-01-01

X-ray crystallography has shown that an antibody paratope typically binds 15–22 amino acids (aa) of an epitope, of which 2–5 randomly distributed amino acids contribute most of the binding energy. In contrast, researchers typically choose for B-cell epitope mapping short peptide antigens in antibody binding assays. Furthermore, short 6–11-aa epitopes, and in particular non-epitopes, are over-represented in published B-cell epitope datasets that are commonly used for development of B-cell epitope prediction approaches from protein antigen sequences. We hypothesized that such suboptimal length peptides result in weak antibody binding and cause false-negative results. We tested the influence of peptide antigen length on antibody binding by analyzing data on more than 900 peptides used for B-cell epitope mapping of immunodominant proteins of Chlamydia spp. We demonstrate that short 7–12-aa peptides of B-cell epitopes bind antibodies poorly; thus, epitope mapping with short peptide antigens falsely classifies many B-cell epitopes as non-epitopes. We also show in published datasets of confirmed epitopes and non-epitopes a direct correlation between length of peptide antigens and antibody binding. Elimination of short, ≤11-aa epitope/non-epitope sequences improved datasets for evaluation of in silico B-cell epitope prediction. Achieving up to 86% accuracy, protein disorder tendency is the best indicator of B-cell epitope regions for chlamydial and published datasets. For B-cell epitope prediction, the most effective approach is plotting disorder of protein sequences with the IUPred-L scale, followed by antibody reactivity testing of 16–30-aa peptides from peak regions. This strategy overcomes the well known inaccuracy of in silico B-cell epitope prediction from primary protein sequences. PMID:27189949

SAMPL4 & DOCK3.7: lessons for automated docking procedures

NASA Astrophysics Data System (ADS)

Coleman, Ryan G.; Sterling, Teague; Weiss, Dahlia R.

2014-03-01

The SAMPL4 challenges were used to test current automated methods for solvation energy, virtual screening, pose and affinity prediction of the molecular docking pipeline DOCK 3.7. Additionally, first-order models of binding affinity were proposed as milestones for any method predicting binding affinity. Several important discoveries about the molecular docking software were made during the challenge: (1) Solvation energies of ligands were five-fold worse than any other method used in SAMPL4, including methods that were similarly fast, (2) HIV Integrase is a challenging target, but automated docking on the correct allosteric site performed well in terms of virtual screening and pose prediction (compared to other methods) but affinity prediction, as expected, was very poor, (3) Molecular docking grid sizes can be very important, serious errors were discovered with default settings that have been adjusted for all future work. Overall, lessons from SAMPL4 suggest many changes to molecular docking tools, not just DOCK 3.7, that could improve the state of the art. Future difficulties and projects will be discussed.

Prediction of the binding mode of N2-phenylguanine derivative inhibitors to herpes simplex virus type 1 thymidine kinase

NASA Astrophysics Data System (ADS)

Gaudio, Anderson Coser; Takahata, Yuji; Richards, William Graham

1998-01-01

The probable binding mode of the herpes simplex virus thymidine kinase (HSV1 TK) N2-[substituted]-phenylguanine inhibitors is proposed. A computational experiment was designed to check some qualitative binding parameters and to calculate the interaction binding energies of alternative binding modes of N2-phenylguanines. The known binding modes of the HSV1 TK natural substrate deoxythymidine and one of its competitive inhibitors ganciclovir were used as templates. Both the qualitative and quantitative parts of the computational experiment indicated that the N2-phenylguanine derivatives bind to the HSV1 TK active site in the deoxythymidine-like binding mode. An experimental observation that N2-phenylguanosine derivatives are not phosphorylated during the interaction with the HSV1 TK gives support to the proposed binding mode.

Predicting the relative binding affinity of mineralocorticoid receptor antagonists by density functional methods

NASA Astrophysics Data System (ADS)

Roos, Katarina; Hogner, Anders; Ogg, Derek; Packer, Martin J.; Hansson, Eva; Granberg, Kenneth L.; Evertsson, Emma; Nordqvist, Anneli

2015-12-01

In drug discovery, prediction of binding affinity ahead of synthesis to aid compound prioritization is still hampered by the low throughput of the more accurate methods and the lack of general pertinence of one method that fits all systems. Here we show the applicability of a method based on density functional theory using core fragments and a protein model with only the first shell residues surrounding the core, to predict relative binding affinity of a matched series of mineralocorticoid receptor (MR) antagonists. Antagonists of MR are used for treatment of chronic heart failure and hypertension. Marketed MR antagonists, spironolactone and eplerenone, are also believed to be highly efficacious in treatment of chronic kidney disease in diabetes patients, but is contra-indicated due to the increased risk for hyperkalemia. These findings and a significant unmet medical need among patients with chronic kidney disease continues to stimulate efforts in the discovery of new MR antagonist with maintained efficacy but low or no risk for hyperkalemia. Applied on a matched series of MR antagonists the quantum mechanical based method gave an R2 = 0.76 for the experimental lipophilic ligand efficiency versus relative predicted binding affinity calculated with the M06-2X functional in gas phase and an R2 = 0.64 for experimental binding affinity versus relative predicted binding affinity calculated with the M06-2X functional including an implicit solvation model. The quantum mechanical approach using core fragments was compared to free energy perturbation calculations using the full sized compound structures.

Calculations of the free energy of interaction of the c-Fos-c-Jun coiled coil: effects of the solvation model and the inclusion of polarization effects.

PubMed

Zuo, Zhili; Gandhi, Neha S; Mancera, Ricardo L

2010-12-27

The leucine zipper region of activator protein-1 (AP-1) comprises the c-Jun and c-Fos proteins and constitutes a well-known coiled coil protein-protein interaction motif. We have used molecular dynamics (MD) simulations in conjunction with the molecular mechanics/Poisson-Boltzmann generalized-Born surface area [MM/PB(GB)SA] methods to predict the free energy of interaction of these proteins. In particular, the influence of the choice of solvation model, protein force field, and water potential on the stability and dynamic properties of the c-Fos-c-Jun complex were investigated. Use of the AMBER polarizable force field ff02 in combination with the polarizable POL3 water potential was found to result in increased stability of the c-Fos-c-Jun complex. MM/PB(GB)SA calculations revealed that MD simulations using the POL3 water potential give the lowest predicted free energies of interaction compared to other nonpolarizable water potentials. In addition, the calculated absolute free energy of binding was predicted to be closest to the experimental value using the MM/GBSA method with independent MD simulation trajectories using the POL3 water potential and the polarizable ff02 force field, while all other binding affinities were overestimated.

Effect of Various Material Properties on the Adhesive Stage of Fretting

NASA Technical Reports Server (NTRS)

Buckley, D. H.

1974-01-01

Various properties of metals and alloys were studied with respect to their effect on the initial stage of the fretting process, namely adhesion. Crystallographic orientation, crystal structure, interfacial binding energies of dissimiliar metal, segregation of alloy constituents and the nature and structure of surface films were found to influence adhesion. High atomic density, low surface energy grain orientations exhibited lower adhesion than other orientations. Knowledge of interfacial surface binding energies assists in predicting adhesive transfer and wear. Selective surface segregation of alloy constituents accomplishes both a reduction in adhesion and improved surface oxidation characteristics. Equivalent surface coverages of various adsorbed species indicate that some are markedly more effective in inhibiting adhesion than others.

The Free Energy Profile of Tubulin Straight-Bent Conformational Changes, with Implications for Microtubule Assembly and Drug Discovery

PubMed Central

Bonomi, Massimiliano; Agard, David A.; Jacobson, Matthew P.

2014-01-01

αβ-tubulin dimers need to convert between a ‘bent’ conformation observed for free dimers in solution and a ‘straight’ conformation required for incorporation into the microtubule lattice. Here, we investigate the free energy landscape of αβ-tubulin using molecular dynamics simulations, emphasizing implications for models of assembly, and modulation of the conformational landscape by colchicine, a tubulin-binding drug that inhibits microtubule polymerization. Specifically, we performed molecular dynamics, potential-of-mean force simulations to obtain the free energy profile for unpolymerized GDP-bound tubulin as a function of the ∼12° intradimer rotation differentiating the straight and bent conformers. Our results predict that the unassembled GDP-tubulin heterodimer exists in a continuum of conformations ranging between straight and bent, but, in agreement with existing structural data, suggests that an intermediate bent state has a lower free energy (by ∼1 kcal/mol) and thus dominates in solution. In agreement with predictions of the lattice model of microtubule assembly, lateral binding of two αβ-tubulins strongly shifts the conformational equilibrium towards the straight state, which is then ∼1 kcal/mol lower in free energy than the bent state. Finally, calculations of colchicine binding to a single αβ-tubulin dimer strongly shifts the equilibrium toward the bent states, and disfavors the straight state to the extent that it is no longer thermodynamically populated. PMID:24516374

A flexible docking scheme to explore the binding selectivity of PDZ domains.

PubMed

Gerek, Z Nevin; Ozkan, S Banu

2010-05-01

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using ROSETTALIGAND, we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density-95/Dlg/ZO-1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 A. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately.

A flexible docking scheme to explore the binding selectivity of PDZ domains

PubMed Central

Gerek, Z Nevin; Ozkan, S Banu

2010-01-01

Modeling of protein binding site flexibility in molecular docking is still a challenging problem due to the large conformational space that needs sampling. Here, we propose a flexible receptor docking scheme: A dihedral restrained replica exchange molecular dynamics (REMD), where we incorporate the normal modes obtained by the Elastic Network Model (ENM) as dihedral restraints to speed up the search towards correct binding site conformations. To our knowledge, this is the first approach that uses ENM modes to bias REMD simulations towards binding induced fluctuations in docking studies. In our docking scheme, we first obtain the deformed structures of the unbound protein as initial conformations by moving along the binding fluctuation mode, and perform REMD using the ENM modes as dihedral restraints. Then, we generate an ensemble of multiple receptor conformations (MRCs) by clustering the lowest replica trajectory. Using RosettaLigand, we dock ligands to the clustered conformations to predict the binding pose and affinity. We apply this method to postsynaptic density-95/Dlg/ZO-1 (PDZ) domains; whose dynamics govern their binding specificity. Our approach produces the lowest energy bound complexes with an average ligand root mean square deviation of 0.36 Å. We further test our method on (i) homologs and (ii) mutant structures of PDZ where mutations alter the binding selectivity. In both cases, our approach succeeds to predict the correct pose and the affinity of binding peptides. Overall, with this approach, we generate an ensemble of MRCs that leads to predict the binding poses and specificities of a protein complex accurately. PMID:20196074

Free Energy Perturbation Calculations of the Thermodynamics of Protein Side-Chain Mutations.

PubMed

Steinbrecher, Thomas; Abel, Robert; Clark, Anthony; Friesner, Richard

2017-04-07

Protein side-chain mutation is fundamental both to natural evolutionary processes and to the engineering of protein therapeutics, which constitute an increasing fraction of important medications. Molecular simulation enables the prediction of the effects of mutation on properties such as binding affinity, secondary and tertiary structure, conformational dynamics, and thermal stability. A number of widely differing approaches have been applied to these predictions, including sequence-based algorithms, knowledge-based potential functions, and all-atom molecular mechanics calculations. Free energy perturbation theory, employing all-atom and explicit-solvent molecular dynamics simulations, is a rigorous physics-based approach for calculating thermodynamic effects of, for example, protein side-chain mutations. Over the past several years, we have initiated an investigation of the ability of our most recent free energy perturbation methodology to model the thermodynamics of protein mutation for two specific problems: protein-protein binding affinities and protein thermal stability. We highlight recent advances in the field and outline current and future challenges. Copyright © 2017 Elsevier Ltd. All rights reserved.

Prediction of Nucleotide Binding Peptides Using Star Graph Topological Indices.

PubMed

Liu, Yong; Munteanu, Cristian R; Fernández Blanco, Enrique; Tan, Zhiliang; Santos Del Riego, Antonino; Pazos, Alejandro

2015-11-01

The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this biological function is an important research topic. The current study proposes a mixed methodology to obtain the first classification model that is able to predict new nucleotide binding peptides, using only the amino acid sequence. Thus, the methodology uses a Star graph molecular descriptor of the peptide sequences and the Machine Learning technique for the best classifier. The best model represents a Random Forest classifier based on two features of the embedded and non-embedded graphs. The performance of the model is excellent, considering similar models in the field, with an Area Under the Receiver Operating Characteristic Curve (AUROC) value of 0.938 and true positive rate (TPR) of 0.886 (test subset). The prediction of new nucleotide binding peptides with this model could be useful for drug target studies in drug development. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Computational Studies of Difference in Binding Modes of Peptide and Non-Peptide Inhibitors to MDM2/MDMX Based on Molecular Dynamics Simulations

PubMed Central

Chen, Jianzhong; Zhang, Dinglin; Zhang, Yuxin; Li, Guohui

2012-01-01

Inhibition of p53-MDM2/MDMX interaction is considered to be a promising strategy for anticancer drug design to activate wild-type p53 in tumors. We carry out molecular dynamics (MD) simulations to study the binding mechanisms of peptide and non-peptide inhibitors to MDM2/MDMX. The rank of binding free energies calculated by molecular mechanics generalized Born surface area (MM-GBSA) method agrees with one of the experimental values. The results suggest that van der Waals energy drives two kinds of inhibitors to MDM2/MDMX. We also find that the peptide inhibitors can produce more interaction contacts with MDM2/MDMX than the non-peptide inhibitors. Binding mode predictions based on the inhibitor-residue interactions show that the π–π, CH–π and CH–CH interactions dominated by shape complimentarity, govern the binding of the inhibitors in the hydrophobic cleft of MDM2/MDMX. Our studies confirm the residue Tyr99 in MDMX can generate a steric clash with the inhibitors due to energy and structure. This finding may theoretically provide help to develop potent dual-specific or MDMX inhibitors. PMID:22408446

Molecular dynamics and Monte Carlo simulations for protein-ligand binding and inhibitor design.

PubMed

Cole, Daniel J; Tirado-Rives, Julian; Jorgensen, William L

2015-05-01

Non-nucleoside inhibitors of HIV reverse transcriptase are an important component of treatment against HIV infection. Novel inhibitors are sought that increase potency against variants that contain the Tyr181Cys mutation. Molecular dynamics based free energy perturbation simulations have been run to study factors that contribute to protein-ligand binding, and the results are compared with those from previous Monte Carlo based simulations and activity data. Predictions of protein-ligand binding modes are very consistent for the two simulation methods; the accord is attributed to the use of an enhanced sampling protocol. The Tyr181Cys binding pocket supports large, hydrophobic substituents, which is in good agreement with experiment. Although some discrepancies exist between the results of the two simulation methods and experiment, free energy perturbation simulations can be used to rapidly test small molecules for gains in binding affinity. Free energy perturbation methods show promise in providing fast, reliable and accurate data that can be used to complement experiment in lead optimization projects. This article is part of a Special Issue entitled "Recent developments of molecular dynamics". Copyright © 2014 Elsevier B.V. All rights reserved.

Prediction of Protein-Protein Interaction Sites Using Electrostatic Desolvation Profiles

PubMed Central

Fiorucci, Sébastien; Zacharias, Martin

2010-01-01

Abstract Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. PMID:20441756

Ligand deconstruction: Why some fragment binding positions are conserved and others are not

PubMed Central

Kozakov, Dima; Hall, David R.; Jehle, Stefan; Luo, Lingqi; Ochiana, Stefan O.; Jones, Elizabeth V.; Pollastri, Michael; Allen, Karen N.; Whitty, Adrian; Vajda, Sandor

2015-01-01

Fragment-based drug discovery (FBDD) relies on the premise that the fragment binding mode will be conserved on subsequent expansion to a larger ligand. However, no general condition has been established to explain when fragment binding modes will be conserved. We show that a remarkably simple condition can be developed in terms of how fragments coincide with binding energy hot spots—regions of the protein where interactions with a ligand contribute substantial binding free energy—the locations of which can easily be determined computationally. Because a substantial fraction of the free energy of ligand binding comes from interacting with the residues in the energetically most important hot spot, a ligand moiety that sufficiently overlaps with this region will retain its location even when other parts of the ligand are removed. This hypothesis is supported by eight case studies. The condition helps identify whether a protein is suitable for FBDD, predicts the size of fragments required for screening, and determines whether a fragment hit can be extended into a higher affinity ligand. Our results show that ligand binding sites can usefully be thought of in terms of an anchor site, which is the top-ranked hot spot and dominates the free energy of binding, surrounded by a number of weaker satellite sites that confer improved affinity and selectivity for a particular ligand and that it is the intrinsic binding potential of the protein surface that determines whether it can serve as a robust binding site for a suitably optimized ligand. PMID:25918377

Computer-Assisted Drug Formulation Design: Novel Approach in Drug Delivery.

PubMed

Metwally, Abdelkader A; Hathout, Rania M

2015-08-03

We hypothesize that, by using several chemo/bio informatics tools and statistical computational methods, we can study and then predict the behavior of several drugs in model nanoparticulate lipid and polymeric systems. Accordingly, two different matrices comprising tripalmitin, a core component of solid lipid nanoparticles (SLN), and PLGA were first modeled using molecular dynamics simulation, and then the interaction of drugs with these systems was studied by means of computing the free energy of binding using the molecular docking technique. These binding energies were hence correlated with the loadings of these drugs in the nanoparticles obtained experimentally from the available literature. The obtained relations were verified experimentally in our laboratory using curcumin as a model drug. Artificial neural networks were then used to establish the effect of the drugs' molecular descriptors on the binding energies and hence on the drug loading. The results showed that the used soft computing methods can provide an accurate method for in silico prediction of drug loading in tripalmitin-based and PLGA nanoparticulate systems. These results have the prospective of being applied to other nano drug-carrier systems, and this integrated statistical and chemo/bio informatics approach offers a new toolbox to the formulation science by proposing what we present as computer-assisted drug formulation design (CADFD).

Free energy component analysis for drug design: a case study of HIV-1 protease-inhibitor binding.

PubMed

Kalra, P; Reddy, T V; Jayaram, B

2001-12-06

A theoretically rigorous and computationally tractable methodology for the prediction of the free energies of binding of protein-ligand complexes is presented. The method formulated involves developing molecular dynamics trajectories of the enzyme, the inhibitor, and the complex, followed by a free energy component analysis that conveys information on the physicochemical forces driving the protein-ligand complex formation and enables an elucidation of drug design principles for a given receptor from a thermodynamic perspective. The complexes of HIV-1 protease with two peptidomimetic inhibitors were taken as illustrative cases. Four-nanosecond-level all-atom molecular dynamics simulations using explicit solvent without any restraints were carried out on the protease-inhibitor complexes and the free proteases, and the trajectories were analyzed via a thermodynamic cycle to calculate the binding free energies. The computed free energies were seen to be in good accord with the reported data. It was noted that the net van der Waals and hydrophobic contributions were favorable to binding while the net electrostatics, entropies, and adaptation expense were unfavorable in these protease-inhibitor complexes. The hydrogen bond between the CH2OH group of the inhibitor at the scissile position and the catalytic aspartate was found to be favorable to binding. Various implicit solvent models were also considered and their shortcomings discussed. In addition, some plausible modifications to the inhibitor residues were attempted, which led to better binding affinities. The generality of the method and the transferability of the protocol with essentially no changes to any other protein-ligand system are emphasized.

Molecular dynamics simulations of energy dissipation and non-thermal diffusion on amorphous solid water.

PubMed

Fredon, A; Cuppen, H M

2018-02-21

Molecules in space are synthesized via a large variety of gas-phase reactions, and reactions on dust-grain surfaces, where the surface acts as a catalyst. Especially, saturated, hydrogen-rich molecules are formed through surface chemistry where the interstellar grains act as a meeting place and absorbing energy. Here we present the results of thousands of molecular dynamics simulations to quantify the outcome of an energy dissipation process. Admolecules on top of an amorphous solid water surface have been given translational energy between 0.5 and 5 eV. Three different surface species are considered, CO 2 , H 2 O and CH 4 , spanning a range in binding energies, number of internal degrees of freedom and molecular weight. The results are compared against a previous study using a crystalline water ice surface. Possible outcomes of a dissipation process are adsorption - possibly after long-range diffusion-, desorption and desorption of a surface molecule. The three admolecules were found to bind at different locations on the surface, particularly in terms of height. Water preferably binds on top of the surface, whereas methane fills the nanopores on the surface. This has direct consequences for desorption, travelled distance, and kick-out probabilities. The admolecules are found to frequently travel several tens of angstroms before stabilizing on a binding site, allowing follow-up reactions en route. We present kick-out probabilities and we have been able to quantify the desorption probability which depends on the binding energy of the species, the translational excitation, and a factor that accounts for difference in binding site height. We provide expressions that can be incorporated in astrochemical models to predict grain surface formation and return into the gas phase of these products.

Converging free energies of binding in cucurbit[7]uril and octa-acid host-guest systems from SAMPL4 using expanded ensemble simulations

NASA Astrophysics Data System (ADS)

Monroe, Jacob I.; Shirts, Michael R.

2014-04-01

Molecular containers such as cucurbit[7]uril (CB7) and the octa-acid (OA) host are ideal simplified model test systems for optimizing and analyzing methods for computing free energies of binding intended for use with biologically relevant protein-ligand complexes. To this end, we have performed initially blind free energy calculations to determine the free energies of binding for ligands of both the CB7 and OA hosts. A subset of the selected guest molecules were those included in the SAMPL4 prediction challenge. Using expanded ensemble simulations in the dimension of coupling host-guest intermolecular interactions, we are able to show that our estimates in most cases can be demonstrated to fully converge and that the errors in our estimates are due almost entirely to the assigned force field parameters and the choice of environmental conditions used to model experiment. We confirm the convergence through the use of alternative simulation methodologies and thermodynamic pathways, analyzing sampled conformations, and directly observing changes of the free energy with respect to simulation time. Our results demonstrate the benefits of enhanced sampling of multiple local free energy minima made possible by the use of expanded ensemble molecular dynamics and may indicate the presence of significant problems with current transferable force fields for organic molecules when used for calculating binding affinities, especially in non-protein chemistries.

Converging free energies of binding in cucurbit[7]uril and octa-acid host-guest systems from SAMPL4 using expanded ensemble simulations.

PubMed

Monroe, Jacob I; Shirts, Michael R

2014-04-01

Molecular containers such as cucurbit[7]uril (CB7) and the octa-acid (OA) host are ideal simplified model test systems for optimizing and analyzing methods for computing free energies of binding intended for use with biologically relevant protein-ligand complexes. To this end, we have performed initially blind free energy calculations to determine the free energies of binding for ligands of both the CB7 and OA hosts. A subset of the selected guest molecules were those included in the SAMPL4 prediction challenge. Using expanded ensemble simulations in the dimension of coupling host-guest intermolecular interactions, we are able to show that our estimates in most cases can be demonstrated to fully converge and that the errors in our estimates are due almost entirely to the assigned force field parameters and the choice of environmental conditions used to model experiment. We confirm the convergence through the use of alternative simulation methodologies and thermodynamic pathways, analyzing sampled conformations, and directly observing changes of the free energy with respect to simulation time. Our results demonstrate the benefits of enhanced sampling of multiple local free energy minima made possible by the use of expanded ensemble molecular dynamics and may indicate the presence of significant problems with current transferable force fields for organic molecules when used for calculating binding affinities, especially in non-protein chemistries.

Synergistic use of compound properties and docking scores in neural network modeling of CYP2D6 binding: predicting affinity and conformational sampling.

PubMed

Bazeley, Peter S; Prithivi, Sridevi; Struble, Craig A; Povinelli, Richard J; Sem, Daniel S

2006-01-01

Cytochrome P450 2D6 (CYP2D6) is used to develop an approach for predicting affinity and relevant binding conformation(s) for highly flexible binding sites. The approach combines the use of docking scores and compound properties as attributes in building a neural network (NN) model. It begins by identifying segments of CYP2D6 that are important for binding specificity, based on structural variability among diverse CYP enzymes. A family of distinct, low-energy conformations of CYP2D6 are generated using simulated annealing (SA) and a collection of 82 compounds with known CYP2D6 affinities are docked. Interestingly, docking poses are observed on the backside of the heme as well as in the known active site. Docking scores for the active site binders, along with compound-specific attributes, are used to train a neural network model to properly bin compounds as strong binders, moderate binders, or nonbinders. Attribute selection is used to preselect the most important scores and compound-specific attributes for the model. A prediction accuracy of 85+/-6% is achieved. Dominant attributes include docking scores for three of the 20 conformations in the ensemble as well as the compound's formal charge, number of aromatic rings, and AlogP. Although compound properties were highly predictive attributes (12% improvement over baseline) in the NN-based prediction of CYP2D6 binders, their combined use with docking score attributes is synergistic (net increase of 23% above baseline). Beyond prediction of affinity, attribute selection provides a way to identify the most relevant protein conformation(s), in terms of binding competence. In the case of CYP2D6, three out of the ensemble of 20 SA-generated structures are found to be the most predictive for binding.

Magnetic properties and core electron binding energies of liquid water

NASA Astrophysics Data System (ADS)

Galamba, N.; Cabral, Benedito J. C.

2018-01-01

The magnetic properties and the core and inner valence electron binding energies of liquid water are investigated. The adopted methodology relies on the combination of molecular dynamics and electronic structure calculations. Born-Oppenheimer molecular dynamics with the Becke and Lee-Yang-Parr functionals for exchange and correlation, respectively, and includes an empirical correction (BLYP-D3) functional and classical molecular dynamics with the TIP4P/2005-F model were carried out. The Keal-Tozer functional was applied for predicting magnetic shielding and spin-spin coupling constants. Core and inner valence electron binding energies in liquid water were calculated with symmetry adapted cluster-configuration interaction. The relationship between the magnetic shielding constant σ(17O), the role played by the oxygen atom as a proton acceptor and donor, and the tetrahedral organisation of liquid water are investigated. The results indicate that the deshielding of the oxygen atom in water is very dependent on the order parameter (q) describing the tetrahedral organisation of the hydrogen bond network. The strong sensitivity of magnetic properties on changes of the electronic density in the nuclei environment is illustrated by a correlation between σ(17O) and the energy gap between the 1a1[O1s] (core) and the 2a1 (inner valence) orbitals of water. Although several studies discussed the eventual connection between magnetic properties and core electron binding energies, such a correlation could not be clearly established. Here, we demonstrate that for liquid water this correlation exists although involving the gap between electron binding energies of core and inner valence orbitals.

Accurate high-throughput structure mapping and prediction with transition metal ion FRET

PubMed Central

Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

2013-01-01

Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

The SAMPL4 host-guest blind prediction challenge: an overview.

PubMed

Muddana, Hari S; Fenley, Andrew T; Mobley, David L; Gilson, Michael K

2014-04-01

Prospective validation of methods for computing binding affinities can help assess their predictive power and thus set reasonable expectations for their performance in drug design applications. Supramolecular host-guest systems are excellent model systems for testing such affinity prediction methods, because their small size and limited conformational flexibility, relative to proteins, allows higher throughput and better numerical convergence. The SAMPL4 prediction challenge therefore included a series of host-guest systems, based on two hosts, cucurbit[7]uril and octa-acid. Binding affinities in aqueous solution were measured experimentally for a total of 23 guest molecules. Participants submitted 35 sets of computational predictions for these host-guest systems, based on methods ranging from simple docking, to extensive free energy simulations, to quantum mechanical calculations. Over half of the predictions provided better correlations with experiment than two simple null models, but most methods underperformed the null models in terms of root mean squared error and linear regression slope. Interestingly, the overall performance across all SAMPL4 submissions was similar to that for the prior SAMPL3 host-guest challenge, although the experimentalists took steps to simplify the current challenge. While some methods performed fairly consistently across both hosts, no single approach emerged as consistent top performer, and the nonsystematic nature of the various submissions made it impossible to draw definitive conclusions regarding the best choices of energy models or sampling algorithms. Salt effects emerged as an issue in the calculation of absolute binding affinities of cucurbit[7]uril-guest systems, but were not expected to affect the relative affinities significantly. Useful directions for future rounds of the challenge might involve encouraging participants to carry out some calculations that replicate each others' studies, and to systematically explore parameter options.

Principal component analysis of binding energies for single-point mutants of hT2R16 bound to an agonist correlate with experimental mutant cell response.

PubMed

Chen, Derek E; Willick, Darryl L; Ruckel, Joseph B; Floriano, Wely B

2015-01-01

Directed evolution is a technique that enables the identification of mutants of a particular protein that carry a desired property by successive rounds of random mutagenesis, screening, and selection. This technique has many applications, including the development of G protein-coupled receptor-based biosensors and designer drugs for personalized medicine. Although effective, directed evolution is not without challenges and can greatly benefit from the development of computational techniques to predict the functional outcome of single-point amino acid substitutions. In this article, we describe a molecular dynamics-based approach to predict the effects of single amino acid substitutions on agonist binding (salicin) to a human bitter taste receptor (hT2R16). An experimentally determined functional map of single-point amino acid substitutions was used to validate the whole-protein molecular dynamics-based predictive functions. Molecular docking was used to construct a wild-type agonist-receptor complex, providing a starting structure for single-point substitution simulations. The effects of each single amino acid substitution in the functional response of the receptor to its agonist were estimated using three binding energy schemes with increasing inclusion of solvation effects. We show that molecular docking combined with molecular mechanics simulations of single-point mutants of the agonist-receptor complex accurately predicts the functional outcome of single amino acid substitutions in a human bitter taste receptor.

Rational design of biaryl pharmacophore inserted noscapine derivatives as potent tubulin binding anticancer agents

NASA Astrophysics Data System (ADS)

Santoshi, Seneha; Manchukonda, Naresh Kumar; Suri, Charu; Sharma, Manya; Sridhar, Balasubramanian; Joseph, Silja; Lopus, Manu; Kantevari, Srinivas; Baitharu, Iswar; Naik, Pradeep Kumar

2015-03-01

We have strategically designed a series of noscapine derivatives by inserting biaryl pharmacophore (a major structural constituent of many of the microtubule-targeting natural anticancer compounds) onto the scaffold structure of noscapine. Molecular interaction of these derivatives with α,β-tubulin heterodimer was investigated by molecular docking, molecular dynamics simulation, and binding free energy calculation. The predictive binding affinity indicates that the newly designed noscapinoids bind to tubulin with a greater affinity. The predictive binding free energy (ΔGbind, pred) of these derivatives (ranging from -5.568 to -5.970 kcal/mol) based on linear interaction energy (LIE) method with a surface generalized Born (SGB) continuum solvation model showed improved binding affinity with tubulin compared to the lead compound, natural α-noscapine (-5.505 kcal/mol). Guided by the computational findings, these new biaryl type α-noscapine congeners were synthesized from 9-bromo-α-noscapine using optimized Suzuki reaction conditions for further experimental evaluation. The derivatives showed improved inhibition of the proliferation of human breast cancer cells (MCF-7), human cervical cancer cells (HeLa) and human lung adenocarcinoma cells (A549), compared to natural noscapine. The cell cycle analysis in MCF-7 further revealed that these compounds alter the cell cycle profile and cause mitotic arrest at G2/M phase more strongly than noscapine. Tubulin binding assay revealed higher binding affinity to tubulin, as suggested by dissociation constant (Kd) of 126 ± 5.0 µM for 5a, 107 ± 5.0 µM for 5c, 70 ± 4.0 µM for 5d, and 68 ± 6.0 µM for 5e compared to noscapine (Kd of 152 ± 1.0 µM). In fact, the experimentally determined value of ΔGbind, expt (calculated from the Kd value) are consistent with the predicted value of ΔGbind, pred calculated based on LIE-SGB. Based on these results, one of the derivative 5e of this series was used for further toxicological evaluation. Treatment of mice with a daily dose of 300 mg/kg and a single dose of 600 mg/kg indicates that the compound does not induce detectable pathological abnormalities in normal tissues. Also there were no significant differences in hematological parameters between the treated and untreated groups. Hence, the newly designed noscapinoid, 5e is an orally bioavailable, safe and effective anticancer agent with a potential for the treatment of cancer and might be a candidate for clinical evaluation.

Predicting the affinity of Farnesoid X Receptor ligands through a hierarchical ranking protocol: a D3R Grand Challenge 2 case study

NASA Astrophysics Data System (ADS)

Réau, Manon; Langenfeld, Florent; Zagury, Jean-François; Montes, Matthieu

2018-01-01

The Drug Design Data Resource (D3R) Grand Challenges are blind contests organized to assess the state-of-the-art methods accuracy in predicting binding modes and relative binding free energies of experimentally validated ligands for a given target. The second stage of the D3R Grand Challenge 2 (GC2) was focused on ranking 102 compounds according to their predicted affinity for Farnesoid X Receptor. In this task, our workflow was ranked 5th out of the 77 submissions in the structure-based category. Our strategy consisted in (1) a combination of molecular docking using AutoDock 4.2 and manual edition of available structures for binding poses generation using SeeSAR, (2) the use of HYDE scoring for pose selection, and (3) a hierarchical ranking using HYDE and MM/GBSA. In this report, we detail our pose generation and ligands ranking protocols and provide guidelines to be used in a prospective computer aided drug design program.

A Hot-Spot Motif Characterizes the Interface between a Designed Ankyrin-Repeat Protein and Its Target Ligand

PubMed Central

Cheung, Luthur Siu-Lun; Kanwar, Manu; Ostermeier, Marc; Konstantopoulos, Konstantinos

2012-01-01

Nonantibody scaffolds such as designed ankyrin repeat proteins (DARPins) can be rapidly engineered to detect diverse target proteins with high specificity and offer an attractive alternative to antibodies. Using molecular simulations, we predicted that the binding interface between DARPin off7 and its ligand (maltose binding protein; MBP) is characterized by a hot-spot motif in which binding energy is largely concentrated on a few amino acids. To experimentally test this prediction, we fused MBP to a transmembrane domain to properly orient the protein into a polymer-cushioned lipid bilayer, and characterized its interaction with off7 using force spectroscopy. Using this, to our knowledge, novel technique along with surface plasmon resonance, we validated the simulation predictions and characterized the effects of select mutations on the kinetics of the off7-MBP interaction. Our integrated approach offers scientific insights on how the engineered protein interacts with the target molecule. PMID:22325262

Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

Stepwise identification of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus type 1 genome boosted by a StepRank scheme.

PubMed

Bi, Jianjun; Song, Rengang; Yang, Huilan; Li, Bingling; Fan, Jianyong; Liu, Zhongrong; Long, Chaoqin

2011-01-01

Identification of immunodominant epitopes is the first step in the rational design of peptide vaccines aimed at T-cell immunity. To date, however, it is yet a great challenge for accurately predicting the potent epitope peptides from a pool of large-scale candidates with an efficient manner. In this study, a method that we named StepRank has been developed for the reliable and rapid prediction of binding capabilities/affinities between proteins and genome-wide peptides. In this procedure, instead of single strategy used in most traditional epitope identification algorithms, four steps with different purposes and thus different computational demands are employed in turn to screen the large-scale peptide candidates that are normally generated from, for example, pathogenic genome. The steps 1 and 2 aim at qualitative exclusion of typical nonbinders by using empirical rule and linear statistical approach, while the steps 3 and 4 focus on quantitative examination and prediction of the interaction energy profile and binding affinity of peptide to target protein via quantitative structure-activity relationship (QSAR) and structure-based free energy analysis. We exemplify this method through its application to binding predictions of the peptide segments derived from the 76 known open-reading frames (ORFs) of herpes simplex virus type 1 (HSV-1) genome with or without affinity to human major histocompatibility complex class I (MHC I) molecule HLA-A*0201, and find that the predictive results are well compatible with the classical anchor residue theory and perfectly match for the extended motif pattern of MHC I-binding peptides. The putative epitopes are further confirmed by comparisons with 11 experimentally measured HLA-A*0201-restrcited peptides from the HSV-1 glycoproteins D and K. We expect that this well-designed scheme can be applied in the computational screening of other viral genomes as well.

Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field.

PubMed

Wang, Lingle; Wu, Yujie; Deng, Yuqing; Kim, Byungchan; Pierce, Levi; Krilov, Goran; Lupyan, Dmitry; Robinson, Shaughnessy; Dahlgren, Markus K; Greenwood, Jeremy; Romero, Donna L; Masse, Craig; Knight, Jennifer L; Steinbrecher, Thomas; Beuming, Thijs; Damm, Wolfgang; Harder, Ed; Sherman, Woody; Brewer, Mark; Wester, Ron; Murcko, Mark; Frye, Leah; Farid, Ramy; Lin, Teng; Mobley, David L; Jorgensen, William L; Berne, Bruce J; Friesner, Richard A; Abel, Robert

2015-02-25

Designing tight-binding ligands is a primary objective of small-molecule drug discovery. Over the past few decades, free-energy calculations have benefited from improved force fields and sampling algorithms, as well as the advent of low-cost parallel computing. However, it has proven to be challenging to reliably achieve the level of accuracy that would be needed to guide lead optimization (∼5× in binding affinity) for a wide range of ligands and protein targets. Not surprisingly, widespread commercial application of free-energy simulations has been limited due to the lack of large-scale validation coupled with the technical challenges traditionally associated with running these types of calculations. Here, we report an approach that achieves an unprecedented level of accuracy across a broad range of target classes and ligands, with retrospective results encompassing 200 ligands and a wide variety of chemical perturbations, many of which involve significant changes in ligand chemical structures. In addition, we have applied the method in prospective drug discovery projects and found a significant improvement in the quality of the compounds synthesized that have been predicted to be potent. Compounds predicted to be potent by this approach have a substantial reduction in false positives relative to compounds synthesized on the basis of other computational or medicinal chemistry approaches. Furthermore, the results are consistent with those obtained from our retrospective studies, demonstrating the robustness and broad range of applicability of this approach, which can be used to drive decisions in lead optimization.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort.

PubMed

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A; Fells, James I; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

Workflows and performances in the ranking prediction of 2016 D3R Grand Challenge 2: lessons learned from a collaborative effort

NASA Astrophysics Data System (ADS)

Gao, Ying-Duo; Hu, Yuan; Crespo, Alejandro; Wang, Deping; Armacost, Kira A.; Fells, James I.; Fradera, Xavier; Wang, Hongwu; Wang, Huijun; Sherborne, Brad; Verras, Andreas; Peng, Zhengwei

2018-01-01

The 2016 D3R Grand Challenge 2 includes both pose and affinity or ranking predictions. This article is focused exclusively on affinity predictions submitted to the D3R challenge from a collaborative effort of the modeling and informatics group. Our submissions include ranking of 102 ligands covering 4 different chemotypes against the FXR ligand binding domain structure, and the relative binding affinity predictions of the two designated free energy subsets of 15 and 18 compounds. Using all the complex structures prepared in the same way allowed us to cover many types of workflows and compare their performances effectively. We evaluated typical workflows used in our daily structure-based design modeling support, which include docking scores, force field-based scores, QM/MM, MMGBSA, MD-MMGBSA, and MacroModel interaction energy estimations. The best performing methods for the two free energy subsets are discussed. Our results suggest that affinity ranking still remains very challenging; that the knowledge of more structural information does not necessarily yield more accurate predictions; and that visual inspection and human intervention are considerably important for ranking. Knowledge of the mode of action and protein flexibility along with visualization tools that depict polar and hydrophobic maps are very useful for visual inspection. QM/MM-based workflows were found to be powerful in affinity ranking and are encouraged to be applied more often. The standardized input and output enable systematic analysis and support methodology development and improvement for high level blinded predictions.

The Formation of Fibrils by Intertwining of Filaments: Model and Application to Amyloid Aβ Protein

PubMed Central

van Gestel, Jeroen; de Leeuw, Simon W.

2007-01-01

We outline a model that describes the interaction of rods that form intertwined bundles. In this simple model, we compare the elastic energy penalty that arises due to the deformation of the rods to the gain in binding energy upon intertwining. We find that, for proper values of the bending Young's modulus and the binding energy, a helical pitch may be found for which the energy of intertwining is most favorable. We apply our description to the problem of Alzheimer's Aβ protein fibrillization. If we forbid configurations that exhibit steric overlap between the protofilaments that make up a protein fibril, our model predicts that fibrils consisting of three protofilaments shall form. This agrees well with experimental results. Our model can also provide an estimate for the helical pitch of suitable fibrils. PMID:17114229

Energy Scaling of Cold Atom-Atom-Ion Three-Body Recombination

NASA Astrophysics Data System (ADS)

Krükow, Artjom; Mohammadi, Amir; Härter, Arne; Denschlag, Johannes Hecker; Pérez-Ríos, Jesús; Greene, Chris H.

2016-05-01

We study three-body recombination of Ba++Rb +Rb in the mK regime where a single 138Ba+ ion in a Paul trap is immersed into a cloud of ultracold 87Rb atoms. We measure the energy dependence of the three-body rate coefficient k3 and compare the results to the theoretical prediction, k3∝Ecol-3 /4, where Ecol is the collision energy. We find agreement if we assume that the nonthermal ion energy distribution is determined by at least two different micromotion induced energy scales. Furthermore, using classical trajectory calculations we predict how the median binding energy of the formed molecules scales with the collision energy. Our studies give new insights into the kinetics of an ion immersed in an ultracold atom cloud and yield important prospects for atom-ion experiments targeting the s -wave regime.

Optimization of binding electrostatics: Charge complementarity in the barnase-barstar protein complex

PubMed Central

lee, Lee-Peng; Tidor, Bruce

2001-01-01

Theoretical and experimental studies have shown that the large desolvation penalty required for polar and charged groups frequently precludes their involvement in electrostatic interactions that contribute strongly to net stability in the folding or binding of proteins in aqueous solution near room temperature. We have previously developed a theoretical framework for computing optimized electrostatic interactions and illustrated use of the algorithm with simplified geometries. Given a receptor and model assumptions, the method computes the ligand-charge distribution that provides the most favorable balance of desolvation and interaction effects on binding. In this paper the method has been extended to treat complexes using actual molecular shapes. The barnase-barstar protein complex was investigated with barnase treated as a target receptor. The atomic point charges of barstar were varied to optimize the electrostatic binding free energy. Barnase and natural barstar form a tight complex (Kd ∼ 10−14 M) with many charged and polar groups near the interface that make this a particularly relevant system for investigating the role of electrostatic effects on binding. The results show that sets of barstar charges (resulting from optimization with different constraints) can be found that give rise to relatively large predicted improvements in electrostatic binding free energy. Principles for enhancing the effect of electrostatic interactions in molecular binding in aqueous environments are discussed in light of the optima. Our findings suggest that, in general, the enhancements in electrostatic binding free energy resulting from modification of polar and charged groups can be substantial. Moreover, a recently proposed definition of electrostatic complementarity is shown to be a useful tool for examining binding interfaces. Finally, calculational results suggest that wild-type barstar is closer to being affinity optimized than is barnase for their mutual binding, consistent with the known roles of these proteins. PMID:11266622

Prediction of protein-protein interaction sites using electrostatic desolvation profiles.

PubMed

Fiorucci, Sébastien; Zacharias, Martin

2010-05-19

Protein-protein complex formation involves removal of water from the interface region. Surface regions with a small free energy penalty for water removal or desolvation may correspond to preferred interaction sites. A method to calculate the electrostatic free energy of placing a neutral low-dielectric probe at various protein surface positions has been designed and applied to characterize putative interaction sites. Based on solutions of the finite-difference Poisson equation, this method also includes long-range electrostatic contributions and the protein solvent boundary shape in contrast to accessible-surface-area-based solvation energies. Calculations on a large set of proteins indicate that in many cases (>90%), the known binding site overlaps with one of the six regions of lowest electrostatic desolvation penalty (overlap with the lowest desolvation region for 48% of proteins). Since the onset of electrostatic desolvation occurs even before direct protein-protein contact formation, it may help guide proteins toward the binding region in the final stage of complex formation. It is interesting that the probe desolvation properties associated with residue types were found to depend to some degree on whether the residue was outside of or part of a binding site. The probe desolvation penalty was on average smaller if the residue was part of a binding site compared to other surface locations. Applications to several antigen-antibody complexes demonstrated that the approach might be useful not only to predict protein interaction sites in general but to map potential antigenic epitopes on protein surfaces. Copyright (c) 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Cooperative binding of drugs on human serum albumin

NASA Astrophysics Data System (ADS)

Varela, L. M.; Pérez-Rodríguez, M.; García, M.

In order to explain the adsorption isotherms of the amphiphilic penicillins nafcillin and cloxacillin onto human serum albumin (HSA), a cooperative multilayer adsorption model is introduced, combining the Brunauer-Emmet-Teller (BET) adsorption isotherm with an amphiphilic ionic adsorbate, whose chemical potential is derived from Guggenheim's theory. The non-cooperative model has been previously proved to qualitatively predict the measured adsorption maxima of these drugs [Varela, L. M., García, M., Pérez-Rodríguez, M., Taboada, P., Ruso, J. M., and Mosquera, V., 2001, J. chem. Phys., 114, 7682]. The surface interactions among adsorbed drug molecules are modelled in a mean-field fashion, so the chemical potential of the adsorbate is assumed to include a term proportional to the surface coverage, the constant of proportionality being the lateral interaction energy between bound molecules. The interaction energies obtained from the empirical binding isotherms are of the order of tenths of the thermal energy, therefore suggesting the principal role of van der Waals forces in the binding process.

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

PubMed Central

2017-01-01

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the geometric and biological complexity. To address this problem we introduce the element-specific persistent homology (ESPH) method. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains important biological information via a multichannel image-like representation. This representation reveals hidden structure-function relationships in biomolecules. We further integrate ESPH and deep convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the deep learning limitations from small and noisy training sets, we propose a multi-task multichannel topological convolutional neural network (MM-TCNN). We demonstrate that TopologyNet outperforms the latest methods in the prediction of protein-ligand binding affinities, mutation induced globular protein folding free energy changes, and mutation induced membrane protein folding free energy changes. Availability: weilab.math.msu.edu/TDL/ PMID:28749969

Computational Study on New Natural Compound Inhibitors of Pyruvate Dehydrogenase Kinases

PubMed Central

Zhou, Xiaoli; Yu, Shanshan; Su, Jing; Sun, Liankun

2016-01-01

Pyruvate dehydrogenase kinases (PDKs) are key enzymes in glucose metabolism, negatively regulating pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibiting PDKs could upregulate PDC activity and drive cells into more aerobic metabolism. Therefore, PDKs are potential targets for metabolism related diseases, such as cancers and diabetes. In this study, a series of computer-aided virtual screening techniques were utilized to discover potential inhibitors of PDKs. Structure-based screening using Libdock was carried out following by ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction. Molecular docking was used to analyze the binding mechanism between these compounds and PDKs. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. From the computational results, two novel natural coumarins compounds (ZINC12296427 and ZINC12389251) from the ZINC database were found binding to PDKs with favorable interaction energy and predicted to be non-toxic. Our study provide valuable information of PDK-coumarins binding mechanisms in PDK inhibitor-based drug discovery. PMID:26959013

Computational Study on New Natural Compound Inhibitors of Pyruvate Dehydrogenase Kinases.

PubMed

Zhou, Xiaoli; Yu, Shanshan; Su, Jing; Sun, Liankun

2016-03-04

Pyruvate dehydrogenase kinases (PDKs) are key enzymes in glucose metabolism, negatively regulating pyruvate dehyrogenase complex (PDC) activity through phosphorylation. Inhibiting PDKs could upregulate PDC activity and drive cells into more aerobic metabolism. Therefore, PDKs are potential targets for metabolism related diseases, such as cancers and diabetes. In this study, a series of computer-aided virtual screening techniques were utilized to discover potential inhibitors of PDKs. Structure-based screening using Libdock was carried out following by ADME (adsorption, distribution, metabolism, excretion) and toxicity prediction. Molecular docking was used to analyze the binding mechanism between these compounds and PDKs. Molecular dynamic simulation was utilized to confirm the stability of potential compound binding. From the computational results, two novel natural coumarins compounds (ZINC12296427 and ZINC12389251) from the ZINC database were found binding to PDKs with favorable interaction energy and predicted to be non-toxic. Our study provide valuable information of PDK-coumarins binding mechanisms in PDK inhibitor-based drug discovery.

Energetic basis for the molecular-scale organization of bone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tao, Jinhui; Battle, Keith C.; Pan, Haihua

2014-12-24

The remarkable properties of bone derive from a highly organized arrangement of co-aligned nm-scale apatite platelets within a fibrillar collagen matrix. The origin of this arrangement is poorly understood and the crystal structures of hydroxyapatite (HAP) and the non-mineralized collagen fibrils alone do not provide an explanation. Moreover, little is known about collagen-apatite interaction energies, which should strongly influence both the molecular-scale organization and the resulting mechanical properties of the composite. We investigated collagen-mineral interactions by combining dynamic force spectroscopy (DFS) measurements of binding energies with molecular dynamics (MD) simulations of binding and AFM observations of collagen adsorption on singlemore » crystals of calcium phosphate for four mineral phases of potential importance in bone formation. In all cases, we observe a strong preferential orientation of collagen binding, but comparison between the observed orientations and TEM analyses native tissues shows only calcium-deficient apatite (CDAP) provides an interface with collagen that is consistent with both. MD simulations predict preferred collagen orientations that agree with observations and results from both MD and DFS reveal large values for the binding energy due to multiple binding sites. These findings reconcile apparent contradictions inherent in a hydroxyapatite or carbonated apatite (CAP) model of bone mineral and provide an energetic rationale for the molecular scale organization of bone.« less

Energetic basis for the molecular-scale organization of bone

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tao, Jinhui; Battle, Keith C.; Pan, Haihua

The remarkable properties of bone derive from a highly organized arrangement of co-aligned nm-scale apatite platelets within a fibrillar collagen matrix. The origin of this arrangement is poorly understood and the crystal structures of hydroxyapatite (HAP) and the non-mineralized collagen fibrils alone do not provide an explanation. Moreover, little is known about collagen-apatite interaction energies, which should strongly influence both the molecular-scale organization and the resulting mechanical properties of the composite. We investigated collagen-mineral interactions by combining dynamic force spectroscopy (DFS) measurements of binding energies with molecular dynamics (MD) simulations of binding and AFM observations of collagen adsorption on singlemore » crystals of calcium phosphate for four mineral phases of potential importance in bone formation. In all cases, we observe a strong preferential orientation of collagen binding, but comparison between the observed orientations and TEM analyses native tissues shows only calcium-deficient apatite (CDAP) provides an interface with collagen that is consistent with both. MD simulations predict preferred collagen orientations that agree with observations and results from both MD and DFS reveal large values for the binding energy due to multiple binding sites. These findings reconcile apparent contradictions inherent in a hydroxyapatite or carbonated apatite (CAP) model of bone mineral and provide an energetic rationale for the molecular scale organization of bone.« less

Tight-Binding study of Boron structures

NASA Astrophysics Data System (ADS)

McGrady, Joseph W.; Papaconstantopoulos, Dimitrios A.; Mehl, Michael J.

2014-10-01

We have performed Linearized Augmented Plane Wave (LAPW) calculations for five crystal structures (alpha, dhcp, sc, fcc, bcc) of Boron which we then fitted to a non-orthogonal tight-binding model following the Naval Research Laboratory Tight-Binding (NRL-TB) method. The predictions of the NRL-TB approach for complicated Boron structures such as R105 (or β-rhombohedral) and T190 are in agreement with recent first-principles calculations. Fully utilizing the computational speed of the NRL-TB method we calculated the energy differences of various structures, including those containing vacancies using supercells with up to 5000 atoms.

Study of protein-probe interaction and protective action of surfactant sodium dodecyl sulphate in urea-denatured HSA using charge transfer fluorescence probe methyl ester of N,N-dimethylamino naphthyl acrylic acid.

PubMed

Mahanta, Subrata; Singh, Rupashree Balia; Guchhait, Nikhil

2009-03-01

We have demonstrated that the intramolecular charge transfer (ICT) probe Methyl ester of N,N-dimethylamino naphthyl acrylic acid (MDMANA) serves as an efficient reporter of the proteinous microenvironment of Human Serum Albumin (HSA). This work reports the binding phenomenon of MDMANA with HSA and spectral modulation thereupon. The extent of binding and free energy change for complexation reaction along with efficient fluorescence resonance energy transfer from Trp-214 of HSA to MDMANA indicates strong binding between probe and protein. Fluorescence anisotropy, red edge excitation shift, acrylamide quenching and time resolved measurements corroborate the binding nature of the probe with protein and predicts that the probe molecule is located at the hydrophobic site of the protein HSA. Due to the strong binding ability of MDMANA with HSA, it is successfully utilized for the study of stabilizing action of anionic surfactant Sodium Dodecyl Sulphate to the unfolding and folding of protein with denaturant urea in concentration range 1M to 9M.

In silico molecular docking analysis of the human Argonaute 2 PAZ domain reveals insights into RNA interference.

PubMed

Kandeel, Mahmoud; Kitade, Yukio

2013-07-01

RNA interference (RNAi) is a critical cellular pathway activated by double stranded RNA and regulates the gene expression of target mRNA. During RNAi, the 3' end of siRNA binds with the PAZ domain, followed by release and rebinding in a cyclic manner, which deemed essential for proper gene silencing. Recently, we provided the forces underlying the recognition of small interfering RNA by PAZ in a computational study based on the structure of Drosophila Argonaute 2 (Ago2) PAZ domain. We have now reanalyzed these data within the view of the new available structures from human Argonauts. While the parameters of weak binding are correlated with higher (RNAi) in the Drosophila model, a different profile is predicted with the human Ago2 PAZ domain. On the basis of the human Ago2 PAZ models, the indicators of stronger binding as the total binding energy and the free energy were associated with better RNAi efficacy. This discrepancy might be attributable to differences in the binding site topology and the difference in the conformation of the bound nucleotides.

Force fields for describing the solution-phase synthesis of shape-selective metal nanoparticles

NASA Astrophysics Data System (ADS)

Zhou, Ya; Al-Saidi, Wissam; Fichthorn, Kristen

2013-03-01

Polyvinylpyrrolidone (PVP) and polyethylene oxide (PEO) are structure-directing agents that exhibit different performance in the polyol synthesis of Ag nanostructures. The success of these structure-directing agents in selective nanostructure synthesis is often attributed to their selective binding to Ag(100) facets. We use first-principles, density-functional theory (DFT) calculations in a vacuum environment to show that PVP has a stronger preference to bind to Ag(100) than to Ag(111), whereas PEO exhibits much weaker selectivity. To understand the role of solvent in the surface-sensitive binding, we develop classical force fields to describe the interactions of the structure-directing (PVP and PEO) and solvent (ethylene glycol) molecules with various Ag substrates. We parameterize the force fields through force-and-energy matching to DFT results using simulated annealing. We validate the force fields by comparisons to DFT and experimental binding energies. Our force fields reproduce the surface-sensitive binding predicted by DFT calculations. Molecular dynamics simulations based on these force fields can be used to reveal the role of solvent, polymer chain length, and polymer concentration in the selective synthesis of Ag nanostructures.

Probing the (110)-Oriented plane of rutile ZnF2: A DFT investigation

NASA Astrophysics Data System (ADS)

Tamijani, Ali Abbaspour; Ebrahimiaqda, Elham

2017-12-01

For many years, rutile-like crystals have given rise to pronounced enthusiasm amongst mineralogists. In this context, rutile-type ZnF2 has found numerous applications across a variety of disciplines, ranging from material sciences to optoelectronics. Surprisingly, very limited literature is concerned with the molecular adsorption on ZnF2 surfaces and related energetics. Additionally, surface probing with small particles is a well-entrenched technique to analyze the interfacial properties. In this regard, small organic species are valuable picks. In the present work, we have employed electronic structure calculations to simulate the adsorption of methane, chloroform, pyrrole, benzene, naphthalene, anthracene, tetracene and pentacene at the (110) plane of rutile ZnF2. Dispersion-corrected DFT method was chosen to predict the binding energies and structures of molecule-adsorbed surfaces. Interestingly, a linear proportionality relationship was found between the binding energies of aromatic adsorbates and their respective molecular lengths. By applying this relationship, we were able to predict the adsorption energy of pentacene on ZnF2 to within 2% of our DFT-based result.

Electromagnetic Nature of Nuclear Energy

NASA Astrophysics Data System (ADS)

Schaeffer, Bernard

2014-09-01

As it is known since two millenaries, there is an attraction between an electric charge and a neutral object. Coulomb found the fundamental laws of electricity two centuries ago. After one century of nuclear physics, the fundamental laws of the strong force are still ignored. It has been found that electric and magnetic Coulomb's laws alone, without any hypothetical centrifugal force, are able to predict the binding energy of the simplest bound nucleus, the deuteron 2 H with a precision of 4 % . The nuclear potential is given by the formula: Uem2 H / A =e2/4 πɛ0 (1/rnp + a - 1/rnp - a ) + μ0 |μnμp |/4 π rnp3. This potential shows a horizontal inflection point where the electric and magnetic forces are equilibrated, coinciding with the experimental deuteron binding energy. Similar results have been obtained for the α particle 4 He where the electric attractive potential is four times larger than that of 2 H while the magnetic repulsion is only 1 . 5 times larger and the 4 HE binding energy six times larger than that of the deuteron. These results, prove the electromagnetic nature of the nuclear energy without the usual assumptions.

Atomistic model for excitons: Capturing Strongly Bound Excitons in Monolayer Transition-Metal Dichalcogenides

NASA Astrophysics Data System (ADS)

Tseng, Frank; Simsek, Ergun; Gunlycke, Daniel

2015-03-01

Monolayer transition-metal dichalcogenides form a direct bandgap predicted in the visible regime making them attractive host materials for various electronic and optoelectronic applications. Due to a weak dielectric screening in these materials, strongly bound electron-hole pairs or excitons have binding energies up to at least several hundred meV's. While the conventional wisdom is to think of excitons as hydrogen-like quasi-particles, we show that the hydrogen model breaks down for these experimentally observed strongly bound, room-temperature excitons. To capture these non-hydrogen-like photo-excitations, we introduce an atomistic model for excitons that predicts both bright excitons and dark excitons, and their broken degeneracy in these two-dimensional materials. For strongly bound exciton states, the lattice potential significantly distorts the envelope wave functions, which affects predicted exciton peak energies. The combination of large binding energies and non-degeneracy of exciton states in monolayer transition metal dichalogendies may furthermore be exploited in room temperature applications where prolonged exciton lifetimes are necessary. This work has been funded by the Office of Naval Research (ONR), directly and through the Naval Research Laboratory (NRL). F.T and E.S acknowledge support from NRL through the NRC Research Associateship Program and ONR Summer Faculty Program, respectively.

Prediction of trivalent actinide amino(poly)carboxylate complex stability constants using linear free energy relationships with the lanthanide series

DOE Office of Scientific and Technical Information (OSTI.GOV)

Uhnak, Nic E.

Prediction of Trivalent Actinide Amino(poly)carboxylate Complex Stability Constants Using Linear Free Energy Relationships with the Lanthanide Series Alternative title: LFER Based Prediction of An(III) APC Stability Constants There is a gap in the literature regarding the complexation of amino(poly)carboxylate (APC) ligands with trivalent actinides (An(III))). The chemistry of the An(III) is nearly identical to that of the trivalent lanthanides Lns, but the An(III) express a slight enhancement when binding APC ligands. Presented in this report is a simple method of predicting the stability constants of the An(III), Pu, Am, Cm, Bk and Cf by using linear free energy relationships (LFER)more » of the An and the lanthanide (Ln) series for 91 APCs. This method produced An stability constants within uncertainty to available literature values for most ligands.« less

Exploring the role of water in molecular recognition: predicting protein ligandability using a combinatorial search of surface hydration sites.

PubMed

Vukovic, Sinisa; Brennan, Paul E; Huggins, David J

2016-09-01

The interaction between any two biological molecules must compete with their interaction with water molecules. This makes water the most important molecule in medicine, as it controls the interactions of every therapeutic with its target. A small molecule binding to a protein is able to recognize a unique binding site on a protein by displacing bound water molecules from specific hydration sites. Quantifying the interactions of these water molecules allows us to estimate the potential of the protein to bind a small molecule. This is referred to as ligandability. In the study, we describe a method to predict ligandability by performing a search of all possible combinations of hydration sites on protein surfaces. We predict ligandability as the summed binding free energy for each of the constituent hydration sites, computed using inhomogeneous fluid solvation theory. We compared the predicted ligandability with the maximum observed binding affinity for 20 proteins in the human bromodomain family. Based on this comparison, it was determined that effective inhibitors have been developed for the majority of bromodomains, in the range from 10 to 100 nM. However, we predict that more potent inhibitors can be developed for the bromodomains BPTF and BRD7 with relative ease, but that further efforts to develop inhibitors for ATAD2 will be extremely challenging. We have also made predictions for the 14 bromodomains with no reported small molecule K d values by isothermal titration calorimetry. The calculations predict that PBRM1(1) will be a challenging target, while others such as TAF1L(2), PBRM1(4) and TAF1(2), should be highly ligandable. As an outcome of this work, we assembled a database of experimental maximal K d that can serve as a community resource assisting medicinal chemistry efforts focused on BRDs. Effective prediction of ligandability would be a very useful tool in the drug discovery process.

Exploring the role of water in molecular recognition: predicting protein ligandability using a combinatorial search of surface hydration sites

NASA Astrophysics Data System (ADS)

Vukovic, Sinisa; Brennan, Paul E.; Huggins, David J.

2016-09-01

The interaction between any two biological molecules must compete with their interaction with water molecules. This makes water the most important molecule in medicine, as it controls the interactions of every therapeutic with its target. A small molecule binding to a protein is able to recognize a unique binding site on a protein by displacing bound water molecules from specific hydration sites. Quantifying the interactions of these water molecules allows us to estimate the potential of the protein to bind a small molecule. This is referred to as ligandability. In the study, we describe a method to predict ligandability by performing a search of all possible combinations of hydration sites on protein surfaces. We predict ligandability as the summed binding free energy for each of the constituent hydration sites, computed using inhomogeneous fluid solvation theory. We compared the predicted ligandability with the maximum observed binding affinity for 20 proteins in the human bromodomain family. Based on this comparison, it was determined that effective inhibitors have been developed for the majority of bromodomains, in the range from 10 to 100 nM. However, we predict that more potent inhibitors can be developed for the bromodomains BPTF and BRD7 with relative ease, but that further efforts to develop inhibitors for ATAD2 will be extremely challenging. We have also made predictions for the 14 bromodomains with no reported small molecule K d values by isothermal titration calorimetry. The calculations predict that PBRM1(1) will be a challenging target, while others such as TAF1L(2), PBRM1(4) and TAF1(2), should be highly ligandable. As an outcome of this work, we assembled a database of experimental maximal K d that can serve as a community resource assisting medicinal chemistry efforts focused on BRDs. Effective prediction of ligandability would be a very useful tool in the drug discovery process.

KFC Server: interactive forecasting of protein interaction hot spots.

PubMed

Darnell, Steven J; LeGault, Laura; Mitchell, Julie C

2008-07-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model-a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein-protein or protein-DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org.

KFC Server: interactive forecasting of protein interaction hot spots

PubMed Central

Darnell, Steven J.; LeGault, Laura; Mitchell, Julie C.

2008-01-01

The KFC Server is a web-based implementation of the KFC (Knowledge-based FADE and Contacts) model—a machine learning approach for the prediction of binding hot spots, or the subset of residues that account for most of a protein interface's; binding free energy. The server facilitates the automated analysis of a user submitted protein–protein or protein–DNA interface and the visualization of its hot spot predictions. For each residue in the interface, the KFC Server characterizes its local structural environment, compares that environment to the environments of experimentally determined hot spots and predicts if the interface residue is a hot spot. After the computational analysis, the user can visualize the results using an interactive job viewer able to quickly highlight predicted hot spots and surrounding structural features within the protein structure. The KFC Server is accessible at http://kfc.mitchell-lab.org. PMID:18539611

SLITHER: a web server for generating contiguous conformations of substrate molecules entering into deep active sites of proteins or migrating through channels in membrane transporters.

PubMed

Lee, Po-Hsien; Kuo, Kuei-Ling; Chu, Pei-Ying; Liu, Eric M; Lin, Jung-Hsin

2009-07-01

Many proteins use a long channel to guide the substrate or ligand molecules into the well-defined active sites for catalytic reactions or for switching molecular states. In addition, substrates of membrane transporters can migrate to another side of cellular compartment by means of certain selective mechanisms. SLITHER (http://bioinfo.mc.ntu.edu.tw/slither/or http://slither.rcas.sinica.edu.tw/) is a web server that can generate contiguous conformations of a molecule along a curved tunnel inside a protein, and the binding free energy profile along the predicted channel pathway. SLITHER adopts an iterative docking scheme, which combines with a puddle-skimming procedure, i.e. repeatedly elevating the potential energies of the identified global minima, thereby determines the contiguous binding modes of substrates inside the protein. In contrast to some programs that are widely used to determine the geometric dimensions in the ion channels, SLITHER can be applied to predict whether a substrate molecule can crawl through an inner channel or a half-channel of proteins across surmountable energy barriers. Besides, SLITHER also provides the list of the pore-facing residues, which can be directly compared with many genetic diseases. Finally, the adjacent binding poses determined by SLITHER can also be used for fragment-based drug design.

Analysis of oxygen binding-energy variations for BaO on W

NASA Astrophysics Data System (ADS)

Haas, G. A.; Shih, A.; Mueller, D.; Thomas, R. E.

Interatomic Auger analyses have been made of different forms of BaO layers on W substrates. Variations in Auger spectroscopy energies of the Ba4dBa5pO2p interatomic Auger transition were found to be largely governed by the O2p binding energy of the BaO adsorbate. This was illustrated by comparing results of the Auger data values with values derived from O2p binding energies using ultraviolet photoelectron spectroscopy. Very good agreement was observed not only for the W<100> substrate but also for the W<110> substrate which showed two oxygen-induced electronics state. Variations in binding energy were noted for different states of BaO lattice formation and for different amounts of oxidation, ranging from the transition of Ba to BaO and continuing to the BaO 2 stoichiometry and beyond. Effects were also reported for adsorbate alignment and thermal activation (i.e., reduction) of the oxidized state. An empirical relationship was found suggesting that the more tightly bound the O2p states of the BaO adsorbate were, the lower its work function would be. This link between binding energy and work function was observed to be valid not only for cases of poisoning by oxidation, but held as well during reactivation by the subsequent reduction of the oxide. In addition, this relationship also appeared to predict the low work function obtained through the introduction of substances such as Sc to the BaO-W system. Possible qualitative reasons which might contribute to this are discussed in terms of enhanced dipole effects and shifts in band structure.

Effect of Solvent and Substrate on the Surface Binding Mode of Carboxylate-Functionalized Aromatic Molecules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domenico, Janna; Foster, Michael E.; Spoerke, Erik D.

Here, the efficiency of dye-sensitized solar cells (DSSCs) is strongly influenced by dye molecule orientation and interactions with the substrate. Understanding the factors controlling the surface orientation of sensitizing organic molecules will aid in the improvement of both traditional DSSCs and other devices that integrate molecular linkers at interfaces. Here, we describe a general approach to understand relative dye–substrate orientation and provide analytical expressions predicting orientation. We consider the effects of substrate, solvent, and protonation state on dye molecule orientation. In the absence of solvent, our model predicts that most carboxylic acid-functionalized molecules prefer to lie flat (parallel) on themore » surface, due to van der Waals interactions, as opposed to a tilted orientation with respect to the surface that is favored by covalent bonding of the carboxylic acid group to the substrate. When solvation effects are considered, however, the molecules are predicted to orient perpendicular to the surface. We extend this approach to help understand and guide the orientation of metal–organic framework (MOF) thin-film growth on various metal–oxide substrates. A two-part analytical model is developed on the basis of the results of DFT calculations and ab initio MD simulations that predicts the binding energy of a molecule by chemical and dispersion forces on rutile and anatase TiO 2 surfaces, and quantifies the dye solvation energy for two solvents. The model is in good agreement with the DFT calculations and enables rapid prediction of dye molecule and MOF linker binding preference on the basis of the size of the adsorbing molecule, identity of the surface, and the solvent environment. We establish the threshold molecular size, governing dye molecule orientation, for each condition.« less

Effect of Solvent and Substrate on the Surface Binding Mode of Carboxylate-Functionalized Aromatic Molecules

DOE PAGES

Domenico, Janna; Foster, Michael E.; Spoerke, Erik D.; ...

2018-04-25

Here, the efficiency of dye-sensitized solar cells (DSSCs) is strongly influenced by dye molecule orientation and interactions with the substrate. Understanding the factors controlling the surface orientation of sensitizing organic molecules will aid in the improvement of both traditional DSSCs and other devices that integrate molecular linkers at interfaces. Here, we describe a general approach to understand relative dye–substrate orientation and provide analytical expressions predicting orientation. We consider the effects of substrate, solvent, and protonation state on dye molecule orientation. In the absence of solvent, our model predicts that most carboxylic acid-functionalized molecules prefer to lie flat (parallel) on themore » surface, due to van der Waals interactions, as opposed to a tilted orientation with respect to the surface that is favored by covalent bonding of the carboxylic acid group to the substrate. When solvation effects are considered, however, the molecules are predicted to orient perpendicular to the surface. We extend this approach to help understand and guide the orientation of metal–organic framework (MOF) thin-film growth on various metal–oxide substrates. A two-part analytical model is developed on the basis of the results of DFT calculations and ab initio MD simulations that predicts the binding energy of a molecule by chemical and dispersion forces on rutile and anatase TiO 2 surfaces, and quantifies the dye solvation energy for two solvents. The model is in good agreement with the DFT calculations and enables rapid prediction of dye molecule and MOF linker binding preference on the basis of the size of the adsorbing molecule, identity of the surface, and the solvent environment. We establish the threshold molecular size, governing dye molecule orientation, for each condition.« less

Systematic study of imidazoles inhibiting IDO1 via the integration of molecular mechanics and quantum mechanics calculations.

PubMed

Zou, Yi; Wang, Fang; Wang, Yan; Guo, Wenjie; Zhang, Yihua; Xu, Qiang; Lai, Yisheng

2017-05-05

Indoleamine 2,3-dioxygenase 1 (IDO1) is regarded as an attractive target for cancer immunotherapy. To rationalize the detailed interactions between IDO1 and its inhibitors at the atomic level, an integrated computational approach by combining molecular mechanics and quantum mechanics methods was employed in this report. Specifically, the binding modes of 20 inhibitors was initially investigated using the induced fit docking (IFD) protocol, which outperformed other two docking protocols in terms of correctly predicting ligand conformations. Secondly, molecular dynamics (MD) simulations and MM/PBSA free energy calculations were employed to determine the dynamic binding process and crucial residues were confirmed through close contact analysis, hydrogen-bond analysis and binding free energy decomposition calculations. Subsequent quantum mechanics and nonbonding interaction analysis were carried out to provide in-depth explanations on the critical role of those key residues, and Arg231 and 7-propionate of the heme group were major contributors to ligand binding, which lowed a great amount of interaction energy. We anticipate that these findings will be valuable for enzymatic studies and rational drug design. Copyright © 2017. Published by Elsevier Masson SAS.

A comparative study based on docking and molecular dynamics simulations over HDAC-tubulin dual inhibitors.

PubMed

Hassanzadeh, Malihe; Bagherzadeh, Kowsar; Amanlou, Massoud

2016-11-01

Nowadays the ability to prediction of complex behavior rationally based on the computational approaches has been a successful technique in drug discovery. In the present study interactions of a new series of hybrids, which were made by linking colchicine as a tubulin inhibitor and suberoylanilide hydroxamic acid (SAHA) as a HDAC inhibitor, with HDAC8 and HDAC1 were investigated and compared. This research has been facilitated by the availability of experimental information besides employing docking methodology as well as classical molecular dynamics simulations and binding free energy calculation were performed. The obtained findings indicate different modes of interactions and inhibition strengths of the studied inhibitors for HDAC8 and HDAC1. HDAC8 binding free energies (-34.35 to -26.27kcal/mol) revealed higher binding affinity to HDAC8 compared to HDAC1 (-33.17 to -7.99kcal/mol). The binding energy contribution of each residue with the hybrid compounds 4a-4e within the active site of HDAC1 and HDAC8 was analyzed and the results confirmed the rule of key amino acids in interaction with the hybrid compounds. Copyright © 2016 Elsevier Inc. All rights reserved.

Improved Binding Free Energy Predictions from Single-Reference Thermodynamic Integration Augmented with Hamiltonian Replica Exchange

DTIC Science & Technology

2011-08-15

Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota , K.; Fukuda,R.;Hasegawa, J.; Ishida,M.;Nakajima,T.; Honda , Y...limitations that are inherent in the molecular dynamics (MD) methods used in the simulations. Conventional FEP and TI free energy calculations are...with HREX (HREX-SRTI), which achieved convergence of solvation free energies for a challenging case of an amide system where conventional FEP and TI

Interaction of E. coli outer-membrane protein A with sugars on the receptors of the brain microvascular endothelial cells.

PubMed

Datta, Deepshikha; Vaidehi, Nagarajan; Floriano, Wely B; Kim, Kwang S; Prasadarao, Nemani V; Goddard, William A

2003-02-01

Esherichia coli, the most common gram-negative bacteria, can penetrate the brain microvascular endothelial cells (BMECs) during the neonatal period to cause meningitis with significant morbidity and mortality. Experimental studies have shown that outer-membrane protein A (OmpA) of E. coli plays a key role in the initial steps of the invasion process by binding to specific sugar moieties present on the glycoproteins of BMEC. These experiments also show that polymers of chitobiose (GlcNAcbeta1-4GlcNAc) block the invasion, while epitopes substituted with the L-fucosyl group do not. We used HierDock computational technique that consists of a hierarchy of coarse grain docking method with molecular dynamics (MD) to predict the binding sites and energies of interactions of GlcNAcbeta1-4GlcNAc and other sugars with OmpA. The results suggest two important binding sites for the interaction of carbohydrate epitopes of BMEC glycoproteins to OmpA. We identify one site as the binding pocket for chitobiose (GlcNAcbeta1-4GlcNAc) in OmpA, while the second region (including loops 1 and 2) may be important for recognition of specific sugars. We find that the site involving loops 1 and 2 has relative binding energies that correlate well with experimental observations. This theoretical study elucidates the interaction sites of chitobiose with OmpA and the binding site predictions made in this article are testable either by mutation studies or invasion assays. These results can be further extended in suggesting possible peptide antagonists and drug design for therapeutic strategies. Copyright 2002 Wiley-Liss, Inc.

Role of R292K mutation in influenza H7N9 neuraminidase toward oseltamivir susceptibility: MD and MM/PB(GB)SA study

NASA Astrophysics Data System (ADS)

Phanich, Jiraphorn; Rungrotmongkol, Thanyada; Kungwan, Nawee; Hannongbua, Supot

2016-10-01

The H7N9 avian influenza virus is a novel re-assortment from at least four different strains of virus. Neuraminidase, which is a glycoprotein on the surface membrane, has been the target for drug treatment. However, some H7N9 strains that have been isolated from patient after drug treatment have a R292K mutation in neuraminidase. This substitution was found to facilitate drug resistance using protein- and virus- assays, in particular it gave a high resistance to the most commonly used drug, oseltamivir. The aim of this research is to understand the source of oseltamivir resistance using MD simulations and the MM/PB(GB)SA binding free energy approaches. Both methods can predict the reduced susceptibility of oseltamivir in good agreement to the IC 50 binding energy, although MM/GBSA underestimates this prediction compared to the MM/PBSA calculation. Electrostatic interaction is the main contribution for oseltamivir binding in terms of both interaction and solvation. We found that the source of the drug resistance is a decrease in the binding interaction combined with the reduction of the dehydration penalty. The smaller K292 mutated residue has a larger binding pocket cavity compared to the wild-type resulting in the loss of drug carboxylate-K292 hydrogen bonding and an increased accessibility for water molecules around the K292 mutated residue. In addition, oseltamivir does not bind well to the R292K mutant complex as shown by the high degree of fluctuation in ligand RMSD during the simulation and the change in angular distribution of bulky side chain groups.

Micro-Environmental Signature of The Interactions between Druggable Target Protein, Dipeptidyl Peptidase-IV, and Anti-Diabetic Drugs.

PubMed

Chakraborty, Chiranjib; Mallick, Bidyut; Sharma, Ashish Ranjan; Sharma, Garima; Jagga, Supriya; Doss, C George Priya; Nam, Ju-Suk; Lee, Sang-Soo

2017-01-01

Druggability of a target protein depends on the interacting micro-environment between the target protein and drugs. Therefore, a precise knowledge of the interacting micro-environment between the target protein and drugs is requisite for drug discovery process. To understand such micro-environment, we performed in silico interaction analysis between a human target protein, Dipeptidyl Peptidase-IV (DPP-4), and three anti-diabetic drugs (saxagliptin, linagliptin and vildagliptin). During the theoretical and bioinformatics analysis of micro-environmental properties, we performed drug-likeness study, protein active site predictions, docking analysis and residual interactions with the protein-drug interface. Micro-environmental landscape properties were evaluated through various parameters such as binding energy, intermolecular energy, electrostatic energy, van der Waals'+H-bond+desolvo energy (E VHD ) and ligand efficiency (LE) using different in silico methods. For this study, we have used several servers and software, such as Molsoft prediction server, CASTp server, AutoDock software and LIGPLOT server. Through micro-environmental study, highest log P value was observed for linagliptin (1.07). Lowest binding energy was also observed for linagliptin with DPP-4 in the binding plot. We also identified the number of H-bonds and residues involved in the hydrophobic interactions between the DPP-4 and the anti-diabetic drugs. During interaction, two H-bonds and nine residues, two H-bonds and eleven residues as well as four H-bonds and nine residues were found between the saxagliptin, linagliptin as well as vildagliptin cases and DPP-4, respectively. Our in silico data obtained for drug-target interactions and micro-environmental signature demonstrates linagliptin as the most stable interacting drug among the tested anti-diabetic medicines.

Predicting protein-protein interactions on a proteome scale by matching evolutionary and structural similarities at interfaces using PRISM.

PubMed

Tuncbag, Nurcan; Gursoy, Attila; Nussinov, Ruth; Keskin, Ozlem

2011-08-11

Prediction of protein-protein interactions at the structural level on the proteome scale is important because it allows prediction of protein function, helps drug discovery and takes steps toward genome-wide structural systems biology. We provide a protocol (termed PRISM, protein interactions by structural matching) for large-scale prediction of protein-protein interactions and assembly of protein complex structures. The method consists of two components: rigid-body structural comparisons of target proteins to known template protein-protein interfaces and flexible refinement using a docking energy function. The PRISM rationale follows our observation that globally different protein structures can interact via similar architectural motifs. PRISM predicts binding residues by using structural similarity and evolutionary conservation of putative binding residue 'hot spots'. Ultimately, PRISM could help to construct cellular pathways and functional, proteome-scale annotation. PRISM is implemented in Python and runs in a UNIX environment. The program accepts Protein Data Bank-formatted protein structures and is available at http://prism.ccbb.ku.edu.tr/prism_protocol/.

Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots.

PubMed

Liu, Qian; Ren, Jing; Song, Jiangning; Li, Jinyan

2015-01-01

A binding hot spot is a small area at a protein-protein interface that can make significant contribution to binding free energy. This work investigates the substantial contribution made by some special co-occurring atomic contacts at a binding hot spot. A co-occurring atomic contact is a pair of atomic contacts that are close to each other with no more than three covalent-bond steps. We found that two kinds of co-occurring atomic contacts can play an important part in the accurate prediction of binding hot spot residues. One is the co-occurrence of two nearby hydrogen bonds. For example, mutations of any residue in a hydrogen bond network consisting of multiple co-occurring hydrogen bonds could disrupt the interaction considerably. The other kind of co-occurring atomic contact is the co-occurrence of a hydrophobic carbon contact and a contact between a hydrophobic carbon atom and a π ring. In fact, this co-occurrence signifies the collective effect of hydrophobic contacts. We also found that the B-factor measurements of several specific groups of amino acids are useful for the prediction of hot spots. Taking the B-factor, individual atomic contacts and the co-occurring contacts as features, we developed a new prediction method and thoroughly assessed its performance via cross-validation and independent dataset test. The results show that our method achieves higher prediction performance than well-known methods such as Robetta, FoldX and Hotpoint. We conclude that these contact descriptors, in particular the novel co-occurring atomic contacts, can be used to facilitate accurate and interpretable characterization of protein binding hot spots.

Co-Occurring Atomic Contacts for the Characterization of Protein Binding Hot Spots

PubMed Central

Liu, Qian; Ren, Jing; Song, Jiangning; Li, Jinyan

2015-01-01

A binding hot spot is a small area at a protein-protein interface that can make significant contribution to binding free energy. This work investigates the substantial contribution made by some special co-occurring atomic contacts at a binding hot spot. A co-occurring atomic contact is a pair of atomic contacts that are close to each other with no more than three covalent-bond steps. We found that two kinds of co-occurring atomic contacts can play an important part in the accurate prediction of binding hot spot residues. One is the co-occurrence of two nearby hydrogen bonds. For example, mutations of any residue in a hydrogen bond network consisting of multiple co-occurring hydrogen bonds could disrupt the interaction considerably. The other kind of co-occurring atomic contact is the co-occurrence of a hydrophobic carbon contact and a contact between a hydrophobic carbon atom and a π ring. In fact, this co-occurrence signifies the collective effect of hydrophobic contacts. We also found that the B-factor measurements of several specific groups of amino acids are useful for the prediction of hot spots. Taking the B-factor, individual atomic contacts and the co-occurring contacts as features, we developed a new prediction method and thoroughly assessed its performance via cross-validation and independent dataset test. The results show that our method achieves higher prediction performance than well-known methods such as Robetta, FoldX and Hotpoint. We conclude that these contact descriptors, in particular the novel co-occurring atomic contacts, can be used to facilitate accurate and interpretable characterization of protein binding hot spots. PMID:26675422

Theoretical structures and binding energies of RNA-RNA/cyanine dyes and spectroscopic properties of cyanine dyes

NASA Astrophysics Data System (ADS)

Salaeh, Salsabila; Chong, Wei Lim; Dokmaisrijan, Supaporn; Payaka, Apirak; Yana, Janchai; Nimmanpipug, Piyarat; Lee, Vannajan Sanghiran; Dumri, Kanchana; Anh, Dau Hung

2014-10-01

Cyanine dyes have been widely used as a fluorescence probe for biomolecules and protein labeling. The mostly used cyanine dyes for nucleic acids labeling are DiSC2(3), DiSC2(5), and DiSC2(7). The possible structures and binding energies of RNA-RNA/Cyanine dyes were predicted theoretically using AutoDock Vina. The results showed that cyanine dyes and bases of RNA-RNA have the van der Waals and pi-pi interactions. The maximum absorption wavelength in the visible region obtained from the TD-DFT calculations of all cyanine dyes in the absence of the RNA-RNA double strand showed the bathochromic shift.

Dimerization of a flocculent protein from Moringa oleifera: experimental evidence and in silico interpretation.

PubMed

Pavankumar, Asalapuram R; Kayathri, Rajarathinam; Murugan, Natarajan A; Zhang, Qiong; Srivastava, Vaibhav; Okoli, Chuka; Bulone, Vincent; Rajarao, Gunaratna K; Ågren, Hans

2014-01-01

Many proteins exist in dimeric and other oligomeric forms to gain stability and functional advantages. In this study, the dimerization property of a coagulant protein (MO2.1) from Moringa oleifera seeds was addressed through laboratory experiments, protein-protein docking studies and binding free energy calculations. The structure of MO2.1 was predicted by homology modelling, while binding free energy and residues-distance profile analyses provided insight into the energetics and structural factors for dimer formation. Since the coagulation activities of the monomeric and dimeric forms of MO2.1 were comparable, it was concluded that oligomerization does not affect the biological activity of the protein.

Water on BN doped benzene: A hard test for exchange-correlation functionals and the impact of exact exchange on weak binding

DOE PAGES

Al-Hamdani, Yasmine S.; Alfè, Dario; von Lilienfeld, O. Anatole; ...

2014-10-22

Density functional theory (DFT) studies of weakly interacting complexes have recently focused on the importance of van der Waals dispersion forces, whereas the role of exchange has received far less attention. Here, by exploiting the subtle binding between water and a boron and nitrogen doped benzene derivative (1,2-azaborine) we show how exact exchange can alter the binding conformation within a complex. Benchmark values have been calculated for three orientations of the water monomer on 1,2-azaborine from explicitly correlated quantum chemical methods, and we have also used diffusion quantum Monte Carlo. For a host of popular DFT exchange-correlation functionals we showmore » that the lack of exact exchange leads to the wrong lowest energy orientation of water on 1,2-azaborine. As such, we suggest that a high proportion of exact exchange and the associated improvement in the electronic structure could be needed for the accurate prediction of physisorption sites on doped surfaces and in complex organic molecules. Meanwhile to predict correct absolute interaction energies an accurate description of exchange needs to be augmented by dispersion inclusive functionals, and certain non-local van der Waals functionals (optB88- and optB86b-vdW) perform very well for absolute interaction energies. Through a comparison with water on benzene and borazine (B₃N₃H₆) we show that these results could have implications for the interaction of water with doped graphene surfaces, and suggest a possible way of tuning the interaction energy.« less

A thermodynamic approach to link self-organization, preferential flow and rainfall-runoff behaviour

NASA Astrophysics Data System (ADS)

Zehe, E.; Ehret, U.; Blume, T.; Kleidon, A.; Scherer, U.; Westhoff, M.

2013-11-01

This study investigates whether a thermodynamically optimal hillslope structure can, if existent, serve as a first guess for uncalibrated predictions of rainfall-runoff. To this end we propose a thermodynamic framework to link rainfall-runoff processes and dynamics of potential energy, kinetic energy and capillary binding energy in catchments and hillslopes. The starting point is that hydraulic equilibrium in soil corresponds to local thermodynamic equilibrium (LTE), characterized by a local maximum entropy/minimum of free energy of soil water. Deviations from LTE occur either due to evaporative losses, which increase absolute values of negative capillary binding energy of soil water and reduce its potential energy, or due to infiltration of rainfall, which increases potential energy of soil water and reduces the strength of capillary binding energy. The amplitude and relaxation time of these deviations depend on climate, vegetation, soil hydraulic functions, topography and density of macropores. Based on this framework we analysed the free energy balance of hillslopes within numerical experiments that perturbed model structures with respect to the surface density of macropores. These model structures have been previously shown to allow successful long-term simulations of the water balances of the Weiherbach and the Malalcahuello catchments, which are located in distinctly different pedological and climatic settings. Our findings offer a new perspective on different functions of preferential flow paths depending on the pedological setting. Free energy dynamics of soil water in the cohesive soils of the Weiherbach is dominated by dynamics of capillary binding energy. Macropores act as dissipative wetting structures by enlarging water flows against steep gradients in soil water potential after long dry spells. This implies accelerated depletion of these gradients and faster relaxation back towards LTE. We found two local optima in macropore density that maximize reduction rates of free energy of soil water during rainfall-driven conditions. These two optima exist because reduction rates of free energy are, in this case, a second-order polynomial of the wetting rate, which implicitly depends on macroporosity. An uncalibrated long-term simulation of the water balance of the Weiherbach catchment based on the first optimum macroporosity performed almost as well as the best fit when macroporosity was calibrated to match rainfall-runoff. In the Malalcahuello catchment we did not find an apparent optimum density of macropores, because free energy dynamics of soil water during rainfall-driven conditions is dominated by increases of potential energy. Macropores act as dissipative drainage structures by enhancing export of potential energy. No optimum macropore density exists in this case because potential energy change rates scale linearly with the wetting rate. We found, however, a distinguished macroporosity that assures steady-state conditions of the potential energy balance of the soil, in the sense that average storage of potential energy is compensated by average potential energy export. This distinguished macroporosity was close to the value that yielded the best fit of rainfall-runoff behaviour during a calibration exercise and allowed a robust estimate of the annual runoff coefficient. Our findings are promising for predictions in ungauged catchments (PUB) as the optimal/distinguished model structures can serve as a first guess for uncalibrated predictions of rainfall-runoff. They also offer an alternative for classifying catchments according to their similarity of the free energy balance components.

Exploring the selectivity of auto-inducer complex with LuxR using molecular docking, mutational studies and molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Rajamanikandan, Sundaraj; Srinivasan, Pappu

2017-03-01

Bacteria communicate with one another using extracellular signaling molecules called auto-inducers (AHLs), a process termed as quorum sensing. The quorum sensing process allows bacteria to regulate various physiological activities. In this regard, quorum sensing master regulator LuxR from Vibrio harveyi represents an attractive therapeutic target for the development of novel anti-quorum sensing agents. Eventhough the binding of AHL complex with LuxR is evidenced in earlier reports, but their mode of binding is not clearly determined. Therefore, in the present work, molecular docking, in silico mutational studies, molecular dynamics simulations and free energy calculations were performed to understand the selectivity of AHL into the binding site of LuxR. The results revealed that Asn133 and Gln137 residues play a crucial role in recognizing AHL more effectively into the binding site of LuxR with good binding free energy. In addition to that, the carbonyl group presents in the lactone ring and amide group of AHL plays a vital role in the formation of hydrogen bond interactions with the protein. Further, structure based virtual screening was performed using ChemBridge database to screen potent lead molecules against LuxR. 4-benzyl-2-pyrrolidinone and N-[2(1-cyclohexen-1-yl) enthyl]-N'(2-ethoxyphenyl) were selected based on dock score, binding affinity and mode of interactions with the receptor. Furthermore, binding free energy, density functional theory and ADME prediction were performed to rank the lead molecules. Thus, the identified lead molecules can be used for the development of anti-quorum sensing drugs.

New Parameters for Higher Accuracy in the Computation of Binding Free Energy Differences upon Alanine Scanning Mutagenesis on Protein-Protein Interfaces.

PubMed

Simões, Inês C M; Costa, Inês P D; Coimbra, João T S; Ramos, Maria J; Fernandes, Pedro A

2017-01-23

Knowing how proteins make stable complexes enables the development of inhibitors to preclude protein-protein (P:P) binding. The identification of the specific interfacial residues that mostly contribute to protein binding, denominated as hot spots, is thus critical. Here, we refine an in silico alanine scanning mutagenesis protocol, based on a residue-dependent dielectric constant version of the Molecular Mechanics/Poisson-Boltzmann Surface Area method. We have used a large data set of structurally diverse P:P complexes to redefine the residue-dependent dielectric constants used in the determination of binding free energies. The accuracy of the method was validated through comparison with experimental data, considering the per-residue P:P binding free energy (ΔΔG binding ) differences upon alanine mutation. Different protocols were tested, i.e., a geometry optimization protocol and three molecular dynamics (MD) protocols: (1) one using explicit water molecules, (2) another with an implicit solvation model, and (3) a third where we have carried out an accelerated MD with explicit water molecules. Using a set of protein dielectric constants (within the range from 1 to 20) we showed that the dielectric constants of 7 for nonpolar and polar residues and 11 for charged residues (and histidine) provide optimal ΔΔG binding predictions. An overall mean unsigned error (MUE) of 1.4 kcal mol -1 relative to the experiment was achieved in 210 mutations only with geometry optimization, which was further reduced with MD simulations (MUE of 1.1 kcal mol -1 for the MD employing explicit solvent). This recalibrated method allows for a better computational identification of hot spots, avoiding expensive and time-consuming experiments or thermodynamic integration/ free energy perturbation/ uBAR calculations, and will hopefully help new drug discovery campaigns in their quest of searching spots of interest for binding small drug-like molecules at P:P interfaces.

Advancing Drug Discovery through Enhanced Free Energy Calculations.

PubMed

Abel, Robert; Wang, Lingle; Harder, Edward D; Berne, B J; Friesner, Richard A

2017-07-18

A principal goal of drug discovery project is to design molecules that can tightly and selectively bind to the target protein receptor. Accurate prediction of protein-ligand binding free energies is therefore of central importance in computational chemistry and computer aided drug design. Multiple recent improvements in computing power, classical force field accuracy, enhanced sampling methods, and simulation setup have enabled accurate and reliable calculations of protein-ligands binding free energies, and position free energy calculations to play a guiding role in small molecule drug discovery. In this Account, we outline the relevant methodological advances, including the REST2 (Replica Exchange with Solute Temperting) enhanced sampling, the incorporation of REST2 sampling with convential FEP (Free Energy Perturbation) through FEP/REST, the OPLS3 force field, and the advanced simulation setup that constitute our FEP+ approach, followed by the presentation of extensive comparisons with experiment, demonstrating sufficient accuracy in potency prediction (better than 1 kcal/mol) to substantially impact lead optimization campaigns. The limitations of the current FEP+ implementation and best practices in drug discovery applications are also discussed followed by the future methodology development plans to address those limitations. We then report results from a recent drug discovery project, in which several thousand FEP+ calculations were successfully deployed to simultaneously optimize potency, selectivity, and solubility, illustrating the power of the approach to solve challenging drug design problems. The capabilities of free energy calculations to accurately predict potency and selectivity have led to the advance of ongoing drug discovery projects, in challenging situations where alternative approaches would have great difficulties. The ability to effectively carry out projects evaluating tens of thousands, or hundreds of thousands, of proposed drug candidates, is potentially transformative in enabling hard to drug targets to be attacked, and in facilitating the development of superior compounds, in various dimensions, for a wide range of targets. More effective integration of FEP+ calculations into the drug discovery process will ensure that the results are deployed in an optimal fashion for yielding the best possible compounds entering the clinic; this is where the greatest payoff is in the exploitation of computer driven design capabilities. A key conclusion from the work described is the surprisingly robust and accurate results that are attainable within the conventional classical simulation, fixed charge paradigm. No doubt there are individual cases that would benefit from a more sophisticated energy model or dynamical treatment, and properties other than protein-ligand binding energies may be more sensitive to these approximations. We conclude that an inflection point in the ability of MD simulations to impact drug discovery has now been attained, due to the confluence of hardware and software development along with the formulation of "good enough" theoretical methods and models.

New generation of docking programs: Supercomputer validation of force fields and quantum-chemical methods for docking.

PubMed

Sulimov, Alexey V; Kutov, Danil C; Katkova, Ekaterina V; Ilin, Ivan S; Sulimov, Vladimir B

2017-11-01

Discovery of new inhibitors of the protein associated with a given disease is the initial and most important stage of the whole process of the rational development of new pharmaceutical substances. New inhibitors block the active site of the target protein and the disease is cured. Computer-aided molecular modeling can considerably increase effectiveness of new inhibitors development. Reliable predictions of the target protein inhibition by a small molecule, ligand, is defined by the accuracy of docking programs. Such programs position a ligand in the target protein and estimate the protein-ligand binding energy. Positioning accuracy of modern docking programs is satisfactory. However, the accuracy of binding energy calculations is too low to predict good inhibitors. For effective application of docking programs to new inhibitors development the accuracy of binding energy calculations should be higher than 1kcal/mol. Reasons of limited accuracy of modern docking programs are discussed. One of the most important aspects limiting this accuracy is imperfection of protein-ligand energy calculations. Results of supercomputer validation of several force fields and quantum-chemical methods for docking are presented. The validation was performed by quasi-docking as follows. First, the low energy minima spectra of 16 protein-ligand complexes were found by exhaustive minima search in the MMFF94 force field. Second, energies of the lowest 8192 minima are recalculated with CHARMM force field and PM6-D3H4X and PM7 quantum-chemical methods for each complex. The analysis of minima energies reveals the docking positioning accuracies of the PM7 and PM6-D3H4X quantum-chemical methods and the CHARMM force field are close to one another and they are better than the positioning accuracy of the MMFF94 force field. Copyright © 2017 Elsevier Inc. All rights reserved.

Experimental and Theoretical Investigations of Infrared Multiple Photon Dissociation Spectra of Aspartic Acid Complexes with Zn2+ and Cd2.

PubMed

Boles, Georgia C; Hightower, Randy L; Coates, Rebecca A; McNary, Christopher P; Berden, Giel; Oomens, Jos; Armentrout, P B

2018-04-12

Complexes of aspartic acid (Asp) cationized with Zn 2+ : Zn(Asp-H) + , Zn(Asp-H) + (ACN) where ACN = acetonitrile, and Zn(Asp-H) + (Asp); as well as with Cd 2+ , CdCl + (Asp), were examined by infrared multiple photon dissociation (IRMPD) action spectroscopy using light generated from a free electron laser. A series of low-energy conformers for each complex was found using quantum chemical calculations to identify the structures formed experimentally. The main binding motif observed for the heavy-metal complex, CdCl + (Asp)[N,CO,CO s ], is a charge-solvated, tridentate structure, where the metal center binds to the backbone amino group and carbonyl oxygens of the backbone and side-chain carboxylic acids. Likewise, the deprotonated Zn(Asp-H) + (ACN) and Zn(Asp-H) + (Asp) complexes show comparable [N,CO - ,CO s ](ACN) and [N,CO - ,CO s ][N,CO,CO s ] coordinations, respectively. Interestingly, there was only minor spectral evidence for the analogous Zn(Asp-H) + [N,CO - ,CO s ] binding motif, even though this species is predicted to be the lowest-energy conformer. Instead, rearrangement and partial dissociation of the amino acid are observed, as spectral features most consistent with the experimental spectrum are exhibited by a four-coordinate Zn(Asp-NH 4 ) + [CO 2 - ,CO s ](NH 3 ) complex. Analysis of the mechanistic pathway leading from the predicted lowest-energy conformer to the isobaric deaminated complex is explored theoretically. Further, comparison of the current work to that of Zn 2+ and Cd 2+ complexes of asparagine (Asn) allows additional conclusions regarding populated conformers and effects of carboxamide versus carboxylic acid binding to be drawn.

Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1.

PubMed

Clark, Anthony J; Gindin, Tatyana; Zhang, Baoshan; Wang, Lingle; Abel, Robert; Murret, Colleen S; Xu, Fang; Bao, Amy; Lu, Nina J; Zhou, Tongqing; Kwong, Peter D; Shapiro, Lawrence; Honig, Barry; Friesner, Richard A

2017-04-07

Direct calculation of relative binding affinities between antibodies and antigens is a long-sought goal. However, despite substantial efforts, no generally applicable computational method has been described. Here, we describe a systematic free energy perturbation (FEP) protocol and calculate the binding affinities between the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class. The protocol has been adapted from successful studies of small molecules to address the challenges associated with modeling protein-protein interactions. Specifically, we built homology models of the three antibody-gp120 complexes, extended the sampling times for large bulky residues, incorporated the modeling of glycans on the surface of gp120, and utilized continuum solvent-based loop prediction protocols to improve sampling. We present three experimental surface plasmon resonance data sets, in which antibody residues in the antibody/gp120 interface were systematically mutated to alanine. The RMS error in the large set (55 total cases) of FEP tests as compared to these experiments, 0.68kcal/mol, is near experimental accuracy, and it compares favorably with the results obtained from a simpler, empirical methodology. The correlation coefficient for the combined data set including residues with glycan contacts, R 2 =0.49, should be sufficient to guide the choice of residues for antibody optimization projects, assuming that this level of accuracy can be realized in prospective prediction. More generally, these results are encouraging with regard to the possibility of using an FEP approach to calculate the magnitude of protein-protein binding affinities. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Experimental and theoretical investigation of topological and energetic characteristics of Sb complexes reversibly binding molecular oxygen.

PubMed

Fukin, Georgy K; Baranov, Evgenii V; Jelsch, Christian; Guillot, Benoît; Poddel'sky, Andrey I; Cherkasov, Vladimir K; Abakumov, Gleb A

2011-07-28

The experimental distribution of electron density in Ph(3)(4,5-OMe-3,6-Bu(t)-Cat)Sb·MeCN (1*) and Ph(3)(4,5-N(2)C(4)H(6)-3,6-Bu(t)-Cat)Sb·MeOH (2*) complexes was studied. According to atoms in molecules theory, the Sb-C(Ph), Sb-O(catecholate), and Sb···N(O) bonds are intermediate, whereas the O-C and C-C bonds are covalent, respectively. The energy of the Sb···N(MeCN) and Sb···O(MeOH) bonds are 7.0 and 11.3 kcal/mol according to the Espinosa equation. Density functional theory and Hartree-Fock calculations were carried out for a series of catecholate and amidophenolate complexes of antimony(V). It was shown that such calculations reliably reproduce geometrical and topological parameters and therefore can be used for a criterion search of dioxygen reversible binding by the catecholate and amidophenolate complexes of antimony(V). It was found that the "critical" value of the HOMO energy vary in the range from -5.197 to -5.061 eV for reversible binding of dioxygen complexes. This can serve as a thermodynamic criterion to predict the possibility of the dioxygen reversible binding by the catecholate and amidophenolate complexes of Sb(V). The HOMO energies correlate with the conversion of the catecholate and amidophenolate complexes in corresponding spiroendoperoxide derivatives as well. The contribution of the atom orbitals of the carbon atoms in the five-membered metallocycle to HOMO in complexes with different substitutes in the 4- and 5-positions of the catecholate ligand allows predicting the place of dioxygen addition. © 2011 American Chemical Society

Strontium and barium in aqueous solution and a potassium channel binding site

NASA Astrophysics Data System (ADS)

Chaudhari, Mangesh I.; Rempe, Susan B.

2018-06-01

Ion hydration structure and free energy establish criteria for understanding selective ion binding in potassium (K+) ion channels and may be significant to understanding blocking mechanisms as well. Recently, we investigated the hydration properties of Ba2+, the most potent blocker of K+ channels among the simple metal ions. Here, we use a similar method of combining ab initio molecular dynamics simulations, statistical mechanical theory, and electronic structure calculations to probe the fundamental hydration properties of Sr2+, which does not block bacterial K+ channels. The radial distribution of water around Sr2+ suggests a stable 8-fold geometry in the local hydration environment, similar to Ba2+. While the predicted hydration free energy of -331.8 kcal/mol is comparable with the experimental result of -334 kcal/mol, the value is significantly more favorable than the -305 kcal/mol hydration free energy of Ba2+. When placed in the innermost K+ channel blocking site, the solvation free energies and lowest energy structures of both Sr2+ and Ba2+ are nearly unchanged compared with their respective hydration properties. This result suggests that the block is not attributable to ion trapping due to +2 charge, and differences in blocking behavior arise due to free energies associated with the exchange of water ligands for channel ligands instead of free energies of transfer from water to the binding site.

Prediction of binding constants of protein ligands: A fast method for the prioritization of hits obtained from de novo design or 3D database search programs

NASA Astrophysics Data System (ADS)

Böhm, Hans-Joachim

1998-07-01

A dataset of 82 protein-ligand complexes of known 3D structure and binding constant Ki was analysed to elucidate the important factors that determine the strength of protein-ligand interactions. The following parameters were investigated: the number and geometry of hydrogen bonds and ionic interactions between the protein and the ligand, the size of the lipophilic contact surface, the flexibility of the ligand, the electrostatic potential in the binding site, water molecules in the binding site, cavities along the protein-ligand interface and specific interactions between aromatic rings. Based on these parameters, a new empirical scoring function is presented that estimates the free energy of binding for a protein-ligand complex of known 3D structure. The function distinguishes between buried and solvent accessible hydrogen bonds. It tolerates deviations in the hydrogen bond geometry of up to 0.25 Å in the length and up to 30 °Cs in the hydrogen bond angle without penalizing the score. The new energy function reproduces the binding constants (ranging from 3.7 × 10-2 M to 1 × 10-14 M, corresponding to binding energies between -8 and -80 kJ/mol) of the dataset with a standard deviation of 7.3 kJ/mol corresponding to 1.3 orders of magnitude in binding affinity. The function can be evaluated very fast and is therefore also suitable for the application in a 3D database search or de novo ligand design program such as LUDI. The physical significance of the individual contributions is discussed.

Dynamic allostery of protein alpha helical coiled-coils

PubMed Central

Hawkins, Rhoda J; McLeish, Tom C.B

2005-01-01

Alpha helical coiled-coils appear in many important allosteric proteins such as the dynein molecular motor and bacteria chemotaxis transmembrane receptors. As a mechanism for transmitting the information of ligand binding to a distant site across an allosteric protein, an alternative to conformational change in the mean static structure is an induced change in the pattern of the internal dynamics of the protein. We explore how ligand binding may change the intramolecular vibrational free energy of a coiled-coil, using parameterized coarse-grained models, treating the case of dynein in detail. The models predict that coupling of slide, bend and twist modes of the coiled-coil transmits an allosteric free energy of ∼2kBT, consistent with experimental results. A further prediction is a quantitative increase in the effective stiffness of the coiled-coil without any change in inherent flexibility of the individual helices. The model provides a possible and experimentally testable mechanism for transmission of information through the alpha helical coiled-coil of dynein. PMID:16849225

Fast, metadynamics-based method for prediction of the stereochemistry-dependent relative free energies of ligand-receptor interactions.

PubMed

Plazinska, Anita; Plazinski, Wojciech; Jozwiak, Krzysztof

2014-04-30

The computational approach applicable for the molecular dynamics (MD)-based techniques is proposed to predict the ligand-protein binding affinities dependent on the ligand stereochemistry. All possible stereoconfigurations are expressed in terms of one set of force-field parameters [stereoconfiguration-independent potential (SIP)], which allows for calculating all relative free energies by only single simulation. SIP can be used for studying diverse, stereoconfiguration-dependent phenomena by means of various computational techniques of enhanced sampling. The method has been successfully tested on the β2-adrenergic receptor (β2-AR) binding the four fenoterol stereoisomers by both metadynamics simulations and replica-exchange MD. Both the methods gave very similar results, fully confirming the presence of stereoselective effects in the fenoterol-β2-AR interactions. However, the metadynamics-based approach offered much better efficiency of sampling which allows for significant reduction of the unphysical region in SIP. Copyright © 2014 Wiley Periodicals, Inc.

Uncertainty Quantification in Alchemical Free Energy Methods.

PubMed

Bhati, Agastya P; Wan, Shunzhou; Hu, Yuan; Sherborne, Brad; Coveney, Peter V

2018-06-12

Alchemical free energy methods have gained much importance recently from several reports of improved ligand-protein binding affinity predictions based on their implementation using molecular dynamics simulations. A large number of variants of such methods implementing different accelerated sampling techniques and free energy estimators are available, each claimed to be better than the others in its own way. However, the key features of reproducibility and quantification of associated uncertainties in such methods have barely been discussed. Here, we apply a systematic protocol for uncertainty quantification to a number of popular alchemical free energy methods, covering both absolute and relative free energy predictions. We show that a reliable measure of error estimation is provided by ensemble simulation-an ensemble of independent MD simulations-which applies irrespective of the free energy method. The need to use ensemble methods is fundamental and holds regardless of the duration of time of the molecular dynamics simulations performed.

The molecular mechanism studies of chirality effect of PHA-739358 on Aurora kinase A by molecular dynamics simulation and free energy calculations

NASA Astrophysics Data System (ADS)

Cheng, Yuanhua; Cui, Wei; Chen, Quan; Tung, Chen-Ho; Ji, Mingjuan; Zhang, Fushi

2011-02-01

Aurora kinase family is one of the emerging targets in oncology drug discovery and several small molecules targeting aurora kinases have been discovered and evaluated under early phase I/II trials. Among them, PHA-739358 (compound 1r) is a 3-aminopyrazole derivative with strong activity against Aurora A under early phase II trial. Inhibitory potency of compound 1r (the benzylic substituent at the pro-R position) is 30 times over that of compound 1s (the benzylic substituent at the pro-S position). In present study, the mechanism of how different configurations influence the binding affinity was investigated using molecular dynamics (MD) simulations, free energy calculations and free energy decomposition analysis. The predicted binding free energies of these two complexes are consistent with the experimental data. The analysis of the individual energy terms indicates that although the van der Waals contribution is important for distinguishing the binding affinities of these two inhibitors, the electrostatic contribution plays a more crucial role in that. Moreover, it is observed that different configurations of the benzylic substituent could form different binding patterns with protein, thus leading to variant inhibitory potency of compounds 1r and 1s. The combination of different molecular modeling techniques is an efficient way to interpret the chirality effects of inhibitors and our work gives valuable information for the chiral drug design in the near future.

Photoelectron spectroscopy of nitromethane anion clusters

NASA Astrophysics Data System (ADS)

Pruitt, Carrie Jo M.; Albury, Rachael M.; Goebbert, Daniel J.

2016-08-01

Nitromethane anion and nitromethane dimer, trimer, and hydrated cluster anions were studied by photoelectron spectroscopy. Vertical detachment energies, estimated electron affinities, and solvation energies were obtained from the photoelectron spectra. Cluster structures were investigated using theoretical calculations. Predicted detachment energies agreed with experiment. Calculations show water binds to nitromethane anion through two hydrogen bonds. The dimer has a non-linear structure with a single ionic Csbnd H⋯O hydrogen bond. The trimer has two different solvent interactions, but both involve the weak Csbnd H⋯O hydrogen bond.

Energetic basis for the molecular-scale organization of bone

DOE PAGES

Tao, Jinhui; Battle, Keith C.; Pan, Haihua; ...

2014-12-24

Here, the remarkable properties of bone derive from a highly organized arrangement of co-aligned nm-scale apatite platelets within a fibrillar collagen matrix. The origin of this arrangement is poorly understood and the crystal structures of hydroxyapatite (HAP) and the non-mineralized collagen fibrils alone do not provide an explanation. Moreover, little is known about collagen-apatite interaction energies, which should strongly influence both the molecular-scale organization and the resulting mechanical properties of the composite. We investigated collagen-mineral interactions by combining dynamic force spectroscopy (DFS) measurements of binding energies with molecular dynamics (MD) simulations of binding and AFM observations of collagen adsorption onmore » single crystals of calcium phosphate for four mineral phases of potential importance in bone formation. In all cases, we observe a strong preferential orientation of collagen binding, but comparison between the observed orientations and TEM analyses native tissues shows only calcium-deficient apatite (CDAP) provides an interface with collagen that is consistent with both. MD simulations predict preferred collagen orientations that agree with observations and results from both MD and DFS reveal large values for the binding energy due to multiple binding sites. These findings reconcile apparent contradictions inherent in a hydroxyapatite or carbonated apatite (CAP) model of bone mineral and provide an energetic rationale for the molecular scale organization of bone.« less

Docking modes of BB-3497 into the PDF active site--a comparison of the pure MM and QM/MM based docking strategies.

PubMed

Kumari, Tripti; Issar, Upasana; Kakkar, Rita

2014-01-01

Peptide deformylase (PDF) has emerged as an important antibacterial drug target. Considerable effort is being directed toward developing peptidic and non-peptidic inhibitors for this metalloprotein. In this work, the known peptidic inhibitor BB-3497 and its various ionization and tautomeric states are evaluated for their inhibition efficiency against PDF using a molecular mechanics (MM) approach as well as a mixed quantum mechanics/molecular mechanics (QM/MM) approach, with an aim to understand the interactions in the binding site. The evaluated Gibbs energies of binding with the mixed QM/MM approach are shown to have the best predictive power. The experimental pose is found to have the most negative Gibbs energy of binding, and also the smallest strain energy. A quantum mechanical evaluation of the active site reveals the requirement of strong chelation by the ligand with the metal ion. The investigated ligand chelates the metal ion through the two oxygens of its reverse hydroxamate moiety, particularly the N-O(-) oxygen, forming strong covalent bonds with the metal ion, which is penta-coordinated. In the uninhibited state, the metal ion is tetrahedrally coordinated, and hence chelation with the inhibitor is associated with an increase of the metal ion coordination. Thus, the strong binding of the ligand at the binding site is accounted for.

Energetic basis for the molecular-scale organization of bone.

PubMed

Tao, Jinhui; Battle, Keith C; Pan, Haihua; Salter, E Alan; Chien, Yung-Ching; Wierzbicki, Andrzej; De Yoreo, James J

2015-01-13

The remarkable properties of bone derive from a highly organized arrangement of coaligned nanometer-scale apatite platelets within a fibrillar collagen matrix. The origin of this arrangement is poorly understood and the crystal structures of hydroxyapatite (HAP) and the nonmineralized collagen fibrils alone do not provide an explanation. Moreover, little is known about collagen-apatite interaction energies, which should strongly influence both the molecular-scale organization and the resulting mechanical properties of the composite. We investigated collagen-mineral interactions by combining dynamic force spectroscopy (DFS) measurements of binding energies with molecular dynamics (MD) simulations of binding and atomic force microscopy (AFM) observations of collagen adsorption on single crystals of calcium phosphate for four mineral phases of potential importance in bone formation. In all cases, we observe a strong preferential orientation of collagen binding, but comparison between the observed orientations and transmission electron microscopy (TEM) analyses of native tissues shows that only calcium-deficient apatite (CDAP) provides an interface with collagen that is consistent with both. MD simulations predict preferred collagen orientations that agree with observations, and results from both MD and DFS reveal large values for the binding energy due to multiple binding sites. These findings reconcile apparent contradictions inherent in a hydroxyapatite or carbonated apatite (CAP) model of bone mineral and provide an energetic rationale for the molecular-scale organization of bone.

Molecular mechanisms underlying deoxy‐ADP.Pi activation of pre‐powerstroke myosin

PubMed Central

Nowakowski, Sarah G.

2017-01-01

Abstract Myosin activation is a viable approach to treat systolic heart failure. We previously demonstrated that striated muscle myosin is a promiscuous ATPase that can use most nucleoside triphosphates as energy substrates for contraction. When 2‐deoxy ATP (dATP) is used, it acts as a myosin activator, enhancing cross‐bridge binding and cycling. In vivo, we have demonstrated that elevated dATP levels increase basal cardiac function and rescues function of infarcted rodent and pig hearts. Here we investigate the molecular mechanism underlying this physiological effect. We show with molecular dynamics simulations that the binding of dADP.Pi (dATP hydrolysis products) to myosin alters the structure and dynamics of the nucleotide binding pocket, myosin cleft conformation, and actin binding sites, which collectively yield a myosin conformation that we predict favors weak, electrostatic binding to actin. In vitro motility assays at high ionic strength were conducted to test this prediction and we found that dATP increased motility. These results highlight alterations to myosin that enhance cross‐bridge formation and reveal a potential mechanism that may underlie dATP‐induced improvements in cardiac function. PMID:28097776

Insights into regioselective metabolism of mefenamic acid by cytochrome P450 BM3 mutants through crystallography, docking, molecular dynamics, and free energy calculations.

PubMed

Capoferri, Luigi; Leth, Rasmus; ter Haar, Ernst; Mohanty, Arun K; Grootenhuis, Peter D J; Vottero, Eduardo; Commandeur, Jan N M; Vermeulen, Nico P E; Jørgensen, Flemming Steen; Olsen, Lars; Geerke, Daan P

2016-03-01

Cytochrome P450 BM3 (CYP102A1) mutant M11 is able to metabolize a wide range of drugs and drug-like compounds. Among these, M11 was recently found to be able to catalyze formation of human metabolites of mefenamic acid and other nonsteroidal anti-inflammatory drugs (NSAIDs). Interestingly, single active-site mutations such as V87I were reported to invert regioselectivity in NSAID hydroxylation. In this work, we combine crystallography and molecular simulation to study the effect of single mutations on binding and regioselective metabolism of mefenamic acid by M11 mutants. The heme domain of the protein mutant M11 was expressed, purified, and crystallized, and its X-ray structure was used as template for modeling. A multistep approach was used that combines molecular docking, molecular dynamics (MD) simulation, and binding free-energy calculations to address protein flexibility. In this way, preferred binding modes that are consistent with oxidation at the experimentally observed sites of metabolism (SOMs) were identified. Whereas docking could not be used to retrospectively predict experimental trends in regioselectivity, we were able to rank binding modes in line with the preferred SOMs of mefenamic acid by M11 and its mutants by including protein flexibility and dynamics in free-energy computation. In addition, we could obtain structural insights into the change in regioselectivity of mefenamic acid hydroxylation due to single active-site mutations. Our findings confirm that use of MD and binding free-energy calculation is useful for studying biocatalysis in those cases in which enzyme binding is a critical event in determining the selective metabolism of a substrate. © 2016 Wiley Periodicals, Inc.

Noncovalent Interactions of DNA Bases with Naphthalene and Graphene.

PubMed

Cho, Yeonchoo; Min, Seung Kyu; Yun, Jeonghun; Kim, Woo Youn; Tkatchenko, Alexandre; Kim, Kwang S

2013-04-09

The complexes of a DNA base bound to graphitic systems are studied. Considering naphthalene as the simplest graphitic system, DNA base-naphthalene complexes are scrutinized at high levels of ab initio theory including coupled cluster theory with singles, doubles, and perturbative triples excitations [CCSD(T)] at the complete basis set (CBS) limit. The stacked configurations are the most stable, where the CCSD(T)/CBS binding energies of guanine, adenine, thymine, and cytosine are 9.31, 8.48, 8.53, 7.30 kcal/mol, respectively. The energy components are investigated using symmetry-adapted perturbation theory based on density functional theory including the dispersion energy. We compared the CCSD(T)/CBS results with several density functional methods applicable to periodic systems. Considering accuracy and availability, the optB86b nonlocal functional and the Tkatchenko-Scheffler functional are used to study the binding energies of nucleobases on graphene. The predicted values are 18-24 kcal/mol, though many-body effects on screening and energy need to be further considered.

Structure and binding energy of the H2S dimer at the CCSD(T) complete basis set limit.

PubMed

Lemke, Kono H

2017-06-21

This study presents results for the binding energy and geometry of the H 2 S dimer which have been computed using Møller-Plesset perturbation theory (MP2, MP4) and coupled cluster (CCSD, CCSD(T)) calculations with basis sets up to aug-cc-pV5Z. Estimates of D e , E ZPE , D o , and dimer geometry have been obtained at each level of theory by taking advantage of the systematic convergence behavior toward the complete basis set (CBS) limit. The CBS limit binding energy values of D e are 1.91 (MP2), 1.75 (MP4), 1.41 (CCSD), and 1.69 kcal/mol (CCSD[T]). The most accurate values for the equilibrium S-S distance r SS (without counterpoise correction) are 4.080 (MP2/aug-cc-pV5Z), 4.131 (MP4/aug-cc-pVQZ), 4.225 (CCSD/aug-cc-pVQZ), and 4.146 Å (CCSD(T)/aug-cc-pVQZ). This study also evaluates the effect of counterpoise correction on the H 2 S dimer geometry and binding energy. As regards the structure of (H 2 S) 2 , MPn, CCSD, and CCSD(T) level values of r SS , obtained by performing geometry optimizations on the counterpoise-corrected potential energy surface, converge systematically to CBS limit values of 4.099 (MP2), 4.146 (MP4), 4.233 (CCSD), and 4.167 Å (CCSD(T)). The corresponding CBS limit values of the equilibrium binding energy D e are 1.88 (MP2), 1.76 (MP4), 1.41 (CCSD), and 1.69 kcal/mol (CCSD(T)), the latter in excellent agreement with the measured binding energy value of 1.68 ± 0.02 kcal/mol reported by Ciaffoni et al. [Appl. Phys. B 92, 627 (2008)]. Combining CBS electronic binding energies D e with E ZPE predicted by CCSD(T) vibrational second-order perturbation theory calculations yields D o = 1.08 kcal/mol, which is around 0.6 kcal/mol smaller than the measured value of 1.7 ± 0.3 kcal/mol. Overall, the results presented here demonstrate that the application of high level calculations, in particular CCSD(T), in combination with augmented correlation consistent basis sets provides valuable insight into the structure and energetics of the hydrogen sulfide dimer.

Structure and binding energy of the H2S dimer at the CCSD(T) complete basis set limit

NASA Astrophysics Data System (ADS)

Lemke, Kono H.

2017-06-01

This study presents results for the binding energy and geometry of the H2S dimer which have been computed using Møller-Plesset perturbation theory (MP2, MP4) and coupled cluster (CCSD, CCSD(T)) calculations with basis sets up to aug-cc-pV5Z. Estimates of De, EZPE, Do, and dimer geometry have been obtained at each level of theory by taking advantage of the systematic convergence behavior toward the complete basis set (CBS) limit. The CBS limit binding energy values of De are 1.91 (MP2), 1.75 (MP4), 1.41 (CCSD), and 1.69 kcal/mol (CCSD[T]). The most accurate values for the equilibrium S-S distance rSS (without counterpoise correction) are 4.080 (MP2/aug-cc-pV5Z), 4.131 (MP4/aug-cc-pVQZ), 4.225 (CCSD/aug-cc-pVQZ), and 4.146 Å (CCSD(T)/aug-cc-pVQZ). This study also evaluates the effect of counterpoise correction on the H2S dimer geometry and binding energy. As regards the structure of (H2S)2, MPn, CCSD, and CCSD(T) level values of rSS, obtained by performing geometry optimizations on the counterpoise-corrected potential energy surface, converge systematically to CBS limit values of 4.099 (MP2), 4.146 (MP4), 4.233 (CCSD), and 4.167 Å (CCSD(T)). The corresponding CBS limit values of the equilibrium binding energy De are 1.88 (MP2), 1.76 (MP4), 1.41 (CCSD), and 1.69 kcal/mol (CCSD(T)), the latter in excellent agreement with the measured binding energy value of 1.68 ± 0.02 kcal/mol reported by Ciaffoni et al. [Appl. Phys. B 92, 627 (2008)]. Combining CBS electronic binding energies De with EZPE predicted by CCSD(T) vibrational second-order perturbation theory calculations yields Do = 1.08 kcal/mol, which is around 0.6 kcal/mol smaller than the measured value of 1.7 ± 0.3 kcal/mol. Overall, the results presented here demonstrate that the application of high level calculations, in particular CCSD(T), in combination with augmented correlation consistent basis sets provides valuable insight into the structure and energetics of the hydrogen sulfide dimer.

Benchmark CCSD(T) and DFT study of binding energies in Be7 - 12: in search of reliable DFT functional for beryllium clusters

NASA Astrophysics Data System (ADS)

Labanc, Daniel; Šulka, Martin; Pitoňák, Michal; Černušák, Ivan; Urban, Miroslav; Neogrády, Pavel

2018-05-01

We present a computational study of the stability of small homonuclear beryllium clusters Be7 - 12 in singlet electronic states. Our predictions are based on highly correlated CCSD(T) coupled cluster calculations. Basis set convergence towards the complete basis set limit as well as the role of the 1s core electron correlation are carefully examined. Our CCSD(T) data for binding energies of Be7 - 12 clusters serve as a benchmark for performance assessment of several density functional theory (DFT) methods frequently used in beryllium cluster chemistry. We observe that, from Be10 clusters on, the deviation from CCSD(T) benchmarks is stable with respect to size, and fluctuating within 0.02 eV error bar for most examined functionals. This opens up the possibility of scaling the DFT binding energies for large Be clusters using CCSD(T) benchmark values for smaller clusters. We also tried to find analogies between the performance of DFT functionals for Be clusters and for the valence-isoelectronic Mg clusters investigated recently in Truhlar's group. We conclude that it is difficult to find DFT functionals that perform reasonably well for both beryllium and magnesium clusters. Out of 12 functionals examined, only the M06-2X functional gives reasonably accurate and balanced binding energies for both Be and Mg clusters.

Secondary metabolites of Mirabilis jalapa structurally inhibit Lactate Dehydrogenase A in silico: a potential cancer treatment

NASA Astrophysics Data System (ADS)

Kusumawati, R.; Nasrullah, A. H.; Pesik, R. N.; Muthmainah; Indarto, D.

2018-03-01

Altered energy metabolism from phosphorylated oxidation to aerobic glycolysis is one of the cancer hallmarks. Lactate dehydrogenase A (LDHA) is a major enzyme that catalyses pyruvate to lactate in such condition. The aim of this study was to explore LDHA inhibitors derived from Indonesian herbal plants. In this study, LDHA and oxamate molecular structures were obtained from protein data bank. As a standard ligand inhibitor, oxamate was molecularly re-validated using Autodock Vina 1.1.2 software and showed binding energy -4.26 ± 0.006 kcal/mol and interacted with LDHA at Gln99, Arg105, Asn137, Arg168, His192, and Thr247 residues. Molecular docking was used to visualize interaction between Indonesian phytochemicals and LDHA. Indonesian phytochemicals with the lowest binding energy and similar residues with standard ligand was Miraxanthin-III (-8.53 ± 0.006 kcal/mol), Vulgaxanthin-I (-8.46 ± 0.006 kcal/mol), Miraxanthin-II (-7.9 ± 0.2 kcal/mol) and Miraxanthin-V (-7.96 ± kcal/mol). Lower energy binding to LDHA and binding site at these residues was predicted to inhibit LDHA activity better than standard ligand. All phytochemicals were found in Mirabilis jalapa plant. Secondary metabolites in Mirabilis jalapa have LDHA inhibitor property in silico. Further in vitro study should be performed to confirm this result.

Designing molecular complexes using free-energy derivatives from liquid-state integral equation theory

NASA Astrophysics Data System (ADS)

Mrugalla, Florian; Kast, Stefan M.

2016-09-01

Complex formation between molecules in solution is the key process by which molecular interactions are translated into functional systems. These processes are governed by the binding or free energy of association which depends on both direct molecular interactions and the solvation contribution. A design goal frequently addressed in pharmaceutical sciences is the optimization of chemical properties of the complex partners in the sense of minimizing their binding free energy with respect to a change in chemical structure. Here, we demonstrate that liquid-state theory in the form of the solute-solute equation of the reference interaction site model provides all necessary information for such a task with high efficiency. In particular, computing derivatives of the potential of mean force (PMF), which defines the free-energy surface of complex formation, with respect to potential parameters can be viewed as a means to define a direction in chemical space toward better binders. We illustrate the methodology in the benchmark case of alkali ion binding to the crown ether 18-crown-6 in aqueous solution. In order to examine the validity of the underlying solute-solute theory, we first compare PMFs computed by different approaches, including explicit free-energy molecular dynamics simulations as a reference. Predictions of an optimally binding ion radius based on free-energy derivatives are then shown to yield consistent results for different ion parameter sets and to compare well with earlier, orders-of-magnitude more costly explicit simulation results. This proof-of-principle study, therefore, demonstrates the potential of liquid-state theory for molecular design problems.

Investigation on the individual contributions of N-H...O=C and C-H...O=C interactions to the binding energies of beta-sheet models.

PubMed

Wang, Chang-Sheng; Sun, Chang-Liang

2010-04-15

In this article, the binding energies of 16 antiparallel and parallel beta-sheet models are estimated using the analytic potential energy function we proposed recently and the results are compared with those obtained from MP2, AMBER99, OPLSAA/L, and CHARMM27 calculations. The comparisons indicate that the analytic potential energy function can produce reasonable binding energies for beta-sheet models. Further comparisons suggest that the binding energy of the beta-sheet models might come mainly from dipole-dipole attractive and repulsive interactions and VDW interactions between the two strands. The dipole-dipole attractive and repulsive interactions are further obtained in this article. The total of N-H...H-N and C=O...O=C dipole-dipole repulsive interaction (the secondary electrostatic repulsive interaction) in the small ring of the antiparallel beta-sheet models is estimated to be about 6.0 kcal/mol. The individual N-H...O=C dipole-dipole attractive interaction is predicted to be -6.2 +/- 0.2 kcal/mol in the antiparallel beta-sheet models and -5.2 +/- 0.6 kcal/mol in the parallel beta-sheet models. The individual C(alpha)-H...O=C attractive interaction is -1.2 +/- 0.2 kcal/mol in the antiparallel beta-sheet models and -1.5 +/- 0.2 kcal/mol in the parallel beta-sheet models. These values are important in understanding the interactions at protein-protein interfaces and developing a more accurate force field for peptides and proteins. 2009 Wiley Periodicals, Inc.

Designing molecular complexes using free-energy derivatives from liquid-state integral equation theory.

PubMed

Mrugalla, Florian; Kast, Stefan M

2016-09-01

Complex formation between molecules in solution is the key process by which molecular interactions are translated into functional systems. These processes are governed by the binding or free energy of association which depends on both direct molecular interactions and the solvation contribution. A design goal frequently addressed in pharmaceutical sciences is the optimization of chemical properties of the complex partners in the sense of minimizing their binding free energy with respect to a change in chemical structure. Here, we demonstrate that liquid-state theory in the form of the solute-solute equation of the reference interaction site model provides all necessary information for such a task with high efficiency. In particular, computing derivatives of the potential of mean force (PMF), which defines the free-energy surface of complex formation, with respect to potential parameters can be viewed as a means to define a direction in chemical space toward better binders. We illustrate the methodology in the benchmark case of alkali ion binding to the crown ether 18-crown-6 in aqueous solution. In order to examine the validity of the underlying solute-solute theory, we first compare PMFs computed by different approaches, including explicit free-energy molecular dynamics simulations as a reference. Predictions of an optimally binding ion radius based on free-energy derivatives are then shown to yield consistent results for different ion parameter sets and to compare well with earlier, orders-of-magnitude more costly explicit simulation results. This proof-of-principle study, therefore, demonstrates the potential of liquid-state theory for molecular design problems.

Various Stone-Wales defects in phagraphene

NASA Astrophysics Data System (ADS)

Openov, L. A.; Podlivaev, A. I.

2016-08-01

Various Stone-Wales defects in phagraphene, which is a graphene allotrope, predicted recently are studied in terms of the nonorthogonal tight-binding model. The energies of the defect formation and the heights of energy barriers preventing the formation and annealing of the defects are found. Corresponding frequency factors in the Arrhenius formula are calculated. The evolution of the defect structure is studied in the real-time mode using the molecular dynamics method.

The DINGO dataset: a comprehensive set of data for the SAMPL challenge

NASA Astrophysics Data System (ADS)

Newman, Janet; Dolezal, Olan; Fazio, Vincent; Caradoc-Davies, Tom; Peat, Thomas S.

2012-05-01

Part of the latest SAMPL challenge was to predict how a small fragment library of 500 commercially available compounds would bind to a protein target. In order to assess the modellers' work, a reasonably comprehensive set of data was collected using a number of techniques. These included surface plasmon resonance, isothermal titration calorimetry, protein crystallization and protein crystallography. Using these techniques we could determine the kinetics of fragment binding, the energy of binding, how this affects the ability of the target to crystallize, and when the fragment did bind, the pose or orientation of binding. Both the final data set and all of the raw images have been made available to the community for scrutiny and further work. This overview sets out to give the parameters of the experiments done and what might be done differently for future studies.

An automated decision-tree approach to predicting protein interaction hot spots.

PubMed

Darnell, Steven J; Page, David; Mitchell, Julie C

2007-09-01

Protein-protein interactions can be altered by mutating one or more "hot spots," the subset of residues that account for most of the interface's binding free energy. The identification of hot spots requires a significant experimental effort, highlighting the practical value of hot spot predictions. We present two knowledge-based models that improve the ability to predict hot spots: K-FADE uses shape specificity features calculated by the Fast Atomic Density Evaluation (FADE) program, and K-CON uses biochemical contact features. The combined K-FADE/CON (KFC) model displays better overall predictive accuracy than computational alanine scanning (Robetta-Ala). In addition, because these methods predict different subsets of known hot spots, a large and significant increase in accuracy is achieved by combining KFC and Robetta-Ala. The KFC analysis is applied to the calmodulin (CaM)/smooth muscle myosin light chain kinase (smMLCK) interface, and to the bone morphogenetic protein-2 (BMP-2)/BMP receptor-type I (BMPR-IA) interface. The results indicate a strong correlation between KFC hot spot predictions and mutations that significantly reduce the binding affinity of the interface. 2007 Wiley-Liss, Inc.

Targeting the cell wall of Mycobacterium tuberculosis: a molecular modeling investigation of the interaction of imipenem and meropenem with L,D-transpeptidase 2.

PubMed

Silva, José Rogério A; Bishai, William R; Govender, Thavendran; Lamichhane, Gyanu; Maguire, Glenn E M; Kruger, Hendrik G; Lameira, Jeronimo; Alves, Cláudio N

2016-01-01

The single crystal X-ray structure of the extracellular portion of the L,D-transpeptidase (ex-LdtMt2 - residues 120-408) enzyme was recently reported. It was observed that imipenem and meropenem inhibit activity of this enzyme, responsible for generating L,D-transpeptide linkages in the peptidoglycan layer of Mycobacterium tuberculosis. Imipenem is more active and isothermal titration calorimetry experiments revealed that meropenem is subjected to an entropy penalty upon binding to the enzyme. Herein, we report a molecular modeling approach to obtain a molecular view of the inhibitor/enzyme interactions. The average binding free energies for nine commercially available inhibitors were calculated using MM/GBSA and Solvation Interaction Energy (SIE) approaches and the calculated energies corresponded well with the available experimentally observed results. The method reproduces the same order of binding energies as experimentally observed for imipenem and meropenem. We have also demonstrated that SIE is a reasonably accurate and cost-effective free energy method, which can be used to predict carbapenem affinities for this enzyme. A theoretical explanation was offered for the experimental entropy penalty observed for meropenem, creating optimism that this computational model can serve as a potential computational model for other researchers in the field.

Partial Ionic Character beyond the Pauling Paradigm: Metal Nanoparticles

DOE PAGES

Duanmu, Kaining; Truhlar, Donald G.

2014-11-12

A canonical perspective on the chemical bond is the Pauling paradigm: a bond in a molecule containing only identical atoms has no ionic character. However, we show that homonuclear silver clusters have very uneven charge distributions (for example, the C 2v structure of Ag 4 has a larger dipole moment than formaldehyde or acetone), and we show how to predict the charge distribution from coordination numbers and Hirshfeld charges. The new charge model is validated against Kohn–Sham calculations of dipole moments with four approximations for the exchange–correlation functional. We report Kohn–Sham studies of the binding energies of CO on silvermore » monomer and silver clusters containing 2–18 atoms. We also find that an accurate charge model is essential for understanding the site dependence of binding. In particular we find that atoms with more positive charges tend to have higher binding energies, which can be used for guidance in catalyst modeling and design. Furthermore, the nonuniform charge distribution of silver clusters predisposes the site preference of binding of carbon monoxide, and we conclude that nonuniform charge distributions are an important property for understanding binding of metal nanoparticles in general.« less

Improving Density Functional Tight Binding Predictions of Free Energy Surfaces for Slow Chemical Reactions in Solution

NASA Astrophysics Data System (ADS)

Kroonblawd, Matthew; Goldman, Nir

2017-06-01

First principles molecular dynamics using highly accurate density functional theory (DFT) is a common tool for predicting chemistry, but the accessible time and space scales are often orders of magnitude beyond the resolution of experiments. Semi-empirical methods such as density functional tight binding (DFTB) offer up to a thousand-fold reduction in required CPU hours and can approach experimental scales. However, standard DFTB parameter sets lack good transferability and calibration for a particular system is usually necessary. Force matching the pairwise repulsive energy term in DFTB to short DFT trajectories can improve the former's accuracy for reactions that are fast relative to DFT simulation times (<10 ps), but the effects on slow reactions and the free energy surface are not well-known. We present a force matching approach to improve the chemical accuracy of DFTB. Accelerated sampling techniques are combined with path collective variables to generate the reference DFT data set and validate fitted DFTB potentials. Accuracy of force-matched DFTB free energy surfaces is assessed for slow peptide-forming reactions by direct comparison to DFT for particular paths. Extensions to model prebiotic chemistry under shock conditions are discussed. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Transformation of cooperative free energies between ligation systems of hemoglobin: resolution of the carbon monoxide binding intermediates.

PubMed

Huang, Y; Ackers, G K

1996-01-23

A strategy has been developed for quantitatively "translating" the distributions of cooperative free energy between different oxygenation analogs of hemoglobin (Hb). The method was used to resolve the cooperative free energies of all eight carbon monoxide binding intermediates. These parameters of the FeCOHb system were determined by thermodynamic transformation of corresponding free energies obtained previously for all species of the Co/FeCO system, i.e., where cobalt-substituted hemes comprise the unligated sites [Speros, P. C., et al. (1991) Biochemistry 30, 7254-7262]. Using hybridized combinations of normal and cobalt-substituted Hb, ligation analog systems Co/FeX (X = CO, CN) were constructed and experimentally quantified. Energetics of cobalt-induced structural perturbation were determined for all species of both the "mixed metal" Co/Fe system and also the ligated Co/FeCN system. It was found that major energetic perturbations of the Co/Fe hybrid species originate from a pure cobalt substitution effect on the alpha subunits. These perturbations are transduced to the beta subunit within the same dimeric half-tetramer, resulting in alteration of the free energies for binding at the nonsubstituted (Fe) sites. Using the linkage strategy developed in this study along with the determined energetics of these couplings, the experimental assembly free energies for the Co/FeCO species were transformed into cooperative free energies of the 10 Fe/FeCO species. The resulting values were found to distribute according to predictions of a symmetry rule mechanism proposed previously [Ackers, G. K., et al. (1992) Science 255, 54-63]. Their distribution is consistent with accurate CO binding data of normal Hb [Perrella, M., et al. (1990b) Biophys. Chem. 37, 211-223] and also with accurate O2 binding data obtained under the same conditions [Chu, A. H., et al. (1984) Biochemistry 23, 604-617].

Identification of curcumin derivatives as human LMTK3 inhibitors for breast cancer: a docking, dynamics, and MM/PBSA approach.

PubMed

Anbarasu, K; Jayanthi, S

2018-05-01

Human lemur tyrosine kinase-3 (LMTK3) is primarily involved in regulation of estrogen receptor-α (ERα) by phosphorylation activity. LMTK3 acts as key biomarker for ERα positive breast cancer and identified as novel drug target for breast cancer. Due to the absence of experimental reports, the computational approach has been followed to screen LMTK3 inhibitors from natural product curcumin derivatives based on rational inhibitor design. The initial virtual screening and re-docking resulted in identification of top three leads with favorable binding energy and strong interactions in critical residues of ATP-binding cavity. ADME prediction confirmed the pharmacological activity of the leads with various properties. The stability and binding affinity of leads were well refined in dynamic system from 25 ns MD simulations. The behavior of protein motion towards closure of ATP-binding cavity was evaluated based on eigenvectors by PCA. In addition, MM/PBSA calculations also confirmed the relative binding free energy of LMTK3-lead complexes in favor of the effective binding. From our study, novel LMTK3 inhibitors tetrahydrocurcumin, curcumin 4,4'-diacetate, and demethoxycurcumin have been proposed with inhibition mechanism. Further experimental evaluation on reported lead candidates might prove its role in breast cancer therapeutics.

Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations.

PubMed

Liu, Kai; Watanabe, Etsurou; Kokubo, Hironori

2017-02-01

The binding mode prediction is of great importance to structure-based drug design. The discrimination of various binding poses of ligand generated by docking is a great challenge not only to docking score functions but also to the relatively expensive free energy calculation methods. Here we systematically analyzed the stability of various ligand poses under molecular dynamics (MD) simulation. First, a data set of 120 complexes was built based on the typical physicochemical properties of drug-like ligands. Three potential binding poses (one correct pose and two decoys) were selected for each ligand from self-docking in addition to the experimental pose. Then, five independent MD simulations for each pose were performed with different initial velocities for the statistical analysis. Finally, the stabilities of ligand poses under MD were evaluated and compared with the native one from crystal structure. We found that about 94% of the native poses were maintained stable during the simulations, which suggests that MD simulations are accurate enough to judge most experimental binding poses as stable properly. Interestingly, incorrect decoy poses were maintained much less and 38-44% of decoys could be excluded just by performing equilibrium MD simulations, though 56-62% of decoys were stable. The computationally-heavy binding free energy calculation can be performed only for these survived poses.

Steric and thermodynamic limits of design for the incorporation of large unnatural amino acids in aminoacyl-tRNA synthetase enzymes.

PubMed

Armen, Roger S; Schiller, Stefan M; Brooks, Charles L

2010-06-01

Orthogonal aminoacyl-tRNA synthetase/tRNA pairs from archaea have been evolved to facilitate site specific in vivo incorporation of unnatural amino acids into proteins in Escherichia coli. Using this approach, unnatural amino acids have been successfully incorporated with high translational efficiency and fidelity. In this study, CHARMM-based molecular docking and free energy calculations were used to evaluate rational design of specific protein-ligand interactions for aminoacyl-tRNA synthetases. A series of novel unnatural amino acid ligands were docked into the p-benzoyl-L-phenylalanine tRNA synthetase, which revealed that the binding pocket of the enzyme does not provide sufficient space for significantly larger ligands. Specific binding site residues were mutated to alanine to create additional space to accommodate larger target ligands, and then mutations were introduced to improve binding free energy. This approach was used to redesign binding sites for several different target ligands, which were then tested against the standard 20 amino acids to verify target specificity. Only the synthetase designed to bind Man-alpha-O-Tyr was predicted to be sufficiently selective for the target ligand and also thermodynamically stable. Our study suggests that extensive redesign of the tRNA synthatase binding pocket for large bulky ligands may be quite thermodynamically unfavorable.

Engineering the Pseudomonas aeruginosa II lectin: designing mutants with changed affinity and specificity

NASA Astrophysics Data System (ADS)

Kříž, Zdeněk; Adam, Jan; Mrázková, Jana; Zotos, Petros; Chatzipavlou, Thomais; Wimmerová, Michaela; Koča, Jaroslav

2014-09-01

This article focuses on designing mutations of the PA-IIL lectin from Pseudomonas aeruginosa that lead to change in specificity. Following the previous results revealing the importance of the amino acid triad 22-23-24 (so-called specificity-binding loop), saturation in silico mutagenesis was performed, with the intent of finding mutations that increase the lectin's affinity and modify its specificity. For that purpose, a combination of docking, molecular dynamics and binding free energy calculation was used. The combination of methods revealed mutations that changed the performance of the wild-type lectin and its mutants to their preferred partners. The mutation at position 22 resulted in 85 % in inactivation of the binding site, and the mutation at 23 did not have strong effects thanks to the side chain being pointed away from the binding site. Molecular dynamics simulations followed by binding free energy calculation were performed on mutants with promising results from docking, and also at those where the amino acid at position 24 was replaced for bulkier or longer polar chain. The key mutants were also prepared in vitro and their binding properties determined by isothermal titration calorimetry. Combination of the used methods proved to be able to predict changes in the lectin performance and helped in explaining the data observed experimentally.

In Silico Analysis of Epitope-Based Vaccine Candidates against Hepatitis B Virus Polymerase Protein

PubMed Central

Zheng, Juzeng; Lin, Xianfan; Wang, Xiuyan; Zheng, Liyu; Lan, Songsong; Jin, Sisi; Ou, Zhanfan; Wu, Jinming

2017-01-01

Hepatitis B virus (HBV) infection has persisted as a major public health problem due to the lack of an effective treatment for those chronically infected. Therapeutic vaccination holds promise, and targeting HBV polymerase is pivotal for viral eradication. In this research, a computational approach was employed to predict suitable HBV polymerase targeting multi-peptides for vaccine candidate selection. We then performed in-depth computational analysis to evaluate the predicted epitopes’ immunogenicity, conservation, population coverage, and toxicity. Lastly, molecular docking and MHC-peptide complex stabilization assay were utilized to determine the binding energy and affinity of epitopes to the HLA-A0201 molecule. Criteria-based analysis provided four predicted epitopes, RVTGGVFLV, VSIPWTHKV, YMDDVVLGA and HLYSHPIIL. Assay results indicated the lowest binding energy and high affinity to the HLA-A0201 molecule for epitopes VSIPWTHKV and YMDDVVLGA and epitopes RVTGGVFLV and VSIPWTHKV, respectively. Regions 307 to 320 and 377 to 387 were considered to have the highest probability to be involved in B cell epitopes. The T cell and B cell epitopes identified in this study are promising targets for an epitope-focused, peptide-based HBV vaccine, and provide insight into HBV-induced immune response. PMID:28509875

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Predicting the Effect of Mutations on Protein-Protein Binding Interactions through Structure-Based Interface Profiles

PubMed Central

Brender, Jeffrey R.; Zhang, Yang

2015-01-01

The formation of protein-protein complexes is essential for proteins to perform their physiological functions in the cell. Mutations that prevent the proper formation of the correct complexes can have serious consequences for the associated cellular processes. Since experimental determination of protein-protein binding affinity remains difficult when performed on a large scale, computational methods for predicting the consequences of mutations on binding affinity are highly desirable. We show that a scoring function based on interface structure profiles collected from analogous protein-protein interactions in the PDB is a powerful predictor of protein binding affinity changes upon mutation. As a standalone feature, the differences between the interface profile score of the mutant and wild-type proteins has an accuracy equivalent to the best all-atom potentials, despite being two orders of magnitude faster once the profile has been constructed. Due to its unique sensitivity in collecting the evolutionary profiles of analogous binding interactions and the high speed of calculation, the interface profile score has additional advantages as a complementary feature to combine with physics-based potentials for improving the accuracy of composite scoring approaches. By incorporating the sequence-derived and residue-level coarse-grained potentials with the interface structure profile score, a composite model was constructed through the random forest training, which generates a Pearson correlation coefficient >0.8 between the predicted and observed binding free-energy changes upon mutation. This accuracy is comparable to, or outperforms in most cases, the current best methods, but does not require high-resolution full-atomic models of the mutant structures. The binding interface profiling approach should find useful application in human-disease mutation recognition and protein interface design studies. PMID:26506533

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity.

PubMed

Korkuć, Paula; Walther, Dirk

2015-01-01

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites.

Physicochemical characteristics of structurally determined metabolite-protein and drug-protein binding events with respect to binding specificity

PubMed Central

Korkuć, Paula; Walther, Dirk

2015-01-01

To better understand and ultimately predict both the metabolic activities as well as the signaling functions of metabolites, a detailed understanding of the physical interactions of metabolites with proteins is highly desirable. Focusing in particular on protein binding specificity vs. promiscuity, we performed a comprehensive analysis of the physicochemical properties of compound-protein binding events as reported in the Protein Data Bank (PDB). We compared the molecular and structural characteristics obtained for metabolites to those of the well-studied interactions of drug compounds with proteins. Promiscuously binding metabolites and drugs are characterized by low molecular weight and high structural flexibility. Unlike reported for drug compounds, low rather than high hydrophobicity appears associated, albeit weakly, with promiscuous binding for the metabolite set investigated in this study. Across several physicochemical properties, drug compounds exhibit characteristic binding propensities that are distinguishable from those associated with metabolites. Prediction of target diversity and compound promiscuity using physicochemical properties was possible at modest accuracy levels only, but was consistently better for drugs than for metabolites. Compound properties capturing structural flexibility and hydrogen-bond formation descriptors proved most informative in PLS-based prediction models. With regard to diversity of enzymatic activities of the respective metabolite target enzymes, the metabolites benzylsuccinate, hypoxanthine, trimethylamine N-oxide, oleoylglycerol, and resorcinol showed very narrow process involvement, while glycine, imidazole, tryptophan, succinate, and glutathione were identified to possess broad enzymatic reaction scopes. Promiscuous metabolites were found to mainly serve as general energy currency compounds, but were identified to also be involved in signaling processes and to appear in diverse organismal systems (digestive and nervous system) suggesting specific molecular and physiological roles of promiscuous metabolites. PMID:26442281

Determining the folding and binding free energy of DNA-based nanodevices and nanoswitches using urea titration curves

PubMed Central

Idili, Andrea

2017-01-01

Abstract DNA nanotechnology takes advantage of the predictability of DNA interactions to build complex DNA-based functional nanoscale structures. However, when DNA functional and responsive units that are based on non-canonical DNA interactions are employed it becomes quite challenging to predict, understand and control their thermodynamics. In response to this limitation, here we demonstrate the use of isothermal urea titration experiments to estimate the free energy involved in a set of DNA-based systems ranging from unimolecular DNA-based nanoswitches to more complex DNA folds (e.g. aptamers) and nanodevices. We propose here a set of fitting equations that allow to analyze the urea titration curves of these DNA responsive units based on Watson–Crick and non-canonical interactions (stem-loop, G-quadruplex, triplex structures) and to correctly estimate their relative folding and binding free energy values under different experimental conditions. The results described herein will pave the way toward the use of urea titration experiments in the field of DNA nanotechnology to achieve easier and more reliable thermodynamic characterization of DNA-based functional responsive units. More generally, our results will be of general utility to characterize other complex supramolecular systems based on different biopolymers. PMID:28605461

All-atomic simulations on human telomeric G-quadruplex DNA binding with thioflavin T.

PubMed

Luo, Di; Mu, Yuguang

2015-04-16

Ligand-stabilized human telomeric G-quadruplex DNA is believed to be an anticancer agent, as it can impede the continuous elongation of telomeres by telomerase in cancer cells. In this study, five well-established human telomeric G-quadruplex DNA models were probed on their binding behaviors with thioflavin T (ThT) via both conventional molecular dynamics (MD) and well-tempered metadynamics (WT-MetaD) simulations. Novel dynamics and characteristic binding patterns were disclosed by the MD simulations. It was observed that the K(+) promoted parallel and hybridized human telomeric G-quadruplex conformations pose higher binding affinities to ThT than the Na(+) and K(+) promoted basket conformations. It is the end, sandwich, and base stacking driven by π-π interactions that are identified as the major binding mechanisms. As the most energy favorable binding mode, the sandwich stacking observed in (3 + 1) hybridized form 1 G-quadruplex conformation is triggered by reversible conformational change of the G-quadruplex. To further examine the free energy landscapes, WT-MetaD simulations were utilized on G-quadruplex-ThT systems. It is found that all of the major binding modes predicted by the MD simulations are confirmed by the WT-MetaD simulations. The results in this work not only accord with existing experimental findings, but also reinforce our understanding on the dynamics of G-quadruplexes and aid future drug developments for G-quadruplex stabilization ligands.

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view.

Pair-Wise and Many-Body Dispersive Interactions Coupled to an Optimally Tuned Range-Separated Hybrid Functional.

PubMed

Agrawal, Piyush; Tkatchenko, Alexandre; Kronik, Leeor

2013-08-13

We propose a nonempirical, pair-wise or many-body dispersion-corrected, optimally tuned range-separated hybrid functional. This functional retains the advantages of the optimal-tuning approach in the prediction of the electronic structure. At the same time, it gains accuracy in the prediction of binding energies for dispersively bound systems, as demonstrated on the S22 and S66 benchmark sets of weakly bound dimers.

Probing ionization potential, electron affinity and self-energy effect on the spectral shape and exciton binding energy of quantum liquid water with self-consistent many-body perturbation theory and the Bethe-Salpeter equation.

PubMed

Ziaei, Vafa; Bredow, Thomas

2018-05-31

An accurate theoretical prediction of ionization potential (IP) and electron affinity (EA) is key in understanding complex photochemical processes in aqueous environments. There have been numerous efforts in literature to accurately predict IP and EA of liquid water, however with often conflicting results depending on the level of theory and the underlying water structures. In a recent study based on hybrid-non-self-consistent many-body perturbation theory (MBPT) Gaiduk et al (2018 Nat. Commun. 9 247) predicted an IP of 10.2 eV and EA of 0.2 eV, resulting in an electronic band gap (i.e. electronic gap (IP-EA) as measured by photoelectron spectroscopy) of about 10 eV, redefining the widely cited experimental gap of 8.7 eV in literature. In the present work, we show that GW self-consistency and an implicit vertex correction in MBPT considerably affect recently reported EA values by Gaiduk et al (2018 Nat. Commun. 9 247) by about 1 eV. Furthermore, the choice of pseudo-potential is critical for an accurate determination of the absolute band positions. Consequently, the self-consistent GW approach with an implicit vertex correction based on projector augmented wave (PAW) method on top of quantum water structures predicts an IP of 10.2, an EA of 1.1, a fundamental gap of 9.1 eV and an exciton binding (Eb) energy of 0.9 eV for the first absorption band of liquid water via the Bethe-Salpeter equation (BSE). Only within such a self-consistent approach a simultanously accurate prediction of IP, EA, Eg, Eb is possible.

Probing ionization potential, electron affinity and self-energy effect on the spectral shape and exciton binding energy of quantum liquid water with self-consistent many-body perturbation theory and the Bethe–Salpeter equation

NASA Astrophysics Data System (ADS)

Ziaei, Vafa; Bredow, Thomas

2018-05-01

An accurate theoretical prediction of ionization potential (IP) and electron affinity (EA) is key in understanding complex photochemical processes in aqueous environments. There have been numerous efforts in literature to accurately predict IP and EA of liquid water, however with often conflicting results depending on the level of theory and the underlying water structures. In a recent study based on hybrid-non-self-consistent many-body perturbation theory (MBPT) Gaiduk et al (2018 Nat. Commun. 9 247) predicted an IP of 10.2 eV and EA of 0.2 eV, resulting in an electronic band gap (i.e. electronic gap (IP-EA) as measured by photoelectron spectroscopy) of about 10 eV, redefining the widely cited experimental gap of 8.7 eV in literature. In the present work, we show that GW self-consistency and an implicit vertex correction in MBPT considerably affect recently reported EA values by Gaiduk et al (2018 Nat. Commun. 9 247) by about 1 eV. Furthermore, the choice of pseudo-potential is critical for an accurate determination of the absolute band positions. Consequently, the self-consistent GW approach with an implicit vertex correction based on projector augmented wave (PAW) method on top of quantum water structures predicts an IP of 10.2, an EA of 1.1, a fundamental gap of 9.1 eV and an exciton binding (Eb) energy of 0.9 eV for the first absorption band of liquid water via the Bethe–Salpeter equation (BSE). Only within such a self-consistent approach a simultanously accurate prediction of IP, EA, Eg, Eb is possible.

B 36N 36 fullerene-like nanocages: A novel material for drug delivery

NASA Astrophysics Data System (ADS)

Ganji, M. D.; Yazdani, H.; Mirnejad, A.

2010-07-01

We study interaction between B 36N 36 fullerene-like nanocage and glycine amino acid from the first- principles. Binding energy is calculated and glycine binding to the pure C 60 fullerene is compared. We also analyze the electronic structure and charge Mulliken population for the energetically most favorable complexes. Our results indicate that glycine can form stable bindings with B 36N 36 nanocage via their carbonyl oxygen (O) active site while, the C 60 fullerene might be unable to form stable bindings to glycine amino acid via their active sites, which is consistence with recent experimental and theoretical investigations. Thus, we arrive at the prediction that the B 36N 36 nanocage can be implemented as a novel material for drug delivery applications.

Energetics of drug-DNA interactions.

PubMed

Chaires, J B

1997-01-01

Understanding the thermodynamics of drug binding to DNA is of both practical and fundamental interest. The practical interest lies in the contribution that thermodynamics can make to the rational design process for the development of new DNA targeted drugs. Thermodynamics offer key insights into the molecular forces that drive complex formation that cannot be obtained by structural or computational studies alone. The fundamental interest in these interactions lies in what they can reveal about the general problems of parsing and predicting ligand binding free energies. For these problems, drug-DNA interactions offer several distinct advantages, among them being that the structures of many drug-DNA complexes are known at high resolution and that such structures reveal that in many cases the drug acts as a rigid body, with little conformational change upon binding. Complete thermodynamic profiles (delta G, delta H, delta S, delta Cp) for numerous drug-DNA interactions have been obtained, with the help of high-sensitivity microcalorimetry. The purpose of this article is to offer a perspective on the interpretation of these thermodynamics parameters, and in particular how they might be correlated with known structural features. Obligatory conformational changes in the DNA to accommodate intercalators and the loss of translational and rotational freedom upon complex formation both present unfavorable free energy barriers for binding. Such barriers must be overcome by favorable free energy contributions from the hydrophobic transfer of ligand from solution into the binding site, polyelectrolyte contributions from coupled ion release, and molecular interactions (hydrogen and ionic bonds, van der Waals interactions) that form within the binding site. Theoretical and semiempirical tools that allow estimates of these contributions to be made will be discussed, and their use in dissecting experimental data illustrated. This process, even at the current level of approximation, can shed considerable light on the drug-DNA binding process.

Identification of Ideal Multi-targeting Bioactive Compounds Against Mur Ligases of Enterobacter aerogenes and Its Binding Mechanism in Comparison with Chemical Inhibitors.

PubMed

Chakkyarath, Vijina; Natarajan, Jeyakumar

2017-10-31

Enterobacter aerogenes have been reported as important opportunistic and multi-resistant bacterial pathogens for humans during the last three decades in hospital wards. The emergence of drug-resistant E. aerogenes demands the need for developing new drugs. Peptidoglycan is an important component of the cell wall of bacteria and the peptidoglycan biochemical pathway is considered as the best source of antibacterial targets. Within this pathway, four Mur ligases MurC, MurD, MurE, and MurF are responsible for the successive additions of L-alanine and suitable targets for developing novel antibacterial drugs. As an inference from this fact, we modeled the three-dimensional structure of above Mur ligases using best template structures available in PDB and analyzed its common binding features. Structural refinement and energy minimization of the predicted Mur ligases models is also being done using molecular dynamics studies. The models of Mur ligases were further investigated for in silico docking studies using bioactive plant compounds from the literature. Interestingly, these results indicate that four plant compounds Isojuripidine, Atroviolacegenin, Porrigenin B, and Nummularogenin showing better docking results in terms of binding energy and number of hydrogen bonds. All these four compounds are spirostan-based compounds with differences in side chains and the amino acid such as ASN, LYS, THR, HIS, ARG (polar) and PHE, GLY, VAL, ALA, MET (non-polar) playing active role in binding site of all four Mur ligases. Overall, in the predicted model, the four plant compounds with its binding features could pave way to design novel multi-targeted antibacterial plant-based bioactive compounds specific to Mur ligases for the treatment of Enterobacter infections.

Genome-wide inference of transcription factor-DNA binding specificity in cell regeneration using a combination strategy.

PubMed

Wang, Xiaofeng; Zhang, Aiqun; Ren, Weizheng; Chen, Caiyu; Dong, Jiahong

2012-11-01

The cell growth, development, and regeneration of tissue and organ are associated with a large number of gene regulation events, which are mediated in part by transcription factors (TFs) binding to cis-regulatory elements involved in the genome. Predicting the binding affinity and inferring the binding specificity of TF-DNA interactions at the genomic level would be fundamentally helpful for our understanding of the molecular mechanism and biological implication underlying sequence-specific TF-DNA recognition. In this study, we report the development of a combination method to characterize the interaction behavior of a 11-mer oligonucleotide segment and its mutations with the Gcn4p protein, a homodimeric, basic leucine zipper TF, and to predict the binding affinity and specificity of potential Gcn4p binders in the genome-wide scale. In this procedure, a position-mutated energy matrix is created based on molecular modeling analysis of native and mutated Gcn4p-DNA complex structures to describe the position-independent interaction energy profile of Gcn4p with different nucleotide types at each position of the oligonucleotide, and the energy terms extracted from the matrix and their interactives are then correlated with experimentally measured affinities of 19268 distinct oligonucleotides using statistical modeling methodology. Subsequently, the best one of built regression models is successfully applied to screen those of potential high-affinity Gcn4p binders from the complete genome. The findings arising from this study are briefly listed below: (i) The 11 positions of oligonucleotides are highly interactive and non-additive in contribution to Gcn4p-DNA binding affinity; (ii) Indirect conformational effects upon nucleotide mutations as well as associated subtle changes in interfacial atomic contacts, but not the direct nonbonded interactions, are primarily responsible for the sequence-specific recognition; (iii) The intrinsic synergistic effects among the sequence positions of oligonucleotides determine Gcn4p-DNA binding affinity and specificity; (iv) Linear regression models in conjunction with variable selection seem to perform fairly well in capturing the internal dependences hidden in the Gcn4p-DNA system, albeit ignoring nonlinear factors may lead the models to systematically underestimate and overestimate high- and low-affinity samples, respectively. © 2012 John Wiley & Sons A/S.

Ground state of excitonic molecules by the Green's-function Monte Carlo method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lee, M.A.; Vashishta, P.; Kalia, R.K.

1983-12-26

The ground-state energy of excitonic molecules is evaluated as a function of the ratio of electron and hole masses, sigma, with use of the Green's-function Monte Carlo method. For all sigma, the Green's-function Monte Carlo energies are significantly lower than the variational estimates and in favorable agreement with experiments. In excitonic rydbergs, the binding energy of the positronium molecule (sigma = 1) is predicted to be -0.06 and for sigma<<1, the Green's-function Monte Carlo energies agree with the ''exact'' limiting behavior, E = -2.346+0.764sigma.

Tertiary structure prediction and identification of druggable pocket in the cancer biomarker – Osteopontin-c

PubMed Central

2014-01-01

Background Osteopontin (Eta, secreted sialoprotein 1, opn) is secreted from different cell types including cancer cells. Three splice variant forms namely osteopontin-a, osteopontin-b and osteopontin-c have been identified. The main astonishing feature is that osteopontin-c is found to be elevated in almost all types of cancer cells. This was the vital point to consider it for sequence analysis and structure predictions which provide ample chances for prognostic, therapeutic and preventive cancer research. Methods Osteopontin-c gene sequence was determined from Breast Cancer sample and was translated to protein sequence. It was then analyzed using various software and web tools for binding pockets, docking and druggability analysis. Due to the lack of homological templates, tertiary structure was predicted using ab-initio method server – I-TASSER and was evaluated after refinement using web tools. Refined structure was compared with known bone sialoprotein electron microscopic structure and docked with CD44 for binding analysis and binding pockets were identified for drug designing. Results Signal sequence of about sixteen amino acid residues was identified using signal sequence prediction servers. Due to the absence of known structures of similar proteins, three dimensional structure of osteopontin-c was predicted using I-TASSER server. The predicted structure was refined with the help of SUMMA server and was validated using SAVES server. Molecular dynamic analysis was carried out using GROMACS software. The final model was built and was used for docking with CD44. Druggable pockets were identified using pocket energies. Conclusions The tertiary structure of osteopontin-c was predicted successfully using the ab-initio method and the predictions showed that osteopontin-c is of fibrous nature comparable to firbronectin. Docking studies showed the significant similarities of QSAET motif in the interaction of CD44 and osteopontins between the normal and splice variant forms of osteopontins and binding pockets analyses revealed several pockets which paved the way to the identification of a druggable pocket. PMID:24401206

Two- and three-body interatomic dispersion energy contributions to binding in molecules and solids

NASA Astrophysics Data System (ADS)

Anatole von Lilienfeld, O.; Tkatchenko, Alexandre

2010-06-01

We present numerical estimates of the leading two- and three-body dispersion energy terms in van der Waals interactions for a broad variety of molecules and solids. The calculations are based on London and Axilrod-Teller-Muto expressions where the required interatomic dispersion energy coefficients, C6 and C9, are computed "on the fly" from the electron density. Inter- and intramolecular energy contributions are obtained using the Tang-Toennies (TT) damping function for short interatomic distances. The TT range parameters are equally extracted on the fly from the electron density using their linear relationship to van der Waals radii. This relationship is empiricially determined for all the combinations of He-Xe rare gas dimers, as well as for the He and Ar trimers. The investigated systems include the S22 database of noncovalent interactions, Ar, benzene and ice crystals, bilayer graphene, C60 dimer, a peptide (Ala10), an intercalated drug-DNA model [ellipticine-d(CG)2], 42 DNA base pairs, a protein (DHFR, 2616 atoms), double stranded DNA (1905 atoms), and 12 molecular crystal polymorphs from crystal structure prediction blind test studies. The two- and three-body interatomic dispersion energies are found to contribute significantly to binding and cohesive energies, for bilayer graphene the latter reaches 50% of experimentally derived binding energy. These results suggest that interatomic three-body dispersion potentials should be accounted for in atomistic simulations when modeling bulky molecules or condensed phase systems.

Two and three-body interatomic dispersion energy contributions to binding in molecules and solids.

DOE Office of Scientific and Technical Information (OSTI.GOV)

von Lilienfeld-Toal, Otto Anatole; Tkatchenko, Alexandre

We present numerical estimates of the leading two- and three-body dispersion energy terms in van der Waals interactions for a broad variety of molecules and solids. The calculations are based on London and Axilrod-Teller-Muto expressions where the required interatomic dispersion energy coefficients, C{sub 6} and C{sub 9}, are computed 'on the fly' from the electron density. Inter- and intramolecular energy contributions are obtained using the Tang-Toennies (TT) damping function for short interatomic distances. The TT range parameters are equally extracted on the fly from the electron density using their linear relationship to van der Waals radii. This relationship is empiriciallymore » determined for all the combinations of He-Xe rare gas dimers, as well as for the He and Ar trimers. The investigated systems include the S22 database of noncovalent interactions, Ar, benzene and ice crystals, bilayer graphene, C{sub 60} dimer, a peptide (Ala{sub 10}), an intercalated drug-DNA model [ellipticine-d(CG){sub 2}], 42 DNA base pairs, a protein (DHFR, 2616 atoms), double stranded DNA (1905 atoms), and 12 molecular crystal polymorphs from crystal structure prediction blind test studies. The two- and three-body interatomic dispersion energies are found to contribute significantly to binding and cohesive energies, for bilayer graphene the latter reaches 50% of experimentally derived binding energy. These results suggest that interatomic three-body dispersion potentials should be accounted for in atomistic simulations when modeling bulky molecules or condensed phase systems.« less

Novel N-allyl/propargyl tetrahydroquinolines: Synthesis via Three-component Cationic Imino Diels-Alder Reaction, Binding Prediction, and Evaluation as Cholinesterase Inhibitors.

PubMed

Rodríguez, Yeray A; Gutiérrez, Margarita; Ramírez, David; Alzate-Morales, Jans; Bernal, Cristian C; Güiza, Fausto M; Romero Bohórquez, Arnold R

2016-10-01

New N-allyl/propargyl 4-substituted 1,2,3,4-tetrahydroquinolines derivatives were efficiently synthesized using acid-catalyzed three components cationic imino Diels-Alder reaction (70-95%). All compounds were tested in vitro as dual acetylcholinesterase and butyryl-cholinesterase inhibitors and their potential binding modes, and affinity, were predicted by molecular docking and binding free energy calculations (∆G) respectively. The compound 4af (IC50 = 72 μm) presented the most effective inhibition against acetylcholinesterase despite its poor selectivity (SI = 2), while the best inhibitory activity on butyryl-cholinesterase was exhibited by compound 4ae (IC50 = 25.58 μm) with considerable selectivity (SI = 0.15). Molecular docking studies indicated that the most active compounds fit in the reported acetylcholinesterase and butyryl-cholinesterase active sites. Moreover, our computational data indicated a high correlation between the calculated ∆G and the experimental activity values in both targets. © 2016 The Authors Chemical Biology & Drug Design Published by John Wiley & Sons Ltd.

Theoretical studies of alkyl radicals in the NaY and HY zeolites.

PubMed

Ghandi, Khashayar; Zahariev, Federico E; Wang, Yan Alexander

2005-08-18

Interplay of quantum mechanical calculations and experimental data on hyperfine coupling constants of ethyl radical in zeolites at several temperatures was engaged to study the geometries and binding energies and to predict the temperature dependence of hyperfine splitting of a series of alkyl radicals in zeolites for the first time. The main focus is on the hyperfine interaction of alkyl radicals in the NaY and HY zeolites. The hyperfine splitting for neutral free radicals and free radical cations is predicted for different zeolite environments. This information can be used to establish the nature of the muoniated alkyl radicals in the NaY and HY zeolites via muSR experiments. The muon hyperfine coupling constants of the ethane radical cation in these zeolites are very large with relatively little dependence on temperature. It was found that the intramolecular dynamics of alkyl free radicals are only weakly affected by their strong binding to zeolites. In contrast, the substrate binding has a significant effect on their intermolecular dynamics.

Predicting MHC-II binding affinity using multiple instance regression

PubMed Central

EL-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

2011-01-01

Reliably predicting the ability of antigen peptides to bind to major histocompatibility complex class II (MHC-II) molecules is an essential step in developing new vaccines. Uncovering the amino acid sequence correlates of the binding affinity of MHC-II binding peptides is important for understanding pathogenesis and immune response. The task of predicting MHC-II binding peptides is complicated by the significant variability in their length. Most existing computational methods for predicting MHC-II binding peptides focus on identifying a nine amino acids core region in each binding peptide. We formulate the problems of qualitatively and quantitatively predicting flexible length MHC-II peptides as multiple instance learning and multiple instance regression problems, respectively. Based on this formulation, we introduce MHCMIR, a novel method for predicting MHC-II binding affinity using multiple instance regression. We present results of experiments using several benchmark datasets that show that MHCMIR is competitive with the state-of-the-art methods for predicting MHC-II binding peptides. An online web server that implements the MHCMIR method for MHC-II binding affinity prediction is freely accessible at http://ailab.cs.iastate.edu/mhcmir. PMID:20855923

Molecular dynamics simulations reveal the allosteric effect of F1174C resistance mutation to ceritinib in ALK-associated lung cancer.

PubMed

Ni, Zhong; Wang, Xiting; Zhang, Tianchen; Jin, Rong Zhong

2016-12-01

Anaplastic lymphoma kinase (ALK) has become as an important target for the treatment of various human cancers, especially non-small-cell lung cancer. A mutation, F1174C, suited in the C-terminal helix αC of ALK and distal from the small-molecule inhibitor ceritinib bound to the ATP-binding site, causes the emergence of drug resistance to ceritinib. However, the detailed mechanism for the allosteric effect of F1174C resistance mutation to ceritinib remains unclear. Here, molecular dynamics (MD) simulations and binding free energy calculations [Molecular Mechanics/Generalized Born Surface Area (MM/GBSA)] were carried out to explore the advent of drug resistance mutation in ALK. MD simulations observed that the exquisite aromatic-aromatic network formed by residues F1098, F1174, F1245, and F1271 in the wild-type ALK-ceritinib complex was disrupted by the F1174C mutation. The resulting mutation allosterically affected the conformational dynamic of P-loop and caused the upward movement of the P-loop from the ATP-binding site, thereby weakening the interaction between ceritinib and the P-loop. The subsequent MM/GBSA binding free energy calculations and decomposition analysis of binding free energy validated this prediction. This study provides mechanistic insight into the allosteric effect of F1174C resistance mutation to ceritinib in ALK and is expected to contribute to design the next-generation of ALK inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

Temperature and doping dependence of the high-energy kink in cuprates.

PubMed

Zemljic, M M; Prelovsek, P; Tohyama, T

2008-01-25

It is shown that spectral functions within the extended t-J model, evaluated using the finite-temperature diagonalization of small clusters, exhibit the high-energy kink in single-particle dispersion consistent with recent angle-resolved photoemission results on hole-doped cuprates. The kink and waterfall-like features persist up to large doping and to temperatures beyond J; hence, the origin can be generally attributed to strong correlations and incoherent hole propagation at large binding energies. In contrast, our analysis predicts that electron-doped cuprates do not exhibit these phenomena in photoemission.

Automated use of mutagenesis data in structure prediction.

PubMed

Nanda, Vikas; DeGrado, William F

2005-05-15

In the absence of experimental structural determination, numerous methods are available to indirectly predict or probe the structure of a target molecule. Genetic modification of a protein sequence is a powerful tool for identifying key residues involved in binding reactions or protein stability. Mutagenesis data is usually incorporated into the modeling process either through manual inspection of model compatibility with empirical data, or through the generation of geometric constraints linking sensitive residues to a binding interface. We present an approach derived from statistical studies of lattice models for introducing mutation information directly into the fitness score. The approach takes into account the phenotype of mutation (neutral or disruptive) and calculates the energy for a given structure over an ensemble of sequences. The structure prediction procedure searches for the optimal conformation where neutral sequences either have no impact or improve stability and disruptive sequences reduce stability relative to wild type. We examine three types of sequence ensembles: information from saturation mutagenesis, scanning mutagenesis, and homologous proteins. Incorporating multiple sequences into a statistical ensemble serves to energetically separate the native state and misfolded structures. As a result, the prediction of structure with a poor force field is sufficiently enhanced by mutational information to improve accuracy. Furthermore, by separating misfolded conformations from the target score, the ensemble energy serves to speed up conformational search algorithms such as Monte Carlo-based methods. Copyright 2005 Wiley-Liss, Inc.

A fragment-based approach to the SAMPL3 Challenge

NASA Astrophysics Data System (ADS)

Kulp, John L.; Blumenthal, Seth N.; Wang, Qiang; Bryan, Richard L.; Guarnieri, Frank

2012-05-01

The success of molecular fragment-based design depends critically on the ability to make predictions of binding poses and of affinity ranking for compounds assembled by linking fragments. The SAMPL3 Challenge provides a unique opportunity to evaluate the performance of a state-of-the-art fragment-based design methodology with respect to these requirements. In this article, we present results derived from linking fragments to predict affinity and pose in the SAMPL3 Challenge. The goal is to demonstrate how incorporating different aspects of modeling protein-ligand interactions impact the accuracy of the predictions, including protein dielectric models, charged versus neutral ligands, ΔΔGs solvation energies, and induced conformational stress. The core method is based on annealing of chemical potential in a Grand Canonical Monte Carlo (GC/MC) simulation. By imposing an initially very high chemical potential and then automatically running a sequence of simulations at successively decreasing chemical potentials, the GC/MC simulation efficiently discovers statistical distributions of bound fragment locations and orientations not found reliably without the annealing. This method accounts for configurational entropy, the role of bound water molecules, and results in a prediction of all the locations on the protein that have any affinity for the fragment. Disregarding any of these factors in affinity-rank prediction leads to significantly worse correlation with experimentally-determined free energies of binding. We relate three important conclusions from this challenge as applied to GC/MC: (1) modeling neutral ligands—regardless of the charged state in the active site—produced better affinity ranking than using charged ligands, although, in both cases, the poses were almost exactly overlaid; (2) simulating explicit water molecules in the GC/MC gave better affinity and pose predictions; and (3) applying a ΔΔGs solvation correction further improved the ranking of the neutral ligands. Using the GC/MC method under a variety of parameters in the blinded SAMPL3 Challenge provided important insights to the relevant parameters and boundaries in predicting binding affinities using simulated annealing of chemical potential calculations.

Investigation of differences in the binding affinities of two analogous ligands for untagged and tagged p38 kinase using thermodynamic integration MD simulation.

PubMed

Sun, Ying-Chieh; Hsu, Wen-Chi; Hsu, Chia-Jen; Chang, Chia-Ming; Cheng, Kai-Hsiang

2015-11-01

Thermodynamic integration (TI) molecular dynamics (MD) simulations for the binding of a pair of a reference ("ref") ligand and an analogous ("analog") ligand to either tagged (with six extra residues at the N-terminus) or untagged p38 kinase proteins were carried out in order to probe how the binding affinity is influenced by the presence or absence of the peptide tag in p38 kinase. This possible effect of protein length on the binding affinity of a ligand-which is seldom addressed in the literature-is important because, even when two labs claim to have performed experiments with the same protein, they may actually have studied variants of the same protein with different lengths because they applied different protein expression conditions/procedures. Thus, if we wanted to compare ligand binding affinities measured in the two labs, it would be necessary to account for any variation in ligand binding affinity with protein length. The pair of ligand-p38 kinase complexes examined in this work (pdb codes: 3d7z and 3lhj, respectively) were ideal for investigating this effect. The experimentally determined binding energy for the ref ligand with the untagged p38 kinase was -10.9 kcal mol(-1), while that for the analog ligand with the tagged p38 kinase was -11.9 kcal mol(-1). The present TI-MD simulation of the mutation of the ref ligand into the analog ligand while the ligand is bound to the untagged p38 kinase predicted that the binding affinity of the analog ligand is 2.0 kcal mol(-1) greater than that of the ref ligand. A similar simulation also indicated that the same was true for ligand binding to the tagged protein, but in this case the binding affinity for the analog ligand is 2.5 kcal mol(-1) larger than that for the ref ligand. These results therefore suggest that the presence of the peptide tag on p38 kinase increased the difference in the binding energies of the ligands by a small amount of 0.5 kcal mol(-1). This result supports the assumption that the presence of a peptide tag has only a minor effect on ΔG values. The error bars in the computed ΔG values were then estimated via confidence interval analysis and a time autocorrelation function for the quantity dV/dλ. The estimated correlation time was ~0.5 ps and the error bar in the ΔG values estimated using nanosecond-scale simulations was ±0.3 kcal mol(-1) at a confidence level of 95%. These predicted results can be verified in future experiments and should prove useful in subsequent similar studies. Graphical Abstract Thermodynamic cycles for binding of two analogous ligands with untagged and tagged p38 kinases and associated Gibbs free energy.

T-Cell Receptors Binding Orientation over Peptide/MHC Class I Is Driven by Long-Range Interactions

PubMed Central

Ferber, Mathias; Zoete, Vincent; Michielin, Olivier

2012-01-01

Crystallographic data about T-Cell Receptor – peptide – major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes. PMID:23251658

Molecular dynamics and MM/GBSA-integrated protocol probing the correlation between biological activities and binding free energies of HIV-1 TAR RNA inhibitors.

PubMed

Peddi, Saikiran Reddy; Sivan, Sree Kanth; Manga, Vijjulatha

2018-02-01

The interaction of HIV-1 transactivator protein Tat with its cognate transactivation response (TAR) RNA has emerged as a promising target for developing antiviral compounds and treating HIV infection, since it is a crucial step for efficient transcription and replication. In the present study, molecular dynamics (MD) simulations and MM/GBSA calculations have been performed on a series of neamine derivatives in order to estimate appropriate MD simulation time for acceptable correlation between ΔG bind and experimental pIC 50 values. Initially, all inhibitors were docked into the active site of HIV-1 TAR RNA. Later to explore various conformations and examine the docking results, MD simulations were carried out. Finally, binding free energies were calculated using MM/GBSA method and were correlated with experimental pIC 50 values at different time scales (0-1 to 0-10 ns). From this study, it is clear that in case of neamine derivatives as simulation time increased the correlation between binding free energy and experimental pIC 50 values increased correspondingly. Therefore, the binding energies which can be interpreted at longer simulation times can be used to predict the bioactivity of new neamine derivatives. Moreover, in this work, we have identified some plausible critical nucleotide interactions with neamine derivatives that are responsible for potent inhibitory activity. Furthermore, we also provide some insights into a new class of oxadiazole-based back bone cyclic peptides designed by incorporating the structural features of neamine derivatives. On the whole, this approach can provide a valuable guidance for designing new potent inhibitors and modify the existing compounds targeting HIV-1 TAR RNA.

T-cell receptors binding orientation over peptide/MHC class I is driven by long-range interactions.

PubMed

Ferber, Mathias; Zoete, Vincent; Michielin, Olivier

2012-01-01

Crystallographic data about T-Cell Receptor - peptide - major histocompatibility complex class I (TCRpMHC) interaction have revealed extremely diverse TCR binding modes triggering antigen recognition. Understanding the molecular basis that governs TCR orientation over pMHC is still a considerable challenge. We present a simplified rigid approach applied on all non-redundant TCRpMHC crystal structures available. The CHARMM force field in combination with the FACTS implicit solvation model is used to study the role of long-distance interactions between the TCR and pMHC. We demonstrate that the sum of the coulomb interactions and the electrostatic solvation energies is sufficient to identify two orientations corresponding to energetic minima at 0° and 180° from the native orientation. Interestingly, these results are shown to be robust upon small structural variations of the TCR such as changes induced by Molecular Dynamics simulations, suggesting that shape complementarity is not required to obtain a reliable signal. Accurate energy minima are also identified by confronting unbound TCR crystal structures to pMHC. Furthermore, we decompose the electrostatic energy into residue contributions to estimate their role in the overall orientation. Results show that most of the driving force leading to the formation of the complex is defined by CDR1,2/MHC interactions. This long-distance contribution appears to be independent from the binding process itself, since it is reliably identified without considering neither short-range energy terms nor CDR induced fit upon binding. Ultimately, we present an attempt to predict the TCR/pMHC binding mode for a TCR structure obtained by homology modeling. The simplicity of the approach and the absence of any fitted parameters make it also easily applicable to other types of macromolecular protein complexes.

Nucleosome stability and accessibility of its DNA to proteins.

PubMed

Prinsen, Peter; Schiessel, Helmut

2010-12-01

In this paper we present a theoretical description of the accessibility of nucleosomal DNA to proteins. We reassess the classical analysis of Polach and Widom (1995) who demonstrated that proteins (in their case restriction enzymes) gain access to buried binding sites inside a nucleosome through spontaneous unwrapping of DNA from the protein spool. We introduce a straightforward nucleosome model the predictions of which show good agreement with experimental data. By fitting the model to the data we obtain the values of two quantities: the adsorption energy to the histone octamer per length of DNA and the extra length that the DNA needs to unwrap beyond the binding site of an enzyme before the enzyme can act as effectively as on bare DNA. Our results indicate that the effective binding energy is surprisingly low which suggests that the nucleosomal parameters are tuned such that two large energies, the DNA bending energy and the pure adsorption energy, nearly cancel. This paper is based on a lecture presented at the summer school "DNA and Chromosomes 2009: Physical and Biological Applications". We follow the lecture as closely as possible which is why we spend more time than usual on issues that are already well-known in the field, and why we discuss some well-known results from a different perspective. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

Steric and Thermodynamic Limits of Design for the Incorporation of Large UnNatural Amino Acids in Aminoacyl-tRNA Synthetase Enzymes

PubMed Central

Armen, Roger S.; Schiller, Stefan M.; Brooks, Charles L.

2015-01-01

Orthogonal aminoacyl-tRNA synthetase/tRNA pairs from archaea have been evolved to facilitate site specific in vivo incorporation of unnatural amino acids into proteins in Escherichia coli. Using this approach, unnatural amino acids have been successfully incorporated with high translational efficiency and fidelity. In this study, CHARMM-based molecular docking and free energy calculations were used to evaluate rational design of specific protein-ligand interactions for aminoacyl-tRNA synthetases. A series of novel unnatural amino acid ligands were docked into the p-benzoyl-L-phenylalanine tRNA synthetase, which revealed that the binding pocket of the enzyme does not provide sufficient space for significantly larger ligands. Specific binding site residues were mutated to alanine to create additional space to accommodate larger target ligands, and then mutations were introduced to improve binding free energy. This approach was used to redesign binding sites for several different target ligands, which were then tested against the standard 20 amino acids to verify target specificity. Only the synthetase designed to bind Man-α-O-Tyr was predicted to be sufficiently selective for the target ligand and also thermodynamically stable. Our study suggests that extensive redesign of the tRNA synthatase binding pocket for large bulky ligands may be quite thermodynamically unfavorable. PMID:20310065

Development of estrogen receptor beta binding prediction model using large sets of chemicals.

PubMed

Sakkiah, Sugunadevi; Selvaraj, Chandrabose; Gong, Ping; Zhang, Chaoyang; Tong, Weida; Hong, Huixiao

2017-11-03

We developed an ER β binding prediction model to facilitate identification of chemicals specifically bind ER β or ER α together with our previously developed ER α binding model. Decision Forest was used to train ER β binding prediction model based on a large set of compounds obtained from EADB. Model performance was estimated through 1000 iterations of 5-fold cross validations. Prediction confidence was analyzed using predictions from the cross validations. Informative chemical features for ER β binding were identified through analysis of the frequency data of chemical descriptors used in the models in the 5-fold cross validations. 1000 permutations were conducted to assess the chance correlation. The average accuracy of 5-fold cross validations was 93.14% with a standard deviation of 0.64%. Prediction confidence analysis indicated that the higher the prediction confidence the more accurate the predictions. Permutation testing results revealed that the prediction model is unlikely generated by chance. Eighteen informative descriptors were identified to be important to ER β binding prediction. Application of the prediction model to the data from ToxCast project yielded very high sensitivity of 90-92%. Our results demonstrated ER β binding of chemicals could be accurately predicted using the developed model. Coupling with our previously developed ER α prediction model, this model could be expected to facilitate drug development through identification of chemicals that specifically bind ER β or ER α .

Endohedral metallofullerene Sc3NC@C84: a theoretical prediction.

PubMed

Wang, Dong-Lai; Xu, Hong-Liang; Su, Zhong-Min; Xin, Guang

2012-11-21

Very recently, two novel Sc(3)NC-based cluster fullerenes Sc(3)NC@C(80) (Wang et. al. J. Am. Chem. Soc. 2010, 132, 16362) and Sc(3)NC@C(78) (Wu et. al. J. Phys. Chem. C 2011, 115, 23755) were prepared and characterized, respectively. Inspired by these findings, the possibility of encapsulating Sc(3)NC cluster in the C(84) fullerene is performed using density functional theory (DFT). Firstly, the isolated pentagon rule (IPR) D(2d) (23) C(84) fullerene is employed to encase the Sc(3)NC cluster: four possible endohedral metallofullerene isomers a-d are designed. The large binding energies (ranging from 163.7 to 210.0 kcal mol(-1)) indicate that the planar quinary cluster Sc(3)NC can be stably encapsulated in the C(84) (isomer 23) cage. Further, we consider the incorporation of Sc(3)NC into the non-IPR C(s) (51365) C(84) cage leading to isomer e and show the high stability of isomer e, which has a larger binding energy, larger HOMO-LUMO gap, higher adiabatic (vertical) ionization potential, and lower adiabatic (vertical) electron affinity than the former four Sc(3)NC@C(84) (isomer 23). Significantly, the predicted binding energy (294.2 kcal mol(-1)) of isomer e is even larger than that (289.2 and 277.7 kcal mol(-1), respectively) of the synthesized Sc(3)NC@C(80) and Sc(3)NC@C(78,) suggesting a considerable possibility for experimental realization. The (13)C NMR chemical shifts and Raman spectra of this a new endofullerene have been explored to assist future experimental characterization.

Alchemical free energy simulations for biological complexes: powerful but temperamental....

PubMed

Aleksandrov, Alexey; Thompson, Damien; Simonson, Thomas

2010-01-01

Free energy simulations compare multiple ligand:receptor complexes by "alchemically" transforming one into another, yielding binding free energy differences. Since their introduction in the 1980s, many technical and theoretical obstacles were surmounted, and the method ("MDFE," since molecular dynamics are often used) has matured into a powerful tool. We describe its current status, its effectiveness, and the challenges it faces. MDFE has provided chemical accuracy for many systems but remains expensive, with significant human overhead costs. The bottlenecks have shifted, partly due to increased computer power. To study diverse sets of ligands, force field availability and accuracy can be a major difficulty. Another difficulty is the frequent need to consider multiple states, related to sidechain protonation or buried waters, for example. Sophisticated, automated methods to sample these states are maturing, such as constant pH simulations. Meanwhile, combinations of MDFE and simpler approaches, like continuum dielectric models, can be very effective. As illustrations, we show how, with careful force field parameterization, MDFE accurately predicts binding specificities between complex tetracycline ligands and their targets. We describe substrate binding to the aspartyl-tRNA synthetase enzyme, where many distinct electrostatic states play a role, and a histidine and a Mg(2+) ion act as coupled switches that help enforce a strict preference for the aspartate substrate, relative to several analogs. Overall, MDFE has achieved a predictive status, where novel ligands can be studied and molecular recognition elucidated in depth. It should play an increasing role in the analysis of complex cellular processes and biomolecular engineering. 2009 John Wiley & Sons, Ltd.

Grain boundaries in bcc-Fe: a density-functional theory and tight-binding study

NASA Astrophysics Data System (ADS)

Wang, Jingliang; Madsen, Georg K. H.; Drautz, Ralf

2018-02-01

Grain boundaries (GBs) have a significant influence on material properties. In the present paper, we calculate the energies of eleven low-Σ ({{Σ }}≤slant 13) symmetrical tilt GBs and two twist GBs in ferromagnetic bcc iron using first-principles density functional theory (DFT) calculations. The results demonstrate the importance of a sufficient sampling of initial rigid body translations in all three directions. We show that the relative GB energies can be explained by the miscoordination of atoms at the GB region. While the main features of the studied GB structures were captured by previous empirical interatomic potential calculations, it is shown that the absolute values of GB energies calculated were substantially underestimated. Based on DFT-calculated GB structures and energies, we construct a new d-band orthogonal tight-binding (TB) model for bcc iron. The TB model is validated by its predictive power on all the studied GBs. We apply the TB model to block boundaries in lath martensite and demonstrate that the experimentally observed GB character distribution can be explained from the viewpoint of interface energy.

Application of the docking program SOL for CSAR benchmark.

PubMed

Sulimov, Alexey V; Kutov, Danil C; Oferkin, Igor V; Katkova, Ekaterina V; Sulimov, Vladimir B

2013-08-26

This paper is devoted to results obtained by the docking program SOL and the post-processing program DISCORE at the CSAR benchmark. SOL and DISCORE programs are described. SOL is the original docking program developed on the basis of the genetic algorithm, MMFF94 force field, rigid protein, precalculated energy grid including desolvation in the frame of simplified GB model, vdW, and electrostatic interactions and taking into account the ligand internal strain energy. An important SOL feature is the single- or multi-processor performance for up to hundreds of CPUs. DISCORE improves the binding energy scoring by the local energy optimization of the ligand docked pose and a simple linear regression on the base of available experimental data. The docking program SOL has demonstrated a good ability for correct ligand positioning in the active sites of the tested proteins in most cases of CSAR exercises. SOL and DISCORE have not demonstrated very exciting results on the protein-ligand binding free energy estimation. Nevertheless, for some target proteins, SOL and DISCORE were among the first in prediction of inhibition activity. Ways to improve SOL and DISCORE are discussed.

Statistical Analysis on the Performance of Molecular Mechanics Poisson–Boltzmann Surface Area versus Absolute Binding Free Energy Calculations: Bromodomains as a Case Study

PubMed Central

2017-01-01

Binding free energy calculations that make use of alchemical pathways are becoming increasingly feasible thanks to advances in hardware and algorithms. Although relative binding free energy (RBFE) calculations are starting to find widespread use, absolute binding free energy (ABFE) calculations are still being explored mainly in academic settings due to the high computational requirements and still uncertain predictive value. However, in some drug design scenarios, RBFE calculations are not applicable and ABFE calculations could provide an alternative. Computationally cheaper end-point calculations in implicit solvent, such as molecular mechanics Poisson–Boltzmann surface area (MMPBSA) calculations, could too be used if one is primarily interested in a relative ranking of affinities. Here, we compare MMPBSA calculations to previously performed absolute alchemical free energy calculations in their ability to correlate with experimental binding free energies for three sets of bromodomain–inhibitor pairs. Different MMPBSA approaches have been considered, including a standard single-trajectory protocol, a protocol that includes a binding entropy estimate, and protocols that take into account the ligand hydration shell. Despite the improvements observed with the latter two MMPBSA approaches, ABFE calculations were found to be overall superior in obtaining correlation with experimental affinities for the test cases considered. A difference in weighted average Pearson () and Spearman () correlations of 0.25 and 0.31 was observed when using a standard single-trajectory MMPBSA setup ( = 0.64 and = 0.66 for ABFE; = 0.39 and = 0.35 for MMPBSA). The best performing MMPBSA protocols returned weighted average Pearson and Spearman correlations that were about 0.1 inferior to ABFE calculations: = 0.55 and = 0.56 when including an entropy estimate, and = 0.53 and = 0.55 when including explicit water molecules. Overall, the study suggests that ABFE calculations are indeed the more accurate approach, yet there is also value in MMPBSA calculations considering the lower compute requirements, and if agreement to experimental affinities in absolute terms is not of interest. Moreover, for the specific protein–ligand systems considered in this study, we find that including an explicit ligand hydration shell or a binding entropy estimate in the MMPBSA calculations resulted in significant performance improvements at a negligible computational cost. PMID:28786670

Density functional theory and chromium: Insights from the dimers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Würdemann, Rolf; Kristoffersen, Henrik H.; Moseler, Michael

2015-03-28

The binding in small Cr clusters is re-investigated, where the correct description of the dimer in three charge states is used as criterion to assign the most suitable density functional theory approximation. The difficulty in chromium arises from the subtle interplay between energy gain from hybridization and energetic cost due to exchange between s and d based molecular orbitals. Variations in published bond lengths and binding energies are shown to arise from insufficient numerical representation of electron density and Kohn-Sham wave-functions. The best functional performance is found for gradient corrected (GGA) functionals and meta-GGAs, where we find severe differences betweenmore » functionals from the same family due to the importance of exchange. Only the “best fit” from Bayesian error estimation is able to predict the correct energetics for all three charge states unambiguously. With this knowledge, we predict small bond-lengths to be exclusively present in Cr{sub 2} and Cr{sub 2}{sup −}. Already for the dimer cation, solely long bond-lengths appear, similar to what is found in the trimer and in chromium bulk.« less

Activity of N-coordinated multi-metal-atom active site structures for Pt-free oxygen reduction reaction catalysis: Role of *OH ligands

DOE PAGES

Holby, Edward F.; Taylor, Christopher D.

2015-03-19

We report calculated oxygen reduction reaction energy pathways on multi-metal-atom structures that have previously been shown to be thermodynamically favorable. We predict that such sites have the ability to spontaneously cleave the O₂ bond and then will proceed to over-bind reaction intermediates. In particular, the *OH bound state has lower energy than the final 2 H₂O state at positive potentials. Contrary to traditional surface catalysts, this *OH binding does not poison the multi-metal-atom site but acts as a modifying ligand that will spontaneously form in aqueous environments leading to new active sites that have higher catalytic activities. These *OH boundmore » structures have the highest calculated activity to date.« less

Heterodimerization of the Entamoeba histolytica EhCPADH virulence complex through molecular dynamics and protein-protein docking.

PubMed

Montaño, Sarita; Orozco, Esther; Correa-Basurto, José; Bello, Martiniano; Chávez-Munguía, Bibiana; Betanzos, Abigail

2017-02-01

EhCPADH is a protein complex involved in the virulence of Entamoeba histolytica, the protozoan responsible for human amebiasis. It is formed by the EhCP112 cysteine protease and the EhADH adhesin. To explore the molecular basis of the complex formation, three-dimensional models were built for both proteins and molecular dynamics simulations (MDS) and docking calculations were performed. Results predicted that the pEhCP112 proenzyme and the mEhCP112 mature enzyme were globular and peripheral membrane proteins. Interestingly, in pEhCP112, the propeptide appeared hiding the catalytic site (C167, H329, N348); while in mEhCP112, this site was exposed and its residues were found structurally closer than in pEhCP112. EhADH emerged as an extended peripheral membrane protein with high fluctuation in Bro1 and V shape domains. 500 ns-long MDS and protein-protein docking predictions evidenced different heterodimeric complexes with the lowest free energy. pEhCP112 interacted with EhADH by the propeptide and C-terminal regions and mEhCP112 by the C-terminal through hydrogen bonds. In contrast, EhADH bound to mEhCP112 by 442-479 residues, adjacent to the target cell-adherence region (480-600 residues), and by the Bro1 domain (9-349 residues). Calculations of the effective binding free energy and per residue free energy decomposition showed that EhADH binds to mEhCP112 with a higher binding energy than to pEhCP112, mainly through van der Waals interactions and the nonpolar part of solvation energy. The EhADH and EhCP112 structural relationship was validated in trophozoites by immunofluorescence, TEM, and immunoprecipitation assays. Experimental findings fair agreed with in silico results.

Improve the prediction of RNA-binding residues using structural neighbours.

PubMed

Li, Quan; Cao, Zanxia; Liu, Haiyan

2010-03-01

The interactions between RNA-binding proteins (RBPs) with RNA play key roles in managing some of the cell's basic functions. The identification and prediction of RNA binding sites is important for understanding the RNA-binding mechanism. Computational approaches are being developed to predict RNA-binding residues based on the sequence- or structure-derived features. To achieve higher prediction accuracy, improvements on current prediction methods are necessary. We identified that the structural neighbors of RNA-binding and non-RNA-binding residues have different amino acid compositions. Combining this structure-derived feature with evolutionary (PSSM) and other structural information (secondary structure and solvent accessibility) significantly improves the predictions over existing methods. Using a multiple linear regression approach and 6-fold cross validation, our best model can achieve an overall correct rate of 87.8% and MCC of 0.47, with a specificity of 93.4%, correctly predict 52.4% of the RNA-binding residues for a dataset containing 107 non-homologous RNA-binding proteins. Compared with existing methods, including the amino acid compositions of structure neighbors lead to clearly improvement. A web server was developed for predicting RNA binding residues in a protein sequence (or structure),which is available at http://mcgill.3322.org/RNA/.

Molecular Docking, Synthesis And Biological Evaluation Of Sulphonylureas/Guanidine Derivatives As Promising Antidiabetics Agent.

PubMed

Panchal, Ishan; Sen, Dhrubo Jyoti; Patel, Ashish D; Shah, Umang; Patel, Mehul; Navle, Archana; Bhavsar, Vashisth

2017-10-02

A series of novel sulphonylureas/guanidine derivatives were designed, synthesized, and evaluated for the treatment of diabetes mellitus. In this study, the designed compounds were docked with AKR1C1 complexes by using glide docking program and docking calculations were performed to predict the binding affinity of the designed compounds with the binding pocket of protein 4YVP and QikProp program was used to predict the ADME/T properties of the analogues. All the targeted derivatives were synthesized and purified by recrystallization. Synthesize compounds were characterized by various physicochemical and various spectroscopic techniques like melting point, thin layer chromatography, infrared spectroscopy (KBr pellets), mass spectroscopy(m/z), 1H NMR (DMSO-d6), and 13C NMR. The synthesized compounds were further studied for biological evolution by alloxan (150 mg/dl, intraperitonial) induced diabetic rat model for in-vivo studies. Among all the synthesized derivatives, 5c and 5d were most potent as per binding energy. Compound 5i have shown a better plasma glucose reduction compared to glibenclamide. Hence, it will further use as a lead compound to develop a more such kind of agent. The docking study revealed that in all designed sulphonylureas/guanidine series of compounds 5c and 5d were found to be most potent compounds as per the binding energy compared to glibenclamide. With the help of details study of in vivo biological activity we observed that compound 5i gives better result compared to glibenclamide as standard. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Renormalization of myoglobin–ligand binding energetics by quantum many-body effects

PubMed Central

Weber, Cédric; Cole, Daniel J.; O’Regan, David D.; Payne, Mike C.

2014-01-01

We carry out a first-principles atomistic study of the electronic mechanisms of ligand binding and discrimination in the myoglobin protein. Electronic correlation effects are taken into account using one of the most advanced methods currently available, namely a linear-scaling density functional theory (DFT) approach wherein the treatment of localized iron 3d electrons is further refined using dynamical mean-field theory. This combination of methods explicitly accounts for dynamical and multireference quantum physics, such as valence and spin fluctuations, of the 3d electrons, while treating a significant proportion of the protein (more than 1,000 atoms) with DFT. The computed electronic structure of the myoglobin complexes and the nature of the Fe–O2 bonding are validated against experimental spectroscopic observables. We elucidate and solve a long-standing problem related to the quantum-mechanical description of the respiration process, namely that DFT calculations predict a strong imbalance between O2 and CO binding, favoring the latter to an unphysically large extent. We show that the explicit inclusion of the many-body effects induced by the Hund’s coupling mechanism results in the correct prediction of similar binding energies for oxy- and carbonmonoxymyoglobin. PMID:24717844

Critical evaluation of methods to incorporate entropy loss upon binding in high-throughput docking.

PubMed

Salaniwal, Sumeet; Manas, Eric S; Alvarez, Juan C; Unwalla, Rayomand J

2007-02-01

Proper accounting of the positional/orientational/conformational entropy loss associated with protein-ligand binding is important to obtain reliable predictions of binding affinity. Herein, we critically examine two simplified statistical mechanics-based approaches, namely a constant penalty per rotor method, and a more rigorous method, referred to here as the partition function-based scoring (PFS) method, to account for such entropy losses in high-throughput docking calculations. Our results on the estrogen receptor beta and dihydrofolate reductase proteins demonstrate that, while the constant penalty method over-penalizes molecules for their conformational flexibility, the PFS method behaves in a more "DeltaG-like" manner by penalizing different rotors differently depending on their residual entropy in the bound state. Furthermore, in contrast to no entropic penalty or the constant penalty approximation, the PFS method does not exhibit any bias towards either rigid or flexible molecules in the hit list. Preliminary enrichment studies using a lead-like random molecular database suggest that an accurate representation of the "true" energy landscape of the protein-ligand complex is critical for reliable predictions of relative binding affinities by the PFS method. Copyright 2006 Wiley-Liss, Inc.

The role of water molecules in computational drug design.

PubMed

de Beer, Stephanie B A; Vermeulen, Nico P E; Oostenbrink, Chris

2010-01-01

Although water molecules are small and only consist of two different atom types, they play various roles in cellular systems. This review discusses their influence on the binding process between biomacromolecular targets and small molecule ligands and how this influence can be modeled in computational drug design approaches. Both the structure and the thermodynamics of active site waters will be discussed as these influence the binding process significantly. Structurally conserved waters cannot always be determined experimentally and if observed, it is not clear if they will be replaced upon ligand binding, even if sufficient space is available. Methods to predict the presence of water in protein-ligand complexes will be reviewed. Subsequently, we will discuss methods to include water in computational drug research. Either as an additional factor in automated docking experiments, or explicitly in detailed molecular dynamics simulations, the effect of water on the quality of the simulations is significant, but not easily predicted. The most detailed calculations involve estimates of the free energy contribution of water molecules to protein-ligand complexes. These calculations are computationally demanding, but give insight in the versatility and importance of water in ligand binding.

Environmental Screening Effects in 2D Materials: Renormalization of the Bandgap, Electronic Structure, and Optical Spectra of Few-Layer Black Phosphorus.

PubMed

Qiu, Diana Y; da Jornada, Felipe H; Louie, Steven G

2017-08-09

Few-layer black phosphorus has recently emerged as a promising 2D semiconductor, notable for its widely tunable bandgap, highly anisotropic properties, and theoretically predicted large exciton binding energies. To avoid degradation, it has become common practice to encapsulate black phosphorus devices. It is generally assumed that this encapsulation does not qualitatively affect their optical properties. Here, we show that the contrary is true. We have performed ab initio GW and GW plus Bethe-Salpeter equation (GW-BSE) calculations to determine the quasiparticle (QP) band structure and optical spectrum of one-layer (1L) through four-layer (4L) black phosphorus, with and without encapsulation between hexagonal boron nitride and sapphire. We show that black phosphorus is exceptionally sensitive to environmental screening. Encapsulation reduces the exciton binding energy in 1L by as much as 70% and completely eliminates the presence of a bound exciton in the 4L structure. The reduction in the exciton binding energies is offset by a similarly large renormalization of the QP bandgap so that the optical gap remains nearly unchanged, but the nature of the excited states and the qualitative features of the absorption spectrum change dramatically.

Alchemical Free Energy Calculations for Nucleotide Mutations in Protein-DNA Complexes.

PubMed

Gapsys, Vytautas; de Groot, Bert L

2017-12-12

Nucleotide-sequence-dependent interactions between proteins and DNA are responsible for a wide range of gene regulatory functions. Accurate and generalizable methods to evaluate the strength of protein-DNA binding have long been sought. While numerous computational approaches have been developed, most of them require fitting parameters to experimental data to a certain degree, e.g., machine learning algorithms or knowledge-based statistical potentials. Molecular-dynamics-based free energy calculations offer a robust, system-independent, first-principles-based method to calculate free energy differences upon nucleotide mutation. We present an automated procedure to set up alchemical MD-based calculations to evaluate free energy changes occurring as the result of a nucleotide mutation in DNA. We used these methods to perform a large-scale mutation scan comprising 397 nucleotide mutation cases in 16 protein-DNA complexes. The obtained prediction accuracy reaches 5.6 kJ/mol average unsigned deviation from experiment with a correlation coefficient of 0.57 with respect to the experimentally measured free energies. Overall, the first-principles-based approach performed on par with the molecular modeling approaches Rosetta and FoldX. Subsequently, we utilized the MD-based free energy calculations to construct protein-DNA binding profiles for the zinc finger protein Zif268. The calculation results compare remarkably well with the experimentally determined binding profiles. The software automating the structure and topology setup for alchemical calculations is a part of the pmx package; the utilities have also been made available online at http://pmx.mpibpc.mpg.de/dna_webserver.html .

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design.

PubMed

Ojewole, Adegoke; Lowegard, Anna; Gainza, Pablo; Reeve, Stephanie M; Georgiev, Ivelin; Anderson, Amy C; Donald, Bruce R

2017-01-01

Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (Reeve et al. Proc Natl Acad Sci U S A 112(3):749-754, 2015), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned continuous rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme's catalytic function but selectively ablate binding of an inhibitor.

In silico study of carvone derivatives as potential neuraminidase inhibitors.

PubMed

Jusoh, Noorakmar; Zainal, Hasanuddin; Abdul Hamid, Azzmer Azzar; Bunnori, Noraslinda M; Abd Halim, Khairul Bariyyah; Abd Hamid, Shafida

2018-03-15

Recent outbreaks of highly pathogenic influenza strains have highlighted the need to develop new anti-influenza drugs. Here, we report an in silico study of carvone derivatives to analyze their binding modes with neuraminidase (NA) active sites. Two proposed carvone analogues, CV(A) and CV(B), with 36 designed ligands were predicted to inhibit NA (PDB ID: 3TI6) using molecular docking. The design is based on structural resemblance with the commercial inhibitor, oseltamivir (OTV), ligand polarity, and amino acid residues in the NA active sites. Docking simulations revealed that ligand A18 has the lowest energy binding (∆G bind ) value of -8.30 kcal mol -1 , comparable to OTV with ∆G bind of -8.72 kcal mol -1 . A18 formed seven hydrogen bonds (H-bonds) at residues Arg292, Arg371, Asp151, Trp178, Glu227, and Tyr406, while eight H-bonds were formed by OTV with amino acids Arg118, Arg292, Arg371, Glu119, Asp151, and Arg152. Molecular dynamics (MD) simulation was conducted to compare the stability between ligand A18 and OTV with NA. Our simulation study showed that the A18-NA complex is as stable as the OTV-NA complex during the MD simulation of 50 ns through the analysis of RMSD, RMSF, total energy, hydrogen bonding, and MM/PBSA free energy calculations.

Prediction of luciferase inhibitors by the high-performance MIEC-GBDT approach based on interaction energetic patterns.

PubMed

Chen, Fu; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

2017-04-12

High-throughput screening (HTS) is widely applied in many fields ranging from drug discovery to clinical diagnostics and toxicity assessment. Firefly luciferase is commonly used as a reporter to monitor the effect of chemical compounds on the activity of a specific target or pathway in HTS. However, the false positive rate of luciferase-based HTS is relatively high because many artifacts or promiscuous compounds that have direct interaction with the luciferase reporter enzyme are usually identified as active compounds (hits). Therefore, it is necessary to develop a rapid screening method to identify these compounds that can inhibit the luciferase activity directly. In this study, a virtual screening (VS) classification model called MIEC-GBDT (MIEC: Molecular Interaction Energy Components; GBDT: Gradient Boosting Decision Tree) was developed to distinguish luciferase inhibitors from non-inhibitors. The MIECs calculated by Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy decomposition were used to energetically characterize the binding pattern of each small molecule at the active site of luciferase, and then the GBDT algorithm was employed to construct the classifiers based on MIECs. The predictions to the test set show that the optimized MIEC-GBDT model outperformed molecular docking and MM/GBSA rescoring. The best MIEC-GBDT model based on the MIECs with the energy terms of ΔG ele , ΔG vdW , ΔG GB , and ΔG SA achieves the prediction accuracies of 87.2% and 90.3% for the inhibitors and non-inhibitors in the test sets, respectively. Moreover, the energetic analysis of the vital residues suggests that the energetic contributions of the vital residues to the binding of inhibitors are quite different from those to the binding of non-inhibitors. These results suggest that the MIEC-GBDT model is reliable and can be used as a powerful tool to identify potential interference compounds in luciferase-based HTS experiments.

2D-QSAR study, molecular docking, and molecular dynamics simulation studies of interaction mechanism between inhibitors and transforming growth factor-beta receptor I (ALK5).

PubMed

Jiang, Meng-Nan; Zhou, Xiao-Ping; Sun, Dong-Ru; Gao, Huan; Zheng, Qing-Chuan; Zhang, Hong-Xing; Liang, Di

2017-11-06

Transforming growth factor type 1 receptor (ALK5) is kinase associated with a wide variety of pathological processes, and inhibition of ALK5 is a good strategy to treat many kinds of cancer and fibrotic diseases. Recently, a series of compounds have been synthesized as ALK5 inhibitors. However, the study of their selectivity against other potential targets remains elusive. In this research, a data-set of ALK5 inhibitors were collected and studied based on the combination of 2D-QSAR, molecular docking and molecular dynamics simulation. The quality of QSAR models were assessed statistically by F, R 2 , and R 2 ADJ , proved to be credible. The cross-validations for the models (q 2 LOO  = 0.571 and 0.629, respectively) showed their robustness, while the external validations (r 2 test  = 0.703 and 0.764, respectively) showed their predictive power. Besides, the predicted binding free energy results calculated by MM/GBSA method were in accordance with the experimental data, and the van der Waals energy term was the factor that had the most significant impact on ligand binding. What is more, several important residues were found to significantly affect the binding affinity. Finally, based on our analyses above, a proposed series of molecules were designed.

Structural study of the Fox-1 RRM protein hydration reveals a role for key water molecules in RRM-RNA recognition

PubMed Central

Blatter, Markus; Cléry, Antoine; Damberger, Fred F.

2017-01-01

Abstract The Fox-1 RNA recognition motif (RRM) domain is an important member of the RRM protein family. We report a 1.8 Å X-ray structure of the free Fox-1 containing six distinct monomers. We use this and the nuclear magnetic resonance (NMR) structure of the Fox-1 protein/RNA complex for molecular dynamics (MD) analyses of the structured hydration. The individual monomers of the X-ray structure show diverse hydration patterns, however, MD excellently reproduces the most occupied hydration sites. Simulations of the protein/RNA complex show hydration consistent with the isolated protein complemented by hydration sites specific to the protein/RNA interface. MD predicts intricate hydration sites with water-binding times extending up to hundreds of nanoseconds. We characterize two of them using NMR spectroscopy, RNA binding with switchSENSE and free-energy calculations of mutant proteins. Both hydration sites are experimentally confirmed and their abolishment reduces the binding free-energy. A quantitative agreement between theory and experiment is achieved for the S155A substitution but not for the S122A mutant. The S155 hydration site is evolutionarily conserved within the RRM domains. In conclusion, MD is an effective tool for predicting and interpreting the hydration patterns of protein/RNA complexes. Hydration is not easily detectable in NMR experiments but can affect stability of protein/RNA complexes. PMID:28505313

Renormalized coupled cluster approaches in the cluster-in-molecule framework: predicting vertical electron binding energies of the anionic water clusters (H2O)(n)(-).

PubMed

Xu, Peng; Gordon, Mark S

2014-09-04

Anionic water clusters are generally considered to be extremely challenging to model using fragmentation approaches due to the diffuse nature of the excess electron distribution. The local correlation coupled cluster (CC) framework cluster-in-molecule (CIM) approach combined with the completely renormalized CR-CC(2,3) method [abbreviated CIM/CR-CC(2,3)] is shown to be a viable alternative for computing the vertical electron binding energies (VEBE). CIM/CR-CC(2,3) with the threshold parameter ζ set to 0.001, as a trade-off between accuracy and computational cost, demonstrates the reliability of predicting the VEBE, with an average percentage error of ∼15% compared to the full ab initio calculation at the same level of theory. The errors are predominantly from the electron correlation energy. The CIM/CR-CC(2,3) approach provides the ease of a black-box type calculation with few threshold parameters to manipulate. The cluster sizes that can be studied by high-level ab initio methods are significantly increased in comparison with full CC calculations. Therefore, the VEBE computed by the CIM/CR-CC(2,3) method can be used as benchmarks for testing model potential approaches in small-to-intermediate-sized water clusters.

Allosteric modulation model of the mu opioid receptor by herkinorin, a potent not alkaloidal agonist

NASA Astrophysics Data System (ADS)

Marmolejo-Valencia, A. F.; Martínez-Mayorga, K.

2017-05-01

Modulation of opioid receptors is the primary choice for pain management and structural information studies have gained new horizons with the recently available X-ray crystal structures. Herkinorin is one of the most remarkable salvinorin A derivative with high affinity for the mu opioid receptor, moderate selectivity and lack of nitrogen atoms on its structure. Surprisingly, binding models for herkinorin are lacking. In this work, we explore binding models of herkinorin using automated docking, molecular dynamics simulations, free energy calculations and available experimental information. Our herkinorin D-ICM-1 binding model predicted a binding free energy of -11.52 ± 1.14 kcal mol-1 by alchemical free energy estimations, which is close to the experimental values -10.91 ± 0.2 and -10.80 ± 0.05 kcal mol-1 and is in agreement with experimental structural information. Specifically, D-ICM-1 molecular dynamics simulations showed a water-mediated interaction between D-ICM-1 and the amino acid H2976.52, this interaction coincides with the co-crystallized ligands. Another relevant interaction, with N1272.63, allowed to rationalize herkinorin's selectivity to mu over delta opioid receptors. Our suggested binding model for herkinorin is in agreement with this and additional experimental data. The most remarkable observation derived from our D-ICM-1 model is that herkinorin reaches an allosteric sodium ion binding site near N1503.35. Key interactions in that region appear relevant for the lack of β-arrestin recruitment by herkinorin. This interaction is key for downstream signaling pathways involved in the development of side effects, such as tolerance. Future SAR studies and medicinal chemistry efforts will benefit from the structural information presented in this work.

Theoretical studies on beta and delta isoform-specific binding mechanisms of phosphoinositide 3-kinase inhibitors.

PubMed

Zhu, Jingyu; Pan, Peichen; Li, Youyong; Wang, Man; Li, Dan; Cao, Biyin; Mao, Xinliang; Hou, Tingjun

2014-03-04

Phosphoinositide 3-kinase (PI3K) is known to be closely related to tumorigenesis and cell proliferation, and controls a variety of cellular processes, including proliferation, growth, apoptosis, migration, metabolism, etc. The PI3K family comprises eight catalytic isoforms, which are subdivided into three classes. Recently, the discovery of inhibitors that block a single isoform of PI3K has continued to attract special attention because they may have higher selectivity for certain tumors and less toxicity for healthy cells. The PI3Kβ and PI3Kδ share fewer studies than α/γ, and therefore, in this work, the combination of molecular dynamics simulations and free energy calculations was employed to explore the binding of three isoform-specific PI3K inhibitors (COM8, IC87114, and GDC-0941) to PI3Kβ or PI3Kδ. The isoform specificities of the studied inhibitors derived from the predicted binding free energies are in good agreement with the experimental data. In addition, the key residues critical for PI3Kβ or PI3Kδ selectivity were highlighted by decomposing the binding free energies into the contributions from individual residues. It was observed that although PI3Kβ and PI3Kδ share the conserved ATP-binding pockets, individual residues do behave differently, particularly the residues critical for PI3Kβ or PI3Kδ selectivity. It can be concluded that the inhibitor specificity between PI3Kβ and PI3Kδ is determined by the additive contributions from multiple residues, not just a single one. This study provides valuable information for understanding the isoform-specific binding mechanisms of PI3K inhibitors, and should be useful for the rational design of novel and selective PI3K inhibitors.

Unraveling the Molecular Mechanism of Benzothiophene and Benzofuran scaffold merged compounds binding to anti-apoptotic Myeloid cell leukemia 1.

PubMed

Marimuthu, Parthiban; Singaravelu, Kalaimathy

2018-05-10

Myeloid cell leukemia 1 (Mcl1), is an anti-apoptotic member of the Bcl-2 family proteins, has gained considerable importance due to its overexpression activity prevents the oncogenic cells to undergo apoptosis. This overexpression activity of Mcl1 eventually develops strong resistance to a wide variety of anticancer agents. Therefore, designing novel inhibitors with potentials to elicit higher binding affinity and specificity to inhibit Mcl1 activity is of greater importance. Thus, Mcl1 acts as an attractive cancer target. Despite recent experimental advancement in the identification and characterization of Benzothiophene and Benzofuran scaffold merged compounds the molecular mechanisms of their binding to Mcl1 are yet to be explored. The current study demonstrates an integrated approach -pharmacophore-based 3D-QSAR, docking, Molecular Dynamics (MD) simulation and free-energy estimation- to access the precise and comprehensive effects of current inhibitors targeting Mcl1 together with its known activity values. The pharmacophore -ANRRR.240- based 3D-QSAR model from the current study provided high confidence (R 2 =0.9154, Q 2 =0.8736, and RMSE=0.3533) values. Furthermore, the docking correctly predicted the binding mode of highly active compound 42. Additionally, the MD simulation for docked complex under explicit-solvent conditions together with free-energy estimation exhibited stable interaction and binding strength over the time period. Also, the decomposition analysis revealed potential energy contributing residues -M231, M250, V253, R265, L267, and F270- to the complex stability. Overall, the current investigation might serve as a valuable insight, either to (i) improve the binding affinity of the current compounds or (ii) discover new generation anti-cancer agents that can effectively downregulate Mcl1 activity.

Role of water molecules in structure and energetics of Pseudomonas aeruginosa lectin I interacting with disaccharides.

PubMed

Nurisso, Alessandra; Blanchard, Bertrand; Audfray, Aymeric; Rydner, Lina; Oscarson, Stefan; Varrot, Annabelle; Imberty, Anne

2010-06-25

Calcium-dependent lectin I from Pseudomonas aeruginosa (PA-IL) binds specifically to oligosaccharides presenting an alpha-galactose residue at their nonreducing end, such as the disaccharides alphaGal1-2betaGalOMe, alphaGal1-3betaGalOMe, and alphaGal1-4betaGalOMe. This provides a unique model for studying the effect of the glycosidic linkage of the ligands on structure and thermodynamics of the complexes by means of experimental and theoretical tools. The structural features of PA-IL in complex with the three disaccharides were established by docking and molecular dynamics simulations and compared with those observed in available crystal structures, including PA-IL.alphaGal1-2betaGalOMe complex, which was solved at 2.4 A resolution and reported herein. The role of a structural bridge water molecule in the binding site of PA-IL was also elucidated through molecular dynamics simulations and free energy calculations. This water molecule establishes three very stable hydrogen bonds with O6 of nonreducing galactose, oxygen from Pro-51 main chain, and nitrogen from Gln-53 main chain of the lectin binding site. Binding free energies for PA-IL in complex with the three disaccharides were investigated, and the results were compared with the experimental data determined by titration microcalorimetry. When the bridge water molecule was included in the free energy calculations, the simulations predicted the correct binding affinity trends with the 1-2-linked disaccharide presenting three times stronger affinity ligand than the other two. These results highlight the role of the water molecule in the binding site of PA-IL and indicate that it should be taken into account when designing glycoderivatives active against P. aeruginosa adhesion.

Isoelectronic bound-exciton photoluminescence in strained beryllium-doped Si0.92Ge0.08 epilayers and Si0.92Ge0.08/Si superlattices at ambient and elevated hydrostatic pressure

NASA Astrophysics Data System (ADS)

Kim, Sangsig; Chang, Ganlin; Herman, Irving P.; Bevk, Joze; Moore, Karen L.; Hall, Dennis G.

1997-03-01

Photoluminescence (PL) from a beryllium-doped Si0.92Ge0.08 epilayer and three different beryllium-doped Si0.92Ge0.08/Si superlattices (SL's) commensurately grown on Si(100) substrates is examined at 9 K at ambient pressure and, for the epilayer and one SL, as a function of hydrostatic pressure. In each structure, excitons bind to the isoelectronic Be pairs in the strained Si0.92Ge0.08 layers. The zero-phonon PL peaks of the epilayer and the in situ doped 50-Å Si0.92Ge0.08/100-Å Si SL shift linearly with pressure toward lower energy at the rate of 0.68+/-0.03 and 0.97+/-0.03 meV/kbar, respectively, which are near the 0.77-meV/kbar value for Si:Be. The PL energies at ambient and elevated pressure are analyzed by accounting for strain, quantum confinement, and exciton binding. A modified Hopfield-Thomas-Lynch model is used to model exciton binding to the Be pairs. This model, in which potential wells bind electrons to a site (that then trap holes), predicts a distribution of electron binding energies when an inhomogeneous distribution of potential-well depths is used. This accounts for the large PL linewidth and the decrease of linewidth with increasing pressure, among other observations. In SL's, the exciton binding energy is shown to depend on the width of the wells as well as the spatial distribution of Be dopants in the superlattice. Also, at and above 58 kbar a very unusual peak is observed in one of the SL's, which is associated with a free-exciton peak in Si, that shifts very fast with pressure (-6.02+/-0.03 meV/kbar).

Assessing the binding of cholinesterase inhibitors by docking and molecular dynamics studies.

PubMed

Ali, M Rejwan; Sadoqi, Mostafa; Møller, Simon G; Boutajangout, Allal; Mezei, Mihaly

2017-09-01

In this report we assessed by docking and molecular dynamics the binding mechanisms of three FDA-approved Alzheimer drugs, inhibitors of the enzyme acetylcholinesterase (AChE): donepezil, galantamine and rivastigmine. Dockings by the softwares Autodock-Vina, PatchDock and Plant reproduced the docked conformations of the inhibitor-enzyme complexes within 2Å of RMSD of the X-ray structure. Free-energy scores show strong affinity of the inhibitors for the enzyme binding pocket. Three independent Molecular Dynamics simulation runs indicated general stability of donepezil, galantamine and rivastigmine in their respective enzyme binding pocket (also referred to as gorge) as well as the tendency to form hydrogen bonds with the water molecules. The binding of rivastigmine in the Torpedo California AChE binding pocket is interesting as it eventually undergoes carbamylation and breaks apart according to the X-ray structure of the complex. Similarity search in the ZINC database and targeted docking on the gorge region of the AChE enzyme gave new putative inhibitor molecules with high predicted binding affinity, suitable for potential biophysical and biological assessments. Copyright © 2017 Elsevier Inc. All rights reserved.

First Principles Predictions of the Structure and Function of G-Protein-Coupled Receptors: Validation for Bovine Rhodopsin

PubMed Central

Trabanino, Rene J.; Hall, Spencer E.; Vaidehi, Nagarajan; Floriano, Wely B.; Kam, Victor W. T.; Goddard, William A.

2004-01-01

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)—one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 Å coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 Å CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 Å CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 Å CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins. PMID:15041637

Prediction of Carbohydrate Binding Sites on Protein Surfaces with 3-Dimensional Probability Density Distributions of Interacting Atoms

PubMed Central

Tsai, Keng-Chang; Jian, Jhih-Wei; Yang, Ei-Wen; Hsu, Po-Chiang; Peng, Hung-Pin; Chen, Ching-Tai; Chen, Jun-Bo; Chang, Jeng-Yih; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Non-covalent protein-carbohydrate interactions mediate molecular targeting in many biological processes. Prediction of non-covalent carbohydrate binding sites on protein surfaces not only provides insights into the functions of the query proteins; information on key carbohydrate-binding residues could suggest site-directed mutagenesis experiments, design therapeutics targeting carbohydrate-binding proteins, and provide guidance in engineering protein-carbohydrate interactions. In this work, we show that non-covalent carbohydrate binding sites on protein surfaces can be predicted with relatively high accuracy when the query protein structures are known. The prediction capabilities were based on a novel encoding scheme of the three-dimensional probability density maps describing the distributions of 36 non-covalent interacting atom types around protein surfaces. One machine learning model was trained for each of the 30 protein atom types. The machine learning algorithms predicted tentative carbohydrate binding sites on query proteins by recognizing the characteristic interacting atom distribution patterns specific for carbohydrate binding sites from known protein structures. The prediction results for all protein atom types were integrated into surface patches as tentative carbohydrate binding sites based on normalized prediction confidence level. The prediction capabilities of the predictors were benchmarked by a 10-fold cross validation on 497 non-redundant proteins with known carbohydrate binding sites. The predictors were further tested on an independent test set with 108 proteins. The residue-based Matthews correlation coefficient (MCC) for the independent test was 0.45, with prediction precision and sensitivity (or recall) of 0.45 and 0.49 respectively. In addition, 111 unbound carbohydrate-binding protein structures for which the structures were determined in the absence of the carbohydrate ligands were predicted with the trained predictors. The overall prediction MCC was 0.49. Independent tests on anti-carbohydrate antibodies showed that the carbohydrate antigen binding sites were predicted with comparable accuracy. These results demonstrate that the predictors are among the best in carbohydrate binding site predictions to date. PMID:22848404

Modeling Fission Product Sorption in Graphite Structures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Szlufarska, Izabela; Morgan, Dane; Allen, Todd

2013-04-08

The goal of this project is to determine changes in adsorption and desorption of fission products to/from nuclear-grade graphite in response to a changing chemical environment. First, the project team will employ principle calculations and thermodynamic analysis to predict stability of fission products on graphite in the presence of structural defects commonly observed in very high- temperature reactor (VHTR) graphites. Desorption rates will be determined as a function of partial pressure of oxygen and iodine, relative humidity, and temperature. They will then carry out experimental characterization to determine the statistical distribution of structural features. This structural information will yield distributionsmore » of binding sites to be used as an input for a sorption model. Sorption isotherms calculated under this project will contribute to understanding of the physical bases of the source terms that are used in higher-level codes that model fission product transport and retention in graphite. The project will include the following tasks: Perform structural characterization of the VHTR graphite to determine crystallographic phases, defect structures and their distribution, volume fraction of coke, and amount of sp2 versus sp3 bonding. This information will be used as guidance for ab initio modeling and as input for sorptivity models; Perform ab initio calculations of binding energies to determine stability of fission products on the different sorption sites present in nuclear graphite microstructures. The project will use density functional theory (DFT) methods to calculate binding energies in vacuum and in oxidizing environments. The team will also calculate stability of iodine complexes with fission products on graphite sorption sites; Model graphite sorption isotherms to quantify concentration of fission products in graphite. The binding energies will be combined with a Langmuir isotherm statistical model to predict the sorbed concentration of fission products on each type of graphite site. The model will include multiple simultaneous adsorbing species, which will allow for competitive adsorption effects between different fission product species and O and OH (for modeling accident conditions).« less

Strong binding and shrinkage of single and double nuclear systems (K−pp, K−ppn, K−K−p and K−K−pp) predicted by Faddeev-Yakubovsky calculations

PubMed Central

MAEDA, Shuji; AKAISHI, Yoshinori; YAMAZAKI, Toshimitsu

2013-01-01

Non-relativistic Faddeev and Faddeev-Yakubovsky calculations were made for K−pp, K−ppn, K−K−p and K−K−pp kaonic nuclear clusters, where the quasi bound states were treated as bound states by employing real separable potential models for the K−-K− and the K−-nucleon interactions as well as for the nucleon-nucleon interaction. The binding energies and spatial shrinkages of these states, obtained for various values of the interaction, were found to increase rapidly with the interaction strength. Their behaviors are shown in a reference diagram, where possible changes by varying the interaction in the dense nuclear medium are given. Using the Λ(1405) ansatz with a PDG mass of 1405 MeV/c2 for K−p, the following ground-state binding energies together with the wave functions were obtained: 51.5 MeV (K−pp), 69 MeV (K−ppn), 30.4 MeV (K−K−p) and 93 MeV (K−K−pp), which are in good agreement with previous results of variational calculation based on the Akaishi-Yamazaki coupled-channel potential. The K−K−pp state has a significantly increased density where the two nucleons are located very close to each other, in spite of the inner NN repulsion. Relativistic corrections on the calculated non-relativistic results indicate substantial lowering of the bound-state masses, especially of K−K−pp, toward the kaon condensation regime. The fact that the recently observed binding energy of K−pp is much larger (by a factor of 2) than the originally predicted one may infer an enhancement of the interaction in dense nuclei by about 25% possibly due to chiral symmetry restoration. In this respect some qualitative accounts are given based on “clearing QCD vacuum” model of Brown, Kubodera and Rho. PMID:24213206

Density-functional tight-binding investigation of the structure, stability and material properties of nickel hydroxide nanotubes

NASA Astrophysics Data System (ADS)

Jahangiri, Soran; Mosey, Nicholas J.

2018-01-01

Nickel hydroxide is a material composed of two-dimensional layers that can be rolled up to form cylindrical nanotubes belonging to a class of inorganic metal hydroxide nanotubes that are candidates for applications in catalysis, energy storage, and microelectronics. The stabilities and other properties of this class of inorganic nanotubes have not yet been investigated in detail. The present study uses self-consistent-charge density-functional tight-binding calculations to examine the stabilities, mechanical properties, and electronic properties of nickel hydroxide nanotubes along with the energetics associated with the adsorption of water by these systems. The tight-binding model was parametrized for this system based on the results of first-principles calculations. The stabilities of the nanotubes were examined by calculating strain energies and performing molecular dynamics simulations. The results indicate that single-walled nickel hydroxide nanotubes are stable at room temperature, which is consistent with experimental investigations. The nanotubes possess size-dependent mechanical properties that are similar in magnitude to those of other inorganic nanotubes. The electronic properties of the nanotubes were also found to be size-dependent and small nickel oxyhydroxide nanotubes are predicted to be semiconductors. Despite this size-dependence, both the mechanical and electronic properties were found to be almost independent of the helical structure of the nanotubes. The calculations also show that water molecules have higher adsorption energies when binding to the interior of the nickel hydroxide nanotubes when compared to adsorption in nanotubes formed from other two-dimensional materials such as graphene. The increased adsorption energy is due to the hydrophilic nature of nickel hydroxide. Due to the broad applications of nickel hydroxide, the nanotubes investigated here are also expected to be used in catalysis, electronics, and clean energy production.

Universal noise and Efimov physics

NASA Astrophysics Data System (ADS)

Nicholson, Amy N.

2016-03-01

Probability distributions for correlation functions of particles interacting via random-valued fields are discussed as a novel tool for determining the spectrum of a theory. In particular, this method is used to determine the energies of universal N-body clusters tied to Efimov trimers, for even N, by investigating the distribution of a correlation function of two particles at unitarity. Using numerical evidence that this distribution is log-normal, an analytical prediction for the N-dependence of the N-body binding energies is made.

Thermodynamic and Kinetic Properties of Intrinsic Defects and Mg Transmutants in 3C-SiC Determined by Density Functional Theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hu, Shenyang Y.; Setyawan, Wahyu; Van Ginhoven, Renee M.

2014-02-20

Density functional theory (DFT) is used to calculate the thermodynamic and kinetic properties of transmutant Mg in 3C-SiC due to high-energy neutron irradiation associated with the fusion nuclear environment. The formation and binding energies of intrinsic defects, Mg-related defects, and clusters in 3C-SiC are systematically calculated. The minimum energy paths and activation energies during point defect migration and small cluster evolution are studied using a generalized solid-state elastic band (G-SSNEB) method with DFT energy calculations. Stable defect structures and possible defect migration mechanisms are identified. The evolution of binding energies during Mg2Si formation demonstrates that the formation of Mg2Si needsmore » to overcome a critical nucleus size and nucleation barrier. It is also found that a compressive stress field exists around the Mg2Si nucleus. These data are important inputs in meso- and macro-scale modeling and experiments to understand and predict the impact of Mg on phase stability, microstructure evolution, and performance of SiC and SiC-based materials during long-term neutron exposures.« less

Structure-based design of ligands for protein basic domains: Application to the HIV-1 Tat protein

NASA Astrophysics Data System (ADS)

Filikov, Anton V.; James, Thomas L.

1998-05-01

A methodology has been developed for designing ligands to bind a flexible basic protein domain where the structure of the domain is essentially known. It is based on an empirical binding free energy function developed for highly charged complexes and on Monte Carlo simulations in internal coordinates with both the ligand and the receptor being flexible. HIV-1 encodes a transactivating regulatory protein called Tat. Binding of the basic domain of Tat to TAR RNA is required for efficient transcription of the viral genome. The structure of a biologically active peptide containing the Tat basic RNA-binding domain is available from NMR studies. The goal of the current project is to design a ligand which will bind to that basic domain and potentially inhibit the TAR-Tat interaction. The basic domain contains six arginine and two lysine residues. Our strategy was to design a ligand for arginine first and then a superligand for the basic domain by joining arginine ligands with a linker. Several possible arginine ligands were obtained by searching the Available Chemicals Directory with DOCK 3.5 software. Phytic acid, which can potentially bind multiple arginines, was chosen as a building block for the superligand. Calorimetric binding studies of several compounds to methylguanidine and Arg-/Lys-containing peptides were performed. The data were used to develop an empirical binding free energy function for prediction of affinity of the ligands for the Tat basic domain. Modeling of the conformations of the complexes with both the superligand and the basic domain being flexible has been carried out via Biased Probability Monte Carlo (BPMC) simulations in internal coordinates (ICM 2.6 suite of programs). The simulations used parameters to ensure correct folding, i.e., consistent with the experimental NMR structure of a 25-residue Tat peptide, from a random starting conformation. Superligands for the basic domain were designed by joining together two molecules of phytic acid with peptidic and peptidomimetic linkers. The linkers were refined by varying the length and side chains of the linking residues, carrying out BPMC simulations, and evaluation of the binding free energy for the best energy conformation. The dissociation constant of the best ligand designed is estimated to be in the low- to mid-nanomolar range.

Protocols Utilizing Constant pH Molecular Dynamics to Compute pH-Dependent Binding Free Energies

PubMed Central

2015-01-01

In protein–ligand binding, the electrostatic environments of the two binding partners may vary significantly in bound and unbound states, which may lead to protonation changes upon binding. In cases where ligand binding results in a net uptake or release of protons, the free energy of binding is pH-dependent. Nevertheless, conventional free energy calculations and molecular docking protocols typically do not rigorously account for changes in protonation that may occur upon ligand binding. To address these shortcomings, we present a simple methodology based on Wyman’s binding polynomial formalism to account for the pH dependence of binding free energies and demonstrate its use on cucurbit[7]uril (CB[7]) host–guest systems. Using constant pH molecular dynamics and a reference binding free energy that is taken either from experiment or from thermodynamic integration computations, the pH-dependent binding free energy is determined. This computational protocol accurately captures the large pKa shifts observed experimentally upon CB[7]:guest association and reproduces experimental binding free energies at different levels of pH. We show that incorrect assignment of fixed protonation states in free energy computations can give errors of >2 kcal/mol in these host–guest systems. Use of the methods presented here avoids such errors, thus suggesting their utility in computing proton-linked binding free energies for protein–ligand complexes. PMID:25134690

Binding selectivity of dibenzo-18-crown-6 for alkali metal cations in aqueous solution: A density functional theory study using a continuum solvation model.

PubMed

Choi, Chang Min; Heo, Jiyoung; Kim, Nam Joon

2012-08-08

Dibenzo-18-crown-6 (DB18C6) exhibits the binding selectivity for alkali metal cations in solution phase. In this study, we investigate the main forces that determine the binding selectivity of DB18C6 for the metal cations in aqueous solution using the density functional theory (DFT) and the conductor-like polarizable continuum model (CPCM). The bond dissociation free energies (BDFE) of DB18C6 complexes with alkali metal cations (M+-DB18C6, M = Li, Na, K, Rb, and Cs) in aqueous solution are calculated at the B3LYP/6-311++G(d,p)//B3LYP/6-31 + G(d) level using the CPCM. It is found that the theoretical BDFE is the largest for K+-DB18C6 and decreases as the size of the metal cation gets larger or smaller than that of K+, which agrees well with previous experimental results. The solvation energy of M+-DB18C6 in aqueous solution plays a key role in determining the binding selectivity of DB18C6. In particular, the non-electrostatic dispersion interaction between the solute and solvent, which depends strongly on the complex structure, is largely responsible for the different solvation energies of M+-DB18C6. This study shows that the implicit solvation model like the CPCM works reasonably well in predicting the binding selectivity of DB18C6 in aqueous solution.

Molecular recognition of avirulence protein (avrxa5) by eukaryotic transcription factor xa5 of rice (Oryza sativa L.): insights from molecular dynamics simulations.

PubMed

Dehury, Budheswar; Maharana, Jitendra; Sahoo, Bikash Ranjan; Sahu, Jagajjit; Sen, Priyabrata; Modi, Mahendra Kumar; Barooah, Madhumita

2015-04-01

The avirulence gene avrxa5 of bacterial blight pathogen Xanthomonas oryzae pv. oryzae (Xoo) recognized by the resistant rice lines having corresponding resistance (xa5) gene in a gene-for-gene manner. We used a combinatorial approach involving protein-protein docking, molecular dynamics (MD) simulations and binding free energy calculations to gain novel insights into the gene-for-gene mechanism that governs the direct interaction of R-Avr protein. From the best three binding poses predicted by molecular docking, MD simulations were performed to explore the dynamic binding mechanism of xa5 and avrxa5. Molecular Mechanics/Poisson Boltzmann Surface Area (MM/PBSA) techniques were employed to calculate the binding free energy and to uncover the thriving force behind the molecular recognition of avrxa5 by eukaryotic transcription factor xa5. Binding free energy analysis revealed van der Waals term as the most constructive component that favors the xa5 and avrxa5 interaction. In addition, hydrogen bonds (H-bonds) and essential electrostatic interactions analysis highlighted amino acid residues Lys54/Asp870, Lys56/Ala868, Lys56/Ala866, Lys56/Glu871, Ile59/His862, Gly61/Phe858, His62/Arg841, His62/Leu856, Ser101/Ala872 and Ser105/Asp870 plays pivotal role for the energetically stability of the R-Avr complex. Insights gained from the present study are expected to unveil the molecular mechanisms that define the transcriptional activator mediated transcriptome modification in host plants. Copyright © 2015 Elsevier Inc. All rights reserved.

Murine Hyperglycemic Vasculopathy and Cardiomyopathy: Whole-Genome Gene Expression Analysis Predicts Cellular Targets and Regulatory Networks Influenced by Mannose Binding Lectin

PubMed Central

Zou, Chenhui; La Bonte, Laura R.; Pavlov, Vasile I.; Stahl, Gregory L.

2012-01-01

Hyperglycemia, in the absence of type 1 or 2 diabetes, is an independent risk factor for cardiovascular disease. We have previously demonstrated a central role for mannose binding lectin (MBL)-mediated cardiac dysfunction in acute hyperglycemic mice. In this study, we applied whole-genome microarray data analysis to investigate MBL’s role in systematic gene expression changes. The data predict possible intracellular events taking place in multiple cellular compartments such as enhanced insulin signaling pathway sensitivity, promoted mitochondrial respiratory function, improved cellular energy expenditure and protein quality control, improved cytoskeleton structure, and facilitated intracellular trafficking, all of which may contribute to the organismal health of MBL null mice against acute hyperglycemia. Our data show a tight association between gene expression profile and tissue function which might be a very useful tool in predicting cellular targets and regulatory networks connected with in vivo observations, providing clues for further mechanistic studies. PMID:22375142

Structure and Stability of Molecular Crystals with Many-Body Dispersion-Inclusive Density Functional Tight Binding.

PubMed

Mortazavi, Majid; Brandenburg, Jan Gerit; Maurer, Reinhard J; Tkatchenko, Alexandre

2018-01-18

Accurate prediction of structure and stability of molecular crystals is crucial in materials science and requires reliable modeling of long-range dispersion interactions. Semiempirical electronic structure methods are computationally more efficient than their ab initio counterparts, allowing structure sampling with significant speedups. We combine the Tkatchenko-Scheffler van der Waals method (TS) and the many-body dispersion method (MBD) with third-order density functional tight-binding (DFTB3) via a charge population-based method. We find an overall good performance for the X23 benchmark database of molecular crystals, despite an underestimation of crystal volume that can be traced to the DFTB parametrization. We achieve accurate lattice energy predictions with DFT+MBD energetics on top of vdW-inclusive DFTB3 structures, resulting in a speedup of up to 3000 times compared with a full DFT treatment. This suggests that vdW-inclusive DFTB3 can serve as a viable structural prescreening tool in crystal structure prediction.

Screening of photosynthetic pigments for herbicidal activity with a new computational molecular approach.

PubMed

Krishnaraj, R Navanietha; Chandran, Saravanan; Pal, Parimal; Berchmans, Sheela

2013-12-01

There is an immense interest among the researchers to identify new herbicides which are effective against the herbs without affecting the environment. In this work, photosynthetic pigments are used as the ligands to predict their herbicidal activity. The enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is a good target for the herbicides. Homology modeling of the target enzyme is done using Modeler 9.11 and the model is validated. Docking studies were performed with AutoDock Vina algorithm to predict the binding of the natural pigments such as β-carotene, chlorophyll a, chlorophyll b, phycoerythrin and phycocyanin to the target. β-carotene, phycoerythrin and phycocyanin have higher binding energies indicating the herbicidal activity of the pigments. This work reports a procedure to screen herbicides with computational molecular approach. These pigments will serve as potential bioherbicides in the future.

The limits of the nuclear landscape explored by the relativistic continuum Hartree-Bogoliubov theory

NASA Astrophysics Data System (ADS)

Xia, X. W.; Lim, Y.; Zhao, P. W.; Liang, H. Z.; Qu, X. Y.; Chen, Y.; Liu, H.; Zhang, L. F.; Zhang, S. Q.; Kim, Y.; Meng, J.

2018-05-01

The ground-state properties of nuclei with 8 ⩽ Z ⩽ 120 from the proton drip line to the neutron drip line have been investigated using the spherical relativistic continuum Hartree-Bogoliubov (RCHB) theory with the relativistic density functional PC-PK1. With the effects of the continuum included, there are totally 9035 nuclei predicted to be bound, which largely extends the existing nuclear landscapes predicted with other methods. The calculated binding energies, separation energies, neutron and proton Fermi surfaces, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, ground-state spins and parities are tabulated. The extension of the nuclear landscape obtained with RCHB is discussed in detail, in particular for the neutron-rich side, in comparison with the relativistic mean field calculations without pairing correlations and also other predicted landscapes. It is found that the coupling between the bound states and the continuum due to the pairing correlations plays an essential role in extending the nuclear landscape. The systematics of the separation energies, radii, densities, potentials and pairing energies of the RCHB calculations are also discussed. In addition, the α-decay energies and proton emitters based on the RCHB calculations are investigated.

On the interactions of nitriles and fluoro-substituted pyridines with silicon tetrafluoride: Computations and thin film IR spectroscopy

NASA Astrophysics Data System (ADS)

Hora, Nicholas J.; Wahl, Benjamin M.; Soares, Camilla; Lara, Skylee A.; Lanska, John R.; Phillips, James A.

2018-04-01

The nature of the interactions between silicon tetrafluoride and series of nitrogen bases, including nitriles (RCN, with R > CH3), pyridine, and various fluoro-substituted pyridines, has been investigated via quantum-chemical computations, low-temperature IR spectroscopy, and bulk reactivity experiments. Using (primarily) M06 with the 6-311+G(2df,2pd) basis set, we obtained equilibrium structures, binding energies, harmonic frequencies, and N-Si potentials in the gas-phase and in bulk dielectric media for an extensive series of 1:1 molecular complexes, including: C6H5CH2CN-SiF4, CH3CH2CN-SiF4, (CH3)3CCN-SiF4, C5H5N-SiF4, 4-FC5H4N-SiF4, 3,5-C5F2H3N-SiF4, 2,6-C5F2H3N-SiF4 and 3,4,5-C5F3H2N-SiF4. In addition, for the analogous 2:1 complexes of pyridine and 3,5-difluororpyridine, we obtained equilibrium structures, binding energies, and harmonic frequencies. The N-Si distances in the 1:1 nitrile complexes are fairly long, ranging from 2.84 Å to 2.88 Å, and the binding energies range from 4.0 to 4.2 kcal/mol (16.7-17.6 kJ/mol). Also, computations predict extremely anharmonic N-Si potentials, for which the inner portions of the curve are preferentially stabilized in dielectric media, which predict an enhancement of these interactions in condensed-phases. However, we see no evidence of bulk reactivity between C6H5CH2CN, CH3CH2CN, or (CH3)3CCN and SiF4, nor any significant interaction between (CH3)3CCN and SiF4 in low temperature IR spectra of solid, (CH3)3CCN/SiF4 thin films. Conversely, the interactions in four of the five 1:1, pyridine-SiF4 complexes are generally stronger; binding energies range from 5.7 to 9.6 kcal/mol (23.8-40.2 kJ/mol), and correspondingly the N-Si distances are relatively short (2.12-2.25 Å). The exception is 2,6-C5F2H3N-SiF4, for which the binding energy is only 3.6 kcal/mol (15.1 kJ/mol), and the N-Si distance is quite long (3.12 Å). In addition, both pyridine and 3,5-difluororpyridine were found to form stable reaction products with SiF4; but no analogous product was obtained with 2,6-difluororpyridine and SiF4, nor was any significant interaction indicated in low-temperature IR spectra of 2,6-difluororpyridine/SiF4 films. By contrast, low temperature spectra of pyridine/SiF4 and 3,5-difluororpyridine/SiF4 thin films are consistent with the presence of a distinct 2:1 reaction product. Moreover, the observed frequencies agree reasonably well with those predicted for the cis, octahedral coordination isomers of the 2:1 molecular complexes, in which the N-Si bonds are compressed slightly relative to those in the predicted gas-phase structures.

Guiding lead optimization with GPCR structure modeling and molecular dynamics.

PubMed

Heifetz, Alexander; James, Tim; Morao, Inaki; Bodkin, Michael J; Biggin, Philip C

2016-10-01

G-protein coupled receptor (GPCR) modeling approaches are widely used in the hit-to-lead and lead optimization stages of drug discovery. Modern protocols that involve molecular dynamics simulation can address key issues such as the free energy of binding (affinity), ligand-induced GPCR flexibility, ligand binding kinetics, conserved water positions and their role in ligand binding and the effects of mutations. The goals of these calculations are to predict the structures of the complexes between existing ligands and their receptors, to understand the key interactions and to utilize these insights in the design of new molecules with improved binding, selectivity or other pharmacological properties. In this review we present a brief survey of various computational approaches illustrated through a hierarchical GPCR modeling protocol and its prospective application in three industrial drug discovery projects. Copyright © 2016 Elsevier Ltd. All rights reserved.

Specificity determinants for the abscisic acid response element.

PubMed

Sarkar, Aditya Kumar; Lahiri, Ansuman

2013-01-01

Abscisic acid (ABA) response elements (ABREs) are a group of cis-acting DNA elements that have been identified from promoter analysis of many ABA-regulated genes in plants. We are interested in understanding the mechanism of binding specificity between ABREs and a class of bZIP transcription factors known as ABRE binding factors (ABFs). In this work, we have modeled the homodimeric structure of the bZIP domain of ABRE binding factor 1 from Arabidopsis thaliana (AtABF1) and studied its interaction with ACGT core motif-containing ABRE sequences. We have also examined the variation in the stability of the protein-DNA complex upon mutating ABRE sequences using the protein design algorithm FoldX. The high throughput free energy calculations successfully predicted the ability of ABF1 to bind to alternative core motifs like GCGT or AAGT and also rationalized the role of the flanking sequences in determining the specificity of the protein-DNA interaction.

Structural determinants of species-selective substrate recognition in human and Drosophila serotonin transporters revealed through computational docking studies

PubMed Central

Kaufmann, Kristian W.; Dawson, Eric S.; Henry, L. Keith; Field, Julie R.; Blakely, Randy D.; Meiler, Jens

2009-01-01

To identify potential determinants of substrate selectivity in serotonin (5-HT) transporters (SERT), models of human and Drosophila serotonin transporters (hSERT, dSERT) were built based on the leucine transporter (LeuTAa) structure reported by Yamashita et al. (Nature 2005;437:215–223), PBDID 2A65. Although the overall amino acid identity between SERTs and the LeuTAa is only 17%, it increases to above 50% in the first shell of the putative 5-HT binding site, allowing de novo computational docking of tryptamine derivatives in atomic detail. Comparison of hSERT and dSERT complexed with substrates pinpoints likely structural determinants for substrate binding. Forgoing the use of experimental transport and binding data of tryptamine derivatives for construction of these models enables us to cHitically assess and validate their predictive power: A single 5-HT binding mode was identified that retains the amine placement observed in the LeuTAa structure, matches site-directed mutagenesis and substituted cysteine accessibility method (SCAM) data, complies with support vector machine derived relations activity relations, and predicts computational binding energies for 5-HT analogs with a significant correlation coefficient (R = 0.72). This binding mode places 5-HT deep in the binding pocket of the SERT with the 5-position near residue hSERT A169/dSERT D164 in transmembrane helix 3, the indole nitrogen next to residue Y176/Y171, and the ethylamine tail under residues F335/F327 and S336/S328 within 4 Å of residue D98. Our studies identify a number of potential contacts whose contribution to substrate binding and transport was previously unsuspected. PMID:18704946

Binding pose and affinity prediction in the 2016 D3R Grand Challenge 2 using the Wilma-SIE method

NASA Astrophysics Data System (ADS)

Hogues, Hervé; Sulea, Traian; Gaudreault, Francis; Corbeil, Christopher R.; Purisima, Enrico O.

2018-01-01

The Farnesoid X receptor (FXR) exhibits significant backbone movement in response to the binding of various ligands and can be a challenge for pose prediction algorithms. As part of the D3R Grand Challenge 2, we tested Wilma-SIE, a rigid-protein docking method, on a set of 36 FXR ligands for which the crystal structures had originally been blinded. These ligands covered several classes of compounds. To overcome the rigid protein limitations of the method, we used an ensemble of publicly available structures for FXR from the PDB. The use of the ensemble allowed Wilma-SIE to predict poses with average and median RMSDs of 2.3 and 1.4 Å, respectively. It was quite clear, however, that had we used a single structure for the receptor the success rate would have been much lower. The most successful predictions were obtained on chemical classes for which one or more crystal structures of the receptor bound to a molecule of the same class was available. In the absence of a crystal structure for the class, observing a consensus binding mode for the ligands of the class using one or more receptor structures of other classes seemed to be indicative of a reasonable pose prediction. Affinity prediction proved to be more challenging with generally poor correlation with experimental IC50s (Kendall tau 0.3). Even when the 36 crystal structures were used the accuracy of the predicted affinities was not appreciably improved. A possible cause of difficulty is the internal energy strain arising from conformational differences in the receptor across complexes, which may need to be properly estimated and incorporated into the SIE scoring function.

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach.

PubMed

Kashyap, Manju; Jaiswal, Varun; Farooq, Umar

2017-09-01

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants. Copyright © 2017 Elsevier B.V. All rights reserved.

Ground state energies from converging and diverging power series expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lisowski, C.; Norris, S.; Pelphrey, R.

2016-10-15

It is often assumed that bound states of quantum mechanical systems are intrinsically non-perturbative in nature and therefore any power series expansion methods should be inapplicable to predict the energies for attractive potentials. However, if the spatial domain of the Schrödinger Hamiltonian for attractive one-dimensional potentials is confined to a finite length L, the usual Rayleigh–Schrödinger perturbation theory can converge rapidly and is perfectly accurate in the weak-binding region where the ground state’s spatial extension is comparable to L. Once the binding strength is so strong that the ground state’s extension is less than L, the power expansion becomes divergent,more » consistent with the expectation that bound states are non-perturbative. However, we propose a new truncated Borel-like summation technique that can recover the bound state energy from the diverging sum. We also show that perturbation theory becomes divergent in the vicinity of an avoided-level crossing. Here the same numerical summation technique can be applied to reproduce the energies from the diverging perturbative sums.« less

Discovery of HIV Type 1 Aspartic Protease Hit Compounds through Combined Computational Approaches.

PubMed

Xanthopoulos, Dimitrios; Kritsi, Eftichia; Supuran, Claudiu T; Papadopoulos, Manthos G; Leonis, Georgios; Zoumpoulakis, Panagiotis

2016-08-05

A combination of computational techniques and inhibition assay experiments was employed to identify hit compounds from commercial libraries with enhanced inhibitory potency against HIV type 1 aspartic protease (HIV PR). Extensive virtual screening with the aid of reliable pharmacophore models yielded five candidate protease inhibitors. Subsequent molecular dynamics and molecular mechanics Poisson-Boltzmann surface area free-energy calculations for the five ligand-HIV PR complexes suggested a high stability of the systems through hydrogen-bond interactions between the ligands and the protease's flaps (Ile50/50'), as well as interactions with residues of the active site (Asp25/25'/29/29'/30/30'). Binding-energy calculations for the three most promising compounds yielded values between -5 and -10 kcal mol(-1) and suggested that van der Waals interactions contribute most favorably to the total energy. The predicted binding-energy values were verified by in vitro inhibition assays, which showed promising results in the high nanomolar range. These results provide structural considerations that may guide further hit-to-lead optimization toward improved anti-HIV drugs. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

pH-selective mutagenesis of protein-protein interfaces: in silico design of therapeutic antibodies with prolonged half-life.

PubMed

Spassov, Velin Z; Yan, Lisa

2013-04-01

Understanding the effects of mutation on pH-dependent protein binding affinity is important in protein design, especially in the area of protein therapeutics. We propose a novel method for fast in silico mutagenesis of protein-protein complexes to calculate the effect of mutation as a function of pH. The free energy differences between the wild type and mutants are evaluated from a molecular mechanics model, combined with calculations of the equilibria of proton binding. The predicted pH-dependent energy profiles demonstrate excellent agreement with experimentally measured pH-dependency of the effect of mutations on the dissociation constants for the complex of turkey ovomucoid third domain (OMTKY3) and proteinase B. The virtual scanning mutagenesis identifies all hotspots responsible for pH-dependent binding of immunoglobulin G (IgG) to neonatal Fc receptor (FcRn) and the results support the current understanding of the salvage mechanism of the antibody by FcRn based on pH-selective binding. The method can be used to select mutations that change the pH-dependent binding profiles of proteins and guide the time consuming and expensive protein engineering experiments. As an application of this method, we propose a computational strategy to search for mutations that can alter the pH-dependent binding behavior of IgG to FcRn with the aim of improving the half-life of therapeutic antibodies in the target organism. Copyright © 2013 Wiley Periodicals, Inc.

Theory of nanobubble formation and induced force in nanochannels

NASA Astrophysics Data System (ADS)

Arai, Noriyoshi; Koishi, Takahiro; Ebisuzaki, Toshikazu

2017-10-01

This paper presents a fundamental theory of nanobubble formation and induced force in confined nanochannels. It is shown that nanobubble formation between hydrophobic plates can be predicted from their surface tension and geometry, with estimated values for the surface free energy and the force acting on the plates in good agreement with the results of molecular dynamics simulation and experimentation. When a bubble is formed between two plates, vertical attractive force and horizontal retract force due to the shifted plates are applied to the plates. The net force exerted on the plates is not dependent on the distance between them. The short-range force between hydrophobic surfaces due to hydrophobic interaction appears to correspond to the force estimated by our theory. We compared between experimental and theoretical values for the binding energy of a molecular motor system to validate our theory. The tendency that the binding energy increases as the size of the protein increases is consistent with the theory.

Conformational Transitions and Convergence of Absolute Binding Free Energy Calculations

PubMed Central

Lapelosa, Mauro; Gallicchio, Emilio; Levy, Ronald M.

2011-01-01

The Binding Energy Distribution Analysis Method (BEDAM) is employed to compute the standard binding free energies of a series of ligands to a FK506 binding protein (FKBP12) with implicit solvation. Binding free energy estimates are in reasonably good agreement with experimental affinities. The conformations of the complexes identified by the simulations are in good agreement with crystallographic data, which was not used to restrain ligand orientations. The BEDAM method is based on λ -hopping Hamiltonian parallel Replica Exchange (HREM) molecular dynamics conformational sampling, the OPLS-AA/AGBNP2 effective potential, and multi-state free energy estimators (MBAR). Achieving converged and accurate results depends on all of these elements of the calculation. Convergence of the binding free energy is tied to the level of convergence of binding energy distributions at critical intermediate states where bound and unbound states are at equilibrium, and where the rate of binding/unbinding conformational transitions is maximal. This finding mirrors similar observations in the context of order/disorder transitions as for example in protein folding. Insights concerning the physical mechanism of ligand binding and unbinding are obtained. Convergence for the largest FK506 ligand is achieved only after imposing strict conformational restraints, which however require accurate prior structural knowledge of the structure of the complex. The analytical AGBNP2 model is found to underestimate the magnitude of the hydrophobic driving force towards binding in these systems characterized by loosely packed protein-ligand binding interfaces. Rescoring of the binding energies using a numerical surface area model corrects this deficiency. This study illustrates the complex interplay between energy models, exploration of conformational space, and free energy estimators needed to obtain robust estimates from binding free energy calculations. PMID:22368530

BeAtMuSiC: Prediction of changes in protein-protein binding affinity on mutations.

PubMed

Dehouck, Yves; Kwasigroch, Jean Marc; Rooman, Marianne; Gilis, Dimitri

2013-07-01

The ability of proteins to establish highly selective interactions with a variety of (macro)molecular partners is a crucial prerequisite to the realization of their biological functions. The availability of computational tools to evaluate the impact of mutations on protein-protein binding can therefore be valuable in a wide range of industrial and biomedical applications, and help rationalize the consequences of non-synonymous single-nucleotide polymorphisms. BeAtMuSiC (http://babylone.ulb.ac.be/beatmusic) is a coarse-grained predictor of the changes in binding free energy induced by point mutations. It relies on a set of statistical potentials derived from known protein structures, and combines the effect of the mutation on the strength of the interactions at the interface, and on the overall stability of the complex. The BeAtMuSiC server requires as input the structure of the protein-protein complex, and gives the possibility to assess rapidly all possible mutations in a protein chain or at the interface, with predictive performances that are in line with the best current methodologies.

CaMELS: In silico prediction of calmodulin binding proteins and their binding sites.

PubMed

Abbasi, Wajid Arshad; Asif, Amina; Andleeb, Saiqa; Minhas, Fayyaz Ul Amir Afsar

2017-09-01

Due to Ca 2+ -dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet-lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet-lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large-margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM-binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome-wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif-based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub-sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels. © 2017 Wiley Periodicals, Inc.

StaRProtein, A Web Server for Prediction of the Stability of Repeat Proteins

PubMed Central

Xu, Yongtao; Zhou, Xu; Huang, Meilan

2015-01-01

Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins. PMID:25807112

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method.

PubMed

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

Prediction of allosteric sites on protein surfaces with an elastic-network-model-based thermodynamic method

NASA Astrophysics Data System (ADS)

Su, Ji Guo; Qi, Li Sheng; Li, Chun Hua; Zhu, Yan Ying; Du, Hui Jing; Hou, Yan Xue; Hao, Rui; Wang, Ji Hua

2014-08-01

Allostery is a rapid and efficient way in many biological processes to regulate protein functions, where binding of an effector at the allosteric site alters the activity and function at a distant active site. Allosteric regulation of protein biological functions provides a promising strategy for novel drug design. However, how to effectively identify the allosteric sites remains one of the major challenges for allosteric drug design. In the present work, a thermodynamic method based on the elastic network model was proposed to predict the allosteric sites on the protein surface. In our method, the thermodynamic coupling between the allosteric and active sites was considered, and then the allosteric sites were identified as those where the binding of an effector molecule induces a large change in the binding free energy of the protein with its ligand. Using the proposed method, two proteins, i.e., the 70 kD heat shock protein (Hsp70) and GluA2 alpha-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptor, were studied and the allosteric sites on the protein surface were successfully identified. The predicted results are consistent with the available experimental data, which indicates that our method is a simple yet effective approach for the identification of allosteric sites on proteins.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Hamdani, Yasmine S.; Alfè, Dario; von Lilienfeld, O. Anatole

Density functional theory (DFT) studies of weakly interacting complexes have recently focused on the importance of van der Waals dispersion forces, whereas the role of exchange has received far less attention. Here, by exploiting the subtle binding between water and a boron and nitrogen doped benzene derivative (1,2-azaborine) we show how exact exchange can alter the binding conformation within a complex. Benchmark values have been calculated for three orientations of the water monomer on 1,2-azaborine from explicitly correlated quantum chemical methods, and we have also used diffusion quantum Monte Carlo. For a host of popular DFT exchange-correlation functionals we showmore » that the lack of exact exchange leads to the wrong lowest energy orientation of water on 1,2-azaborine. As such, we suggest that a high proportion of exact exchange and the associated improvement in the electronic structure could be needed for the accurate prediction of physisorption sites on doped surfaces and in complex organic molecules. Meanwhile to predict correct absolute interaction energies an accurate description of exchange needs to be augmented by dispersion inclusive functionals, and certain non-local van der Waals functionals (optB88- and optB86b-vdW) perform very well for absolute interaction energies. Through a comparison with water on benzene and borazine (B₃N₃H₆) we show that these results could have implications for the interaction of water with doped graphene surfaces, and suggest a possible way of tuning the interaction energy.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Al-Hamdani, Yasmine S.; Michaelides, Angelos, E-mail: angelos.michaelides@ucl.ac.uk; Department of Chemistry, University College London, 20 Gordon Street, London WC1H 0AJ

Density functional theory (DFT) studies of weakly interacting complexes have recently focused on the importance of van der Waals dispersion forces, whereas the role of exchange has received far less attention. Here, by exploiting the subtle binding between water and a boron and nitrogen doped benzene derivative (1,2-azaborine) we show how exact exchange can alter the binding conformation within a complex. Benchmark values have been calculated for three orientations of the water monomer on 1,2-azaborine from explicitly correlated quantum chemical methods, and we have also used diffusion quantum Monte Carlo. For a host of popular DFT exchange-correlation functionals we showmore » that the lack of exact exchange leads to the wrong lowest energy orientation of water on 1,2-azaborine. As such, we suggest that a high proportion of exact exchange and the associated improvement in the electronic structure could be needed for the accurate prediction of physisorption sites on doped surfaces and in complex organic molecules. Meanwhile to predict correct absolute interaction energies an accurate description of exchange needs to be augmented by dispersion inclusive functionals, and certain non-local van der Waals functionals (optB88- and optB86b-vdW) perform very well for absolute interaction energies. Through a comparison with water on benzene and borazine (B{sub 3}N{sub 3}H{sub 6}) we show that these results could have implications for the interaction of water with doped graphene surfaces, and suggest a possible way of tuning the interaction energy.« less

Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins

PubMed Central

Gunner, MR; Baker, Nathan A.

2017-01-01

Proteins change their charge state through protonation and redox reactions as well as through binding charged ligands. The free energy of these reactions are dominated by solvation and electrostatic energies and modulated by protein conformational relaxation in response to the ionization state changes. Although computational methods for calculating these interactions can provide very powerful tools for predicting protein charge states, they include several critical approximations of which users should be aware. This chapter discusses the strengths, weaknesses, and approximations of popular computational methods for predicting charge states and understanding their underlying electrostatic interactions. The goal of this chapter is to inform users about applications and potential caveats of these methods as well as outline directions for future theoretical and computational research. PMID:27497160

Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations

NASA Astrophysics Data System (ADS)

Selwa, Edithe; Elisée, Eddy; Zavala, Agustin; Iorga, Bogdan I.

2018-01-01

Our participation to the D3R Grand Challenge 2 involved a protocol in two steps, with an initial analysis of the available structural data from the PDB allowing the selection of the most appropriate combination of docking software and scoring function. Subsequent docking calculations showed that the pose prediction can be carried out with a certain precision, but this is dependent on the specific nature of the ligands. The correct ranking of docking poses is still a problem and cannot be successful in the absence of good pose predictions. Our free energy calculations on two different subsets provided contrasted results, which might have the origin in non-optimal force field parameters associated with the sulfonamide chemical moiety.

DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2016-01-01

DNA-binding proteins are fundamentally important in cellular processes. Several computational-based methods have been developed to improve the prediction of DNA-binding proteins in previous years. However, insufficient work has been done on the prediction of DNA-binding proteins from protein sequence information. In this paper, a novel predictor, DNABP (DNA-binding proteins), was designed to predict DNA-binding proteins using the random forest (RF) classifier with a hybrid feature. The hybrid feature contains two types of novel sequence features, which reflect information about the conservation of physicochemical properties of the amino acids, and the binding propensity of DNA-binding residues and non-binding propensities of non-binding residues. The comparisons with each feature demonstrated that these two novel features contributed most to the improvement in predictive ability. Furthermore, to improve the prediction performance of the DNABP model, feature selection using the minimum redundancy maximum relevance (mRMR) method combined with incremental feature selection (IFS) was carried out during the model construction. The results showed that the DNABP model could achieve 86.90% accuracy, 83.76% sensitivity, 90.03% specificity and a Matthews correlation coefficient of 0.727. High prediction accuracy and performance comparisons with previous research suggested that DNABP could be a useful approach to identify DNA-binding proteins from sequence information. The DNABP web server system is freely available at http://www.cbi.seu.edu.cn/DNABP/.

Resting energy expenditure and body composition in bedridden institutionalized elderly women with advanced-stage pressure sores.

PubMed

Sergi, Giuseppe; Coin, Alessandra; Mulone, Silvana; Castegnaro, Eugenio; Giantin, Valter; Manzato, Enzo; Busetto, Luca; Inelmen, Emine Meral; Marin, Sara; Enzi, Giuliano

2007-03-01

Our study investigated nutritional status, body composition, and resting energy expenditure (REE) in elderly patients with advanced-stage pressure sores (PS), in addition to researching any hypermetabolic condition and its relationship with PS size. The study involved 52 institutionalized bedridden elderly women (aged 83.7 +/- 6.3 years), divided into two groups: 23 with advanced-stage (stage 3 and 4) PS and 29 without PS. Albumin, prealbumin, and retinol-binding protein were measured in all patients, and fat-free mass (FFM) and fat mass (FM) were obtained by dual-energy x-ray absorptiometry (DEXA). REE was measured by indirect calorimetry and predicted with the Harris-Benedict formula. PS area and volume were also measured. The elderly women with and without PS were comparable in age, FFM, and FM. Mean albumin, prealbumin, and retinol-binding protein values were lower in cases with PS. Unadjusted mean REE was significantly higher in patients with PS (1212.3 +/- 236.7 vs 1085.5 +/- 161.3 kcal/d; p <.05), even after adjusting for FFM or expressed per kilogram of body weight (25.8 +/- 6.7 vs 21.1 +/- 4.0 kcal/d/kg; p <.01). Hypermetabolism, i.e., a measured REE > 110% of the predicted REE, was seen in 74% of patients with PS and 38% of controls. The difference between measured and predicted REE (DeltaREE) correlated with PS volume (r = 0.58; p <.01), but not with area. Advanced-stage PS in elderly women are associated with a hypermetabolic state that is influenced by the volume of the PS.

T-Epitope Designer: A HLA-peptide binding prediction server.

PubMed

Kangueane, Pandjassarame; Sakharkar, Meena Kishore

2005-05-15

The current challenge in synthetic vaccine design is the development of a methodology to identify and test short antigen peptides as potential T-cell epitopes. Recently, we described a HLA-peptide binding model (using structural properties) capable of predicting peptides binding to any HLA allele. Consequently, we have developed a web server named T-EPITOPE DESIGNER to facilitate HLA-peptide binding prediction. The prediction server is based on a model that defines peptide binding pockets using information gleaned from X-ray crystal structures of HLA-peptide complexes, followed by the estimation of peptide binding to binding pockets. Thus, the prediction server enables the calculation of peptide binding to HLA alleles. This model is superior to many existing methods because of its potential application to any given HLA allele whose sequence is clearly defined. The web server finds potential application in T cell epitope vaccine design. http://www.bioinformation.net/ted/

Machine learning in computational docking.

PubMed

Khamis, Mohamed A; Gomaa, Walid; Ahmed, Walaa F

2015-03-01

The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). The methods presented in this paper are specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising. The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind). In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function on PDBbind v2007 achieves a Pearson correlation coefficient between the predicted and experimentally determined binding affinities of 0.803 while the best conventional scoring function achieves 0.644. The best RF-based ranking power ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%. We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models. Copyright © 2015 Elsevier B.V. All rights reserved.

Prediction of core level binding energies in density functional theory: Rigorous definition of initial and final state contributions and implications on the physical meaning of Kohn-Sham energies.

PubMed

Pueyo Bellafont, Noèlia; Bagus, Paul S; Illas, Francesc

2015-06-07

A systematic study of the N(1s) core level binding energies (BE's) in a broad series of molecules is presented employing Hartree-Fock (HF) and the B3LYP, PBE0, and LC-BPBE density functional theory (DFT) based methods with a near HF basis set. The results show that all these methods give reasonably accurate BE's with B3LYP being slightly better than HF but with both PBE0 and LCBPBE being poorer than HF. A rigorous and general decomposition of core level binding energy values into initial and final state contributions to the BE's is proposed that can be used within either HF or DFT methods. The results show that Koopmans' theorem does not hold for the Kohn-Sham eigenvalues. Consequently, Kohn-Sham orbital energies of core orbitals do not provide estimates of the initial state contribution to core level BE's; hence, they cannot be used to decompose initial and final state contributions to BE's. However, when the initial state contribution to DFT BE's is properly defined, the decompositions of initial and final state contributions given by DFT, with several different functionals, are very similar to those obtained with HF. Furthermore, it is shown that the differences of Kohn-Sham orbital energies taken with respect to a common reference do follow the trend of the properly calculated initial state contributions. These conclusions are especially important for condensed phase systems where our results validate the use of band structure calculations to determine initial state contributions to BE shifts.

Computational characterization of how the VX nerve agent binds human serum paraoxonase 1.

PubMed

Fairchild, Steven Z; Peterson, Matthew W; Hamza, Adel; Zhan, Chang-Guo; Cerasoli, Douglas M; Chang, Wenling E

2011-01-01

Human serum paraoxonase 1 (HuPON1) is an enzyme that can hydrolyze various chemical warfare nerve agents including VX. A previous study has suggested that increasing HuPON1's VX hydrolysis activity one to two orders of magnitude would make the enzyme an effective countermeasure for in vivo use against VX. This study helps facilitate further engineering of HuPON1 for enhanced VX-hydrolase activity by computationally characterizing HuPON1's tertiary structure and how HuPON1 binds VX. HuPON1's structure is first predicted through two homology modeling procedures. Docking is then performed using four separate methods, and the stability of each bound conformation is analyzed through molecular dynamics and solvated interaction energy calculations. The results show that VX's lone oxygen atom has a strong preference for forming a direct electrostatic interaction with HuPON1's active site calcium ion. Various HuPON1 residues are also detected that are in close proximity to VX and are therefore potential targets for future mutagenesis studies. These include E53, H115, N168, F222, N224, L240, D269, I291, F292, and V346. Additionally, D183 was found to have a predicted pKa near physiological pH. Given D183's location in HuPON1's active site, this residue could potentially act as a proton donor or accepter during hydrolysis. The results from the binding simulations also indicate that steered molecular dynamics can potentially be used to obtain accurate binding predictions even when starting with a closed conformation of a protein's binding or active site.

Performance of HADDOCK and a simple contact-based protein-ligand binding affinity predictor in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Kurkcuoglu, Zeynep; Koukos, Panagiotis I.; Citro, Nevia; Trellet, Mikael E.; Rodrigues, J. P. G. L. M.; Moreira, Irina S.; Roel-Touris, Jorge; Melquiond, Adrien S. J.; Geng, Cunliang; Schaarschmidt, Jörg; Xue, Li C.; Vangone, Anna; Bonvin, A. M. J. J.

2018-01-01

We present the performance of HADDOCK, our information-driven docking software, in the second edition of the D3R Grand Challenge. In this blind experiment, participants were requested to predict the structures and binding affinities of complexes between the Farnesoid X nuclear receptor and 102 different ligands. The models obtained in Stage1 with HADDOCK and ligand-specific protocol show an average ligand RMSD of 5.1 Å from the crystal structure. Only 6/35 targets were within 2.5 Å RMSD from the reference, which prompted us to investigate the limiting factors and revise our protocol for Stage2. The choice of the receptor conformation appeared to have the strongest influence on the results. Our Stage2 models were of higher quality (13 out of 35 were within 2.5 Å), with an average RMSD of 4.1 Å. The docking protocol was applied to all 102 ligands to generate poses for binding affinity prediction. We developed a modified version of our contact-based binding affinity predictor PRODIGY, using the number of interatomic contacts classified by their type and the intermolecular electrostatic energy. This simple structure-based binding affinity predictor shows a Kendall's Tau correlation of 0.37 in ranking the ligands (7th best out of 77 methods, 5th/25 groups). Those results were obtained from the average prediction over the top10 poses, irrespective of their similarity/correctness, underscoring the robustness of our simple predictor. This results in an enrichment factor of 2.5 compared to a random predictor for ranking ligands within the top 25%, making it a promising approach to identify lead compounds in virtual screening.

Quantum chemical approaches in structure-based virtual screening and lead optimization

NASA Astrophysics Data System (ADS)

Cavasotto, Claudio N.; Adler, Natalia S.; Aucar, Maria G.

2018-05-01

Today computational chemistry is a consolidated tool in drug lead discovery endeavors. Due to methodological developments and to the enormous advance in computer hardware, methods based on quantum mechanics (QM) have gained great attention in the last 10 years, and calculations on biomacromolecules are becoming increasingly explored, aiming to provide better accuracy in the description of protein-ligand interactions and the prediction of binding affinities. In principle, the QM formulation includes all contributions to the energy, accounting for terms usually missing in molecular mechanics force-fields, such as electronic polarization effects, metal coordination, and covalent binding; moreover, QM methods are systematically improvable, and provide a greater degree of transferability. In this mini-review we present recent applications of explicit QM-based methods in small-molecule docking and scoring, and in the calculation of binding free-energy in protein-ligand systems. Although the routine use of QM-based approaches in an industrial drug lead discovery setting remains a formidable challenging task, it is likely they will increasingly become active players within the drug discovery pipeline.

A coarse-grained biophysical model of sequence evolution and the population size dependence of the speciation rate

PubMed Central

Khatri, Bhavin S.; Goldstein, Richard A.

2015-01-01

Speciation is fundamental to understanding the huge diversity of life on Earth. Although still controversial, empirical evidence suggests that the rate of speciation is larger for smaller populations. Here, we explore a biophysical model of speciation by developing a simple coarse-grained theory of transcription factor-DNA binding and how their co-evolution in two geographically isolated lineages leads to incompatibilities. To develop a tractable analytical theory, we derive a Smoluchowski equation for the dynamics of binding energy evolution that accounts for the fact that natural selection acts on phenotypes, but variation arises from mutations in sequences; the Smoluchowski equation includes selection due to both gradients in fitness and gradients in sequence entropy, which is the logarithm of the number of sequences that correspond to a particular binding energy. This simple consideration predicts that smaller populations develop incompatibilities more quickly in the weak mutation regime; this trend arises as sequence entropy poises smaller populations closer to incompatible regions of phenotype space. These results suggest a generic coarse-grained approach to evolutionary stochastic dynamics, allowing realistic modelling at the phenotypic level. PMID:25936759

Energetics of codon-anticodon recognition on the small ribosomal subunit.

PubMed

Almlöf, Martin; Andér, Martin; Aqvist, Johan

2007-01-09

Recent crystal structures of the small ribosomal subunit have made it possible to examine the detailed energetics of codon recognition on the ribosome by computational methods. The binding of cognate and near-cognate anticodon stem loops to the ribosome decoding center, with mRNA containing the Phe UUU and UUC codons, are analyzed here using explicit solvent molecular dynamics simulations together with the linear interaction energy (LIE) method. The calculated binding free energies are in excellent agreement with experimental binding constants and reproduce the relative effects of mismatches in the first and second codon position versus a mismatch at the wobble position. The simulations further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with the Phe UUU codon. It is also found that the ribosome significantly enhances the intrinsic stability differences of codon-anticodon complexes in aqueous solution. Structural analysis of the simulations confirms the previously suggested importance of the universally conserved nucleotides A1492, A1493, and G530 in the decoding process.

Statistical Analysis on the Performance of Molecular Mechanics Poisson-Boltzmann Surface Area versus Absolute Binding Free Energy Calculations: Bromodomains as a Case Study.

PubMed

Aldeghi, Matteo; Bodkin, Michael J; Knapp, Stefan; Biggin, Philip C

2017-09-25

Binding free energy calculations that make use of alchemical pathways are becoming increasingly feasible thanks to advances in hardware and algorithms. Although relative binding free energy (RBFE) calculations are starting to find widespread use, absolute binding free energy (ABFE) calculations are still being explored mainly in academic settings due to the high computational requirements and still uncertain predictive value. However, in some drug design scenarios, RBFE calculations are not applicable and ABFE calculations could provide an alternative. Computationally cheaper end-point calculations in implicit solvent, such as molecular mechanics Poisson-Boltzmann surface area (MMPBSA) calculations, could too be used if one is primarily interested in a relative ranking of affinities. Here, we compare MMPBSA calculations to previously performed absolute alchemical free energy calculations in their ability to correlate with experimental binding free energies for three sets of bromodomain-inhibitor pairs. Different MMPBSA approaches have been considered, including a standard single-trajectory protocol, a protocol that includes a binding entropy estimate, and protocols that take into account the ligand hydration shell. Despite the improvements observed with the latter two MMPBSA approaches, ABFE calculations were found to be overall superior in obtaining correlation with experimental affinities for the test cases considered. A difference in weighted average Pearson ([Formula: see text]) and Spearman ([Formula: see text]) correlations of 0.25 and 0.31 was observed when using a standard single-trajectory MMPBSA setup ([Formula: see text] = 0.64 and [Formula: see text] = 0.66 for ABFE; [Formula: see text] = 0.39 and [Formula: see text] = 0.35 for MMPBSA). The best performing MMPBSA protocols returned weighted average Pearson and Spearman correlations that were about 0.1 inferior to ABFE calculations: [Formula: see text] = 0.55 and [Formula: see text] = 0.56 when including an entropy estimate, and [Formula: see text] = 0.53 and [Formula: see text] = 0.55 when including explicit water molecules. Overall, the study suggests that ABFE calculations are indeed the more accurate approach, yet there is also value in MMPBSA calculations considering the lower compute requirements, and if agreement to experimental affinities in absolute terms is not of interest. Moreover, for the specific protein-ligand systems considered in this study, we find that including an explicit ligand hydration shell or a binding entropy estimate in the MMPBSA calculations resulted in significant performance improvements at a negligible computational cost.

A tungsten-rhenium interatomic potential for point defect studies

DOE PAGES

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-28

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

A tungsten-rhenium interatomic potential for point defect studies

NASA Astrophysics Data System (ADS)

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

2018-05-01

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures in the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancies and self-interstitial defects sufficiently accurately and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).

A tungsten-rhenium interatomic potential for point defect studies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Setyawan, Wahyu; Gao, Ning; Kurtz, Richard J.

A tungsten-rhenium (W-Re) classical interatomic potential is developed within the embedded atom method (EAM) interaction framework. A force-matching method is employed to fit the potential to ab initio forces, energies, and stresses. Simulated annealing is combined with the conjugate gradient technique to search for an optimum potential from over 1000 initial trial sets. The potential is designed for studying point defects in W-Re systems. It gives good predictions of the formation energies of Re defects in W and the binding energies of W self-interstitial clusters with Re. The potential is further evaluated for describing the formation energy of structures inmore » the σ and χ intermetallic phases. The predicted convex-hulls of formation energy are in excellent agreement with ab initio data. In pure Re, the potential can reproduce the formation energies of vacancy and self-interstitial defects sufficiently accurately, and gives the correct ground state self-interstitial configuration. Furthermore, by including liquid structures in the fit, the potential yields a Re melting temperature (3130 K) that is close to the experimental value (3459 K).« less

DFT-based ranking of zinc-binding groups in histone deacetylase inhibitors.

PubMed

Vanommeslaeghe, K; Loverix, S; Geerlings, P; Tourwé, D

2005-11-01

Histone deacetylases (HDACs) have recently attracted considerable interest as targets in the treatment of cell proliferative diseases such as cancer. In the present work, a general framework is proposed for chemical groups that bind into the HDAC catalytic core. Based on this framework, a series of groups was selected for further investigation. A method was developed to rank the HDAC inhibitory potential of these moieties at the B3LYP/6-31G* level, making use of extra diffuse functions and of the PCM solvation model where appropriate. The resulting binding geometries indicate that very stringent constraints should be satisfied in order to have bidental zinc chelation, and even more so to have a strong binding affinity, which makes it difficult to predict the binding mode and affinity of such zinc-binding groups. The chemical hardness and the pK(a) were identified as important criteria for the binding affinity. Also, the hydrophilicity may have a direct influence on the binding affinity. The calculated binding energies were qualitatively validated with experimental results from the literature, and were shown to be meaningful for the purpose of ranking. Additionally, the insights gained from the present work may be useful for increasing the accuracy of QSAR models by providing a rational basis for selecting descriptors.

Study of interactions between metal ions and protein model compounds by energy decomposition analyses and the AMOEBA force field

NASA Astrophysics Data System (ADS)

Jing, Zhifeng; Qi, Rui; Liu, Chengwen; Ren, Pengyu

2017-10-01

The interactions between metal ions and proteins are ubiquitous in biology. The selective binding of metal ions has a variety of regulatory functions. Therefore, there is a need to understand the mechanism of protein-ion binding. The interactions involving metal ions are complicated in nature, where short-range charge-penetration, charge transfer, polarization, and many-body effects all contribute significantly, and a quantitative description of all these interactions is lacking. In addition, it is unclear how well current polarizable force fields can capture these energy terms and whether these polarization models are good enough to describe the many-body effects. In this work, two energy decomposition methods, absolutely localized molecular orbitals and symmetry-adapted perturbation theory, were utilized to study the interactions between Mg2+/Ca2+ and model compounds for amino acids. Comparison of individual interaction components revealed that while there are significant charge-penetration and charge-transfer effects in Ca complexes, these effects can be captured by the van der Waals (vdW) term in the AMOEBA force field. The electrostatic interaction in Mg complexes is well described by AMOEBA since the charge penetration is small, but the distance-dependent polarization energy is problematic. Many-body effects were shown to be important for protein-ion binding. In the absence of many-body effects, highly charged binding pockets will be over-stabilized, and the pockets will always favor Mg and thus lose selectivity. Therefore, many-body effects must be incorporated in the force field in order to predict the structure and energetics of metalloproteins. Also, the many-body effects of charge transfer in Ca complexes were found to be non-negligible. The absorption of charge-transfer energy into the additive vdW term was a main source of error for the AMOEBA many-body interaction energies.

Quantitative Predictions of Binding Free Energy Changes in Drug-Resistant Influenza Neuraminidase

DTIC Science & Technology

2012-08-30

drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD) with Hamiltonian Replica Exchange and...conformations that are virtually identical to WT [10]. Molecular simulations that rigorously model the microscopic structure and thermodynamics PLOS...influenza neuraminidase (NA) that confer drug resistance to two antiviral drugs, zanamivir and oseltamivir. We augmented molecular dynamics (MD) with

ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

PubMed

Comeau, Stephen R; Gatchell, David W; Vajda, Sandor; Camacho, Carlos J

2004-01-01

Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu

The Binding Mode Prediction and Similar Ligand Potency in the Active Site of Vitamin D Receptor with QM/MM Interaction, MESP, and MD Simulation.

PubMed

Selvaraman, Nagamani; Selvam, Saravana Kumar; Muthusamy, Karthikeyan

2016-08-01

Non-secosteroidal ligands are well-known vitamin D receptor (VDR) agonists. In this study, we described a combined QM/MM to define the protein-ligand interaction energy a strong positive correlation in both QM-MM interaction energy and binding free energy against the biological activity. The molecular dynamics simulation study was performed, and specific interactions were extensively studied. The molecular docking results and surface analysis shed light on steric and electrostatic complementarities of these non-secosteroidal ligands to VDR. Finally, the drug likeness properties were also calculated and found within the acceptable range. The results show that bulky group substitutions in side chain decrease the VDR activity, whereas a small substitution increased it. Functional analyses of H393A and H301A mutations substantiate their roles in the VDR agonistic and antagonistic activities. Apart from the His393 and His301, two other amino acids in the hinge region viz. Ser233 and Arg270 acted as an electron donor/acceptor specific to the agonist in the distinct ligand potency. The results from this study disclose the binding mechanism of VDR agonists and structural modifications required to improve the selectivity. © 2016 John Wiley & Sons A/S.

Study of base pair mutations in proline-rich homeodomain (PRH)-DNA complexes using molecular dynamics.

PubMed

Jalili, Seifollah; Karami, Leila; Schofield, Jeremy

2013-06-01

Proline-rich homeodomain (PRH) is a regulatory protein controlling transcription and gene expression processes by binding to the specific sequence of DNA, especially to the sequence 5'-TAATNN-3'. The impact of base pair mutations on the binding between the PRH protein and DNA is investigated using molecular dynamics and free energy simulations to identify DNA sequences that form stable complexes with PRH. Three 20-ns molecular dynamics simulations (PRH-TAATTG, PRH-TAATTA and PRH-TAATGG complexes) in explicit solvent water were performed to investigate three complexes structurally. Structural analysis shows that the native TAATTG sequence forms a complex that is more stable than complexes with base pair mutations. It is also observed that upon mutation, the number and occupancy of the direct and water-mediated hydrogen bonds decrease. Free energy calculations performed with the thermodynamic integration method predict relative binding free energies of 0.64 and 2 kcal/mol for GC to AT and TA to GC mutations, respectively, suggesting that among the three DNA sequences, the PRH-TAATTG complex is more stable than the two mutated complexes. In addition, it is demonstrated that the stability of the PRH-TAATTA complex is greater than that of the PRH-TAATGG complex.

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krishnapriyan, A.; Yang, P.; Niklasson, A. M. N.

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater- Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value ofmore » an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.« less

Numerical Optimization of Density Functional Tight Binding Models: Application to Molecules Containing Carbon, Hydrogen, Nitrogen, and Oxygen

DOE PAGES

Krishnapriyan, A.; Yang, P.; Niklasson, A. M. N.; ...

2017-10-17

New parametrizations for semiempirical density functional tight binding (DFTB) theory have been developed by the numerical optimization of adjustable parameters to minimize errors in the atomization energy and interatomic forces with respect to ab initio calculated data. Initial guesses for the radial dependences of the Slater- Koster bond integrals and overlap integrals were obtained from minimum basis density functional theory calculations. The radial dependences of the pair potentials and the bond and overlap integrals were represented by simple analytic functions. The adjustable parameters in these functions were optimized by simulated annealing and steepest descent algorithms to minimize the value ofmore » an objective function that quantifies the error between the DFTB model and ab initio calculated data. The accuracy and transferability of the resulting DFTB models for the C, H, N, and O system were assessed by comparing the predicted atomization energies and equilibrium molecular geometries of small molecules that were not included in the training data from DFTB to ab initio data. The DFTB models provide accurate predictions of the properties of hydrocarbons and more complex molecules containing C, H, N, and O.« less

Adsorption of CH4 on nitrogen- and boron-containing carbon models of coal predicted by density-functional theory

NASA Astrophysics Data System (ADS)

Liu, Xiao-Qiang; Xue, Ying; Tian, Zhi-Yue; Mo, Jing-Jing; Qiu, Nian-Xiang; Chu, Wei; Xie, He-Ping

2013-11-01

Graphene doped by nitrogen (N) and/or boron (B) is used to represent the surface models of coal with the structural heterogeneity. Through the density functional theory (DFT) calculations, the interactions between coalbed methane (CBM) and coal surfaces have been investigated. Several adsorption sites and orientations of methane (CH4) on graphenes were systematically considered. Our calculations predicted adsorption energies of CH4 on graphenes of up to -0.179 eV, with the strongest binding mode in which three hydrogen atoms of CH4 direct to graphene surface, observed for N-doped graphene, compared to the perfect (-0.154 eV), B-doped (-0.150 eV), and NB-doped graphenes (-0.170 eV). Doping N in graphene increases the adsorption energies of CH4, but slightly reduced binding is found when graphene is doped by B. Our results indicate that all of graphenes act as the role of a weak electron acceptor with respect to CH4. The interactions between CH4 and graphenes are the physical adsorption and slightly depend upon the adsorption sites on graphenes and the orientations of methane as well as the electronegativity of dopant atoms in graphene.

Accounting for receptor flexibility and enhanced sampling methods in computer-aided drug design.

PubMed

Sinko, William; Lindert, Steffen; McCammon, J Andrew

2013-01-01

Protein flexibility plays a major role in biomolecular recognition. In many cases, it is not obvious how molecular structure will change upon association with other molecules. In proteins, these changes can be major, with large deviations in overall backbone structure, or they can be more subtle as in a side-chain rotation. Either way the algorithms that predict the favorability of biomolecular association require relatively accurate predictions of the bound structure to give an accurate assessment of the energy involved in association. Here, we review a number of techniques that have been proposed to accommodate receptor flexibility in the simulation of small molecules binding to protein receptors. We investigate modifications to standard rigid receptor docking algorithms and also explore enhanced sampling techniques, and the combination of free energy calculations and enhanced sampling techniques. The understanding and allowance for receptor flexibility are helping to make computer simulations of ligand protein binding more accurate. These developments may help improve the efficiency of drug discovery and development. Efficiency will be essential as we begin to see personalized medicine tailored to individual patients, which means specific drugs are needed for each patient's genetic makeup. © 2012 John Wiley & Sons A/S.

Defect processes in Be12X (X = Ti, Mo, V, W)

NASA Astrophysics Data System (ADS)

Jackson, M. L.; Burr, P. A.; Grimes, R. W.

2017-08-01

The stability of intrinsic point defects in Be12X intermetallics (where X  =  Ti, V, Mo or W) are predicted using density functional theory simulations and discussed with respect to fusion energy applications. Schottky disorder is found to be the lowest energy complete disorder process, closely matched by Be Frenkel disorder in the cases of Be12V and Be12Ti. Antitisite and X Frenkel disorder are of significantly higher energy. Small clusters of point defects including Be divacancies, Be di-interstitials and accommodation of the X species on two Be sites were considered. Some di-interstitial, divacancy and X2Be combinations exhibit negative binding enthalpy (i.e. clustering is favourable), although this is orientationally dependent. None of the Be12X intermetallics are predicted to exhibit significant non-stoichiometry, ruling out non-stoichiometry as a mechanism for accommodating Be depletion due to neutron transmutation.

Computational Assay of H7N9 Influenza Neuraminidase Reveals R292K Mutation Reduces Drug Binding Affinity

NASA Astrophysics Data System (ADS)

Woods, Christopher J.; Malaisree, Maturos; Long, Ben; McIntosh-Smith, Simon; Mulholland, Adrian J.

2013-12-01

The emergence of a novel H7N9 avian influenza that infects humans is a serious cause for concern. Of the genome sequences of H7N9 neuraminidase available, one contains a substitution of arginine to lysine at position 292, suggesting a potential for reduced drug binding efficacy. We have performed molecular dynamics simulations of oseltamivir, zanamivir and peramivir bound to H7N9, H7N9-R292K, and a structurally related H11N9 neuraminidase. They show that H7N9 neuraminidase is structurally homologous to H11N9, binding the drugs in identical modes. The simulations reveal that the R292K mutation disrupts drug binding in H7N9 in a comparable manner to that observed experimentally for H11N9-R292K. Absolute binding free energy calculations with the WaterSwap method confirm a reduction in binding affinity. This indicates that the efficacy of antiviral drugs against H7N9-R292K will be reduced. Simulations can assist in predicting disruption of binding caused by mutations in neuraminidase, thereby providing a computational `assay.'

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

PubMed

Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

2002-01-01

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

PubMed Central

Garcia Lopez, Sebastian; Kim, Philip M.

2014-01-01

Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

NetMHCIIpan-2.0 - Improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure.

PubMed

Nielsen, Morten; Justesen, Sune; Lund, Ole; Lundegaard, Claus; Buus, Søren

2010-11-13

Binding of peptides to Major Histocompatibility class II (MHC-II) molecules play a central role in governing responses of the adaptive immune system. MHC-II molecules sample peptides from the extracellular space allowing the immune system to detect the presence of foreign microbes from this compartment. Predicting which peptides bind to an MHC-II molecule is therefore of pivotal importance for understanding the immune response and its effect on host-pathogen interactions. The experimental cost associated with characterizing the binding motif of an MHC-II molecule is significant and large efforts have therefore been placed in developing accurate computer methods capable of predicting this binding event. Prediction of peptide binding to MHC-II is complicated by the open binding cleft of the MHC-II molecule, allowing binding of peptides extending out of the binding groove. Moreover, the genes encoding the MHC molecules are immensely diverse leading to a large set of different MHC molecules each potentially binding a unique set of peptides. Characterizing each MHC-II molecule using peptide-screening binding assays is hence not a viable option. Here, we present an MHC-II binding prediction algorithm aiming at dealing with these challenges. The method is a pan-specific version of the earlier published allele-specific NN-align algorithm and does not require any pre-alignment of the input data. This allows the method to benefit also from information from alleles covered by limited binding data. The method is evaluated on a large and diverse set of benchmark data, and is shown to significantly out-perform state-of-the-art MHC-II prediction methods. In particular, the method is found to boost the performance for alleles characterized by limited binding data where conventional allele-specific methods tend to achieve poor prediction accuracy. The method thus shows great potential for efficient boosting the accuracy of MHC-II binding prediction, as accurate predictions can be obtained for novel alleles at highly reduced experimental costs. Pan-specific binding predictions can be obtained for all alleles with know protein sequence and the method can benefit by including data in the training from alleles even where only few binders are known. The method and benchmark data are available at http://www.cbs.dtu.dk/services/NetMHCIIpan-2.0.

Assessing Carbon-Based Anodes for Lithium-Ion Batteries: A Universal Description of Charge-Transfer Binding

DOE PAGES

Liu, Yuanyue; Wang, Y. Morris; Yakobson, Boris I.; ...

2014-07-11

Many key performance characteristics of carbon-based lithium-ion battery anodes are largely determined by the strength of binding between lithium (Li) and sp 2 carbon (C), which can vary significantly with subtle changes in substrate structure, chemistry, and morphology. We use density functional theory calculations to investigate the interactions of Li with a wide variety of sp 2 C substrates, including pristine, defective, and strained graphene, planar C clusters, nanotubes, C edges, and multilayer stacks. In almost all cases, we find a universal linear relation between the Li-C binding energy and the work required to fill previously unoccupied electronic states withinmore » the substrate. This suggests that Li capacity is predominantly determined by two key factors—namely, intrinsic quantum capacitance limitations and the absolute placement of the Fermi level. This simple descriptor allows for straightforward prediction of the Li-C binding energy and related battery characteristics in candidate C materials based solely on the substrate electronic structure. It further suggests specific guidelines for designing more effective C-based anodes. Furthermore, this method should be broadly applicable to charge-transfer adsorption on planar substrates, and provides a phenomenological connection to established principles in supercapacitor and catalyst design.« less

Protein docking prediction using predicted protein-protein interface.

PubMed

Li, Bin; Kihara, Daisuke

2012-01-10

Many important cellular processes are carried out by protein complexes. To provide physical pictures of interacting proteins, many computational protein-protein prediction methods have been developed in the past. However, it is still difficult to identify the correct docking complex structure within top ranks among alternative conformations. We present a novel protein docking algorithm that utilizes imperfect protein-protein binding interface prediction for guiding protein docking. Since the accuracy of protein binding site prediction varies depending on cases, the challenge is to develop a method which does not deteriorate but improves docking results by using a binding site prediction which may not be 100% accurate. The algorithm, named PI-LZerD (using Predicted Interface with Local 3D Zernike descriptor-based Docking algorithm), is based on a pair wise protein docking prediction algorithm, LZerD, which we have developed earlier. PI-LZerD starts from performing docking prediction using the provided protein-protein binding interface prediction as constraints, which is followed by the second round of docking with updated docking interface information to further improve docking conformation. Benchmark results on bound and unbound cases show that PI-LZerD consistently improves the docking prediction accuracy as compared with docking without using binding site prediction or using the binding site prediction as post-filtering. We have developed PI-LZerD, a pairwise docking algorithm, which uses imperfect protein-protein binding interface prediction to improve docking accuracy. PI-LZerD consistently showed better prediction accuracy over alternative methods in the series of benchmark experiments including docking using actual docking interface site predictions as well as unbound docking cases.

Computational and experimental studies of the interaction between phospho-peptides and the C-terminal domain of BRCA1

NASA Astrophysics Data System (ADS)

Anisimov, Victor M.; Ziemys, Arturas; Kizhake, Smitha; Yuan, Ziyan; Natarajan, Amarnath; Cavasotto, Claudio N.

2011-11-01

The C-terminal domain of BRCA1(BRCT) is involved in the DNA repair pathway by recognizing the pSXXF motif in interacting proteins. It has been reported that short peptides containing this motif bind to BRCA1(BRCT) in the micromolar range with high specificity. In this work, the binding of pSXXF peptides has been studied computationally and experimentally in order to characterize their interaction with BRCA1(BRCT). Elucidation of the contacts that drive the protein-ligand interaction is critical for the development of high affinity small-molecule BRCA1 inhibitors. Molecular dynamics (MD) simulations revealed the key role of threonine at the peptide P+2 position in providing structural rigidity to the ligand in the bound state. The mutation at P+1 had minor effects. Peptide extension at the N-terminal position with the naphthyl amino acid exhibited a modest increase in binding affinity, what could be explained by the dispersion interaction of the naphthyl side-chain with a hydrophobic patch. Three in silico end-point methods were considered for the calculation of binding free energy. The Molecular Mechanics Poisson-Boltzmann Surface Area and the Solvated Interaction Energy methods gave reasonable agreement with experimental data, exhibiting a Pearlman predictive index of 0.71 and 0.78, respectively. The MM-quantum mechanics-surface area method yielded improved results, which was characterized by a Pearlman index of 0.78. The correlation coefficients were 0.59, 0.61 and 0.69, respectively. The ability to apply a QM level of theory within an end-point binding free energy protocol may provide a way for a consistent improvement of accuracy in computer-aided drug design.

Unusual structures of MgF5- superhalogen anion

NASA Astrophysics Data System (ADS)

Anusiewicz, Iwona; Skurski, Piotr

2007-05-01

The vertical electron detachment energies (VDE) of three MgF5- anions were calculated at the outer valence Green function level with the 6-311 + G(3df) basis sets. This species was found to form unusual geometrical structures each of which corresponds to an anionic state exhibiting superhalogen nature. The global minimum structure was described as a system in which two central magnesium atoms are linked via symmetrical triangle formed by three fluorine atoms. Extremely large electron binding energies of these anions (exceeding 8.5 eV in all cases) were predicted and discussed.

Exploring the Molecular Design of Protein Interaction Sites with Molecular Dynamics Simulations and Free Energy Calculations†

PubMed Central

Liang, Shide; Li, Liwei; Hsu, Wei-Lun; Pilcher, Meaghan N.; Uversky, Vladimir; Zhou, Yaoqi; Dunker, A. Keith; Meroueh, Samy O.

2009-01-01

The significant work that has been invested toward understanding protein–protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein–protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein–protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes. PMID:19113835

Comparison of the Efficiency of the LIE and MM/GBSA Methods to Calculate Ligand-Binding Energies.

PubMed

Genheden, Samuel; Ryde, Ulf

2011-11-08

We have evaluated the efficiency of two popular end-point methods to calculate ligand-binding free energies, LIE (linear interaction energy) and MM/GBSA (molecular mechanics with generalized Born surface-area solvation), i.e. the computational effort needed to obtain estimates of a similar precision. As a test case, we use the binding of seven biotin analogues to avidin. The energy terms used by MM/GBSA and LIE exhibit a similar correlation time (∼5 ps), and the equilibration time seems also to be similar, although it varies much between the various ligands. The results show that the LIE method is more effective than MM/GBSA, by a factor of 2-7 for a truncated spherical system with a radius of 26 Å and by a factor of 1.0-2.4 for the full avidin tetramer (radius 47 Å). The reason for this is the cost for the MM/GBSA entropy calculations, which more than compensates for the extra simulation of the free ligand in LIE. On the other hand, LIE requires that the protein is neutralized, whereas MM/GBSA has no such requirements. Our results indicate that both the truncation and neutralization of the proteins may slow the convergence and emphasize small differences in the calculations, e.g., differences between the four subunits in avidin. Moreover, LIE cannot take advantage of the fact that avidin is a tetramer. For this test case, LIE gives poor results with the standard parametrization, but after optimizing the scaling factor of the van der Waals terms, reasonable binding affinities can be obtained, although MM/GBSA still gives a significantly better predictive index and correlation to the experimental affinities.

Separating the Role of Protein Restraints and Local Metal-Site Interaction Chemistry in the Thermodynamics of a Zinc Finger Protein

PubMed Central

Dixit, Purushottam D.; Asthagiri, D.

2011-01-01

We express the effective Hamiltonian of an ion-binding site in a protein as a combination of the Hamiltonian of the ion-bound site in vacuum and the restraints of the protein on the site. The protein restraints are described by the quadratic elastic network model. The Hamiltonian of the ion-bound site in vacuum is approximated as a generalized Hessian around the minimum energy configuration. The resultant of the two quadratic Hamiltonians is cast into a pure quadratic form. In the canonical ensemble, the quadratic nature of the resultant Hamiltonian allows us to express analytically the excess free energy, enthalpy, and entropy of ion binding to the protein. The analytical expressions allow us to separate the roles of the dynamic restraints imposed by the protein on the binding site and the temperature-independent chemical effects in metal-ligand coordination. For the consensus zinc-finger peptide, relative to the aqueous phase, the calculated free energy of exchanging Zn2+ with Fe2+, Co2+, Ni2+, and Cd2+ are in agreement with experiments. The predicted excess enthalpy of ion exchange between Zn2+ and Co2+ also agrees with the available experimental estimate. The free energy of applying the protein restraints reveals that relative to Zn2+, the Co2+, and Cd2+-site clusters are more destabilized by the protein restraints. This leads to an experimentally testable hypothesis that a tetrahedral metal binding site with minimal protein restraints will be less selective for Zn2+ over Co2+ and Cd2+ compared to a zinc finger peptide. No appreciable change is expected for Fe2+ and Ni2+. The framework presented here may prove useful in protein engineering to tune metal selectivity. PMID:21943427

External electric field effect on the binding energy of a hydrogenic donor impurity in InGaAsP/InP concentric double quantum rings

NASA Astrophysics Data System (ADS)

Hu, Min; Wang, Hailong; Gong, Qian; Wang, Shumin

2018-04-01

Within the framework of effective-mass envelope-function theory, the ground state binding energy of a hydrogenic donor impurity is calculated in the InGaAsP/InP concentric double quantum rings (CDQRs) using the plane wave method. The effects of geometry, impurity position, external electric field and alloy composition on binding energy are considered. It is shown that the peak value of the binding energy appears in two rings with large gap as the donor impurity moves along the radial direction. The binding energy reaches the peak value at the center of ring height when the donor impurity moves along the axial direction. The binding energy shows nonlinear variation with the increase of ring height. With the external electric field applied along the z-axis, the binding energy of the donor impurity located at zi ≥ 0 decreases while that located at zi < 0 increases. In addition, the binding energy decreases with increasing Ga composition, but increases with the increasing As composition.

N-Ω Interaction from High-Energy Heavy Ion Collisions

NASA Astrophysics Data System (ADS)

Morita, Kenji; Ohnishi, Akira; Hatsuda, Tetsuo

We discuss possible observation of the N-Ω interaction from intensity correlation function in high energy heavy ion collisions. Recently a lattice QCD simulation by the HAL QCD collaboration predicts the existence of a N-Ω bound state in the 5S2 channel. We adopt the N-Ω interaction potential obtained by the lattice simulation and use it to calculate the N-Ω correlation function. We also study the variation of the correlation function with respect to the change of the binding energy and scattering parameters. Our result indicates that heavy ion collisions at RHIC and LHC may provide information on the possible existence of the N-Ω dibaryon.

Naringenin and quercetin--potential anti-HCV agents for NS2 protease targets.

PubMed

Lulu, S Sajitha; Thabitha, A; Vino, S; Priya, A Mohana; Rout, Madhusmita

2016-01-01

Nonstructural proteins of hepatitis C virus had drawn much attention for the scientific fraternity in drug discovery due to its important role in the disease. 3D structure of the protein was predicted using molecular modelling protocol. Docking studies of 10 medicinal plant compounds and three drugs available in the market (control) with NS2 protease were employed by using rigid docking approach of AutoDock 4.2. Among the molecules tested for docking study, naringenin and quercetin revealed minimum binding energy of - 7.97 and - 7.95 kcal/mol with NS2 protease. All the ligands were docked deeply within the binding pocket region of the protein. The docking study results showed that these compounds are potential inhibitors of the target; and also all these docked compounds have good inhibition constant, vdW+Hbond+desolv energy with best RMSD value.

Towards better modelling of drug-loading in solid lipid nanoparticles: Molecular dynamics, docking experiments and Gaussian Processes machine learning.

PubMed

Hathout, Rania M; Metwally, Abdelkader A

2016-11-01

This study represents one of the series applying computer-oriented processes and tools in digging for information, analysing data and finally extracting correlations and meaningful outcomes. In this context, binding energies could be used to model and predict the mass of loaded drugs in solid lipid nanoparticles after molecular docking of literature-gathered drugs using MOE® software package on molecularly simulated tripalmitin matrices using GROMACS®. Consequently, Gaussian processes as a supervised machine learning artificial intelligence technique were used to correlate the drugs' descriptors (e.g. M.W., xLogP, TPSA and fragment complexity) with their molecular docking binding energies. Lower percentage bias was obtained compared to previous studies which allows the accurate estimation of the loaded mass of any drug in the investigated solid lipid nanoparticles by just projecting its chemical structure to its main features (descriptors). Copyright © 2016 Elsevier B.V. All rights reserved.

Computational inhibitor design against malaria plasmepsins.

PubMed

Bjelic, S; Nervall, M; Gutiérrez-de-Terán, H; Ersmark, K; Hallberg, A; Aqvist, J

2007-09-01

Plasmepsins are aspartic proteases involved in the degradation of the host cell hemoglobin that is used as a food source by the malaria parasite. Plasmepsins are highly promising as drug targets, especially when combined with the inhibition of falcipains that are also involved in hemoglobin catabolism. In this review, we discuss the mechanism of plasmepsins I-IV in view of the interest in transition state mimetics as potential compounds for lead development. Inhibitor development against plasmepsin II as well as relevant crystal structures are summarized in order to give an overview of the field. Application of computational techniques, especially binding affinity prediction by the linear interaction energy method, in the development of malarial plasmepsin inhibitors has been highly successful and is discussed in detail. Homology modeling and molecular docking have been useful in the current inhibitor design project, and the combination of such methods with binding free energy calculations is analyzed.

AMOEBA 2.0: A physics-first approach to biomolecular simulations

NASA Astrophysics Data System (ADS)

Rackers, Joshua; Ponder, Jay

The goal of the AMOEBA force field project is to use classical physics to understand and predict the nature of interactions between biological molecules. While making significant advances over the past decade, the ultimate goal of predicting binding energies with ``chemical accuracy'' remains elusive. The primary source of this inaccuracy comes from the physics of how molecules interact at short range. For example, despite AMOEBA's advanced treatment of electrostatics, the force field dramatically overpredicts the electrostatic energy of DNA stacking interactions. AMOEBA 2.0 works to correct these errors by including simple, first principles physics-based terms to account for the quantum mechanical nature of these short-range molecular interactions. We have added a charge penetration term that considerably improves the description of electrostatic interactions at short range. We are reformulating the polarization term of AMOEBA in terms of basic physics assertions. And we are reevaluating the van der Waals term to match ab initio energy decompositions. These additions and changes promise to make AMOEBA more predictive. By including more physical detail of the important short-range interactions of biological molecules, we hope to move closer to the ultimate goal of true predictive power.

Importance of length and sequence order on magnesium binding to surface-bound oligonucleotides studied by second harmonic generation and atomic force microscopy.

PubMed

Holland, Joseph G; Geiger, Franz M

2012-06-07

The binding of magnesium ions to surface-bound single-stranded oligonucleotides was studied under aqueous conditions using second harmonic generation (SHG) and atomic force microscopy (AFM). The effect of strand length on the number of Mg(II) ions bound and their free binding energy was examined for 5-, 10-, 15-, and 20-mers of adenine and guanine at pH 7, 298 K, and 10 mM NaCl. The binding free energies for adenine and guanine sequences were calculated to be -32.1(4) and -35.6(2) kJ/mol, respectively, and invariant with strand length. Furthermore, the ion density for adenine oligonucleotides did not change as strand length increased, with an average value of 2(1) ions/strand. In sharp contrast, guanine oligonucleotides displayed a linear relationship between strand length and ion density, suggesting that cooperativity is important. This data gives predictive capabilities for mixed strands of various lengths, which we exploit for 20-mers of adenines and guanines. In addition, the role sequence order plays in strands of hetero-oligonucleotides was examined for 5'-A(10)G(10)-3', 5'-(AG)(10)-3', and 5'-G(10)A(10)-3' (here the -3' end is chemically modified to bind to the surface). Although the free energy of binding is the same for these three strands (averaged to be -33.3(4) kJ/mol), the total ion density increases when several guanine residues are close to the 3' end (and thus close to the solid support substrate). To further understand these results, we analyzed the height profiles of the functionalized surfaces with tapping-mode atomic force microscopy (AFM). When comparing the average surface height profiles of the oligonucleotide surfaces pre- and post- Mg(II) binding, a positive correlation was found between ion density and the subsequent height decrease following Mg(II) binding, which we attribute to reductions in Coulomb repulsion and strand collapse once a critical number of Mg(II) ions are bound to the strand.

Simultaneous effects of temperature and pressure on the donor binding energy in a V-groove quantum wire

NASA Astrophysics Data System (ADS)

Khordad, R.

2010-03-01

The influence of temperature and pressure, simultaneously, on the binding energy of a hydrogenic donor impurity in a ridge GaAs/Ga 1- xAl xAs quantum wire is studied using a variational procedure within the effective mass approximation. The subband energy and the binding energy of the donor impurity in its ground state as a function of the wire bend width and impurity location at different temperatures and pressures are calculated. The results show that, when the temperature increases, the donor binding energy decreases for a constant applied pressure for all wire bend widths. Also, the binding energy increases by increasing the pressure for a constant temperature for all wire bend widths. In addition, when the temperature and pressure are applied simultaneously the binding energy decreases as the quantum wire bend width increases. On the whole, it is deduced that the temperature and pressure have important effects on the donor binding energy in a V-groove quantum wire.

BindML/BindML+: Detecting Protein-Protein Interaction Interface Propensity from Amino Acid Substitution Patterns.

PubMed

Wei, Qing; La, David; Kihara, Daisuke

2017-01-01

Prediction of protein-protein interaction sites in a protein structure provides important information for elucidating the mechanism of protein function and can also be useful in guiding a modeling or design procedures of protein complex structures. Since prediction methods essentially assess the propensity of amino acids that are likely to be part of a protein docking interface, they can help in designing protein-protein interactions. Here, we introduce BindML and BindML+ protein-protein interaction sites prediction methods. BindML predicts protein-protein interaction sites by identifying mutation patterns found in known protein-protein complexes using phylogenetic substitution models. BindML+ is an extension of BindML for distinguishing permanent and transient types of protein-protein interaction sites. We developed an interactive web-server that provides a convenient interface to assist in structural visualization of protein-protein interactions site predictions. The input data for the web-server are a tertiary structure of interest. BindML and BindML+ are available at http://kiharalab.org/bindml/ and http://kiharalab.org/bindml/plus/ .

Predictive energy landscapes for folding membrane protein assemblies

NASA Astrophysics Data System (ADS)

Truong, Ha H.; Kim, Bobby L.; Schafer, Nicholas P.; Wolynes, Peter G.

2015-12-01

We study the energy landscapes for membrane protein oligomerization using the Associative memory, Water mediated, Structure and Energy Model with an implicit membrane potential (AWSEM-membrane), a coarse-grained molecular dynamics model previously optimized under the assumption that the energy landscapes for folding α-helical membrane protein monomers are funneled once their native topology within the membrane is established. In this study we show that the AWSEM-membrane force field is able to sample near native binding interfaces of several oligomeric systems. By predicting candidate structures using simulated annealing, we further show that degeneracies in predicting structures of membrane protein monomers are generally resolved in the folding of the higher order assemblies as is the case in the assemblies of both nicotinic acetylcholine receptor and V-type Na+-ATPase dimers. The physics of the phenomenon resembles domain swapping, which is consistent with the landscape following the principle of minimal frustration. We revisit also the classic Khorana study of the reconstitution of bacteriorhodopsin from its fragments, which is the close analogue of the early Anfinsen experiment on globular proteins. Here, we show the retinal cofactor likely plays a major role in selecting the final functional assembly.

π-Stacking, C-H/π, and halogen bonding interactions in bromobenzene and mixed bromobenzene-benzene clusters.

PubMed

Reid, Scott A; Nyambo, Silver; Muzangwa, Lloyd; Uhler, Brandon

2013-12-19

Noncovalent interactions play an important role in many chemical and biochemical processes. Building upon our recent study of the homoclusters of chlorobenzene, where π-π stacking and CH/π interactions were identified as the most important binding motifs, in this work we present a study of bromobenzene (PhBr) and mixed bromobenzene-benzene clusters. Electronic spectra in the region of the PhBr monomer S0-S1 (ππ*) transition were obtained using resonant two-photon ionization (R2PI) methods combined with time-of-flight mass analysis. As previously found for related systems, the PhBr cluster spectra show a broad feature whose center is red-shifted from the monomer absorption, and electronic structure calculations indicate the presence of multiple isomers and Franck-Condon activity in low-frequency intermolecular modes. Calculations at the M06-2X/aug-cc-pVDZ level find in total eight minimum energy structures for the PhBr dimer: four π-stacked structures differing in the relative orientation of the Br atoms (denoted D1-D4), one T-shaped structure (D5), and three halogen bonded structures (D6-D8). The calculated binding energies of these complexes, corrected for basis set superposition error (BSSE) and zero-point energy (ZPE), are in the range of -6 to -24 kJ/mol. Time-dependent density functional theory (TDDFT) calculations predict that these isomers absorb over a range that is roughly consistent with the breadth of the experimental spectrum. To examine the influence of dipole-dipole interaction, R2PI spectra were also obtained for the mixed PhBr···benzene dimer, where the spectral congestion is reduced and clear vibrational structure is observed. This structure is well-simulated by Franck-Condon calculations that incorporate the lowest frequency intermolecular modes. Calculations find four minimum energy structures for the mixed dimer and predict that the binding energy of the global minimum is reduced by ~30% relative to the global minimum PhBr dimer structure.

Energies of rare-earth ion states relative to host bands in optical materials from electron photoemission spectroscopy

NASA Astrophysics Data System (ADS)

Thiel, Charles Warren

There are a vast number of applications for rare-earth-activated materials and much of today's cutting-edge optical technology and emerging innovations are enabled by their unique properties. In many of these applications, interactions between the rare-earth ion and the host material's electronic states can enhance or inhibit performance and provide mechanisms for manipulating the optical properties. Continued advances in these technologies require knowledge of the relative energies of rare-earth and crystal band states so that properties of available materials may be fully understood and new materials may be logically developed. Conventional and resonant electron photoemission techniques were used to measure 4f electron and valence band binding energies in important optical materials, including YAG, YAlO3, and LiYF4. The photoemission spectra were theoretically modeled and analyzed to accurately determine relative energies. By combining these energies with ultraviolet spectroscopy, binding energies of excited 4fN-15d and 4fN+1 states were determined. While the 4fN ground-state energies vary considerably between different trivalent ions and lie near or below the top of the valence band in optical materials, the lowest 4f N-15d states have similar energies and are near the bottom of the conduction band. As an example for YAG, the Tb3+ 4f N ground state is in the band gap at 0.7 eV above the valence band while the Lu3+ ground state is 4.7 eV below the valence band maximum; however, the lowest 4fN-15d states are 2.2 eV below the conduction band for both ions. We found that a simple model accurately describes the binding energies of the 4fN, 4fN-1 5d, and 4fN+1 states. The model's success across the entire rare-earth series indicates that measurements on two different ions in a host are sufficient to predict the energies of all rare-earth ions in that host. This information provides new insight into electron transfer transitions, luminescence quenching, and valence stability. All of these results lead to a clearer picture for the host's effect on the rare-earth ion's electron binding energies and will motivate fundamental theoretical analysis and accelerate the development of new optical materials.

Spectroscopic and theoretical investigation of oxali-palladium interactions with β-lactoglobulin.

PubMed

Ghalandari, Behafarid; Divsalar, Adeleh; Saboury, Ali Akbar; Haertlé, Thomas; Parivar, Kazem; Bazl, Roya; Eslami-Moghadam, Mahbube; Amanlou, Massoud

2014-01-24

The possibility of using a small cheap dairy protein, β-lactoglobulin (β-LG), as a carrier for oxali-palladium for drug delivery was studied. Their binding in an aqueous solution at two temperatures of 25 and 37°C was investigated using spectroscopic techniques in combination with a molecular docking study. Fluorescence intensity changes showed combined static and dynamic quenching during β-LG oxali-palladium binding, with the static mode being predominant in the quenching mechanism. The binding and thermodynamic parameters were determined by analyzing the results of quenching and those of the van't Hoff equation. According to obtained results the binding constants at two temperatures of 25 and 37°C are 3.3×10(9) M(-1) and 18.4×10(6) M(-1) respectively. Fluorescence resonance energy transfer (FRET) showed that the experimental results and the molecular docking results were coherent. An absence change of β-LG secondary structure was confirmed by the CD results. Molecular docking results agreed fully with the experimental results since the fluorescence studies also revealed the presence of two binding sites with a negative value for the Gibbs free energy of binding of oxali-palladium to β-LG. Furthermore, molecular docking and experimental results suggest that the hydrophobic effect plays a critical role in the formation of the oxali-palladium complex with β-LG. This agreement between molecular docking and experimental results implies that docking studies may be a suitable method for predicting and confirming experimental results, as shown in this study. Hence, the combination of molecular docking and spectroscopy methods is an effective innovative approach for binding studies, particularly for pharmacophores. Copyright © 2013 Elsevier B.V. All rights reserved.

The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Xia, X. W.; Lim, Y.; Zhao, P. W.

The ground-state properties of nuclei with 8more » $$\\leqslant$$ Z $$\\leqslant$$ 120 from the proton drip line to the neutron drip line have been investigated using the relativistic continuum Hartree-Bogoliubov (RCHB) theory with the relativistic density functional PC-PK1. With the effects of the continuum included, there are totally 9035 nuclei predicted to be bound, which largely extends the existing nuclear landscapes predicted with other methods. The calculated binding energies, separation energies, neutron and proton Fermi surfaces, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, ground-state spins and parities are tabulated. The extension of the nuclear landscape obtained with RCHB is discussed in detail, in particular for the neutron-rich side, in comparison with the relativistic mean field calculations without pairing correlations and also other predicted landscapes. Here, it is found that the coupling between the bound states and the continuum due to the pairing correlations plays an essential role in extending the nuclear landscape. The systematics of the separation energies, radii, densities, potentials and pairing energies of the RCHB calculations are also discussed. In addition, the α-decay energies and proton emitters based on the RCHB calculations are investigated.« less

The limits of the nuclear landscape explored by the relativistic continuum Hartree–Bogoliubov theory

DOE PAGES

Xia, X. W.; Lim, Y.; Zhao, P. W.; ...

2017-11-01

The ground-state properties of nuclei with 8more » $$\\leqslant$$ Z $$\\leqslant$$ 120 from the proton drip line to the neutron drip line have been investigated using the relativistic continuum Hartree-Bogoliubov (RCHB) theory with the relativistic density functional PC-PK1. With the effects of the continuum included, there are totally 9035 nuclei predicted to be bound, which largely extends the existing nuclear landscapes predicted with other methods. The calculated binding energies, separation energies, neutron and proton Fermi surfaces, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, ground-state spins and parities are tabulated. The extension of the nuclear landscape obtained with RCHB is discussed in detail, in particular for the neutron-rich side, in comparison with the relativistic mean field calculations without pairing correlations and also other predicted landscapes. Here, it is found that the coupling between the bound states and the continuum due to the pairing correlations plays an essential role in extending the nuclear landscape. The systematics of the separation energies, radii, densities, potentials and pairing energies of the RCHB calculations are also discussed. In addition, the α-decay energies and proton emitters based on the RCHB calculations are investigated.« less

DNA-linked NanoParticle Lattices with Diamond Symmetry: Stability, Shape and Optical Properties

NASA Astrophysics Data System (ADS)

Emamy, Hamed; Tkachenko, Alexei; Gang, Oleg; Starr, Francis

The linking of nanoparticles (NP) by DNA has been proven to be an effective means to create NP lattices with specific order. Lattices with diamond symmetry are predicted to offer novel photonic properties, but self-assembly of such lattices has proven to be challenging due to the low packing fraction, sensitivity to bond orientation, and local heterogeneity. Recently, we reported an approach to create diamond NP lattices based on the association between anisotropic particles with well-defined tetravalent DNA binding topology and isotropically functionalized NP. Here, we use molecular dynamics simulations to evaluate the Gibbs free energy of these lattices, and thereby determine the stability of these lattices as a function of NP size and DNA stiffness. We also predict the equilibrium shape for the cubic diamond crystallite using the Wulff construction method. Specifically, we predict the equilibrium shape using the surface energy for different crystallographic planes. We evaluate surface energy directly form molecular dynamics simulation, which we correlate with theoretical estimates from the expected number of broken DNA bonds along a facet. Furthermore we study the optical properties of this structure, e.g optical bandgap.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.

PubMed

Feinstein, Wei P; Brylinski, Michal

2015-01-01

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated. In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite. A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins. This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size. Graphical AbstractWe developed a procedure to optimize the box size in molecular docking calculations. Left panel shows the predicted binding pose of NADP (green sticks) compared to the experimental complex structure of human aldose reductase (blue sticks) using a default protocol. Right panel shows the docking accuracy using an optimized box size.

Continuum Electrostatics Approaches to Calculating pKas and Ems in Proteins.

PubMed

Gunner, M R; Baker, N A

2016-01-01

Proteins change their charge state through protonation and redox reactions as well as through binding charged ligands. The free energy of these reactions is dominated by solvation and electrostatic energies and modulated by protein conformational relaxation in response to the ionization state changes. Although computational methods for calculating these interactions can provide very powerful tools for predicting protein charge states, they include several critical approximations of which users should be aware. This chapter discusses the strengths, weaknesses, and approximations of popular computational methods for predicting charge states and understanding the underlying electrostatic interactions. The goal of this chapter is to inform users about applications and potential caveats of these methods as well as outline directions for future theoretical and computational research. © 2016 Elsevier Inc. All rights reserved.

Exploring resistance mechanisms of HCV NS3/4A protease mutations to MK5172: insight from molecular dynamics simulations and free energy calculations.

PubMed

Guan, Yan; Sun, Huiyong; Pan, Peichen; Li, Youyong; Li, Dan; Hou, Tingjun

2015-09-01

Mutations at a number of key positions (Ala156, Asp168 and Arg155) of the HCV NS3/4A protease can induce medium to high resistance to MK5172. The emergence of the resistant mutations seriously compromises the antiviral therapy efficacy to hepatitis C. In this study, molecular dynamics (MD) simulations, free energy calculations and free energy decomposition were used to explore the interaction profiles of MK5172 with the wild-type (WT) and four mutated (R155K, D168A, D168V and A156T) HCV NS3/4A proteases. The binding free energies predicted by Molecular Mechanics/Generalized Born Solvent Area (MM/GBSA) are consistent with the trend of the experimental drug resistance data. The free energy decomposition analysis shows that the resistant mutants may change the protein-MK5172 interaction profiles, resulting in the unbalanced energetic distribution amongst the catalytic triad, the strand 135-139 and the strand 154-160. Moreover, umbrella sampling (US) simulations were employed to elucidate the unbinding processes of MK5172 from the active pockets of the WT HCV NS3/4A protease and the four mutants. The US simulations demonstrate that the dissociation pathways of MK5172 escaping from the binding pockets of the WT and mutants are quite different, and it is quite possible that MK5172 will be harder to get access to the correct binding sites of the mutated proteases, thereafter leading to drug resistance.

Exploring inhibitory potential of Curcumin against various cancer targets by in silico virtual screening.

PubMed

Mahajanakatti, Arpitha Badarinath; Murthy, Geetha; Sharma, Narasimha; Skariyachan, Sinosh

2014-03-01

Various types of cancer accounts for 10% of total death worldwide which necessitates better therapeutic strategies. Curcumin, a curcuminoid present in Curcuma longa, shown to exhibit antioxidant, anti-inflammatory and anticarcinogenic properties. Present study, we aimed to analyze inhibitory properties of curcumin towards virulent proteins for various cancers by computer aided virtual screening. Based on literature studies, twenty two receptors were selected which have critical virulent functions in various cancer. The binding efficiencies of curcumin towards selected targets were studied by molecular docking. Out of all, curcumin showed best results towards epidermal growth factor (EGF), virulent protein of gastric cancer; glutathione-S-transferase Pi gene (GST-PI), virulent protein for prostate cancer; platelet-derived growth factor alpha (PDGFA), virulent protein for mesothelioma and glioma compared with their natural ligands. The calculated binding energies of their docked conformations with curcumin found to be -7.59 kcal/mol, -7.98 kcal/mol and -7.93 kcal/mol respectively. Further, a comparative study was performed to screen binding efficiency of curcumin with two conventional antitumor agents, litreol and triterpene. Docking studies revealed that calculated binding energies of docked complex of litreol and EGF, GST-PI and PDGFA were found to be -5.08 kcal/mol, -3.69 kcal/mol and -1.86 kcal/mol respectively. The calculated binding energies of triterpene with EGF and PDGFA were found to be -4.02 kcal/mol and -3.11 kcal/mol respectively, whereas GST-PI showed +6.07 kcal/mol, indicate poor binding. The predicted pharmacological features of curcumin found to be better than litreol and triterpene. Our study concluded that curcumin has better interacting properties towards these cancer targets than their normal ligands and conventional antitumor agents. Our data pave insight for designing of curcumin as novel inhibitors against various types of cancer.

Major versus minor groove DNA binding of a bisarginylporphyrin hybrid molecule: A molecular mechanics investigation

NASA Astrophysics Data System (ADS)

Gresh, Nohad; Perrée-fauvet, Martine

1999-03-01

On the basis of theoretical computations, we have recently synthesised [Perrée-Fauvet, M. and Gresh, N., Tetrahedron Lett., 36 (1995) 4227] a bisarginyl conjugate of a tricationic porphyrin (BAP), designed to target, in the major groove of DNA, the d(GGC GCC)2 sequence which is part of the primary binding site of the HIV-1 retrovirus site [Wain-Hobson, S. et al., Cell, 40 (1985) 9]. In the theoretical model, the chromophore intercalates at the central d(CpG)2 step and each of the arginyl arms targets O6/N7belonging to guanine bases flanking the intercalation site. Recent IR and UV-visible spectroscopic studies have confirmed the essential features of these theoretical predictions [Mohammadi, S. et al., Biochemistry, 37 (1998) 6165]. In the present study, we compare the energies of competing intercalation modes of BAP to several double-stranded oligonucleotides, according to whether one, two or three N- methylpyridinium rings project into the major groove. Correspondingly, three minor groove binding modes were considered, the arginyl arms now targeting N3, O2 sites belonging to the purine or pyrimidine bases flanking the intercalation site. This investigation has shown that: (i) in both the major and minor grooves, the best-bound complexes have the three N-methylpyridinium rings in the groove opposite to that of the phenyl group bearing the arginyl arms; (ii) major groove binding is preferred over minor groove binding by a significant energy (29 kcal/mol); and (iii) the best-bound sequence in the major groove is d(GGC GCC)2 with two successive guanines upstream from the intercalation. On the other hand, due to the flexibility of the arginyl arms, other GC-rich sequences have close binding energies, two of them being less stable than it by less than 8 kcal/mol. These results serve as the basis for the design of derivatives of BAP with enhanced sequence selectivities in the major groove.

Free energy profiles of cocaine esterase-cocaine binding process by molecular dynamics and potential of mean force simulations.

PubMed

Zhang, Yuxin; Huang, Xiaoqin; Han, Keli; Zheng, Fang; Zhan, Chang-Guo

2016-11-25

The combined molecular dynamics (MD) and potential of mean force (PMF) simulations have been performed to determine the free energy profile of the CocE)-(+)-cocaine binding process in comparison with that of the corresponding CocE-(-)-cocaine binding process. According to the MD simulations, the equilibrium CocE-(+)-cocaine binding mode is similar to the CocE-(-)-cocaine binding mode. However, based on the simulated free energy profiles, a significant free energy barrier (∼5 kcal/mol) exists in the CocE-(+)-cocaine binding process whereas no obvious free energy barrier exists in the CocE-(-)-cocaine binding process, although the free energy barrier of ∼5 kcal/mol is not high enough to really slow down the CocE-(+)-cocaine binding process. In addition, the obtained free energy profiles also demonstrate that (+)-cocaine and (-)-cocaine have very close binding free energies with CocE, with a negligible difference (∼0.2 kcal/mol), which is qualitatively consistent with the nearly same experimental K M values of the CocE enzyme for (+)-cocaine and (-)-cocaine. The consistency between the computational results and available experimental data suggests that the mechanistic insights obtained from this study are reasonable. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Substitutional and interstitial oxygen in wurtzite GaN

NASA Astrophysics Data System (ADS)

Wright, A. F.

2005-11-01

Density-functional theory was used to compute energy-minimum configurations and formation energies of substitutional and interstitial oxygen (O) in wurtzite GaN. The results indicate that O substituted at a N site (ON) acts as a single donor with the ionized state (ON+1) being the most stable O state in p-type GaN. In n-type GaN, interstitial O (OI) is predicted to be a double acceptor and O substituted at a Ga site (OGa) is predicted to be a triple acceptor. The formation energies of these two species are comparable to that of ON in n-type GaN and, as such, they should form and compensate the ON donors. The extent of compensation was estimated for both Ga-rich and N-rich conditions with a total O concentration of 1017cm-3. Ga-rich conditions yielded negligible compensation and an ON concentration in excess of 9.9×1016cm-3. N-rich conditions yielded a 25% lower ON concentration, due to the increased stability of OI and OGa relative to ON, and moderate compensation. These findings are consistent with experimental results indicating that O acts as a donor in GaN(O). Complexes of ON with the Mg acceptor and OI with the Si donor were examined. Binding energies for charge-conserving reactions were ⩾0.5eV, indicating that these complexes can exist in equilibrium at room temperature. Complexes of ON with the Ga vacancy in n-type GaN were also examined and their binding energies were 1.2 and 1.4eV, indicating that appreciable concentrations can exist in equilibrium even at elevated temperatures.

Binding affinities of the farnesoid X receptor in the D3R Grand Challenge 2 estimated by free-energy perturbation and docking

NASA Astrophysics Data System (ADS)

Olsson, Martin A.; García-Sosa, Alfonso T.; Ryde, Ulf

2018-01-01

We have studied the binding of 102 ligands to the farnesoid X receptor within the D3R Grand Challenge 2016 blind-prediction competition. First, we employed docking with five different docking software and scoring functions. The selected docked poses gave an average root-mean-squared deviation of 4.2 Å. Consensus scoring gave decent results with a Kendall's τ of 0.26 ± 0.06 and a Spearman's ρ of 0.41 ± 0.08. For a subset of 33 ligands, we calculated relative binding free energies with free-energy perturbation. Five transformations between the ligands involved a change of the net charge and we implemented and benchmarked a semi-analytic correction (Rocklin et al., J Chem Phys 139:184103, 2013) for artifacts caused by the periodic boundary conditions and Ewald summation. The results gave a mean absolute deviation of 7.5 kJ/mol compared to the experimental estimates and a correlation coefficient of R 2 = 0.1. These results were among the four best in this competition out of 22 submissions. The charge corrections were significant (7-8 kJ/mol) and always improved the results. By employing 23 intermediate states in the free-energy perturbation, there was a proper overlap between all states and the precision was 0.1-0.7 kJ/mol. However, thermodynamic cycles indicate that the sampling was insufficient in some of the perturbations.

Binding free energy and counterion release for adsorption of the antimicrobial peptide lactoferricin B on a POPG membrane.

PubMed

Tolokh, Igor S; Vivcharuk, Victor; Tomberli, Bruno; Gray, C G

2009-09-01

Molecular dynamics (MD) simulations are used to study the interaction of an anionic palmitoyl-oleoyl-phosphatidylglycerol (POPG) bilayer with the cationic antimicrobial peptide bovine lactoferricin (LFCinB) in a 100 mM NaCl solution at 310 K. The interaction of LFCinB with a POPG bilayer is employed as a model system for studying the details of membrane adsorption selectivity of cationic antimicrobial peptides. Seventy eight 4 ns MD production run trajectories of the equilibrated system, with six restrained orientations of LFCinB at 13 different separations from the POPG membrane, are generated to determine the free energy profile for the peptide as a function of the distance between LFCinB and the membrane surface. To calculate the profile for this relatively large system, a variant of constrained MD and thermodynamic integration is used. A simplified method for relating the free energy profile to the LFCinB-POPG membrane binding constant is employed to predict a free energy of adsorption of -5.4+/-1.3 kcal/mol and a corresponding maximum adsorption binding force of about 58 pN. We analyze the results using Poisson-Boltzmann theory. We find the peptide-membrane attraction to be dominated by the entropy increase due to the release of counterions and polarized water from the region between the charged membrane and peptide, as the two approach each other. We contrast these results with those found earlier for adsorption of LFCinB on the mammalianlike palmitoyl-oleoyl-phosphatidylcholine membrane.

Analysis of Immune Complex Structure by Statistical Mechanics and Light Scattering Techniques.

NASA Astrophysics Data System (ADS)

Busch, Nathan Adams

1995-01-01

The size and structure of immune complexes determine their behavior in the immune system. The chemical physics of the complex formation is not well understood; this is due in part to inadequate characterization of the proteins involved, and in part by lack of sufficiently well developed theoretical techniques. Understanding the complex formation will permit rational design of strategies for inhibiting tissue deposition of the complexes. A statistical mechanical model of the proteins based upon the theory of associating fluids was developed. The multipole electrostatic potential for each protein used in this study was characterized for net protein charge, dipole moment magnitude, and dipole moment direction. The binding sites, between the model antigen and antibodies, were characterized for their net surface area, energy, and position relative to the dipole moment of the protein. The equilibrium binding graphs generated with the protein statistical mechanical model compares favorably with experimental data obtained from radioimmunoassay results. The isothermal compressibility predicted by the model agrees with results obtained from dynamic light scattering. The statistical mechanics model was used to investigate association between the model antigen and selected pairs of antibodies. It was found that, in accordance to expectations from thermodynamic arguments, the highest total binding energy yielded complex distributions which were skewed to higher complex size. From examination of the simulated formation of ring structures from linear chain complexes, and from the joint shape probability surfaces, it was found that ring configurations were formed by the "folding" of linear chains until the ends are within binding distance. By comparing the single antigen/two antibody system which differ only in their respective binding site locations, it was found that binding site location influences complex size and shape distributions only when ring formation occurs. The internal potential energy of a ring complex is considerably less than that of the non-associating system; therefore the ring complexes are quite stable and show no evidence of breaking, and collapsing into smaller complexes. The ring formation will occur only in systems where the total free energy of each complex may be minimized. Thus, ring formation will occur even though entropically unfavorable conformations result if the total free energy can be minimized by doing so.

Molecular modeling studies of HIV-1 reverse transcriptase nonnucleoside inhibitors: total energy of complexation as a predictor of drug placement and activity.

PubMed Central

Kroeger Smith, M. B.; Rouzer, C. A.; Taneyhill, L. A.; Smith, N. A.; Hughes, S. H.; Boyer, P. L.; Janssen, P. A.; Moereels, H.; Koymans, L.; Arnold, E.

1995-01-01

Computer modeling studies have been carried out on three nonnucleoside inhibitors complexed with human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT), using crystal coordinate data from a subset of the protein surrounding the binding pocket region. Results from the minimizations of solvated complexes of 2-cyclopropyl-4-methyl-5,11-dihydro-5H-dipyrido[3,2-b :2',3'-e][1,4] diazepin-6-one (nevirapine), alpha-anilino-2, 6-dibromophenylacetamide (alpha-APA), and 8-chloro-tetrahydro-imidazo(4,5,1-jk)(1,4)-benzodiazepin-2(1H)-thi one (TIBO) show that all three inhibitors maintain a very similar conformational shape, roughly overlay each other in the binding pocket, and appear to function as pi-electron donors to aromatic side-chain residues surrounding the pocket. However, side-chain residues adapt to each bound inhibitor in a highly specific manner, closing down around the surface of the drug to make tight van der Waals contacts. Consequently, the results from the calculated minimizations reveal that only when the inhibitors are modeled in a site constructed from coordinate data obtained from their particular RT complex can the calculated binding energies be relied upon to predict the correct orientation of the drug in the pocket. In the correct site, these binding energies correlate with EC50 values determined for all three inhibitors in our laboratory. Analysis of the components of the binding energy reveals that, for all three inhibitors, solvation of the drug is endothermic, but solvation of the protein is exothermic, and the sum favors complex formation. In general, the protein is energetically more stable and the drug less stable in their complexes as compared to the reactant conformations. For all three inhibitors, interaction with the protein in the complex is highly favorable. Interactions of the inhibitors with individual residues correlate with crystallographic and site-specific mutational data. pi-Stacking interactions are important in binding and correlate with drug HOMO RHF/6-31G* energies. Modeling results are discussed with respect to the mechanism of complex formation and the design of nonnucleoside inhibitors that will be more effective against mutants of HIV-1 RT that are resistant to the currently available drugs. PMID:8535257

Insights into the Interactions of Fasciola hepatica Cathepsin L3 with a Substrate and Potential Novel Inhibitors through In Silico Approaches

PubMed Central

Hernández Alvarez, Lilian; Naranjo Feliciano, Dany; Hernández González, Jorge Enrique; de Oliveira Soares, Rosemberg; Barreto Gomes, Diego Enry; Pascutti, Pedro Geraldo

2015-01-01

Background Fasciola hepatica is the causative agent of fascioliasis, a disease affecting grazing animals, causing economic losses in global agriculture and currently being an important human zoonosis. Overuse of chemotherapeutics against fascioliasis has increased the populations of drug resistant parasites. F. hepatica cathepsin L3 is a protease that plays important roles during the life cycle of fluke. Due to its particular collagenolytic activity it is considered an attractive target against the infective phase of F. hepatica. Methodology/Principal Findings Starting with a three dimensional model of FhCL3 we performed a structure-based design of novel inhibitors through a computational study that combined virtual screening, molecular dynamics simulations, and binding free energy (ΔGbind) calculations. Virtual screening was carried out by docking inhibitors obtained from the MYBRIDGE-HitFinder database inside FhCL3 and human cathepsin L substrate-binding sites. On the basis of dock-scores, five compounds were predicted as selective inhibitors of FhCL3. Molecular dynamic simulations were performed and, subsequently, an end-point method was employed to predict ΔGbind values. Two compounds with the best ΔGbind values (-10.68 kcal/mol and -7.16 kcal/mol), comparable to that of the positive control (-10.55 kcal/mol), were identified. A similar approach was followed to structurally and energetically characterize the interface of FhCL3 in complex with a peptidic substrate. Finally, through pair-wise and per-residue free energy decomposition we identified residues that are critical for the substrate/ligand binding and for the enzyme specificity. Conclusions/Significance The present study is the first computer-aided drug design approach against F. hepatica cathepsins. Here we predict the principal determinants of binding of FhCL3 in complex with a natural substrate by detailed energetic characterization of protease interaction surface. We also propose novel compounds as FhCL3 inhibitors. Overall, these results will foster the future rational design of new inhibitors against FhCL3, as well as other F. hepatica cathepsins. PMID:25978322

Insights into the Interactions of Fasciola hepatica Cathepsin L3 with a Substrate and Potential Novel Inhibitors through In Silico Approaches.

PubMed

Hernández Alvarez, Lilian; Naranjo Feliciano, Dany; Hernández González, Jorge Enrique; Soares, R O; Soares, Rosemberg de Oliveira; Barreto Gomes, Diego Enry; Pascutti, Pedro Geraldo

2015-05-01

Fasciola hepatica is the causative agent of fascioliasis, a disease affecting grazing animals, causing economic losses in global agriculture and currently being an important human zoonosis. Overuse of chemotherapeutics against fascioliasis has increased the populations of drug resistant parasites. F. hepatica cathepsin L3 is a protease that plays important roles during the life cycle of fluke. Due to its particular collagenolytic activity it is considered an attractive target against the infective phase of F. hepatica. Starting with a three dimensional model of FhCL3 we performed a structure-based design of novel inhibitors through a computational study that combined virtual screening, molecular dynamics simulations, and binding free energy (ΔGbind) calculations. Virtual screening was carried out by docking inhibitors obtained from the MYBRIDGE-HitFinder database inside FhCL3 and human cathepsin L substrate-binding sites. On the basis of dock-scores, five compounds were predicted as selective inhibitors of FhCL3. Molecular dynamic simulations were performed and, subsequently, an end-point method was employed to predict ΔGbind values. Two compounds with the best ΔGbind values (-10.68 kcal/mol and -7.16 kcal/mol), comparable to that of the positive control (-10.55 kcal/mol), were identified. A similar approach was followed to structurally and energetically characterize the interface of FhCL3 in complex with a peptidic substrate. Finally, through pair-wise and per-residue free energy decomposition we identified residues that are critical for the substrate/ligand binding and for the enzyme specificity. The present study is the first computer-aided drug design approach against F. hepatica cathepsins. Here we predict the principal determinants of binding of FhCL3 in complex with a natural substrate by detailed energetic characterization of protease interaction surface. We also propose novel compounds as FhCL3 inhibitors. Overall, these results will foster the future rational design of new inhibitors against FhCL3, as well as other F. hepatica cathepsins.

Analyzing machupo virus-receptor binding by molecular dynamics simulations.

PubMed

Meyer, Austin G; Sawyer, Sara L; Ellington, Andrew D; Wilke, Claus O

2014-01-01

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein-protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host-virus protein-protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein-protein interactions.

Analyzing machupo virus-receptor binding by molecular dynamics simulations

PubMed Central

Sawyer, Sara L.; Ellington, Andrew D.; Wilke, Claus O.

2014-01-01

In many biological applications, we would like to be able to computationally predict mutational effects on affinity in protein–protein interactions. However, many commonly used methods to predict these effects perform poorly in important test cases. In particular, the effects of multiple mutations, non alanine substitutions, and flexible loops are difficult to predict with available tools and protocols. We present here an existing method applied in a novel way to a new test case; we interrogate affinity differences resulting from mutations in a host–virus protein–protein interface. We use steered molecular dynamics (SMD) to computationally pull the machupo virus (MACV) spike glycoprotein (GP1) away from the human transferrin receptor (hTfR1). We then approximate affinity using the maximum applied force of separation and the area under the force-versus-distance curve. We find, even without the rigor and planning required for free energy calculations, that these quantities can provide novel biophysical insight into the GP1/hTfR1 interaction. First, with no prior knowledge of the system we can differentiate among wild type and mutant complexes. Moreover, we show that this simple SMD scheme correlates well with relative free energy differences computed via free energy perturbation. Second, although the static co-crystal structure shows two large hydrogen-bonding networks in the GP1/hTfR1 interface, our simulations indicate that one of them may not be important for tight binding. Third, one viral site known to be critical for infection may mark an important evolutionary suppressor site for infection-resistant hTfR1 mutants. Finally, our approach provides a framework to compare the effects of multiple mutations, individually and jointly, on protein–protein interactions. PMID:24624315

Testing the Formation Scenarios of Binary Neutron Star Systems with Measurements of the Neutron Star Moment of Inertia

NASA Astrophysics Data System (ADS)

Newton, William G.; Steiner, Andrew W.; Yagi, Kent

2018-03-01

Two low-mass (M < 1.4 M ⊙) neutron stars, J0737-3039B and the companion to J1756-2251, show strong evidence of being formed in an ultra-stripped supernova explosion (US-SN) with a ONeMg or Fe progenitor. Using systematically generated sets of equations of state we map out the relationship between the moment of inertia of J0737-3039A, a candidate for a moment of inertia measurement within a decade, and the binding energy of the two low-mass neutron stars. This relationship, similar to the I-Love-Q relations, is more robust than a previously explored correlation between the binding energy and the slope of the nuclear symmetry energy L. We find that, if either J0737-3039B or the J1756-2251 companion were formed in a US-SN, no more than 0.06 M ⊙ could have been lost from the progenitor core. Furthermore, a measurement of the moment of inertia of J0737-3039A to within 10% accuracy can discriminate between formation scenarios and, given current constraints on the predicted core mass loss, potentially rule them out. Advanced LIGO can potentially measure the neutron star tidal polarizability to equivalent accuracy which, using the I-Love-Q relations, would obtain similar constraints on the formation scenarios. Such information would help constrain important aspects of binary evolution used for population synthesis predictions of the rate of binary neutron star mergers and resulting electromagnetic and gravitational wave signals. Further progress needs to be made in modeling the core-collapse process that leads to low-mass neutron stars, particularly in making robust predictions for the mass loss from the progenitor core.

Unique Organization of Extracellular Amylases into Amylosomes in the Resistant Starch-Utilizing Human Colonic Firmicutes Bacterium Ruminococcus bromii

PubMed Central

Ze, Xiaolei; Ben David, Yonit; Laverde-Gomez, Jenny A.; Dassa, Bareket; Sheridan, Paul O.; Duncan, Sylvia H.; Louis, Petra; Henrissat, Bernard; Juge, Nathalie; Koropatkin, Nicole M.; Bayer, Edward A.

2015-01-01

ABSTRACT Ruminococcus bromii is a dominant member of the human gut microbiota that plays a key role in releasing energy from dietary starches that escape digestion by host enzymes via its exceptional activity against particulate “resistant” starches. Genomic analysis of R. bromii shows that it is highly specialized, with 15 of its 21 glycoside hydrolases belonging to one family (GH13). We found that amylase activity in R. bromii is expressed constitutively, with the activity seen during growth with fructose as an energy source being similar to that seen with starch as an energy source. Six GH13 amylases that carry signal peptides were detected by proteomic analysis in R. bromii cultures. Four of these enzymes are among 26 R. bromii proteins predicted to carry dockerin modules, with one, Amy4, also carrying a cohesin module. Since cohesin-dockerin interactions are known to mediate the formation of protein complexes in cellulolytic ruminococci, the binding interactions of four cohesins and 11 dockerins from R. bromii were investigated after overexpressing them as recombinant fusion proteins. Dockerins possessed by the enzymes Amy4 and Amy9 are predicted to bind a cohesin present in protein scaffoldin 2 (Sca2), which resembles the ScaE cell wall-anchoring protein of a cellulolytic relative, R. flavefaciens. Further complexes are predicted between the dockerin-carrying amylases Amy4, Amy9, Amy10, and Amy12 and two other cohesin-carrying proteins, while Amy4 has the ability to autoaggregate, as its dockerin can recognize its own cohesin. This organization of starch-degrading enzymes is unprecedented and provides the first example of cohesin-dockerin interactions being involved in an amylolytic system, which we refer to as an “amylosome.” PMID:26419877

Alignment-independent technique for 3D QSAR analysis

NASA Astrophysics Data System (ADS)

Wilkes, Jon G.; Stoyanova-Slavova, Iva B.; Buzatu, Dan A.

2016-04-01

Molecular biochemistry is controlled by 3D phenomena but structure-activity models based on 3D descriptors are infrequently used for large data sets because of the computational overhead for determining molecular conformations. A diverse dataset of 146 androgen receptor binders was used to investigate how different methods for defining molecular conformations affect the performance of 3D-quantitative spectral data activity relationship models. Molecular conformations tested: (1) global minimum of molecules' potential energy surface; (2) alignment-to-templates using equal electronic and steric force field contributions; (3) alignment using contributions "Best-for-Each" template; (4) non-energy optimized, non-aligned (2D > 3D). Aggregate predictions from models were compared. Highest average coefficients of determination ranged from R Test 2 = 0.56 to 0.61. The best model using 2D > 3D (imported directly from ChemSpider) produced R Test 2 = 0.61. It was superior to energy-minimized and conformation-aligned models and was achieved in only 3-7 % of the time required using the other conformation strategies. Predictions averaged from models built on different conformations achieved a consensus R Test 2 = 0.65. The best 2D > 3D model was analyzed for underlying structure-activity relationships. For the compound strongest binding to the androgen receptor, 10 substructural features contributing to binding were flagged. Utility of 2D > 3D was compared for two other activity endpoints, each modeling a medium sized data set. Results suggested that large scale, accurate predictions using 2D > 3D SDAR descriptors may be produced for interactions involving endocrine system nuclear receptors and other data sets in which strongest activities are produced by fairly inflexible substrates.

Molecular basis of P450 OleTJE: an investigation of substrate binding mechanism and major pathways

NASA Astrophysics Data System (ADS)

Du, Juan; Liu, Lin; Guo, Li Zhong; Yao, Xiao Jun; Yang, Jian Ming

2017-05-01

Cytochrome P450 OleTJE has attracted much attention for its ability to catalyze the decarboxylation of long chain fatty acids to generate alkenes, which are not only biofuel molecule, but also can be used broadly for making lubricants, polymers and detergents. In this study, the molecular basis of the binding mechanism of P450 OleTJE for arachidic acid, myristic acid, and caprylic acid was investigated by utilizing conventional molecular dynamics simulation and binding free energy calculations. Moreover, random acceleration molecular dynamics (RAMD) simulations were performed to uncover the most probable access/egress channels for different fatty acids. The predicted binding free energy shows an order of arachidic acid < myristic acid < caprylic acid. Key residues interacting with three substrates and residues specifically binding to one of them were identified. The RAMD results suggest the most likely channel for arachidic acid, myristic acid, and caprylic acid are 2e/2b, 2a and 2f/2a, respectively. It is suggested that the reaction is easier to carry out in myristic acid bound system than those in arachidic acid and caprylic acid bound system based on the distance of Hβ atom of substrate relative to P450 OleTJE Compound I states. This study provided novel insight to understand the substrate preference mechanism of P450 OleTJE and valuable information for rational enzyme design for short chain fatty acid decarboxylation.

Key binding and susceptibility of NS3/4A serine protease inhibitors against hepatitis C virus.

PubMed

Meeprasert, Arthitaya; Hannongbua, Supot; Rungrotmongkol, Thanyada

2014-04-28

Hepatitis C virus (HCV) causes an infectious disease that manifests itself as liver inflammation, cirrhosis, and can lead to the development of liver cancer. Its NS3/4A serine protease is a potent target for drug design and development since it is responsible for cleavage of the scissile peptide bonds in the polyprotein important for the HCV life cycle. Herein, the ligand-target interactions and the binding free energy of the four current NS3/4A inhibitors (boceprevir, telaprevir, danoprevir, and BI201335) were investigated by all-atom molecular dynamics simulations with three different initial atomic velocities. The per-residue free energy decomposition suggests that the key residues involved in inhibitor binding were residues 41-43, 57, 81, 136-139, 155-159, and 168 in the NS3 domain. The van der Waals interactions yielded the main driving force for inhibitor binding at the protease active site for the cleavage reaction. In addition, the highest number of hydrogen bonds was formed at the reactive P1 site of the four studied inhibitors. Although the hydrogen bond patterns of these inhibitors were different, their P3 site was most likely to be recognized by the A157 backbone. Both molecular mechanic (MM)/Poisson-Boltzmann surface area and MM/generalized Born surface area approaches predicted the relative binding affinities of the four inhibitors in a somewhat similar trend to their experimentally derived biological activities.

The Strength of Hydrogen Bonds between Fluoro-Organics and Alcohols, a Theoretical Study.

PubMed

Rosenberg, Robert E

2018-05-10

Fluorinated organic compounds are ubiquitous in the pharmaceutical and agricultural industries. To better discern the mode of action of these compounds, it is critical to understand the strengths of hydrogen bonds involving fluorine. There are only a few published examples of the strengths of these bonds. This study provides a high level ab initio study of inter- and intramolecular hydrogen bonds between RF and R'OH, where R and R' are aryl, vinyl, alkyl, and cycloalkyl. Intermolecular binding energies average near 5 kcal/mol, while intramolecular binding energies average about 3 kcal/mol. Inclusion of zero-point energies and applying a counterpoise correction lessen the difference. In both series, modest increases in binding energies are seen with increased acidity of R'OH and increased electron donation of R in RF. In the intramolecular compounds, binding energy increases with the rigidity of the F-(C) n -OH ring. Inclusion of free energy corrections at 298 K results in exoergic binding energies for the intramolecular compounds and endoergic binding energies for the intermolecular compounds. Parameters such as bond lengths, vibrational frequencies, and atomic populations are consistent with formation of a hydrogen bond and with slightly stronger binding in the intermolecular cases over the intramolecular cases. However, these parameters correlated poorly with binding energies.

Blocking Protein kinase C signaling pathway: mechanistic insights into the anti-leishmanial activity of prospective herbal drugs from Withania somnifera

PubMed Central

2012-01-01

Background Leishmaniasis is caused by several species of leishmania protozoan and is one of the major vector-born diseases after malaria and sleeping sickness. Toxicity of available drugs and drug resistance development by protozoa in recent years has made Leishmaniasis cure difficult and challenging. This urges the need to discover new antileishmanial-drug targets and antileishmanial-drug development. Results Tertiary structure of leishmanial protein kinase C was predicted and found stable with a RMSD of 5.8Å during MD simulations. Natural compound withaferin A inhibited the predicted protein at its active site with -28.47 kcal/mol binding free energy. Withanone was also found to inhibit LPKC with good binding affinity of -22.57 kcal/mol. Both withaferin A and withanone were found stable within the binding pocket of predicted protein when MD simulations of ligand-bound protein complexes were carried out to examine the consistency of interactions between the two. Conclusions Leishmanial protein kinase C (LPKC) has been identified as a potential target to develop drugs against Leishmaniasis. We modelled and refined the tertiary structure of LPKC using computational methods such as homology modelling and molecular dynamics simulations. This structure of LPKC was used to reveal mode of inhibition of two previous experimentally reported natural compounds from Withania somnifera - withaferin A and withanone. PMID:23281834

Metal cation dependence of interactions with amino acids: bond dissociation energies of Rb(+) and Cs(+) to the acidic amino acids and their amide derivatives.

PubMed

Armentrout, P B; Yang, Bo; Rodgers, M T

2014-04-24

Metal cation-amino acid interactions are key components controlling the secondary structure and biological function of proteins, enzymes, and macromolecular complexes comprising these species. Determination of pairwise interactions of alkali metal cations with amino acids provides a thermodynamic vocabulary that begins to quantify these fundamental processes. In the present work, we expand a systematic study of such interactions by examining rubidium and cesium cations binding with the acidic amino acids (AA), aspartic acid (Asp) and glutamic acid (Glu), and their amide derivatives, asparagine (Asn) and glutamine (Gln). These eight complexes are formed using electrospray ionization and their bond dissociation energies (BDEs) are determined experimentally using threshold collision-induced dissociation with xenon in a guided ion beam tandem mass spectrometer. Analyses of the energy-dependent cross sections include consideration of unimolecular decay rates, internal energy of the reactant ions, and multiple ion-neutral collisions. Quantum chemical calculations are conducted at the B3LYP, MP2(full), and M06 levels of theory using def2-TZVPPD basis sets, with results showing reasonable agreement with experiment. At 0 and 298 K, most levels of theory predict that the ground-state conformers for M(+)(Asp) and M(+)(Asn) involve tridentate binding of the metal cation to the backbone carbonyl, amino, and side-chain carbonyl groups, although tridentate binding to the carboxylic acid group and side-chain carbonyl is competitive for M(+)(Asn). For the two longer side-chain amino acids, Glu and Gln, multiple structures are competitive. A comparison of these results to those for the smaller alkali cations, Na(+) and K(+), provides insight into the trends in binding energies associated with the molecular polarizability and dipole moment of the side chain. For all four metal cations, the BDEs are inversely correlated with the size of the metal cation and follow the order Asp < Glu < Asn < Gln.

Force-field and quantum-mechanical binding study of selected SAMPL3 host-guest complexes

NASA Astrophysics Data System (ADS)

Hamaguchi, Nobuko; Fusti-Molnar, Laszlo; Wlodek, Stanislaw

2012-05-01

A Merck molecular force field classical potential combined with Poisson-Boltzmann electrostatics (MMFF/PB) has been used to estimate the binding free energy of seven guest molecules (six tertiary amines and one primary amine) into a synthetic receptor (acyclic cucurbit[4]uril congener) and two benzimidazoles into cyclic cucurbit[7]uril (CB[7]) and cucurbit[8]uril (CB[8]) hosts. In addition, binding enthalpies for the benzimidazoles were calculated with density functional theory (DFT) using the B3LYP functional and a polarizable continuum model (PCM). Although in most cases the MMFF/PB approach returned reasonable agreements with the experiment (±2 kcal/mol), significant, much larger deviations were reported in the case of three host-guest pairs. All four binding enthalpy predictions with the DFT/PCM method suffered 70% or larger deviations from the calorimetry data. Results are discussed in terms of the molecular models used for guest-host complexation and the quality of the intermolecular potentials.

Interaction between IGFBP7 and insulin: a theoretical and experimental study

NASA Astrophysics Data System (ADS)

Ruan, Wenjing; Kang, Zhengzhong; Li, Youzhao; Sun, Tianyang; Wang, Lipei; Liang, Lijun; Lai, Maode; Wu, Tao

2016-04-01

Insulin-like growth factor binding protein 7 (IGFBP7) can bind to insulin with high affinity which inhibits the early steps of insulin action. Lack of recognition mechanism impairs our understanding of insulin regulation before it binds to insulin receptor. Here we combine computational simulations with experimental methods to investigate the interaction between IGFBP7 and insulin. Molecular dynamics simulations indicated that His200 and Arg198 in IGFBP7 were key residues. Verified by experimental data, the interaction remained strong in single mutation systems R198E and H200F but became weak in double mutation system R198E-H200F relative to that in wild-type IGFBP7. The results and methods in present study could be adopted in future research of discovery of drugs by disrupting protein-protein interactions in insulin signaling. Nevertheless, the accuracy, reproducibility, and costs of free-energy calculation are still problems that need to be addressed before computational methods can become standard binding prediction tools in discovery pipelines.

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

Nature of the high-binding-energy dip in the low-temperature photoemission spectra of Bi2Sr2CaCu2O8+δ

NASA Astrophysics Data System (ADS)

Dessau, D. S.; Shen, Z.-X.; Wells, B. O.; King, D. M.; Spicer, W. E.; Arko, A. J.; Lombardo, L. W.; Mitzi, D. B.; Kapitulnik, A.

1992-03-01

At the transition to superconductivity, an anomalous high-binding-energy (~=-90 meV) dip appears in the low-temperature photoemission spectra taken along the Γ-M¯ high-symmetry direction of Bi2Sr2CaCu2O8+δ. This paper details experiments which further characterize the energy and k-space dependence of this dip structure. The dip occurs over a wide portion of the Γ-M¯ zone diagonal (110), yet shows minimal energy dispersion. In the spectra taken along the Γ-X zone edge (100), the dip is very weak or not present. We show that these results imply that the dip is not an artifact dependent on the experiment or special features of the band structure and therefore is an intrinsic feature of the superconducting state of Bi2Sr2CaCu2O8+δ. The behavior of the normal-state bands along Γ-M¯ in relation to the local-density-approximation prediction of a Bi-O-based electron ``pocket'' is also discussed, with our data explained most naturally if the Bi-O band remains above the Fermi level for all k.

Structural stability and energetics of grain boundary triple junctions in face centered cubic materials

NASA Astrophysics Data System (ADS)

Adlakha, I.; Solanki, K. N.

2015-03-01

We present a systematic study to elucidate the role of triple junctions (TJs) and their constituent grain boundaries on the structural stability of nanocrystalline materials. Using atomistic simulations along with the nudge elastic band calculations, we explored the atomic structural and thermodynamic properties of TJs in three different fcc materials. We found that the magnitude of excess energy at a TJ was directly related to the atomic density of the metal. Further, the vacancy binding and migration energetics in the vicinity of the TJ were examined as they play a crucial role in the structural stability of NC materials. The resolved line tension which takes into account the stress buildup at the TJ was found to be a good measure in predicting the vacancy binding tendency near the TJ. The activation energy for vacancy migration along the TJ was directly correlated with the measured excess energy. Finally, we show that the resistance for vacancy diffusion increased for TJs with larger excess stored energy and the defect mobility at some TJs is slower than their constituent GBs. Hence, our results have general implications on the diffusional process in NC materials and provide new insight into stabilizing NC materials with tailored TJs.

Deciphering the fluorescence resonance energy transfer from denatured transport protein to anthracene 1,5 disulphonate in reverse micellar environment

NASA Astrophysics Data System (ADS)

Singharoy, Dipti; Bhattacharya, Subhash Chandra

2017-12-01

Constrained environmental effect inside AOT reverse micellar media has been employed in this work to collect the information about energy transfer efficacy between sodium salt of anthracene 1,5 disulphonate (1,5-AS) with model transport proteins, bovine serum albumin (BSA), and human serum albumin (HSA). Steady state, time-resolved fluorescence and circular dichroism techniques have been used for this purpose and corresponding Fӧrster-type resonance energy transfer (FRET) from tryptophan residues to 1,5-AS indicates that 1,5-AS binds in the vicinity of the tryptophan residue (BSA and HSA) with equal strength. Indication of protein damage from fluorescence data and its confirmation has been measured from CD measurement. Molecular modeling study hereby plays a crucial role to predict the minimum energy docked conformation of the probe inside the protein environment. From the docked conformation the distance between 1,5-AS and tryptophan moiety of BSA/HSA has successfully explained the FRET possibility between them. A comparative modeling study between BSA and HSA with 1,5-AS assigning their binding site within specific amino acids plays a crucial role in support of the FRET study.

Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease.

PubMed

Kar, Parimal; Lipowsky, Reinhard; Knecht, Volker

2013-05-16

Both KNI-10033 and KNI-10075 are high affinity preclinical HIV-1 protease (PR) inhibitors with affinities in the picomolar range. In this work, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method has been used to investigate the potency of these two HIV-1 PR inhibitors against the wild-type and mutated proteases assuming that potency correlates with the affinity of the drugs for the target protein. The decomposition of the binding free energy reveals the origin of binding affinities or mutation-induced affinity changes. Our calculations indicate that the mutation I50V causes drug resistance against both inhibitors. On the other hand, we predict that the mutant I84V causes drug resistance against KNI-10075 while KNI-10033 is more potent against the I84V mutant compared to wild-type protease. Drug resistance arises mainly from unfavorable shifts in van der Waals interactions and configurational entropy. The latter indicates that neglecting changes in configurational entropy in the computation of relative binding affinities as often done is not appropriate in general. For the bound complex PR(I50V)-KNI-10075, an increased polar solvation free energy also contributes to the drug resistance. The importance of polar solvation free energies is revealed when interactions governing the binding of KNI-10033 or KNI-10075 to the wild-type protease are compared to the inhibitors darunavir or GRL-06579A. Although the contributions from intermolecular electrostatic and van der Waals interactions as well as the nonpolar component of the solvation free energy are more favorable for PR-KNI-10033 or PR-KNI-10075 compared to PR-DRV or PR-GRL-06579A, both KNI-10033 and KNI-10075 show a similar affinity as darunavir and a lower binding affinity relative to GRL-06579A. This is because of the polar solvation free energy which is less unfavorable for darunavir or GRL-06579A relative to KNI-10033 or KNI-10075. The importance of the polar solvation as revealed here highlights that structural inspection alone is not sufficient for identifying the key contributions to binding affinities and affinity changes for the design of drugs but that solvation effects must be taken into account. A detailed understanding of the molecular forces governing binding and drug resistance might assist in the design of new inhibitors against HIV-1 PR variants that are resistant against current drugs.

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Luo, Heng; Ye, Hao; Ng, Hui Wen

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

PubMed Central

Luo, Heng; Ye, Hao; Ng, Hui Wen; Sakkiah, Sugunadevi; Mendrick, Donna L.; Hong, Huixiao

2016-01-01

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. This algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system. PMID:27558848

sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides

DOE PAGES

Luo, Heng; Ye, Hao; Ng, Hui Wen; ...

2016-08-25

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithmmore » can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.« less

Influence of polarization and self-polarization charges on impurity binding energy in spherical quantum dot with parabolic confinement

NASA Astrophysics Data System (ADS)

Sarkar, Supratik; Sarkar, Samrat; Bose, Chayanika

2018-07-01

We present a general formulation of the ground state binding energy of a shallow hydrogenic impurity in spherical quantum dot with parabolic confinement, considering the effects of polarization and self energy. The variational approach within the effective mass approximation is employed here. The binding energy of an on-center impurity is computed for a GaAs/AlxGa1-xAs quantum dot as a function of the dot size with the dot barrier as parameter. The influence of polarization and self energy are also treated separately. Results indicate that the binding energy increases due to the presence of polarization charge, while decreases due to the self energy of the carrier. An overall enhancement in impurity binding energy, especially for small dots is noted.

Universal binding energy relations in metallic adhesion

NASA Technical Reports Server (NTRS)

Ferrante, J.; Smith, J. R.; Rose, J. H.

1981-01-01

Scaling relations which map metallic adhesive binding energy onto a single universal binding energy curve are discussed in relation to adhesion, friction, and wear in metals. The scaling involved normalizing the energy to the maximum binding energy and normalizing distances by a suitable combination of Thomas-Fermi screening lengths. The universal curve was found to be accurately represented by E*(A*)= -(1+beta A) exp (-Beta A*) where E* is the normalized binding energy, A* is the normalized separation, and beta is the normalized decay constant. The calculated cohesive energies of potassium, barium, copper, molybdenum, and samarium were also found to scale by similar relations, suggesting that the universal relation may be more general than for the simple free electron metals.

PRISM offers a comprehensive genomic approach to transcription factor function prediction

PubMed Central

Wenger, Aaron M.; Clarke, Shoa L.; Guturu, Harendra; Chen, Jenny; Schaar, Bruce T.; McLean, Cory Y.; Bejerano, Gill

2013-01-01

The human genome encodes 1500–2000 different transcription factors (TFs). ChIP-seq is revealing the global binding profiles of a fraction of TFs in a fraction of their biological contexts. These data show that the majority of TFs bind directly next to a large number of context-relevant target genes, that most binding is distal, and that binding is context specific. Because of the effort and cost involved, ChIP-seq is seldom used in search of novel TF function. Such exploration is instead done using expression perturbation and genetic screens. Here we propose a comprehensive computational framework for transcription factor function prediction. We curate 332 high-quality nonredundant TF binding motifs that represent all major DNA binding domains, and improve cross-species conserved binding site prediction to obtain 3.3 million conserved, mostly distal, binding site predictions. We combine these with 2.4 million facts about all human and mouse gene functions, in a novel statistical framework, in search of enrichments of particular motifs next to groups of target genes of particular functions. Rigorous parameter tuning and a harsh null are used to minimize false positives. Our novel PRISM (predicting regulatory information from single motifs) approach obtains 2543 TF function predictions in a large variety of contexts, at a false discovery rate of 16%. The predictions are highly enriched for validated TF roles, and 45 of 67 (67%) tested binding site regions in five different contexts act as enhancers in functionally matched cells. PMID:23382538

Multivariate Analysis of Conformational Changes Induced by Macromolecular Interactions

NASA Astrophysics Data System (ADS)

Mitra, Indranil; Alexov, Emil

2009-11-01

Understanding protein-protein binding and associated conformational changes is critical for both understanding thermodynamics of protein interactions and successful drug discovery. Our study focuses on computational analysis of plausible correlations between induced conformational changes and set of biophysical characteristics of interacting monomers. It was done by comparing 3D structures of unbound and bound monomers to calculate the RMSD which is used as measure of the structural changed induced by the binding. We correlate RMSD with volumetric and interfacial charge of the monomers, the amino acid composition, the energy of binding, and type of amino acids at the interface. as predictors. The data set was analyzed with SVM in R & SPSS which is trained on a combination of a new robust evolutionary conservation signal with the monomeric properties to predict the induced RMSD. The goal of this study is to undergo parametric tests and heirchiacal cluster and discriminant multivariate analysis to find key predictors which will be used to develop algorithm to predict the magnitude of conformational changes provided by the structure of interacting monomers. Results indicate that the most promising predictor is the net charge of the monomers, however, other parameters as the type of amino acids at the interface have significant contribution as well.

Rational Design of Highly Potent and Slow-Binding Cytochrome bc1 Inhibitor as Fungicide by Computational Substitution Optimization

PubMed Central

Hao, Ge-Fei; Yang, Sheng-Gang; Huang, Wei; Wang, Le; Shen, Yan-Qing; Tu, Wen-Long; Li, Hui; Huang, Li-Shar; Wu, Jia-Wei; Berry, Edward A.; Yang, Guang-Fu

2015-01-01

Hit to lead (H2L) optimization is a key step for drug and agrochemical discovery. A critical challenge for H2L optimization is the low efficiency due to the lack of predictive method with high accuracy. We described a new computational method called Computational Substitution Optimization (CSO) that has allowed us to rapidly identify compounds with cytochrome bc1 complex inhibitory activity in the nanomolar and subnanomolar range. The comprehensively optimized candidate has proved to be a slow binding inhibitor of bc1 complex, ~73-fold more potent (Ki = 4.1 nM) than the best commercial fungicide azoxystrobin (AZ; Ki = 297.6 nM) and shows excellent in vivo fungicidal activity against downy mildew and powdery mildew disease. The excellent correlation between experimental and calculated binding free-energy shifts together with further crystallographic analysis confirmed the prediction accuracy of CSO method. To the best of our knowledge, CSO is a new computational approach to substitution-scanning mutagenesis of ligand and could be used as a general strategy of H2L optimisation in drug and agrochemical design.

Lanthanide and transition metal complexes of bioactive coumarins: molecular modeling and spectroscopic studies.

PubMed

Georgieva, I; Mihaylov, Tz; Trendafilova, N

2014-06-01

The present paper summarizes theoretical and spectroscopic investigations on a series of active coumarins and their lanthanide and transition metal complexes with application in medicine and pharmacy. Molecular modeling as well as IR, Raman, NMR and electronic spectral simulations at different levels of theory were performed to obtain important molecular descriptors: total energy, formation energy, binding energy, stability, conformations, structural parameters, electron density distribution, molecular electrostatic potential, Fukui functions, atomic charges, and reactive indexes. The computations are performed both in gas phase and in solution with consideration of the solvent effect on the molecular structural and energetic parameters. The investigations have shown that the advanced computational methods are reliable for prediction of the metal-coumarin binding mode, electron density distribution, thermodynamic properties as well as the strength and nature of the metal-coumarin interaction (not experimentally accessible) and correctly interpret the experimental spectroscopic data. Known results from biological tests for cytotoxic, antimicrobial, anti-fungal, spasmolytic and anti-HIV activities on the studied metal complexes are reported and discussed. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of Li Adsorption on the Electronic and Hydrogen Storage Properties of Acenes: A Dispersion-Corrected TAO-DFT Study

PubMed Central

Seenithurai, Sonai; Chai, Jeng-Da

2016-01-01

Due to the presence of strong static correlation effects and noncovalent interactions, accurate prediction of the electronic and hydrogen storage properties of Li-adsorbed acenes with n linearly fused benzene rings (n = 3–8) has been very challenging for conventional electronic structure methods. To meet the challenge, we study these properties using our recently developed thermally-assisted-occupation density functional theory (TAO-DFT) with dispersion corrections. In contrast to pure acenes, the binding energies of H2 molecules on Li-adsorbed acenes are in the ideal binding energy range (about 20 to 40 kJ/mol per H2). Besides, the H2 gravimetric storage capacities of Li-adsorbed acenes are in the range of 9.9 to 10.7 wt%, satisfying the United States Department of Energy (USDOE) ultimate target of 7.5 wt%. On the basis of our results, Li-adsorbed acenes can be high-capacity hydrogen storage materials for reversible hydrogen uptake and release at ambient conditions. PMID:27609626

RBind: computational network method to predict RNA binding sites.

PubMed

Wang, Kaili; Jian, Yiren; Wang, Huiwen; Zeng, Chen; Zhao, Yunjie

2018-04-26

Non-coding RNA molecules play essential roles by interacting with other molecules to perform various biological functions. However, it is difficult to determine RNA structures due to their flexibility. At present, the number of experimentally solved RNA-ligand and RNA-protein structures is still insufficient. Therefore, binding sites prediction of non-coding RNA is required to understand their functions. Current RNA binding site prediction algorithms produce many false positive nucleotides that are distance away from the binding sites. Here, we present a network approach, RBind, to predict the RNA binding sites. We benchmarked RBind in RNA-ligand and RNA-protein datasets. The average accuracy of 0.82 in RNA-ligand and 0.63 in RNA-protein testing showed that this network strategy has a reliable accuracy for binding sites prediction. The codes and datasets are available at https://zhaolab.com.cn/RBind. yjzhaowh@mail.ccnu.edu.cn. Supplementary data are available at Bioinformatics online.

Predicting protein-binding RNA nucleotides with consideration of binding partners.

PubMed

Tuvshinjargal, Narankhuu; Lee, Wook; Park, Byungkyu; Han, Kyungsook

2015-06-01

In recent years several computational methods have been developed to predict RNA-binding sites in protein. Most of these methods do not consider interacting partners of a protein, so they predict the same RNA-binding sites for a given protein sequence even if the protein binds to different RNAs. Unlike the problem of predicting RNA-binding sites in protein, the problem of predicting protein-binding sites in RNA has received little attention mainly because it is much more difficult and shows a lower accuracy on average. In our previous study, we developed a method that predicts protein-binding nucleotides from an RNA sequence. In an effort to improve the prediction accuracy and usefulness of the previous method, we developed a new method that uses both RNA and protein sequence data. In this study, we identified effective features of RNA and protein molecules and developed a new support vector machine (SVM) model to predict protein-binding nucleotides from RNA and protein sequence data. The new model that used both protein and RNA sequence data achieved a sensitivity of 86.5%, a specificity of 86.2%, a positive predictive value (PPV) of 72.6%, a negative predictive value (NPV) of 93.8% and Matthews correlation coefficient (MCC) of 0.69 in a 10-fold cross validation; it achieved a sensitivity of 58.8%, a specificity of 87.4%, a PPV of 65.1%, a NPV of 84.2% and MCC of 0.48 in independent testing. For comparative purpose, we built another prediction model that used RNA sequence data alone and ran it on the same dataset. In a 10 fold-cross validation it achieved a sensitivity of 85.7%, a specificity of 80.5%, a PPV of 67.7%, a NPV of 92.2% and MCC of 0.63; in independent testing it achieved a sensitivity of 67.7%, a specificity of 78.8%, a PPV of 57.6%, a NPV of 85.2% and MCC of 0.45. In both cross-validations and independent testing, the new model that used both RNA and protein sequences showed a better performance than the model that used RNA sequence data alone in most performance measures. To the best of our knowledge, this is the first sequence-based prediction of protein-binding nucleotides in RNA which considers the binding partner of RNA. The new model will provide valuable information for designing biochemical experiments to find putative protein-binding sites in RNA with unknown structure. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Influence of metal cofactors and water on the catalytic mechanism of creatininase-creatinine in aqueous solution from molecular dynamics simulation and quantum study

NASA Astrophysics Data System (ADS)

Lee, Vannajan Sanghiran; Kodchakorn, Kanchanok; Jitonnom, Jitrayut; Nimmanpipug, Piyarat; Kongtawelert, Prachya; Premanode, Bhusana

2010-10-01

The reaction mechanism of creatinine-creatininase binding to form creatine as a final product has been investigated by using a combined ab initio quantum mechanical/molecular mechanical approach and classical molecular dynamics (MD) simulations. In MD simulations, an X-ray crystal structure of the creatininase/creatinine was modified for creatininase/creatinine complexes and the MD simulations were run for free creatininase and creatinine in water. MD results reveal that two X-ray water molecules can be retained in the active site as catalytic water. The binding free energy from Molecular Mechanics Poisson-Boltzmann Surface Area calculation predicted the strong binding of creatinine with Zn2+, Asp45 and Glu183. Two step mechanisms via Mn2+/Zn2+ (as in X-ray structure) and Zn2+/Zn2+ were proposed for water adding step and ring opening step with two catalytic waters. The pathway using synchronous transit methods with local density approximations with PWC functional for the fragment in the active region were obtained. Preferable pathway Zn2+/Zn2+ was observed due to lower activation energy in water adding step. The calculated energy in the second step for both systems were comparable with the barrier of 26.03 and 24.44 kcal/mol for Mn2+/Zn2+ and Zn2+/Zn2+, respectively.

Theory for rates, equilibrium constants, and Brønsted slopes in F1-ATPase single molecule imaging experiments

PubMed Central

Volkán-Kacsó, Sándor; Marcus, Rudolph A.

2015-01-01

A theoretical model of elastically coupled reactions is proposed for single molecule imaging and rotor manipulation experiments on F1-ATPase. Stalling experiments are considered in which rates of individual ligand binding, ligand release, and chemical reaction steps have an exponential dependence on rotor angle. These data are treated in terms of the effect of thermodynamic driving forces on reaction rates, and lead to equations relating rate constants and free energies to the stalling angle. These relations, in turn, are modeled using a formalism originally developed to treat electron and other transfer reactions. During stalling the free energy profile of the enzymatic steps is altered by a work term due to elastic structural twisting. Using biochemical and single molecule data, the dependence of the rate constant and equilibrium constant on the stall angle, as well as the Børnsted slope are predicted and compared with experiment. Reasonable agreement is found with stalling experiments for ATP and GTP binding. The model can be applied to other torque-generating steps of reversible ligand binding, such as ADP and Pi release, when sufficient data become available. PMID:26483483

Modeling the Binding of Neurotransmitter Transporter Inhibitors with Molecular Dynamics and Free Energy Calculations

NASA Astrophysics Data System (ADS)

Jean, Bernandie

The monoamine transporter (MAT) proteins responsible for the reuptake of the neurotransmitter substrates, dopamine, serotonin, and norepinephrine, are drug targets for the treatment of psychiatric disorders including depression, anxiety, and attention deficit hyperactivity disorder. Small molecules that inhibit these proteins can serve as useful therapeutic agents. However, some dopamine transporter (DAT) inhibitors, such as cocaine and methamphetamine, are highly addictive and abusable. Efforts have been made to develop small molecules that will inhibit the transporters and elucidate specific binding site interactions. This work provides knowledge of molecular interactions associated with MAT inhibitors by offering an atomistic perspective that can guide designs of new pharmacotherapeutics with enhanced activity. The work described herein evaluates intermolecular interactions using computational methods to reveal the mechanistic detail of inhibitors binding in the DAT. Because cocaine recognizes the extracellular-facing or outward-facing (OF) DAT conformation and benztropine recognizes the intracellular-facing or inward-facing (IF) conformation, it was postulated that behaviorally "typical" (abusable, locomotor psychostimulant) inhibitors stabilize the OF DAT and "atypical" (little or no abuse potential) inhibitors favor IF DAT. Indeed, behaviorally-atypical cocaine analogs have now been shown to prefer the OF DAT conformation. Specifically, the binding interactions of two cocaine analogs, LX10 and LX11, were studied in the OF DAT using molecular dynamics simulations. LX11 was able to interact with residues of transmembrane helix 8 and bind in a fashion that allowed for hydration of the primary binding site (S1) from the intracellular space, thus impacting the intracellular interaction network capable of regulating conformational transitions in DAT. Additionally, a novel serotonin transporter (SERT) inhibitor previously discovered through virtual screening at the SERT secondary binding site (S2) was studied. Intermolecular interactions between SM11 and SERT have been assessed using binding free energy calculations to predict the ligand-binding site and optimize ligand-binding interactions. Results indicate the addition of atoms to the 4-chlorobenzyl moiety were most energetically favorable. The simulations carried out in DAT and SERT were supported by experimental results. Furthermore, the co-crystal structures of DAT and SERT share similar ligand-binding interactions with the homology models used in this study.

Nucleation time of nanoscale water bridges.

PubMed

Szoszkiewicz, Robert; Riedo, Elisa

2005-09-23

Water capillaries bind together grains of sand. They also can bind an atomic force microscope tip to a substrate. The kinetics of capillary condensation at the nanoscale is studied here using friction force microscopy. At 40% relative humidity we find that the meniscus nucleation times increase from 0.7 to 4.2 ms when the temperature decreases from 332 to 299 K. The nucleation times grow exponentially with the inverse temperature 1/T obeying an Arrhenius law. We obtain a nucleation energy barrier of 7.8 x 10(-20) J and an attempt frequency ranging between 4 and 250 GHz, in excellent agreement with theoretical predictions. These results provide direct experimental evidence that capillary condensation is a thermally activated phenomenon.

ONIOM DFT/PM3 calculations on the interaction between dapivirine and HIV-1 reverse transcriptase, a theoretical study.

PubMed

Liang, Y H; Chen, F E

2007-08-01

Theoretical investigations of the interaction between dapivirine and the HIV-1 RT binding site have been performed by the ONIOM2 (B3LYP/6-31G (d,p): PM3) and B3LYP/6-31G (d,p) methods. The results derived from this study indicate that this inhibitor dapivirine forms two hydrogen bonds with Lys101 and exhibits strong π-π stacking or H…π interaction with Tyr181 and Tyr188. These interactions play a vital role in stabilizing the NNIBP/dapivirine complex. Additionally, the predicted binding energy of the BBF optimized structure for this complex system is -18.20 kcal/mol.

Binding free energy calculations between bovine β-lactoglobulin and four fatty acids using the MMGBSA method.

PubMed

Bello, Martiniano

2014-10-01

The bovine dairy protein β-lactoglobulin (βlg) is a promiscuous protein that has the ability to bind several hydrophobic ligands. In this study, based on known experimental data, the dynamic interaction mechanism between bovine βlg and four fatty acids was investigated by a protocol combining molecular dynamics (MD) simulations and molecular mechanics generalized Born surface area (MMGBSA) binding free energy calculations. Energetic analyses revealed binding free energy trends that corroborated known experimental findings; larger ligand size corresponded to greater binding affinity. Finally, binding free energy decomposition provided detailed information about the key residues stabilizing the complex. © 2014 Wiley Periodicals, Inc.

Edible oil structures at low and intermediate concentrations. I. Modeling, computer simulation, and predictions for X ray scattering

NASA Astrophysics Data System (ADS)

Pink, David A.; Quinn, Bonnie; Peyronel, Fernanda; Marangoni, Alejandro G.

2013-12-01

Triacylglycerols (TAGs) are biologically important molecules which form the recently discovered highly anisotropic crystalline nanoplatelets (CNPs) and, ultimately, the large-scale fat crystal networks in edible oils. Identifying the hierarchies of these networks and how they spontaneously self-assemble is important to understanding their functionality and oil binding capacity. We have modelled CNPs and studied how they aggregate under the assumption that all CNPs are present before aggregation begins and that their solubility in the liquid oil is very low. We represented CNPs as rigid planar arrays of spheres with diameter ≈50 nm and defined the interaction between spheres in terms of a Hamaker coefficient, A, and a binding energy, VB. We studied three cases: weak binding, |VB|/kBT ≪ 1, physically realistic binding, VB = Vd(R, Δ), so that |VB|/kBT ≈ 1, and Strong binding with |VB|/kBT ≫ 1. We divided the concentration of CNPs, ϕ, with 0≤ϕ= 10-2 (solid fat content) ≤1, into two regions: Low and intermediate concentrations with 0<ϕ<0.25 and high concentrations with 0.25 < ϕ and considered only the first case. We employed Monte Carlo computer simulation to model CNP aggregation and analyzed them using static structure functions, S(q). We found that strong binding cases formed aggregates with fractal dimension, D, 1.7≤D ≤1.8, in accord with diffusion limited cluster-cluster aggregation (DLCA) and weak binding formed aggregates with D =3, indicating a random distribution of CNPs. We found that models with physically realistic intermediate binding energies formed linear multilayer stacks of CNPs (TAGwoods) with fractal dimension D =1 for ϕ =0.06,0.13, and 0.22. TAGwood lengths were greater at lower ϕ than at higher ϕ, where some of the aggregates appeared as thick CNPs. We increased the spatial scale and modelled the TAGwoods as rigid linear arrays of spheres of diameter ≈500 nm, interacting via the attractive van der Waals interaction. We found that TAGwoods aggregated via DLCA into clusters with fractal dimension D =1.7-1.8. As the simulations were run further, TAGwoods relaxed their positions in order to maximize the attractive interaction making the process look like reaction limited cluster-cluster aggregation with the fractal dimension increasing to D =2.0-2.1. For higher concentrations of CNPs, many TAGwood clusters were formed and, because of their weak interactions, were distributed randomly with D =3.0. We summarize the hierarchy of structures and make predictions for X-ray scattering.

Ground-State Properties of Mg Isotopes in and Beyond the Island of Inversion through Reaction Cross Sections

NASA Astrophysics Data System (ADS)

Watanabe, Shin; Minomo, Kosho; Shimada, Mitsuhiro; Tagami, Shingo; Kimura, Masaaki; Takechi, Maya; Fukuda, Mitsunori; Nishimura, Daiki; Suzuki, Takeshi; Matsumoto, Takuma; Shimizu, Yoshifumi R.; Yahiro, Masanobu

We analyze recently measured total reaction cross sections (σR) for 24-38Mg incident on 12C targets at 240 MeV/nucleon by using the microscopic framework based on the double folding model and antisymmetrized molecular dynamics (AMD). The framework reproduces not only the measured σR but also other existing measured ground-state properties of Mg Isotopes (spin parity, total binding energy, one-neutron separation energy, and 2+ and 4+ excitation energies) quite well. AMD predicts large deformation from 31Mg19 to a drip-line nucleus 40Mg28, indicating that both the N = 20 and 28 magicities disappear.

Zinc finger protein binding to DNA: an energy perspective using molecular dynamics simulation and free energy calculations on mutants of both zinc finger domains and their specific DNA bases.

PubMed

Hamed, Mazen Y; Arya, Gaurav

2016-05-01

Energy calculations based on MM-GBSA were employed to study various zinc finger protein (ZF) motifs binding to DNA. Mutants of both the DNA bound to their specific amino acids were studied. Calculated energies gave evidence for a relationship between binding energy and affinity of ZF motifs to their sites on DNA. ΔG values were -15.82(12), -3.66(12), and -12.14(11.6) kcal/mol for finger one, finger two, and finger three, respectively. The mutations in the DNA bases reduced the value of the negative energies of binding (maximum value for ΔΔG = 42Kcal/mol for F1 when GCG mutated to GGG, and ΔΔG = 22 kcal/mol for F2, the loss in total energy of binding originated in the loss in electrostatic energies upon mutation (r = .98). The mutations in key amino acids in the ZF motif in positions-1, 2, 3, and 6 showed reduced binding energies to DNA with correlation coefficients between total free energy and electrostatic was .99 and with Van der Waal was .93. Results agree with experimentally found selectivity which showed that Arginine in position-1 is specific to G, while Aspartic acid (D) in position 2 plays a complicated role in binding. There is a correlation between the MD calculated free energies of binding and those obtained experimentally for prepared ZF motifs bound to triplet bases in other reports (), our results may help in the design of ZF motifs based on the established recognition codes based on energies and contributing energies to the total energy.

Topology-based modeling of intrinsically disordered proteins: balancing intrinsic folding and intermolecular interactions.

PubMed

Ganguly, Debabani; Chen, Jianhan

2011-04-01

Coupled binding and folding is frequently involved in specific recognition of so-called intrinsically disordered proteins (IDPs), a newly recognized class of proteins that rely on a lack of stable tertiary fold for function. Here, we exploit topology-based Gō-like modeling as an effective tool for the mechanism of IDP recognition within the theoretical framework of minimally frustrated energy landscape. Importantly, substantial differences exist between IDPs and globular proteins in both amino acid sequence and binding interface characteristics. We demonstrate that established Gō-like models designed for folded proteins tend to over-estimate the level of residual structures in unbound IDPs, whereas under-estimating the strength of intermolecular interactions. Such systematic biases have important consequences in the predicted mechanism of interaction. A strategy is proposed to recalibrate topology-derived models to balance intrinsic folding propensities and intermolecular interactions, based on experimental knowledge of the overall residual structure level and binding affinity. Applied to pKID/KIX, the calibrated Gō-like model predicts a dominant multistep sequential pathway for binding-induced folding of pKID that is initiated by KIX binding via the C-terminus in disordered conformations, followed by binding and folding of the rest of C-terminal helix and finally the N-terminal helix. This novel mechanism is consistent with key observations derived from a recent NMR titration and relaxation dispersion study and provides a molecular-level interpretation of kinetic rates derived from dispersion curve analysis. These case studies provide important insight into the applicability and potential pitfalls of topology-based modeling for studying IDP folding and interaction in general. Copyright © 2011 Wiley-Liss, Inc.

A study of planar anchor groups for graphene-based single-molecule electronics.

PubMed

Bailey, Steven; Visontai, David; Lambert, Colin J; Bryce, Martin R; Frampton, Harry; Chappell, David

2014-02-07

To identify families of stable planar anchor groups for use in single molecule electronics, we report detailed results for the binding energies of two families of anthracene and pyrene derivatives adsorbed onto graphene. We find that all the selected derivatives functionalized with either electron donating or electron accepting substituents bind more strongly to graphene than the parent non-functionalized anthracene or pyrene. The binding energy is sensitive to the detailed atomic alignment of substituent groups over the graphene substrate leading to larger than expected binding energies for -OH and -CN derivatives. Furthermore, the ordering of the binding energies within the anthracene and pyrene series does not simply follow the electron affinities of the substituents. Energy barriers to rotation or displacement on the graphene surface are much lower than binding energies for adsorption and therefore at room temperature, although the molecules are bound to the graphene, they are almost free to move along the graphene surface. Binding energies can be increased by incorporating electrically inert side chains and are sensitive to the conformation of such chains.

A study of planar anchor groups for graphene-based single-molecule electronics

NASA Astrophysics Data System (ADS)

Bailey, Steven; Visontai, David; Lambert, Colin J.; Bryce, Martin R.; Frampton, Harry; Chappell, David

2014-02-01

To identify families of stable planar anchor groups for use in single molecule electronics, we report detailed results for the binding energies of two families of anthracene and pyrene derivatives adsorbed onto graphene. We find that all the selected derivatives functionalized with either electron donating or electron accepting substituents bind more strongly to graphene than the parent non-functionalized anthracene or pyrene. The binding energy is sensitive to the detailed atomic alignment of substituent groups over the graphene substrate leading to larger than expected binding energies for -OH and -CN derivatives. Furthermore, the ordering of the binding energies within the anthracene and pyrene series does not simply follow the electron affinities of the substituents. Energy barriers to rotation or displacement on the graphene surface are much lower than binding energies for adsorption and therefore at room temperature, although the molecules are bound to the graphene, they are almost free to move along the graphene surface. Binding energies can be increased by incorporating electrically inert side chains and are sensitive to the conformation of such chains.

Theory and Normal Mode Analysis of Change in Protein Vibrational Dynamics on Ligand Binding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mortisugu, Kei; Njunda, Brigitte; Smith, Jeremy C

2009-12-01

The change of protein vibrations on ligand binding is of functional and thermodynamic importance. Here, this process is characterized using a simple analytical 'ball-and-spring' model and all-atom normal-mode analysis (NMA) of the binding of the cancer drug, methotrexate (MTX) to its target, dihydrofolate reductase (DHFR). The analytical model predicts that the coupling between protein vibrations and ligand external motion generates entropy-rich, low-frequency vibrations in the complex. This is consistent with the atomistic NMA which reveals vibrational softening in forming the DHFR-MTX complex, a result also in qualitative agreement with neutron-scattering experiments. Energy minimization of the atomistic bound-state (B) structure whilemore » gradually decreasing the ligand interaction to zero allows the generation of a hypothetical 'intermediate' (I) state, without the ligand force field but with a structure similar to that of B. In going from I to B, it is found that the vibrational entropies of both the protein and MTX decrease while the complex structure becomes enthalpically stabilized. However, the relatively weak DHFR:MTX interaction energy results in the net entropy gain arising from coupling between the protein and MTX external motion being larger than the loss of vibrational entropy on complex formation. This, together with the I structure being more flexible than the unbound structure, results in the observed vibrational softening on ligand binding.« less

Wettability of graphitic-carbon and silicon surfaces: MD modeling and theoretical analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ramos-Alvarado, Bladimir; Kumar, Satish; Peterson, G. P.

2015-07-28

The wettability of graphitic carbon and silicon surfaces was numerically and theoretically investigated. A multi-response method has been developed for the analysis of conventional molecular dynamics (MD) simulations of droplets wettability. The contact angle and indicators of the quality of the computations are tracked as a function of the data sets analyzed over time. This method of analysis allows accurate calculations of the contact angle obtained from the MD simulations. Analytical models were also developed for the calculation of the work of adhesion using the mean-field theory, accounting for the interfacial entropy changes. A calibration method is proposed to providemore » better predictions of the respective contact angles under different solid-liquid interaction potentials. Estimations of the binding energy between a water monomer and graphite match those previously reported. In addition, a breakdown in the relationship between the binding energy and the contact angle was observed. The macroscopic contact angles obtained from the MD simulations were found to match those predicted by the mean-field model for graphite under different wettability conditions, as well as the contact angles of Si(100) and Si(111) surfaces. Finally, an assessment of the effect of the Lennard-Jones cutoff radius was conducted to provide guidelines for future comparisons between numerical simulations and analytical models of wettability.« less

Implications of Climate Volatility for Agricultural Commodity Markets in the Presence of Biofuel Mandates

NASA Astrophysics Data System (ADS)

Verma, M.; Diffenbaugh, N. S.; Hertel, T. W.; Beckman, J.

2011-12-01

In presence of bio-fuels, link between energy and agricultural commodity markets has become more complex. An increase in ethanol production to minimum 15bn gallons a year - Renewable Fuel Standard (RFS) and current technically permissible maximum 10% blending limit - Blend Wall (BW); make the link even stronger. If oil prices in future do not rise significantly from their current levels, this minimum production requirement would likely be binding. In such a scenario any fluctuation in crop production will have to be absorbed by the non-ethanol usage of the crop and would translate into crop prices adjusting to clear the markets and therefore the commodity prices will be more volatile. At high oil prices it is possible that the BW may become binding, severing the link between oil prices and commodity prices as well, potentially leading to higher price volatility. Hertel and Beckman (2010) find that, with both RFS and BW simultaneously binding, corn price volatility due to supply side shocks (which could arise from extreme climate events) could be more than 50% as large as in the absence of bio-fuel policies. So energy markets are important determinants of agricultural commodity price volatility. This proposal intends to introduce the increased supply side volatility on account of climate change and volatility, in the framework. Global warming on account of increased GHG concentrations is expected to increase the intensity and frequency of hot extremes in US (Diffenbaugh et al. 2008) and therefore affect corn yields. With supply shocks expected to increase, binding RFS and BW will exacerbate the volatility, while if they are non-binding then the price changes could be cushioned. We propose to model the impacts of climate changes and volatility on commodity prices by linking three main components - a. Projections for change in temperature and precipitation using climate model b. A statistical model to predict impacts of change in climate variable on corn yields in US c. Computable General Equilibrium economic model that uses the results of the two above as inputs, to predict commodity prices under alternative energy price scenarios We start with the high resolution projections on temperature and precipitation for US corn-belt for years 2020-2040. A modified version of statistical relationship estimated by Schlenker and Roberts, is used to translate climate variables' change into yield changes for each. Shocks are sampled from this distribution to decipher the corresponding volatility in commodity prices. All else constant, the increased supply side variability should result in increased price volatility; high oil prices however give markets an incentive to produce more than 15bn gallons ethanol a year (non-binding RFS) and part of supply fluctuation in crop production can be borne by ethanol production and impact of climate change on crop prices would be less dramatic than it would have been if the entire adjustment was to come through non-ethanol usage. So impact of climate change clearly depends on energy markets and policy decisions and results should provide insights into impact of climate change on agricultural prices under different energy market scenarios.

Drug Distribution. Part 1. Models to Predict Membrane Partitioning.

PubMed

Nagar, Swati; Korzekwa, Ken

2017-03-01

Tissue partitioning is an important component of drug distribution and half-life. Protein binding and lipid partitioning together determine drug distribution. Two structure-based models to predict partitioning into microsomal membranes are presented. An orientation-based model was developed using a membrane template and atom-based relative free energy functions to select drug conformations and orientations for neutral and basic drugs. The resulting model predicts the correct membrane positions for nine compounds tested, and predicts the membrane partitioning for n = 67 drugs with an average fold-error of 2.4. Next, a more facile descriptor-based model was developed for acids, neutrals and bases. This model considers the partitioning of neutral and ionized species at equilibrium, and can predict membrane partitioning with an average fold-error of 2.0 (n = 92 drugs). Together these models suggest that drug orientation is important for membrane partitioning and that membrane partitioning can be well predicted from physicochemical properties.

Spectroscopic and molecular modeling study on the separate and simultaneous bindings of alprazolam and fluoxetine hydrochloride to human serum albumin (HSA): With the aim of the drug interactions probing

NASA Astrophysics Data System (ADS)

Dangkoob, Faeze; Housaindokht, Mohmmad Reza; Asoodeh, Ahmad; Rajabi, Omid; Rouhbakhsh Zaeri, Zeinab; Verdian Doghaei, Asma

2015-02-01

The objective of the present research is to study the interaction of separate and simultaneous of alprazolam (ALP) and fluoxetine hydrochloride (FLX) with human serum albumin (HSA) in phosphate buffer (pH 7.4) using different kinds of spectroscopic, cyclic voltammetry and molecular modeling techniques. The absorbance spectra of protein, drugs and protein-drug showed complex formation between the drugs and HSA. Fluorescence analysis demonstrated that ALP and FLX could quench the fluorescence spectrum of HSA and demonstrated the conformational change of HSA in the presence of both drugs. Also, fluorescence quenching mechanism of HSA-drug complexes both separately and simultaneously was suggested as static quenching. The analysis of UV absorption data and the fluorescence quenching of HSA in the binary and ternary systems showed that FLX decreased the binding affinity between ALP and HSA. On the contrary, ALP increased the binding affinity of FLX and HSA. The results of synchronous fluorescence and three-dimensional fluorescence spectra indicated that the binding of drugs to HSA would modify the microenvironment around the Trp and Tyr residues and the conformation of HSA. The distances between Trp residue and the binding sites of the drugs were estimated according to the Förster theory, and it was demonstrated that non-radiative energy transfer from HSA to the drugs occurred with a high probability. Moreover, according to CV measurements, the decrease of peak current in the cyclic voltammogram of the both drugs in the presence of HSA revealed that they interacted with albumin and binding constants were calculated for binary systems which were in agreement with the binding constants obtained from UV absorption and fluorescence spectroscopy. The prediction of the best binding sites of ALP and FLX in binary and ternary systems in molecular modeling approach was done using of Gibbs free energy.

Spectroscopic and molecular modeling study on the separate and simultaneous bindings of alprazolam and fluoxetine hydrochloride to human serum albumin (HSA): with the aim of the drug interactions probing.

PubMed

Dangkoob, Faeze; Housaindokht, Mohmmad Reza; Asoodeh, Ahmad; Rajabi, Omid; Rouhbakhsh Zaeri, Zeinab; Verdian Doghaei, Asma

2015-02-25

The objective of the present research is to study the interaction of separate and simultaneous of alprazolam (ALP) and fluoxetine hydrochloride (FLX) with human serum albumin (HSA) in phosphate buffer (pH 7.4) using different kinds of spectroscopic, cyclic voltammetry and molecular modeling techniques. The absorbance spectra of protein, drugs and protein-drug showed complex formation between the drugs and HSA. Fluorescence analysis demonstrated that ALP and FLX could quench the fluorescence spectrum of HSA and demonstrated the conformational change of HSA in the presence of both drugs. Also, fluorescence quenching mechanism of HSA-drug complexes both separately and simultaneously was suggested as static quenching. The analysis of UV absorption data and the fluorescence quenching of HSA in the binary and ternary systems showed that FLX decreased the binding affinity between ALP and HSA. On the contrary, ALP increased the binding affinity of FLX and HSA. The results of synchronous fluorescence and three-dimensional fluorescence spectra indicated that the binding of drugs to HSA would modify the microenvironment around the Trp and Tyr residues and the conformation of HSA. The distances between Trp residue and the binding sites of the drugs were estimated according to the Förster theory, and it was demonstrated that non-radiative energy transfer from HSA to the drugs occurred with a high probability. Moreover, according to CV measurements, the decrease of peak current in the cyclic voltammogram of the both drugs in the presence of HSA revealed that they interacted with albumin and binding constants were calculated for binary systems which were in agreement with the binding constants obtained from UV absorption and fluorescence spectroscopy. The prediction of the best binding sites of ALP and FLX in binary and ternary systems in molecular modeling approach was done using of Gibbs free energy. Copyright © 2014 Elsevier B.V. All rights reserved.

Isolation of anticancer drug TAXOL from Pestalotiopsis breviseta with apoptosis and B-Cell lymphoma protein docking studies.

PubMed

Kathiravan, G; Sureban, Sripathi M; Sree, Harsha N; Bhuvaneshwari, V; Kramony, Evelin

2012-12-01

Extraction and investigation of TAXOL from Pestalotiopsis breviseta (Sacc.) using protein docking, which is a computational technique that samples conformations of small molecules in protein-binding sites. Scoring functions are used to assess which of these conformations best complements the protein binding site and active site prediction. Coelomycetous fungi P. breviseta (Sacc.) Steyaert was screened for the production of TAXOL, an anticancer drug. TAXOL PRODUCTION WAS CONFIRMED BY THE FOLLOWING METHODS: Ultraviolet (UV) spectroscopic analysis, Infrared analysis, High performance liquid chromatography analysis (HPLC), and Liquid chromatography mass spectrum (LC-MASS). TAXOL produced by the fungi was compared with authentic TAXOL, and protein docking studies were performed. The BCL2 protein of human origin showed a higher affinity toward the compound paclitaxel. It has the binding energy value of -13.0061 (KJ/Mol) with four hydrogen bonds.

Isolation of anticancer drug TAXOL from Pestalotiopsis breviseta with apoptosis and B-Cell lymphoma protein docking studies

PubMed Central

Kathiravan, G.; Sureban, Sripathi M.; Sree, Harsha N.; Bhuvaneshwari, V.; Kramony, Evelin

2012-01-01

Background: Extraction and investigation of TAXOL from Pestalotiopsis breviseta (Sacc.) using protein docking, which is a computational technique that samples conformations of small molecules in protein-binding sites. Scoring functions are used to assess which of these conformations best complements the protein binding site and active site prediction. Materials and Methods: Coelomycetous fungi P. breviseta (Sacc.) Steyaert was screened for the production of TAXOL, an anticancer drug. Results: TAXOL production was confirmed by the following methods: Ultraviolet (UV) spectroscopic analysis, Infrared analysis, High performance liquid chromatography analysis (HPLC), and Liquid chromatography mass spectrum (LC-MASS). TAXOL produced by the fungi was compared with authentic TAXOL, and protein docking studies were performed. Conclusion: The BCL2 protein of human origin showed a higher affinity toward the compound paclitaxel. It has the binding energy value of −13.0061 (KJ/Mol) with four hydrogen bonds. PMID:24808664

Coarse-graining, Electrostatics and pH effects in phospholipid systems

NASA Astrophysics Data System (ADS)

Travesset, Alex; Vangaveti, Sweta

2010-03-01

We introduce a minimal free energy describing the interaction of charged groups and counterions including both classical electrostatic and specific interactions. The predictions of the model are compared against the standard model for describing ions next to charged interfaces, consisting of Poisson-Boltzmann theory with additional constants describing ion binding, which are specific to the counterion and the interfacial charge (``chemical binding''). It is shown that the ``chemical'' model can be appropriately described by an underlying ``physical'' model over several decades in concentration, but the extracted binding constants are not uniquely defined, as they differ depending on the particular observable quantity being studied. It is also shown that electrostatic correlations for divalent (or higher valence) ions enhance the surface charge by increasing deprotonation, an effect not properly accounted within chemical models. The model is applied to the charged phospholipids phosphatidylserine, Phosphatidc acid and Phosphoinositides and implications for different biological processes are discussed.

Identification of DNA primase inhibitors via a combined fragment-based and virtual screening

NASA Astrophysics Data System (ADS)

Ilic, Stefan; Akabayov, Sabine R.; Arthanari, Haribabu; Wagner, Gerhard; Richardson, Charles C.; Akabayov, Barak

2016-11-01

The structural differences between bacterial and human primases render the former an excellent target for drug design. Here we describe a technique for selecting small molecule inhibitors of the activity of T7 DNA primase, an ideal model for bacterial primases due to their common structural and functional features. Using NMR screening, fragment molecules that bind T7 primase were identified and then exploited in virtual filtration to select larger molecules from the ZINC database. The molecules were docked to the primase active site using the available primase crystal structure and ranked based on their predicted binding energies to identify the best candidates for functional and structural investigations. Biochemical assays revealed that some of the molecules inhibit T7 primase-dependent DNA replication. The binding mechanism was delineated via NMR spectroscopy. Our approach, which combines fragment based and virtual screening, is rapid and cost effective and can be applied to other targets.

Theoretical study of transition-metal ions bound to benzene

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.; Partridge, Harry; Langhoff, Stephen R.

1992-01-01

Theoretical binding energies are reported for all first-row and selected second-row transition metal ions (M+) bound to benzene. The calculations employ basis sets of at least double-zeta plus polarization quality and account for electron correlation using the modified coupled-pair functional method. While the bending is predominantly electrostatic, the binding energies are significantly increased by electron correlation, because the donation from the metal d orbitals to the benzene pi* orbitals is not well described at the self-consistent-field level. The uncertainties in the computed binding energies are estimated to be about 5 kcal/mol. Although the calculated and experimental binding energies generally agree to within their combined uncertainties, it is likely that the true binding energies lie in the lower portion of the experimental range. This is supported by the very good agreement between the theoretical and recent experimental binding energies for AgC6H6(+).

Fast de novo discovery of low-energy protein loop conformations.

PubMed

Wong, Samuel W K; Liu, Jun S; Kou, S C

2017-08-01

In the prediction of protein structure from amino acid sequence, loops are challenging regions for computational methods. Since loops are often located on the protein surface, they can have significant roles in determining protein functions and binding properties. Loop prediction without the aid of a structural template requires extensive conformational sampling and energy minimization, which are computationally difficult. In this article we present a new de novo loop sampling method, the Parallely filtered Energy Targeted All-atom Loop Sampler (PETALS) to rapidly locate low energy conformations. PETALS explores both backbone and side-chain positions of the loop region simultaneously according to the energy function selected by the user, and constructs a nonredundant ensemble of low energy loop conformations using filtering criteria. The method is illustrated with the DFIRE potential and DiSGro energy function for loops, and shown to be highly effective at discovering conformations with near-native (or better) energy. Using the same energy function as the DiSGro algorithm, PETALS samples conformations with both lower RMSDs and lower energies. PETALS is also useful for assessing the accuracy of different energy functions. PETALS runs rapidly, requiring an average time cost of 10 minutes for a length 12 loop on a single 3.2 GHz processor core, comparable to the fastest existing de novo methods for generating an ensemble of conformations. Proteins 2017; 85:1402-1412. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.