ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Simultaneous optimization of biomolecular energy function on features from small molecules and macromolecules

PubMed Central

Park, Hahnbeom; Bradley, Philip; Greisen, Per; Liu, Yuan; Mulligan, Vikram Khipple; Kim, David E.; Baker, David; DiMaio, Frank

2017-01-01

Most biomolecular modeling energy functions for structure prediction, sequence design, and molecular docking, have been parameterized using existing macromolecular structural data; this contrasts molecular mechanics force fields which are largely optimized using small-molecule data. In this study, we describe an integrated method that enables optimization of a biomolecular modeling energy function simultaneously against small-molecule thermodynamic data and high-resolution macromolecular structural data. We use this approach to develop a next-generation Rosetta energy function that utilizes a new anisotropic implicit solvation model, and an improved electrostatics and Lennard-Jones model, illustrating how energy functions can be considerably improved in their ability to describe large-scale energy landscapes by incorporating both small-molecule and macromolecule data. The energy function improves performance in a wide range of protein structure prediction challenges, including monomeric structure prediction, protein-protein and protein-ligand docking, protein sequence design, and prediction of the free energy changes by mutation, while reasonably recapitulating small-molecule thermodynamic properties. PMID:27766851

Challenges and Development of a Multi-Scale Computational Model for Photosystem I Decoupled Energy Conversion

DTIC Science & Technology

2013-06-01

Applications of Molecular Modeling to Challenges in Clean Energy; Fitzgerald, G., et al .; ACS Symposium Series; American Chemical Society: Washington, DC...to 178 In Applications of Molecular Modeling to Challenges in Clean Energy; Fitzgerald, G., et al .; ACS Symposium Series; American Chemical Society...Washington, DC, 2013. developmodels of spectral properties and energy transfer kinetics (20–22). Ivashin et al . optimized select ligands (α

Packaging double-helical DNA into viral capsids.

PubMed

LaMarque, Jaclyn C; Le, Thuc-Vy L; Harvey, Stephen C

2004-02-15

DNA packaging in bacteriophage P4 has been examined using a molecular mechanics model with a reduced representation containing one pseudoatom per turn of the double helix. The model is a discretized version of an elastic continuum model. The DNA is inserted piecewise into the model capsid, with the structure being reoptimized after each piece is inserted. Various optimization protocols were investigated, and it was found that molecular dynamics at a very low temperature (0.3 K) produces the optimal packaged structure. This structure is a concentric spool, rather than the coaxial spool that has been commonly accepted for so many years. This geometry, which was originally suggested by Hall and Schellman in 1982 (Biopolymers Vol. 21, pp. 2011-2031), produces a lower overall elastic energy than coaxial spooling. Copyright 2003 Wiley Periodicals, Inc.

Introductory Molecular Orbital Theory: An Honors General Chemistry Computational Lab as Implemented Using Three-Dimensional Modeling Software

ERIC Educational Resources Information Center

Ruddick, Kristie R.; Parrill, Abby L.; Petersen, Richard L.

2012-01-01

In this study, a computational molecular orbital theory experiment was implemented in a first-semester honors general chemistry course. Students used the GAMESS (General Atomic and Molecular Electronic Structure System) quantum mechanical software (as implemented in ChemBio3D) to optimize the geometry for various small molecules. Extended Huckel…

The anesthetic action of some polyhalogenated ethers-Monte Carlo method based QSAR study.

PubMed

Golubović, Mlađan; Lazarević, Milan; Zlatanović, Dragan; Krtinić, Dane; Stoičkov, Viktor; Mladenović, Bojan; Milić, Dragan J; Sokolović, Dušan; Veselinović, Aleksandar M

2018-04-13

Up to this date, there has been an ongoing debate about the mode of action of general anesthetics, which have postulated many biological sites as targets for their action. However, postoperative nausea and vomiting are common problems in which inhalational agents may have a role in their development. When a mode of action is unknown, QSAR modelling is essential in drug development. To investigate the aspects of their anesthetic, QSAR models based on the Monte Carlo method were developed for a set of polyhalogenated ethers. Until now, their anesthetic action has not been completely defined, although some hypotheses have been suggested. Therefore, a QSAR model should be developed on molecular fragments that contribute to anesthetic action. QSAR models were built on the basis of optimal molecular descriptors based on the SMILES notation and local graph invariants, whereas the Monte Carlo optimization method with three random splits into the training and test set was applied for model development. Different methods, including novel Index of ideality correlation, were applied for the determination of the robustness of the model and its predictive potential. The Monte Carlo optimization process was capable of being an efficient in silico tool for building up a robust model of good statistical quality. Molecular fragments which have both positive and negative influence on anesthetic action were determined. The presented study can be useful in the search for novel anesthetics. Copyright © 2018 Elsevier Ltd. All rights reserved.

Drude polarizable force field for aliphatic ketones and aldehydes, and their associated acyclic carbohydrates

NASA Astrophysics Data System (ADS)

Small, Meagan C.; Aytenfisu, Asaminew H.; Lin, Fang-Yu; He, Xibing; MacKerell, Alexander D.

2017-04-01

The majority of computer simulations exploring biomolecular function employ Class I additive force fields (FF), which do not treat polarization explicitly. Accordingly, much effort has been made into developing models that go beyond the additive approximation. Development and optimization of the Drude polarizable FF has yielded parameters for selected lipids, proteins, DNA and a limited number of carbohydrates. The work presented here details parametrization of aliphatic aldehydes and ketones (viz. acetaldehyde, propionaldehyde, butaryaldehyde, isobutaryaldehyde, acetone, and butanone) as well as their associated acyclic sugars ( d-allose and d-psicose). LJ parameters are optimized targeting experimental heats of vaporization and molecular volumes, while the electrostatic parameters are optimized targeting QM water interactions, dipole moments, and molecular polarizabilities. Bonded parameters are targeted to both QM and crystal survey values, with the models for ketones and aldehydes shown to be in good agreement with QM and experimental target data. The reported heats of vaporization and molecular volumes represent a compromise between the studied model compounds. Simulations of the model compounds show an increase in the magnitude and the fluctuations of the dipole moments in moving from gas phase to condensed phases, which is a phenomenon that the additive FF is intrinsically unable to reproduce. The result is a polarizable model for aliphatic ketones and aldehydes including the acyclic sugars d-allose and d-psicose, thereby extending the available biomolecules in the Drude polarizable FF.

Drude polarizable force field for aliphatic ketones and aldehydes, and their associated acyclic carbohydrates.

PubMed

Small, Meagan C; Aytenfisu, Asaminew H; Lin, Fang-Yu; He, Xibing; MacKerell, Alexander D

2017-04-01

The majority of computer simulations exploring biomolecular function employ Class I additive force fields (FF), which do not treat polarization explicitly. Accordingly, much effort has been made into developing models that go beyond the additive approximation. Development and optimization of the Drude polarizable FF has yielded parameters for selected lipids, proteins, DNA and a limited number of carbohydrates. The work presented here details parametrization of aliphatic aldehydes and ketones (viz. acetaldehyde, propionaldehyde, butaryaldehyde, isobutaryaldehyde, acetone, and butanone) as well as their associated acyclic sugars (D-allose and D-psicose). LJ parameters are optimized targeting experimental heats of vaporization and molecular volumes, while the electrostatic parameters are optimized targeting QM water interactions, dipole moments, and molecular polarizabilities. Bonded parameters are targeted to both QM and crystal survey values, with the models for ketones and aldehydes shown to be in good agreement with QM and experimental target data. The reported heats of vaporization and molecular volumes represent a compromise between the studied model compounds. Simulations of the model compounds show an increase in the magnitude and the fluctuations of the dipole moments in moving from gas phase to condensed phases, which is a phenomenon that the additive FF is intrinsically unable to reproduce. The result is a polarizable model for aliphatic ketones and aldehydes including the acyclic sugars D-allose and D-psicose, thereby extending the available biomolecules in the Drude polarizable FF.

Drude Polarizable Force Field for Aliphatic Ketones and Aldehydes, and their Associated Acyclic Carbohydrates

PubMed Central

Small, Meagan C.; Aytenfisu, Asaminew H.; Lin, Fang-Yu; He, Xibing; MacKerell, Alexander D.

2017-01-01

The majority of computer simulations exploring biomolecular function employ Class I additive force fields (FF), which do not treat polarization explicitly. Accordingly, much effort has been made into developing models that go beyond the additive approximation. Development and optimization of the Drude polarizable FF has yielded parameters for selected lipids, proteins, DNA and a limited number of carbohydrates. The work presented here details parametrization of aliphatic aldehydes and ketones (viz. acetaldehyde, propionaldehyde, butaryaldehyde, isobutaryaldehyde, acetone, and butanone) as well as their associated acyclic sugars (D-allose and D-psicose). LJ parameters are optimized targeting experimental heats of vaporization and molecular volumes, while the electrostatic parameters are optimized targeting QM water interactions, dipole moments, and molecular polarizabilities. Bonded parameters are targeted to both QM and crystal survey values, with the models for ketones and aldehydes shown to be in good agreement with QM and experimental target data. The reported heats of vaporization and molecular volumes represent a compromise between the studied model compounds. Simulations of the model compounds show an increase in the magnitude and the fluctuations of the dipole moments in moving from gas phase to condensed phases, which is a phenomenon that the additive FF is intrinsically unable to reproduce. The result is a polarizable model for aliphatic ketones and aldehydes including the acyclic sugars D-allose and D-psicose, thereby extending the available biomolecules in the Drude polarizable FF. PMID:28190218

Extended Lagrangian formulation of charge-constrained tight-binding molecular dynamics.

PubMed

Cawkwell, M J; Coe, J D; Yadav, S K; Liu, X-Y; Niklasson, A M N

2015-06-09

The extended Lagrangian Born-Oppenheimer molecular dynamics formalism [Niklasson, Phys. Rev. Lett., 2008, 100, 123004] has been applied to a tight-binding model under the constraint of local charge neutrality to yield microcanonical trajectories with both precise, long-term energy conservation and a reduced number of self-consistent field optimizations at each time step. The extended Lagrangian molecular dynamics formalism restores time reversal symmetry in the propagation of the electronic degrees of freedom, and it enables the efficient and accurate self-consistent optimization of the chemical potential and atomwise potential energy shifts in the on-site elements of the tight-binding Hamiltonian that are required when enforcing local charge neutrality. These capabilities are illustrated with microcanonical molecular dynamics simulations of a small metallic cluster using an sd-valent tight-binding model for titanium. The effects of weak dissipation on the propagation of the auxiliary degrees of freedom for the chemical potential and on-site Hamiltonian matrix elements that is used to counteract the accumulation of numerical noise during trajectories was also investigated.

The ribosome as an optimal decoder: a lesson in molecular recognition.

PubMed

Savir, Yonatan; Tlusty, Tsvi

2013-04-11

The ribosome is a complex molecular machine that, in order to synthesize proteins, has to decode mRNAs by pairing their codons with matching tRNAs. Decoding is a major determinant of fitness and requires accurate and fast selection of correct tRNAs among many similar competitors. However, it is unclear whether the modern ribosome, and in particular its large conformational changes during decoding, are the outcome of adaptation to its task as a decoder or the result of other constraints. Here, we derive the energy landscape that provides optimal discrimination between competing substrates and thereby optimal tRNA decoding. We show that the measured landscape of the prokaryotic ribosome is sculpted in this way. This model suggests that conformational changes of the ribosome and tRNA during decoding are means to obtain an optimal decoder. Our analysis puts forward a generic mechanism that may be utilized broadly by molecular recognition systems. Copyright © 2013 Elsevier Inc. All rights reserved.

Optimized theory for simple and molecular fluids.

PubMed

Marucho, M; Montgomery Pettitt, B

2007-03-28

An optimized closure approximation for both simple and molecular fluids is presented. A smooth interpolation between Perkus-Yevick and hypernetted chain closures is optimized by minimizing the free energy self-consistently with respect to the interpolation parameter(s). The molecular version is derived from a refinement of the method for simple fluids. In doing so, a method is proposed which appropriately couples an optimized closure with the variant of the diagrammatically proper integral equation recently introduced by this laboratory [K. M. Dyer et al., J. Chem. Phys. 123, 204512 (2005)]. The simplicity of the expressions involved in this proposed theory has allowed the authors to obtain an analytic expression for the approximate excess chemical potential. This is shown to be an efficient tool to estimate, from first principles, the numerical value of the interpolation parameters defining the aforementioned closure. As a preliminary test, representative models for simple fluids and homonuclear diatomic Lennard-Jones fluids were analyzed, obtaining site-site correlation functions in excellent agreement with simulation data.

Virtual screening and optimization of Type II inhibitors of JAK2 from a natural product library.

PubMed

Ma, Dik-Lung; Chan, Daniel Shiu-Hin; Wei, Guo; Zhong, Hai-Jing; Yang, Hui; Leung, Lai To; Gullen, Elizabeth A; Chiu, Pauline; Cheng, Yung-Chi; Leung, Chung-Hang

2014-11-21

Amentoflavone has been identified as a JAK2 inhibitor by structure-based virtual screening of a natural product library. In silico optimization using the DOLPHIN model yielded analogues with enhanced potency against JAK2 activity and HCV activity in cellulo. Molecular modeling and kinetic experiments suggested that the analogues may function as Type II inhibitors of JAK2.

Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models

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

Esposito, Emilio Xavier, E-mail: emilio@exeResearch.com; The Chem21 Group, Inc., 1780 Wilson Drive, Lake Forest, IL 60045; Hopfinger, Anton J., E-mail: hopfingr@gmail.com

2015-10-01

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted frommore » the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. - Highlights: • Proposed toxicity mechanism of action for decorated nanotubes complexes • Discussion of the key molecular features for each endpoint's mechanism of action • Unique mechanisms of action for each of the six biological systems • Hypothesized mechanisms of action based on QSAR/QNAR predictive models.« less

A "Reverse-Schur" Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design.

PubMed

Bardhan, Jaydeep P; Altman, Michael D; Tidor, B; White, Jacob K

2009-01-01

We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule's electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts-in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method.

A “Reverse-Schur” Approach to Optimization With Linear PDE Constraints: Application to Biomolecule Analysis and Design

PubMed Central

Bardhan, Jaydeep P.; Altman, Michael D.

2009-01-01

We present a partial-differential-equation (PDE)-constrained approach for optimizing a molecule’s electrostatic interactions with a target molecule. The approach, which we call reverse-Schur co-optimization, can be more than two orders of magnitude faster than the traditional approach to electrostatic optimization. The efficiency of the co-optimization approach may enhance the value of electrostatic optimization for ligand-design efforts–in such projects, it is often desirable to screen many candidate ligands for their viability, and the optimization of electrostatic interactions can improve ligand binding affinity and specificity. The theoretical basis for electrostatic optimization derives from linear-response theory, most commonly continuum models, and simple assumptions about molecular binding processes. Although the theory has been used successfully to study a wide variety of molecular binding events, its implications have not yet been fully explored, in part due to the computational expense associated with the optimization. The co-optimization algorithm achieves improved performance by solving the optimization and electrostatic simulation problems simultaneously, and is applicable to both unconstrained and constrained optimization problems. Reverse-Schur co-optimization resembles other well-known techniques for solving optimization problems with PDE constraints. Model problems as well as realistic examples validate the reverse-Schur method, and demonstrate that our technique and alternative PDE-constrained methods scale very favorably compared to the standard approach. Regularization, which ordinarily requires an explicit representation of the objective function, can be included using an approximate Hessian calculated using the new BIBEE/P (boundary-integral-based electrostatics estimation by preconditioning) method. PMID:23055839

Variational Koopman models: Slow collective variables and molecular kinetics from short off-equilibrium simulations

NASA Astrophysics Data System (ADS)

Wu, Hao; Nüske, Feliks; Paul, Fabian; Klus, Stefan; Koltai, Péter; Noé, Frank

2017-04-01

Markov state models (MSMs) and master equation models are popular approaches to approximate molecular kinetics, equilibria, metastable states, and reaction coordinates in terms of a state space discretization usually obtained by clustering. Recently, a powerful generalization of MSMs has been introduced, the variational approach conformation dynamics/molecular kinetics (VAC) and its special case the time-lagged independent component analysis (TICA), which allow us to approximate slow collective variables and molecular kinetics by linear combinations of smooth basis functions or order parameters. While it is known how to estimate MSMs from trajectories whose starting points are not sampled from an equilibrium ensemble, this has not yet been the case for TICA and the VAC. Previous estimates from short trajectories have been strongly biased and thus not variationally optimal. Here, we employ the Koopman operator theory and the ideas from dynamic mode decomposition to extend the VAC and TICA to non-equilibrium data. The main insight is that the VAC and TICA provide a coefficient matrix that we call Koopman model, as it approximates the underlying dynamical (Koopman) operator in conjunction with the basis set used. This Koopman model can be used to compute a stationary vector to reweight the data to equilibrium. From such a Koopman-reweighted sample, equilibrium expectation values and variationally optimal reversible Koopman models can be constructed even with short simulations. The Koopman model can be used to propagate densities, and its eigenvalue decomposition provides estimates of relaxation time scales and slow collective variables for dimension reduction. Koopman models are generalizations of Markov state models, TICA, and the linear VAC and allow molecular kinetics to be described without a cluster discretization.

Exploring possible mechanisms of action for the nanotoxicity and protein binding of decorated nanotubes: interpretation of physicochemical properties from optimal QSAR models.

PubMed

Esposito, Emilio Xavier; Hopfinger, Anton J; Shao, Chi-Yu; Su, Bo-Han; Chen, Sing-Zuo; Tseng, Yufeng Jane

2015-10-01

Carbon nanotubes have become widely used in a variety of applications including biosensors and drug carriers. Therefore, the issue of carbon nanotube toxicity is increasingly an area of focus and concern. While previous studies have focused on the gross mechanisms of action relating to nanomaterials interacting with biological entities, this study proposes detailed mechanisms of action, relating to nanotoxicity, for a series of decorated (functionalized) carbon nanotube complexes based on previously reported QSAR models. Possible mechanisms of nanotoxicity for six endpoints (bovine serum albumin, carbonic anhydrase, chymotrypsin, hemoglobin along with cell viability and nitrogen oxide production) have been extracted from the corresponding optimized QSAR models. The molecular features relevant to each of the endpoint respective mechanism of action for the decorated nanotubes are also discussed. Based on the molecular information contained within the optimal QSAR models for each nanotoxicity endpoint, either the decorator attached to the nanotube is directly responsible for the expression of a particular activity, irrespective of the decorator's 3D-geometry and independent of the nanotube, or those decorators having structures that place the functional groups of the decorators as far as possible from the nanotube surface most strongly influence the biological activity. These molecular descriptors are further used to hypothesize specific interactions involved in the expression of each of the six biological endpoints. Copyright © 2015 Elsevier Inc. All rights reserved.

Molecular modeling of calmodulin: a comparison with crystallographic data

NASA Technical Reports Server (NTRS)

McDonald, J. J.; Rein, R.

1989-01-01

Two methods of side-chain placement on a modeled protein have been examined. Two molecular models of calmodulin were constructed that differ in the treatment of side chains prior to optimization of the molecule. A virtual bond analysis program developed by Purisima and Scheraga was used to determine the backbone conformation based on 2.2 angstroms resolution C alpha coordinates for the molecules. In the first model, side chains were initially constructed in an extended conformation. In the second model, a conformational grid search technique was employed. Calcium ions were treated explicitly during energy optimization using CHARMM. The models are compared to a recently published refined crystal structure of calmodulin. The results indicate that the initial choices for side-chains, but also significant effects on the main-chain conformation and supersecondary structure. The conformational differences are discussed. Analysis of these and other methods makes possible the formulation of a methodology for more appropriate side-chain placement in modeled proteins.

Potent New Small-Molecule Inhibitor of Botulinum Neurotoxin Serotype A Endopeptidase Developed by Synthesis-Based Computer-Aided Molecular Design

DTIC Science & Technology

2009-11-01

dynamics of the complex predicted by multiple molecular dynamics simulations , and discuss further structural optimization to achieve better in vivo efficacy...complex with BoNTAe and the dynamics of the complex predicted by multiple molecular dynamics simulations (MMDSs). On the basis of the 3D model, we discuss...is unlimited whereas AHP exhibited 54% inhibition under the same conditions (Table 1). Computer Simulation Twenty different molecular dynamics

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

PubMed

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

Glioblastoma, a brief review of history, molecular genetics, animal models and novel therapeutic strategies.

PubMed

Agnihotri, Sameer; Burrell, Kelly E; Wolf, Amparo; Jalali, Sharzhad; Hawkins, Cynthia; Rutka, James T; Zadeh, Gelareh

2013-02-01

Glioblastoma (GBM) is the most common and lethal primary brain tumor. Over the past few years tremendous genomic and proteomic characterization along with robust animal models of GBM have provided invaluable data that show that "GBM", although histologically indistinguishable from one another, are comprised of molecularly heterogenous diseases. In addition, robust pre-clinical models and a better understanding of the core pathways disrupted in GBM are providing a renewed optimism for novel strategies targeting these devastating tumors. Here, we summarize a brief history of the disease, our current molecular knowledge, lessons from animal models and emerging concepts of angiogenesis, invasion, and metabolism in GBM that may lend themselves to therapeutic targeting.

Higher-Order Extended Lagrangian Born–Oppenheimer Molecular Dynamics for Classical Polarizable Models

DOE PAGES

Albaugh, Alex; Head-Gordon, Teresa; Niklasson, Anders M. N.

2018-01-09

Generalized extended Lagrangian Born−Oppenheimer molecular dynamics (XLBOMD) methods provide a framework for fast iteration-free simulations of models that normally require expensive electronic ground state optimizations prior to the force evaluations at every time step. XLBOMD uses dynamically driven auxiliary degrees of freedom that fluctuate about a variationally optimized ground state of an approximate “shadow” potential which approximates the true reference potential. While the requirements for such shadow potentials are well understood, constructing such potentials in practice has previously been ad hoc, and in this work, we present a systematic development of XLBOMD shadow potentials that match the reference potential tomore » any order. We also introduce a framework for combining friction-like dissipation for the auxiliary degrees of freedom with general-order integration, a combination that was not previously possible. These developments are demonstrated with a simple fluctuating charge model and point induced dipole polarization models.« less

Higher-Order Extended Lagrangian Born-Oppenheimer Molecular Dynamics for Classical Polarizable Models.

PubMed

Albaugh, Alex; Head-Gordon, Teresa; Niklasson, Anders M N

2018-02-13

Generalized extended Lagrangian Born-Oppenheimer molecular dynamics (XLBOMD) methods provide a framework for fast iteration-free simulations of models that normally require expensive electronic ground state optimizations prior to the force evaluations at every time step. XLBOMD uses dynamically driven auxiliary degrees of freedom that fluctuate about a variationally optimized ground state of an approximate "shadow" potential which approximates the true reference potential. While the requirements for such shadow potentials are well understood, constructing such potentials in practice has previously been ad hoc, and in this work, we present a systematic development of XLBOMD shadow potentials that match the reference potential to any order. We also introduce a framework for combining friction-like dissipation for the auxiliary degrees of freedom with general-order integration, a combination that was not previously possible. These developments are demonstrated with a simple fluctuating charge model and point induced dipole polarization models.

Higher-Order Extended Lagrangian Born–Oppenheimer Molecular Dynamics for Classical Polarizable Models

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

Albaugh, Alex; Head-Gordon, Teresa; Niklasson, Anders M. N.

Generalized extended Lagrangian Born−Oppenheimer molecular dynamics (XLBOMD) methods provide a framework for fast iteration-free simulations of models that normally require expensive electronic ground state optimizations prior to the force evaluations at every time step. XLBOMD uses dynamically driven auxiliary degrees of freedom that fluctuate about a variationally optimized ground state of an approximate “shadow” potential which approximates the true reference potential. While the requirements for such shadow potentials are well understood, constructing such potentials in practice has previously been ad hoc, and in this work, we present a systematic development of XLBOMD shadow potentials that match the reference potential tomore » any order. We also introduce a framework for combining friction-like dissipation for the auxiliary degrees of freedom with general-order integration, a combination that was not previously possible. These developments are demonstrated with a simple fluctuating charge model and point induced dipole polarization models.« less

A COMPUTER MODELING STUDY OF BINDING PROPERTIES OF CHIRAL NUCLEOPEPTIDE FOR BIOMEDICAL APPLICATIONS.

PubMed

Pirtskhalava, M; Egoyan, A; Mirtskhulava, M; Roviello, G

2017-12-01

Nucleopeptides often show interesting properties of molecular binding that render them good candidates for development of innovative drugs for anticancer and antiviral therapies. In this work we present results of computer modeling of interactions between the molecules of hexathymine nucleopeptide (T6) and poly rA RNA (A18). The results of geometry optimization calculated using Hyperchem software and our own computer program for molecular docking show that molecules establish stable complexes due to the complementary-nucleobase interaction and the electrostatic interaction between the negative phosphate group of poly rA and the positively-charged residues present in the cationic nucleopeptide structure. Computer modeling makes it possible to find the optimal binding configuration of the molecules of a nucleopeptide and poly rA RNA and to estimate the binding energy between the molecules.

Molecular taxonomy of phytopathogenic fungi: a case study in Peronospora.

PubMed

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M Teresa; Martín, María P

2009-07-29

Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence.

Molecular Taxonomy of Phytopathogenic Fungi: A Case Study in Peronospora

PubMed Central

Göker, Markus; García-Blázquez, Gema; Voglmayr, Hermann; Tellería, M. Teresa; Martín, María P.

2009-01-01

Background Inappropriate taxon definitions may have severe consequences in many areas. For instance, biologically sensible species delimitation of plant pathogens is crucial for measures such as plant protection or biological control and for comparative studies involving model organisms. However, delimiting species is challenging in the case of organisms for which often only molecular data are available, such as prokaryotes, fungi, and many unicellular eukaryotes. Even in the case of organisms with well-established morphological characteristics, molecular taxonomy is often necessary to emend current taxonomic concepts and to analyze DNA sequences directly sampled from the environment. Typically, for this purpose clustering approaches to delineate molecular operational taxonomic units have been applied using arbitrary choices regarding the distance threshold values, and the clustering algorithms. Methodology Here, we report on a clustering optimization method to establish a molecular taxonomy of Peronospora based on ITS nrDNA sequences. Peronospora is the largest genus within the downy mildews, which are obligate parasites of higher plants, and includes various economically important pathogens. The method determines the distance function and clustering setting that result in an optimal agreement with selected reference data. Optimization was based on both taxonomy-based and host-based reference information, yielding the same outcome. Resampling and permutation methods indicate that the method is robust regarding taxon sampling and errors in the reference data. Tests with newly obtained ITS sequences demonstrate the use of the re-classified dataset in molecular identification of downy mildews. Conclusions A corrected taxonomy is provided for all Peronospora ITS sequences contained in public databases. Clustering optimization appears to be broadly applicable in automated, sequence-based taxonomy. The method connects traditional and modern taxonomic disciplines by specifically addressing the issue of how to optimally account for both traditional species concepts and genetic divergence. PMID:19641601

Quantum optimal control of isomerization dynamics of a one-dimensional reaction-path model dominated by a competing dissociation channel

NASA Astrophysics Data System (ADS)

Kurosaki, Yuzuru; Artamonov, Maxim; Ho, Tak-San; Rabitz, Herschel

2009-07-01

Quantum wave packet optimal control simulations with intense laser pulses have been carried out for studying molecular isomerization dynamics of a one-dimensional (1D) reaction-path model involving a dominant competing dissociation channel. The 1D intrinsic reaction coordinate model mimics the ozone open→cyclic ring isomerization along the minimum energy path that successively connects the ozone cyclic ring minimum, the transition state (TS), the open (global) minimum, and the dissociative O2+O asymptote on the O3 ground-state A1' potential energy surface. Energetically, the cyclic ring isomer, the TS barrier, and the O2+O dissociation channel lie at ˜0.05, ˜0.086, and ˜0.037 hartree above the open isomer, respectively. The molecular orientation of the modeled ozone is held constant with respect to the laser-field polarization and several optimal fields are found that all produce nearly perfect isomerization. The optimal control fields are characterized by distinctive high temporal peaks as well as low frequency components, thereby enabling abrupt transfer of the time-dependent wave packet over the TS from the open minimum to the targeted ring minimum. The quick transition of the ozone wave packet avoids detrimental leakage into the competing O2+O channel. It is possible to obtain weaker optimal laser fields, resulting in slower transfer of the wave packets over the TS, when a reduced level of isomerization is satisfactory.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.

PubMed

Toropova, Alla P; Toropov, Andrey A

2017-06-05

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by n validation =29, r 2 validation =0.8596, RMSE validation =0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization and benchmarking of a perturbative Metropolis Monte Carlo quantum mechanics/molecular mechanics program

NASA Astrophysics Data System (ADS)

Feldt, Jonas; Miranda, Sebastião; Pratas, Frederico; Roma, Nuno; Tomás, Pedro; Mata, Ricardo A.

2017-12-01

In this work, we present an optimized perturbative quantum mechanics/molecular mechanics (QM/MM) method for use in Metropolis Monte Carlo simulations. The model adopted is particularly tailored for the simulation of molecular systems in solution but can be readily extended to other applications, such as catalysis in enzymatic environments. The electrostatic coupling between the QM and MM systems is simplified by applying perturbation theory to estimate the energy changes caused by a movement in the MM system. This approximation, together with the effective use of GPU acceleration, leads to a negligible added computational cost for the sampling of the environment. Benchmark calculations are carried out to evaluate the impact of the approximations applied and the overall computational performance.

Optimization and benchmarking of a perturbative Metropolis Monte Carlo quantum mechanics/molecular mechanics program.

PubMed

Feldt, Jonas; Miranda, Sebastião; Pratas, Frederico; Roma, Nuno; Tomás, Pedro; Mata, Ricardo A

2017-12-28

In this work, we present an optimized perturbative quantum mechanics/molecular mechanics (QM/MM) method for use in Metropolis Monte Carlo simulations. The model adopted is particularly tailored for the simulation of molecular systems in solution but can be readily extended to other applications, such as catalysis in enzymatic environments. The electrostatic coupling between the QM and MM systems is simplified by applying perturbation theory to estimate the energy changes caused by a movement in the MM system. This approximation, together with the effective use of GPU acceleration, leads to a negligible added computational cost for the sampling of the environment. Benchmark calculations are carried out to evaluate the impact of the approximations applied and the overall computational performance.

Optimization strategies for molecular dynamics programs on Cray computers and scalar work stations

NASA Astrophysics Data System (ADS)

Unekis, Michael J.; Rice, Betsy M.

1994-12-01

We present results of timing runs and different optimization strategies for a prototype molecular dynamics program that simulates shock waves in a two-dimensional (2-D) model of a reactive energetic solid. The performance of the program may be improved substantially by simple changes to the Fortran or by employing various vendor-supplied compiler optimizations. The optimum strategy varies among the machines used and will vary depending upon the details of the program. The effect of various compiler options and vendor-supplied subroutine calls is demonstrated. Comparison is made between two scalar workstations (IBM RS/6000 Model 370 and Model 530) and several Cray supercomputers (X-MP/48, Y-MP8/128, and C-90/16256). We find that for a scientific application program dominated by sequential, scalar statements, a relatively inexpensive high-end work station such as the IBM RS/60006 RISC series will outperform single processor performance of the Cray X-MP/48 and perform competitively with single processor performance of the Y-MP8/128 and C-9O/16256.

Molecular Cooperativity Governs Diverse and Monoallelic Olfactory Receptor Expression

NASA Astrophysics Data System (ADS)

Xing, Jianhua; Tian, Xiaojun; Zhang, Hang; Sannerud, Jens

Multiple-objective optimization is common in biological systems. In the mammalian olfactory system, each sensory neuron stochastically expresses only one out of up to thousands of olfactory receptor (OR) gene alleles; at organism level the types of expressed ORs need to be maximized. The molecular mechanism of this Nobel-Prize winning puzzle remains unresolved after decades of extensive studies. Existing models focus only on monoallele activation, and cannot explain recent observations in mutants, especially the reduced global diversity of expressed ORs in G9a/GLP knockouts. In this work we integrated existing information on OR expression, and proposed an evolutionarily optimized three-layer regulation mechanism, which includes zonal segregation, epigenetic and enhancer competition coupled to a negative feedback loop. This model not only recapitulates monoallelic OR expression, but also elucidates how the olfactory system maximizes and maintains the diversity of OR expression. The model is validated by several experimental results, and particularly underscores cooperativity and synergy as a general design principle of multi-objective optimization in biology. The work is supported by the NIGMS/DMS Mathematical Biology program.

[Screen potential CYP450 2E1 inhibitors from Chinese herbal medicine based on support vector regression and molecular docking method].

PubMed

Chen, Xi; Lu, Fang; Jiang, Lu-di; Cai, Yi-Lian; Li, Gong-Yu; Zhang, Yan-Ling

2016-07-01

Inhibition of cytochrome P450 (CYP450) enzymes is the most common reasons for drug interactions, so the study on early prediction of CYPs inhibitors can help to decrease the incidence of adverse reactions caused by drug interactions.CYP450 2E1(CYP2E1), as a key role in drug metabolism process, has broad spectrum of drug metabolism substrate. In this study, 32 CYP2E1 inhibitors were collected for the construction of support vector regression (SVR) model. The test set data were used to verify CYP2E1 quantitative models and obtain the optimal prediction model of CYP2E1 inhibitor. Meanwhile, one molecular docking program, CDOCKER, was utilized to analyze the interaction pattern between positive compounds and active pocket to establish the optimal screening model of CYP2E1 inhibitors.SVR model and molecular docking prediction model were combined to screen traditional Chinese medicine database (TCMD), which could improve the calculation efficiency and prediction accuracy. 6 376 traditional Chinese medicine (TCM) compounds predicted by SVR model were obtained, and in further verification by using molecular docking model, 247 TCM compounds with potential inhibitory activities against CYP2E1 were finally retained. Some of them have been verified by experiments. The results demonstrated that this study could provide guidance for the virtual screening of CYP450 inhibitors and the prediction of CYPs-mediated DDIs, and also provide references for clinical rational drug use. Copyright© by the Chinese Pharmaceutical Association.

Transitioning NWChem to the Next Generation of Manycore Machines

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

Bylaska, Eric J.; Apra, E; Kowalski, Karol

The NorthWest chemistry (NWChem) modeling software is a popular molecular chemistry simulation software that was designed from the start to work on massively parallel processing supercomputers [1-3]. It contains an umbrella of modules that today includes self-consistent eld (SCF), second order Møller-Plesset perturbation theory (MP2), coupled cluster (CC), multiconguration self-consistent eld (MCSCF), selected conguration interaction (CI), tensor contraction engine (TCE) many body methods, density functional theory (DFT), time-dependent density functional theory (TDDFT), real-time time-dependent density functional theory, pseudopotential plane-wave density functional theory (PSPW), band structure (BAND), ab initio molecular dynamics (AIMD), Car-Parrinello molecular dynamics (MD), classical MD, hybrid quantum mechanicsmore » molecular mechanics (QM/MM), hybrid ab initio molecular dynamics molecular mechanics (AIMD/MM), gauge independent atomic orbital nuclear magnetic resonance (GIAO NMR), conductor like screening solvation model (COSMO), conductor-like screening solvation model based on density (COSMO-SMD), and reference interaction site model (RISM) solvation models, free energy simulations, reaction path optimization, parallel in time, among other capabilities [4]. Moreover, new capabilities continue to be added with each new release.« less

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

The R.E.D. tools: advances in RESP and ESP charge derivation and force field library building.

PubMed

Dupradeau, François-Yves; Pigache, Adrien; Zaffran, Thomas; Savineau, Corentin; Lelong, Rodolphe; Grivel, Nicolas; Lelong, Dimitri; Rosanski, Wilfried; Cieplak, Piotr

2010-07-28

Deriving atomic charges and building a force field library for a new molecule are key steps when developing a force field required for conducting structural and energy-based analysis using molecular mechanics. Derivation of popular RESP charges for a set of residues is a complex and error prone procedure because it depends on numerous input parameters. To overcome these problems, the R.E.D. Tools (RESP and ESP charge Derive, ) have been developed to perform charge derivation in an automatic and straightforward way. The R.E.D. program handles chemical elements up to bromine in the periodic table. It interfaces different quantum mechanical programs employed for geometry optimization and computing molecular electrostatic potential(s), and performs charge fitting using the RESP program. By defining tight optimization criteria and by controlling the molecular orientation of each optimized geometry, charge values are reproduced at any computer platform with an accuracy of 0.0001 e. The charges can be fitted using multiple conformations, making them suitable for molecular dynamics simulations. R.E.D. allows also for defining charge constraints during multiple molecule charge fitting, which are used to derive charges for molecular fragments. Finally, R.E.D. incorporates charges into a force field library, readily usable in molecular dynamics computer packages. For complex cases, such as a set of homologous molecules belonging to a common family, an entire force field topology database is generated. Currently, the atomic charges and force field libraries have been developed for more than fifty model systems and stored in the RESP ESP charge DDataBase. Selected results related to non-polarizable charge models are presented and discussed.

Prediction of molecular crystal structures by a crystallographic QM/MM model with full space-group symmetry.

PubMed

Mörschel, Philipp; Schmidt, Martin U

2015-01-01

A crystallographic quantum-mechanical/molecular-mechanical model (c-QM/MM model) with full space-group symmetry has been developed for molecular crystals. The lattice energy was calculated by quantum-mechanical methods for short-range interactions and force-field methods for long-range interactions. The quantum-mechanical calculations covered the interactions within the molecule and the interactions of a reference molecule with each of the surrounding 12-15 molecules. The interactions with all other molecules were treated by force-field methods. In each optimization step the energies in the QM and MM shells were calculated separately as single-point energies; after adding both energy contributions, the crystal structure (including the lattice parameters) was optimized accordingly. The space-group symmetry was maintained throughout. Crystal structures with more than one molecule per asymmetric unit, e.g. structures with Z' = 2, hydrates and solvates, have been optimized as well. Test calculations with different quantum-mechanical methods on nine small organic molecules revealed that the density functional theory methods with dispersion correction using the B97-D functional with 6-31G* basis set in combination with the DREIDING force field reproduced the experimental crystal structures with good accuracy. Subsequently the c-QM/MM method was applied to nine compounds from the CCDC blind tests resulting in good energy rankings and excellent geometric accuracies.

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.

Biotechnology and genetic engineering in the new drug development. Part III. Biocatalysis, metabolic engineering and molecular modelling.

PubMed

Stryjewska, Agnieszka; Kiepura, Katarzyna; Librowski, Tadeusz; Lochyński, Stanisław

2013-01-01

Industrial biotechnology has been defined as the use and application of biotechnology for the sustainable processing and production of chemicals, materials and fuels. It makes use of biocatalysts such as microbial communities, whole-cell microorganisms or purified enzymes. In the review these processes are described. Drug design is an iterative process which begins when a chemist identifies a compound that displays an interesting biological profile and ends when both the activity profile and the chemical synthesis of the new chemical entity are optimized. Traditional approaches to drug discovery rely on a stepwise synthesis and screening program for large numbers of compounds to optimize activity profiles. Over the past ten to twenty years, scientists have used computer models of new chemical entities to help define activity profiles, geometries and relativities. This article introduces inter alia the concepts of molecular modelling and contains references for further reading.

Combining configurational energies and forces for molecular force field optimization

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

Vlcek, Lukas; Sun, Weiwei; Kent, Paul R. C.

While quantum chemical simulations have been increasingly used as an invaluable source of information for atomistic model development, the high computational expenses typically associated with these techniques often limit thorough sampling of the systems of interest. It is therefore of great practical importance to use all available information as efficiently as possible, and in a way that allows for consistent addition of constraints that may be provided by macroscopic experiments. We propose a simple approach that combines information from configurational energies and forces generated in a molecular dynamics simulation to increase the effective number of samples. Subsequently, this information ismore » used to optimize a molecular force field by minimizing the statistical distance similarity metric. We also illustrate the methodology on an example of a trajectory of configurations generated in equilibrium molecular dynamics simulations of argon and water and compare the results with those based on the force matching method.« less

Combining configurational energies and forces for molecular force field optimization

DOE PAGES

Vlcek, Lukas; Sun, Weiwei; Kent, Paul R. C.

2017-07-21

While quantum chemical simulations have been increasingly used as an invaluable source of information for atomistic model development, the high computational expenses typically associated with these techniques often limit thorough sampling of the systems of interest. It is therefore of great practical importance to use all available information as efficiently as possible, and in a way that allows for consistent addition of constraints that may be provided by macroscopic experiments. We propose a simple approach that combines information from configurational energies and forces generated in a molecular dynamics simulation to increase the effective number of samples. Subsequently, this information ismore » used to optimize a molecular force field by minimizing the statistical distance similarity metric. We also illustrate the methodology on an example of a trajectory of configurations generated in equilibrium molecular dynamics simulations of argon and water and compare the results with those based on the force matching method.« less

Extraction of gelatin from salmon (Salmo salar) fish skin using trypsin-aided process: optimization by Plackett-Burman and response surface methodological approaches.

PubMed

Fan, HuiYin; Dumont, Marie-Josée; Simpson, Benjamin K

2017-11-01

Gelatin from salmon ( Salmo salar ) skin with high molecular weight protein chains ( α -chains) was extracted using trypsin-aided process. Response surface methodology was used to optimise the extraction parameters. Yield, hydroxyproline content and protein electrophoretic profile via sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of gelatin were used as responses in the optimization study. The optimum conditions were determined as: trypsin concentration at 1.49 U/g; extraction temperature at 45 °C; and extraction time at 6 h 16 min. This response surface optimized model was significant and produced an experimental value (202.04 ± 8.64%) in good agreement with the predicted value (204.19%). Twofold higher yields of gelatin with high molecular weight protein chains were achieved in the optimized process with trypsin treatment when compared to the process without trypsin.

Optimisation of the synthesis of vancomycin-selective molecularly imprinted polymer nanoparticles using automatic photoreactor

PubMed Central

2014-01-01

A novel optimized protocol for solid-state synthesis of molecularly imprinted polymer nanoparticles (nanoMIPs) with specificity for antibiotic vancomycin is described. The experimental objective was optimization of the synthesis parameters (factors) affecting the yield of obtained nanoparticles which have been synthesized using the first prototype of an automated solid-phase synthesizer. Applications of experimental design (or design of experiments) in optimization of nanoMIP yield were carried out using MODDE 9.0 software. The factors chosen in the model were the amount of functional monomers in the polymerization mixture, irradiation time, temperature during polymerization, and elution temperature. In general, it could be concluded that the irradiation time is the most important and the temperature was the least important factor which influences the yield of nanoparticles. Overall, the response surface methodology proved to be an effective tool in reducing time required for optimization of complex experimental conditions. PMID:24685151

Optimisation of the synthesis of vancomycin-selective molecularly imprinted polymer nanoparticles using automatic photoreactor.

PubMed

Muzyka, Kateryna; Karim, Khalku; Guerreiro, Antonio; Poma, Alessandro; Piletsky, Sergey

2014-03-31

A novel optimized protocol for solid-state synthesis of molecularly imprinted polymer nanoparticles (nanoMIPs) with specificity for antibiotic vancomycin is described. The experimental objective was optimization of the synthesis parameters (factors) affecting the yield of obtained nanoparticles which have been synthesized using the first prototype of an automated solid-phase synthesizer. Applications of experimental design (or design of experiments) in optimization of nanoMIP yield were carried out using MODDE 9.0 software. The factors chosen in the model were the amount of functional monomers in the polymerization mixture, irradiation time, temperature during polymerization, and elution temperature. In general, it could be concluded that the irradiation time is the most important and the temperature was the least important factor which influences the yield of nanoparticles. Overall, the response surface methodology proved to be an effective tool in reducing time required for optimization of complex experimental conditions.

Optimisation of the synthesis of vancomycin-selective molecularly imprinted polymer nanoparticles using automatic photoreactor

NASA Astrophysics Data System (ADS)

Muzyka, Kateryna; Karim, Khalku; Guerreiro, Antonio; Poma, Alessandro; Piletsky, Sergey

2014-03-01

A novel optimized protocol for solid-state synthesis of molecularly imprinted polymer nanoparticles (nanoMIPs) with specificity for antibiotic vancomycin is described. The experimental objective was optimization of the synthesis parameters (factors) affecting the yield of obtained nanoparticles which have been synthesized using the first prototype of an automated solid-phase synthesizer. Applications of experimental design (or design of experiments) in optimization of nanoMIP yield were carried out using MODDE 9.0 software. The factors chosen in the model were the amount of functional monomers in the polymerization mixture, irradiation time, temperature during polymerization, and elution temperature. In general, it could be concluded that the irradiation time is the most important and the temperature was the least important factor which influences the yield of nanoparticles. Overall, the response surface methodology proved to be an effective tool in reducing time required for optimization of complex experimental conditions.

Automated parameterization of intermolecular pair potentials using global optimization techniques

NASA Astrophysics Data System (ADS)

Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk

2014-12-01

In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.

Computational Optimization and Characterization of Molecularly Imprinted Polymers

NASA Astrophysics Data System (ADS)

Terracina, Jacob J.

Molecularly imprinted polymers (MIPs) are a class of materials containing sites capable of selectively binding to the imprinted target molecule. Computational chemistry techniques were used to study the effect of different fabrication parameters (the monomer-to-target ratios, pre-polymerization solvent, temperature, and pH) on the formation of the MIP binding sites. Imprinted binding sites were built in silico for the purposes of better characterizing the receptor - ligand interactions. Chiefly, the sites were characterized with respect to their selectivities and the heterogeneity between sites. First, a series of two-step molecular mechanics (MM) and quantum mechanics (QM) computational optimizations of monomer -- target systems was used to determine optimal monomer-to-target ratios for the MIPs. Imidazole- and xanthine-derived target molecules were studied. The investigation included both small-scale models (one-target) and larger scale models (five-targets). The optimal ratios differed between the small and larger scales. For the larger models containing multiple targets, binding-site surface area analysis was used to evaluate the heterogeneity of the sites. The more fully surrounded sites had greater binding energies. Molecular docking was then used to measure the selectivities of the QM-optimized binding sites by comparing the binding energies of the imprinted target to that of a structural analogue. Selectivity was also shown to improve as binding sites become more fully encased by the monomers. For internal sites, docking consistently showed selectivity favoring the molecules that had been imprinted via QM geometry optimizations. The computationally imprinted sites were shown to exhibit size-, shape-, and polarity-based selectivity. This represented a novel approach to investigate the selectivity and heterogeneity of imprinted polymer binding sites, by applying the rapid orientation screening of MM docking to the highly accurate QM-optimized geometries. Next, we sought to computationally construct and investigate binding sites for their enantioselectivity. Again, a two-step MM [special characters removed] QM optimization scheme was used to "computationally imprint" chiral molecules. Using docking techniques, the imprinted binding sites were shown to exhibit an enantioselective preference for the imprinted molecule over its enantiomer. Docking of structurally similar chiral molecules showed that the sites computationally imprinted with R- or S-tBOC-tyrosine were able to differentiate between R- and S-forms of other tyrosine derivatives. The cross-enantioselectivity did not hold for chiral molecules that did not share the tyrosine H-bonding functional group orientations. Further analysis of the individual monomer - target interactions within the binding site led us to conclude that H-bonding functional groups that are located immediately next to the target's chiral center, and therefore spatially fixed relative to the chiral center, will have a stronger contribution to the enantioselectivity of the site than those groups separated from the chiral center by two or more rotatable bonds. These models were the first computationally imprinted binding sites to exhibit this enantioselective preference for the imprinted target molecules. Finally, molecular dynamics (MD) was used to quantify H-bonding interactions between target molecules, monomers, and solvents representative of the pre-polymerization matrix. It was found that both target dimerization and solvent interference decrease the number of monomer - target H-bonds present. Systems were optimized via simulated annealing to create binding sites that were then subjected to molecular docking analysis. Docking showed that the presence of solvent had a detrimental effect on the sensitivity and selectivity of the sites, and that solvents with more H-bonding capabilities were more disruptive to the binding properties of the site. Dynamic simulations also showed that increasing the temperature of the solution can significantly decrease the number of H-bonds formed between the targets and monomers. It is believed that the monomer - target complexes formed within the pre-polymerization matrix are translated into the selective binding cavities formed during polymerization. Elucidating the nature of these interactions in silico improves our understanding of MIPs, ultimately allowing for more optimized sensing materials.

QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

PubMed

Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

2013-06-01

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quantum mechanical/molecular mechanical/continuum style solvation model: time-dependent density functional theory.

PubMed

Thellamurege, Nandun M; Cui, Fengchao; Li, Hui

2013-08-28

A combined quantum mechanical/molecular mechanical/continuum (QM/MMpol/C) style method is developed for time-dependent density functional theory (TDDFT, including long-range corrected TDDFT) method, induced dipole polarizable force field, and induced surface charge continuum model. Induced dipoles and induced charges are included in the TDDFT equations to solve for the transition energies, relaxed density, and transition density. Analytic gradient is derived and implemented for geometry optimization and molecular dynamics simulation. QM/MMpol/C style DFT and TDDFT methods are used to study the hydrogen bonding of the photoactive yellow protein chromopore in ground state and excited state.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

Computational studies of novel chymase inhibitors against cardiovascular and allergic diseases: mechanism and inhibition.

PubMed

Arooj, Mahreen; Thangapandian, Sundarapandian; John, Shalini; Hwang, Swan; Park, Jong K; Lee, Keun W

2012-12-01

To provide a new idea for drug design, a computational investigation is performed on chymase and its novel 1,4-diazepane-2,5-diones inhibitors that explores the crucial molecular features contributing to binding specificity. Molecular docking studies of inhibitors within the active site of chymase were carried out to rationalize the inhibitory properties of these compounds and understand their inhibition mechanism. The density functional theory method was used to optimize molecular structures with the subsequent analysis of highest occupied molecular orbital, lowest unoccupied molecular orbital, and molecular electrostatic potential maps, which revealed that negative potentials near 1,4-diazepane-2,5-diones ring are essential for effective binding of inhibitors at active site of enzyme. The Bayesian model with receiver operating curve statistic of 0.82 also identified arylsulfonyl and aminocarbonyl as the molecular features favoring and not favoring inhibition of chymase, respectively. Moreover, genetic function approximation was applied to construct 3D quantitative structure-activity relationships models. Two models (genetic function approximation model 1 r(2) = 0.812 and genetic function approximation model 2 r(2) = 0.783) performed better in terms of correlation coefficients and cross-validation analysis. In general, this study is used as example to illustrate how combinational use of 2D/3D quantitative structure-activity relationships modeling techniques, molecular docking, frontier molecular orbital density fields (highest occupied molecular orbital and lowest unoccupied molecular orbital), and molecular electrostatic potential analysis may be useful to gain an insight into the binding mechanism between enzyme and its inhibitors. © 2012 John Wiley & Sons A/S.

An alternative covariance estimator to investigate genetic heterogeneity in populations

USDA-ARS?s Scientific Manuscript database

Genomic predictions and GWAS have used mixed models for identification of associations and trait predictions. In both cases, the covariance between individuals for performance is estimated using molecular markers. Mixed model properties indicate that the use of the data for prediction is optimal if ...

Postprocessing of docked protein-ligand complexes using implicit solvation models.

PubMed

Lindström, Anton; Edvinsson, Lotta; Johansson, Andreas; Andersson, C David; Andersson, Ida E; Raubacher, Florian; Linusson, Anna

2011-02-28

Molecular docking plays an important role in drug discovery as a tool for the structure-based design of small organic ligands for macromolecules. Possible applications of docking are identification of the bioactive conformation of a protein-ligand complex and the ranking of different ligands with respect to their strength of binding to a particular target. We have investigated the effect of implicit water on the postprocessing of binding poses generated by molecular docking using MM-PB/GB-SA (molecular mechanics Poisson-Boltzmann and generalized Born surface area) methodology. The investigation was divided into three parts: geometry optimization, pose selection, and estimation of the relative binding energies of docked protein-ligand complexes. Appropriate geometry optimization afforded more accurate binding poses for 20% of the complexes investigated. The time required for this step was greatly reduced by minimizing the energy of the binding site using GB solvation models rather than minimizing the entire complex using the PB model. By optimizing the geometries of docking poses using the GB(HCT+SA) model then calculating their free energies of binding using the PB implicit solvent model, binding poses similar to those observed in crystal structures were obtained. Rescoring of these poses according to their calculated binding energies resulted in improved correlations with experimental binding data. These correlations could be further improved by applying the postprocessing to several of the most highly ranked poses rather than focusing exclusively on the top-scored pose. The postprocessing protocol was successfully applied to the analysis of a set of Factor Xa inhibitors and a set of glycopeptide ligands for the class II major histocompatibility complex (MHC) A(q) protein. These results indicate that the protocol for the postprocessing of docked protein-ligand complexes developed in this paper may be generally useful for structure-based design in drug discovery.

Simulating the flow of entangled polymers.

PubMed

Masubuchi, Yuichi

2014-01-01

To optimize automation for polymer processing, attempts have been made to simulate the flow of entangled polymers. In industry, fluid dynamics simulations with phenomenological constitutive equations have been practically established. However, to account for molecular characteristics, a method to obtain the constitutive relationship from the molecular structure is required. Molecular dynamics simulations with atomic description are not practical for this purpose; accordingly, coarse-grained models with reduced degrees of freedom have been developed. Although the modeling of entanglement is still a challenge, mesoscopic models with a priori settings to reproduce entangled polymer dynamics, such as tube models, have achieved remarkable success. To use the mesoscopic models as staging posts between atomistic and fluid dynamics simulations, studies have been undertaken to establish links from the coarse-grained model to the atomistic and macroscopic simulations. Consequently, integrated simulations from materials chemistry to predict the macroscopic flow in polymer processing are forthcoming.

Optimization of Polyplex Formation between DNA Oligonucleotide and Poly(ʟ-Lysine): Experimental Study and Modeling Approach.

PubMed

Vasiliu, Tudor; Cojocaru, Corneliu; Rotaru, Alexandru; Pricope, Gabriela; Pinteala, Mariana; Clima, Lilia

2017-06-17

The polyplexes formed by nucleic acids and polycations have received a great attention owing to their potential application in gene therapy. In our study, we report experimental results and modeling outcomes regarding the optimization of polyplex formation between the double-stranded DNA (dsDNA) and poly(ʟ-Lysine) (PLL). The quantification of the binding efficiency during polyplex formation was performed by processing of the images captured from the gel electrophoresis assays. The design of experiments (DoE) and response surface methodology (RSM) were employed to investigate the coupling effect of key factors (pH and N/P ratio) affecting the binding efficiency. According to the experimental observations and response surface analysis, the N/P ratio showed a major influence on binding efficiency compared to pH. Model-based optimization calculations along with the experimental confirmation runs unveiled the maximal binding efficiency (99.4%) achieved at pH 5.4 and N/P ratio 125. To support the experimental data and reveal insights of molecular mechanism responsible for the polyplex formation between dsDNA and PLL, molecular dynamics simulations were performed at pH 5.4 and 7.4.

Optimization of Polyplex Formation between DNA Oligonucleotide and Poly(l-Lysine): Experimental Study and Modeling Approach

PubMed Central

Vasiliu, Tudor; Cojocaru, Corneliu; Rotaru, Alexandru; Pricope, Gabriela; Pinteala, Mariana; Clima, Lilia

2017-01-01

The polyplexes formed by nucleic acids and polycations have received a great attention owing to their potential application in gene therapy. In our study, we report experimental results and modeling outcomes regarding the optimization of polyplex formation between the double-stranded DNA (dsDNA) and poly(l-Lysine) (PLL). The quantification of the binding efficiency during polyplex formation was performed by processing of the images captured from the gel electrophoresis assays. The design of experiments (DoE) and response surface methodology (RSM) were employed to investigate the coupling effect of key factors (pH and N/P ratio) affecting the binding efficiency. According to the experimental observations and response surface analysis, the N/P ratio showed a major influence on binding efficiency compared to pH. Model-based optimization calculations along with the experimental confirmation runs unveiled the maximal binding efficiency (99.4%) achieved at pH 5.4 and N/P ratio 125. To support the experimental data and reveal insights of molecular mechanism responsible for the polyplex formation between dsDNA and PLL, molecular dynamics simulations were performed at pH 5.4 and 7.4. PMID:28629130

From laptop to benchtop to bedside: Structure-based Drug Design on Protein Targets

PubMed Central

Chen, Lu; Morrow, John K.; Tran, Hoang T.; Phatak, Sharangdhar S.; Du-Cuny, Lei; Zhang, Shuxing

2013-01-01

As an important aspect of computer-aided drug design, structure-based drug design brought a new horizon to pharmaceutical development. This in silico method permeates all aspects of drug discovery today, including lead identification, lead optimization, ADMET prediction and drug repurposing. Structure-based drug design has resulted in fruitful successes drug discovery targeting protein-ligand and protein-protein interactions. Meanwhile, challenges, noted by low accuracy and combinatoric issues, may also cause failures. In this review, state-of-the-art techniques for protein modeling (e.g. structure prediction, modeling protein flexibility, etc.), hit identification/optimization (e.g. molecular docking, focused library design, fragment-based design, molecular dynamic, etc.), and polypharmacology design will be discussed. We will explore how structure-based techniques can facilitate the drug discovery process and interplay with other experimental approaches. PMID:22316152

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Ligand-guided optimization of CXCR4 homology models for virtual screening using a multiple chemotype approach

NASA Astrophysics Data System (ADS)

Neves, Marco A. C.; Simões, Sérgio; Sá e Melo, M. Luisa

2010-12-01

CXCR4 is a G-protein coupled receptor for CXCL12 that plays an important role in human immunodeficiency virus infection, cancer growth and metastasization, immune cell trafficking and WHIM syndrome. In the absence of an X-ray crystal structure, theoretical modeling of the CXCR4 receptor remains an important tool for structure-function analysis and to guide the discovery of new antagonists with potential clinical use. In this study, the combination of experimental data and molecular modeling approaches allowed the development of optimized ligand-receptor models useful for elucidation of the molecular determinants of small molecule binding and functional antagonism. The ligand-guided homology modeling approach used in this study explicitly re-shaped the CXCR4 binding pocket in order to improve discrimination between known CXCR4 antagonists and random decoys. Refinement based on multiple test-sets with small compounds from single chemotypes provided the best early enrichment performance. These results provide an important tool for structure-based drug design and virtual ligand screening of new CXCR4 antagonists.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium.

PubMed

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-12-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

Molecular Dynamics Modeling and Simulation of Diamond Cutting of Cerium

NASA Astrophysics Data System (ADS)

Zhang, Junjie; Zheng, Haibing; Shuai, Maobing; Li, Yao; Yang, Yang; Sun, Tao

2017-07-01

The coupling between structural phase transformations and dislocations induces challenges in understanding the deformation behavior of metallic cerium at the nanoscale. In the present work, we elucidate the underlying mechanism of cerium under ultra-precision diamond cutting by means of molecular dynamics modeling and simulations. The molecular dynamics model of diamond cutting of cerium is established by assigning empirical potentials to describe atomic interactions and evaluating properties of two face-centered cubic cerium phases. Subsequent molecular dynamics simulations reveal that dislocation slip dominates the plastic deformation of cerium under the cutting process. In addition, the analysis based on atomic radial distribution functions demonstrates that there are trivial phase transformations from the γ-Ce to the δ-Ce occurred in both machined surface and formed chip. Following investigations on machining parameter dependence reveal the optimal machining conditions for achieving high quality of machined surface of cerium.

From Geometry Optimization to Time Dependent Molecular Structure Modeling: Method Developments, ab initio Theories and Applications

NASA Astrophysics Data System (ADS)

Liang, Wenkel

This dissertation consists of two general parts: (I) developments of optimization algorithms (both nuclear and electronic degrees of freedom) for time-independent molecules and (II) novel methods, first-principle theories and applications in time dependent molecular structure modeling. In the first part, we discuss in specific two new algorithms for static geometry optimization, the eigenspace update (ESU) method in nonredundant internal coordinate that exhibits an enhanced performace with up to a factor of 3 savings in computational cost for large-sized molecular systems; the Car-Parrinello density matrix search (CP-DMS) method that enables direct minimization of the SCF energy as an effective alternative to conventional diagonalization approach. For the second part, we consider the time dependence and first presents two nonadiabatic dynamic studies that model laser controlled molecular photo-dissociation for qualitative understandings of intense laser-molecule interaction, using ab initio direct Ehrenfest dynamics scheme implemented with real-time time-dependent density functional theory (RT-TDDFT) approach developed in our group. Furthermore, we place our special interest on the nonadiabatic electronic dynamics in the ultrafast time scale, and presents (1) a novel technique that can not only obtain energies but also the electron densities of doubly excited states within a single determinant framework, by combining methods of CP-DMS with RT-TDDFT; (2) a solvated first-principles electronic dynamics method by incorporating the polarizable continuum solvation model (PCM) to RT-TDDFT, which is found to be very effective in describing the dynamical solvation effect in the charge transfer process and yields a consistent absorption spectrum in comparison to the conventional linear response results in solution. (3) applications of the PCM-RT-TDDFT method to study the intramolecular charge-transfer (CT) dynamics in a C60 derivative. Such work provides insights into the characteristics of ultrafast dynamics in photoexcited fullerene derivatives, and aids in the rational design for pre-dissociative exciton in the intramolecular CT process in organic solar cells.

MoCha: Molecular Characterization of Unknown Pathways.

PubMed

Lobo, Daniel; Hammelman, Jennifer; Levin, Michael

2016-04-01

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

An implicit divalent counterion force field for RNA molecular dynamics

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

Henke, Paul S.; Mak, Chi H., E-mail: cmak@usc.edu; Center of Applied Mathematical Sciences, University of Southern California, Los Angeles, California 90089

How to properly account for polyvalent counterions in a molecular dynamics simulation of polyelectrolytes such as nucleic acids remains an open question. Not only do counterions such as Mg{sup 2+} screen electrostatic interactions, they also produce attractive intrachain interactions that stabilize secondary and tertiary structures. Here, we show how a simple force field derived from a recently reported implicit counterion model can be integrated into a molecular dynamics simulation for RNAs to realistically reproduce key structural details of both single-stranded and base-paired RNA constructs. This divalent counterion model is computationally efficient. It works with existing atomistic force fields, or coarse-grainedmore » models may be tuned to work with it. We provide optimized parameters for a coarse-grained RNA model that takes advantage of this new counterion force field. Using the new model, we illustrate how the structural flexibility of RNA two-way junctions is modified under different salt conditions.« less

Development of simulation approach for two-dimensional chiral molecular self-assembly driven by hydrogen bond at the liquid/solid interface

NASA Astrophysics Data System (ADS)

Qin, Yuan; Yao, Man; Hao, Ce; Wan, Lijun; Wang, Yunhe; Chen, Ting; Wang, Dong; Wang, Xudong; Chen, Yonggang

2017-09-01

Two-dimensional (2D) chiral self-assembly system of 5-(benzyloxy)-isophthalic acid derivative/(S)-(+)-2-octanol/highly oriented pyrolytic graphite was studied. A combined density functional theory/molecular mechanics/molecular dynamics (DFT/MM/MD) approach for system of 2D chiral molecular self-assembly driven by hydrogen bond at the liquid/solid interface was thus proposed. Structural models of the chiral assembly were built on the basis of scanning tunneling microscopy (STM) images and simplified for DFT geometry optimization. Merck Molecular Force Field (MMFF) was singled out as the suitable force field by comparing the optimized configurations of MM and DFT. MM and MD simulations for hexagonal unit model which better represented the 2D assemble network were then preformed with MMFF. The adhesion energy, evolution of self-assembly process and characteristic parameters of hydrogen bond were obtained and analyzed. According to the above simulation, the stabilities of the clockwise and counterclockwise enantiomorphous networks were evaluated. The calculational results were supported by STM observations and the feasibility of the simulation method was confirmed by two other systems in the presence of chiral co-absorbers (R)-(-)-2-octanol and achiral co-absorbers 1-octanol. This theoretical simulation method assesses the stability trend of 2D enantiomorphous assemblies with atomic scale and can be applied to the similar hydrogen bond driven 2D chirality of molecular self-assembly system.

Irrelevance of the Power Stroke for the Directionality, Stopping Force, and Optimal Efficiency of Chemically Driven Molecular Machines

PubMed Central

Astumian, R. Dean

2015-01-01

A simple model for a chemically driven molecular walker shows that the elastic energy stored by the molecule and released during the conformational change known as the power-stroke (i.e., the free-energy difference between the pre- and post-power-stroke states) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Further, the apportionment of the dependence on the externally applied force between the forward and reverse rate constants of the power-stroke (or indeed among all rate constants) is irrelevant for determining the directionality, stopping force, and efficiency of the motor. Arguments based on the principle of microscopic reversibility demonstrate that this result is general for all chemically driven molecular machines, and even more broadly that the relative energies of the states of the motor have no role in determining the directionality, stopping force, or optimal efficiency of the machine. Instead, the directionality, stopping force, and optimal efficiency are determined solely by the relative heights of the energy barriers between the states. Molecular recognition—the ability of a molecular machine to discriminate between substrate and product depending on the state of the machine—is far more important for determining the intrinsic directionality and thermodynamics of chemo-mechanical coupling than are the details of the internal mechanical conformational motions of the machine. In contrast to the conclusions for chemical driving, a power-stroke is very important for the directionality and efficiency of light-driven molecular machines and for molecular machines driven by external modulation of thermodynamic parameters. PMID:25606678

Clustering molecular dynamics trajectories for optimizing docking experiments.

PubMed

De Paris, Renata; Quevedo, Christian V; Ruiz, Duncan D; Norberto de Souza, Osmar; Barros, Rodrigo C

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.

Novel ligands of Choline Acetyltransferase designed by in silico molecular docking, hologram QSAR and lead optimization

NASA Astrophysics Data System (ADS)

Kumar, Rajnish; Långström, Bengt; Darreh-Shori, Taher

2016-08-01

Recent reports have brought back the acetylcholine synthesizing enzyme, choline acetyltransferase in the mainstream research in dementia and the cholinergic anti-inflammatory pathway. Here we report, a specific strategy for the design of novel ChAT ligands based on molecular docking, Hologram Quantitative Structure Activity Relationship (HQSAR) and lead optimization. Molecular docking was performed on a series of ChAT inhibitors to decipher the molecular fingerprint of their interaction with the active site of ChAT. Then robust statistical fragment HQSAR models were developed. A library of novel ligands was generated based on the pharmacophoric and shape similarity scoring function, and evaluated in silico for their molecular interactions with ChAT. Ten of the top scoring invented compounds are reported here. We confirmed the activity of α-NETA, the only commercially available ChAT inhibitor, and one of the seed compounds in our model, using a new simple colorimetric ChAT assay (IC50 ~ 88 nM). In contrast, α-NETA exhibited an IC50 of ~30 μM for the ACh-degrading cholinesterases. In conclusion, the overall results may provide useful insight for discovering novel ChAT ligands and potential positron emission tomography tracers as in vivo functional biomarkers of the health of central cholinergic system in neurodegenerative disorders, such as Alzheimer’s disease.

Targeting CYP51 for drug design by the contributions of molecular modeling.

PubMed

Rabelo, Vitor W; Santos, Taísa F; Terra, Luciana; Santana, Marcos V; Castro, Helena C; Rodrigues, Carlos R; Abreu, Paula A

2017-02-01

CYP51 is an enzyme of sterol biosynthesis pathway present in animals, plants, protozoa and fungi. This enzyme is described as an important drug target that is still of interest. Therefore, in this work, we reviewed the structure and function of CYP51 and explored the molecular modeling approaches for the development of new antifungal and antiprotozoans that target this enzyme. Crystallographic structures of CYP51 of some organisms have already been described in the literature, which enable the construction of homology models of other organisms' enzymes and molecular docking studies of new ligands. The binding mode and interactions of some new series of azoles with antifungal or antiprotozoan activities has been studied and showed important residues of the active site. Molecular modeling is an important tool to be explored for the discovery and optimization of CYP51 inhibitors with better activities, pharmacokinetics, and toxicological profiles. © 2016 Société Française de Pharmacologie et de Thérapeutique.

BioMOL: a computer-assisted biological modeling tool for complex chemical mixtures and biological processes at the molecular level.

PubMed Central

Klein, Michael T; Hou, Gang; Quann, Richard J; Wei, Wei; Liao, Kai H; Yang, Raymond S H; Campain, Julie A; Mazurek, Monica A; Broadbelt, Linda J

2002-01-01

A chemical engineering approach for the rigorous construction, solution, and optimization of detailed kinetic models for biological processes is described. This modeling capability addresses the required technical components of detailed kinetic modeling, namely, the modeling of reactant structure and composition, the building of the reaction network, the organization of model parameters, the solution of the kinetic model, and the optimization of the model. Even though this modeling approach has enjoyed successful application in the petroleum industry, its application to biomedical research has just begun. We propose to expand the horizons on classic pharmacokinetics and physiologically based pharmacokinetics (PBPK), where human or animal bodies were often described by a few compartments, by integrating PBPK with reaction network modeling described in this article. If one draws a parallel between an oil refinery, where the application of this modeling approach has been very successful, and a human body, the individual processing units in the oil refinery may be considered equivalent to the vital organs of the human body. Even though the cell or organ may be much more complicated, the complex biochemical reaction networks in each organ may be similarly modeled and linked in much the same way as the modeling of the entire oil refinery through linkage of the individual processing units. The integrated chemical engineering software package described in this article, BioMOL, denotes the biological application of molecular-oriented lumping. BioMOL can build a detailed model in 1-1,000 CPU sec using standard desktop hardware. The models solve and optimize using standard and widely available hardware and software and can be presented in the context of a user-friendly interface. We believe this is an engineering tool with great promise in its application to complex biological reaction networks. PMID:12634134

In Silico Exploration of 1,7-Diazacarbazole Analogs as Checkpoint Kinase 1 Inhibitors by Using 3D QSAR, Molecular Docking Study, and Molecular Dynamics Simulations.

PubMed

Gao, Xiaodong; Han, Liping; Ren, Yujie

2016-05-05

Checkpoint kinase 1 (Chk1) is an important serine/threonine kinase with a self-protection function. The combination of Chk1 inhibitors and anti-cancer drugs can enhance the selectivity of tumor therapy. In this work, a set of 1,7-diazacarbazole analogs were identified as potent Chk1 inhibitors through a series of computer-aided drug design processes, including three-dimensional quantitative structure-activity relationship (3D-QSAR) modeling, molecular docking, and molecular dynamics simulations. The optimal QSAR models showed significant cross-validated correlation q² values (0.531, 0.726), fitted correlation r² coefficients (higher than 0.90), and standard error of prediction (less than 0.250). These results suggested that the developed models possess good predictive ability. Moreover, molecular docking and molecular dynamics simulations were applied to highlight the important interactions between the ligand and the Chk1 receptor protein. This study shows that hydrogen bonding and electrostatic forces are key interactions that confer bioactivity.

ls1 mardyn: The Massively Parallel Molecular Dynamics Code for Large Systems.

PubMed

Niethammer, Christoph; Becker, Stefan; Bernreuther, Martin; Buchholz, Martin; Eckhardt, Wolfgang; Heinecke, Alexander; Werth, Stephan; Bungartz, Hans-Joachim; Glass, Colin W; Hasse, Hans; Vrabec, Jadran; Horsch, Martin

2014-10-14

The molecular dynamics simulation code ls1 mardyn is presented. It is a highly scalable code, optimized for massively parallel execution on supercomputing architectures and currently holds the world record for the largest molecular simulation with over four trillion particles. It enables the application of pair potentials to length and time scales that were previously out of scope for molecular dynamics simulation. With an efficient dynamic load balancing scheme, it delivers high scalability even for challenging heterogeneous configurations. Presently, multicenter rigid potential models based on Lennard-Jones sites, point charges, and higher-order polarities are supported. Due to its modular design, ls1 mardyn can be extended to new physical models, methods, and algorithms, allowing future users to tailor it to suit their respective needs. Possible applications include scenarios with complex geometries, such as fluids at interfaces, as well as nonequilibrium molecular dynamics simulation of heat and mass transfer.

Self-optimized construction of transition rate matrices from accelerated atomistic simulations with Bayesian uncertainty quantification

NASA Astrophysics Data System (ADS)

Swinburne, Thomas D.; Perez, Danny

2018-05-01

A massively parallel method to build large transition rate matrices from temperature-accelerated molecular dynamics trajectories is presented. Bayesian Markov model analysis is used to estimate the expected residence time in the known state space, providing crucial uncertainty quantification for higher-scale simulation schemes such as kinetic Monte Carlo or cluster dynamics. The estimators are additionally used to optimize where exploration is performed and the degree of temperature acceleration on the fly, giving an autonomous, optimal procedure to explore the state space of complex systems. The method is tested against exactly solvable models and used to explore the dynamics of C15 interstitial defects in iron. Our uncertainty quantification scheme allows for accurate modeling of the evolution of these defects over timescales of several seconds.

Symbol interval optimization for molecular communication with drift.

PubMed

Kim, Na-Rae; Eckford, Andrew W; Chae, Chan-Byoung

2014-09-01

In this paper, we propose a symbol interval optimization algorithm in molecular communication with drift. Proper symbol intervals are important in practical communication systems since information needs to be sent as fast as possible with low error rates. There is a trade-off, however, between symbol intervals and inter-symbol interference (ISI) from Brownian motion. Thus, we find proper symbol interval values considering the ISI inside two kinds of blood vessels, and also suggest no ISI system for strong drift models. Finally, an isomer-based molecule shift keying (IMoSK) is applied to calculate achievable data transmission rates (achievable rates, hereafter). Normalized achievable rates are also obtained and compared in one-symbol ISI and no ISI systems.

ChemTS: an efficient python library for de novo molecular generation.

PubMed

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

ChemTS: an efficient python library for de novo molecular generation

NASA Astrophysics Data System (ADS)

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-12-01

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

Structural characterization/correlation of calorimetric properties of coal fluids: Final report, September 1, 1985--August 31, 1988

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

Starling, K.E.; Mallinson, R.G.; Li, M.H.

The objective of this research is to examine the relationship between the calorimetric properties of coal fluids and their molecular functional group composition. Coal fluid samples which have had their calorimetric properties measured are characterized using proton NMR, IR, and elemental analysis. These characterizations are then used in a chemical structural model to determine the composition of the coal fluid in terms of the important molecular functional groups. These functional groups are particularly important in determining the intramolecular based properties of a fluid, such as ideal gas heat capacities. Correlational frameworks for ideal gas heat capacities are then examined withinmore » an existing equation of state methodology to determine an optimal correlation. The optimal correlation for obtaining the characterization/chemical structure information and the sensitivity of the correlation to the characterization and structural model is examined. 8 refs.« less

Structural characterization/correlation of calorimetric properties of coal fluids: Second annual report, September 1, 1986-August 31, 1987

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

Starling, K.E.; Mallinson, R.G.; Li, M.H.

The objective of this research is to examine the relationship between the calorimetric properties of coal fluids and their molecular functional group composition. Coal fluid samples which have had their calorimetric properties measured are characterized using proton NMR, ir, and elemental analysis. These characterizations are then used in a chemical structural model to determine the composition of the coal fluid in terms of the important molecular functional groups. These functional groups are particularly important in determining the intramolecular based properties of a fluid, such as ideal gas heat capacities. Correlational frameworks for ideal gas heat capacities are then examined withinmore » an existing equation of state methodology to determine an optimal correlation. The optimal correlation for obtaining the characterization/chemical structure information and the sensitivity of the correlation to the characterization and structural model is examined.« less

Structural characterization/correlation of calorimetric properties of coal fluids. First annual report, September 1, 1985-August 31, 1986

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

Starling, K.E.; Mallinson, R.G.; Li, M.H.

The objective of this research is to examine the relationship between the calorimetric properties of coal liquids and their molecular functional group composition. Coal liquid samples which have had their calorimetric properties measured are characterized using proton NMR, ir and elemental analysis. These characterizations are then used in a chemical structural model to determine the composition of the coal liquid in terms of the important molecular functional groups. These functional groups are particularly important in determining the intramolecular based properties of a fluid, such as ideal gas heat capacities. Correlational frameworks for heat capacities will then be examined within anmore » existing equation of state methodology to determine an optimal correlation. Also, the optimal recipe for obtaining the characterization/chemical structure information and the sensitivity of the correlation to the characterization and structural model will be examined and determined. 7 refs.« less

Machine Learning Force Field Parameters from Ab Initio Data

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

Li, Ying; Li, Hui; Pickard, Frank C.

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. The results were further improved by introducing an offset factor duringmore » the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.« less

Optimizing Likelihood Models for Particle Trajectory Segmentation in Multi-State Systems.

PubMed

Young, Dylan Christopher; Scrimgeour, Jan

2018-06-19

Particle tracking offers significant insight into the molecular mechanics that govern the behav- ior of living cells. The analysis of molecular trajectories that transition between different motive states, such as diffusive, driven and tethered modes, is of considerable importance, with even single trajectories containing significant amounts of information about a molecule's environment and its interactions with cellular structures. Hidden Markov models (HMM) have been widely adopted to perform the segmentation of such complex tracks. In this paper, we show that extensive analysis of hidden Markov model outputs using data derived from multi-state Brownian dynamics simulations can be used both for the optimization of the likelihood models used to describe the states of the system and for characterization of the technique's failure mechanisms. This analysis was made pos- sible by the implementation of parallelized adaptive direct search algorithm on a Nvidia graphics processing unit. This approach provides critical information for the visualization of HMM failure and successful design of particle tracking experiments where trajectories contain multiple mobile states. © 2018 IOP Publishing Ltd.

State-space reduction and equivalence class sampling for a molecular self-assembly model.

PubMed

Packwood, Daniel M; Han, Patrick; Hitosugi, Taro

2016-07-01

Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.

Force Field for Peptides and Proteins based on the Classical Drude Oscillator

PubMed Central

Lopes, Pedro E.M.; Huang, Jing; Shim, Jihyun; Luo, Yun; Li, Hui; Roux, Benoît; MacKerell, Alexander D.

2013-01-01

Presented is a polarizable force field based on a classical Drude oscillator framework, currently implemented in the programs CHARMM and NAMD, for modeling and molecular dynamics (MD) simulation studies of peptides and proteins. Building upon parameters for model compounds representative of the functional groups in proteins, the development of the force field focused on the optimization of the parameters for the polypeptide backbone and the connectivity between the backbone and side chains. Optimization of the backbone electrostatic parameters targeted quantum mechanical conformational energies, interactions with water, molecular dipole moments and polarizabilities and experimental condensed phase data for short polypeptides such as (Ala)5. Additional optimization of the backbone φ, ψ conformational preferences included adjustments of the tabulated two-dimensional spline function through the CMAP term. Validation of the model included simulations of a collection of peptides and proteins. This 1st generation polarizable model is shown to maintain the folded state of the studied systems on the 100 ns timescale in explicit solvent MD simulations. The Drude model typically yields larger RMS differences as compared to the additive CHARMM36 force field (C36) and shows additional flexibility as compared to the additive model. Comparison with NMR chemical shift data shows a small degradation of the polarizable model with respect to the additive, though the level of agreement may be considered satisfactory, while for residues shown to have significantly underestimated S2 order parameters in the additive model, improvements are calculated with the polarizable model. Analysis of dipole moments associated with the peptide backbone and tryptophan side chains show the Drude model to have significantly larger values than those present in C36, with the dipole moments of the peptide backbone enhanced to a greater extent in sheets versus helices and the dipoles of individual moieties observed to undergo significant variations during the MD simulations. Although there are still some limitations, the presented model, termed Drude-2013, is anticipated to yield a molecular picture of peptide and protein structure and function that will be of increased physical validity and internal consistency in a computationally accessible fashion. PMID:24459460

System geometry optimization for molecular breast tomosynthesis with focusing multi-pinhole collimators

NASA Astrophysics Data System (ADS)

van Roosmalen, Jarno; Beekman, Freek J.; Goorden, Marlies C.

2018-01-01

Imaging of 99mTc-labelled tracers is gaining popularity for detecting breast tumours. Recently, we proposed a novel design for molecular breast tomosynthesis (MBT) based on two sliding focusing multi-pinhole collimators that scan a modestly compressed breast. Simulation studies indicate that MBT has the potential to improve the tumour-to-background contrast-to-noise ratio significantly over state-of-the-art planar molecular breast imaging. The aim of the present paper is to optimize the collimator-detector geometry of MBT. Using analytical models, we first optimized sensitivity at different fixed system resolutions (ranging from 5 to 12 mm) by tuning the pinhole diameters and the distance between breast and detector for a whole series of automatically generated multi-pinhole designs. We evaluated both MBT with a conventional continuous crystal detector with 3.2 mm intrinsic resolution and with a pixelated detector with 1.6 mm pixels. Subsequently, full system simulations of a breast phantom containing several lesions were performed for the optimized geometry at each system resolution for both types of detector. From these simulations, we found that tumour-to-background contrast-to-noise ratio was highest for systems in the 7 mm-10 mm system resolution range over which it hardly varied. No significant differences between the two detector types were found.

Novel cationic lipid nanoparticles as an ophthalmic delivery system for multicomponent drugs: development, characterization, in vitro permeation, in vivo pharmacokinetic, and molecular dynamics studies.

PubMed

Wang, Jialu; Zhao, Fang; Liu, Rui; Chen, Jingjing; Zhang, Qinghua; Lao, Ruijuan; Wang, Ze; Jin, Xin; Liu, Changxiao

2017-01-01

The purpose of this study was to prepare, optimize, and characterize a cationic lipid nanoparticle (CLN) system containing multicomponent drugs using a molecular dynamics model as a novel method of evaluating formulations. Puerarin (PUE) and scutellarin (SCU) were used as model drugs. CLNs were successfully prepared using melt-emulsion ultrasonication and low temperature-solidification technique. The properties of CLNs such as morphology, particle size, zeta potential, entrapment efficiency (EE), drug loading (DL), and drug release behavior were investigated. The CLNs were evaluated by corneal permeation, preocular retention time, and pharmacokinetics in the aqueous humor. Additionally, a molecular dynamics model was used to evaluate the formulation. Electron microscopy results showed that the nanoparticles were approximately spherical in shape. The EE (%) and DL (%) values of PUE and SCU in the optimal formulation were 56.60±3.73, 72.31±1.96 and 1.68±0.17, 2.44±1.14, respectively. The pharmacokinetic study in the aqueous humor showed that compared with the PUE and SCU solution, the area under the concentration-time curve (AUC) value of PUE was enhanced by 2.33-fold for PUE-SCU CLNs ( p <0.01), and the SCU AUC was enhanced by 2.32-fold ( p <0.01). In the molecular dynamics model, PUE and SCU passed through the POPC bilayer, with an obvious difference in the free energy well depth. It was found that the maximum free energy required for PUE and SCU transmembrane movement was ~15 and 88 kJ·mol -1 , respectively. These findings indicated that compared with SCU, PUE easily passed through the membrane. The diffusion coefficient for PUE and SCU were 4.1×10 -3 ±0.0027 and 1.0×10 -3 ±0.0006 e -5 cm 2 ·s -1 , respectively. Data from the molecular dynamics model were consistent with the experimental data. All data indicated that CLNs have a great potential for ocular administration and can be used as an ocular delivery system for multicomponent drugs. Moreover, the molecular dynamics model can also be used as a novel method for evaluating formulations.

Thermodynamic metrics and optimal paths.

PubMed

Sivak, David A; Crooks, Gavin E

2012-05-11

A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.

Equilibrium star formation in a constant Q disc: model optimization and initial tests

NASA Astrophysics Data System (ADS)

Zheng, Zheng; Meurer, Gerhardt R.; Heckman, Timothy M.; Thilker, David A.; Zwaan, Martin A.

2013-10-01

We develop a model for the distribution of the interstellar medium (ISM) and star formation in galaxies based on recent studies that indicate that galactic discs stabilize to a constant stability parameter, which we combine with prescriptions of how the phases of the ISM are determined and for the star formation law (SFL). The model predicts the gas surface mass density and star formation intensity of a galaxy given its rotation curve, stellar surface mass density and the gas velocity dispersion. This model is tested on radial profiles of neutral and molecular ISM surface mass density and star formation intensity of 12 galaxies selected from the H I Nearby Galaxy Survey sample. Our tests focus on intermediate radii (0.3 to 1 times the optical radius) because there are insufficient data to test the outer discs and the fits are less accurate in detail in the centre. Nevertheless, the model produces reasonable agreement with the ISM mass and star formation rate integrated over the central region in all but one case. To optimize the model, we evaluate four recipes for the stability parameter, three recipes for apportioning the ISM into molecular and neutral components, and eight versions of the SFL. We find no clear-cut best prescription for the two-fluid (gas and stars) stability parameter Q2f and therefore for simplicity, we use the Wang and Silk approximation (QWS). We found that an empirical scaling between the molecular-to-neutral ISM ratio (Rmol) and the stellar surface mass density proposed by Leroy et al. works marginally better than the other two prescriptions for this ratio in predicting the ISM profiles, and noticeably better in predicting the star formation intensity from the ISM profiles produced by our model with the SFLs we tested. Thus, in the context of our modelled ISM profiles, the linear molecular SFL and the two-component SFL work better than the other prescriptions we tested. We incorporate these relations into our `constant Q disc' model.

Molecular docking and 3D-QSAR studies on inhibitors of DNA damage signaling enzyme human PARP-1.

PubMed

Fatima, Sabiha; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

2012-08-01

Poly (ADP-ribose) polymerase-1 (PARP-1) operates in a DNA damage signaling network. Molecular docking and three dimensional-quantitative structure activity relationship (3D-QSAR) studies were performed on human PARP-1 inhibitors. Docked conformation obtained for each molecule was used as such for 3D-QSAR analysis. Molecules were divided into a training set and a test set randomly in four different ways, partial least square analysis was performed to obtain QSAR models using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Derived models showed good statistical reliability that is evident from their r², q²(loo) and r²(pred) values. To obtain a consensus for predictive ability from all the models, average regression coefficient r²(avg) was calculated. CoMFA and CoMSIA models showed a value of 0.930 and 0.936, respectively. Information obtained from the best 3D-QSAR model was applied for optimization of lead molecule and design of novel potential inhibitors.

Polarizable six-point water models from computational and empirical optimization.

PubMed

Tröster, Philipp; Lorenzen, Konstantin; Tavan, Paul

2014-02-13

Tröster et al. (J. Phys. Chem B 2013, 117, 9486-9500) recently suggested a mixed computational and empirical approach to the optimization of polarizable molecular mechanics (PMM) water models. In the empirical part the parameters of Buckingham potentials are optimized by PMM molecular dynamics (MD) simulations. The computational part applies hybrid calculations, which combine the quantum mechanical description of a H2O molecule by density functional theory (DFT) with a PMM model of its liquid phase environment generated by MD. While the static dipole moments and polarizabilities of the PMM water models are fixed at the experimental gas phase values, the DFT/PMM calculations are employed to optimize the remaining electrostatic properties. These properties cover the width of a Gaussian inducible dipole positioned at the oxygen and the locations of massless negative charge points within the molecule (the positive charges are attached to the hydrogens). The authors considered the cases of one and two negative charges rendering the PMM four- and five-point models TL4P and TL5P. Here we extend their approach to three negative charges, thus suggesting the PMM six-point model TL6P. As compared to the predecessors and to other PMM models, which also exhibit partial charges at fixed positions, TL6P turned out to predict all studied properties of liquid water at p0 = 1 bar and T0 = 300 K with a remarkable accuracy. These properties cover, for instance, the diffusion constant, viscosity, isobaric heat capacity, isothermal compressibility, dielectric constant, density, and the isobaric thermal expansion coefficient. This success concurrently provides a microscopic physical explanation of corresponding shortcomings of previous models. It uniquely assigns the failures of previous models to substantial inaccuracies in the description of the higher electrostatic multipole moments of liquid phase water molecules. Resulting favorable properties concerning the transferability to other temperatures and conditions like the melting of ice are also discussed.

Final report for the DOE Early Career Award #DE-SC0003912

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

Jayaraman, Arthi

This DoE supported early career project was aimed at developing computational models, theory and simulation methods that would be then be used to predict assembly and morphology in polymer nanocomposites. In particular, the focus was on composites in active layers of devices, containing conducting polymers that act as electron donors and nanoscale additives that act as electron acceptors. During the course this work, we developed the first of its kind molecular models to represent conducting polymers enabling simulations at the experimentally relevant length and time scales. By comparison with experimentally observed morphologies we validated these models. Furthermore, using these modelsmore » and molecular dynamics simulations on graphical processing units (GPUs) we predicted the molecular level design features in polymers and additive that lead to morphologies with optimal features for charge carrier behavior in solar cells. Additionally, we also predicted computationally new design rules for better dispersion of additives in polymers that have been confirmed through experiments. Achieving dispersion in polymer nanocomposites is valuable to achieve controlled macroscopic properties of the composite. The results obtained during the course of this DOE funded project enables optimal design of higher efficiency organic electronic and photovoltaic devices and improve every day life with engineering of these higher efficiency devices.« less

Molecular Modeling of the Binding Structures in the Interlayer Adsorption of a Tetracycline Antibiotic by Smectite Clays

NASA Astrophysics Data System (ADS)

Aristilde, L.

2009-12-01

A controlling factor in the fate of antibiotics in the environment is their sequestration in soil particles including clay minerals. Of special interest is the interlayer adsorption by smectite clays, which has been shown to influence both the bioavailability and persistence of antibiotics in the soil environment. However, the interlayer structures of the bound antibiotics, essential to an accurate understanding of the adsorption mechanisms, are not well understood. Molecular simulations of oxytetracycline (OTC) with a model montmorillonite (MONT) clay were performed to gain insights into these structures for tetracycline antibiotics. Monte Carlo simulations were used for explorations of the clay layer spacing required for the adsorption of the antibiotic under different hydration states of the clay interlayer; these preliminary results were validated with previous X-ray diffraction patterns obtained following sorption experiments of OTC with MONT. Molecular dynamics relaxation simulations were performed subsequently in order to obtain geometry-optimized structures of the binding conformations of the intercalated antibiotic in the model MONT layers. This study contributes to a mechanistic understanding of the factors controlling the interlayer adsorption of the tetracycline antibiotics by the expandable smectite clay minerals. Figure 1. Optimized Monte Carlo simulation cell of OTC in the interlayer of MONT: perspective side view (top) and bottom view (bottom).

Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model.

PubMed

Ni, Jingchao; Koyuturk, Mehmet; Tong, Hanghang; Haines, Jonathan; Xu, Rong; Zhang, Xiang

2016-11-10

Accurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. However, different diseases tend to manifest in different tissues, and the molecular networks in different tissues are usually different. An ideal method should be able to incorporate tissue-specific molecular networks for different diseases. In this paper, we develop a robust and flexible method to integrate tissue-specific molecular networks for disease gene prioritization. Our method allows each disease to have its own tissue-specific network(s). We formulate the problem of candidate gene prioritization as an optimization problem based on network propagation. When there are multiple tissue-specific networks available for a disease, our method can automatically infer the relative importance of each tissue-specific network. Thus it is robust to the noisy and incomplete network data. To solve the optimization problem, we develop fast algorithms which have linear time complexities in the number of nodes in the molecular networks. We also provide rigorous theoretical foundations for our algorithms in terms of their optimality and convergence properties. Extensive experimental results show that our method can significantly improve the accuracy of candidate gene prioritization compared with the state-of-the-art methods. In our experiments, we compare our methods with 7 popular network-based disease gene prioritization algorithms on diseases from Online Mendelian Inheritance in Man (OMIM) database. The experimental results demonstrate that our methods recover true associations more accurately than other methods in terms of AUC values, and the performance differences are significant (with paired t-test p-values less than 0.05). This validates the importance to integrate tissue-specific molecular networks for studying disease gene prioritization and show the superiority of our network models and ranking algorithms toward this purpose. The source code and datasets are available at http://nijingchao.github.io/CRstar/ .

Insights from molecular modeling and dynamics simulation of pathogen resistance (R) protein from brinjal.

PubMed

Shrivastava, Dipty; Nain, Vikrant; Sahi, Shakti; Verma, Anju; Sharma, Priyanka; Sharma, Prakash Chand; Kumar, Polumetla Ananda

2011-01-22

Resistance (R) protein recognizes molecular signature of pathogen infection and activates downstream hypersensitive response signalling in plants. R protein works as a molecular switch for pathogen defence signalling and represent one of the largest plant gene family. Hence, understanding molecular structure and function of R proteins has been of paramount importance for plant biologists. The present study is aimed at predicting structure of R proteins signalling domains (CC-NBS) by creating a homology model, refining and optimising the model by molecular dynamics simulation and comparing ADP and ATP binding. Based on sequence similarity with proteins of known structures, CC-NBS domains were initially modelled using CED- 4 (cell death abnormality protein) and APAF-1 (apoptotic protease activating factor) as multiple templates. The final CC-NBS structural model was built and optimized by molecular dynamic simulation for 5 nanoseconds (ns). Docking of ADP and ATP at active site shows that both ligand bind specifically with same residues and with minor difference (1 Kcal/mol) in binding energy. Sharing of binding site by ADP and ATP and low difference in their binding site makes CC-NBS suitable for working as molecular switch. Furthermore, structural superimposition elucidate that CC-NBS and CARD (caspase recruitment domains) domain of CED-4 have low RMSD value of 0.9 A° Availability of 3D structural model for both CC and NBS domains will . help in getting deeper insight in these pathogen defence genes.

Optimization and comprehensive characterization of a faithful tissue culture model of the benign and malignant human prostate.

PubMed

Maund, Sophia Lisette; Nolley, Rosalie; Peehl, Donna Mae

2014-02-01

Few preclinical models accurately depict normal human prostate tissue or primary prostate cancer (PCa). In vitro systems typically lack complex cellular interactions among structured prostatic epithelia and a stromal microenvironment, and genetic and molecular fidelity are concerns in both in vitro and in vivo models. 'Tissue slice cultures' (TSCs) provide realistic preclinical models of diverse tissues and organs, but have not been fully developed or widely utilized for prostate studies. Problems encountered include degeneration of differentiated secretory cells, basal cell hyperplasia, and poor survival of PCa. Here, we optimized, characterized, and applied a TSC model of primary human PCa and benign prostate tissue that overcomes many deficiencies of current in vitro models. Tissue cores from fresh prostatectomy specimens were precision-cut at 300 μm and incubated in a rotary culture apparatus. The ability of varied culture conditions to faithfully maintain benign and cancer cell and tissue structure and function over time was evaluated by immunohistological and biochemical assays. After optimization of the culture system, molecular and cellular responses to androgen ablation and to piperlongumine (PL), purported to specifically reduce androgen signaling in PCa, were investigated. Optimized culture conditions successfully maintained the structural and functional fidelity of both benign and PCa TSCs for 5 days. TSCs exhibited androgen dependence, appropriately undergoing ductal degeneration, reduced proliferation, and decreased prostate-specific antigen expression upon androgen ablation. Further, TSCs revealed cancer-specific reduction of androgen receptor and increased apoptosis upon treatment with PL, validating data from cell lines. We demonstrate a TSC model that authentically recapitulates the structural, cellular, and genetic characteristics of the benign and malignant human prostate, androgen dependence of the native tissue, and cancer-specific response to a potentially new therapeutic for PCa. The work described herein provides a basis for advancing the experimental utility of the TSC model.

Molecular dynamics of reversible self-healing materials

NASA Astrophysics Data System (ADS)

Madden, Ian; Luijten, Erik

Hydrolyzable polymers have numerous industrial applications as degradable materials. Recent experimental work by Cheng and co-workers has introduced the concept of hindered urea bond (HUB) chemistry to design self-healing systems. Important control parameters are the steric hindrance of the HUB structures, which is used to tune the hydrolytic degradation kinetics, and their density. We employ molecular dynamics simulations of polymeric interfaces to systematically explore the role of these properties in a coarse-grained model, and make direct comparison to experimental data. Our model provides direct insight into the self-healing process, permitting optimization of the control parameters.

ChemTS: an efficient python library for de novo molecular generation

PubMed Central

Yang, Xiufeng; Zhang, Jinzhe; Yoshizoe, Kazuki; Terayama, Kei; Tsuda, Koji

2017-01-01

Abstract Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS. PMID:29435094

Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH)

DTIC Science & Technology

2013-09-01

Optimization of the Nonradiative Lifetime of Molecular- Beam-Epitaxy (MBE)-Grown Undoped GaAs/AlGaAs Double Heterostructures (DH) by P...it to the originator. Army Research Laboratory Adelphi, MD 20783-1197 ARL-TR-6660 September 2013 Optimization of the Nonradiative ...REPORT TYPE Final 3. DATES COVERED (From - To) FY2013 4. TITLE AND SUBTITLE Optimization of the Nonradiative Lifetime of Molecular-Beam-Epitaxy

Experimental and Modeling Studies of the Combustion Characteristics of Conventional and Alternative Jet Fuels. Final Report

NASA Technical Reports Server (NTRS)

Meeks, Ellen; Naik, Chitral V.; Puduppakkam, Karthik V.; Modak, Abhijit; Egolfopoulos, Fokion N.; Tsotsis, Theo; Westbrook, Charles K.

2011-01-01

The objectives of this project have been to develop a comprehensive set of fundamental data regarding the combustion behavior of jet fuels and appropriately associated model fuels. Based on the fundamental study results, an auxiliary objective was to identify differentiating characteristics of molecular fuel components that can be used to explain different fuel behavior and that may ultimately be used in the planning and design of optimal fuel-production processes. The fuels studied in this project were Fischer-Tropsch (F-T) fuels and biomass-derived jet fuels that meet certain specifications of currently used jet propulsion applications. Prior to this project, there were no systematic experimental flame data available for such fuels. One of the key goals has been to generate such data, and to use this data in developing and verifying effective kinetic models. The models have then been reduced through automated means to enable multidimensional simulation of the combustion characteristics of such fuels in real combustors. Such reliable kinetic models, validated against fundamental data derived from laminar flames using idealized flow models, are key to the development and design of optimal combustors and fuels. The models provide direct information about the relative contribution of different molecular constituents to the fuel performance and can be used to assess both combustion and emissions characteristics.

Dynamic Histogram Analysis To Determine Free Energies and Rates from Biased Simulations.

PubMed

Stelzl, Lukas S; Kells, Adam; Rosta, Edina; Hummer, Gerhard

2017-12-12

We present an algorithm to calculate free energies and rates from molecular simulations on biased potential energy surfaces. As input, it uses the accumulated times spent in each state or bin of a histogram and counts of transitions between them. Optimal unbiased equilibrium free energies for each of the states/bins are then obtained by maximizing the likelihood of a master equation (i.e., first-order kinetic rate model). The resulting free energies also determine the optimal rate coefficients for transitions between the states or bins on the biased potentials. Unbiased rates can be estimated, e.g., by imposing a linear free energy condition in the likelihood maximization. The resulting "dynamic histogram analysis method extended to detailed balance" (DHAMed) builds on the DHAM method. It is also closely related to the transition-based reweighting analysis method (TRAM) and the discrete TRAM (dTRAM). However, in the continuous-time formulation of DHAMed, the detailed balance constraints are more easily accounted for, resulting in compact expressions amenable to efficient numerical treatment. DHAMed produces accurate free energies in cases where the common weighted-histogram analysis method (WHAM) for umbrella sampling fails because of slow dynamics within the windows. Even in the limit of completely uncorrelated data, where WHAM is optimal in the maximum-likelihood sense, DHAMed results are nearly indistinguishable. We illustrate DHAMed with applications to ion channel conduction, RNA duplex formation, α-helix folding, and rate calculations from accelerated molecular dynamics. DHAMed can also be used to construct Markov state models from biased or replica-exchange molecular dynamics simulations. By using binless WHAM formulated as a numerical minimization problem, the bias factors for the individual states can be determined efficiently in a preprocessing step and, if needed, optimized globally afterward.

Improving the Molecular Ion Signal Intensity for In Situ Liquid SIMS Analysis.

PubMed

Zhou, Yufan; Yao, Juan; Ding, Yuanzhao; Yu, Jiachao; Hua, Xin; Evans, James E; Yu, Xiaofei; Lao, David B; Heldebrant, David J; Nune, Satish K; Cao, Bin; Bowden, Mark E; Yu, Xiao-Ying; Wang, Xue-Lin; Zhu, Zihua

2016-12-01

In situ liquid secondary ion mass spectrometry (SIMS) enabled by system for analysis at the liquid vacuum interface (SALVI) has proven to be a promising new tool to provide molecular information at solid-liquid and liquid-vacuum interfaces. However, the initial data showed that useful signals in positive ion spectra are too weak to be meaningful in most cases. In addition, it is difficult to obtain strong negative molecular ion signals when m/z>200. These two drawbacks have been the biggest obstacle towards practical use of this new analytical approach. In this study, we report that strong and reliable positive and negative molecular signals are achievable after optimizing the SIMS experimental conditions. Four model systems, including a 1,8-diazabicycloundec-7-ene (DBU)-base switchable ionic liquid, a live Shewanella oneidensis biofilm, a hydrated mammalian epithelia cell, and an electrolyte popularly used in Li ion batteries were studied. A signal enhancement of about two orders of magnitude was obtained in comparison with non-optimized conditions. Therefore, molecular ion signal intensity has become very acceptable for use of in situ liquid SIMS to study solid-liquid and liquid-vacuum interfaces. Graphical Abstract ᅟ.

Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments

PubMed Central

De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.

2015-01-01

Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944

Prediction of chemical biodegradability using support vector classifier optimized with differential evolution.

PubMed

Cao, Qi; Leung, K M

2014-09-22

Reliable computer models for the prediction of chemical biodegradability from molecular descriptors and fingerprints are very important for making health and environmental decisions. Coupling of the differential evolution (DE) algorithm with the support vector classifier (SVC) in order to optimize the main parameters of the classifier resulted in an improved classifier called the DE-SVC, which is introduced in this paper for use in chemical biodegradability studies. The DE-SVC was applied to predict the biodegradation of chemicals on the basis of extensive sample data sets and known structural features of molecules. Our optimization experiments showed that DE can efficiently find the proper parameters of the SVC. The resulting classifier possesses strong robustness and reliability compared with grid search, genetic algorithm, and particle swarm optimization methods. The classification experiments conducted here showed that the DE-SVC exhibits better classification performance than models previously used for such studies. It is a more effective and efficient prediction model for chemical biodegradability.

Optimization simulated injection molding process for ultrahigh molecular weight polyethylene nanocomposite hip liner using response surface methodology and simulation of mechanical behavior.

PubMed

Heidari, Behzad Shiroud; Davachi, Seyed Mohammad; Moghaddam, Amin Hedayati; Seyfi, Javad; Hejazi, Iman; Sahraeian, Razi; Rashedi, Hamid

2018-05-01

In this study, injection molding process of ultrahigh molecular weight polyethylene (UHMWPE) reinforced with nano-hydroxyapatite (nHA) was simulated and optimized through minimizing the shrinkage and warpage of the hip liners as an essential part of a hip prosthesis. Fractional factorial design (FFD) was applied to the design of the experiment, modeling, and optimizing the shrinkage and warpage of UHMWPE/nHA composite liners. The Analysis of variance (ANOVA) was applied to find the importance of operative parameters and their effects. In this experiment, seven input parameters were surveyed, including mold temperature (A), melt temperature (B), injection time (C), packing time (D), packing pressure (E), coolant temperature (F), and type of liner (G). Two models were capable of predicting warpage and volumetric shrinkage (%) in different conditions with R 2 of 0.9949 and 0.9989, respectively. According to the models, the optimized values of warpage and volumetric shrinkage are 0.287222 mm and 13.6613%, respectively. Meanwhile, a finite element analysis (FE analysis) was also carried out to examine the stress distribution in liners under the force values of demanding and daily activities. The Von-Mises stress distribution showed that both of the liners can be applied to all activities with no failure. However, UHMWPE/nHA liner is more resistant to the highest loads than UHMWPE liner due to the effect of nHA in the nanocomposite. Finally, according to the results of injection molding simulations, optimization, structural analysis as well as the tensile strength and wear resistance, UHMWPE/nHA liner is recommended for the production of a hip prosthesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

The molecular matching problem

NASA Technical Reports Server (NTRS)

Kincaid, Rex K.

1993-01-01

Molecular chemistry contains many difficult optimization problems that have begun to attract the attention of optimizers in the Operations Research community. Problems including protein folding, molecular conformation, molecular similarity, and molecular matching have been addressed. Minimum energy conformations for simple molecular structures such as water clusters, Lennard-Jones microclusters, and short polypeptides have dominated the literature to date. However, a variety of interesting problems exist and we focus here on a molecular structure matching (MSM) problem.

Optimized acoustic biochip integrated with microfluidics for biomarkers detection in molecular diagnostics.

PubMed

Papadakis, G; Friedt, J M; Eck, M; Rabus, D; Jobst, G; Gizeli, E

2017-09-01

The development of integrated platforms incorporating an acoustic device as the detection element requires addressing simultaneously several challenges of technological and scientific nature. The present work was focused on the design of a microfluidic module, which, combined with a dual or array type Love wave acoustic chip could be applied to biomedical applications and molecular diagnostics. Based on a systematic study we optimized the mechanics of the flow cell attachment and the sealing material so that fluidic interfacing/encapsulation would impose minimal losses to the acoustic wave. We have also investigated combinations of operating frequencies with waveguide materials and thicknesses for maximum sensitivity during the detection of protein and DNA biomarkers. Within our investigations neutravidin was used as a model protein biomarker and unpurified PCR amplified Salmonella DNA as the model genetic target. Our results clearly indicate the need for experimental verification of the optimum engineering and analytical parameters, in order to develop commercially viable systems for integrated analysis. The good reproducibility of the signal together with the ability of the array biochip to detect multiple samples hold promise for the future use of the integrated system in a Lab-on-a-Chip platform for application to molecular diagnostics.

Profiling the interaction mechanism of quinoline/quinazoline derivatives as MCHR1 antagonists: an in silico method.

PubMed

Wu, Mingwei; Li, Yan; Fu, Xinmei; Wang, Jinghui; Zhang, Shuwei; Yang, Ling

2014-09-01

Melanin concentrating hormone receptor 1 (MCHR1), a crucial regulator of energy homeostasis involved in the control of feeding and energy metabolism, is a promising target for treatment of obesity. In the present work, the up-to-date largest set of 181 quinoline/quinazoline derivatives as MCHR1 antagonists was subjected to both ligand- and receptor-based three-dimensional quantitative structure-activity (3D-QSAR) analysis applying comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The optimal predictable CoMSIA model exhibited significant validity with the cross-validated correlation coefficient (Q²) = 0.509, non-cross-validated correlation coefficient (R²(ncv)) = 0.841 and the predicted correlation coefficient (R²(pred)) = 0.745. In addition, docking studies and molecular dynamics (MD) simulations were carried out for further elucidation of the binding modes of MCHR1 antagonists. MD simulations in both water and lipid bilayer systems were performed. We hope that the obtained models and information may help to provide an insight into the interaction mechanism of MCHR1 antagonists and facilitate the design and optimization of novel antagonists as anti-obesity agents.

Small business development for molecular diagnostics.

PubMed

Anagostou, Anthanasia; Liotta, Lance A

2012-01-01

Molecular profiling, which is the application of molecular diagnostics technology to tissue and blood -specimens, is an integral element in the new era of molecular medicine and individualized therapy. Molecular diagnostics is a fertile ground for small business development because it can generate products that meet immediate demands in the health-care sector: (a) Detection of disease risk, or early-stage disease, with a higher specificity and sensitivity compared to previous testing methods, and (b) "Companion diagnostics" for stratifying patients to receive a treatment choice optimized to their individual disease. This chapter reviews the promise and challenges of business development in this field. Guidelines are provided for the creation of a business model and the generation of a marketing plan around a candidate molecular diagnostic product. Steps to commercialization are outlined using existing molecular diagnostics companies as learning examples.

Prediction of Sliding Friction Coefficient Based on a Novel Hybrid Molecular-Mechanical Model.

PubMed

Zhang, Xiaogang; Zhang, Yali; Wang, Jianmei; Sheng, Chenxing; Li, Zhixiong

2018-08-01

Sliding friction is a complex phenomenon which arises from the mechanical and molecular interactions of asperities when examined in a microscale. To reveal and further understand the effects of micro scaled mechanical and molecular components of friction coefficient on overall frictional behavior, a hybrid molecular-mechanical model is developed to investigate the effects of main factors, including different loads and surface roughness values, on the sliding friction coefficient in a boundary lubrication condition. Numerical modelling was conducted using a deterministic contact model and based on the molecular-mechanical theory of friction. In the contact model, with given external loads and surface topographies, the pressure distribution, real contact area, and elastic/plastic deformation of each single asperity contact were calculated. Then asperity friction coefficient was predicted by the sum of mechanical and molecular components of friction coefficient. The mechanical component was mainly determined by the contact width and elastic/plastic deformation, and the molecular component was estimated as a function of the contact area and interfacial shear stress. Numerical results were compared with experimental results and a good agreement was obtained. The model was then used to predict friction coefficients in different operating and surface conditions. Numerical results explain why applied load has a minimum effect on the friction coefficients. They also provide insight into the effect of surface roughness on the mechanical and molecular components of friction coefficients. It is revealed that the mechanical component dominates the friction coefficient when the surface roughness is large (Rq > 0.2 μm), while the friction coefficient is mainly determined by the molecular component when the surface is relatively smooth (Rq < 0.2 μm). Furthermore, optimal roughness values for minimizing the friction coefficient are recommended.

Matching mice to malignancy: molecular subgroups and models of medulloblastoma

PubMed Central

Lau, Jasmine; Schmidt, Christin; Markant, Shirley L.; Taylor, Michael D.; Wechsler-Reya, Robert J.

2012-01-01

Introduction Medulloblastoma, the largest group of embryonal brain tumors, has historically been classified into five variants based on histopathology. More recently, epigenetic and transcriptional analyses of primary tumors have sub-classified medulloblastoma into four to six subgroups, most of which are incongruous with histopathological classification. Discussion Improved stratification is required for prognosis and development of targeted treatment strategies, to maximize cure and minimize adverse effects. Several mouse models of medulloblastoma have contributed both to an improved understanding of progression and to developmental therapeutics. In this review, we summarize the classification of human medulloblastoma subtypes based on histopathology and molecular features. We describe existing genetically engineered mouse models, compare these to human disease, and discuss the utility of mouse models for developmental therapeutics. Just as accurate knowledge of the correct molecular subtype of medulloblastoma is critical to the development of targeted therapy in patients, we propose that accurate modeling of each subtype of medulloblastoma in mice will be necessary for preclinical evaluation and optimization of those targeted therapies. PMID:22315164

Optimal time points sampling in pathway modelling.

PubMed

Hu, Shiyan

2004-01-01

Modelling cellular dynamics based on experimental data is at the heart of system biology. Considerable progress has been made to dynamic pathway modelling as well as the related parameter estimation. However, few of them gives consideration for the issue of optimal sampling time selection for parameter estimation. Time course experiments in molecular biology rarely produce large and accurate data sets and the experiments involved are usually time consuming and expensive. Therefore, to approximate parameters for models with only few available sampling data is of significant practical value. For signal transduction, the sampling intervals are usually not evenly distributed and are based on heuristics. In the paper, we investigate an approach to guide the process of selecting time points in an optimal way to minimize the variance of parameter estimates. In the method, we first formulate the problem to a nonlinear constrained optimization problem by maximum likelihood estimation. We then modify and apply a quantum-inspired evolutionary algorithm, which combines the advantages of both quantum computing and evolutionary computing, to solve the optimization problem. The new algorithm does not suffer from the morass of selecting good initial values and being stuck into local optimum as usually accompanied with the conventional numerical optimization techniques. The simulation results indicate the soundness of the new method.

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

PubMed

Toropov, A A; Toropova, A P; Raska, I

2008-04-01

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

Systematic and efficient side chain optimization for molecular docking using a cheapest-path procedure.

PubMed

Schumann, Marcel; Armen, Roger S

2013-05-30

Molecular docking of small-molecules is an important procedure for computer-aided drug design. Modeling receptor side chain flexibility is often important or even crucial, as it allows the receptor to adopt new conformations as induced by ligand binding. However, the accurate and efficient incorporation of receptor side chain flexibility has proven to be a challenge due to the huge computational complexity required to adequately address this problem. Here we describe a new docking approach with a very fast, graph-based optimization algorithm for assignment of the near-optimal set of residue rotamers. We extensively validate our approach using the 40 DUD target benchmarks commonly used to assess virtual screening performance and demonstrate a large improvement using the developed side chain optimization over rigid receptor docking (average ROC AUC of 0.693 vs. 0.623). Compared to numerous benchmarks, the overall performance is better than nearly all other commonly used procedures. Furthermore, we provide a detailed analysis of the level of receptor flexibility observed in docking results for different classes of residues and elucidate potential avenues for further improvement. Copyright © 2013 Wiley Periodicals, Inc.

Foraging on the potential energy surface: a swarm intelligence-based optimizer for molecular geometry.

PubMed

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D; Sebastiani, Daniel

2012-11-21

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

Foraging on the potential energy surface: A swarm intelligence-based optimizer for molecular geometry

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Falk von Rudorff, Guido; Wolf, Sebastian; Kabbe, Gabriel; Schärf, Daniel; Kühne, Thomas D.; Sebastiani, Daniel

2012-11-01

We present a stochastic, swarm intelligence-based optimization algorithm for the prediction of global minima on potential energy surfaces of molecular cluster structures. Our optimization approach is a modification of the artificial bee colony (ABC) algorithm which is inspired by the foraging behavior of honey bees. We apply our modified ABC algorithm to the problem of global geometry optimization of molecular cluster structures and show its performance for clusters with 2-57 particles and different interatomic interaction potentials.

OPTIMIZATION OF MODERN DISPERSIVE RAMAN SPECTROMETERS FOR MOLECULAR SPECIATION OF ORGANICS IN WATER

EPA Science Inventory

Pesticides and industrial chemicals are typically complex organic molecules with multiple heteroatoms that can ionize in water. However, models for understanding the behavior of these chemicals in the environment typically assume that they exist exclusively as neutral species --...

Transitioning NWChem to the Next Generation of Manycore Machines

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

Bylaska, Eric J.; Apra, Edoardo; Kowalski, Karol

The NorthWest Chemistry (NWChem) modeling software is a popular molecular chemistry simulation software that was designed from the start to work on massively parallel processing supercomputers[6, 28, 49]. It contains an umbrella of modules that today includes Self Consistent Field (SCF), second order Mller-Plesset perturbation theory (MP2), Coupled Cluster, multi-conguration selfconsistent eld (MCSCF), selected conguration interaction (CI), tensor contraction engine (TCE) many body methods, density functional theory (DFT), time-dependent density functional theory (TDDFT), real time time-dependent density functional theory, pseudopotential plane-wave density functional theory (PSPW), band structure (BAND), ab initio molecular dynamics, Car-Parrinello molecular dynamics, classical molecular dynamics (MD), QM/MM,more » AIMD/MM, GIAO NMR, COSMO, COSMO-SMD, and RISM solvation models, free energy simulations, reaction path optimization, parallel in time, among other capabilities[ 22]. Moreover new capabilities continue to be added with each new release.« less

Molecular Treatment of Nano-Kaolinite Generations.

PubMed

Táborosi, Attila; Szilagyi, Robert K; Zsirka, Balázs; Fónagy, Orsolya; Horváth, Erzsébet; Kristóf, János

2018-06-18

A procedure is developed for defining a compositionally and structurally realistic, atomic-scale description of exfoliated clay nanoparticles from the kaolinite family of phylloaluminosilicates. By use of coordination chemical principles, chemical environments within a nanoparticle can be separated into inner, outer, and peripheral spheres. The edges of the molecular models of nanoparticles were protonated in a validated manner to achieve charge neutrality. Structural optimizations using semiempirical methods (NDDO Hamiltonians and DFTB formalism) and ab initio density functionals with a saturated basis set revealed previously overlooked molecular origins of morphological changes as a result of exfoliation. While the use of semiempirical methods is desirable for the treatment of nanoparticles composed of tens of thousands of atoms, the structural accuracy is rather modest in comparison to DFT methods. We report a comparative survey of our infrared data for untreated crystalline and various exfoliated states of kaolinite and halloysite. Given the limited availability of experimental techniques for providing direct structural information about nano-kaolinite, the vibrational spectra can be considered as an essential tool for validating structural models. The comparison of experimental and calculated stretching and bending frequencies further justified the use of the preferred level of theory. Overall, an optimal molecular model of the defect-free, ideal nano-kaolinite can be composed with respect to stationary structure and curvature of the potential energy surface using the PW91/SVP level of theory with empirical dispersion correction (PW91+D) and polarizable continuum solvation model (PCM) without the need for a scaled quantum chemical force field. This validated theoretical approach is essential in order to follow the formation of exfoliated clays and their surface reactivity that is experimentally unattainable.

Chiral micellar electrokinetic chromatography (CMEKC)-atmospheric pressure photoionization of benzoin derivatives using mixed molecular micelles

PubMed Central

He, Jun; Shamsi, Shahab A.

2012-01-01

In the present work we report, for the first time, the successful on-line coupling of chiral micellar electrokinetic chromatography (CMEKC) to atmospheric pressure photo-ionization mass spectrometry (APPI-MS). Four structurally similar neutral test solutes (e.g., benzoin derivatives) were successfully ionized by APPI-MS. The mass spectra in the positive ion mode showed that the protonated molecular ions of benzoins are not the most abundant fragment ions. Simultaneous enantioseparation by CMEKC and on-line APPI-MS detection of four photoinitiators: hydrobenzoin (HBNZ), benzoin (BNZ), benzoin methyl ether (BME), benzoin ethyl ether (BEE), were achieved using an optimized molar ratio of mixed molecular micelle of two polymeric chiral surfactants (polysodium N-undecenoxy carbonyl-L-leucinate and polysodium N-undecenoyl-L,L-leucylvalinate). The CMEKC conditions, such as voltage, chiral polymeric surfactant concentration, buffer pH, and BGE concentration, were optimized using a multivariate central composite design (CCD). The sheath liquid composition (involving % v/v methanol, dopant concentration, electrolyte additive concentration, and flow rate) and spray chamber parameters (drying gas flow rate, drying gas temperature, and vaporizer temperature) were also optimized with CCD. Models built based on the CCD results and response surface method was used to analyze the interactions between factors and their effects on the responses. The final overall optimum conditions for CMEKC-APPI-MS were also predicted and found in agreement with the experimentally optimized parameters. PMID:21500208

An Efficient Variable Screening Method for Effective Surrogate Models for Reliability-Based Design Optimization

DTIC Science & Technology

2014-04-01

surrogate model generation is difficult for high -dimensional problems, due to the curse of dimensionality. Variable screening methods have been...a variable screening model was developed for the quasi-molecular treatment of ion-atom collision [16]. In engineering, a confidence interval of...for high -level radioactive waste [18]. Moreover, the design sensitivity method can be extended to the variable screening method because vital

Hormone purification by isoelectric focusing in space

NASA Technical Reports Server (NTRS)

Bier, M.

1988-01-01

The objective of the program was the definition and development of optimal methods for electrophoretic separations in microgravity. The approach is based on a triad consisting of ground based experiments, mathematical modeling and experiments in microgravity. Zone electrophoresis is a rate process, where separation is achieved in uniform buffers on the basis of differences in electrophoretic mobilities. Optimization and modeling of continuous flow electrophoresis mainly concern the hydrodynamics of the flow process, including gravity dependent fluid convection due to density gradients and gravity independent electroosmosis. Optimization of focusing requires a more complex model describing the molecular transport processes involved in electrophoresis of interacting systems. Three different focusing instruments were designed, embodying novel principles of fluid stabilization. Fluid stability was achieved by: (1) flow streamlining by means of membrane elements in combination with rapid fluid recycling; (2) apparatus rotation in combination with said membrane elements; and (3) shear stress induced by rapid recycling through a narrow gap channel.

Computational strategies for the automated design of RNA nanoscale structures from building blocks using NanoTiler.

PubMed

Bindewald, Eckart; Grunewald, Calvin; Boyle, Brett; O'Connor, Mary; Shapiro, Bruce A

2008-10-01

One approach to designing RNA nanoscale structures is to use known RNA structural motifs such as junctions, kissing loops or bulges and to construct a molecular model by connecting these building blocks with helical struts. We previously developed an algorithm for detecting internal loops, junctions and kissing loops in RNA structures. Here we present algorithms for automating or assisting many of the steps that are involved in creating RNA structures from building blocks: (1) assembling building blocks into nanostructures using either a combinatorial search or constraint satisfaction; (2) optimizing RNA 3D ring structures to improve ring closure; (3) sequence optimisation; (4) creating a unique non-degenerate RNA topology descriptor. This effectively creates a computational pipeline for generating molecular models of RNA nanostructures and more specifically RNA ring structures with optimized sequences from RNA building blocks. We show several examples of how the algorithms can be utilized to generate RNA tecto-shapes.

Computational strategies for the automated design of RNA nanoscale structures from building blocks using NanoTiler☆

PubMed Central

Bindewald, Eckart; Grunewald, Calvin; Boyle, Brett; O’Connor, Mary; Shapiro, Bruce A.

2013-01-01

One approach to designing RNA nanoscale structures is to use known RNA structural motifs such as junctions, kissing loops or bulges and to construct a molecular model by connecting these building blocks with helical struts. We previously developed an algorithm for detecting internal loops, junctions and kissing loops in RNA structures. Here we present algorithms for automating or assisting many of the steps that are involved in creating RNA structures from building blocks: (1) assembling building blocks into nanostructures using either a combinatorial search or constraint satisfaction; (2) optimizing RNA 3D ring structures to improve ring closure; (3) sequence optimisation; (4) creating a unique non-degenerate RNA topology descriptor. This effectively creates a computational pipeline for generating molecular models of RNA nanostructures and more specifically RNA ring structures with optimized sequences from RNA building blocks. We show several examples of how the algorithms can be utilized to generate RNA tecto-shapes. PMID:18838281

Emergence of robust growth laws from optimal regulation of ribosome synthesis.

PubMed

Scott, Matthew; Klumpp, Stefan; Mateescu, Eduard M; Hwa, Terence

2014-08-22

Bacteria must constantly adapt their growth to changes in nutrient availability; yet despite large-scale changes in protein expression associated with sensing, adaptation, and processing different environmental nutrients, simple growth laws connect the ribosome abundance and the growth rate. Here, we investigate the origin of these growth laws by analyzing the features of ribosomal regulation that coordinate proteome-wide expression changes with cell growth in a variety of nutrient conditions in the model organism Escherichia coli. We identify supply-driven feedforward activation of ribosomal protein synthesis as the key regulatory motif maximizing amino acid flux, and autonomously guiding a cell to achieve optimal growth in different environments. The growth laws emerge naturally from the robust regulatory strategy underlying growth rate control, irrespective of the details of the molecular implementation. The study highlights the interplay between phenomenological modeling and molecular mechanisms in uncovering fundamental operating constraints, with implications for endogenous and synthetic design of microorganisms. © 2014 The Authors. Published under the terms of the CC BY 4.0 license.

Optimized "detectors" for dynamics analysis in solid-state NMR

NASA Astrophysics Data System (ADS)

Smith, Albert A.; Ernst, Matthias; Meier, Beat H.

2018-01-01

Relaxation in nuclear magnetic resonance (NMR) results from stochastic motions that modulate anisotropic NMR interactions. Therefore, measurement of relaxation-rate constants can be used to characterize molecular-dynamic processes. The motion is often characterized by Markov processes using an auto-correlation function, which is assumed to be a sum of multiple decaying exponentials. We have recently shown that such a model can lead to severe misrepresentation of the real motion, when the real correlation function is more complex than the model. Furthermore, multiple distributions of motion may yield the same set of dynamics data. Therefore, we introduce optimized dynamics "detectors" to characterize motions which are linear combinations of relaxation-rate constants. A detector estimates the average or total amplitude of motion for a range of motional correlation times. The information obtained through the detectors is less specific than information obtained using an explicit model, but this is necessary because the information contained in the relaxation data is ambiguous, if one does not know the correct motional model. On the other hand, if one has a molecular dynamics trajectory, one may calculate the corresponding detector responses, allowing direct comparison to experimental NMR dynamics analysis. We describe how to construct a set of optimized detectors for a given set of relaxation measurements. We then investigate the properties of detectors for a number of different data sets, thus gaining an insight into the actual information content of the NMR data. Finally, we show an example analysis of ubiquitin dynamics data using detectors, using the DIFRATE software.

Emotion: The Self-regulatory Sense

PubMed Central

2014-01-01

While emotion is a central component of human health and well-being, traditional approaches to understanding its biological function have been wanting. A dynamic systems model, however, broadly redefines and recasts emotion as a primary sensory system—perhaps the first sensory system to have emerged, serving the ancient autopoietic function of “self-regulation.” Drawing upon molecular biology and revelations from the field of epigenetics, the model suggests that human emotional perceptions provide an ongoing stream of “self-relevant” sensory information concerning optimally adaptive states between the organism and its immediate environment, along with coupled behavioral corrections that honor a universal self-regulatory logic, one still encoded within cellular signaling and immune functions. Exemplified by the fundamental molecular circuitry of sensorimotor control in the E coli bacterium, the model suggests that the hedonic (affective) categories emerge directly from positive and negative feedback processes, their good/bad binary appraisals relating to dual self-regulatory behavioral regimes—evolutionary purposes, through which organisms actively participate in natural selection, and through which humans can interpret optimal or deficit states of balanced being and becoming. The self-regulatory sensory paradigm transcends anthropomorphism, unites divergent theoretical perspectives and isolated bodies of literature, while challenging time-honored assumptions. While suppressive regulatory strategies abound, it suggests that emotions are better understood as regulating us, providing a service crucial to all semantic language, learning systems, evaluative decision-making, and fundamental to optimal physical, mental, and social health. PMID:24808986

Synthetic biology and molecular genetics in non-conventional yeasts: Current tools and future advances.

PubMed

Wagner, James M; Alper, Hal S

2016-04-01

Coupling the tools of synthetic biology with traditional molecular genetic techniques can enable the rapid prototyping and optimization of yeast strains. While the era of yeast synthetic biology began in the well-characterized model organism Saccharomyces cerevisiae, it is swiftly expanding to include non-conventional yeast production systems such as Hansenula polymorpha, Kluyveromyces lactis, Pichia pastoris, and Yarrowia lipolytica. These yeasts already have roles in the manufacture of vaccines, therapeutic proteins, food additives, and biorenewable chemicals, but recent synthetic biology advances have the potential to greatly expand and diversify their impact on biotechnology. In this review, we summarize the development of synthetic biological tools (including promoters and terminators) and enabling molecular genetics approaches that have been applied in these four promising alternative biomanufacturing platforms. An emphasis is placed on synthetic parts and genome editing tools. Finally, we discuss examples of synthetic tools developed in other organisms that can be adapted or optimized for these hosts in the near future. Copyright © 2015 Elsevier Inc. All rights reserved.

Prioritizing therapeutic targets using patient-derived xenograft models

PubMed Central

Lodhia, K.A; Hadley, A; Haluska, P; Scott, C.L

2015-01-01

Effective systemic treatment of cancer relies on the delivery of agents with optimal therapeutic potential. The molecular age of medicine has provided genomic tools that can identify a large number of potential therapeutic targets in individual patients, heralding the promise of personalized treatment. However, determining which potential targets actually drive tumor growth and should be prioritized for therapy is challenging. Indeed, reliable molecular matches of target and therapeutic agent have been stringently validated in the clinic for only a small number of targets. Patient-derived xenografts (PDX) are tumor models developed in immunocompromised mice using tumor procured directly from the patient. As patient surrogates, PDX models represent a powerful tool for addressing individualized therapy. Challenges include humanizing the immune system of PDX models and ensuring high quality molecular annotation, in order to maximise insights for the clinic. Importantly, PDX can be sampled repeatedly and in parallel, to reveal clonal evolution, which may predict mechanisms of drug resistance and inform therapeutic strategy design. PMID:25783201

Optimization of linear and branched alkane interactions with water to simulate hydrophobic hydration

NASA Astrophysics Data System (ADS)

Ashbaugh, Henry S.; Liu, Lixin; Surampudi, Lalitanand N.

2011-08-01

Previous studies of simple gas hydration have demonstrated that the accuracy of molecular simulations at capturing the thermodynamic signatures of hydrophobic hydration is linked both to the fidelity of the water model at replicating the experimental liquid density at ambient pressure and an accounting of polarization interactions between the solute and water. We extend those studies to examine alkane hydration using the transferable potentials for phase equilibria united-atom model for linear and branched alkanes, developed to reproduce alkane phase behavior, and the TIP4P/2005 model for water, which provides one of the best descriptions of liquid water for the available fixed-point charge models. Alkane site/water oxygen Lennard-Jones cross interactions were optimized to reproduce the experimental alkane hydration free energies over a range of temperatures. The optimized model reproduces the hydration free energies of the fitted alkanes with a root mean square difference between simulation and experiment of 0.06 kcal/mol over a wide temperature range, compared to 0.44 kcal/mol for the parent model. The optimized model accurately reproduces the temperature dependence of hydrophobic hydration, as characterized by the hydration enthalpies, entropies, and heat capacities, as well as the pressure response, as characterized by partial molar volumes.

Hit to Lead optimization of a novel class of squarate-containing polo-like kinases inhibitors.

PubMed

Zhang, Qingwei; Xia, Zhiren; Mitten, Michael J; Lasko, Loren M; Klinghofer, Vered; Bouska, Jennifer; Johnson, Eric F; Penning, Thomas D; Luo, Yan; Giranda, Vincent L; Shoemaker, Alexander R; Stewart, Kent D; Djuric, Stevan W; Vasudevan, Anil

2012-12-15

A high throughput screening (HTS) hit, 1 (Plk1 K(i)=2.2 μM) was optimized and evaluated for the enzymatic inhibition of Plk-1 kinase. Molecular modeling suggested the importance of adding a hydrophobic aromatic amine side chain in order to improve the potency by a classic kinase H-donor-acceptor binding mode. Extensive SAR studies led to the discovery of 49 (Plk1 K(i)=5 nM; EC(50)=1.05 μM), which demonstrated moderate efficacy at 100 mpk in a MiaPaCa tumor model, with no overt toxicity. Copyright © 2012 Elsevier Ltd. All rights reserved.

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

Sivak, David; Crooks, Gavin

A fundamental problem in modern thermodynamics is how a molecular-scale machine performs useful work, while operating away from thermal equilibrium without excessive dissipation. To this end, we derive a friction tensor that induces a Riemannian manifold on the space of thermodynamic states. Within the linear-response regime, this metric structure controls the dissipation of finite-time transformations, and bestows optimal protocols with many useful properties. We discuss the connection to the existing thermodynamic length formalism, and demonstrate the utility of this metric by solving for optimal control parameter protocols in a simple nonequilibrium model.

Clustering and optimal arrangement of enzymes in reaction-diffusion systems.

PubMed

Buchner, Alexander; Tostevin, Filipe; Gerland, Ulrich

2013-05-17

Enzymes within biochemical pathways are often colocalized, yet the consequences of specific spatial enzyme arrangements remain poorly understood. We study the impact of enzyme arrangement on reaction efficiency within a reaction-diffusion model. The optimal arrangement transitions from a cluster to a distributed profile as a single parameter, which controls the probability of reaction versus diffusive loss of pathway intermediates, is varied. We introduce the concept of enzyme exposure to explain how this transition arises from the stochastic nature of molecular reactions and diffusion.

Structural modeling of Ge6.25As32.5Se61.25 using a combination of reverse Monte Carlo and Ab initio molecular dynamics.

PubMed

Opletal, George; Drumm, Daniel W; Wang, Rong P; Russo, Salvy P

2014-07-03

Ternary glass structures are notoriously difficult to model accurately, and yet prevalent in several modern endeavors. Here, a novel combination of Reverse Monte Carlo (RMC) modeling and ab initio molecular dynamics (MD) is presented, rendering these complicated structures computationally tractable. A case study (Ge6.25As32.5Se61.25 glass) illustrates the effects of ab initio MD quench rates and equilibration temperatures, and the combined approach's efficacy over standard RMC or random insertion methods. Submelting point MD quenches achieve the most stable, realistic models, agreeing with both experimental and fully ab initio results. The simple approach of RMC followed by ab initio geometry optimization provides similar quality to the RMC-MD combination, for far fewer resources.

Optimization of Analytical Potentials for Coarse-Grained Biopolymer Models.

PubMed

Mereghetti, Paolo; Maccari, Giuseppe; Spampinato, Giulia Lia Beatrice; Tozzini, Valentina

2016-08-25

The increasing trend in the recent literature on coarse grained (CG) models testifies their impact in the study of complex systems. However, the CG model landscape is variegated: even considering a given resolution level, the force fields are very heterogeneous and optimized with very different parametrization procedures. Along the road for standardization of CG models for biopolymers, here we describe a strategy to aid building and optimization of statistics based analytical force fields and its implementation in the software package AsParaGS (Assisted Parameterization platform for coarse Grained modelS). Our method is based on the use and optimization of analytical potentials, optimized by targeting internal variables statistical distributions by means of the combination of different algorithms (i.e., relative entropy driven stochastic exploration of the parameter space and iterative Boltzmann inversion). This allows designing a custom model that endows the force field terms with a physically sound meaning. Furthermore, the level of transferability and accuracy can be tuned through the choice of statistical data set composition. The method-illustrated by means of applications to helical polypeptides-also involves the analysis of two and three variable distributions, and allows handling issues related to the FF term correlations. AsParaGS is interfaced with general-purpose molecular dynamics codes and currently implements the "minimalist" subclass of CG models (i.e., one bead per amino acid, Cα based). Extensions to nucleic acids and different levels of coarse graining are in the course.

Theoretical calculations of Electron Paramagnetic Resonance parameters of liquid phase Orotic acid radical

NASA Astrophysics Data System (ADS)

Sarikaya, Ebru Karakaş; Dereli, Ömer

2017-02-01

To obtain liquid phase molecular structure, conformational analysis of Orotic acid was performed and six conformers were determined. For these conformations, eight possible radicals were modelled by using Density Functional Theory computations with respect to molecular structure. Electron Paramagnetic Resonance parameters of these model radicals were calculated and then they were compared with the experimental ones. Geometry optimizations of the molecule and modeled radicals were performed using Becke's three-parameter hybrid-exchange functional combined with the Lee-Yang-Parr correlation functional of Density Functional Theory and 6-311++G(d,p) basis sets in p-dioxane solution. Because Orotic acid can be mutagenic in mammalian somatic cells and it is also mutagenic for bacteria and yeast, it has been studied.

Bioenergy Research | Bioenergy | NREL

Science.gov Websites

range of research from exploring biomass at the molecular level through biorefinery process optimization Bioenergetics We work at the molecular and cellular level to understand and optimize microbial production of biofuels and bioproducts. fanciful illustration of fuel nozzle with molecular structures drawn in

Defining therapy goals for major molecular remission in chronic myeloid leukemia: results of the randomized CML Study IV.

PubMed

Saussele, Susanne; Hehlmann, Rüdiger; Fabarius, Alice; Jeromin, Sabine; Proetel, Ulrike; Rinaldetti, Sebastien; Kohlbrenner, Katharina; Einsele, Hermann; Falge, Christiane; Kanz, Lothar; Neubauer, Andreas; Kneba, Michael; Stegelmann, Frank; Pfreundschuh, Michael; Waller, Cornelius F; Oppliger Leibundgut, Elisabeth; Heim, Dominik; Krause, Stefan W; Hofmann, Wolf-Karsten; Hasford, Joerg; Pfirrmann, Markus; Müller, Martin C; Hochhaus, Andreas; Lauseker, Michael

2018-05-01

Major molecular remission (MMR) is an important therapy goal in chronic myeloid leukemia (CML). So far, MMR is not a failure criterion according to ELN management recommendation leading to uncertainties when to change therapy in CML patients not reaching MMR after 12 months. At monthly landmarks, for different molecular remission status Hazard ratios (HR) were estimated for patients registered to CML study IV who were divided in a learning and a validation sample. The minimum HR for MMR was found at 2.5 years with 0.28 (compared to patients without remission). In the validation sample, a significant advantage for progression-free survival (PFS) for patients in MMR could be detected (p-value 0.007). The optimal time to predict PFS in patients with MMR could be validated in an independent sample at 2.5 years. With our model we provide a suggestion when to define lack of MMR as therapy failure and thus treatment change should be considered. The optimal response time for 1% BCR-ABL at about 12-15 months was confirmed and for deep molecular remission no specific time point was detected. Nevertheless, it was demonstrated that the earlier the MMR is achieved the higher is the chance to attain deep molecular response later.

Anomalous waterlike behavior in spherically-symmetric water models optimized with the relative entropy.

PubMed

Chaimovich, Aviel; Shell, M Scott

2009-03-28

Recent efforts have attempted to understand many of liquid water's anomalous properties in terms of effective spherically-symmetric pairwise molecular interactions entailing two characteristic length scales (so-called "core-softened" potentials). In this work, we examine the extent to which such simple descriptions of water are representative of the true underlying interactions by extracting coarse-grained potential functions that are optimized to reproduce the behavior of an all-atom model. To perform this optimization, we use a novel procedure based upon minimizing the relative entropy, a quantity that measures the extent to which a coarse-grained configurational ensemble overlaps with a reference all-atom one. We show that the optimized spherically-symmetric water models exhibit notable variations with the state conditions at which they were optimized, reflecting in particular the shifting accessibility of networked hydrogen bonding interactions. Moreover, we find that water's density and diffusivity anomalies are only reproduced when the effective coarse-grained potentials are allowed to vary with state. Our results therefore suggest that no state-independent spherically-symmetric potential can fully capture the interactions responsible for water's unique behavior; rather, the particular way in which the effective interactions vary with temperature and density contributes significantly to anomalous properties.

Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

PubMed

Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

2018-06-12

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

Optimizing the Immuno-Surface Characteristics for Bio-Sensors and Filters Through Modeling and Experiments

DTIC Science & Technology

2005-06-01

immobilization of antibodies o Adsorbed, aminophase, heterobifunctional crosslinkers (GMBS, BMPS, EMCS) o GMBS attaches the most antibodies o ProteinA ...play a role in getting the antigen close enough to the immuno-surface to potentially interact as well as the short range molecular forces that

Docking-based Screening of Ficus religiosa Phytochemicals as Inhibitors of Human Histamine H2 Receptor.

PubMed

Chaudhary, Amit; Yadav, Birendra Singh; Singh, Swati; Maurya, Pramod Kumar; Mishra, Alok; Srivastva, Shweta; Varadwaj, Pritish Kumar; Singh, Nand Kumar; Mani, Ashutosh

2017-10-01

Ficus religiosa L. is generally known as Peepal and belongs to family Moraceae . The tree is a source of many compounds having high medicinal value. In gastrointestinal tract, histamine H2 receptors have key role in histamine-stimulated gastric acid secretion. Their over stimulation causes its excessive production which is responsible for gastric ulcer. This study aims to screen the range of phytochemicals present in F. religiosa for binding with human histamine H2 and identify therapeutics for a gastric ulcer from the plant. In this work, a 3D-structure of human histamine H2 receptor was modeled by using homology modeling and the predicted model was validated using PROCHECK. Docking studies were also performed to assess binding affinities between modeled receptor and 34 compounds. Molecular dynamics simulations were done to identify most stable receptor-ligand complexes. Absorption, distribution, metabolism, excretion, and screening was done to evaluate pharmacokinetic properties of compounds. The results suggest that seven ligands, namely, germacrene, bergaptol, lanosterol, Ergost-5-en-3beta-ol, α-amyrin acetate, bergapten, and γ-cadinene showed better binding affinities. Among seven phytochemicals, lanosterol and α-amyrin acetate were found to have greater stability during simulation studies. These two compounds may be a suitable therapeutic agent against histamine H2 receptor. This study was performed to screen antiulcer compounds from F. religiosa . Molecular modeling, molecular docking and MD simulation studies were performed with selected phytochemicals from F. religiosa . The analysis suggests that Lanosterol and α-amyrin may be a suitable therapeutic agent against histamine H2 receptor. This study facilitates initiation of the herbal drug discovery process for the antiulcer activity. Abbreviations used: ADMET: Absorption, distribution, metabolism, excretion and toxicity, DOPE: Discrete Optimized Potential Energy, OPLS: Optimized potential for liquid simulations, RMSD: Root-mean-square deviation, HOA: Human oral absorption, MW: Molecular weight, SP: Standard-precision, XP: Extra-precision, GPCRs: G protein-coupled receptors, SASA: Solvent accessible surface area, Rg: Radius of gyration, NHB: Number of hydrogen bond.

Hybrid Quantum Mechanics/Molecular Mechanics Solvation Scheme for Computing Free Energies of Reactions at Metal-Water Interfaces.

PubMed

Faheem, Muhammad; Heyden, Andreas

2014-08-12

We report the development of a quantum mechanics/molecular mechanics free energy perturbation (QM/MM-FEP) method for modeling chemical reactions at metal-water interfaces. This novel solvation scheme combines planewave density function theory (DFT), periodic electrostatic embedded cluster method (PEECM) calculations using Gaussian-type orbitals, and classical molecular dynamics (MD) simulations to obtain a free energy description of a complex metal-water system. We derive a potential of mean force (PMF) of the reaction system within the QM/MM framework. A fixed-size, finite ensemble of MM conformations is used to permit precise evaluation of the PMF of QM coordinates and its gradient defined within this ensemble. Local conformations of adsorbed reaction moieties are optimized using sequential MD-sampling and QM-optimization steps. An approximate reaction coordinate is constructed using a number of interpolated states and the free energy difference between adjacent states is calculated using the QM/MM-FEP method. By avoiding on-the-fly QM calculations and by circumventing the challenges associated with statistical averaging during MD sampling, a computational speedup of multiple orders of magnitude is realized. The method is systematically validated against the results of ab initio QM calculations and demonstrated for C-C cleavage in double-dehydrogenated ethylene glycol on a Pt (111) model surface.

Why do we need three levels to understand the molecular optical response?

NASA Astrophysics Data System (ADS)

Perez-Moreno, Javier; Clays, Koen; Kuzyk, Mark G.

2011-10-01

Traditionally, the nonlinear optical response at the molecular level has been modeled using the two-level approximation, under the assumption that the behavior of the exact sum-over-states (SOS) expressions for the molecular polarizabilities is well represented by the contribution of only two levels. We show how, a rigorous application of the Thomas-Kuhn sum-rules over the SOS expression for the diagonal component of the first-hyperpolarziability proves that the two-level approximation is unphysical. In addition, we indicate how the contributions of potentially infinite number of states to the SOS expressions for the first-hyperpolarizability are well represented by the contributions of a generic three-level system. This explains why the analysis of the three-level model in conjugation with the sum rules has lead to successful paradigms for the optimization of organic chromophores.

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

PubMed

Yao, Kun; Herr, John E; Toth, David W; Mckintyre, Ryker; Parkhill, John

2018-02-28

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Improving the Molecular Ion Signal Intensity for In Situ Liquid SIMS Analysis

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

Zhou, Yufan; Yao, Juan; Ding, Yuanzhao

In situ liquid secondary ion mass spectrometry (SIMS) enabled by system for analysis at the liquid vacuum interface (SALVI) has proven to be a promising new tool to provide molecular information at solid–liquid and liquid–vacuum interfaces. However, the initial data showed that useful signals in positive ion spectra are too weak to be meaningful in most cases. In addition, it is difficult to obtain strong negative molecular ion signals when m/z>200. These two drawbacks have been the biggest obstacle towards practical use of this new analytical approach. In this study, we report that strong and reliable positive and negative molecularmore » signals are achievable after optimizing the SIMS experimental conditions. Four model systems, including a 1,8-diazabicycloundec-7-ene (DBU)-base switchable ionic liquid, a live Shewanella oneidensis biofilm, a hydrated mammalian epithelia cell, and an electrolyte popularly used in Li ion batteries were studied. A signal enhancement of about two orders of magnitude was obtained in comparison with non-optimized conditions. Therefore, molecular ion signal intensity has become very acceptable to use for in situ liquid SIMS to study solid–liquid and liquid–vacuum interfaces.« less

Chiral micellar electrokinetic chromatography-atmospheric pressure photoionization of benzoin derivatives using mixed molecular micelles.

PubMed

He, Jun; Shamsi, Shahab A

2011-05-01

In the present work we report, for the first time, the successful on-line coupling of chiral MEKC (CMEKC) to atmospheric pressure photoionization MS (APPI-MS). Four structurally similar neutral test solutes (e.g. benzoin (BNZ) derivatives) were successfully ionized by APPI-MS. The mass spectra in the positive ion mode showed that the protonated molecular ions of BNZs are not the most abundant fragment ions. Simultaneous enantioseparation by CMEKC and on-line APPI-MS detection of four photoinitiators, hydrobenzoin, BNZ, benzoin methyl ether, benzoin ethyl ether, were achieved using an optimized molar ratio of mixed molecular micelle of two polymeric chiral surfactants (polysodium N-undecenoxy carbonyl-L-leucinate and polysodium N-undecenoyl-L,L-leucylvalinate). The CMEKC conditions, such as voltage, chiral polymeric surfactant concentration, buffer pH, and BGE concentration, were optimized using a multivariate central composite design (CCD). The sheath liquid composition (involving %v/v methanol, dopant concentration, electrolyte additive concentration, and flow rate) and spray chamber parameters (drying gas flow rate, drying gas temperature, and vaporizer temperature) were also optimized with CCD. Models built based on the CCD results and response surface method were used to analyze the interactions between factors and their effects on the responses. The final overall optimum conditions for CMEKC-APPI-MS were also predicted and found in agreement with the experimentally optimized parameters. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimization of physico-chemical properties of gelatin extracted from fish skin of rainbow trout (Onchorhynchus mykiss).

PubMed

Tabarestani, H Shahiri; Maghsoudlou, Y; Motamedzadegan, A; Mahoonak, A R Sadeghi

2010-08-01

Physico-chemical properties of gelatin extracted from rainbow trout (Onchorhynchus mykiss) skin were optimized using response surface methodology (RSM). Central rotatable composite design was applied to study the combined effects of NaOH concentration (0.01-0.21 N), acetic acid concentration (0.01-0.21 N) and pre-treatment time (1-3h) on yield, molecular weight distribution, gel strength, viscosity and melting point of gelatin. Regression models were developed to predict the variables. Predict values of multiple response at optimal condition were that yield=9.36%, alpha(1)/alpha(2) chain ratio=1.76, beta chain percent=32.81, gel strength=459 g, viscosity=3.2 mPa s and melting point=20.4 degrees C. The optimal condition was obtained using 0.19 N NaOH and 0.121 N acetic acid for 3h. The results showed that the concentration of H(+) during pre-treatment had significant effect on molecular weight distribution, melting point and gel strength. The concentration of OH(-) had significant effect on viscosity and for extraction yield, pretreatment time was the critical factor. (c) 2010 Elsevier Ltd. All rights reserved.

Atomistic Molecular Dynamics Simulations of Carbon Dioxide Diffusivity in n-Hexane, n-Decane, n-Hexadecane, Cyclohexane, and Squalane.

PubMed

Moultos, Othonas A; Tsimpanogiannis, Ioannis N; Panagiotopoulos, Athanassios Z; Trusler, J P Martin; Economou, Ioannis G

2016-12-22

Atomistic molecular dynamics simulations were carried out to obtain the diffusion coefficients of CO 2 in n-hexane, n-decane, n-hexadecane, cyclohexane, and squalane at temperatures up to 423.15 K and pressures up to 65 MPa. Three popular models were used for the representation of hydrocarbons: the united atom TraPPE (TraPPE-UA), the all-atom OPLS, and an optimized version of OPLS, namely, L-OPLS. All models qualitatively reproduce the pressure dependence of the diffusion coefficient of CO 2 in hydrocarbons measured recently, and L-OPLS was found to be the most accurate. Specifically for n-alkanes, L-OPLS also reproduced the measured viscosities and densities much more accurately than the original OPLS and TraPPE-UA models, indicating that the optimization of the torsional potential is crucial for the accurate description of transport properties of long chain molecules. The three force fields predict different microscopic properties such as the mean square radius of gyration for the n-alkane molecules and pair correlation functions for the CO 2 -n-alkane interactions. CO 2 diffusion coefficients in all hydrocarbons studied are shown to deviate significantly from the Stokes-Einstein behavior.

Generative Recurrent Networks for De Novo Drug Design.

PubMed

Gupta, Anvita; Müller, Alex T; Huisman, Berend J H; Fuchs, Jens A; Schneider, Petra; Schneider, Gisbert

2018-01-01

Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short-term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine-tuned the RNN's predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN-LSTM system for high-impact use cases, such as low-data drug discovery, fragment based molecular design, and hit-to-lead optimization for diverse drug targets. © 2017 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.

Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids

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

Dunn, Nicholas J. H.; Noid, W. G., E-mail: wnoid@chem.psu.edu

2015-12-28

The present work investigates the capability of bottom-up coarse-graining (CG) methods for accurately modeling both structural and thermodynamic properties of all-atom (AA) models for molecular liquids. In particular, we consider 1, 2, and 3-site CG models for heptane, as well as 1 and 3-site CG models for toluene. For each model, we employ the multiscale coarse-graining method to determine interaction potentials that optimally approximate the configuration dependence of the many-body potential of mean force (PMF). We employ a previously developed “pressure-matching” variational principle to determine a volume-dependent contribution to the potential, U{sub V}(V), that approximates the volume-dependence of the PMF.more » We demonstrate that the resulting CG models describe AA density fluctuations with qualitative, but not quantitative, accuracy. Accordingly, we develop a self-consistent approach for further optimizing U{sub V}, such that the CG models accurately reproduce the equilibrium density, compressibility, and average pressure of the AA models, although the CG models still significantly underestimate the atomic pressure fluctuations. Additionally, by comparing this array of models that accurately describe the structure and thermodynamic pressure of heptane and toluene at a range of different resolutions, we investigate the impact of bottom-up coarse-graining upon thermodynamic properties. In particular, we demonstrate that U{sub V} accounts for the reduced cohesion in the CG models. Finally, we observe that bottom-up coarse-graining introduces subtle correlations between the resolution, the cohesive energy density, and the “simplicity” of the model.« less

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.

Computer modeling of the neurotoxin binding site of acetylcholine receptor spanning residues 185 through 196

NASA Technical Reports Server (NTRS)

Garduno-Juarez, R.; Shibata, M.; Zielinski, T. J.; Rein, R.

1987-01-01

A model of the complex between the acetylcholine receptor and the snake neurotoxin, cobratoxin, was built by molecular model building and energy optimization techniques. The experimentally identified functionally important residues of cobratoxin and the dodecapeptide corresponding to the residues 185-196 of acetylcholine receptor alpha subunit were used to build the model. Both cis and trans conformers of cyclic L-cystine portion of the dodecapeptide were examined. Binding residues independently identified on cobratoxin are shown to interact with the dodecapeptide AChR model.

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Punctuated equilibrium and shock waves in molecular models of biological evolution.

PubMed

Saakian, David B; Ghazaryan, Makar H; Hu, Chin-Kun

2014-08-01

We consider the dynamics in infinite population evolution models with a general symmetric fitness landscape. We find shock waves, i.e., discontinuous transitions in the mean fitness, in evolution dynamics even with smooth fitness landscapes, which means that the search for the optimal evolution trajectory is more complicated. These shock waves appear in the case of positive epistasis and can be used to represent punctuated equilibria in biological evolution during long geological time scales. We find exact analytical solutions for discontinuous dynamics at the large-genome-length limit and derive optimal mutation rates for a fixed fitness landscape to send the population from the initial configuration to some final configuration in the fastest way.

The unlikely high efficiency of a molecular motor based on active motion

NASA Astrophysics Data System (ADS)

Ebeling, W.

2015-07-01

The efficiency of a simple model of a motor converting chemical into mechanical energy is studied analytically. The model motor shows interesting properties corresponding qualitatively to motors investigated in experiments. The efficiency increases with the load and may for low loss reach high values near to 100 percent in a narrow regime of optimal load. It is shown that the optimal load and the maximal efficiency depend by universal power laws on the dimensionless loss parameter. Stochastic effects decrease the stability of motor regimes with high efficiency and make them unlikely. Numerical studies show efficiencies below the theoretical optimum and demonstrate that special ratchet profiles my stabilize efficient regimes.

The right time to learn: mechanisms and optimization of spaced learning

PubMed Central

Smolen, Paul; Zhang, Yili; Byrne, John H.

2016-01-01

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

Use of Molecular Dynamics for the Refinement of an Electrostatic Model for the In Silico Design of a Polymer Antidote for the Anticoagulant Fondaparinux

PubMed Central

Kwok, Ezra; Gopaluni, Bhushan; Kizhakkedathu, Jayachandran N.

2013-01-01

Molecular dynamics (MD) simulations results are herein incorporated into an electrostatic model used to determine the structure of an effective polymer-based antidote to the anticoagulant fondaparinux. In silico data for the polymer or its cationic binding groups has not, up to now, been available, and experimental data on the structure of the polymer-fondaparinux complex is extremely limited. Consequently, the task of optimizing the polymer structure is a daunting challenge. MD simulations provided a means to gain microscopic information on the interactions of the binding groups and fondaparinux that would have otherwise been inaccessible. This was used to refine the electrostatic model and improve the quantitative model predictions of binding affinity. Once refined, the model provided guidelines to improve electrostatic forces between candidate polymers and fondaparinux in order to increase association rate constants. PMID:27006916

Statistical modelling coupled with LC-MS analysis to predict human upper intestinal absorption of phytochemical mixtures.

PubMed

Selby-Pham, Sophie N B; Howell, Kate S; Dunshea, Frank R; Ludbey, Joel; Lutz, Adrian; Bennett, Louise

2018-04-15

A diet rich in phytochemicals confers benefits for health by reducing the risk of chronic diseases via regulation of oxidative stress and inflammation (OSI). For optimal protective bio-efficacy, the time required for phytochemicals and their metabolites to reach maximal plasma concentrations (T max ) should be synchronised with the time of increased OSI. A statistical model has been reported to predict T max of individual phytochemicals based on molecular mass and lipophilicity. We report the application of the model for predicting the absorption profile of an uncharacterised phytochemical mixture, herein referred to as the 'functional fingerprint'. First, chemical profiles of phytochemical extracts were acquired using liquid chromatography mass spectrometry (LC-MS), then the molecular features for respective components were used to predict their plasma absorption maximum, based on molecular mass and lipophilicity. This method of 'functional fingerprinting' of plant extracts represents a novel tool for understanding and optimising the health efficacy of plant extracts. Copyright © 2017 Elsevier Ltd. All rights reserved.

Optimization of Manganese Reduction in Biotreated POME onto 3A Molecular Sieve and Clinoptilolite Zeolites.

PubMed

Jami, Mohammed S; Rosli, Nurul-Shafiqah; Amosa, Mutiu K

2016-06-01

Availability of quality-certified water is pertinent to the production of food and pharmaceutical products. Adverse effects of manganese content of water on the corrosion of vessels and reactors necessitate that process water is scrutinized for allowable concentration levels before being applied in the production processes. In this research, optimization of the adsorption process conditions germane to the removal of manganese from biotreated palm oil mill effluent (BPOME) using zeolite 3A subsequent to a comparative adsorption with clinoptilolite was studied. A face-centered central composite design (FCCCD) of the response surface methodology (RSM) was adopted for the study. Analysis of variance (ANOVA) for response surface quadratic model revealed that the model was significant with dosage and agitation speed connoting the main significant process factors for the optimization. R(2) of 0.9478 yielded by the model was in agreement with predicted R(2). Langmuir and pseudo-second-order suggest the adsorption mechanism involved monolayer adsorption and cation exchanging.

Optimized Mouse Models for Liver Fibrosis.

PubMed

Kim, Yong Ook; Popov, Yury; Schuppan, Detlef

2017-01-01

Fibrosis is the excessive accumulation of extracellular matrix components due to chronic injury, with collagens as predominant structural components. Liver fibrosis can progress to cirrhosis, which is characterized by a severe distortion of the delicate hepatic vascular architecture, the shunting of the blood supply away from hepatocytes and the resultant functional liver failure. Cirrhosis is associated with a highly increased morbidity and mortality and represents the major hard endpoint in clinical studies of chronic liver diseases. Moreover, cirrhosis is a strong cofactor of primary liver cancer. In vivo models are indispensable tools to study the cellular and molecular mechanisms of liver fibrosis and to develop specific antifibrotic therapies towards clinical translation. Here, we provide a detailed description of select optimized mouse models of liver fibrosis and state-of-the-art fibrosis readouts.

Optimization and characterization of spray-dried IgG formulations: a design of experiment approach.

PubMed

Faghihi, Homa; Najafabadi, Abdolhosein Rouholamini; Vatanara, Alireza

2017-10-24

The purpose of the present study is to optimize a spray-dried formulation as a model antibody regarding stability and aerodynamic property for further aerosol therapy of this group of macromolecules. A three-factor, three-level, Box-Behnken design was employed milligrams of Cysteine (X 1 ), Trehalose (X 2 ), and Tween 20 (X 3 ) as independent variables. The dependent variables were quantified and the optimized formulation was prepared accordingly. SEC-HPLC and FTIR-spectroscopy were conducted to evaluate the molecular and structural status of spray-dried preparations. Particle characterization of optimized sample was performed with the aid of DSC, SEM, and TSI examinations. Experimental responses of a total of 17 formulations resulted in yield values, (Y 1 ), ranging from 21.1 ± 0.2 to 40.2 ± 0.1 (%); beta-sheet content, (Y 2 ), from 66.22 ± 0.19 to 73.78 ± 0.26 (%); amount of aggregation following process, (Y 3 ), ranging from 0.11 ± 0.03 to 0.95 ± 0.03 (%); and amount of aggregation upon storage, (Y 4 ), from 0.81 ± 0.01 to 3.13 ± 0.64 (%) as dependent variables. Results-except for those of the beta sheet content-were fitted to quadratic models describing the inherent relationship between main factors. Co-application of Cysteine and Tween 20 preserved antibody molecules from molecular degradation and improved immediate and accelerated stability of spry-dried antibodies. Validation of the optimization study indicated high degree of prognostic ability of response surface methodology in preparation of stable spray-dried IgG. Graphical abstract Spray drying of IgG in the presence of Trehalose, Cysteine and Tween 20.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins.

PubMed

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-05

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

QM/MM Geometry Optimization on Extensive Free-Energy Surfaces for Examination of Enzymatic Reactions and Design of Novel Functional Properties of Proteins

NASA Astrophysics Data System (ADS)

Hayashi, Shigehiko; Uchida, Yoshihiro; Hasegawa, Taisuke; Higashi, Masahiro; Kosugi, Takahiro; Kamiya, Motoshi

2017-05-01

Many remarkable molecular functions of proteins use their characteristic global and slow conformational dynamics through coupling of local chemical states in reaction centers with global conformational changes of proteins. To theoretically examine the functional processes of proteins in atomic detail, a methodology of quantum mechanical/molecular mechanical (QM/MM) free-energy geometry optimization is introduced. In the methodology, a geometry optimization of a local reaction center is performed with a quantum mechanical calculation on a free-energy surface constructed with conformational samples of the surrounding protein environment obtained by a molecular dynamics simulation with a molecular mechanics force field. Geometry optimizations on extensive free-energy surfaces by a QM/MM reweighting free-energy self-consistent field method designed to be variationally consistent and computationally efficient have enabled examinations of the multiscale molecular coupling of local chemical states with global protein conformational changes in functional processes and analysis and design of protein mutants with novel functional properties.

Molecular replacement: tricks and treats

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

Abergel, Chantal, E-mail: chantal.abergel@igs.cnrs-mrs.fr

2013-11-01

To be successful, molecular replacement relies on the quality of the model and of the crystallographic data. Some tricks that could be applied to the models or to the crystal to increase the success rate of MR are discussed here. Molecular replacement is the method of choice for X-ray crystallographic structure determination provided that suitable structural homologues are available in the PDB. Presently, there are ∼80 000 structures in the PDB (8074 were deposited in the year 2012 alone), of which ∼70% have been solved by molecular replacement. For successful molecular replacement the model must cover at least 50% ofmore » the total structure and the C{sub α} r.m.s.d. between the core model and the structure to be solved must be less than 2 Å. Here, an approach originally implemented in the CaspR server (http://www.igs.cnrs-mrs.fr/Caspr2/index.cgi) based on homology modelling to search for a molecular-replacement solution is discussed. How the use of as much information as possible from different sources can improve the model(s) is briefly described. The combination of structural information with distantly related sequences is crucial to optimize the multiple alignment that will define the boundaries of the core domains. PDB clusters (sequences with ≥30% identical residues) can also provide information on the eventual changes in conformation and will help to explore the relative orientations assumed by protein subdomains. Normal-mode analysis can also help in generating series of conformational models in the search for a molecular-replacement solution. Of course, finding a correct solution is only the first step and the accuracy of the identified solution is as important as the data quality to proceed through refinement. Here, some possible reasons for failure are discussed and solutions are proposed using a set of successful examples.« less

Thermodynamic properties for applications in chemical industry via classical force fields.

PubMed

Guevara-Carrion, Gabriela; Hasse, Hans; Vrabec, Jadran

2012-01-01

Thermodynamic properties of fluids are of key importance for the chemical industry. Presently, the fluid property models used in process design and optimization are mostly equations of state or G (E) models, which are parameterized using experimental data. Molecular modeling and simulation based on classical force fields is a promising alternative route, which in many cases reasonably complements the well established methods. This chapter gives an introduction to the state-of-the-art in this field regarding molecular models, simulation methods, and tools. Attention is given to the way modeling and simulation on the scale of molecular force fields interact with other scales, which is mainly by parameter inheritance. Parameters for molecular force fields are determined both bottom-up from quantum chemistry and top-down from experimental data. Commonly used functional forms for describing the intra- and intermolecular interactions are presented. Several approaches for ab initio to empirical force field parameterization are discussed. Some transferable force field families, which are frequently used in chemical engineering applications, are described. Furthermore, some examples of force fields that were parameterized for specific molecules are given. Molecular dynamics and Monte Carlo methods for the calculation of transport properties and vapor-liquid equilibria are introduced. Two case studies are presented. First, using liquid ammonia as an example, the capabilities of semi-empirical force fields, parameterized on the basis of quantum chemical information and experimental data, are discussed with respect to thermodynamic properties that are relevant for the chemical industry. Second, the ability of molecular simulation methods to describe accurately vapor-liquid equilibrium properties of binary mixtures containing CO(2) is shown.

Evaluation of protein-protein docking model structures using all-atom molecular dynamics simulations combined with the solution theory in the energy representation

NASA Astrophysics Data System (ADS)

Takemura, Kazuhiro; Guo, Hao; Sakuraba, Shun; Matubayasi, Nobuyuki; Kitao, Akio

2012-12-01

We propose a method to evaluate binding free energy differences among distinct protein-protein complex model structures through all-atom molecular dynamics simulations in explicit water using the solution theory in the energy representation. Complex model structures are generated from a pair of monomeric structures using the rigid-body docking program ZDOCK. After structure refinement by side chain optimization and all-atom molecular dynamics simulations in explicit water, complex models are evaluated based on the sum of their conformational and solvation free energies, the latter calculated from the energy distribution functions obtained from relatively short molecular dynamics simulations of the complex in water and of pure water based on the solution theory in the energy representation. We examined protein-protein complex model structures of two protein-protein complex systems, bovine trypsin/CMTI-1 squash inhibitor (PDB ID: 1PPE) and RNase SA/barstar (PDB ID: 1AY7), for which both complex and monomer structures were determined experimentally. For each system, we calculated the energies for the crystal complex structure and twelve generated model structures including the model most similar to the crystal structure and very different from it. In both systems, the sum of the conformational and solvation free energies tended to be lower for the structure similar to the crystal. We concluded that our energy calculation method is useful for selecting low energy complex models similar to the crystal structure from among a set of generated models.

Evaluation of protein-protein docking model structures using all-atom molecular dynamics simulations combined with the solution theory in the energy representation.

PubMed

Takemura, Kazuhiro; Guo, Hao; Sakuraba, Shun; Matubayasi, Nobuyuki; Kitao, Akio

2012-12-07

We propose a method to evaluate binding free energy differences among distinct protein-protein complex model structures through all-atom molecular dynamics simulations in explicit water using the solution theory in the energy representation. Complex model structures are generated from a pair of monomeric structures using the rigid-body docking program ZDOCK. After structure refinement by side chain optimization and all-atom molecular dynamics simulations in explicit water, complex models are evaluated based on the sum of their conformational and solvation free energies, the latter calculated from the energy distribution functions obtained from relatively short molecular dynamics simulations of the complex in water and of pure water based on the solution theory in the energy representation. We examined protein-protein complex model structures of two protein-protein complex systems, bovine trypsin/CMTI-1 squash inhibitor (PDB ID: 1PPE) and RNase SA/barstar (PDB ID: 1AY7), for which both complex and monomer structures were determined experimentally. For each system, we calculated the energies for the crystal complex structure and twelve generated model structures including the model most similar to the crystal structure and very different from it. In both systems, the sum of the conformational and solvation free energies tended to be lower for the structure similar to the crystal. We concluded that our energy calculation method is useful for selecting low energy complex models similar to the crystal structure from among a set of generated models.

Harnessing Integrative Omics to Facilitate Molecular Imaging of the Human Epidermal Growth Factor Receptor Family for Precision Medicine.

PubMed

Pool, Martin; de Boer, H Rudolf; Hooge, Marjolijn N Lub-de; van Vugt, Marcel A T M; de Vries, Elisabeth G E

2017-01-01

Cancer is a growing problem worldwide. The cause of death in cancer patients is often due to treatment-resistant metastatic disease. Many molecularly targeted anticancer drugs have been developed against 'oncogenic driver' pathways. However, these treatments are usually only effective in properly selected patients. Resistance to molecularly targeted drugs through selective pressure on acquired mutations or molecular rewiring can hinder their effectiveness. This review summarizes how molecular imaging techniques can potentially facilitate the optimal implementation of targeted agents. Using the human epidermal growth factor receptor (HER) family as a model in (pre)clinical studies, we illustrate how molecular imaging may be employed to characterize whole body target expression as well as monitor drug effectiveness and the emergence of tumor resistance. We further discuss how an integrative omics discovery platform could guide the selection of 'effect sensors' - new molecular imaging targets - which are dynamic markers that indicate treatment effectiveness or resistance.

Applications of SMART: A DRDC Atmospheric Radiative Transfer Library Optimized for Wide Band Computations

DTIC Science & Technology

2011-06-28

DRDC accurate refracted path calculation – 2-stream (flux) and DISORT (N-stream) MS calculations – Lambert and sea surface (DRDC analytical model ) BRDF ...display a currently valid OMB control number. 1. REPORT DATE 28 JUN 2011 2. REPORT TYPE 3. DATES COVERED 00-00-2011 to 00-00-2011 4. TITLE AND...radiance – MODTRAN molecular extinctions (CK) • Seamless integration of MOD4v3r1 – MODTRAN and DRDC aerosol models – Falling snow model (DRDC

Molecular design and MD simulations of epitaxial superlattice of self-assembling ternary lipid bilayers

NASA Astrophysics Data System (ADS)

Chou, George; Vaughn, Mark; Cheng, K.

2011-10-01

Multicomponent lipid bilayers represent an important model system for studying cell membranes. At present, an ordered multicomponent phospholipid/cholesterol bilayer system involving charged lipid is still not available. Using a lipid superlattice (SL) model, a 13 x 15 x 15 nm^3 ternary phosphatidylcholine/phosphatidylserine/cholesterol bilayer system in water with simultaneous headgroup SL and acyl chain SL at different depths, or epitaxial SL, of the bilayer has been designed with atomistic detail. The arrangements of this epitaxial SL system were optimized by only two molecular parameters, lattice space and rotational angle of the lipids. Using atomistic MD simulations, we demonstrated the stability of the ordered structures for more than 100 ns. A positional restrained system was also used as a control. This system will provide new insights into understanding the nanodomain structures of cell membranes at the molecular level.

Simplified molecular input line entry system-based: QSAR modelling for MAP kinase-interacting protein kinase (MNK1).

PubMed

Begum, S; Achary, P Ganga Raju

2015-01-01

Quantitative structure-activity relationship (QSAR) models were built for the prediction of inhibition (pIC50, i.e. negative logarithm of the 50% effective concentration) of MAP kinase-interacting protein kinase (MNK1) by 43 potent inhibitors. The pIC50 values were modelled with five random splits, with the representations of the molecular structures by simplified molecular input line entry system (SMILES). QSAR model building was performed by the Monte Carlo optimisation using three methods: classic scheme; balance of correlations; and balance correlation with ideal slopes. The robustness of these models were checked by parameters as rm(2), r(*)m(2), [Formula: see text] and randomisation technique. The best QSAR model based on single optimal descriptors was applied to study in vitro structure-activity relationships of 6-(4-(2-(piperidin-1-yl) ethoxy) phenyl)-3-(pyridin-4-yl) pyrazolo [1,5-a] pyrimidine derivatives as a screening tool for the development of novel potent MNK1 inhibitors. The effects of alkyl group, -OH, -NO2, F, Cl, Br, I, etc. on the IC50 values towards the inhibition of MNK1 were also reported.

Discrete Biogeography Based Optimization for Feature Selection in Molecular Signatures.

PubMed

Liu, Bo; Tian, Meihong; Zhang, Chunhua; Li, Xiangtao

2015-04-01

Biomarker discovery from high-dimensional data is a complex task in the development of efficient cancer diagnoses and classification. However, these data are usually redundant and noisy, and only a subset of them present distinct profiles for different classes of samples. Thus, selecting high discriminative genes from gene expression data has become increasingly interesting in the field of bioinformatics. In this paper, a discrete biogeography based optimization is proposed to select the good subset of informative gene relevant to the classification. In the proposed algorithm, firstly, the fisher-markov selector is used to choose fixed number of gene data. Secondly, to make biogeography based optimization suitable for the feature selection problem; discrete migration model and discrete mutation model are proposed to balance the exploration and exploitation ability. Then, discrete biogeography based optimization, as we called DBBO, is proposed by integrating discrete migration model and discrete mutation model. Finally, the DBBO method is used for feature selection, and three classifiers are used as the classifier with the 10 fold cross-validation method. In order to show the effective and efficiency of the algorithm, the proposed algorithm is tested on four breast cancer dataset benchmarks. Comparison with genetic algorithm, particle swarm optimization, differential evolution algorithm and hybrid biogeography based optimization, experimental results demonstrate that the proposed method is better or at least comparable with previous method from literature when considering the quality of the solutions obtained. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Mathematical and Computational Modeling in Complex Biological Systems

PubMed Central

Li, Wenyang; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology. PMID:28386558

Mathematical and Computational Modeling in Complex Biological Systems.

PubMed

Ji, Zhiwei; Yan, Ke; Li, Wenyang; Hu, Haigen; Zhu, Xiaoliang

2017-01-01

The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.

Modeling of lipase catalyzed ring-opening polymerization of epsilon-caprolactone.

PubMed

Sivalingam, G; Madras, Giridhar

2004-01-01

Enzymatic ring-opening polymerization of epsilon-caprolactone by various lipases was investigated in toluene at various temperatures. The determination of molecular weight and structural identification was carried out with gel permeation chromatography and proton NMR, respectively. Among the various lipases employed, an immobilized lipase from Candida antartica B (Novozym 435) showed the highest catalytic activity. The polymerization of epsilon-caprolactone by Novozym 435 showed an optimal temperature of 65 degrees C and an optimum toluene content of 50/50 v/v of toluene and epsilon-caprolactone. As lipases can degrade polyesters, a maximum in the molecular weight with time was obtained due to the competition of ring opening polymerization and degradation by specific chain end scission. The optimum temperature, toluene content, and the variation of molecular weight with time are consistent with earlier observations. A comprehensive model based on continuous distribution kinetics was developed to model these phenomena. The model accounts for simultaneous polymerization, degradation and enzyme deactivation and provides a technique to determine the rate coefficients for these processes. The dependence of these rate coefficients with temperature and monomer concentration is also discussed.

Separated-pair independent particle model and the generalized Brillouin theorem: ab initio calculations on the dissociation of polyatomic molecules

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

Sundberg, Kenneth Randall

1976-01-01

A method is developed to optimize the separated-pair independent particle (SPIP) wave function; it is a special case of the separated-pair theory obtained by using two-term natural expansions of the geminals. The orbitals are optimized by a theory based on the generalized Brillouin theorem and iterative configuration interaction (CI) calculations in the space of the SPIP function and its single excitations. The geminal expansion coefficients are optimized by serial 2 x 2 CI calculations. Formulas are derived for the matrix elements. An algorithm to implement the method is presented, and the work needed to evaluate the molecular integrals is discussed.

The electronegativity equalization method and the split charge equilibration applied to organic systems: parametrization, validation, and comparison.

PubMed

Verstraelen, Toon; Van Speybroeck, Veronique; Waroquier, Michel

2009-07-28

An extensive benchmark of the electronegativity equalization method (EEM) and the split charge equilibration (SQE) model on a very diverse set of organic molecules is presented. These models efficiently compute atomic partial charges and are used in the development of polarizable force fields. The predicted partial charges that depend on empirical parameters are calibrated to reproduce results from quantum mechanical calculations. Recently, SQE is presented as an extension of the EEM to obtain the correct size dependence of the molecular polarizability. In this work, 12 parametrization protocols are applied to each model and the optimal parameters are benchmarked systematically. The training data for the empirical parameters comprise of MP2/Aug-CC-pVDZ calculations on 500 organic molecules containing the elements H, C, N, O, F, S, Cl, and Br. These molecules have been selected by an ingenious and autonomous protocol from an initial set of almost 500,000 small organic molecules. It is clear that the SQE model outperforms the EEM in all benchmark assessments. When using Hirshfeld-I charges for the calibration, the SQE model optimally reproduces the molecular electrostatic potential from the ab initio calculations. Applications on chain molecules, i.e., alkanes, alkenes, and alpha alanine helices, confirm that the EEM gives rise to a divergent behavior for the polarizability, while the SQE model shows the correct trends. We conclude that the SQE model is an essential component of a polarizable force field, showing several advantages over the original EEM.

Universal optimal working cycles of molecular motors.

PubMed

Efremov, Artem; Wang, Zhisong

2011-04-07

Molecular motors capable of directional track-walking or rotation are abundant in living cells, and inspire the emerging field of artificial nanomotors. Some biomotors can convert 90% of free energy from chemical fuels into usable mechanical work, and the same motors still maintain a speed sufficient for cellular functions. This study exposed a new regime of universal optimization that amounts to a thermodynamically best working regime for molecular motors but is unfamiliar in macroscopic engines. For the ideal case of zero energy dissipation, the universally optimized working cycle for molecular motors is infinitely slow like Carnot cycle for heat engines. But when a small amount of energy dissipation reduces energy efficiency linearly from 100%, the speed is recovered exponentially due to Boltzmann's law. Experimental data on a major biomotor (kinesin) suggest that the regime of universal optimization has been largely approached in living cells, underpinning the extreme efficiency-speed trade-off in biomotors. The universal optimization and its practical approachability are unique thermodynamic advantages of molecular systems over macroscopic engines in facilitating motor functions. The findings have important implications for the natural evolution of biomotors as well as the development of artificial counterparts.

Concentrating phenolic acids from Lonicera japonica by nanofiltration technology

NASA Astrophysics Data System (ADS)

Li, Cunyu; Ma, Yun; Li, Hongyang; Peng, Guoping

2017-03-01

Response surface analysis methodology was used to optimize the concentrate process of phenolic acids from Lonicera japonica by nanofiltration technique. On the basis of the influences of pressure, temperature and circulating volume, the retention rate of neochlorogenic acid, chlorogenic acid and 4-dicaffeoylquinic acid were selected as index, molecular weight cut-off of nanofiltration membrane, concentration and pH were selected as influencing factors during concentrate process. The experiment mathematical model was arranged according to Box-Behnken central composite experiment design. The optimal concentrate conditions were as following: nanofiltration molecular weight cut-off, 150 Da; solutes concentration, 18.34 µg/mL; pH, 4.26. The predicted value of retention rate was 97.99% under the optimum conditions, and the experimental value was 98.03±0.24%, which was in accordance with the predicted value. These results demonstrate that the combination of Box-Behnken design and response surface analysis can well optimize the concentrate process of Lonicera japonica water-extraction by nanofiltration, and the results provide the basis for nanofiltration concentrate for heat-sensitive traditional Chinese medicine.

Towards computational materials design from first principles using alchemical changes and derivatives.

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

von Lilienfeld-Toal, Otto Anatole

2010-11-01

The design of new materials with specific physical, chemical, or biological properties is a central goal of much research in materials and medicinal sciences. Except for the simplest and most restricted cases brute-force computational screening of all possible compounds for interesting properties is beyond any current capacity due to the combinatorial nature of chemical compound space (set of stoichiometries and configurations). Consequently, when it comes to computationally optimizing more complex systems, reliable optimization algorithms must not only trade-off sufficient accuracy and computational speed of the models involved, they must also aim for rapid convergence in terms of number of compoundsmore » 'visited'. I will give an overview on recent progress on alchemical first principles paths and gradients in compound space that appear to be promising ingredients for more efficient property optimizations. Specifically, based on molecular grand canonical density functional theory an approach will be presented for the construction of high-dimensional yet analytical property gradients in chemical compound space. Thereafter, applications to molecular HOMO eigenvalues, catalyst design, and other problems and systems shall be discussed.« less

Sequential Injection Analysis for Optimization of Molecular Biology Reactions

PubMed Central

Allen, Peter B.; Ellington, Andrew D.

2011-01-01

In order to automate the optimization of complex biochemical and molecular biology reactions, we developed a Sequential Injection Analysis (SIA) device and combined this with a Design of Experiment (DOE) algorithm. This combination of hardware and software automatically explores the parameter space of the reaction and provides continuous feedback for optimizing reaction conditions. As an example, we optimized the endonuclease digest of a fluorogenic substrate, and showed that the optimized reaction conditions also applied to the digest of the substrate outside of the device, and to the digest of a plasmid. The sequential technique quickly arrived at optimized reaction conditions with less reagent use than a batch process (such as a fluid handling robot exploring multiple reaction conditions in parallel) would have. The device and method should now be amenable to much more complex molecular biology reactions whose variable spaces are correspondingly larger. PMID:21338059

Mutagenicity, anticancer activity and blood brain barrier: similarity and dissimilarity of molecular alerts.

PubMed

Toropov, Andrey A; Toropova, Alla P; Benfenati, Emilio; Salmona, Mario

2018-06-01

The aim of the present work is an attempt to define computable measure of similarity between different endpoints. The similarity of structural alerts of different biochemical endpoints can be used to solve tasks of medicinal chemistry. Optimal descriptors are a tool to build up models for different endpoints. The optimal descriptor is calculated with simplified molecular input-line entry system (SMILES). A group of elements (single symbol or pair of symbols) can represent any SMILES. Each element of SMILES can be represented by so-called correlation weight i.e. coefficient that should be used to calculate descriptor. Numerical data on the correlation weights are calculated by the Monte Carlo method, i.e. by optimization procedure, which gives maximal correlation coefficient between the optimal descriptor and endpoint for the training set. Statistically stable correlation weights observed in several runs of the optimization can be examined as structural alerts, which are promoters of the increase or the decrease of a biochemical activity of a substance. Having data on several runs of the optimization correlation weights, one can extract list of promoters of increase and list of promoters of decrease for an endpoint. The study of similarity and dissimilarity of the above lists has been carried out for the following pairs of endpoints: (i) mutagenicity and anticancer activity; (ii) mutagenicity and blood brain barrier; and (iii) blood brain barrier and anticancer activity. The computational experiment confirms that similarity and dissimilarity for pairs of endpoints can be measured.

Protein folding optimization based on 3D off-lattice model via an improved artificial bee colony algorithm.

PubMed

Li, Bai; Lin, Mu; Liu, Qiao; Li, Ya; Zhou, Changjun

2015-10-01

Protein folding is a fundamental topic in molecular biology. Conventional experimental techniques for protein structure identification or protein folding recognition require strict laboratory requirements and heavy operating burdens, which have largely limited their applications. Alternatively, computer-aided techniques have been developed to optimize protein structures or to predict the protein folding process. In this paper, we utilize a 3D off-lattice model to describe the original protein folding scheme as a simplified energy-optimal numerical problem, where all types of amino acid residues are binarized into hydrophobic and hydrophilic ones. We apply a balance-evolution artificial bee colony (BE-ABC) algorithm as the minimization solver, which is featured by the adaptive adjustment of search intensity to cater for the varying needs during the entire optimization process. In this work, we establish a benchmark case set with 13 real protein sequences from the Protein Data Bank database and evaluate the convergence performance of BE-ABC algorithm through strict comparisons with several state-of-the-art ABC variants in short-term numerical experiments. Besides that, our obtained best-so-far protein structures are compared to the ones in comprehensive previous literature. This study also provides preliminary insights into how artificial intelligence techniques can be applied to reveal the dynamics of protein folding. Graphical Abstract Protein folding optimization using 3D off-lattice model and advanced optimization techniques.

Metabolic Network Modeling of Microbial Interactions in Natural and Engineered Environmental Systems

PubMed Central

Perez-Garcia, Octavio; Lear, Gavin; Singhal, Naresh

2016-01-01

We review approaches to characterize metabolic interactions within microbial communities using Stoichiometric Metabolic Network (SMN) models for applications in environmental and industrial biotechnology. SMN models are computational tools used to evaluate the metabolic engineering potential of various organisms. They have successfully been applied to design and optimize the microbial production of antibiotics, alcohols and amino acids by single strains. To date however, such models have been rarely applied to analyze and control the metabolism of more complex microbial communities. This is largely attributed to the diversity of microbial community functions, metabolisms, and interactions. Here, we firstly review different types of microbial interaction and describe their relevance for natural and engineered environmental processes. Next, we provide a general description of the essential methods of the SMN modeling workflow including the steps of network reconstruction, simulation through Flux Balance Analysis (FBA), experimental data gathering, and model calibration. Then we broadly describe and compare four approaches to model microbial interactions using metabolic networks, i.e., (i) lumped networks, (ii) compartment per guild networks, (iii) bi-level optimization simulations, and (iv) dynamic-SMN methods. These approaches can be used to integrate and analyze diverse microbial physiology, ecology and molecular community data. All of them (except the lumped approach) are suitable for incorporating species abundance data but so far they have been used only to model simple communities of two to eight different species. Interactions based on substrate exchange and competition can be directly modeled using the above approaches. However, interactions based on metabolic feedbacks, such as product inhibition and synthropy require extensions to current models, incorporating gene regulation and compounding accumulation mechanisms. SMN models of microbial interactions can be used to analyze complex “omics” data and to infer and optimize metabolic processes. Thereby, SMN models are suitable to capitalize on advances in high-throughput molecular and metabolic data generation. SMN models are starting to be applied to describe microbial interactions during wastewater treatment, in-situ bioremediation, microalgae blooms methanogenic fermentation, and bioplastic production. Despite their current challenges, we envisage that SMN models have future potential for the design and development of novel growth media, biochemical pathways and synthetic microbial associations. PMID:27242701

Optimization of the GBMV2 implicit solvent force field for accurate simulation of protein conformational equilibria.

PubMed

Lee, Kuo Hao; Chen, Jianhan

2017-06-15

Accurate treatment of solvent environment is critical for reliable simulations of protein conformational equilibria. Implicit treatment of solvation, such as using the generalized Born (GB) class of models arguably provides an optimal balance between computational efficiency and physical accuracy. Yet, GB models are frequently plagued by a tendency to generate overly compact structures. The physical origins of this drawback are relatively well understood, and the key to a balanced implicit solvent protein force field is careful optimization of physical parameters to achieve a sufficient level of cancellation of errors. The latter has been hampered by the difficulty of generating converged conformational ensembles of non-trivial model proteins using the popular replica exchange sampling technique. Here, we leverage improved sampling efficiency of a newly developed multi-scale enhanced sampling technique to re-optimize the generalized-Born with molecular volume (GBMV2) implicit solvent model with the CHARMM36 protein force field. Recursive optimization of key GBMV2 parameters (such as input radii) and protein torsion profiles (via the CMAP torsion cross terms) has led to a more balanced GBMV2 protein force field that recapitulates the structures and stabilities of both helical and β-hairpin model peptides. Importantly, this force field appears to be free of the over-compaction bias, and can generate structural ensembles of several intrinsically disordered proteins of various lengths that seem highly consistent with available experimental data. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

MIRATE: MIps RATional dEsign Science Gateway.

PubMed

Busato, Mirko; Distefano, Rosario; Bates, Ferdia; Karim, Kal; Bossi, Alessandra Maria; López Vilariño, José Manuel; Piletsky, Sergey; Bombieri, Nicola; Giorgetti, Alejandro

2018-06-13

Molecularly imprinted polymers (MIPs) are high affinity robust synthetic receptors, which can be optimally synthesized and manufactured more economically than their biological equivalents (i.e. antibody). In MIPs production, rational design based on molecular modeling is a commonly employed technique. This mostly aids in (i) virtual screening of functional monomers (FMs), (ii) optimization of monomer-template ratio, and (iii) selectivity analysis. We present MIRATE, an integrated science gateway for the intelligent design of MIPs. By combining and adapting multiple state-of-the-art bioinformatics tools into automated and innovative pipelines, MIRATE guides the user through the entire process of MIPs' design. The platform allows the user to fully customize each stage involved in the MIPs' design, with the main goal to support the synthesis in the wet-laboratory. MIRATE is freely accessible with no login requirement at http://mirate.di.univr.it/. All major browsers are supported.

Optimization of applied voltages for on-chip concentration of DNA using nanoslit

NASA Astrophysics Data System (ADS)

Azuma, Naoki; Itoh, Shintaro; Fukuzawa, Kenji; Zhang, Hedong

2017-12-01

On-chip sample concentration is an effective pretreatment to improve the detection sensitivity of lab-on-a-chip devices for biochemical analysis. In a previous study, we successfully achieved DNA sample concentration using a nanoslit fabricated in the microchannel of a device designed for DNA size separation. The nanoslit was a channel with a depth smaller than the diameter of a random coil-shaped DNA molecule. The concentration was achieved using the entropy trap at the boundary between the microchannel and the nanoslit. DNA molecules migrating toward the nanoslit owing to electrophoresis were trapped in front of the nanoslit and the concentration was enhanced over time. In this study, we successfully maximize the molecular concentration by optimizing the applied voltage for electrophoresis and verifying the effect of temperature. In addition, we propose a model formula that predicts the molecular concentration, the validity of which is confirmed through comparison with experimental results.

Commentary: Why Pharmaceutical Scientists in Early Drug Discovery Are Critical for Influencing the Design and Selection of Optimal Drug Candidates.

PubMed

Landis, Margaret S; Bhattachar, Shobha; Yazdanian, Mehran; Morrison, John

2018-01-01

This commentary reflects the collective view of pharmaceutical scientists from four different organizations with extensive experience in the field of drug discovery support. Herein, engaging discussion is presented on the current and future approaches for the selection of the most optimal and developable drug candidates. Over the past two decades, developability assessment programs have been implemented with the intention of improving physicochemical and metabolic properties. However, the complexity of both new drug targets and non-traditional drug candidates provides continuing challenges for developing formulations for optimal drug delivery. The need for more enabled technologies to deliver drug candidates has necessitated an even more active role for pharmaceutical scientists to influence many key molecular parameters during compound optimization and selection. This enhanced role begins at the early in vitro screening stages, where key learnings regarding the interplay of molecular structure and pharmaceutical property relationships can be derived. Performance of the drug candidates in formulations intended to support key in vivo studies provides important information on chemotype-formulation compatibility relationships. Structure modifications to support the selection of the solid form are also important to consider, and predictive in silico models are being rapidly developed in this area. Ultimately, the role of pharmaceutical scientists in drug discovery now extends beyond rapid solubility screening, early form assessment, and data delivery. This multidisciplinary role has evolved to include the practice of proactively taking part in the molecular design to better align solid form and formulation requirements to enhance developability potential.

Design, synthesis, molecular modeling and biological evaluation of novel 2,3-dihydrophthalazine-1,4-dione derivatives as potential anticonvulsant agents

NASA Astrophysics Data System (ADS)

El-Helby, Abdel Ghany A.; Ayyad, Rezk R.; Sakr, Helmy M.; Abdelrahim, Adel S.; El-Adl, K.; Sherbiny, Farag S.; Eissa, Ibrahim H.; Khalifa, Mohamed M.

2017-02-01

In view of their expected anticonvulsant activity, some novel derivatives of 2,3-dihydrophthalazine-1,4-dione 4-22 were designed, synthesized and evaluated using pentylenetetrazole (PTZ) and picrotoxin as convulsion-inducing models. Moreover, the most active compounds were tested against electrical induced convulsion using maximal electroshock (MES) models of seizures. Most of the tested compounds showed considerable anticonvulsant activity in at least one of the anticonvulsant tests. Compounds 13 and 14g were proved to be the most potent compounds of this series with relatively low toxicity in the median lethal dose test when compared with the reference drug. Molecular modeling studies were done to verify the biological activity. The obtained results showed that the most potent compounds could be useful as a template for future design, optimization, and investigation to produce more active analogues.

Computational modeling and in-vitro/in-silico correlation of phospholipid-based prodrugs for targeted drug delivery in inflammatory bowel disease

NASA Astrophysics Data System (ADS)

Dahan, Arik; Markovic, Milica; Keinan, Shahar; Kurnikov, Igor; Aponick, Aaron; Zimmermann, Ellen M.; Ben-Shabat, Shimon

2017-11-01

Targeting drugs to the inflamed intestinal tissue(s) represents a major advancement in the treatment of inflammatory bowel disease (IBD). In this work we present a powerful in-silico modeling approach to guide the molecular design of novel prodrugs targeting the enzyme PLA2, which is overexpressed in the inflamed tissues of IBD patients. The prodrug consists of the drug moiety bound to the sn-2 position of phospholipid (PL) through a carbonic linker, aiming to allow PLA2 to release the free drug. The linker length dictates the affinity of the PL-drug conjugate to PLA2, and the optimal linker will enable maximal PLA2-mediated activation. Thermodynamic integration and Weighted Histogram Analysis Method (WHAM)/Umbrella Sampling method were used to compute the changes in PLA2 transition state binding free energy of the prodrug molecule (ΔΔGtr) associated with decreasing/increasing linker length. The simulations revealed that 6-carbons linker is the optimal one, whereas shorter or longer linkers resulted in decreased PLA2-mediated activation. These in-silico results were shown to be in excellent correlation with experimental in-vitro data. Overall, this modern computational approach enables optimization of the molecular design of novel prodrugs, which may allow targeting the free drug specifically to the diseased intestinal tissue of IBD patients.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated Training of ReaxFF Reactive Force Fields for Energetics of Enzymatic Reactions.

PubMed

Trnka, Tomáš; Tvaroška, Igor; Koča, Jaroslav

2018-01-09

Computational studies of the reaction mechanisms of various enzymes are nowadays based almost exclusively on hybrid QM/MM models. Unfortunately, the success of this approach strongly depends on the selection of the QM region, and computational cost is a crucial limiting factor. An interesting alternative is offered by empirical reactive molecular force fields, especially the ReaxFF potential developed by van Duin and co-workers. However, even though an initial parametrization of ReaxFF for biomolecules already exists, it does not provide the desired level of accuracy. We have conducted a thorough refitting of the ReaxFF force field to improve the description of reaction energetics. To minimize the human effort required, we propose a fully automated approach to generate an extensive training set comprised of thousands of different geometries and molecular fragments starting from a few model molecules. Electrostatic parameters were optimized with QM electrostatic potentials as the main target quantity, avoiding excessive dependence on the choice of reference atomic charges and improving robustness and transferability. The remaining force field parameters were optimized using the VD-CMA-ES variant of the CMA-ES optimization algorithm. This method is able to optimize hundreds of parameters simultaneously with unprecedented speed and reliability. The resulting force field was validated on a real enzymatic system, ppGalNAcT2 glycosyltransferase. The new force field offers excellent qualitative agreement with the reference QM/MM reaction energy profile, matches the relative energies of intermediate and product minima almost exactly, and reduces the overestimation of transition state energies by 27-48% compared with the previous parametrization.

Discrete Optimization of Electronic Hyperpolarizabilities in a Chemical Subspace

DTIC Science & Technology

2009-05-01

molecular design. Methods for optimization in discrete spaces have been studied extensively and recently reviewed ( 5). Optimization methods include...integer programming, as in branch-and-bound techniques (including dead-end elimination [ 6]), simulated annealing ( 7), and genetic algorithms ( 8...These algorithms have found renewed interest and application in molecular and materials design (9- 12) . Recently, new approaches have been

Linear Discriminant Analysis for the in Silico Discovery of Mechanism-Based Reversible Covalent Inhibitors of a Serine Protease: Application of Hydration Thermodynamics Analysis and Semi-empirical Molecular Orbital Calculation.

PubMed

Masuda, Yosuke; Yoshida, Tomoki; Yamaotsu, Noriyuki; Hirono, Shuichi

2018-01-01

We recently reported that the Gibbs free energy of hydrolytic water molecules (ΔG wat ) in acyl-trypsin intermediates calculated by hydration thermodynamics analysis could be a useful metric for estimating the catalytic rate constants (k cat ) of mechanism-based reversible covalent inhibitors. For thorough evaluation, the proposed method was tested with an increased number of covalent ligands that have no corresponding crystal structures. After modeling acyl-trypsin intermediate structures using flexible molecular superposition, ΔG wat values were calculated according to the proposed method. The orbital energies of antibonding π* molecular orbitals (MOs) of carbonyl C=O in covalently modified catalytic serine (E orb ) were also calculated by semi-empirical MO calculations. Then, linear discriminant analysis (LDA) was performed to build a model that can discriminate covalent inhibitor candidates from substrate-like ligands using ΔG wat and E orb . The model was built using a training set (10 compounds) and then validated by a test set (4 compounds). As a result, the training set and test set ligands were perfectly discriminated by the model. Hydrolysis was slower when (1) the hydrolytic water molecule has lower ΔG wat ; (2) the covalent ligand presents higher E orb (higher reaction barrier). Results also showed that the entropic term of hydrolytic water molecule (-TΔS wat ) could be used for estimating k cat and for covalent inhibitor optimization; when the rotational freedom of the hydrolytic water molecule is limited, the chance for favorable interaction with the electrophilic acyl group would also be limited. The method proposed in this study would be useful for screening and optimizing the mechanism-based reversible covalent inhibitors.

Bioethanol production optimization: a thermodynamic analysis.

PubMed

Alvarez, Víctor H; Rivera, Elmer Ccopa; Costa, Aline C; Filho, Rubens Maciel; Wolf Maciel, Maria Regina; Aznar, Martín

2008-03-01

In this work, the phase equilibrium of binary mixtures for bioethanol production by continuous extractive process was studied. The process is composed of four interlinked units: fermentor, centrifuge, cell treatment unit, and flash vessel (ethanol-congener separation unit). A proposal for modeling the vapor-liquid equilibrium in binary mixtures found in the flash vessel has been considered. This approach uses the Predictive Soave-Redlich-Kwong equation of state, with original and modified molecular parameters. The congeners considered were acetic acid, acetaldehyde, furfural, methanol, and 1-pentanol. The results show that the introduction of new molecular parameters r and q in the UNIFAC model gives more accurate predictions for the concentration of the congener in the gas phase for binary and ternary systems.

Protein engineering and the use of molecular modeling and simulation: the case of heterodimeric Fc engineering.

PubMed

Spreter Von Kreudenstein, Thomas; Lario, Paula I; Dixit, Surjit B

2014-01-01

Computational and structure guided methods can make significant contributions to the development of solutions for difficult protein engineering problems, including the optimization of next generation of engineered antibodies. In this paper, we describe a contemporary industrial antibody engineering program, based on hypothesis-driven in silico protein optimization method. The foundational concepts and methods of computational protein engineering are discussed, and an example of a computational modeling and structure-guided protein engineering workflow is provided for the design of best-in-class heterodimeric Fc with high purity and favorable biophysical properties. We present the engineering rationale as well as structural and functional characterization data on these engineered designs. Copyright © 2013 Elsevier Inc. All rights reserved.

Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem.

PubMed

Hyde, Damon; Schulz, Ralf; Brooks, Dana; Miller, Eric; Ntziachristos, Vasilis

2009-04-01

Hybrid imaging systems combining x-ray computed tomography (CT) and fluorescence tomography can improve fluorescence imaging performance by incorporating anatomical x-ray CT information into the optical inversion problem. While the use of image priors has been investigated in the past, little is known about the optimal use of forward photon propagation models in hybrid optical systems. In this paper, we explore the impact on reconstruction accuracy of the use of propagation models of varying complexity, specifically in the context of these hybrid imaging systems where significant structural information is known a priori. Our results demonstrate that the use of generically known parameters provides near optimal performance, even when parameter mismatch remains.

Learning To Fold Proteins Using Energy Landscape Theory

PubMed Central

Schafer, N.P.; Kim, B.L.; Zheng, W.; Wolynes, P.G.

2014-01-01

This review is a tutorial for scientists interested in the problem of protein structure prediction, particularly those interested in using coarse-grained molecular dynamics models that are optimized using lessons learned from the energy landscape theory of protein folding. We also present a review of the results of the AMH/AMC/AMW/AWSEM family of coarse-grained molecular dynamics protein folding models to illustrate the points covered in the first part of the article. Accurate coarse-grained structure prediction models can be used to investigate a wide range of conceptual and mechanistic issues outside of protein structure prediction; specifically, the paper concludes by reviewing how AWSEM has in recent years been able to elucidate questions related to the unusual kinetic behavior of artificially designed proteins, multidomain protein misfolding, and the initial stages of protein aggregation. PMID:25308991

ECUT: Energy Conversion and Utilization Technologies program - Biocatalysis research activity

NASA Technical Reports Server (NTRS)

Wilcox, R.

1984-01-01

The activities of the Biocatalysis Research Activity are organized into the Biocatalysis and Molecular Modeling work elements and a supporting planning and analysis function. In the Biocatalysis work element, progress is made in developing a method for stabilizing genetically engineered traits in microorganisms, refining a technique for monitoring cells that are genetically engineered, and identifying strains of fungi for highly efficient preprocessing of biomass for optimizing the efficiency of bioreactors. In the Molecular Modeling work element, a preliminary model of the behavior of enzymes is developed. A preliminary investigation of the potential for synthesizing enzymes for use in electrochemical processes is completed. Contact with industry and universities is made to define key biocatalysis technical issues and to broaden the range of potential participants in the activity. Analyses are conducted to identify and evaluate potential concepts for future research funding.

Gyre and gimble: a maximum-likelihood replacement for Patterson correlation refinement.

PubMed

McCoy, Airlie J; Oeffner, Robert D; Millán, Claudia; Sammito, Massimo; Usón, Isabel; Read, Randy J

2018-04-01

Descriptions are given of the maximum-likelihood gyre method implemented in Phaser for optimizing the orientation and relative position of rigid-body fragments of a model after the orientation of the model has been identified, but before the model has been positioned in the unit cell, and also the related gimble method for the refinement of rigid-body fragments of the model after positioning. Gyre refinement helps to lower the root-mean-square atomic displacements between model and target molecular-replacement solutions for the test case of antibody Fab(26-10) and improves structure solution with ARCIMBOLDO_SHREDDER.

Earth-Abundant Materials as Photosensitizers in the Molecular Assemblies for Solar Energy Conversion

DTIC Science & Technology

2013-03-31

experimentally by several research groups ,3-8   which provide us with a starting point and a set of benchmarks for our theoretical calculations. In this...binding mode. All the nonequivalent linker positions on the dyes were modeled: two nonequivalent carboxylic acid groups on 1 and 2 and two... nonequivalent cyanide groups on 3. All optimizations were performed in vacuum. Interfacial Electron Transfer Simulations. All model systems were composed of a

Consistent integration of experimental and ab initio data into molecular and coarse-grained models

NASA Astrophysics Data System (ADS)

Vlcek, Lukas

As computer simulations are increasingly used to complement or replace experiments, highly accurate descriptions of physical systems at different time and length scales are required to achieve realistic predictions. The questions of how to objectively measure model quality in relation to reference experimental or ab initio data, and how to transition seamlessly between different levels of resolution are therefore of prime interest. To address these issues, we use the concept of statistical distance to define a measure of similarity between statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the systems' measurable properties. Through systematic coarse-graining, we arrive at appropriate expressions for optimization loss functions consistently incorporating microscopic ab initio data as well as macroscopic experimental data. The design of coarse-grained and multiscale models is then based on factoring the model system partition function into terms describing the system at different resolution levels. The optimization algorithm takes advantage of thermodynamic perturbation expressions for fast exploration of the model parameter space, enabling us to scan millions of parameter combinations per hour on a single CPU. The robustness and generality of the new model optimization framework and its efficient implementation are illustrated on selected examples including aqueous solutions, magnetic systems, and metal alloys.

Application of biomarkers in cancer risk management: evaluation from stochastic clonal evolutionary and dynamic system optimization points of view.

PubMed

Li, Xiaohong; Blount, Patricia L; Vaughan, Thomas L; Reid, Brian J

2011-02-01

Aside from primary prevention, early detection remains the most effective way to decrease mortality associated with the majority of solid cancers. Previous cancer screening models are largely based on classification of at-risk populations into three conceptually defined groups (normal, cancer without symptoms, and cancer with symptoms). Unfortunately, this approach has achieved limited successes in reducing cancer mortality. With advances in molecular biology and genomic technologies, many candidate somatic genetic and epigenetic "biomarkers" have been identified as potential predictors of cancer risk. However, none have yet been validated as robust predictors of progression to cancer or shown to reduce cancer mortality. In this Perspective, we first define the necessary and sufficient conditions for precise prediction of future cancer development and early cancer detection within a simple physical model framework. We then evaluate cancer risk prediction and early detection from a dynamic clonal evolution point of view, examining the implications of dynamic clonal evolution of biomarkers and the application of clonal evolution for cancer risk management in clinical practice. Finally, we propose a framework to guide future collaborative research between mathematical modelers and biomarker researchers to design studies to investigate and model dynamic clonal evolution. This approach will allow optimization of available resources for cancer control and intervention timing based on molecular biomarkers in predicting cancer among various risk subsets that dynamically evolve over time.

Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

NASA Astrophysics Data System (ADS)

Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

2017-03-01

Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

NASA Astrophysics Data System (ADS)

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; Stuehn, Torsten

2017-11-01

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach, the theoretical modeling and scaling laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. These two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.

Molecular Cardiac Surgery with Recirculating Delivery (MCARD): Procedure and Vector Transfer.

PubMed

Katz, Michael G; Fargnoli, Anthony S; Kendle, Andrew P; Bridges, Charles R

2017-01-01

Despite progress in clinical treatment, cardiovascular diseases are still the leading cause of morbidity and mortality worldwide. Therefore, novel therapeutic approaches are needed, targeting the underlying molecular mechanisms of disease with improved outcomes for patients. Gene therapy is one of the most promising fields for the development of new treatments for the advanced stages of cardiovascular diseases. The establishment of clinically relevant methods of gene transfer remains one of the principal limitations on the effectiveness of gene therapy. Recently, there have been significant advances in direct and transvascular gene delivery methods. The ideal gene transfer method should be explored in clinically relevant large animal models of heart disease to evaluate the roles of specific molecular pathways in disease pathogenesis. Characteristics of the optimal technique for gene delivery include low morbidity, an increased myocardial transcapillary gradient, esxtended vector residence time in the myocytes, and the exclusion of residual vector from the systemic circulation after delivery to minimize collateral expression and immune response. Here we describe myocardial gene transfer techniques with molecular cardiac surgery with recirculating delivery in a large animal model of post ischemic heart failure.

Modeling the intermolecular interactions: molecular structure of N-3-hydroxyphenyl-4-methoxybenzamide.

PubMed

Karabulut, Sedat; Namli, Hilmi; Kurtaran, Raif; Yildirim, Leyla Tatar; Leszczynski, Jerzy

2014-03-01

The title compound, N-3-hydroxyphenyl-4-methoxybenzamide (3) was prepared by the acylation reaction of 3-aminophenol (1) and 4-metoxybenzoylchloride (2) in THF and characterized by ¹H NMR, ¹³C NMR and elemental analysis. Molecular structure of the crystal was determined by single crystal X-ray diffraction and DFT calculations. 3 crystallizes in monoclinic P2₁/c space group. The influence of intermolecular interactions (dimerization and crystal packing) on molecular geometry has been evaluated by calculations performed for three different models; monomer (3), dimer (4) and dimer with added unit cell contacts (5). Molecular structure of 3, 4 and 5 was optimized by applying B3LYP method with 6-31G+(d,p) basis set in gas phase and compared with X-ray crystallographic data including bond lengths, bond angles and selected dihedral angles. It has been concluded that although the crystal packing and dimerization have a minor effect on bond lengths and angles, however, these interactions are important for the dihedral angles and the rotational conformation of aromatic rings. Copyright © 2013 Elsevier Inc. All rights reserved.

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things

PubMed Central

Akan, Ozgur B.

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics. PMID:29415019

Modeling convection-diffusion-reaction systems for microfluidic molecular communications with surface-based receivers in Internet of Bio-Nano Things.

PubMed

Kuscu, Murat; Akan, Ozgur B

2018-01-01

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics.

Computational modeling on the recognition of the HRE motif by HIF-1: molecular docking and molecular dynamics studies.

PubMed

Sokkar, Pandian; Sathis, Vani; Ramachandran, Murugesan

2012-05-01

Hypoxia inducible factor-1 (HIF-1) is a bHLH-family transcription factor that controls genes involved in glycolysis, angiogenesis, migration, as well as invasion factors that are important for tumor progression and metastasis. HIF-1, a heterodimer of HIF-1α and HIF-1β, binds to the hypoxia responsive element (HRE) present in the promoter regions of hypoxia responsive genes, such as vascular endothelial growth factor (VEGF). Neither the structure of free HIF-1 nor that of its complex with HRE is available. Computational modeling of the transcription factor-DNA complex has always been challenging due to their inherent flexibility and large conformational space. The present study aims to model the interaction between the DNA-binding domain of HIF-1 and HRE. Experiments showed that rigid macromolecular docking programs (HEX and GRAMM-X) failed to predict the optimal dimerization of individually modeled HIF-1 subunits. Hence, the HIF-1 heterodimer was modeled based on the phosphate system positive regulatory protein (PHO4) homodimer. The duplex VEGF-DNA segment containing HRE with flanking nucleotides was modeled in the B form and equilibrated via molecular dynamics (MD) simulation. A rigid docking approach was used to predict the crude binding mode of HIF-1 dimer with HRE, in which the putative contacts were found to be present. An MD simulation (5 ns) of the HIF-1-HRE complex in explicit water was performed to account for its flexibility and to optimize its interactions. All of the conserved amino acid residues were found to play roles in the recognition of HRE. The present work, which sheds light on the recognition of HRE by HIF-1, could be beneficial in the design of peptide or small molecule therapeutics that can mimic HIF-1 and bind with the HRE sequence.

Depth-resolved analytical model and correction algorithm for photothermal optical coherence tomography

PubMed Central

Lapierre-Landry, Maryse; Tucker-Schwartz, Jason M.; Skala, Melissa C.

2016-01-01

Photothermal OCT (PT-OCT) is an emerging molecular imaging technique that occupies a spatial imaging regime between microscopy and whole body imaging. PT-OCT would benefit from a theoretical model to optimize imaging parameters and test image processing algorithms. We propose the first analytical PT-OCT model to replicate an experimental A-scan in homogeneous and layered samples. We also propose the PT-CLEAN algorithm to reduce phase-accumulation and shadowing, two artifacts found in PT-OCT images, and demonstrate it on phantoms and in vivo mouse tumors. PMID:27446693

Constraint Logic Programming approach to protein structure prediction.

PubMed

Dal Palù, Alessandro; Dovier, Agostino; Fogolari, Federico

2004-11-30

The protein structure prediction problem is one of the most challenging problems in biological sciences. Many approaches have been proposed using database information and/or simplified protein models. The protein structure prediction problem can be cast in the form of an optimization problem. Notwithstanding its importance, the problem has very seldom been tackled by Constraint Logic Programming, a declarative programming paradigm suitable for solving combinatorial optimization problems. Constraint Logic Programming techniques have been applied to the protein structure prediction problem on the face-centered cube lattice model. Molecular dynamics techniques, endowed with the notion of constraint, have been also exploited. Even using a very simplified model, Constraint Logic Programming on the face-centered cube lattice model allowed us to obtain acceptable results for a few small proteins. As a test implementation their (known) secondary structure and the presence of disulfide bridges are used as constraints. Simplified structures obtained in this way have been converted to all atom models with plausible structure. Results have been compared with a similar approach using a well-established technique as molecular dynamics. The results obtained on small proteins show that Constraint Logic Programming techniques can be employed for studying protein simplified models, which can be converted into realistic all atom models. The advantage of Constraint Logic Programming over other, much more explored, methodologies, resides in the rapid software prototyping, in the easy way of encoding heuristics, and in exploiting all the advances made in this research area, e.g. in constraint propagation and its use for pruning the huge search space.

Multi-Scale Computational Enzymology: Enhancing Our Understanding of Enzymatic Catalysis

PubMed Central

Gherib, Rami; Dokainish, Hisham M.; Gauld, James W.

2014-01-01

Elucidating the origin of enzymatic catalysis stands as one the great challenges of contemporary biochemistry and biophysics. The recent emergence of computational enzymology has enhanced our atomistic-level description of biocatalysis as well the kinetic and thermodynamic properties of their mechanisms. There exists a diversity of computational methods allowing the investigation of specific enzymatic properties. Small or large density functional theory models allow the comparison of a plethora of mechanistic reactive species and divergent catalytic pathways. Molecular docking can model different substrate conformations embedded within enzyme active sites and determine those with optimal binding affinities. Molecular dynamics simulations provide insights into the dynamics and roles of active site components as well as the interactions between substrate and enzymes. Hybrid quantum mechanical/molecular mechanical (QM/MM) can model reactions in active sites while considering steric and electrostatic contributions provided by the surrounding environment. Using previous studies done within our group, on OvoA, EgtB, ThrRS, LuxS and MsrA enzymatic systems, we will review how these methods can be used either independently or cooperatively to get insights into enzymatic catalysis. PMID:24384841

Simulations of molecular self-assembled monolayers on surfaces: packing structures, formation processes and functions tuned by intermolecular and interfacial interactions.

PubMed

Wen, Jin; Li, Wei; Chen, Shuang; Ma, Jing

2016-08-17

Surfaces modified with a functional molecular monolayer are essential for the fabrication of nano-scale electronics or machines with novel physical, chemical, and/or biological properties. Theoretical simulation based on advanced quantum chemical and classical models is at present a necessary tool in the development, design, and understanding of the interfacial nanostructure. The nanoscale surface morphology, growth processes, and functions are controlled by not only the electronic structures (molecular energy levels, dipole moments, polarizabilities, and optical properties) of building units but also the subtle balance between intermolecular and interfacial interactions. The switchable surfaces are also constructed by introducing stimuli-responsive units like azobenzene derivatives. To bridge the gap between experiments and theoretical models, opportunities and challenges for future development of modelling of ferroelectricity, entropy, and chemical reactions of surface-supported monolayers are also addressed. Theoretical simulations will allow us to obtain important and detailed information about the structure and dynamics of monolayer modified interfaces, which will guide the rational design and optimization of dynamic interfaces to meet challenges of controlling optical, electrical, and biological functions.

Spontaneous polarization and dielectric relaxation dynamics of ferroelectric liquid crystals derived from 2(S)-[2(S)-ethylhexyolxy] propionic acid and its (S, R)-diastereomer

NASA Astrophysics Data System (ADS)

Huang, Lei-Ching; Fu, Chao-Ming

2015-09-01

The spontaneous polarization and molecular dynamics of four ferroelectric liquid crystals (FLCs) with two different kinds of core rings and two types of diastereomeric structures were investigated in this study. The FLCs with a biphenyl ring core structure showed higher spontaneous polarization than the FLCs with a naphthalene ring core structure. The complex dielectric spectra exhibited the Goldstone mode in the ferroelectric (SmC*) phase for all FLCs. The complex dielectric spectra of the four FLCs can be optimally fitted by the Debye model and the Cole-Cole model. Moreover, the Goldstone mode was enhanced under low DC bias fields for the FLCs with the (S, R)- diastereomeric structure, whereas the mode was suppressed for the FLCs with the (S, S)- diastereomeric structure. A microscopic molecular dynamic model is proposed to describe the underlying mechanism of the particular enhancement of the Goldstone mode. The experimental results of dielectric spectra and spontaneous polarization are explained in the discussion of the mesomorphic properties related to the FLC molecular structure.

Molecular beacon sequence design algorithm.

PubMed

Monroe, W Todd; Haselton, Frederick R

2003-01-01

A method based on Web-based tools is presented to design optimally functioning molecular beacons. Molecular beacons, fluorogenic hybridization probes, are a powerful tool for the rapid and specific detection of a particular nucleic acid sequence. However, their synthesis costs can be considerable. Since molecular beacon performance is based on its sequence, it is imperative to rationally design an optimal sequence before synthesis. The algorithm presented here uses simple Microsoft Excel formulas and macros to rank candidate sequences. This analysis is carried out using mfold structural predictions along with other free Web-based tools. For smaller laboratories where molecular beacons are not the focus of research, the public domain algorithm described here may be usefully employed to aid in molecular beacon design.

The Molecular Structure of a Phosphatidylserine Bilayer Determined by Scattering and Molecular Dynamics Simulations

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

Pan, Jianjun; Cheng, Xiaolin; Monticelli, Luca

2014-01-01

Phosphatidylserine (PS) lipids play essential roles in biological processes, including enzyme activation and apoptosis. We report on the molecular structure and atomic scale interactions of a fluid bilayer composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidylserine (POPS). A scattering density profile model, aided by molecular dynamics (MD) simulations, was developed to jointly refine different contrast small-angle neutron and X-ray scattering data, which yielded a lipid area of 62.7 A2 at 25 C. MD simulations with POPS lipid area constrained at different values were also performed using all-atom and aliphatic united-atom models. The optimal simulated bilayer was obtained using a model-free comparison approach. Examination of themore » simulated bilayer, which agrees best with the experimental scattering data, reveals a preferential interaction between Na+ ions and the terminal serine and phosphate moieties. Long-range inter-lipid interactions were identified, primarily between the positively charged ammonium, and the negatively charged carboxylic and phosphate oxygens. The area compressibility modulus KA of the POPS bilayer was derived by quantifying lipid area as a function of surface tension from area-constrained MD simulations. It was found that POPS bilayers possess a much larger KA than that of neutral phosphatidylcholine lipid bilayers. We propose that the unique molecular features of POPS bilayers may play an important role in certain physiological functions.« less

Computational modeling in melanoma for novel drug discovery.

PubMed

Pennisi, Marzio; Russo, Giulia; Di Salvatore, Valentina; Candido, Saverio; Libra, Massimo; Pappalardo, Francesco

2016-06-01

There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.

Polarizable atomic multipole-based force field for DOPC and POPE membrane lipids

NASA Astrophysics Data System (ADS)

Chu, Huiying; Peng, Xiangda; Li, Yan; Zhang, Yuebin; Min, Hanyi; Li, Guohui

2018-04-01

A polarizable atomic multipole-based force field for the membrane bilayer models 1,2-dioleoyl-phosphocholine (DOPC) and 1-palmitoyl-2-oleoyl-phosphatidylethanolamine (POPE) has been developed. The force field adopts the same framework as the Atomic Multipole Optimized Energetics for Biomolecular Applications (AMOEBA) model, in which the charge distribution of each atom is represented by the permanent atomic monopole, dipole and quadrupole moments. Many-body polarization including the inter- and intra-molecular polarization is modelled in a consistent manner with distributed atomic polarizabilities. The van der Waals parameters were first transferred from existing AMOEBA parameters for small organic molecules and then optimised by fitting to ab initio intermolecular interaction energies between models and a water molecule. Molecular dynamics simulations of the two aqueous DOPC and POPE membrane bilayer systems, consisting of 72 model molecules, were then carried out to validate the force field parameters. Membrane width, area per lipid, volume per lipid, deuterium order parameters, electron density profile, etc. were consistent with experimental values.

Prospective Design of Anti‐Transferrin Receptor Bispecific Antibodies for Optimal Delivery into the Human Brain

PubMed Central

Kanodia, JS; Gadkar, K; Bumbaca, D; Zhang, Y; Tong, RK; Luk, W; Hoyte, K; Lu, Y; Wildsmith, KR; Couch, JA; Watts, RJ; Dennis, MS; Ernst, JA; Scearce‐Levie, K; Atwal, JK; Joseph, S

2016-01-01

Anti‐transferrin receptor (TfR)‐based bispecific antibodies have shown promise for boosting antibody uptake in the brain. Nevertheless, there are limited data on the molecular properties, including affinity required for successful development of TfR‐based therapeutics. A complex nonmonotonic relationship exists between affinity of the anti‐TfR arm and brain uptake at therapeutically relevant doses. However, the quantitative nature of this relationship and its translatability to humans is heretofore unexplored. Therefore, we developed a mechanistic pharmacokinetic‐pharmacodynamic (PK‐PD) model for bispecific anti‐TfR/BACE1 antibodies that accounts for antibody‐TfR interactions at the blood‐brain barrier (BBB) as well as the pharmacodynamic (PD) effect of anti‐BACE1 arm. The calibrated model correctly predicted the optimal anti‐TfR affinity required to maximize brain exposure of therapeutic antibodies in the cynomolgus monkey and was scaled to predict the optimal affinity of anti‐TfR bispecifics in humans. Thus, this model provides a framework for testing critical translational predictions for anti‐TfR bispecific antibodies, including choice of candidate molecule for clinical development. PMID:27299941

Multistep modeling of protein structure: application to bungarotoxin

NASA Technical Reports Server (NTRS)

Srinivasan, S.; Shibata, M.; Rein, R.

1986-01-01

Modelling of bungarotoxin in atomic details is presented in this article. The model-building procedure utilizes the low-resolution crystal coordinates of the c-alpha atoms of bungarotoxin, sequence homology within the neurotoxin family, as well as high-resolution x-ray diffraction data of cobratoxin and erabutoxin. Our model-building procedure involves: (a) principles of comparative modelling, (b) embedding procedures of distance geometry, and (c) use of molecular mechanics for optimizing packing. The model is not only consistent with the c-alpha coordinates of crystal structure, but also agrees with solution conformational features of the triple-stranded beta sheet as observed by NOE measurements.

Optimization of time-course experiments for kinetic model discrimination.

PubMed

Lages, Nuno F; Cordeiro, Carlos; Sousa Silva, Marta; Ponces Freire, Ana; Ferreira, António E N

2012-01-01

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction.In this work, a method is proposed to optimize the design of enzyme kinetic assays with the goal of selecting a model among a set of candidates. We focus on models with systems of ordinary differential equations as the underlying mathematical description. The method provides a design where an extension of the Kullback-Leibler distance, computed over the time courses predicted by the models, is maximized. Given the asymmetric nature this measure, a generalized differential evolution algorithm for multi-objective optimization problems was used.The kinetics of yeast glyoxalase I (EC 4.4.1.5) was chosen as a difficult test case to evaluate the method. Although a single-substrate kinetic model is usually considered, a two-substrate mechanism has also been proposed for this enzyme. We designed an experiment capable of discriminating between the two models by optimizing the initial substrate concentrations of glyoxalase I, in the presence of the subsequent pathway enzyme, glyoxalase II (EC 3.1.2.6). This discriminatory experiment was conducted in the laboratory and the results indicate a two-substrate mechanism for the kinetics of yeast glyoxalase I.

Modeling and simulation of the debonding process of composite solid propellants

NASA Astrophysics Data System (ADS)

Feng, Tao; Xu, Jin-sheng; Han, Long; Chen, Xiong

2017-07-01

In order to study the damage evolution law of composite solid propellants, the molecular dynamics particle filled algorithm was used to establish the mesoscopic structure model of HTPB(Hydroxyl-terminated polybutadiene) propellants. The cohesive element method was employed for the adhesion interface between AP(Ammonium perchlorate) particle and HTPB matrix and the bilinear cohesive zone model was used to describe the mechanical response of the interface elements. The inversion analysis method based on Hooke-Jeeves optimization algorithm was employed to identify the parameters of cohesive zone model(CZM) of the particle/binder interface. Then, the optimized parameters were applied to the commercial finite element software ABAQUS to simulate the damage evolution process for AP particle and HTPB matrix, including the initiation, development, gathering and macroscopic crack. Finally, the stress-strain simulation curve was compared with the experiment curves. The result shows that the bilinear cohesive zone model can accurately describe the debonding and fracture process between the AP particles and HTPB matrix under the uniaxial tension loading.

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

Kadoura, Ahmad, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Sun, Shuyu, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa; Siripatana, Adil, E-mail: ahmad.kadoura@kaust.edu.sa, E-mail: adil.siripatana@kaust.edu.sa, E-mail: shuyu.sun@kaust.edu.sa, E-mail: omar.knio@kaust.edu.sa

In this work, two Polynomial Chaos (PC) surrogates were generated to reproduce Monte Carlo (MC) molecular simulation results of the canonical (single-phase) and the NVT-Gibbs (two-phase) ensembles for a system of normalized structureless Lennard-Jones (LJ) particles. The main advantage of such surrogates, once generated, is the capability of accurately computing the needed thermodynamic quantities in a few seconds, thus efficiently replacing the computationally expensive MC molecular simulations. Benefiting from the tremendous computational time reduction, the PC surrogates were used to conduct large-scale optimization in order to propose single-site LJ models for several simple molecules. Experimental data, a set of supercriticalmore » isotherms, and part of the two-phase envelope, of several pure components were used for tuning the LJ parameters (ε, σ). Based on the conducted optimization, excellent fit was obtained for different noble gases (Ar, Kr, and Xe) and other small molecules (CH{sub 4}, N{sub 2}, and CO). On the other hand, due to the simplicity of the LJ model used, dramatic deviations between simulation and experimental data were observed, especially in the two-phase region, for more complex molecules such as CO{sub 2} and C{sub 2} H{sub 6}.« less

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

PubMed Central

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-01-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements. PMID:27112127

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate.

PubMed

Pradines, Joël R; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-26

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

Combining measurements to estimate properties and characterization extent of complex biochemical mixtures; applications to Heparan Sulfate

NASA Astrophysics Data System (ADS)

Pradines, Joël R.; Beccati, Daniela; Lech, Miroslaw; Ozug, Jennifer; Farutin, Victor; Huang, Yongqing; Gunay, Nur Sibel; Capila, Ishan

2016-04-01

Complex mixtures of molecular species, such as glycoproteins and glycosaminoglycans, have important biological and therapeutic functions. Characterization of these mixtures with analytical chemistry measurements is an important step when developing generic drugs such as biosimilars. Recent developments have focused on analytical methods and statistical approaches to test similarity between mixtures. The question of how much uncertainty on mixture composition is reduced by combining several measurements still remains mostly unexplored. Mathematical frameworks to combine measurements, estimate mixture properties, and quantify remaining uncertainty, i.e. a characterization extent, are introduced here. Constrained optimization and mathematical modeling are applied to a set of twenty-three experimental measurements on heparan sulfate, a mixture of linear chains of disaccharides having different levels of sulfation. While this mixture has potentially over two million molecular species, mathematical modeling and the small set of measurements establish the existence of nonhomogeneity of sulfate level along chains and the presence of abundant sulfate repeats. Constrained optimization yields not only estimations of sulfate repeats and sulfate level at each position in the chains but also bounds on these levels, thereby estimating the extent of characterization of the sulfation pattern which is achieved by the set of measurements.

eXtended CASA Line Analysis Software Suite (XCLASS)

NASA Astrophysics Data System (ADS)

Möller, T.; Endres, C.; Schilke, P.

2017-02-01

The eXtended CASA Line Analysis Software Suite (XCLASS) is a toolbox for the Common Astronomy Software Applications package (CASA) containing new functions for modeling interferometric and single dish data. Among the tools is the myXCLASS program which calculates synthetic spectra by solving the radiative transfer equation for an isothermal object in one dimension, whereas the finite source size and dust attenuation are considered as well. Molecular data required by the myXCLASS program are taken from an embedded SQLite3 database containing entries from the Cologne Database for Molecular Spectroscopy (CDMS) and JPL using the Virtual Atomic and Molecular Data Center (VAMDC) portal. Additionally, the toolbox provides an interface for the model optimizer package Modeling and Analysis Generic Interface for eXternal numerical codes (MAGIX), which helps to find the best description of observational data using myXCLASS (or another external model program), that is, finding the parameter set that most closely reproduces the data. http://www.astro.uni-koeln.de/projects/schilke/myXCLASSInterface A copy of the code is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/598/A7

Multiensemble Markov models of molecular thermodynamics and kinetics.

PubMed

Wu, Hao; Paul, Fabian; Wehmeyer, Christoph; Noé, Frank

2016-06-07

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models-clustering of high-dimensional spaces and modeling of complex many-state systems-with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein-ligand binding model.

Synthesis, geometry optimization, spectroscopic investigations (UV/Vis, excited states, FT-IR) and application of new azomethine dyes

NASA Astrophysics Data System (ADS)

Shahab, Siyamak; Sheikhi, Masoome; Filippovich, Liudmila; Kumar, Rakesh; Dikusar, Evgenij; Yahyaei, Hooriye; Khaleghian, Mehrnoosh

2017-11-01

In the present work, the quantum theoretical calculations of the molecular structures of the four new synthesized azomethine dyes such as: (E)-N-(4-butoxybenzylidene)-4-((E)-phenyldiazenyl)aniline (PAZB-6), (E)-N-(4-(benzyloxy)benzylidene)-4-((E))-phenyldiazenyl)aniline (PAZB-7), 4-((E)-4-((E)-phenyldiazenyl)phenyl)imino)methyl)phenol (PAZB-8), (E)-N-(4-methoxybenzylidene)-4-((E))-phenyldiazenyl)aniline (PAZB-9) have been predicted using Density Functional Theory in the solvent Dimethylformamide. The geometries of the azomethine dyes were optimized by PBE1PBE/6-31+G* level of theory. The electronic spectra of the title compounds in the solvent DMF was carried out by TDPBE1PBE/6-31+G* method. FT-IR spectra of the title compounds are recorded and discussed. Frontier molecular orbitals, molecular electrostatic potential, electronic properties, natural charges and Natural Bond Orbital (NBO) analysis of the mentioned compounds were investigated and discussed by theoretical calculations. The azomethine dyes were synthesized after quantum chemical modeling for optical applications. A new study of anisotropy of thermal and electrical conductivity of the colored stretched PVA-films have been undertaken.

Effective removal of hydrogen sulfide using 4A molecular sieve zeolite synthesized from attapulgite.

PubMed

Liu, Xinpeng; Wang, Rui

2017-03-15

In this work, 4A molecular sieve zeolite was synthesized from attapulgite (ATP) in different conditions and was applied initially for H 2 S removal. The sorbent was characterized by scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectra and N 2 adsorption/desorption. The effects of the synthesis condition and adsorption temperature were studied by dynamic adsorption experiment. The optimal adsorption temperature is 50°C. The H 2 S adsorption results have showed that the optimal synthesis conditions are as follows: the ratio of silicon to aluminum and ratio of sodium to silicon are both 1.5, the ratio of water to sodium is 30, crystallization temperature and crystallization time is 90°C, 4h, respectively. The breakthrough and saturation sulfur sorption capacities of zeolite synthesized under optimum conditions are up to nearly 10 and 15mg/g-sorbent, respectively, and the H 2 S removal rate is nearly 100%. The adsorption kinetics nonlinear fitting results show that the adsorption system follows Bingham model. These results indicate that 4A molecular sieve zeolite synthesized from attapulgite can be used for H 2 S removal promisingly. Copyright © 2016 Elsevier B.V. All rights reserved.

From Mollusks to Medicine: A Venomics Approach for the Discovery and Characterization of Therapeutics from Terebridae Peptide Toxins.

PubMed

Verdes, Aida; Anand, Prachi; Gorson, Juliette; Jannetti, Stephen; Kelly, Patrick; Leffler, Abba; Simpson, Danny; Ramrattan, Girish; Holford, Mandë

2016-04-19

Animal venoms comprise a diversity of peptide toxins that manipulate molecular targets such as ion channels and receptors, making venom peptides attractive candidates for the development of therapeutics to benefit human health. However, identifying bioactive venom peptides remains a significant challenge. In this review we describe our particular venomics strategy for the discovery, characterization, and optimization of Terebridae venom peptides, teretoxins. Our strategy reflects the scientific path from mollusks to medicine in an integrative sequential approach with the following steps: (1) delimitation of venomous Terebridae lineages through taxonomic and phylogenetic analyses; (2) identification and classification of putative teretoxins through omics methodologies, including genomics, transcriptomics, and proteomics; (3) chemical and recombinant synthesis of promising peptide toxins; (4) structural characterization through experimental and computational methods; (5) determination of teretoxin bioactivity and molecular function through biological assays and computational modeling; (6) optimization of peptide toxin affinity and selectivity to molecular target; and (7) development of strategies for effective delivery of venom peptide therapeutics. While our research focuses on terebrids, the venomics approach outlined here can be applied to the discovery and characterization of peptide toxins from any venomous taxa.

Hsp100/ClpB Chaperone Function and Mechanism

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

Vierling, Elizabeth

2015-01-27

The supported research investigated the mechanism of action of a unique class of molecular chaperones in higher plants, the Hsp100/ClpB proteins, with the ultimate goal of defining how these chaperones influence plant growth, development, stress tolerance and productivity. Molecular chaperones are essential effectors of cellular “protein quality control”, which comprises processes that ensure the proper folding, localization, activation and turnover of proteins. Hsp100/ClpB proteins are required for temperature acclimation in plants, optimal seed yield, and proper chloroplast development. The model plant Arabidopsis thaliana and genetic and molecular approaches were used to investigate two of the three members of the Hsp100/ClpBmore » proteins in plants, cytosolic AtHsp101 and chloroplast-localized AtClpB-p. Investigating the chaperone activity of the Hsp100/ClpB proteins addresses DOE goals in that this activity impacts how “plants generate and assemble components” as well as “allowing for their self repair”. Additionally, Hsp100/ClpB protein function in plants is directly required for optimal “utilization of biological energy” and is involved in “mechanisms that control the architecture of energy transduction systems”.« less

Prospective Molecular Profiling of Canine Cancers Provides a Clinically Relevant Comparative Model for Evaluating Personalized Medicine (PMed) Trials

PubMed Central

Mazcko, Christina; Cherba, David; Hendricks, William; Lana, Susan; Ehrhart, E. J.; Charles, Brad; Fehling, Heather; Kumar, Leena; Vail, David; Henson, Michael; Childress, Michael; Kitchell, Barbara; Kingsley, Christopher; Kim, Seungchan; Neff, Mark; Davis, Barbara

2014-01-01

Background Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. Methodology A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Conclusions Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development. PMID:24637659

Prospective molecular profiling of canine cancers provides a clinically relevant comparative model for evaluating personalized medicine (PMed) trials.

PubMed

Paoloni, Melissa; Webb, Craig; Mazcko, Christina; Cherba, David; Hendricks, William; Lana, Susan; Ehrhart, E J; Charles, Brad; Fehling, Heather; Kumar, Leena; Vail, David; Henson, Michael; Childress, Michael; Kitchell, Barbara; Kingsley, Christopher; Kim, Seungchan; Neff, Mark; Davis, Barbara; Khanna, Chand; Trent, Jeffrey

2014-01-01

Molecularly-guided trials (i.e. PMed) now seek to aid clinical decision-making by matching cancer targets with therapeutic options. Progress has been hampered by the lack of cancer models that account for individual-to-individual heterogeneity within and across cancer types. Naturally occurring cancers in pet animals are heterogeneous and thus provide an opportunity to answer questions about these PMed strategies and optimize translation to human patients. In order to realize this opportunity, it is now necessary to demonstrate the feasibility of conducting molecularly-guided analysis of tumors from dogs with naturally occurring cancer in a clinically relevant setting. A proof-of-concept study was conducted by the Comparative Oncology Trials Consortium (COTC) to determine if tumor collection, prospective molecular profiling, and PMed report generation within 1 week was feasible in dogs. Thirty-one dogs with cancers of varying histologies were enrolled. Twenty-four of 31 samples (77%) successfully met all predefined QA/QC criteria and were analyzed via Affymetrix gene expression profiling. A subsequent bioinformatics workflow transformed genomic data into a personalized drug report. Average turnaround from biopsy to report generation was 116 hours (4.8 days). Unsupervised clustering of canine tumor expression data clustered by cancer type, but supervised clustering of tumors based on the personalized drug report clustered by drug class rather than cancer type. Collection and turnaround of high quality canine tumor samples, centralized pathology, analyte generation, array hybridization, and bioinformatic analyses matching gene expression to therapeutic options is achievable in a practical clinical window (<1 week). Clustering data show robust signatures by cancer type but also showed patient-to-patient heterogeneity in drug predictions. This lends further support to the inclusion of a heterogeneous population of dogs with cancer into the preclinical modeling of personalized medicine. Future comparative oncology studies optimizing the delivery of PMed strategies may aid cancer drug development.

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

Insight into the grain boundary effect on the ionic transport of yttria-stabilized zirconia at elevated temperatures from a molecular modeling perspective

NASA Astrophysics Data System (ADS)

Chang, Kai-Shiun; Lin, Yi-Feng; Tung, Kuo-Lun

A molecular dynamics (MD) simulation is used to reveal the grain boundary effect on the ionic transport of yttria-stabilized zirconia (YSZ). The oxygen ion displacements and diffusivities of the ideal and grain boundary-inserted YSZ models are analyzed at elevated temperatures. An optimized Y 2O 3 concentration within YSZ for the best ionic conductivity is achieved by balancing the trade-off between the increased vacancies and the decreased accessible free space. The mass transfer resistance of the grain boundary in YSZ can be more easily found at higher temperatures by observing the oxygen ion diffusivities or traveling trajectories. At lower temperatures, the grain interior and the grain boundary control the ionic transport. In contrast, the grain boundary effect on the diffusion barrier is gradually eliminated at elevated temperatures. The modeled results in this work agree well with previous experimental data.

The atomic scale structure of CXV carbon: wide-angle x-ray scattering and modeling studies.

PubMed

Hawelek, L; Brodka, A; Dore, J C; Honkimaki, V; Burian, A

2013-11-13

The disordered structure of commercially available CXV activated carbon produced from finely powdered wood-based carbon has been studied using the wide-angle x-ray scattering technique, molecular dynamics and density functional theory simulations. The x-ray scattering data has been converted to the real space representation in the form of the pair correlation function via the Fourier transform. Geometry optimizations using classical molecular dynamics based on the reactive empirical bond order potential and density functional theory at the B3LYP/6-31g* level have been performed to generate nanoscale models of CXV carbon consistent with the experimental data. The final model of the structure comprises four chain-like and buckled graphitic layers containing a small percentage of four-fold coordinated atoms (sp(3) defects) in each layer. The presence of non-hexagonal rings in the atomic arrangement has been also considered.

ATK-ForceField: a new generation molecular dynamics software package

NASA Astrophysics Data System (ADS)

Schneider, Julian; Hamaekers, Jan; Chill, Samuel T.; Smidstrup, Søren; Bulin, Johannes; Thesen, Ralph; Blom, Anders; Stokbro, Kurt

2017-12-01

ATK-ForceField is a software package for atomistic simulations using classical interatomic potentials. It is implemented as a part of the Atomistix ToolKit (ATK), which is a Python programming environment that makes it easy to create and analyze both standard and highly customized simulations. This paper will focus on the atomic interaction potentials, molecular dynamics, and geometry optimization features of the software, however, many more advanced modeling features are available. The implementation details of these algorithms and their computational performance will be shown. We present three illustrative examples of the types of calculations that are possible with ATK-ForceField: modeling thermal transport properties in a silicon germanium crystal, vapor deposition of selenium molecules on a selenium surface, and a simulation of creep in a copper polycrystal.

Allocating dissipation across a molecular machine cycle to maximize flux

PubMed Central

Brown, Aidan I.; Sivak, David A.

2017-01-01

Biomolecular machines consume free energy to break symmetry and make directed progress. Nonequilibrium ATP concentrations are the typical free energy source, with one cycle of a molecular machine consuming a certain number of ATP, providing a fixed free energy budget. Since evolution is expected to favor rapid-turnover machines that operate efficiently, we investigate how this free energy budget can be allocated to maximize flux. Unconstrained optimization eliminates intermediate metastable states, indicating that flux is enhanced in molecular machines with fewer states. When maintaining a set number of states, we show that—in contrast to previous findings—the flux-maximizing allocation of dissipation is not even. This result is consistent with the coexistence of both “irreversible” and reversible transitions in molecular machine models that successfully describe experimental data, which suggests that, in evolved machines, different transitions differ significantly in their dissipation. PMID:29073016

Molecular approaches to third generation photovoltaics: photochemical up-conversion

NASA Astrophysics Data System (ADS)

Cheng, Yuen Yap; Fückel, Burkhard; Roberts, Derrick A.; Khoury, Tony; Clady, Rapha"l. G. C. R.; Tayebjee, Murad J. Y.; Piper, Roland; Ekins-Daukes, N. J.; Crossley, Maxwell J.; Schmidt, Timothy W.

2010-08-01

We have investigated a photochemical up-conversion system comprising a molecular mixture of a palladium porphyrin to harvest light, and a polycyclic aromatic hydrocarbon to emit light. The energy of harvested photons is stored as molecular triplet states which then annihilate to bring about up-converted fluorescence. The limiting efficiency of such triplet-triplet annihilation up-conversion has been believed to be 11% for some time. However, by rigorously investigating the kinetics of delayed fluorescence following pulsed excitation, we demonstrate instantaneous annihilation efficiencies exceeding 40%, and limiting efficiencies for the current system of ~60%. We attribute the high efficiencies obtained to the electronic structure of the emitting molecule, which exhibits an exceptionally high T2 molecular state. We utilize the kinetic data obtained to model an up-converting layer irradiated with broadband sunlight, finding that ~3% efficiencies can be obtained with the current system, with this improving dramatically upon optimization of various parameters.

Fragmentation-based QM/MM simulations: length dependence of chain dynamics and hydrogen bonding of polyethylene oxide and polyethylene in aqueous solutions.

PubMed

Li, Hui; Li, Wei; Li, Shuhua; Ma, Jing

2008-06-12

Molecular fragmentation quantum mechanics (QM) calculations have been combined with molecular mechanics (MM) to construct the fragmentation QM/MM method for simulations of dilute solutions of macromolecules. We adopt the electrostatics embedding QM/MM model, where the low-cost generalized energy-based fragmentation calculations are employed for the QM part. Conformation energy calculations, geometry optimizations, and Born-Oppenheimer molecular dynamics simulations of poly(ethylene oxide), PEO(n) (n = 6-20), and polyethylene, PE(n) ( n = 9-30), in aqueous solution have been performed within the framework of both fragmentation and conventional QM/MM methods. The intermolecular hydrogen bonding and chain configurations obtained from the fragmentation QM/MM simulations are consistent with the conventional QM/MM method. The length dependence of chain conformations and dynamics of PEO and PE oligomers in aqueous solutions is also investigated through the fragmentation QM/MM molecular dynamics simulations.

Photostability can be significantly modulated by molecular packing in glasses

DOE Data Explorer

Ediger, Mark [University of Wisconsin-Madison; de Pablo, Juan [University of Chicago; Anthony, Lucas [University of Chicago; Qiu, Yue [University of Chicago

2016-04-10

While previous work has demonstrated that molecular packing in organic crystals can strongly influence photochemical stability, efforts to tune photostability in amorphous materials have shown much smaller effects. Here we show that physical vapor deposition can substantially improve the photostability of organic glasses. Disperse Orange 37 (DO37), an azobenzene derivative, is studied as a model system. Photostability is assessed through changes in the density and molecular orientation of glassy thin films during light irradiation. By optimizing the substrate temperature used for deposition, we can increase photostability by a factor of 50 relative to the liquid-cooled glass. Photostability correlates with glass density, with density increases of up to 1.3%. Coarse-grained molecular simulations, which mimic glass preparation and the photoisomerization reaction, also indicate that glasses with higher density have substantially increased photostability. These results provide insights that may assist in the design of organic photovoltaics and light emission devices with longer lifetimes.

Fluorescent sensor systems based on nanostructured polymeric membranes for selective recognition of Aflatoxin B1.

PubMed

Sergeyeva, Tetyana; Yarynka, Daria; Piletska, Elena; Lynnik, Rostyslav; Zaporozhets, Olga; Brovko, Oleksandr; Piletsky, Sergey; El'skaya, Anna

2017-12-01

Nanostructured polymeric membranes for selective recognition of aflatoxin B1 were synthesized in situ and used as highly sensitive recognition elements in the developed fluorescent sensor. Artificial binding sites capable of selective recognition of aflatoxin B1 were formed in the structure of the polymeric membranes using the method of molecular imprinting. A composition of molecularly imprinted polymer (MIP) membranes was optimized using the method of computational modeling. The MIP membranes were synthesized using the non-toxic close structural analogue of aflatoxin B1, ethyl-2-oxocyclopentanecarboxylate as a dummy template. The MIP membranes with the optimized composition demonstrated extremely high selectivity towards aflatoxin B1 (AFB1). Negligible binding of close structural analogues of AFB1 - aflatoxins B2 (AFB2), aflatoxin G2 (AFG2), and ochratoxin A (OTA) was demonstrated. Binding of AFB1 by the MIP membranes was investigated as a function of both type and concentration of the functional monomer in the initial monomer composition used for the membranes' synthesis, as well as sample composition. The conditions of the solid-phase extraction of the mycotoxin using the MIP membrane as a stationary phase (pH, ionic strength, buffer concentration, volume of the solution, ratio between water and organic solvent, filtration rate) were optimized. The fluorescent sensor system based on the optimized MIP membranes provided a possibility of AFB1 detection within the range 14-500ngmL -1 demonstrating detection limit (3Ϭ) of 14ngmL -1 . The developed technique was successfully applied for the analysis of model solutions and waste waters from bread-making plants. Copyright © 2017 Elsevier B.V. All rights reserved.

Optimization and Evaluation of Gastroretentive Ranitidine HCl Microspheres by Using Factorial Design with Improved Bioavailability and Mucosal Integrity in Ulcer Model.

PubMed

Khattab, Abeer; Zaki, Nashwah

2017-05-01

The purpose of our investigation was to develop and optimize the drug entrapment efficiency and bioadhesion properties of mucoadhesive chitosan microspheres containing ranitidine HCl prepared by an ionotropic gelation method as a gastroretentive delivery system; thus, we improved their protective and therapeutic gastric effects in an ulcer model. A 3 × 2 2 full factorial design was adopted to study the effect of three different factors, i.e., the type of polymer at three levels (chitosan, chitosan/hydroxypropylmethylcellulose, and chitosan/methylcellulose), the type of solvent at two levels (acetic acid and lactic acid), and the type of chitosan at two levels (low molecular weight (LMW) and high molecular weight (HMW)). The studied responses were particle size, swelling index, drug entrapment efficiency, bioadhesion (as determined by wash-off and rinsing tests), and T 80% of drug release. Studies of the in vivo mucoadhesion and in vivo protective and healing effects of the optimized formula against gastric ulcers were carried out using albino rats (with induced gastric ulceration) and were compared to the effects of free ranitidine powder. A pharmacokinetic study in rabbits showed a significant, 2.1-fold increase in theAUC 0-24 of the ranitidine microspheres compared to free ranitidine after oral administration. The optimized formula showed higher drug entrapment efficiency and mucoadhesion properties and had more protective and healing effects on induced gastric ulcers in rats than ranitidine powder. In conclusion, the prolonged gastrointestinal residence time and the stability of the mucoadhesive microspheres of ranitidine as well as the synergistic healing effect of chitosan could contribute to increasing the potential of its anti-gastric ulcer activity.

Utilization of group theory in studies of molecular clusters

NASA Astrophysics Data System (ADS)

Ocak, Mahir E.

The structure of the molecular symmetry group of molecular clusters was analyzed and it is shown that the molecular symmetry group of a molecular cluster can be written as direct products and semidirect products of its subgroups. Symmetry adaptation of basis functions in direct product groups and semidirect product groups was considered in general and the sequential symmetry adaptation procedure which is already known for direct product groups was extended to the case of semidirect product groups. By using the sequential symmetry adaptation procedure a new method for calculating the VRT spectra of molecular clusters which is named as Monomer Basis Representation (MBR) method is developed. In the MBR method, calculations starts with a single monomer with the purpose of obtaining an optimized basis for that monomer as a linear combination of some primitive basis functions. Then, an optimized basis for each identical monomer is generated from the optimized basis of this monomer. By using the optimized bases of the monomers, a basis is generated generated for the solution of the full problem, and the VRT spectra of the cluster is obtained by using this basis. Since an optimized basis is used for each monomer which has a much smaller size than the primitive basis from which the optimized bases are generated, the MBR method leads to an exponential optimization in the size of the basis that is required for the calculations. Application of the MBR method has been illustrated by calculating the VRT spectra of water dimer by using the SAPT-5st potential surface of Groenenboom et al. The rest of the calculations are in good agreement with both the original calculations of Groenenboom et al. and also with the experimental results. Comparing the size of the optimized basis with the size of the primitive basis, it can be said that the method works efficiently. Because of its efficiency, the MBR method can be used for studies of clusters bigger than dimers. Thus, MBR method can be used for studying the many-body terms and for deriving accurate potential surfaces.

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

PubMed Central

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-01-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3. PMID:25644994

Drug Target Optimization in Chronic Myeloid Leukemia Using Innovative Computational Platform

NASA Astrophysics Data System (ADS)

Chuang, Ryan; Hall, Benjamin A.; Benque, David; Cook, Byron; Ishtiaq, Samin; Piterman, Nir; Taylor, Alex; Vardi, Moshe; Koschmieder, Steffen; Gottgens, Berthold; Fisher, Jasmin

2015-02-01

Chronic Myeloid Leukemia (CML) represents a paradigm for the wider cancer field. Despite the fact that tyrosine kinase inhibitors have established targeted molecular therapy in CML, patients often face the risk of developing drug resistance, caused by mutations and/or activation of alternative cellular pathways. To optimize drug development, one needs to systematically test all possible combinations of drug targets within the genetic network that regulates the disease. The BioModelAnalyzer (BMA) is a user-friendly computational tool that allows us to do exactly that. We used BMA to build a CML network-model composed of 54 nodes linked by 104 interactions that encapsulates experimental data collected from 160 publications. While previous studies were limited by their focus on a single pathway or cellular process, our executable model allowed us to probe dynamic interactions between multiple pathways and cellular outcomes, suggest new combinatorial therapeutic targets, and highlight previously unexplored sensitivities to Interleukin-3.

QSAR, molecular docking studies of thiophene and imidazopyridine derivatives as polo-like kinase 1 inhibitors

NASA Astrophysics Data System (ADS)

Cao, Shandong

2012-08-01

The purpose of the present study was to develop in silico models allowing for a reliable prediction of polo-like kinase inhibitors based on a large diverse dataset of 136 compounds. As an effective method, quantitative structure activity relationship (QSAR) was applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). The proposed QSAR models showed reasonable predictivity of thiophene analogs (Rcv2=0.533, Rpred2=0.845) and included four molecular descriptors, namely IC3, RDF075m, Mor02m and R4e+. The optimal model for imidazopyridine derivatives (Rcv2=0.776, Rpred2=0.876) was shown to perform good in prediction accuracy, using GATS2m and BEHe1 descriptors. Analysis of the contour maps helped to identify structural requirements for the inhibitors and served as a basis for the design of the next generation of the inhibitor analogues. Docking studies were also employed to position the inhibitors into the polo-like kinase active site to determine the most probable binding mode. These studies may help to understand the factors influencing the binding affinity of chemicals and to develop alternative methods for prescreening and designing of polo-like kinase inhibitors.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald.

PubMed

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2014-02-28

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations.

Scalable Evaluation of Polarization Energy and Associated Forces in Polarizable Molecular Dynamics: II.Towards Massively Parallel Computations using Smooth Particle Mesh Ewald

PubMed Central

Lagardère, Louis; Lipparini, Filippo; Polack, Étienne; Stamm, Benjamin; Cancès, Éric; Schnieders, Michael; Ren, Pengyu; Maday, Yvon; Piquemal, Jean-Philip

2015-01-01

In this paper, we present a scalable and efficient implementation of point dipole-based polarizable force fields for molecular dynamics (MD) simulations with periodic boundary conditions (PBC). The Smooth Particle-Mesh Ewald technique is combined with two optimal iterative strategies, namely, a preconditioned conjugate gradient solver and a Jacobi solver in conjunction with the Direct Inversion in the Iterative Subspace for convergence acceleration, to solve the polarization equations. We show that both solvers exhibit very good parallel performances and overall very competitive timings in an energy-force computation needed to perform a MD step. Various tests on large systems are provided in the context of the polarizable AMOEBA force field as implemented in the newly developed Tinker-HP package which is the first implementation for a polarizable model making large scale experiments for massively parallel PBC point dipole models possible. We show that using a large number of cores offers a significant acceleration of the overall process involving the iterative methods within the context of spme and a noticeable improvement of the memory management giving access to very large systems (hundreds of thousands of atoms) as the algorithm naturally distributes the data on different cores. Coupled with advanced MD techniques, gains ranging from 2 to 3 orders of magnitude in time are now possible compared to non-optimized, sequential implementations giving new directions for polarizable molecular dynamics in periodic boundary conditions using massively parallel implementations. PMID:26512230

A study of N-methylacetamide in water clusters: based on atom-bond electronegativity equalization method fused into molecular mechanics.

PubMed

Yang, Zhong-Zhi; Qian, Ping

2006-08-14

N-methylacetamide (NMA) is a very interesting compound and often serves as a model of the peptide bond. The interaction between NMA and water provides a convenient prototype for the solvation of the peptides in aqueous solutions. Here we present NMA-water potential model based on atom-bond electronegativity equalization method fused into molecular mechanics (ABEEM/MM) that is to take ABEEM charges of all atoms, bonds, and lone-pair electrons of NMA and water molecules into the electrostatic interaction term in molecular mechanics. The model has the following characters: (1)it allows the charges in system to fluctuate responding to the ambient environment; (2) for two major types of intermolecular hydrogen bonds, which are the hydrogen bond forming between the lone-pair electron on amide oxygen and the water hydrogen, and the one forming between the lone-pair electron on water oxygen and the amide hydrogen, we take special treatments in describing the electrostatic interaction by the use of the parameters k(lpO=, H) and k(lpO(-), HN(-)), respectively. The newly constructed potential model based on ABEEM/MM is first applied to amide-water clusters and reproduces gas-phase state properties of NMA(H(2)O)(n) (n=1-3) including optimal structures, dipole moments, ABEEM charge distributions, energy difference of the isolated trans- and cis-NMA, interaction energies, hydrogen bonding cooperative effects, and so on, whose results show the good agreement with those measured by available experiments and calculated by ab initio methods. In order to further test the reasonableness of this model and the correctness and transferability of the parameters, many static properties of the larger NMA-water complexes NMA(H(2)O)(n) (n=4-6) are also studied including optimal structures and interaction energies. The results also show fair consistency with those of our quantum chemistry calculations.

Optimization of Benzoisothiazole dioxide inhibitory activity of the NS5B polymerase of HCV genotype 4 using ligand-steered homological modeling, reaction-driven scaffold-hopping and Enovo workflow.

PubMed

Mahmoud, Amr Hamed; Mohamed Abouzid, Khaled Abouzid; El Ella, Dalal Abd El Rahman Abou; Hamid Ismail, Mohamed Abdel

2011-01-01

Infection caused by hepatitis C virus (HCV) is a significant world health problem for which novel therapies are in urgent demand. The virus is highly prevalent in the Middle East and Africa particularly Egypt with more than 90% of infections due to genotype 4. Nonstructural (NS5B) viral proteins have emerged as an attractive target for HCV antivirals discovery. A potent class of inhibitors having benzisothiazole dioxide scaffold has been identified on this target, however they were mainly active on genotype 1 while exhibiting much lowered activity on other genotypes due to the high degree of mutation of its binding site. Based on this fact, we employed a novel strategy to optimize this class on genotype 4. This strategy depends on using a refined ligand-steered homological model of this genotype to study the mutation binding energies of the binding site amino acid residues, the essential features for interaction and provide a structure-based pharmacophore model that can aid optimization. This model was applied on a focused library which was generated using a reaction-driven scaffold-hopping strategy. The hits retrieved were subjected to Enovo pipeline pilot optimization workflow that employs R-group enumeration, core-constrained protein docking using modified CDOCKER and finally ranking of poses using an accurate molecular mechanics generalized Born with surface area method.

Atomic modeling of cryo-electron microscopy reconstructions--joint refinement of model and imaging parameters.

PubMed

Chapman, Michael S; Trzynka, Andrew; Chapman, Brynmor K

2013-04-01

When refining the fit of component atomic structures into electron microscopic reconstructions, use of a resolution-dependent atomic density function makes it possible to jointly optimize the atomic model and imaging parameters of the microscope. Atomic density is calculated by one-dimensional Fourier transform of atomic form factors convoluted with a microscope envelope correction and a low-pass filter, allowing refinement of imaging parameters such as resolution, by optimizing the agreement of calculated and experimental maps. A similar approach allows refinement of atomic displacement parameters, providing indications of molecular flexibility even at low resolution. A modest improvement in atomic coordinates is possible following optimization of these additional parameters. Methods have been implemented in a Python program that can be used in stand-alone mode for rigid-group refinement, or embedded in other optimizers for flexible refinement with stereochemical restraints. The approach is demonstrated with refinements of virus and chaperonin structures at resolutions of 9 through 4.5 Å, representing regimes where rigid-group and fully flexible parameterizations are appropriate. Through comparisons to known crystal structures, flexible fitting by RSRef is shown to be an improvement relative to other methods and to generate models with all-atom rms accuracies of 1.5-2.5 Å at resolutions of 4.5-6 Å. Copyright © 2013 Elsevier Inc. All rights reserved.

Dynamic optimal control of homeostasis: an integrative system approach for modeling of the central nitrogen metabolism in Saccharomyces cerevisiae.

PubMed

van Riel, N A; Giuseppin, M L; Verrips, C T

2000-01-01

The theory of dynamic optimal metabolic control (DOMC), as developed by Giuseppin and Van Riel (Metab. Eng., 2000), is applied to model the central nitrogen metabolism (CNM) in Saccharomyces cerevisiae. The CNM represents a typical system encountered in advanced metabolic engineering. The CNM is the source of the cellular amino acids and proteins, including flavors and potentially valuable biomolecules; therefore, it is also of industrial interest. In the DOMC approach the cell is regarded as an optimally controlled system. Given the metabolic genotype, the cell faces a control problem to maintain an optimal flux distribution in a changing environment. The regulation is based on strategies and balances feedback control of homeostasis and feedforward regulation for adaptation. The DOMC approach is an integrative, holistic approach, not based on mechanistic descriptions and (therefore) not biased by the variation present in biochemical and molecular biological data. It is an effective tool to structure the rapidly increasing amount of data on the function of genes and pathways. The DOMC model is used successfully to predict the responses of pulses of ammonia and glutamine to nitrogen-limited continuous cultures of a wild-type strain and a glutamine synthetase-negative mutant. The simulation results are validated with experimental data.

Multiensemble Markov models of molecular thermodynamics and kinetics

PubMed Central

Wu, Hao; Paul, Fabian; Noé, Frank

2016-01-01

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model. PMID:27226302

Thermodynamic models for vapor-liquid equilibria of nitrogen + oxygen + carbon dioxide at low temperatures

NASA Astrophysics Data System (ADS)

Vrabec, Jadran; Kedia, Gaurav Kumar; Buchhauser, Ulrich; Meyer-Pittroff, Roland; Hasse, Hans

2009-02-01

For the design and optimization of CO 2 recovery from alcoholic fermentation processes by distillation, models for vapor-liquid equilibria (VLE) are needed. Two such thermodynamic models, the Peng-Robinson equation of state (EOS) and a model based on Henry's law constants, are proposed for the ternary mixture N 2 + O 2 + CO 2. Pure substance parameters of the Peng-Robinson EOS are taken from the literature, whereas the binary parameters of the Van der Waals one-fluid mixing rule are adjusted to experimental binary VLE data. The Peng-Robinson EOS describes both binary and ternary experimental data well, except at high pressures approaching the critical region. A molecular model is validated by simulation using binary and ternary experimental VLE data. On the basis of this model, the Henry's law constants of N 2 and O 2 in CO 2 are predicted by molecular simulation. An easy-to-use thermodynamic model, based on those Henry's law constants, is developed to reliably describe the VLE in the CO 2-rich region.

A Synergistic Approach to Interpreting Planetary Atmospheres

NASA Astrophysics Data System (ADS)

Batalha, Natasha E.

We will soon have the technological capability to measure the atmospheric composition of temperate Earth-sized planets orbiting nearby stars. Interpreting these atmospheric signals poses a new challenge to planetary science. In contrast to jovian-like atmospheres, whose bulk compositions consist of hydrogen and helium, terrestrial planet atmospheres are likely comprised of high mean molecular weight secondary atmospheres, which have gone through a high degree of evolution. For example, present-day Mars has a frozen surface with a thin tenuous atmosphere, but 4 billion years ago it may have been warmed by a thick greenhouse atmosphere. Several processes contribute to a planet's atmospheric evolution: stellar evolution, geological processes, atmospheric escape, biology, etc. Each of these individual processes affects the planetary system as a whole and therefore they all must be considered in the modeling of terrestrial planets. In order to demonstrate the intricacies in modeling terrestrial planets, I use early Mars as a case study. I leverage a combination of one-dimensional climate, photochemical and energy balance models in order to create one self-consistent model that closely matches currently available climate data. One-dimensional models can address several processes: the influence of greenhouse gases on heating, the effect of the planet's geological processes (i.e. volcanoes and the carbonatesilicate cycle) on the atmosphere, the effect of rainfall on atmospheric composition and the stellar irradiance. After demonstrating the number of assumptions required to build a model, I look towards what exactly we can learn from remote observations of temperate Earths and Super Earths. However, unlike in-situ observations from our own solar system, remote sensing techniques need to be developed and understood in order to accurately characterize exo-atmospheres. I describe the models used to create synthetic transit transmission observations, which includes models of transit spectroscopy and instrumental noise. Using these, I lay the framework for an information content-based approach to optimize our observations and maximize the retrievable information from exoatmospheres. First I test the method on observing strategies of the well-studied, low-mean-molecular weight atmospheres of warm-Neptunes and hot Jupiters. Upon verifying the methodology, I finally address optimal observing strategies for temperate, high-mean-molecular weight atmospheres (Earths/super-Earths). iv.

A Method for Combining Experimentation and Molecular Dynamics Simulation to Improve Cohesive Zone Models for Metallic Microstructures

NASA Technical Reports Server (NTRS)

Hochhalter, J. D.; Glaessgen, E. H.; Ingraffea, A. R.; Aquino, W. A.

2009-01-01

Fracture processes within a material begin at the nanometer length scale at which the formation, propagation, and interaction of fundamental damage mechanisms occur. Physics-based modeling of these atomic processes quickly becomes computationally intractable as the system size increases. Thus, a multiscale modeling method, based on the aggregation of fundamental damage processes occurring at the nanoscale within a cohesive zone model, is under development and will enable computationally feasible and physically meaningful microscale fracture simulation in polycrystalline metals. This method employs atomistic simulation to provide an optimization loop with an initial prediction of a cohesive zone model (CZM). This initial CZM is then applied at the crack front region within a finite element model. The optimization procedure iterates upon the CZM until the finite element model acceptably reproduces the near-crack-front displacement fields obtained from experimental observation. With this approach, a comparison can be made between the original CZM predicted by atomistic simulation and the converged CZM that is based on experimental observation. Comparison of the two CZMs gives insight into how atomistic simulation scales.

Towards the optimal design of an uncemented acetabular component using genetic algorithms

NASA Astrophysics Data System (ADS)

Ghosh, Rajesh; Pratihar, Dilip Kumar; Gupta, Sanjay

2015-12-01

Aseptic loosening of the acetabular component (hemispherical socket of the pelvic bone) has been mainly attributed to bone resorption and excessive generation of wear particle debris. The aim of this study was to determine optimal design parameters for the acetabular component that would minimize bone resorption and volumetric wear. Three-dimensional finite element models of intact and implanted pelvises were developed using data from computed tomography scans. A multi-objective optimization problem was formulated and solved using a genetic algorithm. A combination of suitable implant material and corresponding set of optimal thicknesses of the component was obtained from the Pareto-optimal front of solutions. The ultra-high-molecular-weight polyethylene (UHMWPE) component generated considerably greater volumetric wear but lower bone density loss compared to carbon-fibre reinforced polyetheretherketone (CFR-PEEK) and ceramic. CFR-PEEK was located in the range between ceramic and UHMWPE. Although ceramic appeared to be a viable alternative to cobalt-chromium-molybdenum alloy, CFR-PEEK seems to be the most promising alternative material.

A group contribution method for associating chain molecules based on the statistical associating fluid theory (SAFT-γ)

NASA Astrophysics Data System (ADS)

Lymperiadis, Alexandros; Adjiman, Claire S.; Galindo, Amparo; Jackson, George

2007-12-01

A predictive group-contribution statistical associating fluid theory (SAFT-γ) is developed by extending the molecular-based SAFT-VR equation of state [A. Gil-Villegas et al. J. Chem. Phys. 106, 4168 (1997)] to treat heteronuclear molecules which are formed from fused segments of different types. Our models are thus a heteronuclear generalization of the standard models used within SAFT, comparable to the optimized potentials for the liquid state OPLS models commonly used in molecular simulation; an advantage of our SAFT-γ over simulation is that an algebraic description for the thermodynamic properties of the model molecules can be developed. In our SAFT-γ approach, each functional group in the molecule is modeled as a united-atom spherical (square-well) segment. The different groups are thus characterized by size (diameter), energy (well depth) and range parameters representing the dispersive interaction, and by shape factor parameters (which denote the extent to which each group contributes to the overall molecular properties). For associating groups a number of bonding sites are included on the segment: in this case the site types, the number of sites of each type, and the appropriate association energy and range parameters also have to be specified. A number of chemical families (n-alkanes, branched alkanes, n-alkylbenzenes, mono- and diunsaturated hydrocarbons, and n-alkan-1-ols) are treated in order to assess the quality of the SAFT-γ description of the vapor-liquid equilibria and to estimate the parameters of various functional groups. The group parameters for the functional groups present in these compounds (CH3, CH2, CH3CH, ACH, ACCH2, CH2, CH , and OH) together with the unlike energy parameters between groups of different types are obtained from an optimal description of the pure component phase equilibria. The approach is found to describe accurately the vapor-liquid equilibria with an overall %AAD of 3.60% for the vapor pressure and 0.86% for the saturated liquid density. The fluid phase equilibria of some larger compounds comprising these groups, which are not included in the optimization database and some binary mixtures are examined to confirm the predictive capability of the SAFT-γ approach. A key advantage of our method is that the binary interaction parameters between groups can be estimated directly from an examination of pure components alone. This means that as a first approximation the fluid-phase equilibria of mixtures of compounds comprising the groups considered can be predicted without the need for any adjustment of the binary interaction parameters (which is common in other approaches). The special case of molecular models comprising tangentially bonded (all-atom and united-atom) segments is considered separately; we comment on the adequacy of such models in representing the properties of real molecules.

A group contribution method for associating chain molecules based on the statistical associating fluid theory (SAFT-gamma).

PubMed

Lymperiadis, Alexandros; Adjiman, Claire S; Galindo, Amparo; Jackson, George

2007-12-21

A predictive group-contribution statistical associating fluid theory (SAFT-gamma) is developed by extending the molecular-based SAFT-VR equation of state [A. Gil-Villegas et al. J. Chem. Phys. 106, 4168 (1997)] to treat heteronuclear molecules which are formed from fused segments of different types. Our models are thus a heteronuclear generalization of the standard models used within SAFT, comparable to the optimized potentials for the liquid state OPLS models commonly used in molecular simulation; an advantage of our SAFT-gamma over simulation is that an algebraic description for the thermodynamic properties of the model molecules can be developed. In our SAFT-gamma approach, each functional group in the molecule is modeled as a united-atom spherical (square-well) segment. The different groups are thus characterized by size (diameter), energy (well depth) and range parameters representing the dispersive interaction, and by shape factor parameters (which denote the extent to which each group contributes to the overall molecular properties). For associating groups a number of bonding sites are included on the segment: in this case the site types, the number of sites of each type, and the appropriate association energy and range parameters also have to be specified. A number of chemical families (n-alkanes, branched alkanes, n-alkylbenzenes, mono- and diunsaturated hydrocarbons, and n-alkan-1-ols) are treated in order to assess the quality of the SAFT-gamma description of the vapor-liquid equilibria and to estimate the parameters of various functional groups. The group parameters for the functional groups present in these compounds (CH(3), CH(2), CH(3)CH, ACH, ACCH(2), CH(2)=, CH=, and OH) together with the unlike energy parameters between groups of different types are obtained from an optimal description of the pure component phase equilibria. The approach is found to describe accurately the vapor-liquid equilibria with an overall %AAD of 3.60% for the vapor pressure and 0.86% for the saturated liquid density. The fluid phase equilibria of some larger compounds comprising these groups, which are not included in the optimization database and some binary mixtures are examined to confirm the predictive capability of the SAFT-gamma approach. A key advantage of our method is that the binary interaction parameters between groups can be estimated directly from an examination of pure components alone. This means that as a first approximation the fluid-phase equilibria of mixtures of compounds comprising the groups considered can be predicted without the need for any adjustment of the binary interaction parameters (which is common in other approaches). The special case of molecular models comprising tangentially bonded (all-atom and united-atom) segments is considered separately; we comment on the adequacy of such models in representing the properties of real molecules.

Mathematical modeling of cell adhesion in shear flow: application to targeted drug delivery in inflammation and cancer metastasis.

PubMed

Jadhav, Sameer; Eggleton, Charles D; Konstantopoulos, Konstantinos

2007-01-01

Cell adhesion plays a pivotal role in diverse biological processes that occur in the dynamic setting of the vasculature, including inflammation and cancer metastasis. Although complex, the naturally occurring processes that have evolved to allow for cell adhesion in the vasculature can be exploited to direct drug carriers to targeted cells and tissues. Fluid (blood) flow influences cell adhesion at the mesoscale by affecting the mechanical response of cell membrane, the intercellular contact area and collisional frequency, and at the nanoscale level by modulating the kinetics and mechanics of receptor-ligand interactions. Consequently, elucidating the molecular and biophysical nature of cell adhesion requires a multidisciplinary approach involving the synthesis of fundamentals from hydrodynamic flow, molecular kinetics and cell mechanics with biochemistry/molecular cell biology. To date, significant advances have been made in the identification and characterization of the critical cell adhesion molecules involved in inflammatory disorders, and, to a lesser degree, in cancer metastasis. Experimental work at the nanoscale level to determine the lifetime, interaction distance and strain responses of adhesion receptor-ligand bonds has been spurred by the advent of atomic force microscopy and biomolecular force probes, although our current knowledge in this area is far from complete. Micropipette aspiration assays along with theoretical frameworks have provided vital information on cell mechanics. Progress in each of the aforementioned research areas is key to the development of mathematical models of cell adhesion that incorporate the appropriate biological, kinetic and mechanical parameters that would lead to reliable qualitative and quantitative predictions. These multiscale mathematical models can be employed to predict optimal drug carrier-cell binding through isolated parameter studies and engineering optimization schemes, which will be essential for developing effective drug carriers for delivery of therapeutic agents to afflicted sites of the host.

Diverse ways of perturbing the human arachidonic acid metabolic network to control inflammation.

PubMed

Meng, Hu; Liu, Ying; Lai, Luhua

2015-08-18

Inflammation and other common disorders including diabetes, cardiovascular disease, and cancer are often the result of several molecular abnormalities and are not likely to be resolved by a traditional single-target drug discovery approach. Though inflammation is a normal bodily reaction, uncontrolled and misdirected inflammation can cause inflammatory diseases such as rheumatoid arthritis and asthma. Nonsteroidal anti-inflammatory drugs including aspirin, ibuprofen, naproxen, or celecoxib are commonly used to relieve aches and pains, but often these drugs have undesirable and sometimes even fatal side effects. To facilitate safer and more effective anti-inflammatory drug discovery, a balanced treatment strategy should be developed at the biological network level. In this Account, we focus on our recent progress in modeling the inflammation-related arachidonic acid (AA) metabolic network and subsequent multiple drug design. We first constructed a mathematical model of inflammation based on experimental data and then applied the model to simulate the effects of commonly used anti-inflammatory drugs. Our results indicated that the model correctly reproduced the established bleeding and cardiovascular side effects. Multitarget optimal intervention (MTOI), a Monte Carlo simulated annealing based computational scheme, was then developed to identify key targets and optimal solutions for controlling inflammation. A number of optimal multitarget strategies were discovered that were both effective and safe and had minimal associated side effects. Experimental studies were performed to evaluate these multitarget control solutions further using different combinations of inhibitors to perturb the network. Consequently, simultaneous control of cyclooxygenase-1 and -2 and leukotriene A4 hydrolase, as well as 5-lipoxygenase and prostaglandin E2 synthase were found to be among the best solutions. A single compound that can bind multiple targets presents advantages including low risk of drug-drug interactions and robustness regarding concentration fluctuations. Thus, we developed strategies for multiple-target drug design and successfully discovered several series of multiple-target inhibitors. Optimal solutions for a disease network often involve mild but simultaneous interventions of multiple targets, which is in accord with the philosophy of traditional Chinese medicine (TCM). To this end, our AA network model can aptly explain TCM anti-inflammatory herbs and formulas at the molecular level. We also aimed to identify activators for several enzymes that appeared to have increased activity based on MTOI outcomes. Strategies were then developed to predict potential allosteric sites and to discover enzyme activators based on our hypothesis that combined treatment with the projected activators and inhibitors could balance different AA network pathways, control inflammation, and reduce associated adverse effects. Our work demonstrates that the integration of network modeling and drug discovery can provide novel solutions for disease control, which also calls for new developments in drug design concepts and methodologies. With the rapid accumulation of quantitative data and knowledge of the molecular networks of disease, we can expect an increase in the development and use of quantitative disease models to facilitate efficient and safe drug discovery.

Optimal use of novel agents in chronic lymphocytic leukemia.

PubMed

Smith, Mitchell R; Weiss, Robert F

2018-05-07

Novel agents are changing therapy for patients with CLL, but their optimal use remains unclear. We model the clinical situation in which CLL responds to therapy, but resistant clones, generally carrying del17p, progress and lead to relapse. Sub-clones of varying growth rates and treatment sensitivity affect predicted therapy outcomes. We explore effects of different approaches to starting novel agent in relation to bendamustine-rituximab induction therapy: at initiation of therapy, at the end of chemo-immunotherapy, at molecular relapse, or at clinical detection of relapse. The outcomes differ depending on the underlying clonal architecture, raising the concept that personalized approaches based on clinical evaluation of each patient's clonal architecture might optimize outcomes while minimizing toxicity and cost. Copyright © 2018 Elsevier Ltd. All rights reserved.

Molecular modeling of directed self-assembly of block copolymers: Fundamental studies of processing conditions and evolutionary pattern design

NASA Astrophysics Data System (ADS)

Khaira, Gurdaman Singh

Rapid progress in the semi-conductor industry has pushed for smaller feature sizes on integrated electronic circuits. Current photo-lithographic techniques for nanofabrication have reached their technical limit and are problematic when printing features small enough to meet future industrial requirements. "Bottom-up'' techniques, such as the directed self-assembly (DSA) of block copolymers (BCP), are the primary contenders to compliment current "top-down'' photo-lithography ones. For industrial requirements, the defect density from DSA needs to be less than 1 defect per 10 cm by 10 cm. Knowledge of both material synthesis and the thermodynamics of the self-assembly process are required before optimal operating conditions can be found to produce results adequate for industry. The work present in this thesis is divided into three chapters, each discussing various aspects of DSA as studied via a molecular model that contains the essential physics of BCP self-assembly. Though there are various types of guiding fields that can be used to direct BCPs over large wafer areas with minimum defects, this study focuses only on chemically patterned substrates. The first chapter addresses optimal pattern design by describing a framework where molecular simulations of various complexities are coupled with an advanced optimization technique to find a pattern that directs a target morphology. It demonstrates the first ever study where BCP self-assembly on a patterned substrate is optimized using a three-dimensional description of the block-copolymers. For problems pertaining to DSA, the methodology is shown to converge much faster than the traditional random search approach. The second chapter discusses the metrology of BCP thin films using TEM tomography and X-ray scattering techniques, such as CDSAXS and GISAXS. X-ray scattering has the advantage of being able to quickly probe the average structure of BCP morphologies over large wafer areas; however, deducing the BCP morphology from the information in inverse space is a challenging task. Using the optimization techniques and molecular simulations discussed in the first chapter, a methodology to reconstruct BCP morphology from X-ray scattering data is described. It is shown that only a handful of simulation parameters that come directly from experiment are able to describe the morphologies observed from real X-ray scattering experiments. The last chapter focuses on the use of solvents to assist the self-assembly of BCPs. Additional functionality to capture the process of solvent annealing is also discussed. The bulk behavior of solvated mixtures of BCPs with solvents of various affinities is described, and the results are consistent with the experimentally observed behavior of BCPs in the presence of solvents.

Computer-assisted determination of minimum energy conformations. 7: A pharmacophore model of the active region of the alpha2-adrenoceptor

NASA Astrophysics Data System (ADS)

Ashman, William P.; Mickiewicz, A. P.; Nelson, Todd M.

1992-09-01

Molecular modeling and computational chemistry techniques are used to analyze compounds in developing pharmacophores of biological receptors to use as templates in structure activity relationship studies and to design new chemicals having physiological activity of interest. In this study, the results of x-ray crystal analyses and PM3 semi-empirical molecular orbital conformational analyses are used to determine the three-dimensional representations of selected adrenergic compounds known to be agonists with the alpha2-adrenoceptor in achieving optimized geometries and electrostatic parameters. The alpha2-adrenergic agonists interact with the adrenergic system receptors to produce various increases or decreases in hemodynamic responses (i.e., hypertension, hypotension, and bradycardia) and sedation. A pharmacophore model of the active region of the alpha2-adrenoceptor is described based on the superimposition of common structural, electrostatic, and physicochemical features of the compounds. Using the model to predict compound adrenergic activity and to design alpha2-adrenergic compounds is discussed.

PREFMD: a web server for protein structure refinement via molecular dynamics simulations.

PubMed

Heo, Lim; Feig, Michael

2018-03-15

Refinement of protein structure models is a long-standing problem in structural bioinformatics. Molecular dynamics-based methods have emerged as an avenue to achieve consistent refinement. The PREFMD web server implements an optimized protocol based on the method successfully tested in CASP11. Validation with recent CASP refinement targets shows consistent and more significant improvement in global structure accuracy over other state-of-the-art servers. PREFMD is freely available as a web server at http://feiglab.org/prefmd. Scripts for running PREFMD as a stand-alone package are available at https://github.com/feiglab/prefmd.git. feig@msu.edu. Supplementary data are available at Bioinformatics online.

Coherent Amplification of Ultrafast Molecular Dynamics in an Optical Oscillator

NASA Astrophysics Data System (ADS)

Aharonovich, Igal; Pe'er, Avi

2016-02-01

Optical oscillators present a powerful optimization mechanism. The inherent competition for the gain resources between possible modes of oscillation entails the prevalence of the most efficient single mode. We harness this "ultrafast" coherent feedback to optimize an optical field in time, and show that, when an optical oscillator based on a molecular gain medium is synchronously pumped by ultrashort pulses, a temporally coherent multimode field can develop that optimally dumps a general, dynamically evolving vibrational wave packet, into a single vibrational target state. Measuring the emitted field opens a new window to visualization and control of fast molecular dynamics. The realization of such a coherent oscillator with hot alkali dimers appears within experimental reach.

Parallel Molecular Distributed Detection With Brownian Motion.

PubMed

Rogers, Uri; Koh, Min-Sung

2016-12-01

This paper explores the in vivo distributed detection of an undesired biological agent's (BAs) biomarkers by a group of biological sized nanomachines in an aqueous medium under drift. The term distributed, indicates that the system information relative to the BAs presence is dispersed across the collection of nanomachines, where each nanomachine possesses limited communication, computation, and movement capabilities. Using Brownian motion with drift, a probabilistic detection and optimal data fusion framework, coined molecular distributed detection, will be introduced that combines theory from both molecular communication and distributed detection. Using the optimal data fusion framework as a guide, simulation indicates that a sub-optimal fusion method exists, allowing for a significant reduction in implementation complexity while retaining BA detection accuracy.

Molecular properties of steroids involved in their effects on the biophysical state of membranes.

PubMed

Wenz, Jorge J

2015-10-01

The activity of steroids on membranes was studied in relation to their ordering, rigidifying, condensing and/or raft promoting ability. The structures of 82 steroids were modeled by a semi-empirical procedure (AM1) and 245 molecular descriptors were next computed on the optimized energy conformations. Principal component analysis, mean contrasting and logistic regression were used to correlate the molecular properties with 212 cases of documented activities. It was possible to group steroids based on their properties and activities, indicating that steroids having similar molecular properties have similar activities on membranes. Steroids having high values of area, partition coefficient, volume, number of rotatable bonds, molar refractivity, polarizability or mass displayed ordering, rigidifying, condensing and/or raft promoting activity on membranes higher than those steroids having low values in such molecular properties. After a variable selection procedure circumventing correlation problems among descriptors, area and log P were found as the most relevant properties in governing and predicting the activity of steroids on membranes. A logistic regression model as a function of the area and log P of the steroids is proposed, which is able to predict correctly 92.5% of the cases. A rationale of the findings is discussed. Copyright © 2015 Elsevier B.V. All rights reserved.

Combined 3D-QSAR and molecular docking study on 7,8-dialkyl-1,3-diaminopyrrolo-[3,2-f] Quinazoline series compounds to understand the binding mechanism of DHFR inhibitors

NASA Astrophysics Data System (ADS)

Aouidate, Adnane; Ghaleb, Adib; Ghamali, Mounir; Chtita, Samir; Choukrad, M'barek; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-07-01

A series of nineteen DHFR inhibitors was studied based on the combination of two computational techniques namely, three-dimensional quantitative structure activity relationship (3D-QSAR) and molecular docking. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were developed using 19 molecules having pIC50 ranging from 9.244 to 5.839. The best CoMFA and CoMSIA models show conventional determination coefficients R2 of 0.96 and 0.93 as well as the Leave One Out cross-validation determination coefficients Q2 of 0.64 and 0.72, respectively. The predictive ability of those models was evaluated by the external validation using a test set of five compounds with predicted determination coefficients R2test of 0.92 and 0.94, respectively. The binding mode between this kind of compounds and the DHFR enzyme in addition to the key amino acid residues were explored by molecular docking simulation. Contour maps and molecular docking identified that the R1 and R2 natures at the pyrazole moiety are the important features for the optimization of the binding affinity to the DHFR receptor. According to the good concordance between the CoMFA/CoMSIA contour maps and docking results, the obtained information was explored to design novel molecules.

Efficient approach to include molecular polarizations using charge and atom dipole response kernels to calculate free energy gradients in the QM/MM scheme.

PubMed

Asada, Toshio; Ando, Kanta; Sakurai, Koji; Koseki, Shiro; Nagaoka, Masataka

2015-10-28

An efficient approach to evaluate free energy gradients (FEGs) within the quantum mechanical/molecular mechanical (QM/MM) framework has been proposed to clarify reaction processes on the free energy surface (FES) in molecular assemblies. The method is based on response kernel approximations denoted as the charge and the atom dipole response kernel (CDRK) model that include explicitly induced atom dipoles. The CDRK model was able to reproduce polarization effects for both electrostatic interactions between QM and MM regions and internal energies in the QM region obtained by conventional QM/MM methods. In contrast to charge response kernel (CRK) models, CDRK models could be applied to various kinds of molecules, even linear or planer molecules, without using imaginary interaction sites. Use of the CDRK model enabled us to obtain FEGs on QM atoms in significantly reduced computational time. It was also clearly demonstrated that the time development of QM forces of the solvated propylene carbonate radical cation (PC˙(+)) provided reliable results for 1 ns molecular dynamics (MD) simulation, which were quantitatively in good agreement with expensive QM/MM results. Using FEG and nudged elastic band (NEB) methods, we found two optimized reaction paths on the FES for decomposition reactions to generate CO2 molecules from PC˙(+), whose reaction is known as one of the degradation mechanisms in the lithium-ion battery. Two of these reactions proceed through an identical intermediate structure whose molecular dipole moment is larger than that of the reactant to be stabilized in the solvent, which has a high relative dielectric constant. Thus, in order to prevent decomposition reactions, PC˙(+) should be modified to have a smaller dipole moment along two reaction paths.

System Design for Nano-Network Communications

NASA Astrophysics Data System (ADS)

ShahMohammadian, Hoda

The potential applications of nanotechnology in a wide range of areas necessities nano-networking research. Nano-networking is a new type of networking which has emerged by applying nanotechnology to communication theory. Therefore, this dissertation presents a framework for physical layer communications in a nano-network and addresses some of the pressing unsolved challenges in designing a molecular communication system. The contribution of this dissertation is proposing well-justified models for signal propagation, noise sources, optimum receiver design and synchronization in molecular communication channels. The design of any communication system is primarily based on the signal propagation channel and noise models. Using the Brownian motion and advection molecular statistics, separate signal propagation and noise models are presented for diffusion-based and flow-based molecular communication channels. It is shown that the corrupting noise of molecular channels is uncorrelated and non-stationary with a signal dependent magnitude. The next key component of any communication system is the reception and detection process. This dissertation provides a detailed analysis of the effect of the ligand-receptor binding mechanism on the received signal, and develops the first optimal receiver design for molecular communications. The bit error rate performance of the proposed receiver is evaluated and the impact of medium motion on the receiver performance is investigated. Another important feature of any communication system is synchronization. In this dissertation, the first blind synchronization algorithm is presented for the molecular communication channels. The proposed algorithm uses a non-decision directed maximum likelihood criterion for estimating the channel delay. The Cramer-Rao lower bound is also derived and the performance of the proposed synchronization algorithm is evaluated by investigating its mean square error.

3D Printing of Biomolecular Models for Research and Pedagogy

PubMed Central

Da Veiga Beltrame, Eduardo; Tyrwhitt-Drake, James; Roy, Ian; Shalaby, Raed; Suckale, Jakob; Pomeranz Krummel, Daniel

2017-01-01

The construction of physical three-dimensional (3D) models of biomolecules can uniquely contribute to the study of the structure-function relationship. 3D structures are most often perceived using the two-dimensional and exclusively visual medium of the computer screen. Converting digital 3D molecular data into real objects enables information to be perceived through an expanded range of human senses, including direct stereoscopic vision, touch, and interaction. Such tangible models facilitate new insights, enable hypothesis testing, and serve as psychological or sensory anchors for conceptual information about the functions of biomolecules. Recent advances in consumer 3D printing technology enable, for the first time, the cost-effective fabrication of high-quality and scientifically accurate models of biomolecules in a variety of molecular representations. However, the optimization of the virtual model and its printing parameters is difficult and time consuming without detailed guidance. Here, we provide a guide on the digital design and physical fabrication of biomolecule models for research and pedagogy using open source or low-cost software and low-cost 3D printers that use fused filament fabrication technology. PMID:28362403

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.

Mathematical modeling for novel cancer drug discovery and development.

PubMed

Zhang, Ping; Brusic, Vladimir

2014-10-01

Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments. This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment. Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.

Biolayer modeling and optimization for the SPARROW biosensor

NASA Astrophysics Data System (ADS)

Feng, Ke

2007-12-01

Biosensor direct detection of molecular binding events is of significant interest in applications from molecular screening for cancer drug design to bioagent detection for homeland security and defense. The Stacked Planar Affinity Regulated Resonant Optical Waveguide (SPARROW) structure based on coupled waveguides was recently developed to achieve increased sensitivity within a fieldable biosensor device configuration. Under ideal operating conditions, modification of the effective propagation constant of the structure's sensing waveguide through selective attachment of specific targets to probes on the waveguide surface results in a change in the coupling characteristics of the guide over a specifically designed interaction length with the analyte. Monitoring the relative power in each waveguide after interaction enables 'recognition' of those targets which have selectively bound to the surface. However, fabrication tolerances, waveguide interface roughness, biolayer surface roughness and biolayer partial coverage have an effect on biosensor behavior and achievable limit of detection (LOD). In addition to these influences which play a role in device optimization, the influence of the spatially random surface loading of molecular binding events has to be considered, especially for low surface coverage. In this dissertation an analytic model is established for the SPARROW biosensor which accounts for these nonidealities with which the design of the biosensor can be guided and optimized. For the idealized case of uniform waveguide transducer layers and biolayer, both theoretical simulation (analytical expression) and computer simulation (numerical calculation) are completed. For the nonideal case of an inhomogeneous transducer with nonideal waveguide and biolayer surfaces, device output power is affected by such physical influences as surface scattering, coupling length, absorption, and percent coverage of binding events. Using grating and perturbation techniques we explore the influence of imperfect surfaces and random surface loading on scattering loss and coupling length. Results provide a range of achievable limits of detection in the SPARROW device for a given target size, surface loading, and detectable optical power.

Molecular simulations of electrolyte structure and dynamics in lithium-sulfur battery solvents

NASA Astrophysics Data System (ADS)

Park, Chanbum; Kanduč, Matej; Chudoba, Richard; Ronneburg, Arne; Risse, Sebastian; Ballauff, Matthias; Dzubiella, Joachim

2018-01-01

The performance of modern lithium-sulfur (Li/S) battery systems critically depends on the electrolyte and solvent compositions. For fundamental molecular insights and rational guidance of experimental developments, efficient and sufficiently accurate molecular simulations are thus in urgent need. Here, we construct a molecular dynamics (MD) computer simulation model of representative state-of-the art electrolyte-solvent systems for Li/S batteries constituted by lithium-bis(trifluoromethane)sulfonimide (LiTFSI) and LiNO3 electrolytes in mixtures of the organic solvents 1,2-dimethoxyethane (DME) and 1,3-dioxolane (DOL). We benchmark and verify our simulations by comparing structural and dynamic features with various available experimental reference systems and demonstrate their applicability for a wide range of electrolyte-solvent compositions. For the state-of-the-art battery solvent, we finally calculate and discuss the detailed composition of the first lithium solvation shell, the temperature dependence of lithium diffusion, as well as the electrolyte conductivities and lithium transference numbers. Our model will serve as a basis for efficient future predictions of electrolyte structure and transport in complex electrode confinements for the optimization of modern Li/S batteries (and related devices).

Testing the limits of sensitivity in a solid-state structural investigation by combined X-ray powder diffraction, solid-state NMR, and molecular modelling.

PubMed

Filip, Xenia; Borodi, Gheorghe; Filip, Claudiu

2011-10-28

A solid state structural investigation of ethoxzolamide is performed on microcrystalline powder by using a multi-technique approach that combines X-ray powder diffraction (XRPD) data analysis based on direct space methods with information from (13)C((15)N) solid-state Nuclear Magnetic Resonance (SS-NMR) and molecular modeling. Quantum chemical computations of the crystal were employed for geometry optimization and chemical shift calculations based on the Gauge Including Projector Augmented-Wave (GIPAW) method, whereas a systematic search in the conformational space was performed on the isolated molecule using a molecular mechanics (MM) approach. The applied methodology proved useful for: (i) removing ambiguities in the XRPD crystal structure determination process and further refining the derived structure solutions, and (ii) getting important insights into the relationship between the complex network of non-covalent interactions and the induced supra-molecular architectures/crystal packing patterns. It was found that ethoxzolamide provides an ideal case study for testing the accuracy with which this methodology allows to distinguish between various structural features emerging from the analysis of the powder diffraction data. This journal is © the Owner Societies 2011

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

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

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Scalable and fast heterogeneous molecular simulation with predictive parallelization schemes

DOE PAGES

Guzman, Horacio V.; Junghans, Christoph; Kremer, Kurt; ...

2017-11-27

Multiscale and inhomogeneous molecular systems are challenging topics in the field of molecular simulation. In particular, modeling biological systems in the context of multiscale simulations and exploring material properties are driving a permanent development of new simulation methods and optimization algorithms. In computational terms, those methods require parallelization schemes that make a productive use of computational resources for each simulation and from its genesis. Here, we introduce the heterogeneous domain decomposition approach, which is a combination of an heterogeneity-sensitive spatial domain decomposition with an a priori rearrangement of subdomain walls. Within this approach and paper, the theoretical modeling and scalingmore » laws for the force computation time are proposed and studied as a function of the number of particles and the spatial resolution ratio. We also show the new approach capabilities, by comparing it to both static domain decomposition algorithms and dynamic load-balancing schemes. Specifically, two representative molecular systems have been simulated and compared to the heterogeneous domain decomposition proposed in this work. Finally, these two systems comprise an adaptive resolution simulation of a biomolecule solvated in water and a phase-separated binary Lennard-Jones fluid.« less

Molecular Hybridization of Potent and Selective γ-Hydroxybutyric Acid (GHB) Ligands: Design, Synthesis, Binding Studies, and Molecular Modeling of Novel 3-Hydroxycyclopent-1-enecarboxylic Acid (HOCPCA) and trans-γ-Hydroxycrotonic Acid (T-HCA) Analogs.

PubMed

Krall, Jacob; Jensen, Claus Hatt; Bavo, Francesco; Falk-Petersen, Christina Birkedahl; Haugaard, Anne Stæhr; Vogensen, Stine Byskov; Tian, Yongsong; Nittegaard-Nielsen, Mia; Sigurdardóttir, Sara Björk; Kehler, Jan; Kongstad, Kenneth Thermann; Gloriam, David E; Clausen, Rasmus Prætorius; Harpsøe, Kasper; Wellendorph, Petrine; Frølund, Bente

2017-11-09

γ-Hydroxybutyric acid (GHB) is a neuroactive substance with specific high-affinity binding sites. To facilitate target identification and ligand optimization, we herein report a comprehensive structure-affinity relationship study for novel ligands targeting these binding sites. A molecular hybridization strategy was used based on the conformationally restricted 3-hydroxycyclopent-1-enecarboxylic acid (HOCPCA) and the linear GHB analog trans-4-hydroxycrotonic acid (T-HCA). In general, all structural modifications performed on HOCPCA led to reduced affinity. In contrast, introduction of diaromatic substituents into the 4-position of T-HCA led to high-affinity analogs (medium nanomolar K i ) for the GHB high-affinity binding sites as the most high-affinity analogs reported to date. The SAR data formed the basis for a three-dimensional pharmacophore model for GHB ligands, which identified molecular features important for high-affinity binding, with high predictive validity. These findings will be valuable in the further processes of both target characterization and ligand identification for the high-affinity GHB binding sites.

Experimental Design for Parameter Estimation of Gene Regulatory Networks

PubMed Central

Timmer, Jens

2012-01-01

Systems biology aims for building quantitative models to address unresolved issues in molecular biology. In order to describe the behavior of biological cells adequately, gene regulatory networks (GRNs) are intensively investigated. As the validity of models built for GRNs depends crucially on the kinetic rates, various methods have been developed to estimate these parameters from experimental data. For this purpose, it is favorable to choose the experimental conditions yielding maximal information. However, existing experimental design principles often rely on unfulfilled mathematical assumptions or become computationally demanding with growing model complexity. To solve this problem, we combined advanced methods for parameter and uncertainty estimation with experimental design considerations. As a showcase, we optimized three simulated GRNs in one of the challenges from the Dialogue for Reverse Engineering Assessment and Methods (DREAM). This article presents our approach, which was awarded the best performing procedure at the DREAM6 Estimation of Model Parameters challenge. For fast and reliable parameter estimation, local deterministic optimization of the likelihood was applied. We analyzed identifiability and precision of the estimates by calculating the profile likelihood. Furthermore, the profiles provided a way to uncover a selection of most informative experiments, from which the optimal one was chosen using additional criteria at every step of the design process. In conclusion, we provide a strategy for optimal experimental design and show its successful application on three highly nonlinear dynamic models. Although presented in the context of the GRNs to be inferred for the DREAM6 challenge, the approach is generic and applicable to most types of quantitative models in systems biology and other disciplines. PMID:22815723

Molecular design for enhancement of ocular penetration.

PubMed

Shirasaki, Yoshihisa

2008-07-01

Over the past two decades, many oral drugs have been designed in consideration of physicochemical properties to attain optimal pharmacokinetic properties. This strategy significantly reduced attrition in drug development owing to inadequate pharmacokinetics during the last decade. On the other hand, most ophthalmic drugs are generated from reformulation of other therapeutic dosage forms. Therefore, the modification of formulations has been used mainly as the approach to improve ocular pharmacokinetics. However, to maximize ocular pharmacokinetic properties, a specific molecular design for ocular drug is preferable. Passive diffusion of drugs across the cornea membranes requires appropriate lipophilicity and aqueous solubility. Improvement of such physicochemical properties has been achieved by structure optimization or prodrug approaches. This review discusses the current knowledge about ophthalmic drugs adapted from systemic drugs and molecular design for ocular drugs. I propose the approaches for molecular design to obtain the optimal ocular penetration into anterior segment based on published studies to date.

Coenzyme Q10-Loaded Fish Oil-Based Bigel System: Probing the Delivery Across Porcine Skin and Possible Interaction with Fish Oil Fatty Acids.

PubMed

Zulfakar, Mohd Hanif; Chan, Lee Mei; Rehman, Khurram; Wai, Lam Kok; Heard, Charles M

2018-04-01

Coenzyme Q10 (CoQ10) is a vitamin-like oil-soluble molecule that has anti-oxidant and anti-ageing effects. To determine the most optimal CoQ10 delivery vehicle, CoQ10 was solubilised in both water and fish oil, and formulated into hydrogel, oleogel and bigel. Permeability of CoQ10 from each formulation across porcine ear skin was then evaluated. Furthermore, the effects of the omega-3 fatty eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids from fish oil on skin permeation were investigated by means of nuclear magnetic resonance (NMR) and computerised molecular modelling docking experiments. The highest drug permeation was achieved with the bigel formulation that proved to be the most effective vehicle in delivering CoQ10 across the skin membrane due to a combination of its adhesive, viscous and lipophilic properties. Furthermore, the interactions between CoQ10 and fatty acids revealed by NMR and molecular modelling experiments likely accounted for skin permeability of CoQ10. NMR data showed dose-dependent changes in proton chemical shifts in EPA and DHA. Molecular modelling revealed complex formation and large binding energies between fatty acids and CoQ10. This study advances the knowledge about bigels as drug delivery vehicles and highlights the use of NMR and molecular docking studies for the prediction of the influence of drug-excipient relationships at the molecular level.

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.

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.

Characterization, phase solubility and molecular modeling of α-cyclodextrin/pyrimethamine inclusion complex

NASA Astrophysics Data System (ADS)

Araujo, Marcia Valeria Gaspar de; Macedo, Osmir F. L.; Nascimento, Cristiane da Cunha; Conegero, Leila Souza; Barreto, Ledjane Silva; Almeida, Luis Eduardo; Costa, Nivan Bezerra da; Gimenez, Iara F.

2009-02-01

An inclusion complex between the dihydrofolate reductase inhibitor pyrimethamine (PYR) and α-cyclodextrin (α-CD) was prepared and characterized. From the phase-solubility diagram, a linear increase of PYR solubility was verified as a function of α-CD concentration, suggesting the formation of a soluble complex. A 1:1 host-guest stoichiometry can be proposed according to the Job's plot, obtained from the difference of PYR fluorescence intensity in the presence and absence of α-CD. Differential scanning calorimetry (DSC) measurements provided additional evidences of complexation such as the absence of the endothermic peak assigned to the melting of the drug. The inclusion mode characterized by two-dimensional 1H NMR spectroscopy (ROESY) involves penetration of the p-chlorophenyl ring into the α-CD cavity, in agreement to the orientation optimized by molecular modeling methods.

Optimizing physical energy functions for protein folding.

PubMed

Fujitsuka, Yoshimi; Takada, Shoji; Luthey-Schulten, Zaida A; Wolynes, Peter G

2004-01-01

We optimize a physical energy function for proteins with the use of the available structural database and perform three benchmark tests of the performance: (1) recognition of native structures in the background of predefined decoy sets of Levitt, (2) de novo structure prediction using fragment assembly sampling, and (3) molecular dynamics simulations. The energy parameter optimization is based on the energy landscape theory and uses a Monte Carlo search to find a set of parameters that seeks the largest ratio deltaE(s)/DeltaE for all proteins in a training set simultaneously. Here, deltaE(s) is the stability gap between the native and the average in the denatured states and DeltaE is the energy fluctuation among these states. Some of the energy parameters optimized are found to show significant correlation with experimentally observed quantities: (1) In the recognition test, the optimized function assigns the lowest energy to either the native or a near-native structure among many decoy structures for all the proteins studied. (2) Structure prediction with the fragment assembly sampling gives structure models with root mean square deviation less than 6 A in one of the top five cluster centers for five of six proteins studied. (3) Structure prediction using molecular dynamics simulation gives poorer performance, implying the importance of having a more precise description of local structures. The physical energy function solely inferred from a structural database neither utilizes sequence information from the family of the target nor the outcome of the secondary structure prediction but can produce the correct native fold for many small proteins. Copyright 2003 Wiley-Liss, Inc.

Modeling and verification of process parameters for the production of tannase by Aspergillus oryzae under submerged fermentation using agro-wastes.

PubMed

Varadharajan, Venkatramanan; Vadivel, Sudhan Shanmuga; Ramaswamy, Arulvel; Sundharamurthy, Venkatesaprabhu; Chandrasekar, Priyadharshini

2017-01-01

Tannase production by Aspergillus oryzae using various agro-wastes as substrates by submerged fermentation was studied in this research. The microbe was isolated from degrading corn kernel obtained from the corn fields at Tiruchengode, India. The microbial identification was done using 18S rRNA gene analysis. The agro-wastes chosen for the study were pomegranate rind, Cassia auriculata flower, black gram husk, and tea dust. The process parameters chosen for optimization study were substrate concentration, pH, temperature, and incubation period. During one variable at a time optimization, the pomegranate rind extract produced maximum tannase activity of 138.12 IU/mL and it was chosen as the best substrate for further experiments. The quadratic model was found to be the effective model for prediction of tannase production by A. oryzae. The optimized conditions predicted by response surface methodology (RSM) with genetic algorithm (GA) were 1.996% substrate concentration, pH of 4.89, temperature of 34.91 °C, and an incubation time of 70.65 H with maximum tannase activity of 138.363 IU/mL. The confirmatory experiment under optimized conditions showed tannase activity of 139.22 IU/mL. Hence, RSM-GA pair was successfully used in this study to optimize the process parameters required for the production of tannase using pomegranate rind. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Predicting DPP-IV inhibitors with machine learning approaches

NASA Astrophysics Data System (ADS)

Cai, Jie; Li, Chanjuan; Liu, Zhihong; Du, Jiewen; Ye, Jiming; Gu, Qiong; Xu, Jun

2017-04-01

Dipeptidyl peptidase IV (DPP-IV) is a promising Type 2 diabetes mellitus (T2DM) drug target. DPP-IV inhibitors prolong the action of glucagon-like peptide-1 (GLP-1) and gastric inhibitory peptide (GIP), improve glucose homeostasis without weight gain, edema, and hypoglycemia. However, the marketed DPP-IV inhibitors have adverse effects such as nasopharyngitis, headache, nausea, hypersensitivity, skin reactions and pancreatitis. Therefore, it is still expected for novel DPP-IV inhibitors with minimal adverse effects. The scaffolds of existing DPP-IV inhibitors are structurally diversified. This makes it difficult to build virtual screening models based upon the known DPP-IV inhibitor libraries using conventional QSAR approaches. In this paper, we report a new strategy to predict DPP-IV inhibitors with machine learning approaches involving naïve Bayesian (NB) and recursive partitioning (RP) methods. We built 247 machine learning models based on 1307 known DPP-IV inhibitors with optimized molecular properties and topological fingerprints as descriptors. The overall predictive accuracies of the optimized models were greater than 80%. An external test set, composed of 65 recently reported compounds, was employed to validate the optimized models. The results demonstrated that both NB and RP models have a good predictive ability based on different combinations of descriptors. Twenty "good" and twenty "bad" structural fragments for DPP-IV inhibitors can also be derived from these models for inspiring the new DPP-IV inhibitor scaffold design.

Molecular Hydrogen as an Emerging Therapeutic Medical Gas for Neurodegenerative and Other Diseases

PubMed Central

Ohno, Kinji; Ito, Mikako; Ichihara, Masatoshi; Ito, Masafumi

2012-01-01

Effects of molecular hydrogen on various diseases have been documented for 63 disease models and human diseases in the past four and a half years. Most studies have been performed on rodents including two models of Parkinson's disease and three models of Alzheimer's disease. Prominent effects are observed especially in oxidative stress-mediated diseases including neonatal cerebral hypoxia; Parkinson's disease; ischemia/reperfusion of spinal cord, heart, lung, liver, kidney, and intestine; transplantation of lung, heart, kidney, and intestine. Six human diseases have been studied to date: diabetes mellitus type 2, metabolic syndrome, hemodialysis, inflammatory and mitochondrial myopathies, brain stem infarction, and radiation-induced adverse effects. Two enigmas, however, remain to be solved. First, no dose-response effect is observed. Rodents and humans are able to take a small amount of hydrogen by drinking hydrogen-rich water, but marked effects are observed. Second, intestinal bacteria in humans and rodents produce a large amount of hydrogen, but an addition of a small amount of hydrogen exhibits marked effects. Further studies are required to elucidate molecular bases of prominent hydrogen effects and to determine the optimal frequency, amount, and method of hydrogen administration for each human disease. PMID:22720117

Study of lithium cation in water clusters: based on atom-bond electronegativity equalization method fused into molecular mechanics.

PubMed

Li, Xin; Yang, Zhong-Zhi

2005-05-12

We present a potential model for Li(+)-water clusters based on a combination of the atom-bond electronegativity equalization and molecular mechanics (ABEEM/MM) that is to take ABEEM charges of the cation and all atoms, bonds, and lone pairs of water molecules into the intermolecular electrostatic interaction term in molecular mechanics. The model allows point charges on cationic site and seven sites of an ABEEM-7P water molecule to fluctuate responding to the cluster geometry. The water molecules in the first sphere of Li(+) are strongly structured and there is obvious charge transfer between the cation and the water molecules; therefore, the charge constraint on the ionic cluster includes the charged constraint on the Li(+) and the first-shell water molecules and the charge neutrality constraint on each water molecule in the external hydration shells. The newly constructed potential model based on ABEEM/MM is first applied to ionic clusters and reproduces gas-phase state properties of Li(+)(H(2)O)(n) (n = 1-6 and 8) including optimized geometries, ABEEM charges, binding energies, frequencies, and so on, which are in fair agreement with those measured by available experiments and calculated by ab initio methods. Prospects and benefits introduced by this potential model are pointed out.

High-Efficiency Multiscale Modeling of Cell Deformations in Confined Microenvironments in Microcirculation and Microfluidics

NASA Astrophysics Data System (ADS)

Lu, Huijie; Peng, Zhangli

2017-11-01

We developed a high-efficiency multiscale modeling method to predict the stress and deformation of cells during the interactions with their microenvironments in microcirculation and microfluidics, including red blood cells (RBCs) and circulating tumor cells (CTCs). There are more than 1 billion people in the world suffering from RBC diseases. The mechanical properties of RBCs are changed in these diseases due to molecular structure alternations, which is not only important for understanding the disease pathology but also provides an opportunity for diagnostics. On the other hand, the mechanical properties of cancer cells are also altered compared to healthy cells. This can lead to acquired ability to cross the narrow capillary networks and endothelial gaps, which is crucial for metastasis, the leading cause of cancer mortality. Therefore, it is important to predict the deformation and stress of RBCs and CTCs in microcirculations. We develop a high-efficiency multiscale model of cell-fluid interaction. We pass the information from our molecular scale models to the cell scale to study the effect of molecular mutations. Using our high-efficiency boundary element methods of fluids, we will be able to run 3D simulations using a single CPU within several hours, which will enable us to run extensive parametric studies and optimization.

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.

Novel naïve Bayes classification models for predicting the carcinogenicity of chemicals.

PubMed

Zhang, Hui; Cao, Zhi-Xing; Li, Meng; Li, Yu-Zhi; Peng, Cheng

2016-11-01

The carcinogenicity prediction has become a significant issue for the pharmaceutical industry. The purpose of this investigation was to develop a novel prediction model of carcinogenicity of chemicals by using a naïve Bayes classifier. The established model was validated by the internal 5-fold cross validation and external test set. The naïve Bayes classifier gave an average overall prediction accuracy of 90 ± 0.8% for the training set and 68 ± 1.9% for the external test set. Moreover, five simple molecular descriptors (e.g., AlogP, Molecular weight (M W ), No. of H donors, Apol and Wiener) considered as important for the carcinogenicity of chemicals were identified, and some substructures related to the carcinogenicity were achieved. Thus, we hope the established naïve Bayes prediction model could be applied to filter early-stage molecules for this potential carcinogenicity adverse effect; and the identified five simple molecular descriptors and substructures of carcinogens would give a better understanding of the carcinogenicity of chemicals, and further provide guidance for medicinal chemists in the design of new candidate drugs and lead optimization, ultimately reducing the attrition rate in later stages of drug development. Copyright © 2016 Elsevier Ltd. All rights reserved.

Machine Learning Prediction of the Energy Gap of Graphene Nanoflakes Using Topological Autocorrelation Vectors.

PubMed

Fernandez, Michael; Abreu, Jose I; Shi, Hongqing; Barnard, Amanda S

2016-11-14

The possibility of band gap engineering in graphene opens countless new opportunities for application in nanoelectronics. In this work, the energy gaps of 622 computationally optimized graphene nanoflakes were mapped to topological autocorrelation vectors using machine learning techniques. Machine learning modeling revealed that the most relevant correlations appear at topological distances in the range of 1 to 42 with prediction accuracy higher than 80%. The data-driven model can statistically discriminate between graphene nanoflakes with different energy gaps on the basis of their molecular topology.

Quantum chemical study of the mechanism of action of vitamin K epoxide reductase (VKOR)

NASA Astrophysics Data System (ADS)

Deerfield, David, II; Davis, Charles H.; Wymore, Troy; Stafford, Darrel W.; Pedersen, Lee G.

Possible model, but simplistic, mechanisms for the action of vitamin K epoxide reductase (VKOR) are investigated with quantum mechanical methods (B3LYP/6-311G**). The geometries of proposed model intermediates in the mechanisms are energy optimized. Finally, the energetics of the proposed (pseudo-enzymatic) pathways are compared. We find that the several pathways are all energetically feasible. These results will be useful for designing quantum mechanical/molecular mechanical method (QM/MM) studies of the enzymatic pathway once three-dimensional structural data are determined and available for VKOR.

Optimal thermoelectric figure of merit of a molecular junction

NASA Astrophysics Data System (ADS)

Murphy, Padraig; Mukerjee, Subroto; Moore, Joel

2008-10-01

We show that a molecular junction can give large values of the thermoelectric figure of merit ZT , and so it could be used as a solid-state energy-conversion device that operates close to the Carnot efficiency. The mechanism is similar to the Mahan-Sofo model for bulk thermoelectrics—the Lorenz number goes to zero violating the Wiedemann-Franz law while the thermopower remains nonzero. The molecular state through which charge is transported must be weakly coupled to the leads, and the energy level of the state must be of order kBT away from the Fermi energy of the leads. In practice, the figure of merit is limited by the phonon thermal conductance; we show that the largest possible Z Ttilde ( Gtilde thph)-1/2 , where Gtilde thph is the phonon thermal conductance divided by the thermal conductance quantum.

Assessing the Efficacy of Poly(N-isopropylacrylamide) for Drug Delivery Applications Using Molecular Dynamics Simulations.

PubMed

Moghadam, Soroush; Larson, Ronald G

2017-02-06

All-atom molecular dynamic simulations (AA-MD) are performed for aqueous solutions of hydrophobic drug molecules (phenytoin) with model polymer excipients, namely, (1) N-isopropylacrylamide, (pNIPAAm), (2) pNIPAAm-co-acrylamide (Am), and (3) pNIPAAm-co-dimethylacrylamide (DMA). After validating the force field parameters using the well-known lower critical solution behavior of pNIPAAm, we simulate the polymer-drug complex in water and its behavior at temperatures below (295 K) and above the LCST (310 K). Using radial distribution functions, we find that there is an optimum comonomer molar fraction of around 20-30% DMA at which interaction with phenytoin drug molecules is strongest, consistent with recent experimental findings. The results provide evidence that molecular simulations are able to provide guidance in the optimization of novel polymer excipients for drug release.

Could LogP be a principal determinant of biological activity in 18-crown-6 ethers? Synthesis of biologically active adamantane-substituted diaza-crowns.

PubMed

Supek, Fran; Ramljak, Tatjana Šumanovac; Marjanović, Marko; Buljubašić, Maja; Kragol, Goran; Ilić, Nataša; Smuc, Tomislav; Zahradka, Davor; Mlinarić-Majerski, Kata; Kralj, Marijeta

2011-08-01

18-crown-6 ethers are known to exert their biological activity by transporting K(+) ions across cell membranes. Using non-linear Support Vector Machines regression, we searched for structural features that influence antiproliferative activity in a diverse set of 19 known oxa-, monoaza- and diaza-18-crown-6 ethers. Here, we show that the logP of the molecule is the most important molecular descriptor, among ∼1300 tested descriptors, in determining biological potency (R(2)(cv) = 0.704). The optimal logP was at 5.5 (Ghose-Crippen ALOGP estimate) while both higher and lower values were detrimental to biological potency. After controlling for logP, we found that the antiproliferative activity of the molecule was generally not affected by side chain length, molecular symmetry, or presence of side chain amide links. To validate this QSAR model, we synthesized six novel, highly lipophilic diaza-18-crown-6 derivatives with adamantane moieties attached to the side arms. These compounds have near-optimal logP values and consequently exhibit strong growth inhibition in various human cancer cell lines and a bacterial system. The bioactivities of different diaza-18-crown-6 analogs in Bacillus subtilis and cancer cells were correlated, suggesting conserved molecular features may be mediating the cytotoxic response. We conclude that relying primarily on the logP is a sensible strategy in preparing future 18-crown-6 analogs with optimized biological activity. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

RNA unrestrained molecular dynamics ensemble improves agreement with experimental NMR data compared to single static structure: a test case

NASA Astrophysics Data System (ADS)

Beckman, Robert A.; Moreland, David; Louise-May, Shirley; Humblet, Christine

2006-05-01

Nuclear magnetic resonance (NMR) provides structural and dynamic information reflecting an average, often non-linear, of multiple solution-state conformations. Therefore, a single optimized structure derived from NMR refinement may be misleading if the NMR data actually result from averaging of distinct conformers. It is hypothesized that a conformational ensemble generated by a valid molecular dynamics (MD) simulation should be able to improve agreement with the NMR data set compared with the single optimized starting structure. Using a model system consisting of two sequence-related self-complementary ribonucleotide octamers for which NMR data was available, 0.3 ns particle mesh Ewald MD simulations were performed in the AMBER force field in the presence of explicit water and counterions. Agreement of the averaged properties of the molecular dynamics ensembles with NMR data such as homonuclear proton nuclear Overhauser effect (NOE)-based distance constraints, homonuclear proton and heteronuclear 1H-31P coupling constant ( J) data, and qualitative NMR information on hydrogen bond occupancy, was systematically assessed. Despite the short length of the simulation, the ensemble generated from it agreed with the NMR experimental constraints more completely than the single optimized NMR structure. This suggests that short unrestrained MD simulations may be of utility in interpreting NMR results. As expected, a 0.5 ns simulation utilizing a distance dependent dielectric did not improve agreement with the NMR data, consistent with its inferior exploration of conformational space as assessed by 2-D RMSD plots. Thus, ability to rapidly improve agreement with NMR constraints may be a sensitive diagnostic of the MD methods themselves.

From in silica to in silico: retention thermodynamics at solid-liquid interfaces.

PubMed

El Hage, Krystel; Bemish, Raymond J; Meuwly, Markus

2018-06-28

The dynamics of solvated molecules at the solid/liquid interface is essential for a molecular-level understanding for the solution thermodynamics in reversed phase liquid chromatography (RPLC). The heterogeneous nature of the systems and the competing intermolecular interactions makes solute retention in RPLC a surprisingly challenging problem which benefits greatly from modelling at atomistic resolution. However, the quality of the underlying computational model needs to be sufficiently accurate to provide a realistic description of the energetics and dynamics of systems, especially for solution-phase simulations. Here, the retention thermodynamics and the retention mechanism of a range of benzene-derivatives in C18 stationary-phase chains in contact with water/methanol mixtures is studied using point charge (PC) and multipole (MTP) electrostatic models. The results demonstrate that free energy simulations with a faithful MTP representation of the computational model provide quantitative and molecular level insight into the thermodynamics of adsorption/desorption in chromatographic systems while a conventional PC representation fails in doing so. This provides a rational basis to develop more quantitative and validated models for the optimization of separation systems.

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

Zhang, Na; Zhang, Peng; Kang, Wei

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters aremore » systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately.« less

Ab initio modeling of steady-state and time-dependent charge transport in hole-only α-NPD devices

NASA Astrophysics Data System (ADS)

Liu, Feilong; Massé, Andrea; Friederich, Pascal; Symalla, Franz; Nitsche, Robert; Wenzel, Wolfgang; Coehoorn, Reinder; Bobbert, Peter A.

2016-12-01

We present an ab initio modeling study of steady-state and time-dependent charge transport in hole-only devices of the amorphous molecular semiconductor α-NPD [N ,N'-Di(1 -naphthyl)-N ,N'-diphenyl-(1 ,1'-biphenyl)-4 ,4'-diamine] . The study is based on the microscopic information obtained from atomistic simulations of the morphology and density functional theory calculations of the molecular hole energies, reorganization energies, and transfer integrals. Using stochastic approaches, the microscopic information obtained in simulation boxes at a length scale of ˜10 nm is expanded and employed in one-dimensional (1D) and three-dimensional (3D) master-equation modeling of the charge transport at the device scale of ˜100 nm. Without any fit parameter, predicted current density-voltage and impedance spectroscopy data obtained with the 3D modeling are in very good agreement with measured data on devices with different α-NPD layer thicknesses in a wide range of temperatures, bias voltages, and frequencies. Similarly good results are obtained with the computationally much more efficient 1D modeling after optimizing a hopping prefactor.

Fragment-Based Drug Discovery of Potent Protein Kinase C Iota Inhibitors.

PubMed

Kwiatkowski, Jacek; Liu, Boping; Tee, Doris Hui Ying; Chen, Guoying; Ahmad, Nur Huda Binte; Wong, Yun Xuan; Poh, Zhi Ying; Ang, Shi Hua; Tan, Eldwin Sum Wai; Ong, Esther Hq; Nurul Dinie; Poulsen, Anders; Pendharkar, Vishal; Sangthongpitag, Kanda; Lee, May Ann; Sepramaniam, Sugunavathi; Ho, Soo Yei; Cherian, Joseph; Hill, Jeffrey; Keller, Thomas H; Hung, Alvin W

2018-05-24

Protein kinase C iota (PKC-ι) is an atypical kinase implicated in the promotion of different cancer types. A biochemical screen of a fragment library has identified several hits from which an azaindole-based scaffold was chosen for optimization. Driven by a structure-activity relationship and supported by molecular modeling, a weakly bound fragment was systematically grown into a potent and selective inhibitor against PKC-ι.

Geometry-dependent distributed polarizability models for the water molecule

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

Loboda, Oleksandr; Ingrosso, Francesca; Ruiz-López, Manuel F.

2016-01-21

Geometry-dependent distributed polarizability models have been constructed by fits to ab initio calculations at the coupled cluster level of theory with up to noniterative triple excitations in an augmented triple-zeta quality basis set for the water molecule in the field of a point charge. The investigated models include (i) charge-flow polarizabilities between chemically bonded atoms, (ii) isotropic or anisotropic dipolar polarizabilities on oxygen atom or on all atoms, and (iii) combinations of models (i) and (ii). For each model, the polarizability parameters have been optimized to reproduce the induction energy of a water molecule polarized by a point charge successivelymore » occupying a grid of points surrounding the molecule. The quality of the models is ascertained by examining their ability to reproduce these induction energies as well as the molecular dipolar and quadrupolar polarizabilities. The geometry dependence of the distributed polarizability models has been explored by changing bond lengths and HOH angle to generate 125 molecular structures (reduced to 75 symmetry-unique ones). For each considered model, the distributed polarizability components have been fitted as a function of the geometry by a Taylor expansion in monomer coordinate displacements up to the sum of powers equal to 4.« less

Analytic gradient for second order Møller-Plesset perturbation theory with the polarizable continuum model based on the fragment molecular orbital method.

PubMed

Nagata, Takeshi; Fedorov, Dmitri G; Li, Hui; Kitaura, Kazuo

2012-05-28

A new energy expression is proposed for the fragment molecular orbital method interfaced with the polarizable continuum model (FMO/PCM). The solvation free energy is shown to be more accurate on a set of representative polypeptides with neutral and charged residues, in comparison to the original formulation at the same level of the many-body expansion of the electrostatic potential determining the apparent surface charges. The analytic first derivative of the energy with respect to nuclear coordinates is formulated at the second-order Møller-Plesset (MP2) perturbation theory level combined with PCM, for which we derived coupled perturbed Hartree-Fock equations. The accuracy of the analytic gradient is demonstrated on test calculations in comparison to numeric gradient. Geometry optimization of the small Trp-cage protein (PDB: 1L2Y) is performed with FMO/PCM/6-31(+)G(d) at the MP2 and restricted Hartree-Fock with empirical dispersion (RHF/D). The root mean square deviations between the FMO optimized and NMR experimental structure are found to be 0.414 and 0.426 Å for RHF/D and MP2, respectively. The details of the hydrogen bond network in the Trp-cage protein are revealed.

Analytic gradient for second order Møller-Plesset perturbation theory with the polarizable continuum model based on the fragment molecular orbital method

NASA Astrophysics Data System (ADS)

Nagata, Takeshi; Fedorov, Dmitri G.; Li, Hui; Kitaura, Kazuo

2012-05-01

A new energy expression is proposed for the fragment molecular orbital method interfaced with the polarizable continuum model (FMO/PCM). The solvation free energy is shown to be more accurate on a set of representative polypeptides with neutral and charged residues, in comparison to the original formulation at the same level of the many-body expansion of the electrostatic potential determining the apparent surface charges. The analytic first derivative of the energy with respect to nuclear coordinates is formulated at the second-order Møller-Plesset (MP2) perturbation theory level combined with PCM, for which we derived coupled perturbed Hartree-Fock equations. The accuracy of the analytic gradient is demonstrated on test calculations in comparison to numeric gradient. Geometry optimization of the small Trp-cage protein (PDB: 1L2Y) is performed with FMO/PCM/6-31(+)G(d) at the MP2 and restricted Hartree-Fock with empirical dispersion (RHF/D). The root mean square deviations between the FMO optimized and NMR experimental structure are found to be 0.414 and 0.426 Å for RHF/D and MP2, respectively. The details of the hydrogen bond network in the Trp-cage protein are revealed.

Determination of solute descriptors by chromatographic methods.

PubMed

Poole, Colin F; Atapattu, Sanka N; Poole, Salwa K; Bell, Andrea K

2009-10-12

The solvation parameter model is now well established as a useful tool for obtaining quantitative structure-property relationships for chemical, biomedical and environmental processes. The model correlates a free-energy related property of a system to six free-energy derived descriptors describing molecular properties. These molecular descriptors are defined as L (gas-liquid partition coefficient on hexadecane at 298K), V (McGowan's characteristic volume), E (excess molar refraction), S (dipolarity/polarizability), A (hydrogen-bond acidity), and B (hydrogen-bond basicity). McGowan's characteristic volume is trivially calculated from structure and the excess molar refraction can be calculated for liquids from their refractive index and easily estimated for solids. The remaining four descriptors are derived by experiment using (largely) two-phase partitioning, chromatography, and solubility measurements. In this article, the use of gas chromatography, reversed-phase liquid chromatography, micellar electrokinetic chromatography, and two-phase partitioning for determining solute descriptors is described. A large database of experimental retention factors and partition coefficients is constructed after first applying selection tools to remove unreliable experimental values and an optimized collection of varied compounds with descriptor values suitable for calibrating chromatographic systems is presented. These optimized descriptors are demonstrated to be robust and more suitable than other groups of descriptors characterizing the separation properties of chromatographic systems.

A kinetic investigation of interacting, stimulated T cells identifies conditions for rapid functional enhancement, minimal phenotype differentiation, and improved adoptive cell transfer tumor eradication.

PubMed

Zhou, Jing; Bethune, Michael T; Malkova, Natalia; Sutherland, Alexander M; Comin-Anduix, Begonya; Su, Yapeng; Baltimore, David; Ribas, Antoni; Heath, James R

2018-01-01

For adoptive cell transfer (ACT) immunotherapy of tumor-reactive T cells, an effective therapeutic outcome depends upon cell dose, cell expansion in vivo through a minimally differentiated phenotype, long term persistence, and strong cytolytic effector function. An incomplete understanding of the biological coupling between T cell expansion, differentiation, and response to stimulation hinders the co-optimization of these factors. We report on a biophysical investigation of how the short-term kinetics of T cell functional activation, through molecular stimulation and cell-cell interactions, competes with phenotype differentiation. T cells receive molecular stimulation for a few minutes to a few hours in bulk culture. Following this priming period, the cells are then analyzed at the transcriptional level, or isolated as single cells, with continuing molecular stimulation, within microchambers for analysis via 11-plex secreted protein assays. We resolve a rapid feedback mechanism, promoted by T cell-T cell contact interactions, which strongly amplifies T cell functional performance while yielding only minimal phenotype differentiation. When tested in mouse models of ACT, optimally primed T cells lead to complete tumor eradication. A similar kinetic process is identified in CD8+ and CD4+ T cells collected from a patient with metastatic melanoma.

A kinetic investigation of interacting, stimulated T cells identifies conditions for rapid functional enhancement, minimal phenotype differentiation, and improved adoptive cell transfer tumor eradication

PubMed Central

Zhou, Jing; Bethune, Michael T.; Malkova, Natalia; Sutherland, Alexander M.; Comin-Anduix, Begonya; Su, Yapeng; Baltimore, David; Ribas, Antoni

2018-01-01

For adoptive cell transfer (ACT) immunotherapy of tumor-reactive T cells, an effective therapeutic outcome depends upon cell dose, cell expansion in vivo through a minimally differentiated phenotype, long term persistence, and strong cytolytic effector function. An incomplete understanding of the biological coupling between T cell expansion, differentiation, and response to stimulation hinders the co-optimization of these factors. We report on a biophysical investigation of how the short-term kinetics of T cell functional activation, through molecular stimulation and cell-cell interactions, competes with phenotype differentiation. T cells receive molecular stimulation for a few minutes to a few hours in bulk culture. Following this priming period, the cells are then analyzed at the transcriptional level, or isolated as single cells, with continuing molecular stimulation, within microchambers for analysis via 11-plex secreted protein assays. We resolve a rapid feedback mechanism, promoted by T cell—T cell contact interactions, which strongly amplifies T cell functional performance while yielding only minimal phenotype differentiation. When tested in mouse models of ACT, optimally primed T cells lead to complete tumor eradication. A similar kinetic process is identified in CD8+ and CD4+ T cells collected from a patient with metastatic melanoma. PMID:29360859

Highly predictive and interpretable models for PAMPA permeability.

PubMed

Sun, Hongmao; Nguyen, Kimloan; Kerns, Edward; Yan, Zhengyin; Yu, Kyeong Ri; Shah, Pranav; Jadhav, Ajit; Xu, Xin

2017-02-01

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery. Copyright © 2016 Elsevier Ltd. All rights reserved.

Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARγ

PubMed Central

Zhang, Jun; Hao, Qing-Qing; Liu, Xin; Jing, Zhi; Jia, Wen-Qing; Wang, Shu-Qing; Xu, Wei-Ren; Cheng, Xian-Chao; Wang, Run-Ling

2017-01-01

Telmisartan, a bifunctional agent of blood pressure lowering and glycemia reduction, was previously reported to antagonize angiotensin II type 1 (AT1) receptor and partially activate peroxisome proliferator-activated receptor γ (PPARγ) simultaneously. Through the modification to telmisartan, researchers designed and obtained imidazo-\\pyridine derivatives with the IC50s of 0.49∼94.1 nM against AT1 and EC50s of 20∼3640 nM towards PPARγ partial activation. For minutely inquiring the interaction modes with the relevant receptor and analyzing the structure-activity relationships, molecular docking and 3D-QSAR (Quantitative structure-activity relationships) analysis of these imidazo-\\pyridines on dual targets were conducted in this work. Docking approaches of these derivatives with both receptors provided explicit interaction behaviors and excellent matching degree with the binding pockets. The best CoMFA (Comparative Molecular Field Analysis) models exhibited predictive results of q2=0.553, r2=0.954, SEE=0.127, r2pred=0.779 for AT1 and q2=0.503, r2=1.00, SEE=0.019, r2pred=0.604 for PPARγ, respectively. The contour maps from the optimal model showed detailed information of structural features (steric and electrostatic fields) towards the biological activity. Combining the bioisosterism with the valuable information from above studies, we designed six molecules with better predicted activities towards AT1 and PPARγ partial activation. Overall, these results could be useful for designing potential dual AT1 antagonists and partial PPARγ agonists. PMID:28445965

Molecular docking, 3D-QSAR and structural optimization on imidazo-pyridine derivatives dually targeting AT1 and PPARg.

PubMed

Zhang, Jun; Hao, Qing-Qing; Liu, Xin; Jing, Zhi; Jia, Wen-Qing; Wang, Shu-Qing; Xu, Wei-Ren; Cheng, Xian-Chao; Wang, Run-Ling

2017-04-11

Telmisartan, a bifunctional agent of blood pressure lowering and glycemia reduction, was previously reported to antagonize angiotensin II type 1 (AT1) receptor and partially activate peroxisome proliferator-activated receptor γ (PPARγ) simultaneously. Through the modification to telmisartan, researchers designed and obtained imidazo-\\pyridine derivatives with the IC50s of 0.49~94.1 nM against AT1 and EC50s of 20~3640 nM towards PPARγ partial activation. For minutely inquiring the interaction modes with the relevant receptor and analyzing the structure-activity relationships, molecular docking and 3D-QSAR (Quantitative structure-activity relationships) analysis of these imidazo-\\pyridines on dual targets were conducted in this work. Docking approaches of these derivatives with both receptors provided explicit interaction behaviors and excellent matching degree with the binding pockets. The best CoMFA (Comparative Molecular Field Analysis) models exhibited predictive results of q2=0.553, r2=0.954, SEE=0.127, r2pred=0.779 for AT1 and q2=0.503, r2=1.00, SEE=0.019, r2pred=0.604 for PPARγ, respectively. The contour maps from the optimal model showed detailed information of structural features (steric and electrostatic fields) towards the biological activity. Combining the bioisosterism with the valuable information from above studies, we designed six molecules with better predicted activities towards AT1 and PPARγ partial activation. Overall, these results could be useful for designing potential dual AT1 antagonists and partial PPARγ agonists.

Multi-objective Optimization of Molecular Distillation Conditions for Oleic Acid from a Rich-in-Fatty Acid Model Mixture.

PubMed

Ketenoğlu, Onur; Erdoğdu, Ferruh; Tekin, Aziz

2018-01-01

Oleic acid is a commercially valuable compound and has many positive health effects. Determining optimum conditions in a physical separation process is an industrially significant point due to environmental and health related concerns. Molecular distillation avoids the use of chemicals and adverse effects of high temperature application. The objective of this study was to determine the molecular distillation conditions for oleic acid to increase its purity and distillation yield in a model fatty acid mixture. For this purpose, a short-path evaporator column was used. Evaporation temperature ranged from 110 to 190℃, while absolute pressure was from 0.05 to 5 mmHg. Results showed that elevating temperature generally increased distillation yield until a maximum evaporation temperature. Vacuum application also affected the yield at a given temperature, and amount of distillate increased at higher vacuums except the case applied at 190℃. A multi-objective optimization procedure was then used for maximizing both yield and oleic acid amounts in distillate simultaneously, and an optimum point of 177.36℃ and 0.051 mmHg was determined for this purpose. Results also demonstrated that evaporation of oleic acid was also suppressed by a secondary dominant fatty acid of olive oil - palmitic acid, which tended to evaporate easier than oleic acid at lower evaporation temperatures, and increasing temperature achieved to transfer more oleic acid to distillate. At 110℃ and 0.05 mmHg, oleic and palmitic acid concentrations in distillate were 63.67% and 24.32%, respectively. Outcomes of this study are expected to be useful for industrial process conditions.

Nonparametric density estimation and optimal bandwidth selection for protein unfolding and unbinding data

NASA Astrophysics Data System (ADS)

Bura, E.; Zhmurov, A.; Barsegov, V.

2009-01-01

Dynamic force spectroscopy and steered molecular simulations have become powerful tools for analyzing the mechanical properties of proteins, and the strength of protein-protein complexes and aggregates. Probability density functions of the unfolding forces and unfolding times for proteins, and rupture forces and bond lifetimes for protein-protein complexes allow quantification of the forced unfolding and unbinding transitions, and mapping the biomolecular free energy landscape. The inference of the unknown probability distribution functions from the experimental and simulated forced unfolding and unbinding data, as well as the assessment of analytically tractable models of the protein unfolding and unbinding requires the use of a bandwidth. The choice of this quantity is typically subjective as it draws heavily on the investigator's intuition and past experience. We describe several approaches for selecting the "optimal bandwidth" for nonparametric density estimators, such as the traditionally used histogram and the more advanced kernel density estimators. The performance of these methods is tested on unimodal and multimodal skewed, long-tailed distributed data, as typically observed in force spectroscopy experiments and in molecular pulling simulations. The results of these studies can serve as a guideline for selecting the optimal bandwidth to resolve the underlying distributions from the forced unfolding and unbinding data for proteins.

The p40 Subunit of Interleukin (IL)-12 Promotes Stabilization and Export of the p35 Subunit

PubMed Central

Jalah, Rashmi; Rosati, Margherita; Ganneru, Brunda; Pilkington, Guy R.; Valentin, Antonio; Kulkarni, Viraj; Bergamaschi, Cristina; Chowdhury, Bhabadeb; Zhang, Gen-Mu; Beach, Rachel Kelly; Alicea, Candido; Broderick, Kate E.; Sardesai, Niranjan Y.; Pavlakis, George N.; Felber, Barbara K.

2013-01-01

IL-12 is a 70-kDa heterodimeric cytokine composed of the p35 and p40 subunits. To maximize cytokine production from plasmid DNA, molecular steps controlling IL-12p70 biosynthesis at the posttranscriptional and posttranslational levels were investigated. We show that the combination of RNA/codon-optimized gene sequences and fine-tuning of the relative expression levels of the two subunits within a cell resulted in increased production of the IL-12p70 heterodimer. We found that the p40 subunit plays a critical role in enhancing the stability, intracellular trafficking, and export of the p35 subunit. This posttranslational regulation mediated by the p40 subunit is conserved in mammals. Based on these findings, dual gene expression vectors were generated, producing an optimal ratio of the two subunits, resulting in a ∼1 log increase in human, rhesus, and murine IL-12p70 production compared with vectors expressing the wild type sequences. Such optimized DNA plasmids also produced significantly higher levels of systemic bioactive IL-12 upon in vivo DNA delivery in mice compared with plasmids expressing the wild type sequences. A single therapeutic injection of an optimized murine IL-12 DNA plasmid showed significantly more potent control of tumor development in the B16 melanoma cancer model in mice. Therefore, the improved IL-12p70 DNA vectors have promising potential for in vivo use as molecular vaccine adjuvants and in cancer immunotherapy. PMID:23297419

Molecular docking and 3D-QSAR studies on triazolinone and pyridazinone, non-nucleoside inhibitor of HIV-1 reverse transcriptase.

PubMed

Sivan, Sree Kanth; Manga, Vijjulatha

2010-06-01

Nonnucleoside reverse transcriptase inhibitors (NNRTIs) are allosteric inhibitors of the HIV-1 reverse transcriptase. Recently a series of Triazolinone and Pyridazinone were reported as potent inhibitors of HIV-1 wild type reverse transcriptase. In the present study, docking and 3D quantitative structure activity relationship (3D QSAR) studies involving comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 31 molecules. Ligands were built and minimized using Tripos force field and applying Gasteiger-Hückel charges. These ligands were docked into protein active site using GLIDE 4.0. The docked poses were analyzed; the best docked poses were selected and aligned. CoMFA and CoMSIA fields were calculated using SYBYL6.9. The molecules were divided into training set and test set, a PLS analysis was performed and QSAR models were generated. The model showed good statistical reliability which is evident from the r2 nv, q2 loo and r2 pred values. The CoMFA model provides the most significant correlation of steric and electrostatic fields with biological activities. The CoMSIA model provides a correlation of steric, electrostatic, acceptor and hydrophobic fields with biological activities. The information rendered by 3D QSAR model initiated us to optimize the lead and design new potential inhibitors.

Using FT-IR Spectroscopy to Elucidate the Structures of Ablative Polymers

NASA Technical Reports Server (NTRS)

Fan, Wendy

2011-01-01

The composition and structure of an ablative polymer has a multifaceted influence on its thermal, mechanical and ablative properties. Understanding the molecular level information is critical to the optimization of material performance because it helps to establish correlations with the macroscopic properties of the material, the so-called structure-property relationship. Moreover, accurate information of molecular structures is also essential to predict the thermal decomposition pathways as well as to identify decomposition species that are fundamentally important to modeling work. In this presentation, I will describe the use of infrared transmission spectroscopy (FT-IR) as a convenient tool to aid the discovery and development of thermal protection system materials.

Computational drug discovery

PubMed Central

Ou-Yang, Si-sheng; Lu, Jun-yan; Kong, Xiang-qian; Liang, Zhong-jie; Luo, Cheng; Jiang, Hualiang

2012-01-01

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field. PMID:22922346

A device-oriented optimizer for solving ground state problems on an approximate quantum computer, Part II: Experiments for interacting spin and molecular systems

NASA Astrophysics Data System (ADS)

Kandala, Abhinav; Mezzacapo, Antonio; Temme, Kristan; Bravyi, Sergey; Takita, Maika; Chavez-Garcia, Jose; Córcoles, Antonio; Smolin, John; Chow, Jerry; Gambetta, Jay

Hybrid quantum-classical algorithms can be used to find variational solutions to generic quantum problems. Here, we present an experimental implementation of a device-oriented optimizer that uses superconducting quantum hardware. The experiment relies on feedback between the quantum device and classical optimization software which is robust to measurement noise. Our device-oriented approach uses naturally available interactions for the preparation of trial states. We demonstrate the application of this technique for solving interacting spin and molecular structure problems.

Development of reactive force fields using ab initio molecular dynamics simulation minimally biased to experimental data

NASA Astrophysics Data System (ADS)

Chen, Chen; Arntsen, Christopher; Voth, Gregory A.

2017-10-01

Incorporation of quantum mechanical electronic structure data is necessary to properly capture the physics of many chemical processes. Proton hopping in water, which involves rearrangement of chemical and hydrogen bonds, is one such example of an inherently quantum mechanical process. Standard ab initio molecular dynamics (AIMD) methods, however, do not yet accurately predict the structure of water and are therefore less than optimal for developing force fields. We have instead utilized a recently developed method which minimally biases AIMD simulations to match limited experimental data to develop novel multiscale reactive molecular dynamics (MS-RMD) force fields by using relative entropy minimization. In this paper, we present two new MS-RMD models using such a parameterization: one which employs water with harmonic internal vibrations and another which uses anharmonic water. We show that the newly developed MS-RMD models very closely reproduce the solvation structure of the hydrated excess proton in the target AIMD data. We also find that the use of anharmonic water increases proton hopping, thereby increasing the proton diffusion constant.

Highly organized smectic-like packing in vapor-deposited glasses of a liquid crystal

DOE PAGES

Gujral, Ankit; Gomez, Jaritza; Jiang, Jing; ...

2016-12-26

Glasses of a model smectic liquid crystal-forming molecule, itraconazole, were prepared by vapor deposition onto substrates with temperatures ranging from T substrate = 0.78T g to 1.02T g, where T g (=330 K) is the glass transition temperature. The films were characterized using X-ray scattering techniques. For T substrate near and below T g, glasses with layered smectic-like structures can be prepared and the layer spacing can be tuned by 16% through the choice of T substrate. Remarkably, glasses prepared with T substrate values above T g exhibit levels of structural organization much higher than that of a thermally annealedmore » film. These results are explained by a mechanism based upon a preferred molecular orientation and enhanced molecular motion at the free surface, indicating that molecular organization in the glass is independent of the anchoring preferred at the substrate. Furthermore, these results suggest new strategies for optimizing molecular packing within active layers of organic electronic and optoelectronic devices.« less

Extended Lagrangian Excited State Molecular Dynamics

DOE PAGES

Bjorgaard, Josiah August; Sheppard, Daniel Glen; Tretiak, Sergei; ...

2018-01-09

In this work, an extended Lagrangian framework for excited state molecular dynamics (XL-ESMD) using time-dependent self-consistent field theory is proposed. The formulation is a generalization of the extended Lagrangian formulations for ground state Born–Oppenheimer molecular dynamics [Phys. Rev. Lett. 2008 100, 123004]. The theory is implemented, demonstrated, and evaluated using a time-dependent semiempirical model, though it should be generally applicable to ab initio theory. The simulations show enhanced energy stability and a significantly reduced computational cost associated with the iterative solutions of both the ground state and the electronically excited states. Relaxed convergence criteria can therefore be used both formore » the self-consistent ground state optimization and for the iterative subspace diagonalization of the random phase approximation matrix used to calculate the excited state transitions. In conclusion, the XL-ESMD approach is expected to enable numerically efficient excited state molecular dynamics for such methods as time-dependent Hartree–Fock (TD-HF), Configuration Interactions Singles (CIS), and time-dependent density functional theory (TD-DFT).« less

Molecular Mechanism of Wide Photoabsorption Spectral Shifts of Color Variants of Human Cellular Retinol Binding Protein II.

PubMed

Cheng, Cheng; Kamiya, Motoshi; Uchida, Yoshihiro; Hayashi, Shigehiko

2015-10-21

Color variants of human cellular retinol binding protein II (hCRBPII) created by protein engineering were recently shown to exhibit anomalously wide photoabsorption spectral shifts over ∼200 nm across the visible region. The remarkable phenomenon provides a unique opportunity to gain insight into the molecular basis of the color tuning of retinal binding proteins for understanding of color vision as well as for engineering of novel color variants of retinal binding photoreceptor proteins employed in optogenetics. Here, we report a theoretical investigation of the molecular mechanism underlying the anomalously wide spectral shifts of the color variants of hCRBPII. Computational modeling of the color variants with hybrid molecular simulations of free energy geometry optimization succeeded in reproducing the experimentally observed wide spectral shifts, and revealed that protein flexibility, through which the active site structure of the protein and bound water molecules is altered by remote mutations, plays a significant role in inducing the large spectral shifts.

Photostability Can Be Significantly Modulated by Molecular Packing in Glasses

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

Qiu, Yue; Antony, Lucas W.; de Pablo, Juan J.

2016-08-12

While previous work has demonstrated that molecular packing in organic crystals can strongly influence photochemical stability, efforts to tune photostability in amorphous materials have shown much smaller effects. Here we show that physical vapor deposition can substantially improve the photostability of organic glasses. Disperse Orange 37 (DO37), an azobenzene derivative, is studied as a model system. Photostability is assessed through changes in the density and molecular orientation of glassy thin films during light irradiation. By optimizing the substrate temperature used for deposition, we can increase photostability by a factor of 50 relative to the liquid-cooled glass. Photostability correlates with glassmore » density, with density increases of up to 1.3%. Coarse-grained molecular simulations, which mimic glass preparation and the photoisomerization reaction, also indicate that glasses with higher density have substantially increased photostability. These results provide insights that may assist in the design of organic photovoltaics and light emission devices with longer lifetimes.« less

Extended Lagrangian Excited State Molecular Dynamics

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

Bjorgaard, Josiah August; Sheppard, Daniel Glen; Tretiak, Sergei

In this work, an extended Lagrangian framework for excited state molecular dynamics (XL-ESMD) using time-dependent self-consistent field theory is proposed. The formulation is a generalization of the extended Lagrangian formulations for ground state Born–Oppenheimer molecular dynamics [Phys. Rev. Lett. 2008 100, 123004]. The theory is implemented, demonstrated, and evaluated using a time-dependent semiempirical model, though it should be generally applicable to ab initio theory. The simulations show enhanced energy stability and a significantly reduced computational cost associated with the iterative solutions of both the ground state and the electronically excited states. Relaxed convergence criteria can therefore be used both formore » the self-consistent ground state optimization and for the iterative subspace diagonalization of the random phase approximation matrix used to calculate the excited state transitions. In conclusion, the XL-ESMD approach is expected to enable numerically efficient excited state molecular dynamics for such methods as time-dependent Hartree–Fock (TD-HF), Configuration Interactions Singles (CIS), and time-dependent density functional theory (TD-DFT).« less

Extended Lagrangian Excited State Molecular Dynamics.

PubMed

Bjorgaard, J A; Sheppard, D; Tretiak, S; Niklasson, A M N

2018-02-13

An extended Lagrangian framework for excited state molecular dynamics (XL-ESMD) using time-dependent self-consistent field theory is proposed. The formulation is a generalization of the extended Lagrangian formulations for ground state Born-Oppenheimer molecular dynamics [Phys. Rev. Lett. 2008 100, 123004]. The theory is implemented, demonstrated, and evaluated using a time-dependent semiempirical model, though it should be generally applicable to ab initio theory. The simulations show enhanced energy stability and a significantly reduced computational cost associated with the iterative solutions of both the ground state and the electronically excited states. Relaxed convergence criteria can therefore be used both for the self-consistent ground state optimization and for the iterative subspace diagonalization of the random phase approximation matrix used to calculate the excited state transitions. The XL-ESMD approach is expected to enable numerically efficient excited state molecular dynamics for such methods as time-dependent Hartree-Fock (TD-HF), Configuration Interactions Singles (CIS), and time-dependent density functional theory (TD-DFT).

Engineering controllable bidirectional molecular motors based on myosin

PubMed Central

Chen, Lu; Nakamura, Muneaki; Schindler, Tony D.; Parker, David; Bryant, Zev

2012-01-01

Cytoskeletal motors drive the transport of organelles and molecular cargoes within cells1, and have potential applications in molecular detection and diagnostic devices2,3. Engineering molecular motors with dynamically controllable properties will allow selective perturbation of mechanical processes in living cells, and yield optimized device components for complex tasks such as molecular sorting and directed assembly3. Biological motors have previously been modified by introducing activation/deactivation switches that respond to metal ions4,5 and other signals6. Here we show that myosin motors can be engineered to reversibly change their direction of motion in response to a calcium signal. Building on previous protein engineering studies7–11 and guided by a structural model12 for the redirected power stroke of myosin VI, we constructed bidirectional myosins through the rigid recombination of structural modules. The performance of the motors was confirmed using gliding filament assays and single fluorophore tracking. Our general strategy, in which external signals trigger changes in the geometry and mechanics of myosin lever arms, should enable spatiotemporal control over a range of motor properties including processivity, stride size13, and branchpoint turning14. PMID:22343382

Engineering controllable bidirectional molecular motors based on myosin

NASA Astrophysics Data System (ADS)

Chen, Lu; Nakamura, Muneaki; Schindler, Tony D.; Parker, David; Bryant, Zev

2012-04-01

Cytoskeletal motors drive the transport of organelles and molecular cargoes within cells and have potential applications in molecular detection and diagnostic devices. Engineering molecular motors with controllable properties will allow selective perturbation of mechanical processes in living cells and provide optimized device components for tasks such as molecular sorting and directed assembly. Biological motors have previously been modified by introducing activation/deactivation switches that respond to metal ions and other signals. Here, we show that myosin motors can be engineered to reversibly change their direction of motion in response to a calcium signal. Building on previous protein engineering studies and guided by a structural model for the redirected power stroke of myosin VI, we have constructed bidirectional myosins through the rigid recombination of structural modules. The performance of the motors was confirmed using gliding filament assays and single fluorophore tracking. Our strategy, in which external signals trigger changes in the geometry and mechanics of myosin lever arms, should make it possible to achieve spatiotemporal control over a range of motor properties including processivity, stride size and branchpoint turning.

Modeling of direct detection Doppler wind lidar. I. The edge technique.

PubMed

McKay, J A

1998-09-20

Analytic models, based on a convolution of a Fabry-Perot etalon transfer function with a Gaussian spectral source, are developed for the shot-noise-limited measurement precision of Doppler wind lidars based on the edge filter technique by use of either molecular or aerosol atmospheric backscatter. The Rayleigh backscatter formulation yields a map of theoretical sensitivity versus etalon parameters, permitting design optimization and showing that the optimal system will have a Doppler measurement uncertainty no better than approximately 2.4 times that of a perfect, lossless receiver. An extension of the models to include the effect of limited etalon aperture leads to a condition for the minimum aperture required to match light collection optics. It is shown that, depending on the choice of operating point, the etalon aperture finesse must be 4-15 to avoid degradation of measurement precision. A convenient, closed-form expression for the measurement precision is obtained for spectrally narrow backscatter and is shown to be useful for backscatter that is spectrally broad as well. The models are extended to include extrinsic noise, such as solar background or the Rayleigh background on an aerosol Doppler lidar. A comparison of the model predictions with experiment has not yet been possible, but a comparison with detailed instrument modeling by McGill and Spinhirne shows satisfactory agreement. The models derived here will be more conveniently implemented than McGill and Spinhirne's and more readily permit physical insights to the optimization and limitations of the double-edge technique.

Detection of MDR1 mRNA expression with optimized gold nanoparticle beacon

NASA Astrophysics Data System (ADS)

Zhou, Qiumei; Qian, Zhiyu; Gu, Yueqing

2016-03-01

MDR1 (multidrug resistance gene) mRNA expression is a promising biomarker for the prediction of doxorubicin resistance in clinic. However, the traditional technical process in clinic is complicated and cannot perform the real-time detection mRNA in living single cells. In this study, the expression of MDR1 mRNA was analyzed based on optimized gold nanoparticle beacon in tumor cells. Firstly, gold nanoparticle (AuNP) was modified by thiol-PEG, and the MDR1 beacon sequence was screened and optimized using a BLAST bioinformatics strategy. Then, optimized MDR1 molecular beacons were characterized by transmission electron microscope, UV-vis and fluorescence spectroscopies. The cytotoxicity of MDR1 molecular beacon on L-02, K562 and K562/Adr cells were investigated by MTT assay, suggesting that MDR1 molecular beacon was low inherent cytotoxicity. Dark field microscope was used to investigate the cellular uptake of hDAuNP beacon assisted with ultrasound. Finally, laser scanning confocal microscope images showed that there was a significant difference in MDR1 mRNA expression in K562 and K562/Adr cells, which was consistent with the results of q-PCR measurement. In summary, optimized MDR1 molecular beacon designed in this study is a reliable strategy for detection MDR1 mRNA expression in living tumor cells, and will be a promising strategy for in guiding patient treatment and management in individualized medication.

Combined Atomistic Molecular Calculations and Experimental Investigations for the Architecture, Screening, Optimization, and Characterization of Pyrazinamide Containing Oral Film Formulations for Tuberculosis Management.

PubMed

Adeleke, Oluwatoyin A; Monama, Nkwe O; Tsai, Pei-Chin; Sithole, Happy M; Michniak-Kohn, Bozena B

2016-02-01

To date, effective treatment, prophylaxis, and control of tuberculosis (TB) infection is mainly dependent on the use of drugs. However, patient noncompliance with prescribed anti-TB treatment schemes remains a major problem confronting successful pharmacotherapeutic outcomes. Thus, the development of alternative delivery systems that can improve adherence for the existing anti-TB bioactives has been intensified in recent times. The aim of this investigation was to engineer an optimal, thermodynamically stable oral film (OF) formulation containing a key anti-TB agent, pyrazinamide (PYZ), employing molecular modeling and experimental tools. Four PYZ-loaded film variants (OF 1, OF 2, OF 3, OF 4) were constructed in silico and then prepared in vitro using the Accelrys Materials Studio software and solvent casting method, respectively. Screening and selection of the optimal OF was based on the computation of the total interaction energy (ET), kinetic energy (EK), solubility parameter (S), and cohesive energy density (CED) as well as determining mass, thickness, dissolution and disintegration times, dissolution pH, drug loading capacity, and surface morphology in vitro. OF 2 was selected as the optimal formulation as it displayed the lowest ET (-8006.28 kcal/mol), dissolution time (9.96 min), disintegration time (56.49 s), and weight (39.33 mg); moderate EK (1052.98 kcal/mol); highest S (44.55 (J/cm(3))(0.5)) and CED (1.99 × 10(9) J/m(3)), slim dimension (166 μm), good and unvarying drug loading capacity (98.04%), acceptable dissolution pH (6.70), and well-layered surface topography. The drug release behavior of the optimal OF 2 was best elucidated with the zero order (R(2) = 0.97) and Korsmeyer-Peppas (R(2) = 0.99) models. X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and differential scanning calorimetry (DSC) analyses showed that OF 2 was made of physically mixed multiple component polymeric and nonpolymeric compounds. OF 2 was semicrystalline in nature and displayed a dual phased ex vivo mucosal permeation pattern. In silico and in vitro physicomechanical quantities revealed OF 2's flexibility, robustness, and compressibility. OF 2 was most stable under controlled environmental humidity, pressure, and temperature conditions in silico and in vitro. OF 2 was potentially non-cytotoxic and biocompatible. Succinctly, this work demonstrated the applicability of a combination of atomistic molecular mechanics and dynamics calculations as well as experimental analyses to the fabrication, screening, optimization, and characterization of drug formulations. Lastly, the fabricated OF 2 formulation can function as a potential alternative for the effective loading and delivery of PYZ.

Modelling the growth of plants with a uniform growth logistics.

PubMed

Kilian, H G; Bartkowiak, D; Kazda, M; Kaufmann, D

2014-05-21

The increment model has previously been used to describe the growth of plants in general. Here, we examine how the same logistics enables the development of different superstructures. Data from the literature are analyzed with the increment model. Increments are growth-invariant molecular clusters, treated as heuristic particles. This approach formulates the law of mass action for multi-component systems, describing the general properties of superstructures which are optimized via relaxation processes. The daily growth patterns of hypocotyls can be reproduced implying predetermined growth invariant model parameters. In various species, the coordinated formation and death of fine roots are modeled successfully. Their biphasic annual growth follows distinct morphological programs but both use the same logistics. In tropical forests, distributions of the diameter in breast height of trees of different species adhere to the same pattern. Beyond structural fluctuations, competition and cooperation within and between the species may drive optimization. All superstructures of plants examined so far could be reproduced with our approach. With genetically encoded growth-invariant model parameters (interaction with the environment included) perfect morphological development runs embedded in the uniform logistics of the increment model. Copyright © 2014 Elsevier Ltd. All rights reserved.

A coupled two-dimensional main chain torsional potential for protein dynamics: generation and implementation.

PubMed

Li, Yongxiu; Gao, Ya; Zhang, Xuqiang; Wang, Xingyu; Mou, Lirong; Duan, Lili; He, Xiao; Mei, Ye; Zhang, John Z H

2013-09-01

Main chain torsions of alanine dipeptide are parameterized into coupled 2-dimensional Fourier expansions based on quantum mechanical (QM) calculations at M06 2X/aug-cc-pvtz//HF/6-31G** level. Solvation effect is considered by employing polarizable continuum model. Utilization of the M06 2X functional leads to precise potential energy surface that is comparable to or even better than MP2 level, but with much less computational demand. Parameterization of the 2D expansions is against the full main chain torsion space instead of just a few low energy conformations. This procedure is similar to that for the development of AMBER03 force field, except unique weighting factor was assigned to all the grid points. To avoid inconsistency between quantum mechanical calculations and molecular modeling, the model peptide is further optimized at molecular mechanics level with main chain dihedral angles fixed before the calculation of the conformational energy on molecular mechanical level at each grid point, during which generalized Born model is employed. Difference in solvation models at quantum mechanics and molecular mechanics levels makes this parameterization procedure less straightforward. All force field parameters other than main chain torsions are taken from existing AMBER force field. With this new main chain torsion terms, we have studied the main chain dihedral distributions of ALA dipeptide and pentapeptide in aqueous solution. The results demonstrate that 2D main chain torsion is effective in delineating the energy variation associated with rotations along main chain dihedrals. This work is an implication for the necessity of more accurate description of main chain torsions in the future development of ab initio force field and it also raises a challenge to the development of quantum mechanical methods, especially the quantum mechanical solvation models.

Geometry-dependent atomic multipole models for the water molecule.

PubMed

Loboda, O; Millot, C

2017-10-28

Models of atomic electric multipoles for the water molecule have been optimized in order to reproduce the electric potential around the molecule computed by ab initio calculations at the coupled cluster level of theory with up to noniterative triple excitations in an augmented triple-zeta quality basis set. Different models of increasing complexity, from atomic charges up to models containing atomic charges, dipoles, and quadrupoles, have been obtained. The geometry dependence of these atomic multipole models has been investigated by changing bond lengths and HOH angle to generate 125 molecular structures (reduced to 75 symmetry-unique ones). For several models, the atomic multipole components have been fitted as a function of the geometry by a Taylor series of fourth order in monomer coordinate displacements.

Geometry-dependent atomic multipole models for the water molecule

NASA Astrophysics Data System (ADS)

Loboda, O.; Millot, C.

2017-10-01

Models of atomic electric multipoles for the water molecule have been optimized in order to reproduce the electric potential around the molecule computed by ab initio calculations at the coupled cluster level of theory with up to noniterative triple excitations in an augmented triple-zeta quality basis set. Different models of increasing complexity, from atomic charges up to models containing atomic charges, dipoles, and quadrupoles, have been obtained. The geometry dependence of these atomic multipole models has been investigated by changing bond lengths and HOH angle to generate 125 molecular structures (reduced to 75 symmetry-unique ones). For several models, the atomic multipole components have been fitted as a function of the geometry by a Taylor series of fourth order in monomer coordinate displacements.

Improved building up a model of toxicity towards Pimephales promelas by the Monte Carlo method.

PubMed

Toropova, Alla P; Toropov, Andrey A; Raskova, Maria; Raska, Ivan

2016-12-01

By optimization of so-called correlation weights of attributes of simplified molecular input-line entry system (SMILES) quantitative structure - activity relationships (QSAR) for toxicity towards Pimephales promelas are established. A new SMILES attribute has been utilized in this work. This attribute is a molecular descriptor, which reflects (i) presence of different kinds of bonds (double, triple, and stereo chemical bonds); (ii) presence of nitrogen, oxygen, sulphur, and phosphorus atoms; and (iii) presence of fluorine, chlorine, bromine, and iodine atoms. The statistical characteristics of the best model are the following: n=226, r 2 =0.7630, RMSE=0.654 (training set); n=114, r 2 =0.7024, RMSE=0.766 (calibration set); n=226, r 2 =0.6292, RMSE=0.870 (validation set). A new criterion to select a preferable split into the training and validation sets are suggested and discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Vibrational, spectroscopic, molecular docking and density functional theory studies on N-(5-aminopyridin-2-yl)acetamide

NASA Astrophysics Data System (ADS)

Asath, R. Mohamed; Rekha, T. N.; Premkumar, S.; Mathavan, T.; Benial, A. Milton Franklin

2016-12-01

Conformational analysis was carried out for N-(5-aminopyridin-2-yl)acetamide (APA) molecule. The most stable, optimized structure was predicted by the density functional theory calculations using the B3LYP functional with cc-pVQZ basis set. The optimized structural parameters and vibrational frequencies were calculated. The experimental and theoretical vibrational frequencies were assigned and compared. Ultraviolet-visible spectrum was simulated and validated experimentally. The molecular electrostatic potential surface was simulated. Frontier molecular orbitals and related molecular properties were computed, which reveals that the higher molecular reactivity and stability of the APA molecule and further density of states spectrum was simulated. The natural bond orbital analysis was also performed to confirm the bioactivity of the APA molecule. Antidiabetic activity was studied based on the molecular docking analysis and the APA molecule was identified that it can act as a good inhibitor against diabetic nephropathy.

Identification of ligand efficient, fragment-like hits from an HTS library: structure-based virtual screening and docking investigations of 2H- and 3H-pyrazolo tautomers for Aurora kinase A selectivity.

PubMed

Sarvagalla, Sailu; Singh, Vivek Kumar; Ke, Yi-Yu; Shiao, Hui-Yi; Lin, Wen-Hsing; Hsieh, Hsing-Pang; Hsu, John T A; Coumar, Mohane Selvaraj

2015-01-01

Furanopyrimidine 1 (IC50 = 273 nM, LE = 0.36, LELP = 10.28) was recently identified by high-throughput screening (HTS) of an in-house library (125,000 compounds) as an Aurora kinase inhibitor. Structure-based hit optimization resulted in lead molecules with in vivo efficacy in a mouse tumour xenograft model, but no oral bioavailability. This is attributed to "molecular obesity", a common problem during hit to lead evolution during which degradation of important molecular properties such as molecular weight (MW) and lipophilicity occurs. This could be effectively tackled by the right choice of hit compounds for optimization. In this regard, ligand efficiency (LE) and ligand efficiency dependent lipophilicity (LELP) indices are more often used to choose fragment-like hits for optimization. To identify hits with appropriate LE, we used a MW cut-off <250, and pyrazole structure to filter HTS library. Next, structure-based virtual screening using software (Libdock and Glide) in the Aurora A crystal structure (PDB ID: 3E5A) was carried out, and the top scoring 18 compounds tested for Aurora A enzyme inhibition. This resulted in the identification of a novel tetrahydro-pyrazolo-isoquinoline hit 7 (IC50 = 852 nM, LE = 0.44, LELP = 8.36) with fragment-like properties suitable for further hit optimization. Moreover, hit 7 was found to be selective for Aurora A (Aurora B IC50 = 35,150 nM) and the possible reasons for selectivity investigated by docking two tautomeric forms (2H- and 3H-pyrazole) of 7 in Auroras A and B (PDB ID: 4AF3) crystal structures. This docking study shows that the major 3H-pyrazole tautomer of 7 binds in Aurora A stronger than in Aurora B.

Identification of ligand efficient, fragment-like hits from an HTS library: structure-based virtual screening and docking investigations of 2 H- and 3 H-pyrazolo tautomers for Aurora kinase A selectivity

NASA Astrophysics Data System (ADS)

Sarvagalla, Sailu; Singh, Vivek Kumar; Ke, Yi-Yu; Shiao, Hui-Yi; Lin, Wen-Hsing; Hsieh, Hsing-Pang; Hsu, John T. A.; Coumar, Mohane Selvaraj

2015-01-01

Furanopyrimidine 1 (IC50 = 273 nM, LE = 0.36, LELP = 10.28) was recently identified by high-throughput screening (HTS) of an in-house library (125,000 compounds) as an Aurora kinase inhibitor. Structure-based hit optimization resulted in lead molecules with in vivo efficacy in a mouse tumour xenograft model, but no oral bioavailability. This is attributed to "molecular obesity", a common problem during hit to lead evolution during which degradation of important molecular properties such as molecular weight (MW) and lipophilicity occurs. This could be effectively tackled by the right choice of hit compounds for optimization. In this regard, ligand efficiency (LE) and ligand efficiency dependent lipophilicity (LELP) indices are more often used to choose fragment-like hits for optimization. To identify hits with appropriate LE, we used a MW cut-off <250, and pyrazole structure to filter HTS library. Next, structure-based virtual screening using software (Libdock and Glide) in the Aurora A crystal structure (PDB ID: 3E5A) was carried out, and the top scoring 18 compounds tested for Aurora A enzyme inhibition. This resulted in the identification of a novel tetrahydro-pyrazolo-isoquinoline hit 7 (IC50 = 852 nM, LE = 0.44, LELP = 8.36) with fragment-like properties suitable for further hit optimization. Moreover, hit 7 was found to be selective for Aurora A (Aurora B IC50 = 35,150 nM) and the possible reasons for selectivity investigated by docking two tautomeric forms (2 H- and 3 H-pyrazole) of 7 in Auroras A and B (PDB ID: 4AF3) crystal structures. This docking study shows that the major 3 H-pyrazole tautomer of 7 binds in Aurora A stronger than in Aurora B.

Rigorous force field optimization principles based on statistical distance minimization

DOE PAGES

Vlcek, Lukas; Chialvo, Ariel A.

2015-10-12

We use the concept of statistical distance to define a measure of distinguishability between a pair of statistical mechanical systems, i.e., a model and its target, and show that its minimization leads to general convergence of the model’s static measurable properties to those of the target. Here we exploit this feature to define a rigorous basis for the development of accurate and robust effective molecular force fields that are inherently compatible with coarse-grained experimental data. The new model optimization principles and their efficient implementation are illustrated through selected examples, whose outcome demonstrates the higher robustness and predictive accuracy of themore » approach compared to other currently used methods, such as force matching and relative entropy minimization. We also discuss relations between the newly developed principles and established thermodynamic concepts, which include the Gibbs-Bogoliubov inequality and the thermodynamic length.« less

Using molecular diagnostic testing to personalize the treatment of patients with gastrointestinal stromal tumors.

PubMed

Bannon, Amber E; Klug, Lillian R; Corless, Christopher L; Heinrich, Michael C

2017-05-01

The diagnosis and treatment of gastrointestinal stromal tumor (GIST) has emerged as a paradigm for modern cancer treatment ('precision medicine'), as it highlights the importance of matching molecular defects with specific therapies. Over the past two decades, the molecular classification and diagnostic work up of GIST has been radically transformed, accompanied by the development of molecular therapies for specific subgroups of GIST. This review summarizes the developments in the field of molecular diagnosis of GIST, particularly as they relate to optimizing medical therapy. Areas covered: Based on an extensive literature search of the molecular and clinical aspects of GIST, the authors review the most important developments in this field with an emphasis on the differential diagnosis of GIST including mutation testing, therapeutic implications of each molecular subtype, and emerging technologies relevant to the field. Expert commentary: The use of molecular diagnostics to classify GIST has been shown to be successful in optimizing patient treatment, but these methods remain under-utilized. In order to facilitate efficient and comprehensive molecular testing, the authors have developed a decision tree to aid clinicians.

Structure and dynamics of human vimentin intermediate filament dimer and tetramer in explicit and implicit solvent models.

PubMed

Qin, Zhao; Buehler, Markus J

2011-01-01

Intermediate filaments, in addition to microtubules and microfilaments, are one of the three major components of the cytoskeleton in eukaryotic cells, and play an important role in mechanotransduction as well as in providing mechanical stability to cells at large stretch. The molecular structures, mechanical and dynamical properties of the intermediate filament basic building blocks, the dimer and the tetramer, however, have remained elusive due to persistent experimental challenges owing to the large size and fibrillar geometry of this protein. We have recently reported an atomistic-level model of the human vimentin dimer and tetramer, obtained through a bottom-up approach based on structural optimization via molecular simulation based on an implicit solvent model (Qin et al. in PLoS ONE 2009 4(10):e7294, 9). Here we present extensive simulations and structural analyses of the model based on ultra large-scale atomistic-level simulations in an explicit solvent model, with system sizes exceeding 500,000 atoms and simulations carried out at 20 ns time-scales. We report a detailed comparison of the structural and dynamical behavior of this large biomolecular model with implicit and explicit solvent models. Our simulations confirm the stability of the molecular model and provide insight into the dynamical properties of the dimer and tetramer. Specifically, our simulations reveal a heterogeneous distribution of the bending stiffness along the molecular axis with the formation of rather soft and highly flexible hinge-like regions defined by non-alpha-helical linker domains. We report a comparison of Ramachandran maps and the solvent accessible surface area between implicit and explicit solvent models, and compute the persistence length of the dimer and tetramer structure of vimentin intermediate filaments for various subdomains of the protein. Our simulations provide detailed insight into the dynamical properties of the vimentin dimer and tetramer intermediate filament building blocks, which may guide the development of novel coarse-grained models of intermediate filaments, and could also help in understanding assembly mechanisms.

Sustainable design and manufacturing of multifunctional polymer nanocomposite coatings: A multiscale systems approach

NASA Astrophysics Data System (ADS)

Xiao, Jie

Polymer nanocomposites have a great potential to be a dominant coating material in a wide range of applications in the automotive, aerospace, ship-making, construction, and pharmaceutical industries. However, how to realize design sustainability of this type of nanostructured materials and how to ensure the true optimality of the product quality and process performance in coating manufacturing remain as a mountaintop area. The major challenges arise from the intrinsic multiscale nature of the material-process-product system and the need to manipulate the high levels of complexity and uncertainty in design and manufacturing processes. This research centers on the development of a comprehensive multiscale computational methodology and a computer-aided tool set that can facilitate multifunctional nanocoating design and application from novel function envisioning and idea refinement, to knowledge discovery and design solution derivation, and further to performance testing in industrial applications and life cycle analysis. The principal idea is to achieve exceptional system performance through concurrent characterization and optimization of materials, product and associated manufacturing processes covering a wide range of length and time scales. Multiscale modeling and simulation techniques ranging from microscopic molecular modeling to classical continuum modeling are seamlessly coupled. The tight integration of different methods and theories at individual scales allows the prediction of macroscopic coating performance from the fundamental molecular behavior. Goal-oriented design is also pursued by integrating additional methods for bio-inspired dynamic optimization and computational task management that can be implemented in a hierarchical computing architecture. Furthermore, multiscale systems methodologies are developed to achieve the best possible material application towards sustainable manufacturing. Automotive coating manufacturing, that involves paint spay and curing, is specifically discussed in this dissertation. Nevertheless, the multiscale considerations for sustainable manufacturing, the novel concept of IPP control, and the new PPDE-based optimization method are applicable to other types of manufacturing, e.g., metal coating development through electroplating. It is demonstrated that the methodological development in this dissertation can greatly facilitate experimentalists in novel material invention and new knowledge discovery. At the same time, they can provide scientific guidance and reveal various new opportunities and effective strategies for sustainable manufacturing.

Optical properties of InAsBi and optimal designs of lattice-matched and strain-balanced III-V semiconductor superlattices

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

Webster, P. T., E-mail: preston.t.webster@asu.edu; Riordan, N. A.; Gogineni, C.

The optical properties of bulk InAs{sub 0.936}Bi{sub 0.064} grown by molecular beam epitaxy on a (100)-oriented GaSb substrate are measured using spectroscopic ellipsometry. The index of refraction and absorption coefficient are measured over photon energies ranging from 44 meV to 4.4 eV and are used to identify the room temperature bandgap energy of bulk InAs{sub 0.936}Bi{sub 0.064} as 60.6 meV. The bandgap of InAsBi is expressed as a function of Bi mole fraction using the band anticrossing model and a characteristic coupling strength of 1.529 eV between the Bi impurity state and the InAs valence band. These results are programmed into a software toolmore » that calculates the miniband structure of semiconductor superlattices and identifies optimal designs in terms of maximizing the electron-hole wavefunction overlap as a function of transition energy. These functionalities are demonstrated by mapping the design spaces of lattice-matched GaSb/InAs{sub 0.911}Sb{sub 0.089} and GaSb/InAs{sub 0.932}Bi{sub 0.068} and strain-balanced InAs/InAsSb, InAs/GaInSb, and InAs/InAsBi superlattices on GaSb. The absorption properties of each of these material systems are directly compared by relating the wavefunction overlap square to the absorption coefficient of each optimized design. Optimal design criteria are provided for key detector wavelengths for each superlattice system. The optimal design mid-wave infrared InAs/InAsSb superlattice is grown using molecular beam epitaxy, and its optical properties are evaluated using spectroscopic ellipsometry and photoluminescence spectroscopy.« less

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

Effect of diatomic molecular properties on binary laser pulse optimizations of quantum gate operations.

PubMed

Zaari, Ryan R; Brown, Alex

2011-07-28

The importance of the ro-vibrational state energies on the ability to produce high fidelity binary shaped laser pulses for quantum logic gates is investigated. The single frequency 2-qubit ACNOT(1) and double frequency 2-qubit NOT(2) quantum gates are used as test cases to examine this behaviour. A range of diatomics is sampled. The laser pulses are optimized using a genetic algorithm for binary (two amplitude and two phase parameter) variation on a discretized frequency spectrum. The resulting trends in the fidelities were attributed to the intrinsic molecular properties and not the choice of method: a discretized frequency spectrum with genetic algorithm optimization. This is verified by using other common laser pulse optimization methods (including iterative optimal control theory), which result in the same qualitative trends in fidelity. The results differ from other studies that used vibrational state energies only. Moreover, appropriate choice of diatomic (relative ro-vibrational state arrangement) is critical for producing high fidelity optimized quantum logic gates. It is also suggested that global phase alignment imposes a significant restriction on obtaining high fidelity regions within the parameter search space. Overall, this indicates a complexity in the ability to provide appropriate binary laser pulse control of diatomics for molecular quantum computing. © 2011 American Institute of Physics

Molecular dynamics in drug design: new generations of compstatin analogs.

PubMed

Tamamis, Phanourios; López de Victoria, Aliana; Gorham, Ronald D; Bellows-Peterson, Meghan L; Pierou, Panayiota; Floudas, Christodoulos A; Morikis, Dimitrios; Archontis, Georgios

2012-05-01

We report the computational and rational design of new generations of potential peptide-based inhibitors of the complement protein C3 from the compstatin family. The binding efficacy of the peptides is tested by extensive molecular dynamics-based structural and physicochemical analysis, using 32 atomic detail trajectories in explicit water for 22 peptides bound to human, rat or mouse target protein C3, with a total of 257 ns. The criteria for the new design are: (i) optimization for C3 affinity and for the balance between hydrophobicity and polarity to improve solubility compared to known compstatin analogs; and (ii) development of dual specificity, human-rat/mouse C3 inhibitors, which could be used in animal disease models. Three of the new analogs are analyzed in more detail as they possess strong and novel binding characteristics and are promising candidates for further optimization. This work paves the way for the development of an improved therapeutic for age-related macular degeneration, and other complement system-mediated diseases, compared to known compstatin variants. © 2012 John Wiley & Sons A/S.

Polymerization and Structure of Bio-Based Plastics: A Computer Simulation

NASA Astrophysics Data System (ADS)

Khot, Shrikant N.; Wool, Richard P.

2001-03-01

We recently examined several hundred chemical pathways to convert chemically functionalized plant oil triglycerides, monoglycerides and reactive diluents into high performance plastics with a broad range of properties (US Patent No. 6,121,398). The resulting polymers had linear, branched, light- and highly-crosslinked chain architectures and could be used as pressure sensitive adhesives, elastomers and high performance rigid thermoset composite resins. To optimize the molecular design and minimize the number of chemical trials in this system with excess degrees of freedom, we developed a computer simulation of the free radical polymerization process. The triglyceride structure, degree of chemical substitution, mole fractions, fatty acid distribution function, and reaction kinetic parameters were used as initial inputs on a 3d lattice simulation. The evolution of the network fractal structure was computed and used to measure crosslink density, dangling ends, degree of reaction and defects in the lattice. The molecular connectivity was used to determine strength via a vector percolation model of fracture. The simulation permitted the optimal design of new bio-based materials with respect to monomer selection, cure reaction conditions and desired properties. Supported by the National Science Foundation

A novel approach for the removal of radiocesium from aqueous solution by ZSM-5 molecular sieve.

PubMed

Gao, Xiaoqing; Zhang, Peng; Yang, Junqiang; Sun, Xuejie; Fu, Yi; Shi, Keliang; Chai, Zhifang; Wu, Wangsuo

2018-05-21

Finding an approach for pretreatment of radionuclides from contaminated water are interesting topics of research. In present work, the ZSM-5 molecular sieve was characterized with different techniques such as zeta potential, SEM, FT-IR and XRD to clarify the surface properties of sample and applied as a sorbent to concentrate and recover Cs(I) from aqueous solution. The effect of environmental conditions such as contact time, ionic strength, content of sorbent and solution pH on Cs(I) uptake were optimized using batch techniques. Different kinetic and isotherm models were utilized to evaluate the experimental data and the correlation parameters were obtained. Based on the sorption/desorption experiment, it can be deduced that the ZSM-5 molecular sieve has potential application for the rapid and quantitative recovery of radiocesium from wastewater. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automated Protein Biomarker Analysis: on-line extraction of clinical samples by Molecularly Imprinted Polymers

NASA Astrophysics Data System (ADS)

Rossetti, Cecilia; Świtnicka-Plak, Magdalena A.; Grønhaug Halvorsen, Trine; Cormack, Peter A. G.; Sellergren, Börje; Reubsaet, Léon

2017-03-01

Robust biomarker quantification is essential for the accurate diagnosis of diseases and is of great value in cancer management. In this paper, an innovative diagnostic platform is presented which provides automated molecularly imprinted solid-phase extraction (MISPE) followed by liquid chromatography-mass spectrometry (LC-MS) for biomarker determination using ProGastrin Releasing Peptide (ProGRP), a highly sensitive biomarker for Small Cell Lung Cancer, as a model. Molecularly imprinted polymer microspheres were synthesized by precipitation polymerization and analytical optimization of the most promising material led to the development of an automated quantification method for ProGRP. The method enabled analysis of patient serum samples with elevated ProGRP levels. Particularly low sample volumes were permitted using the automated extraction within a method which was time-efficient, thereby demonstrating the potential of such a strategy in a clinical setting.

Ion specific correlations in bulk and at biointerfaces.

PubMed

Kalcher, I; Horinek, D; Netz, R R; Dzubiella, J

2009-10-21

Ion specific effects are ubiquitous in any complex colloidal or biological fluid in bulk or at interfaces. The molecular origins of these 'Hofmeister effects' are not well understood and their theoretical description poses a formidable challenge to the modeling and simulation community. On the basis of the combination of atomistically resolved molecular dynamics (MD) computer simulations and statistical mechanics approaches, we present a few selected examples of specific electrolyte effects in bulk, at simple neutral and charged interfaces, and on a short α-helical peptide. The structural complexity in these strongly Coulomb-correlated systems is highlighted and analyzed in the light of available experimental data. While in general the comparison of MD simulations to experiments often lacks quantitative agreement, mostly because molecular force fields and coarse-graining procedures remain to be optimized, the consensus as regards trends provides important insights into microscopic hydration and binding mechanisms.

Parameterizing the Morse Potential for Coarse-Grained Modeling of Blood Plasma

PubMed Central

Zhang, Na; Zhang, Peng; Kang, Wei; Bluestein, Danny; Deng, Yuefan

2014-01-01

Multiscale simulations of fluids such as blood represent a major computational challenge of coupling the disparate spatiotemporal scales between molecular and macroscopic transport phenomena characterizing such complex fluids. In this paper, a coarse-grained (CG) particle model is developed for simulating blood flow by modifying the Morse potential, traditionally used in Molecular Dynamics for modeling vibrating structures. The modified Morse potential is parameterized with effective mass scales for reproducing blood viscous flow properties, including density, pressure, viscosity, compressibility and characteristic flow dynamics of human blood plasma fluid. The parameterization follows a standard inverse-problem approach in which the optimal micro parameters are systematically searched, by gradually decoupling loosely correlated parameter spaces, to match the macro physical quantities of viscous blood flow. The predictions of this particle based multiscale model compare favorably to classic viscous flow solutions such as Counter-Poiseuille and Couette flows. It demonstrates that such coarse grained particle model can be applied to replicate the dynamics of viscous blood flow, with the advantage of bridging the gap between macroscopic flow scales and the cellular scales characterizing blood flow that continuum based models fail to handle adequately. PMID:24910470

QSPR models of n-octanol/water partition coefficients and aqueous solubility of halogenated methyl-phenyl ethers by DFT method.

PubMed

Zeng, Xiao-Lan; Wang, Hong-Jun; Wang, Yan

2012-02-01

The possible molecular geometries of 134 halogenated methyl-phenyl ethers were optimized at B3LYP/6-31G(*) level with Gaussian 98 program. The calculated structural parameters were taken as theoretical descriptors to establish two new novel QSPR models for predicting aqueous solubility (-lgS(w,l)) and n-octanol/water partition coefficient (lgK(ow)) of halogenated methyl-phenyl ethers. The two models achieved in this work both contain three variables: energy of the lowest unoccupied molecular orbital (E(LUMO)), most positive atomic partial charge in molecule (q(+)), and quadrupole moment (Q(yy) or Q(zz)), of which R values are 0.992 and 0.970 respectively, their standard errors of estimate in modeling (SD) are 0.132 and 0.178, respectively. The results of leave-one-out (LOO) cross-validation for training set and validation with external test sets both show that the models obtained exhibited optimum stability and good predictive power. We suggests that two QSPR models derived here can be used to predict S(w,l) and K(ow) accurately for non-tested halogenated methyl-phenyl ethers congeners. Copyright © 2011 Elsevier Ltd. All rights reserved.

A polarizable QM/MM approach to the molecular dynamics of amide groups solvated in water

NASA Astrophysics Data System (ADS)

Schwörer, Magnus; Wichmann, Christoph; Tavan, Paul

2016-03-01

The infrared (IR) spectra of polypeptides are dominated by the so-called amide bands. Because they originate from the strongly polar and polarizable amide groups (AGs) making up the backbone, their spectral positions sensitively depend on the local electric fields. Aiming at accurate computations of these IR spectra by molecular dynamics (MD) simulations, which derive atomic forces from a hybrid quantum and molecular mechanics (QM/MM) Hamiltonian, here we consider the effects of solvation in bulk liquid water on the amide bands of the AG model compound N-methyl-acetamide (NMA). As QM approach to NMA we choose grid-based density functional theory (DFT). For the surrounding MM water, we develop, largely based on computations, a polarizable molecular mechanics (PMM) model potential called GP6P, which features six Gaussian electrostatic sources (one induced dipole, five static partial charge distributions) and, therefore, avoids spurious distortions of the DFT electron density in hybrid DFT/PMM simulations. Bulk liquid GP6P is shown to have favorable properties at the thermodynamic conditions of the parameterization and beyond. Lennard-Jones (LJ) parameters of the DFT fragment NMA are optimized by comparing radial distribution functions in the surrounding GP6P liquid with reference data obtained from a "first-principles" DFT-MD simulation. Finally, IR spectra of NMA in GP6P water are calculated from extended DFT/PMM-MD trajectories, in which the NMA is treated by three different DFT functionals (BP, BLYP, B3LYP). Method-specific frequency scaling factors are derived from DFT-MD simulations of isolated NMA. The DFT/PMM-MD simulations with GP6P and with the optimized LJ parameters then excellently predict the effects of aqueous solvation and deuteration observed in the IR spectra of NMA. As a result, the methods required to accurately compute such spectra by DFT/PMM-MD also for larger peptides in aqueous solution are now at hand.

A polarizable QM/MM approach to the molecular dynamics of amide groups solvated in water

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

Schwörer, Magnus; Wichmann, Christoph; Tavan, Paul, E-mail: tavan@physik.uni-muenchen.de

2016-03-21

The infrared (IR) spectra of polypeptides are dominated by the so-called amide bands. Because they originate from the strongly polar and polarizable amide groups (AGs) making up the backbone, their spectral positions sensitively depend on the local electric fields. Aiming at accurate computations of these IR spectra by molecular dynamics (MD) simulations, which derive atomic forces from a hybrid quantum and molecular mechanics (QM/MM) Hamiltonian, here we consider the effects of solvation in bulk liquid water on the amide bands of the AG model compound N-methyl-acetamide (NMA). As QM approach to NMA we choose grid-based density functional theory (DFT). Formore » the surrounding MM water, we develop, largely based on computations, a polarizable molecular mechanics (PMM) model potential called GP6P, which features six Gaussian electrostatic sources (one induced dipole, five static partial charge distributions) and, therefore, avoids spurious distortions of the DFT electron density in hybrid DFT/PMM simulations. Bulk liquid GP6P is shown to have favorable properties at the thermodynamic conditions of the parameterization and beyond. Lennard-Jones (LJ) parameters of the DFT fragment NMA are optimized by comparing radial distribution functions in the surrounding GP6P liquid with reference data obtained from a “first-principles” DFT-MD simulation. Finally, IR spectra of NMA in GP6P water are calculated from extended DFT/PMM-MD trajectories, in which the NMA is treated by three different DFT functionals (BP, BLYP, B3LYP). Method-specific frequency scaling factors are derived from DFT-MD simulations of isolated NMA. The DFT/PMM-MD simulations with GP6P and with the optimized LJ parameters then excellently predict the effects of aqueous solvation and deuteration observed in the IR spectra of NMA. As a result, the methods required to accurately compute such spectra by DFT/PMM-MD also for larger peptides in aqueous solution are now at hand.« less

Modeling of crude oil biodegradation using two phase partitioning bioreactor.

PubMed

Fakhru'l-Razi, A; Peyda, Mazyar; Ab Karim Ghani, Wan Azlina Wan; Abidin, Zurina Zainal; Zakaria, Mohamad Pauzi; Moeini, Hassan

2014-01-01

In this work, crude oil biodegradation has been optimized in a solid-liquid two phase partitioning bioreactor (TPPB) by applying a response surface methodology based d-optimal design. Three key factors including phase ratio, substrate concentration in solid organic phase, and sodium chloride concentration in aqueous phase were taken as independent variables, while the efficiency of the biodegradation of absorbed crude oil on polymer beads was considered to be the dependent variable. Commercial thermoplastic polyurethane (Desmopan®) was used as the solid phase in the TPPB. The designed experiments were carried out batch wise using a mixed acclimatized bacterial consortium. Optimum combinations of key factors with a statistically significant cubic model were used to maximize biodegradation in the TPPB. The validity of the model was successfully verified by the good agreement between the model-predicted and experimental results. When applying the optimum parameters, gas chromatography-mass spectrometry showed a significant reduction in n-alkanes and low molecular weight polycyclic aromatic hydrocarbons. This consequently highlights the practical applicability of TPPB in crude oil biodegradation. © 2014 American Institute of Chemical Engineers.

An optimal strategy for functional mapping of dynamic trait loci.

PubMed

Jin, Tianbo; Li, Jiahan; Guo, Ying; Zhou, Xiaojing; Yang, Runqing; Wu, Rongling

2010-02-01

As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.

Optimal Implementations for Reliable Circadian Clocks

NASA Astrophysics Data System (ADS)

Hasegawa, Yoshihiko; Arita, Masanori

2014-09-01

Circadian rhythms are acquired through evolution to increase the chances for survival through synchronizing with the daylight cycle. Reliable synchronization is realized through two trade-off properties: regularity to keep time precisely, and entrainability to synchronize the internal time with daylight. We find by using a phase model with multiple inputs that achieving the maximal limit of regularity and entrainability entails many inherent features of the circadian mechanism. At the molecular level, we demonstrate the role sharing of two light inputs, phase advance and delay, as is well observed in mammals. At the behavioral level, the optimal phase-response curve inevitably contains a dead zone, a time during which light pulses neither advance nor delay the clock. We reproduce the results of phase-controlling experiments entrained by two types of periodic light pulses. Our results indicate that circadian clocks are designed optimally for reliable clockwork through evolution.

Construction and Maintenance of the Optimal Photosynthetic Systems of the Leaf, Herbaceous Plant and Tree: an Eco-developmental Treatise

PubMed Central

TERASHIMA, ICHIRO; ARAYA, TAKAO; MIYAZAWA, SHIN-ICHI; SONE, KOSEI; YANO, SATOSHI

2004-01-01

• Background and Aims The paper by Monsi and Saeki in 1953 (Japanese Journal of Botany 14: 22–52) was pioneering not only in mathematical modelling of canopy photosynthesis but also in eco-developmental studies of seasonal changes in leaf canopies. • Scope Construction and maintenance mechanisms of efficient photosynthetic systems at three different scaling levels—single leaves, herbaceous plants and trees—are reviewed mainly based on the nitrogen optimization theory. First, the nitrogen optimization theory with respect to the canopy and the single leaf is briefly introduced. Secondly, significance of leaf thickness in CO2 diffusion in the leaf and in leaf photosynthesis is discussed. Thirdly, mechanisms of adjustment of photosynthetic properties of the leaf within the herbaceous plant individual throughout its life are discussed. In particular, roles of sugar sensing, redox control and of cytokinin are highlighted. Finally, the development of a tree is considered. • Conclusions Various mechanisms contribute to construction and maintenance of efficient photosynthetic systems. Molecular backgrounds of these ecologically important mechanisms should be clarified. The construction mechanisms of the tree cannot be explained solely by the nitrogen optimization theory. It is proposed that the pipe model theory in its differential form could be a potential tool in future studies in this research area. PMID:15598701

Mathematical inference and control of molecular networks from perturbation experiments

NASA Astrophysics Data System (ADS)

Mohammed-Rasheed, Mohammed

One of the main challenges facing biologists and mathematicians in the post genomic era is to understand the behavior of molecular networks and harness this understanding into an educated intervention of the cell. The cell maintains its function via an elaborate network of interconnecting positive and negative feedback loops of genes, RNA and proteins that send different signals to a large number of pathways and molecules. These structures are referred to as genetic regulatory networks (GRNs) or molecular networks. GRNs can be viewed as dynamical systems with inherent properties and mechanisms, such as steady-state equilibriums and stability, that determine the behavior of the cell. The biological relevance of the mathematical concepts are important as they may predict the differentiation of a stem cell, the maintenance of a normal cell, the development of cancer and its aberrant behavior, and the design of drugs and response to therapy. Uncovering the underlying GRN structure from gene/protein expression data, e.g., microarrays or perturbation experiments, is called inference or reverse engineering of the molecular network. Because of the high cost and time consuming nature of biological experiments, the number of available measurements or experiments is very small compared to the number of molecules (genes, RNA and proteins). In addition, the observations are noisy, where the noise is due to the measurements imperfections as well as the inherent stochasticity of genetic expression levels. Intra-cellular activities and extra-cellular environmental attributes are also another source of variability. Thus, the inference of GRNs is, in general, an under-determined problem with a highly noisy set of observations. The ultimate goal of GRN inference and analysis is to be able to intervene within the network, in order to force it away from undesirable cellular states and into desirable ones. However, it remains a major challenge to design optimal intervention strategies in order to affect the time evolution of molecular activity in a desirable manner. In this proposal, we address both the inference and control problems of GRNs. In the first part of the thesis, we consider the control problem. We assume that we are given a general topology network structure, whose dynamics follow a discrete-time Markov chain model. We subsequently develop a comprehensive framework for optimal perturbation control of the network. The aim of the perturbation is to drive the network away from undesirable steady-states and to force it to converge to a unique desirable steady-state. The proposed framework does not make any assumptions about the topology of the initial network (e.g., ergodicity, weak and strong connectivity), and is thus applicable to general topology networks. We define the optimal perturbation as the minimum-energy perturbation measured in terms of the Frobenius norm between the initial and perturbed networks. We subsequently demonstrate that there exists at most one optimal perturbation that forces the network into the desirable steady-state. In the event where the optimal perturbation does not exist, we construct a family of sub-optimal perturbations that approximate the optimal solution arbitrarily closely. In the second part of the thesis, we address the inference problem of GRNs from time series data. We model the dynamics of the molecules using a system of ordinary differential equations corrupted by additive white noise. For large-scale networks, we formulate the inference problem as a constrained maximum likelihood estimation problem. We derive the molecular interactions that maximize the likelihood function while constraining the network to be sparse. We further propose a procedure to recover weak interactions based on the Bayesian information criterion. For small-size networks, we investigated the inference of a globally stable 7-gene melanoma genetic regulatory network from genetic perturbation experiments. We considered five melanoma cell lines, who exhibit different motility/invasion behavior under the same perturbation experiment of gene Wnt5a. The results of the simulations validate both the steady state levels and the experimental data of the perturbation experiments of all five cell lines. The goal of this study is to answer important questions that link the response of the network to perturbations, as measured by the experiments, to its structure, i.e., connectivity. Answers to these questions shed novel insights on the structure of networks and how they react to perturbations.

Feedback quantum control of molecular electronic population transfer

NASA Astrophysics Data System (ADS)

Bardeen, Christopher J.; Yakovlev, Vladislav V.; Wilson, Kent R.; Carpenter, Scott D.; Weber, Peter M.; Warren, Warren S.

1997-11-01

Feedback quantum control, where the sample `teaches' a computer-controlled arbitrary lightform generator to find the optimal light field, is experimentally demonstrated for a molecular system. Femtosecond pulses tailored by a computer-controlled acousto-optic pulse shaper excite fluorescence from laser dye molecules in solution. Fluorescence and laser power are monitored, and the computer uses the experimental data and a genetic algorithm to optimize population transfer from ground to first excited state. Both efficiency (the ratio of excited state population to laser energy) and effectiveness (total excited state population) are optimized. Potential use as an `automated theory tester' is discussed.

ATOMIC RESOLUTION CRYO ELECTRON MICROSCOPY OF MACROMOLECULAR COMPLEXES

PubMed Central

ZHOU, Z. HONG

2013-01-01

Single-particle cryo electron microscopy (cryoEM) is a technique for determining three-dimensional (3D) structures from projection images of molecular complexes preserved in their “native,” noncrystalline state. Recently, atomic or near-atomic resolution structures of several viruses and protein assemblies have been determined by single-particle cryoEM, allowing ab initio atomic model building by following the amino acid side chains or nucleic acid bases identifiable in their cryoEM density maps. In particular, these cryoEM structures have revealed extended arms contributing to molecular interactions that are otherwise not resolved by the conventional structural method of X-ray crystallography at similar resolutions. High-resolution cryoEM requires careful consideration of a number of factors, including proper sample preparation to ensure structural homogeneity, optimal configuration of electron imaging conditions to record high-resolution cryoEM images, accurate determination of image parameters to correct image distortions, efficient refinement and computation to reconstruct a 3D density map, and finally appropriate choice of modeling tools to construct atomic models for functional interpretation. This progress illustrates the power of cryoEM and ushers it into the arsenal of structural biology, alongside conventional techniques of X-ray crystallography and NMR, as a major tool (and sometimes the preferred one) for the studies of molecular interactions in supramolecular assemblies or machines. PMID:21501817

Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

NASA Astrophysics Data System (ADS)

Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

2013-12-01

P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

Molecular modelling study of changes induced by netropsin binding to nucleosome core particles.

PubMed Central

Pérez, J J; Portugal, J

1990-01-01

It is well known that certain sequence-dependent modulators in structure appear to determine the rotational positioning of DNA on the nucleosome core particle. That preference is rather weak and could be modified by some ligands as netropsin, a minor-groove binding antibiotic. We have undertaken a molecular modelling approach to calculate the relative energy of interaction between a DNA molecule and the protein core particle. The histones particle is considered as a distribution of positive charges on the protein surface that interacts with the DNA molecule. The molecular electrostatic potentials for the DNA, simulated as a discontinuous cylinder, were calculated using the values for all the base pairs. Computing these parameters, we calculated the relative energy of interaction and the more stable rotational setting of DNA. The binding of four molecules of netropsin to this model showed that a new minimum of energy is obtained when the DNA turns toward the protein surface by about 180 degrees, so a new energetically favoured structure appears where netropsin binding sites are located facing toward the histones surface. The effect of netropsin could be explained in terms of an induced change in the phasing of DNA on the core particle. The induced rotation is considered to optimize non-bonded contacts between the netropsin molecules and the DNA backbone. PMID:2165249

The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials

DOE PAGES

Witman, Matthew; Ling, Sanliang; Jawahery, Sudi; ...

2017-03-30

For applications of metal–organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material’s Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework’s intrinsic flexibility whereby performance is either improved or reduced with increasingmore » flexibility, depending on the material’s pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials’ selectivity can actually be improved by flexibility. In conclusion, equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.« less

The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials

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

Witman, Matthew; Ling, Sanliang; Jawahery, Sudi

For applications of metal–organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material’s Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework’s intrinsic flexibility whereby performance is either improved or reduced with increasingmore » flexibility, depending on the material’s pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials’ selectivity can actually be improved by flexibility. In conclusion, equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.« less

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

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

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

Cross-scale efficient tensor contractions for coupled cluster computations through multiple programming model backends

DOE PAGES

Ibrahim, Khaled Z.; Epifanovsky, Evgeny; Williams, Samuel; ...

2017-03-08

Coupled-cluster methods provide highly accurate models of molecular structure through explicit numerical calculation of tensors representing the correlation between electrons. These calculations are dominated by a sequence of tensor contractions, motivating the development of numerical libraries for such operations. While based on matrix–matrix multiplication, these libraries are specialized to exploit symmetries in the molecular structure and in electronic interactions, and thus reduce the size of the tensor representation and the complexity of contractions. The resulting algorithms are irregular and their parallelization has been previously achieved via the use of dynamic scheduling or specialized data decompositions. We introduce our efforts tomore » extend the Libtensor framework to work in the distributed memory environment in a scalable and energy-efficient manner. We achieve up to 240× speedup compared with the optimized shared memory implementation of Libtensor. We attain scalability to hundreds of thousands of compute cores on three distributed-memory architectures (Cray XC30 and XC40, and IBM Blue Gene/Q), and on a heterogeneous GPU-CPU system (Cray XK7). As the bottlenecks shift from being compute-bound DGEMM's to communication-bound collectives as the size of the molecular system scales, we adopt two radically different parallelization approaches for handling load-imbalance, tasking and bulk synchronous models. Nevertheless, we preserve a unified interface to both programming models to maintain the productivity of computational quantum chemists.« less

A molecular model for the active site of S-adenosyl- l-homocysteine hydrolase

NASA Astrophysics Data System (ADS)

Yeh, Jerry C.; Borchardt, Ronald T.; Vedani, Angelo

1991-06-01

S-adenosyl- l-homocysteine hydrolase (AdoHcy hydrolase, EC 3.3.1.1.), a specific target for antiviral drug design, catalyzes the hydrolysis of AdoHcy to adenosine (Ado) and homocysteine (Hcy) as well as the synthesis of AdoHcy from Ado and Hcy. The enzyme isolated from different sources has been shown to contain tightly bound NAD+. Based on the 2.0 Å-resolution X-ray crystal structure of dogfish lactate dehydrogenase (LDH), which is functionally homologous to AdoHcy hydrolase, and the primary sequence of rat liver AdoHcy hydrolase, we have derived a molecular model of an extended active site for AdoHcy hydrolase. The computational mutation was performed using the software MUTAR (Yeh et al., University of Kansas, Lawrence), followed by molecular mechanics optimizations using the programs AMBER (Singh et al., University of California, San Francisco) and YETI (Vedani, University of Kansas). Solvation of the model structure was achieved by use of the program SOLVGEN (Jacober, University of Kansas); 56 water molecules were explicitly included in all refinements. Some of these may be involved in the catalytic reaction. We also studied a model of the complex of AdoHcy hydrolase with NAD+, as well as the ternary complexes of the redox reaction catalyzed by AdoHcy hydrolase and has been used to differentiate the relative binding strength of inhibitors.

Optimal sensitivity for molecular recognition MAC-mode AFM

PubMed

Schindler; Badt; Hinterdorfer; Kienberger; Raab; Wielert-Badt; Pastushenko

2000-02-01

Molecular recognition force microscopy (MRFM) using the magnetic AC mode (MAC mode) atomic force microscope (AFM) was recently investigated to locate and probe recognition sites. A flexible crosslinker carrying a ligand is bound to the tip for the molecular recognition of receptors on the surface of a sample. In this report, the driving frequency is calculated which optimizes the sensitivity (S). The sensitivity of MRFM is defined as the relative change of the magnetically excited cantilever deflection amplitude arising from a crosslinker/antibody/antigen connection that is characterized by a very small force constant. The sensitivity is calculated in a damped oscillator model with a certain value of quality factor Q, which, together with load, defines the frequency response (unloaded oscillator shows resonance at Q > 0.707). If Q < 1, the greatest value of S corresponds to zero driving frequency omega (measured in units of eigenfrequency). Therefore, for Q < 1, MAC-mode has no advantage in comparison with DC-mode. Two additional extremes are found at omegaL = (1 - 1/Q)(1/2) and omegaR = (1 + 1/Q)(1/2), with corresponding sensitivities S(L) = Q2/(2Q - 1), S(R) = Q2/(2Q + 1). The L-extreme exists only for Q > 1, and then S(L) > S(R), i.e. the L-extreme is the main one. For Q > 1, S(L) > 1, and for Q > 2.41, S(R) > 1. These are the critical Q-values, above which selecting driving frequency equal to sigmaL or sigmaR brings advantage to MAC mode vs. DC mode. Satisfactory quality of the oscillator model is demonstrated by comparison of some results with those calculated within the classical description of cantilevers.

Free Energy-Based Virtual Screening and Optimization of RNase H Inhibitors of HIV-1 Reverse Transcriptase.

PubMed

Zhang, Baofeng; D'Erasmo, Michael P; Murelli, Ryan P; Gallicchio, Emilio

2016-09-30

We report the results of a binding free energy-based virtual screening campaign of a library of 77 α-hydroxytropolone derivatives against the challenging RNase H active site of the reverse transcriptase (RT) enzyme of human immunodeficiency virus-1. Multiple protonation states, rotamer states, and binding modalities of each compound were individually evaluated. The work involved more than 300 individual absolute alchemical binding free energy parallel molecular dynamics calculations and over 1 million CPU hours on national computing clusters and a local campus computational grid. The thermodynamic and structural measures obtained in this work rationalize a series of characteristics of this system useful for guiding future synthetic and biochemical efforts. The free energy model identified key ligand-dependent entropic and conformational reorganization processes difficult to capture using standard docking and scoring approaches. Binding free energy-based optimization of the lead compounds emerging from the virtual screen has yielded four compounds with very favorable binding properties, which will be the subject of further experimental investigations. This work is one of the few reported applications of advanced-binding free energy models to large-scale virtual screening and optimization projects. It further demonstrates that, with suitable algorithms and automation, advanced-binding free energy models can have a useful role in early-stage drug-discovery programs.

Amber Plug-In for Protein Shop

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

Oliva, Ricardo

2004-05-10

The Amber Plug-in for ProteinShop has two main components: an AmberEngine library to compute the protein energy models, and a module to solve the energy minimization problem using an optimization algorithm in the OPTI-+ library. Together, these components allow the visualization of the protein folding process in ProteinShop. AmberEngine is a object-oriented library to compute molecular energies based on the Amber model. The main class is called ProteinEnergy. Its main interface methods are (1) "init" to initialize internal variables needed to compute the energy. (2) "eval" to evaluate the total energy given a vector of coordinates. Additional methods allow themore » user to evaluate the individual components of the energy model (bond, angle, dihedral, non-bonded-1-4, and non-bonded energies) and to obtain the energy of each individual atom. The Amber Engine library source code includes examples and test routines that illustrate the use of the library in stand alone programs. The energy minimization module uses the AmberEngine library and the nonlinear optimization library OPT++. OPT++ is open source software available under the GNU Lesser General Public License. The minimization module currently makes use of the LBFGS optimization algorithm in OPT++ to perform the energy minimization. Future releases may give the user a choice of other algorithms available in OPT++.« less

Experimental design, modeling and optimization of polyplex formation between DNA oligonucleotides and branched polyethylenimine.

PubMed

Clima, Lilia; Ursu, Elena L; Cojocaru, Corneliu; Rotaru, Alexandru; Barboiu, Mihail; Pinteala, Mariana

2015-09-28

The complexes formed by DNA and polycations have received great attention owing to their potential application in gene therapy. In this study, the binding efficiency between double-stranded oligonucleotides (dsDNA) and branched polyethylenimine (B-PEI) has been quantified by processing of the images captured from the gel electrophoresis assays. The central composite experimental design has been employed to investigate the effects of controllable factors on the binding efficiency. On the basis of experimental data and the response surface methodology, a multivariate regression model has been constructed and statistically validated. The model has enabled us to predict the binding efficiency depending on experimental factors, such as concentrations of dsDNA and B-PEI as well as the initial pH of solution. The optimization of the binding process has been performed using simplex and gradient methods. The optimal conditions determined for polyplex formation have yielded a maximal binding efficiency close to 100%. In order to reveal the mechanism of complex formation at the atomic-scale, a molecular dynamic simulation has been carried out. According to the computation results, B-PEI amine hydrogen atoms have interacted with oxygen atoms from dsDNA phosphate groups. These interactions have led to the formation of hydrogen bonds between macromolecules, stabilizing the polyplex structure.

Quantum optimal control pathways of ozone isomerization dynamics subject to competing dissociation: A two-state one-dimensional model

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

Kurosaki, Yuzuru, E-mail: kurosaki.yuzuru@jaea.go.jp; Ho, Tak-San, E-mail: tsho@Princeton.EDU; Rabitz, Herschel, E-mail: hrabitz@Princeton.EDU

We construct a two-state one-dimensional reaction-path model for ozone open → cyclic isomerization dynamics. The model is based on the intrinsic reaction coordinate connecting the cyclic and open isomers with the O{sub 2} + O asymptote on the ground-state {sup 1}A{sup ′} potential energy surface obtained with the high-level ab initio method. Using this two-state model time-dependent wave packet optimal control simulations are carried out. Two possible pathways are identified along with their respective band-limited optimal control fields; for pathway 1 the wave packet initially associated with the open isomer is first pumped into a shallow well on the excitedmore » electronic state potential curve and then driven back to the ground electronic state to form the cyclic isomer, whereas for pathway 2 the corresponding wave packet is excited directly to the primary well of the excited state potential curve. The simulations reveal that the optimal field for pathway 1 produces a final yield of nearly 100% with substantially smaller intensity than that obtained in a previous study [Y. Kurosaki, M. Artamonov, T.-S. Ho, and H. Rabitz, J. Chem. Phys. 131, 044306 (2009)] using a single-state one-dimensional model. Pathway 2, due to its strong coupling to the dissociation channel, is less effective than pathway 1. The simulations also show that nonlinear field effects due to molecular polarizability and hyperpolarizability are small for pathway 1 but could become significant for pathway 2 because much higher field intensity is involved in the latter. The results suggest that a practical control may be feasible with the aid of a few lowly excited electronic states for ozone isomerization.« less

Scoring of Side-Chain Packings: An Analysis of Weight Factors and Molecular Dynamics Structures.

PubMed

Colbes, Jose; Aguila, Sergio A; Brizuela, Carlos A

2018-02-26

The protein side-chain packing problem (PSCPP) is a central task in computational protein design. The problem is usually modeled as a combinatorial optimization problem, which consists of searching for a set of rotamers, from a given rotamer library, that minimizes a scoring function (SF). The SF is a weighted sum of terms, that can be decomposed in physics-based and knowledge-based terms. Although there are many methods to obtain approximate solutions for this problem, all of them have similar performances and there has not been a significant improvement in recent years. Studies on protein structure prediction and protein design revealed the limitations of current SFs to achieve further improvements for these two problems. In the same line, a recent work reported a similar result for the PSCPP. In this work, we ask whether or not this negative result regarding further improvements in performance is due to (i) an incorrect weighting of the SFs terms or (ii) the constrained conformation resulting from the protein crystallization process. To analyze these questions, we (i) model the PSCPP as a bi-objective combinatorial optimization problem, optimizing, at the same time, the two most important terms of two SFs of state-of-the-art algorithms and (ii) performed a preprocessing relaxation of the crystal structure through molecular dynamics to simulate the protein in the solvent and evaluated the performance of these two state-of-the-art SFs under these conditions. Our results indicate that (i) no matter what combination of weight factors we use the current SFs will not lead to better performances and (ii) the evaluated SFs will not be able to improve performance on relaxed structures. Furthermore, the experiments revealed that the SFs and the methods are biased toward crystallized structures.

Toward Mycobacterium tuberculosis DXR inhibitor design: homology modeling and molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Singh, Nidhi; Avery, Mitchell A.; McCurdy, Christopher R.

2007-09-01

Mycobacterium tuberculosis 1-deoxy- d-xylulose-5-phosphate reductoisomerase ( MtDXR) is a potential target for antitubercular chemotherapy. In the absence of its crystallographic structure, our aim was to develop a structural model of MtDXR. This will allow us to gain early insight into the structure and function of the enzyme and its likely binding to ligands and cofactors and thus, facilitate structure-based inhibitor design. To achieve this goal, initial models of MtDXR were generated using MODELER. The best quality model was refined using a series of minimizations and molecular dynamics simulations. A protein-ligand complex was also developed from the initial homology model of the target protein by including information about the known ligand as spatial restraints and optimizing the mutual interactions between the ligand and the binding site. The final model was evaluated on the basis of its ability to explain several site-directed mutagenesis data. Furthermore, a comparison of the homology model with the X-ray structure published in the final stages of the project shows excellent agreement and validates the approach. The knowledge gained from the current study should prove useful in the design and development of inhibitors as potential novel therapeutic agents against tuberculosis by either de novo drug design or virtual screening of large chemical databases.

Optimization of nanoparticles for cardiovascular tissue engineering.

PubMed

Izadifar, Mohammad; Kelly, Michael E; Haddadi, Azita; Chen, Xiongbiao

2015-06-12

Nano-particulate delivery systems have increasingly been playing important roles in cardiovascular tissue engineering. Properties of nanoparticles (e.g. size, polydispersity, loading capacity, zeta potential, morphology) are essential to system functions. Notably, these characteristics are regulated by fabrication variables, but in a complicated manner. This raises a great need to optimize fabrication process variables to ensure the desired nanoparticle characteristics. This paper presents a comprehensive experimental study on this matter, along with a novel method, the so-called Geno-Neural approach, to analyze, predict and optimize fabrication variables for desired nanoparticle characteristics. Specifically, ovalbumin was used as a protein model of growth factors used in cardiovascular tissue regeneration, and six fabrication variables were examined with regard to their influence on the characteristics of nanoparticles made from high molecular weight poly(lactide-co-glycolide). The six-factor five-level central composite rotatable design was applied to the conduction of experiments, and based on the experimental results, a geno-neural model was developed to determine the optimum fabrication conditions. For desired particle sizes of 150, 200, 250 and 300 nm, respectively, the optimum conditions to achieve the low polydispersity index, higher negative zeta potential and higher loading capacity were identified based on the developed geno-neural model and then evaluated experimentally. The experimental results revealed that the polymer and the external aqueous phase concentrations and their interactions with other fabrication variables were the most significant variables to affect the size, polydispersity index, zeta potential, loading capacity and initial burst release of the nanoparticles, while the electron microscopy images of the nanoparticles showed their spherical geometries with no sign of large pores or cracks on their surfaces. The release study revealed that the onset of the third phase of release can be affected by the polymer concentration. Circular dichroism spectroscopy indicated that ovalbumin structural integrity is preserved during the encapsulation process. Findings from this study would greatly contribute to the design of high molecular weight poly(lactide-co-glycolide) nanoparticles for prolonged release patterns in cardiovascular engineering.

WavePacket: A Matlab package for numerical quantum dynamics.II: Open quantum systems, optimal control, and model reduction

NASA Astrophysics Data System (ADS)

Schmidt, Burkhard; Hartmann, Carsten

2018-07-01

WavePacket is an open-source program package for numeric simulations in quantum dynamics. It can solve time-independent or time-dependent linear Schrödinger and Liouville-von Neumann-equations in one or more dimensions. Also coupled equations can be treated, which allows, e.g., to simulate molecular quantum dynamics beyond the Born-Oppenheimer approximation. Optionally accounting for the interaction with external electric fields within the semi-classical dipole approximation, WavePacket can be used to simulate experiments involving tailored light pulses in photo-induced physics or chemistry. Being highly versatile and offering visualization of quantum dynamics 'on the fly', WavePacket is well suited for teaching or research projects in atomic, molecular and optical physics as well as in physical or theoretical chemistry. Building on the previous Part I [Comp. Phys. Comm. 213, 223-234 (2017)] which dealt with closed quantum systems and discrete variable representations, the present Part II focuses on the dynamics of open quantum systems, with Lindblad operators modeling dissipation and dephasing. This part also describes the WavePacket function for optimal control of quantum dynamics, building on rapid monotonically convergent iteration methods. Furthermore, two different approaches to dimension reduction implemented in WavePacket are documented here. In the first one, a balancing transformation based on the concepts of controllability and observability Gramians is used to identify states that are neither well controllable nor well observable. Those states are either truncated or averaged out. In the other approach, the H2-error for a given reduced dimensionality is minimized by H2 optimal model reduction techniques, utilizing a bilinear iterative rational Krylov algorithm. The present work describes the MATLAB version of WavePacket 5.3.0 which is hosted and further developed at the Sourceforge platform, where also extensive Wiki-documentation as well as numerous worked-out demonstration examples with animated graphics can be found.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

Force field development with GOMC, a fast new Monte Carlo molecular simulation code

NASA Astrophysics Data System (ADS)

Mick, Jason Richard

In this work GOMC (GPU Optimized Monte Carlo) a new fast, flexible, and free molecular Monte Carlo code for the simulation atomistic chemical systems is presented. The results of a large Lennard-Jonesium simulation in the Gibbs ensemble is presented. Force fields developed using the code are also presented. To fit the models a quantitative fitting process is outlined using a scoring function and heat maps. The presented n-6 force fields include force fields for noble gases and branched alkanes. These force fields are shown to be the most accurate LJ or n-6 force fields to date for these compounds, capable of reproducing pure fluid behavior and binary mixture behavior to a high degree of accuracy.

O-desmethylquinine as a cyclooxygenase-2 (COX-2) inhibitors using AutoDock Vina

NASA Astrophysics Data System (ADS)

Damayanti, Sophi; Mahardhika, Andhika Bintang; Ibrahim, Slamet; Chong, Wei Lim; Lee, Vannajan Sanghiran; Tjahjono, Daryono Hadi

2014-10-01

Computational approach was employed to evaluate the biological activity of novel cyclooxygenase-2 COX-2 inhibitor, O-desmethylquinine, in comparison to quinine as common inhibitor which can also be used an agent of antipyretic, antimalaria, analgesic and antiinflamation. The molecular models of the compound were constructed and optimized with the density function theory with at the B3LYP/6-31G (d,p) level using Gaussian 09 program. Molecular docking studies of the compounds were done to obtain the COX-2 complex structures and their binding energies were analyzed using the AutoDock Vina. The results of docking of the two ligands were comparable and cannot be differentiated from the energy scoring function with AutoDock Vina.

Proline 68 enhances photoisomerization yield in photoactive yellow protein.

PubMed

Rupenyan, Alisa B; Vreede, Jocelyne; van Stokkum, Ivo H M; Hospes, Marijke; Kennis, John T M; Hellingwerf, Klaas J; Groot, Marie Louise

2011-05-26

In proteins and enzymes, the local environment of an active cofactor plays an important role in controlling the outcome of a functional reaction. In photoactive yellow protein (PYP), it ensures photoisomerization of the chromophore, a prerequisite for formation of a signaling state. PYP is the prototype of a PAS domain, and the preferred model system for the studies of molecular mechanisms of biological light sensing. We investigated the effect of replacing proline-68, positioned near but not in direct contact with the chromophore, with other neutral amino acids (alanine, glycine, and valine), using ultrafast spectroscopy probing the visible and the mid-IR spectral regions, and molecular simulation to understand the interactions tuning the efficiency of light signaling. Transient absorption measurements indicate that the quantum yield of isomerization in the mutants is lower than the yield observed for the wild type. Subpicosecond mid-IR spectra and molecular dynamics simulations of the four proteins reveal that the hydrogen bond interactions around the chromophore and the access of water molecules in the active site of the protein determine the efficiency of photoisomerization. The mutants provide additional hydrogen bonds to the chromophore, directly and by allowing more water molecules access to its binding pocket. We conclude that proline-68 in the wild type protein optimizes the yield of photochemistry by maintaining a weak hydrogen bond with the chromophore, at the same time restraining the entrance of water molecules close to the alkylic part of pCa. This study provides a molecular basis for the structural optimization of biological light sensing.

Automated bond order assignment as an optimization problem.

PubMed

Dehof, Anna Katharina; Rurainski, Alexander; Bui, Quang Bao Anh; Böcker, Sebastian; Lenhof, Hans-Peter; Hildebrandt, Andreas

2011-03-01

Numerous applications in Computational Biology process molecular structures and hence strongly rely not only on correct atomic coordinates but also on correct bond order information. For proteins and nucleic acids, bond orders can be easily deduced but this does not hold for other types of molecules like ligands. For ligands, bond order information is not always provided in molecular databases and thus a variety of approaches tackling this problem have been developed. In this work, we extend an ansatz proposed by Wang et al. that assigns connectivity-based penalty scores and tries to heuristically approximate its optimum. In this work, we present three efficient and exact solvers for the problem replacing the heuristic approximation scheme of the original approach: an A*, an ILP and an fixed-parameter approach (FPT) approach. We implemented and evaluated the original implementation, our A*, ILP and FPT formulation on the MMFF94 validation suite and the KEGG Drug database. We show the benefit of computing exact solutions of the penalty minimization problem and the additional gain when computing all optimal (or even suboptimal) solutions. We close with a detailed comparison of our methods. The A* and ILP solution are integrated into the open-source C++ LGPL library BALL and the molecular visualization and modelling tool BALLView and can be downloaded from our homepage www.ball-project.org. The FPT implementation can be downloaded from http://bio.informatik.uni-jena.de/software/.

Luminescent probes for optical in vivo imaging

NASA Astrophysics Data System (ADS)

Texier, Isabelle; Josserand, Veronique; Garanger, Elisabeth; Razkin, Jesus; Jin, Zhaohui; Dumy, Pascal; Favrot, Marie; Boturyn, Didier; Coll, Jean-Luc

2005-04-01

Going along with instrumental development for small animal fluorescence in vivo imaging, we are developing molecular fluorescent probes, especially for tumor targeting. Several criteria have to be taken into account for the optimization of the luminescent label. It should be adapted to the in vivo imaging optical conditions : red-shifted absorption and emission, limited overlap between absorption and emission for a good signal filtering, optimized luminescence quantum yield, limited photo-bleaching. Moreover, the whole probe should fulfill the biological requirements for in vivo labeling : adapted blood-time circulation, biological conditions compatibility, low toxicity. We here demonstrate the ability of the imaging fluorescence set-up developed in LETI to image the bio-distribution of molecular probes on short times after injection. Targeting with Cy5 labeled holo-transferrin of subcutaneous TS/Apc (angiogenic murine breast carcinoma model) or IGROV1 (human ovarian cancer) tumors was achieved. Differences in the kinetics of the protein uptake by the tumors were evidenced. IGROV1 internal metastatic nodes implanted in the peritoneal cavity could be detected in nude mice. However, targeted metastatic nodes in lung cancer could only be imaged after dissection of the mouse. These results validate our fluorescence imaging set-up and the use of Cy5 as a luminescent label. New fluorescent probes based on this dye and a molecular delivery template (the RAFT molecule) can thus be envisioned.

Molecular inspired models for prediction and control of directional FSO/RF wireless networks

NASA Astrophysics Data System (ADS)

Llorca, Jaime; Milner, Stuart D.; Davis, Christopher C.

2010-08-01

Directional wireless networks using FSO and RF transmissions provide wireless backbone support for mobile communications in dynamic environments. The heterogeneous and dynamic nature of such networks challenges their robustness and requires self-organization mechanisms to assure end-to-end broadband connectivity. We developed a framework based on the definition of a potential energy function to characterize robustness in communication networks and the study of first and second order variations of the potential energy to provide prediction and control strategies for network performance optimization. In this paper, we present non-convex molecular potentials such as the Morse Potential, used to describe the potential energy of bonds within molecules, for the characterization of communication links in the presence of physical constraints such as the power available at the network nodes. The inclusion of the Morse Potential translates into adaptive control strategies where forces on network nodes drive the release, retention or reconfiguration of communication links for network performance optimization. Simulation results show the effectiveness of our self-organized control mechanism, where the physical topology reorganizes to maximize the number of source to destination communicating pairs. Molecular Normal Mode Analysis (NMA) techniques for assessing network performance degradation in dynamic networks are also presented. Preliminary results show correlation between peaks in the eigenvalues of the Hessian of the network potential and network degradation.

Developing novel C-4 analogues of pyrrole-based antitubulin agents: weak but critical hydrogen bonding in the colchicine site†

PubMed Central

Da, Chenxiao; Telang, Nakul; Hall, Kayleigh; Kluball, Emily; Barelli, Peter; Finzel, Kara; Jia, Xin; Gupton, John T.; Mooberry, Susan L.; Kellogg, Glen E.

2013-01-01

The synthesis, biological evaluation and molecular modeling of a series of pyrrole compounds related to 3,5-dibromo-4-(3,4-dimethoxyphenyl)-1H-pyrrole-2-carboxylic acid that evaluates and optimizes C-4 substituents are reported. The key factor for microtubule depolymerization activity appears to be the presence of an appropriately positioned acceptor for Cys241β in the otherwise hydrophobic subpocket A. PMID:23457660

Discovery of Hyperpolarized Molecular Imaging Biomarkers in a Novel Prostate Tissue Slice Culture Model

DTIC Science & Technology

2013-06-01

research is to optimize an MRS-compatible, 3D Tissue Culture Bioreactor for use with primary human prostate tissue cultures (TSCs) and use it to...Tissue Culture Bioreactor ” to be submitted to the Journal Magnetic Resonance in Medicine. CONCLUSIONS: We have engineered a robust MR compatible 3D ...loss of structure, function or metabolism within a NMR compatible 3-D tissue culture bioreactor , and that magnetic resonance spectroscopy studies of

Coupled elasticity–diffusion model for the effects of cytoskeleton deformation on cellular uptake of cylindrical nanoparticles

PubMed Central

Wang, Jizeng; Li, Long

2015-01-01

Molecular dynamic simulations and experiments have recently demonstrated how cylindrical nanoparticles (CNPs) with large aspect ratios penetrate animal cells and inevitably deform cytoskeletons. Thus, a coupled elasticity–diffusion model was adopted to elucidate this interesting biological phenomenon by considering the effects of elastic deformations of cytoskeleton and membrane, ligand–receptor binding and receptor diffusion. The mechanism by which the binding energy drives the CNPs with different orientations to enter host cells was explored. This mechanism involved overcoming the resistance caused by cytoskeleton and membrane deformations and the change in configurational entropy of the ligand–receptor bonds and free receptors. Results showed that deformation of the cytoskeleton significantly influenced the engulfing process by effectively slowing down and even hindering the entry of the CNPs. Additionally, the engulfing depth was determined quantitatively. CNPs preferred or tended to vertically attack target cells until they were stuck in the cytoskeleton as implied by the speed of vertically oriented CNPs that showed much faster initial engulfing speeds than horizontally oriented CNPs. These results elucidated the most recent molecular dynamics simulations and experimental observations on the cellular uptake of carbon nanotubes and phagocytosis of filamentous Escherichia coli bacteria. The most efficient engulfment showed the stiffness-dependent optimal radius of the CNPs. Cytoskeleton stiffness exhibited more significant influence on the optimal sizes of the vertical uptake than the horizontal uptake. PMID:25411410

Optimizing Noble Gas-Water Interactions via Monte Carlo Simulations.

PubMed

Warr, Oliver; Ballentine, Chris J; Mu, Junju; Masters, Andrew

2015-11-12

In this work we present optimized noble gas-water Lennard-Jones 6-12 pair potentials for each noble gas. Given the significantly different atomic nature of water and the noble gases, the standard Lorentz-Berthelot mixing rules produce inaccurate unlike molecular interactions between these two species. Consequently, we find simulated Henry's coefficients deviate significantly from their experimental counterparts for the investigated thermodynamic range (293-353 K at 1 and 10 atm), due to a poor unlike potential well term (εij). Where εij is too high or low, so too is the strength of the resultant noble gas-water interaction. This observed inadequacy in using the Lorentz-Berthelot mixing rules is countered in this work by scaling εij for helium, neon, argon, and krypton by factors of 0.91, 0.8, 1.1, and 1.05, respectively, to reach a much improved agreement with experimental Henry's coefficients. Due to the highly sensitive nature of the xenon εij term, coupled with the reasonable agreement of the initial values, no scaling factor is applied for this noble gas. These resulting optimized pair potentials also accurately predict partitioning within a CO2-H2O binary phase system as well as diffusion coefficients in ambient water. This further supports the quality of these interaction potentials. Consequently, they can now form a well-grounded basis for the future molecular modeling of multiphase geological systems.

Deep Eutectic Solvents Modified Molecular Imprinted Polymers for Optimized Purification of Chlorogenic Acid from Honeysuckle.

PubMed

Li, Guizhen; Wang, Wei; Wang, Qian; Zhu, Tao

2016-02-01

Deep eutectic solvents (DES) were synthesized with choline chloride (ChCl), and DES modified molecular imprinted polymers (DES-MIPs), DES modified non-imprinted polymers (DES-NIPs, without template), MIPs and NIPs were prepared in an identical procedure. Fourier transform infrared spectrometer (FT-IR) and field emission scanning electron microscopy (FE-SEM) were used to characterize the obtained polymers. Rebinding experiment and solid-phase extraction (SPE) were used to prove the high selectivity adsorption properties of the polymers. Box-Behnken design (BBD) with three factors was used to optimize the extraction condition of chlorogenic acid (CA) from honeysuckles. The optimum extraction conditions were found to be ultrasonic time optimized (20 min), the volume fraction of ethanol (60%) and ratio of liquid to material (15 mL g(-1)). Under these conditions, the mean extraction yield of CA was 12.57 mg g(-1), which was in good agreement with the predicted BBD model value. Purification of hawthorn extract was achieved by SPE process, and SPE recoveries of CA were 72.56, 64.79, 69.34 and 60.08% by DES-MIPs, DES-NIPs, MIPs and NIPs, respectively. The results showed DES-MIPs had potential for promising functional adsorption material for the purification of bioactive compounds. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

AMMOS2: a web server for protein-ligand-water complexes refinement via molecular mechanics.

PubMed

Labbé, Céline M; Pencheva, Tania; Jereva, Dessislava; Desvillechabrol, Dimitri; Becot, Jérôme; Villoutreix, Bruno O; Pajeva, Ilza; Miteva, Maria A

2017-07-03

AMMOS2 is an interactive web server for efficient computational refinement of protein-small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein-ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein-ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein-ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein-ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein-ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein-ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

An efficient approach to selective electromembrane extraction of naproxen by means of molecularly imprinted polymer-coated multi-walled carbon nanotubes-reinforced hollow fibers.

PubMed

Tahmasebi, Zeinab; Davarani, Saied Saeed Hosseiny; Asgharinezhad, Ali Akbar

2016-10-28

In this work, a novel microextraction technique using molecularly imprinted polymer-coated multi-walled carbon nanotubes (MIP-MWCNTs) in electromembrane extraction (EME) procedure is described. The method in combination with HPLC-UV was utilized to determine naproxen, as an acidic model drug, in urine, plasma and wastewater samples. For this purpose, MIP-MWCNTs were placed in the pores of polypropylene hollow fiber. The MIP-MWCNTs-EME method has the advantages of high selectivity and cleanup of MIP along with high enrichment ability of the EME in a single step extraction. Continuing with the research, optimization of the factors affecting the migration of naproxen from sample solutions to MIP-MWCNTs sites and then into the lumen of hollow fiber was explored. Under the optimized conditions, the limit of detection (LOD) of the developed method was calculated to be 0.3μgL -1 . All relative standard deviations (RSDs) were lower than 3%. Linearity of the method was obtained within the range of 1-1000μgL -1 with the coefficient of determination (r 2 ) being higher than 0.999. Under the optimized conditions, an extraction recovery of 66% was obtained, which corresponded to a preconcentration factor of 88. Finally, the developed method was satisfactorily used to determine naproxen in urine, plasma and wastewater samples. Copyright © 2016 Elsevier B.V. All rights reserved.

AMMOS2: a web server for protein–ligand–water complexes refinement via molecular mechanics

PubMed Central

Labbé, Céline M.; Pencheva, Tania; Jereva, Dessislava; Desvillechabrol, Dimitri; Becot, Jérôme; Villoutreix, Bruno O.; Pajeva, Ilza

2017-01-01

Abstract AMMOS2 is an interactive web server for efficient computational refinement of protein–small organic molecule complexes. The AMMOS2 protocol employs atomic-level energy minimization of a large number of experimental or modeled protein–ligand complexes. The web server is based on the previously developed standalone software AMMOS (Automatic Molecular Mechanics Optimization for in silico Screening). AMMOS utilizes the physics-based force field AMMP sp4 and performs optimization of protein–ligand interactions at five levels of flexibility of the protein receptor. The new version 2 of AMMOS implemented in the AMMOS2 web server allows the users to include explicit water molecules and individual metal ions in the protein–ligand complexes during minimization. The web server provides comprehensive analysis of computed energies and interactive visualization of refined protein–ligand complexes. The ligands are ranked by the minimized binding energies allowing the users to perform additional analysis for drug discovery or chemical biology projects. The web server has been extensively tested on 21 diverse protein–ligand complexes. AMMOS2 minimization shows consistent improvement over the initial complex structures in terms of minimized protein–ligand binding energies and water positions optimization. The AMMOS2 web server is freely available without any registration requirement at the URL: http://drugmod.rpbs.univ-paris-diderot.fr/ammosHome.php. PMID:28486703

Development of ocular drug delivery systems using molecularly imprinted soft contact lenses.

PubMed

Tashakori-Sabzevar, Faezeh; Mohajeri, Seyed Ahmad

2015-05-01

Recently, significant advances have been made in order to optimize drug delivery to ocular tissues. The main problems in ocular drug delivery are poor bioavailability and uncontrollable drug delivery of conventional ophthalmic preparations (e.g. eye drops). Hydrogels have been investigated since 1965 as new ocular drug delivery systems. Increase of hydrogel loading capacity, optimization of drug residence time on the ocular surface and biocompatibility with the eye tissue has been the main focus of previous studies. Molecular imprinting technology provided the opportunity to fulfill the above-mentioned objectives. Molecularly imprinted soft contact lenses (SCLs) have high potentials as novel drug delivery systems for the treatment of eye disorders. This technique is used for the preparation of polymers with specific binding sites for a template molecule. Previous studies indicated that molecular imprinting technology could be successfully applied for the preparation of SCLs as ocular drug delivery systems. Previous research, particularly in vivo studies, demonstrated that molecular imprinting is a versatile and effective method in optimizing the drug release behavior and enhancing the loading capacity of SCLs as new ocular drug delivery systems. This review highlights various potentials of molecularly imprinted contact lenses in enhancing the drug-loading capacity and controlling the drug release, compared to other ocular drug delivery systems. We have also studied the effects of contributing factors such as the type of comonomer, template/functional monomer molar ratio, crosslinker concentration in drug-loading capacity, and the release properties of molecularly imprinted hydrogels.

Virtual Institute of Microbial Stress and Survival: Deduction of Stress Response Pathways in Metal and Radionuclide Reducing Microorganisms

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

None

2004-04-17

The projects application goals are to: (1) To understand bacterial stress-response to the unique stressors in metal/radionuclide contamination sites; (2) To turn this understanding into a quantitative, data-driven model for exploring policies for natural and biostimulatory bioremediation; (3) To implement proposed policies in the field and compare results to model predictions; and (4) Close the experimental/computation cycle by using discrepancies between models and predictions to drive new measurements and construction of new models. The projects science goals are to: (1) Compare physiological and molecular response of three target microorganisms to environmental perturbation; (2) Deduce the underlying regulatory pathways that controlmore » these responses through analysis of phenotype, functional genomic, and molecular interaction data; (3) Use differences in the cellular responses among the target organisms to understand niche specific adaptations of the stress and metal reduction pathways; (4) From this analysis derive an understanding of the mechanisms of pathway evolution in the environment; and (5) Ultimately, derive dynamical models for the control of these pathways to predict how natural stimulation can optimize growth and metal reduction efficiency at field sites.« less

Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.

PubMed

Kumar, Ashwani; Chauhan, Shilpi

2017-01-01

SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R 2  = 0.8350, Q 2 test  = 0.7491 for the test set and R 2  = 0.9655, Q 2 ext  = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

QSPR prediction of the hydroxyl radical rate constant of water contaminants.

PubMed

Borhani, Tohid Nejad Ghaffar; Saniedanesh, Mohammadhossein; Bagheri, Mehdi; Lim, Jeng Shiun

2016-07-01

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

Insight into the Li2CO3-K2CO3 eutectic mixture from classical molecular dynamics: Thermodynamics, structure, and dynamics

NASA Astrophysics Data System (ADS)

Corradini, Dario; Coudert, François-Xavier; Vuilleumier, Rodolphe

2016-03-01

We use molecular dynamics simulations to study the thermodynamics, structure, and dynamics of the Li2CO3-K2CO3 (62:38 mol. %) eutectic mixture. We present a new classical non-polarizable force field for this molten salt mixture, optimized using experimental and first principles molecular dynamics simulations data as reference. This simple force field allows efficient molecular simulations of phenomena at long time scales. We use this optimized force field to describe the behavior of the eutectic mixture in the 900-1100 K temperature range, at pressures between 0 and 5 GPa. After studying the equation of state in these thermodynamic conditions, we present molecular insight into the structure and dynamics of the melt. In particular, we present an analysis of the temperature and pressure dependence of the eutectic mixture's self-diffusion coefficients, viscosity, and ionic conductivity.

Insight into the Li2CO3-K2CO3 eutectic mixture from classical molecular dynamics: Thermodynamics, structure, and dynamics.

PubMed

Corradini, Dario; Coudert, François-Xavier; Vuilleumier, Rodolphe

2016-03-14

We use molecular dynamics simulations to study the thermodynamics, structure, and dynamics of the Li2CO3-K2CO3 (62:38 mol. %) eutectic mixture. We present a new classical non-polarizable force field for this molten salt mixture, optimized using experimental and first principles molecular dynamics simulations data as reference. This simple force field allows efficient molecular simulations of phenomena at long time scales. We use this optimized force field to describe the behavior of the eutectic mixture in the 900-1100 K temperature range, at pressures between 0 and 5 GPa. After studying the equation of state in these thermodynamic conditions, we present molecular insight into the structure and dynamics of the melt. In particular, we present an analysis of the temperature and pressure dependence of the eutectic mixture's self-diffusion coefficients, viscosity, and ionic conductivity.

jMetalCpp: optimizing molecular docking problems with a C++ metaheuristic framework.

PubMed

López-Camacho, Esteban; García Godoy, María Jesús; Nebro, Antonio J; Aldana-Montes, José F

2014-02-01

Molecular docking is a method for structure-based drug design and structural molecular biology, which attempts to predict the position and orientation of a small molecule (ligand) in relation to a protein (receptor) to produce a stable complex with a minimum binding energy. One of the most widely used software packages for this purpose is AutoDock, which incorporates three metaheuristic techniques. We propose the integration of AutoDock with jMetalCpp, an optimization framework, thereby providing both single- and multi-objective algorithms that can be used to effectively solve docking problems. The resulting combination of AutoDock + jMetalCpp allows users of the former to easily use the metaheuristics provided by the latter. In this way, biologists have at their disposal a richer set of optimization techniques than those already provided in AutoDock. Moreover, designers of metaheuristic techniques can use molecular docking for case studies, which can lead to more efficient algorithms oriented to solving the target problems.  jMetalCpp software adapted to AutoDock is freely available as a C++ source code at http://khaos.uma.es/AutodockjMetal/.

Software Applications on the Peregrine System | High-Performance Computing

Science.gov Websites

programming and optimization. Gaussian Chemistry Program for calculating molecular electronic structure and Materials Science Open-source classical molecular dynamics program designed for massively parallel systems framework Q-Chem Chemistry ab initio quantum chemistry package for predictin molecular structures

Control of nitromethane photoionization efficiency with shaped femtosecond pulses.

PubMed

Roslund, Jonathan; Shir, Ofer M; Dogariu, Arthur; Miles, Richard; Rabitz, Herschel

2011-04-21

The applicability of adaptive femtosecond pulse shaping is studied for achieving selectivity in the photoionization of low-density polyatomic targets. In particular, optimal dynamic discrimination (ODD) techniques exploit intermediate molecular electronic resonances that allow a significant increase in the photoionization efficiency of nitromethane with shaped near-infrared femtosecond pulses. The intensity bias typical of high-photon number, nonresonant ionization is accounted for by reference to a strictly intensity-dependent process. Closed-loop adaptive learning is then able to discover a pulse form that increases the ionization efficiency of nitromethane by ∼150%. The optimally induced molecular dynamics result from entry into a region of parameter space inaccessible with intensity-only control. Finally, the discovered pulse shape is demonstrated to interact with the molecular system in a coherent fashion as assessed from the asymmetry between the response to the optimal field and its time-reversed counterpart.

Vibrational spectroscopic and structural investigations on fullerene: A DFT approach

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

Christy, P. Anto; Premkumar, S.; Asath, R. Mohamed

2016-05-06

The molecular structure of fullerene (C{sub 60}) molecule was optimized by the DFT/B3LYP method with 6-31G and 6-31G(d,p) basis sets using Gaussian 09 program. The vibrational frequencies were calculated for the optimized molecular structure of the molecule. The calculated vibrational frequencies confirm that the molecular structure of the molecule was located at the minimum energy potential energy surface. The calculated vibrational frequencies were assigned on the basis of functional group analysis and also confirmed using the GaussView 05 software. The frontier molecular orbitals analysis was carried out. The FMOs related molecular properties were predicted. The higher ionization potential, higher electronmore » affinity, higher softness, lower band gap energy and lower hardness values were obtained, which confirm that the fullerene molecule has a higher molecular reactivity. The Mulliken atomic charge distribution of the molecule was also calculated. Hence, these results play an important role due to its potential applications as drug delivery devices.« less

A global optimization perspective on molecular clusters.

PubMed

Marques, J M C; Pereira, F B; Llanio-Trujillo, J L; Abreu, P E; Albertí, M; Aguilar, A; Pirani, F; Bartolomei, M

2017-04-28

Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters; some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca 2+ ions with various types of solvents.This article is part of the themed issue 'Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces'. © 2017 The Author(s).

A global optimization perspective on molecular clusters

PubMed Central

Pereira, F. B.; Llanio-Trujillo, J. L.; Abreu, P. E.; Albertí, M.; Aguilar, A.; Pirani, F.; Bartolomei, M.

2017-01-01

Although there is a long history behind the idea of chemical structure, this is a key concept that continues to challenge chemists. Chemical structure is fundamental to understanding most of the properties of matter and its knowledge for complex systems requires the use of state-of-the-art techniques, either experimental or theoretical. From the theoretical view point, one needs to establish the interaction potential among the atoms or molecules of the system, which contains all the information regarding the energy landscape, and employ optimization algorithms to discover the relevant stationary points. In particular, global optimization methods are of major importance to search for the low-energy structures of molecular aggregates. We review the application of global optimization techniques to several molecular clusters; some new results are also reported. Emphasis is given to evolutionary algorithms and their application in the study of the microsolvation of alkali-metal and Ca2+ ions with various types of solvents. This article is part of the themed issue ‘Theoretical and computational studies of non-equilibrium and non-statistical dynamics in the gas phase, in the condensed phase and at interfaces’. PMID:28320902

A novel far-red fluorescent xenograft model of ovarian carcinoma for preclinical evaluation of HER2-targeted immunotoxins

PubMed Central

Zdobnova, Tatiana; Sokolova, Evgeniya; Stremovskiy, Oleg; Karpenko, Dmitry; Telford, William; Turchin, Ilya; Balalaeva, Irina; Deyev, Sergey

2015-01-01

We have created a novel fluorescent model of a human ovarian carcinoma xenograft overexpressing receptor HER2, a promising molecular target of solid tumors. The model is based on a newly generated SKOV-kat cell line stably expressing far-red fluorescent protein Katushka. Katushka is most suitable for the in vivo imaging due to an optimal combination of high brightness and emission in the “window of tissue transparency”. The relevance of the fluorescent model for the in vivo monitoring of tumor growth and response to treatment was demonstrated using a newly created HER2-targeted recombinant immunotoxin based on the 4D5scFv antibody and a fragment of the Pseudomonas exotoxin A. PMID:26436696

Comparative molecular field analysis and molecular docking studies on novel aryl chalcone derivatives against an important drug target cysteine protease in Plasmodium falciparum.

PubMed

Thillainayagam, Mahalakshmi; Anbarasu, Anand; Ramaiah, Sudha

2016-08-21

The computational studies namely molecular docking simulations and Comparative Molecular Field Analysis (CoMFA) are executed on series of 52 novel aryl chalcones derivatives using Plasmodium falciparum cysteine proteases (falcipain - 2) as vital target. In the present study, the correlation between different molecular field effects namely steric and electrostatic interactions and chemical structures to the inhibitory activities of novel aryl chalcone derivatives is inferred to perceive the major structural prerequisites for the rational design and development of potent and novel lead anti-malarial compound. The apparent binding conformations of all the compounds at the active site of falcipain - 2 and the hydrogen-bond interactions which could be used to modify the inhibitory activities are identified by using Surflex-dock study. Statistically significant CoMFA model has been developed with the cross-validated correlation coefficient (q(2)) of 0.912 and the non-cross-validated correlation coefficient (r(2)) of 0.901. Standard error of estimation (SEE) of 0.210, with the optimum number of components is ten. The predictability of the derived model is examined with a test set consists of sixteen compounds and the predicted r(2) value is found to be 0.924. The docking and QSAR study results confer crucial suggestions for the optimization of novel 1,3-diphenyl-2-propen-1-one derivatives and synthesis of effective anti- malarial compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Multiscale Molecular Simulation of Solution Processing of SMDPPEH: PCBM Small-Molecule Organic Solar Cells.

PubMed

Lee, Cheng-Kuang; Pao, Chun-Wei

2016-08-17

Solution-processed small-molecule organic solar cells are a promising renewable energy source because of their low production cost, mechanical flexibility, and light weight relative to their pure inorganic counterparts. In this work, we developed a coarse-grained (CG) Gay-Berne ellipsoid molecular simulation model based on atomistic trajectories from all-atom molecular dynamics simulations of smaller system sizes to systematically study the nanomorphology of the SMDPPEH/PCBM/solvent ternary blend during solution processing, including the blade-coating process by applying external shear to the solution. With the significantly reduced overall system degrees of freedom and computational acceleration from GPU, we were able to go well beyond the limitation of conventional all-atom molecular simulations with a system size on the order of hundreds of nanometers with mesoscale molecular detail. Our simulations indicate that, similar to polymer solar cells, the optimal blending ratio in small-molecule organic solar cells must provide the highest specific interfacial area for efficient exciton dissociation, while retaining balanced hole/electron transport pathway percolation. We also reveal that blade-coating processes have a significant impact on nanomorphology. For given donor/acceptor blending ratios, applying an external shear force can effectively promote donor/acceptor phase segregation and stacking in the SMDPPEH domains. The present study demonstrated the capability of an ellipsoid-based coarse-grained model for studying the nanomorphology evolution of small-molecule organic solar cells during solution processing/blade-coating and provided links between fabrication protocols and device nanomorphologies.

Statistical molecular design of balanced compound libraries for QSAR modeling.

PubMed

Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

2010-01-01

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

Combined quantum mechanical and molecular mechanical method for metal-organic frameworks: proton topologies of NU-1000.

PubMed

Wu, Xin-Ping; Gagliardi, Laura; Truhlar, Donald G

2018-01-17

Metal-organic frameworks (MOFs) are materials with applications in catalysis, gas separations, and storage. Quantum mechanical (QM) calculations can provide valuable guidance to understand and predict their properties. In order to make the calculations faster, rather than modeling these materials as periodic (infinite) systems, it is useful to construct finite models (called cluster models) and use subsystem methods such as fragment methods or combined quantum mechanical and molecular mechanical (QM/MM) methods. Here we employ a QM/MM methodology to study one particular MOF that has been of widespread interest because of its wide pores and good solvent and thermal stability, namely NU-1000, which contains hexanuclear zirconium nodes and 1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy 4- ) linkers. A modified version of the Bristow-Tiana-Walsh transferable force field has been developed to allow QM/MM calculations on NU-1000; we call the new parametrization the NU1T force field. We consider isomeric structures corresponding to various proton topologies of the [Zr 6 (μ 3 -O) 8 O 8 H 16 ] 8+ node of NU-1000, and we compute their relative energies using a QM/MM scheme designed for the present kind of problem. We compared the results to full quantum mechanical (QM) energy calculations and found that the QM/MM models can reproduce the full QM relative energetics (which span a range of 334 kJ mol -1 ) with a mean unsigned deviation (MUD) of only 2 kJ mol -1 . Furthermore, we found that the structures optimized by QM/MM are nearly identical to their full QM optimized counterparts.

Path integral molecular dynamic simulation of flexible molecular systems in their ground state: Application to the water dimer

NASA Astrophysics Data System (ADS)

Schmidt, Matthew; Roy, Pierre-Nicholas

2018-03-01

We extend the Langevin equation Path Integral Ground State (LePIGS), a ground state quantum molecular dynamics method, to simulate flexible molecular systems and calculate both energetic and structural properties. We test the approach with the H2O and D2O monomers and dimers. We systematically optimize all simulation parameters and use a unity trial wavefunction. We report ground state energies, dissociation energies, and structural properties using three different water models, two of which are empirically based, q-TIP4P/F and q-SPC/Fw, and one which is ab initio, MB-pol. We demonstrate that our energies calculated from LePIGS can be merged seamlessly with low temperature path integral molecular dynamics calculations and note the similarities between the two methods. We also benchmark our energies against previous diffusion Monte Carlo calculations using the same potentials and compare to experimental results. We further demonstrate that accurate vibrational energies of the H2O and D2O monomer can be calculated from imaginary time correlation functions generated from the LePIGS simulations using solely the unity trial wavefunction.

Molecular structure, vibrational spectra, NLO and MEP analysis of bis[2-hydroxy-кO-N-(2-pyridyl)-1-naphthaldiminato-кN]zinc(II)

NASA Astrophysics Data System (ADS)

Tanak, Hasan; Toy, Mehmet

2013-11-01

The molecular geometry and vibrational frequencies of bis[2-hydroxy-кO-N-(2-pyridyl)-1-naphthaldiminato-кN]zinc(II) in the ground state have been calculated by using the Hartree-Fock (HF) and density functional method (B3LYP) with 6-311G(d,p) basis set. The results of the optimized molecular structure are presented and compared with the experimental X-ray diffraction. The energetic and atomic charge behavior of the title compound in solvent media has been examined by applying the Onsager and the polarizable continuum model. To investigate second order nonlinear optical properties of the title compound, the electric dipole (μ), linear polarizability (α) and first-order hyperpolarizability (β) were computed using the density functional B3LYP and CAM-B3LYP methods with the 6-31+G(d) basis set. According to our calculations, the title compound exhibits nonzero (β) value revealing second order NLO behavior. In addition, DFT calculations of the title compound, molecular electrostatic potential (MEP), frontier molecular orbitals, and thermodynamic properties were performed at B3LYP/6-311G(d,p) level of theory.

Tunable molecular orientation and elevated thermal stability of vapor-deposited organic semiconductors

PubMed Central

Walters, Diane M.; Lyubimov, Ivan; de Pablo, Juan J.; Ediger, M. D.

2015-01-01

Physical vapor deposition is commonly used to prepare organic glasses that serve as the active layers in light-emitting diodes, photovoltaics, and other devices. Recent work has shown that orienting the molecules in such organic semiconductors can significantly enhance device performance. We apply a high-throughput characterization scheme to investigate the effect of the substrate temperature (Tsubstrate) on glasses of three organic molecules used as semiconductors. The optical and material properties are evaluated with spectroscopic ellipsometry. We find that molecular orientation in these glasses is continuously tunable and controlled by Tsubstrate/Tg, where Tg is the glass transition temperature. All three molecules can produce highly anisotropic glasses; the dependence of molecular orientation upon substrate temperature is remarkably similar and nearly independent of molecular length. All three compounds form “stable glasses” with high density and thermal stability, and have properties similar to stable glasses prepared from model glass formers. Simulations reproduce the experimental trends and explain molecular orientation in the deposited glasses in terms of the surface properties of the equilibrium liquid. By showing that organic semiconductors form stable glasses, these results provide an avenue for systematic performance optimization of active layers in organic electronics. PMID:25831545

Electric Field Tuning Molecular Packing and Electrical Properties of Solution-Shearing Coated Organic Semiconducting Thin Films

DOE PAGES

Molina-Lopez, Francisco; Yan, Hongping; Gu, Xiaodan; ...

2017-01-17

Recent improvements in solution-coated organic semiconductors (OSCs) evidence their high potential for cost-efficient organic electronics and sensors. Molecular packing structure determines the charge transport property of molecular solids. However, it remains challenging to control the molecular packing structure for a given OSC. Here, the application of alternating electric fields is reported to fine-tune the crystal packing of OSC solution-shearing coated at ambient conditions. First, a theoretical model based on dielectrophoresis is developed to guide the selection of the optimal conditions (frequency and amplitude) of the electric field applied through the solution-shearing blade during coating of OSC thin films. Next, electricmore » field-induced polymorphism is demonstrated for OSCs with both herringbone and 2D brick-wall packing motifs in 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene and 6,13-bis(triisopropylsilylethynyl) pentacene, respectively. Favorable molecular packing can be accessible in some cases, resulting in higher charge carrier mobilities. In conclusion, this work provides a new approach to tune the properties of solution-coated OSCs in functional devices for high-performance printed electronics.« less

Progression to multi-scale models and the application to food system intervention strategies.

PubMed

Gröhn, Yrjö T

2015-02-01

The aim of this article is to discuss how the systems science approach can be used to optimize intervention strategies in food animal systems. It advocates the idea that the challenges of maintaining a safe food supply are best addressed by integrating modeling and mathematics with biological studies critical to formulation of public policy to address these challenges. Much information on the biology and epidemiology of food animal systems has been characterized through single-discipline methods, but until now this information has not been thoroughly utilized in a fully integrated manner. The examples are drawn from our current research. The first, explained in depth, uses clinical mastitis to introduce the concept of dynamic programming to optimize management decisions in dairy cows (also introducing the curse of dimensionality problem). In the second example, a compartmental epidemic model for Johne's disease with different intervention strategies is optimized. The goal of the optimization strategy depends on whether there is a relationship between Johne's and Crohn's disease. If so, optimization is based on eradication of infection; if not, it is based on the cow's performance only (i.e., economic optimization, similar to the mastitis example). The third example focuses on food safety to introduce risk assessment using Listeria monocytogenes and Salmonella Typhimurium. The last example, practical interventions to effectively manage antibiotic resistance in beef and dairy cattle systems, introduces meta-population modeling that accounts for bacterial growth not only in the host (cow), but also in the cow's feed, drinking water and the housing environment. Each example stresses the need to progress toward multi-scale modeling. The article ends with examples of multi-scale systems, from food supply systems to Johne's disease. Reducing the consequences of foodborne illnesses (i.e., minimizing disease occurrence and associated costs) can only occur through an understanding of the system as a whole, including all its complexities. Thus the goal of future research should be to merge disciplines such as molecular biology, applied mathematics and social sciences to gain a better understanding of complex systems such as the food supply chain. Copyright © 2014 Elsevier B.V. All rights reserved.

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

PubMed Central

Margolin, Adam A.; Bilal, Erhan; Huang, Erich; Norman, Thea C.; Ottestad, Lars; Mecham, Brigham H.; Sauerwine, Ben; Kellen, Michael R.; Mangravite, Lara M.; Furia, Matthew D.; Vollan, Hans Kristian Moen; Rueda, Oscar M.; Guinney, Justin; Deflaux, Nicole A.; Hoff, Bruce; Schildwachter, Xavier; Russnes, Hege G.; Park, Daehoon; Vang, Veronica O.; Pirtle, Tyler; Youseff, Lamia; Citro, Craig; Curtis, Christina; Kristensen, Vessela N.; Hellerstein, Joseph; Friend, Stephen H.; Stolovitzky, Gustavo; Aparicio, Samuel; Caldas, Carlos; Børresen-Dale, Anne-Lise

2013-01-01

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models. PMID:23596205

Extension of the AMBER molecular dynamics software to Intel's Many Integrated Core (MIC) architecture

NASA Astrophysics Data System (ADS)

Needham, Perri J.; Bhuiyan, Ashraf; Walker, Ross C.

2016-04-01

We present an implementation of explicit solvent particle mesh Ewald (PME) classical molecular dynamics (MD) within the PMEMD molecular dynamics engine, that forms part of the AMBER v14 MD software package, that makes use of Intel Xeon Phi coprocessors by offloading portions of the PME direct summation and neighbor list build to the coprocessor. We refer to this implementation as pmemd MIC offload and in this paper present the technical details of the algorithm, including basic models for MPI and OpenMP configuration, and analyze the resultant performance. The algorithm provides the best performance improvement for large systems (>400,000 atoms), achieving a ∼35% performance improvement for satellite tobacco mosaic virus (1,067,095 atoms) when 2 Intel E5-2697 v2 processors (2 ×12 cores, 30M cache, 2.7 GHz) are coupled to an Intel Xeon Phi coprocessor (Model 7120P-1.238/1.333 GHz, 61 cores). The implementation utilizes a two-fold decomposition strategy: spatial decomposition using an MPI library and thread-based decomposition using OpenMP. We also present compiler optimization settings that improve the performance on Intel Xeon processors, while retaining simulation accuracy.

Disentangling the Role of Entanglement Density and Molecular Alignment in the Mechanical Response of Glassy Polymers

NASA Astrophysics Data System (ADS)

O'Connor, Thomas; Robbins, Mark

Glassy polymers are a ubiquitous part of modern life, but much about their mechanical properties remains poorly understood. Since chains in glassy states are hindered from exploring their conformational entropy, they can't be understood with common entropic network models. Additionally, glassy states are highly sensitive to material history and nonequilibrium distributions of chain alignment and entanglement can be produced during material processing. Understanding how these far-from equilibrium states impact mechanical properties is analytically challenging but essential to optimizing processing methods. We use molecular dynamics simulations to study the yield and strain hardening of glassy polymers as separate functions of the degree of molecular alignment and inter-chain entanglement. We vary chain alignment and entanglement with three different preparation protocols that mimic common processing conditions in and out of solution. We compare our results to common mechanical models of amorphous polymers and assess their applicability to different experimental processing conditions. This research was performed within the Center for Materials in Extreme Dynamic Environments (CMEDE) under the Hopkins Extreme Materials Institute at Johns Hopkins University. Financial support was provided by Grant W911NF-12-2-0022.

Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2009-08-21

Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6+/-0.2 kcal mol(-1). In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

Optical measurement of transverse molecular diffusion in a microchannel.

PubMed Central

Kamholz, A E; Schilling, E A; Yager, P

2001-01-01

Quantitative analysis of molecular diffusion is a necessity for the efficient design of most microfluidic devices as well as an important biophysical method in its own right. This study demonstrates the rapid measurement of diffusion coefficients of large and small molecules in a microfluidic device, the T-sensor, by means of conventional epifluorescence microscopy. Data were collected by monitoring the transverse flux of analyte from a sample stream into a second stream flowing alongside it. As indicated by the low Reynolds numbers of the system (< 1), flow is laminar, and molecular transport between streams occurs only by diffusion. Quantitative determinations were made by fitting data with predictions of a one-dimensional model. Analysis was made of the flow development and its effect on the distribution of diffusing analyte using a three-dimensional modeling software package. Diffusion coefficients were measured for four fluorescently labeled molecules: fluorescein-biotin, insulin, ovalbumin, and streptavidin. The resulting values differed from accepted results by an average of 2.4%. Microfluidic system parameters can be selected to achieve accurate diffusion coefficient measurements and to optimize other microfluidic devices that rely on precise transverse transport of molecules. PMID:11259309

Water activity and mobility in solutions of glycerol and small molecular weight sugars: Implication for cryo- and lyopreservation

NASA Astrophysics Data System (ADS)

He, Xiaoming; Fowler, Alex; Toner, Mehmet

2006-10-01

In this study, the free volume models, originally developed for large molecular weight polymer-solvent systems, were used to study the water activity and mobility in solutions of four small molecular weight cryo-/lyoprotectants, viz., glycerol, a monosaccharide (fructose), and two disaccharides (sucrose and trehalose). The free volume model parameters were determined by fitting the models to available experimental data using a nonlinear optimization procedure. It was found that free volume models could accurately predict the available experimental data, which suggests that the free volume models might be generally applicable to aqueous solutions of small molecular weight cryo-/lyoprotectants. Furthermore, several models for estimating the mutual diffusion coefficient were tested using available experimental data for aqueous solutions of glycerol and a better method to estimate the mutual diffusion coefficient was proposed. Free volume models were used to predict and analyze the water activity and mobility in solutions of four cryo-/lyoprotectants under conditions frequently encountered in cryo-/lyopreservation applications. It was found that the water mobility in the glassy state of the above four solutions is essentially negligible in the case of cryopreservation with storage temperature lower than -110°C. However, the water mobility in a glass at higher temperature (>-80°C) may be significant. As a result, a subcooling of up to 50°C may be necessary for the long-term cryo-/lyopreservation of biomaterials depending on the water content and the type of cryo-/lyoprotectants. It was further shown that trehalose might be the best of the four protectants studied for lyopreservation (water mass fraction ⩽0.1) when the storage temperature is above the room temperature. The results from this study might be useful for the development of more effective protocols for both cryopreservation and lyopreservation of living cells and other biomaterials.

Artificial Bee Colony Optimization of Capping Potentials for Hybrid Quantum Mechanical/Molecular Mechanical Calculations.

PubMed

Schiffmann, Christoph; Sebastiani, Daniel

2011-05-10

We present an algorithmic extension of a numerical optimization scheme for analytic capping potentials for use in mixed quantum-classical (quantum mechanical/molecular mechanical, QM/MM) ab initio calculations. Our goal is to minimize bond-cleavage-induced perturbations in the electronic structure, measured by means of a suitable penalty functional. The optimization algorithm-a variant of the artificial bee colony (ABC) algorithm, which relies on swarm intelligence-couples deterministic (downhill gradient) and stochastic elements to avoid local minimum trapping. The ABC algorithm outperforms the conventional downhill gradient approach, if the penalty hypersurface exhibits wiggles that prevent a straight minimization pathway. We characterize the optimized capping potentials by computing NMR chemical shifts. This approach will increase the accuracy of QM/MM calculations of complex biomolecules.

First-Principles Quantum Dynamics of Singlet Fission: Coherent versus Thermally Activated Mechanisms Governed by Molecular π Stacking

NASA Astrophysics Data System (ADS)

Tamura, Hiroyuki; Huix-Rotllant, Miquel; Burghardt, Irene; Olivier, Yoann; Beljonne, David

2015-09-01

Singlet excitons in π -stacked molecular crystals can split into two triplet excitons in a process called singlet fission that opens a route to carrier multiplication in photovoltaics. To resolve controversies about the mechanism of singlet fission, we have developed a first principles nonadiabatic quantum dynamical model that reveals the critical role of molecular stacking symmetry and provides a unified picture of coherent versus thermally activated singlet fission mechanisms in different acenes. The slip-stacked equilibrium packing structure of pentacene derivatives is found to enhance ultrafast singlet fission mediated by a coherent superexchange mechanism via higher-lying charge transfer states. By contrast, the electronic couplings for singlet fission strictly vanish at the C2 h symmetric equilibrium π stacking of rubrene. In this case, singlet fission is driven by excitations of symmetry-breaking intermolecular vibrations, rationalizing the experimentally observed temperature dependence. Design rules for optimal singlet fission materials therefore need to account for the interplay of molecular π -stacking symmetry and phonon-induced coherent or thermally activated mechanisms.

Initiative for Molecular Profiling and Advanced Cancer Therapy and challenges in the implementation of precision medicine.

PubMed

Tsimberidou, Apostolia-Maria

In the last decade, breakthroughs in technology have improved our understanding of genomic, transcriptional, proteomic, epigenetic aberrations and immune mechanisms in carcinogenesis. Genomics and model systems have enabled the validation of novel therapeutic strategies. Based on these developments, in 2007, we initiated the IMPACT (Initiative for Molecular Profiling and Advanced Cancer Therapy) study, the first personalized medicine program for patients with advanced cancer at The University of Texas MD Anderson Cancer Center. We demonstrated that in patients referred for Phase I clinical trials, the use of tumor molecular profiling and treatment with matched targeted therapy was associated with encouraging rates of response, progression-free survival and overall survival compared to non-matched therapy. We are currently conducting IMPACT2, a randomized study evaluating molecular profiling and targeted agents in patients with metastatic cancer. Optimization of innovative biomarker-driven clinical trials that include targeted therapy and/or immunotherapeutic approaches for carefully selected patients will accelerate the development of novel drugs and the implementation of precision medicine. Copyright © 2017 Elsevier Inc. All rights reserved.

Variational Identification of Markovian Transition States

NASA Astrophysics Data System (ADS)

Martini, Linda; Kells, Adam; Covino, Roberto; Hummer, Gerhard; Buchete, Nicolae-Viorel; Rosta, Edina

2017-07-01

We present a method that enables the identification and analysis of conformational Markovian transition states from atomistic or coarse-grained molecular dynamics (MD) trajectories. Our algorithm is presented by using both analytical models and examples from MD simulations of the benchmark system helix-forming peptide Ala5 , and of larger, biomedically important systems: the 15-lipoxygenase-2 enzyme (15-LOX-2), the epidermal growth factor receptor (EGFR) protein, and the Mga2 fungal transcription factor. The analysis of 15-LOX-2 uses data generated exclusively from biased umbrella sampling simulations carried out at the hybrid ab initio density functional theory (DFT) quantum mechanics/molecular mechanics (QM/MM) level of theory. In all cases, our method automatically identifies the corresponding transition states and metastable conformations in a variationally optimal way, with the input of a set of relevant coordinates, by accurately reproducing the intrinsic slowest relaxation rate of each system. Our approach offers a general yet easy-to-implement analysis method that provides unique insight into the molecular mechanism and the rare but crucial (i.e., rate-limiting) transition states occurring along conformational transition paths in complex dynamical systems such as molecular trajectories.

Probing the effects of surface hydrophobicity and tether orientation on antibody-antigen binding

NASA Astrophysics Data System (ADS)

Bush, Derek B.; Knotts, Thomas A.

2017-04-01

Antibody microarrays have the potential to revolutionize molecular detection for many applications, but their current use is limited by poor reliability, and efforts to change this have not yielded fruitful results. One difficulty which limits the rational engineering of next-generation devices is that little is known, at the molecular level, about the antibody-antigen binding process near solid surfaces. Atomic-level structural information is scant because typical experimental techniques (X-ray crystallography and NMR) cannot be used to image proteins bound to surfaces. To overcome this limitation, this study uses molecular simulation and an advanced, experimentally validated, coarse-grain, protein-surface model to compare fab-lysozyme binding in bulk solution and when the fab is tethered to hydrophobic and hydrophilic surfaces. The results show that the tether site in the fab, as well as the surface hydrophobicity, significantly impacts the binding process and suggests that the optimal design involves tethering fabs upright on a hydrophilic surface. The results offer an unprecedented, molecular-level picture of the binding process and give hope that the rational design of protein-microarrays is possible.

Strategies for the Optimization of Natural Leads to Anticancer Drugs or Drug Candidates

PubMed Central

Xiao, Zhiyan; Morris-Natschke, Susan L.; Lee, Kuo-Hsiung

2015-01-01

Natural products have made significant contribution to cancer chemotherapy over the past decades and remain an indispensable source of molecular and mechanistic diversity for anticancer drug discovery. More often than not, natural products may serve as leads for further drug development rather than as effective anticancer drugs by themselves. Generally, optimization of natural leads into anticancer drugs or drug candidates should not only address drug efficacy, but also improve ADMET profiles and chemical accessibility associated with the natural leads. Optimization strategies involve direct chemical manipulation of functional groups, structure-activity relationship-directed optimization and pharmacophore-oriented molecular design based on the natural templates. Both fundamental medicinal chemistry principles (e.g., bio-isosterism) and state-of-the-art computer-aided drug design techniques (e.g., structure-based design) can be applied to facilitate optimization efforts. In this review, the strategies to optimize natural leads to anticancer drugs or drug candidates are illustrated with examples and described according to their purposes. Furthermore, successful case studies on lead optimization of bioactive compounds performed in the Natural Products Research Laboratories at UNC are highlighted. PMID:26359649

Developmental stage related patterns of codon usage and genomic GC content: searching for evolutionary fingerprints with models of stem cell differentiation

PubMed Central

2007-01-01

Background The usage of synonymous codons shows considerable variation among mammalian genes. How and why this usage is non-random are fundamental biological questions and remain controversial. It is also important to explore whether mammalian genes that are selectively expressed at different developmental stages bear different molecular features. Results In two models of mouse stem cell differentiation, we established correlations between codon usage and the patterns of gene expression. We found that the optimal codons exhibited variation (AT- or GC-ending codons) in different cell types within the developmental hierarchy. We also found that genes that were enriched (developmental-pivotal genes) or specifically expressed (developmental-specific genes) at different developmental stages had different patterns of codon usage and local genomic GC (GCg) content. Moreover, at the same developmental stage, developmental-specific genes generally used more GC-ending codons and had higher GCg content compared with developmental-pivotal genes. Further analyses suggest that the model of translational selection might be consistent with the developmental stage-related patterns of codon usage, especially for the AT-ending optimal codons. In addition, our data show that after human-mouse divergence, the influence of selective constraints is still detectable. Conclusion Our findings suggest that developmental stage-related patterns of gene expression are correlated with codon usage (GC3) and GCg content in stem cell hierarchies. Moreover, this paper provides evidence for the influence of natural selection at synonymous sites in the mouse genome and novel clues for linking the molecular features of genes to their patterns of expression during mammalian ontogenesis. PMID:17349061

Optimization and modeling for the synthesis of sterol esters from deodorizer distillate by lipase-catalyzed esterification.

PubMed

Zhang, Xinyu; Yu, Jiang; Zeng, Aiwu

2017-03-01

In this paper, cotton seed oil deodorizer distillate (CSODD), was recovered to obtain fatty acid sterol ester (FASE), which is one of the biological activated substances added as human therapeutic to lower cholesterol. Esterification reactions were carried out using Candida rugosa lipase as a catalyst, and the conversion of phytosterol was optimized using response surface methodology. The highest conversion (90.8 ± 0.4%) was reached at 0.84 wt% enzyme load, 1:25 solvent/CSODD mass ratio, and 44.2 °C after 12 H reaction. A kinetic model based on the reaction rate equation was developed to describe the reaction process. The activation energy of the reaction was calculated to be 56.9 kJ/mol and the derived kinetic parameters provided indispensable basics for further study. The optimization and kinetic research of synthesizing FASE from deodorizer distillate provided necessary information for the industrial applications in the near future. Experimental results showed that the proposed process is a promising alternative to recycle sterol esters from vegetable oil deodorizer distillates in a mild, efficient, and environmental friendly method. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

Naturally-Occurring Canine Invasive Urothelial Carcinoma: A Model for Emerging Therapies

PubMed Central

Sommer, Breann C.; Dhawan, Deepika; Ratliff, Timothy L.; Knapp, Deborah W.

2018-01-01

The development of targeted therapies and the resurgence of immunotherapy offer enormous potential to dramatically improve the outlook for patients with invasive urothelial carcinoma (InvUC). Optimization of these therapies, however, is crucial as only a minority of patients achieve dramatic remission, and toxicities are common. With the complexities of the therapies, and the growing list of possible drug combinations to test, highly relevant animal models are needed to assess and select the most promising approaches to carry forward into human trials. The animal model(s) should possess key features that dictate success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). While it may not be possible to create these collective features in experimental models, these features are present in naturally-occurring InvUC in pet dogs. Naturally occurring canine InvUC closely mimics muscle-invasive bladder cancer in humans in regards to cellular and molecular features, molecular subtypes, biological behavior (sites and frequency of metastasis), and response to therapy. Clinical treatment trials in pet dogs with InvUC are considered a win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. This review will provide an overview of canine InvUC, the similarities to the human condition, and the potential for dogs with InvUC to serve as a model to predict the outcomes of targeted therapy and immunotherapy in humans. PMID:29732386

Naturally-Occurring Canine Invasive Urothelial Carcinoma: A Model for Emerging Therapies.

PubMed

Sommer, Breann C; Dhawan, Deepika; Ratliff, Timothy L; Knapp, Deborah W

2018-04-26

The development of targeted therapies and the resurgence of immunotherapy offer enormous potential to dramatically improve the outlook for patients with invasive urothelial carcinoma (InvUC). Optimization of these therapies, however, is crucial as only a minority of patients achieve dramatic remission, and toxicities are common. With the complexities of the therapies, and the growing list of possible drug combinations to test, highly relevant animal models are needed to assess and select the most promising approaches to carry forward into human trials. The animal model(s) should possess key features that dictate success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). While it may not be possible to create these collective features in experimental models, these features are present in naturally-occurring InvUC in pet dogs. Naturally occurring canine InvUC closely mimics muscle-invasive bladder cancer in humans in regards to cellular and molecular features, molecular subtypes, biological behavior (sites and frequency of metastasis), and response to therapy. Clinical treatment trials in pet dogs with InvUC are considered a win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. This review will provide an overview of canine InvUC, the similarities to the human condition, and the potential for dogs with InvUC to serve as a model to predict the outcomes of targeted therapy and immunotherapy in humans.

Concept of combinatorial de novo design of drug-like molecules by particle swarm optimization.

PubMed

Hartenfeller, Markus; Proschak, Ewgenij; Schüller, Andreas; Schneider, Gisbert

2008-07-01

We present a fast stochastic optimization algorithm for fragment-based molecular de novo design (COLIBREE, Combinatorial Library Breeding). The search strategy is based on a discrete version of particle swarm optimization. Molecules are represented by a scaffold, which remains constant during optimization, and variable linkers and side chains. Different linkers represent virtual chemical reactions. Side-chain building blocks were obtained from pseudo-retrosynthetic dissection of large compound databases. Here, ligand-based design was performed using chemically advanced template search (CATS) topological pharmacophore similarity to reference ligands as fitness function. A weighting scheme was included for particle swarm optimization-based molecular design, which permits the use of many reference ligands and allows for positive and negative design to be performed simultaneously. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor subtype-selective agonists. The results demonstrate the ability of the technique to cope with large combinatorial chemistry spaces and its applicability to focused library design. The technique was able to perform exploitation of a known scheme and at the same time explorative search for novel ligands within the framework of a given molecular core structure. It thereby represents a practical solution for compound screening in the early hit and lead finding phase of a drug discovery project.

Optimizing water permeability through the hourglass shape of aquaporins

PubMed Central

Gravelle, Simon; Joly, Laurent; Detcheverry, François; Ybert, Christophe; Cottin-Bizonne, Cécile; Bocquet, Lydéric

2013-01-01

The ubiquitous aquaporin channels are able to conduct water across cell membranes, combining the seemingly antagonist functions of a very high selectivity with a remarkable permeability. Whereas molecular details are obvious keys to perform these tasks, the overall efficiency of transport in such nanopores is also strongly limited by viscous dissipation arising at the connection between the nanoconstriction and the nearby bulk reservoirs. In this contribution, we focus on these so-called entrance effects and specifically examine whether the characteristic hourglass shape of aquaporins may arise from a geometrical optimum for such hydrodynamic dissipation. Using a combination of finite-element calculations and analytical modeling, we show that conical entrances with suitable opening angle can indeed provide a large increase of the overall channel permeability. Moreover, the optimal opening angles that maximize the permeability are found to compare well with the angles measured in a large variety of aquaporins. This suggests that the hourglass shape of aquaporins could be the result of a natural selection process toward optimal hydrodynamic transport. Finally, in a biomimetic perspective, these results provide guidelines to design artificial nanopores with optimal performances. PMID:24067650

Progress with modeling activity landscapes in drug discovery.

PubMed

Vogt, Martin

2018-04-19

Activity landscapes (ALs) are representations and models of compound data sets annotated with a target-specific activity. In contrast to quantitative structure-activity relationship (QSAR) models, ALs aim at characterizing structure-activity relationships (SARs) on a large-scale level encompassing all active compounds for specific targets. The popularity of AL modeling has grown substantially with the public availability of large activity-annotated compound data sets. AL modeling crucially depends on molecular representations and similarity metrics used to assess structural similarity. Areas covered: The concepts of AL modeling are introduced and its basis in quantitatively assessing molecular similarity is discussed. The different types of AL modeling approaches are introduced. AL designs can broadly be divided into three categories: compound-pair based, dimensionality reduction, and network approaches. Recent developments for each of these categories are discussed focusing on the application of mathematical, statistical, and machine learning tools for AL modeling. AL modeling using chemical space networks is covered in more detail. Expert opinion: AL modeling has remained a largely descriptive approach for the analysis of SARs. Beyond mere visualization, the application of analytical tools from statistics, machine learning and network theory has aided in the sophistication of AL designs and provides a step forward in transforming ALs from descriptive to predictive tools. To this end, optimizing representations that encode activity relevant features of molecules might prove to be a crucial step.

Structural refinement of the hERG1 pore and voltage-sensing domains with ROSETTA-membrane and molecular dynamics simulations.

PubMed

Subbotina, Julia; Yarov-Yarovoy, Vladimir; Lees-Miller, James; Durdagi, Serdar; Guo, Jiqing; Duff, Henry J; Noskov, Sergei Yu

2010-11-01

The hERG1 gene (Kv11.1) encodes a voltage-gated potassium channel. Mutations in this gene lead to one form of the Long QT Syndrome (LQTS) in humans. Promiscuous binding of drugs to hERG1 is known to alter the structure/function of the channel leading to an acquired form of the LQTS. Expectably, creation and validation of reliable 3D model of the channel have been a key target in molecular cardiology and pharmacology for the last decade. Although many models were built, they all were limited to pore domain. In this work, a full model of the hERG1 channel is developed which includes all transmembrane segments. We tested a template-driven de-novo design with ROSETTA-membrane modeling using side-chain placements optimized by subsequent molecular dynamics (MD) simulations. Although backbone templates for the homology modeled parts of the pore and voltage sensors were based on the available structures of KvAP, Kv1.2 and Kv1.2-Kv2.1 chimera channels, the missing parts are modeled de-novo. The impact of several alignments on the structure of the S4 helix in the voltage-sensing domain was also tested. Herein, final models are evaluated for consistency to the reported structural elements discovered mainly on the basis of mutagenesis and electrophysiology. These structural elements include salt bridges and close contacts in the voltage-sensor domain; and the topology of the extracellular S5-pore linker compared with that established by toxin foot-printing and nuclear magnetic resonance studies. Implications of the refined hERG1 model to binding of blockers and channels activators (potent new ligands for channel activations) are discussed. © 2010 Wiley-Liss, Inc.

Optimization of medium components and physicochemical parameters to simultaneously enhance microbial growth and production of lypolitic enzymes by Stenotrophomonas sp.

PubMed

Mazzucotelli, Cintia Anabela; Agüero, María Victoria; Del Rosario Moreira, María; Ansorena, María Roberta

2016-05-01

The optimization of lipase and esterase production (LP and EP) and bacterial growth (BG) of a Stenotrophomonas sp. strain was developed. For this purpose, the effect of five different medium components and three physicochemical parameters were evaluated using a Plackett-Burman statistical design. Among eight variables, stirring speed, pH, and peptone concentration were found to be the most effective factors on the three responses under evaluation. An optimization study applying Box-Behnken response surface methodology was used to study the interactive effects of the three selected variables on LP/EP and microorganism growth. Predicted models were found to be significant with high regression coefficients (90%-99%). By using the desirability function approach, the optimum condition applying simultaneous optimization of the three responses under study resulted to be: stirring speed of 100 rpm, pH of 7.5, and a peptone concentration of 10 g/L, with a desirability value of 0.977. Under these optimal conditions, it is possible to achieve in the optimized medium a 15-fold increase in esterase productivity, a 117-fold increase in lipase production, and a 9-log CFU/mL increase in BG, compared with the basal medium without agitation. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Development of the Molecular Adsorber Coating for Spacecraft and Instrument Interiors

NASA Technical Reports Server (NTRS)

Abraham, Nithin

2011-01-01

On-orbit Molecular Contamination occurs when materials outgas and deposit onto very sensitive interior surfaces of the spacecraft and instruments. The current solution, Molecular Adsorber Pucks, has disadvantages, which are reviewed. A new innovative solution, Molecular Adsorber Coating (MAC), is currently being formulated, optimized, and tested. It is a sprayable alternative composed of Zeolite-based coating with adsorbing properties.

Modeling and parameter identification of the simultaneous saccharification-fermentation process for ethanol production.

PubMed

Ochoa, Silvia; Yoo, Ahrim; Repke, Jens-Uwe; Wozny, Günter; Yang, Dae Ryook

2007-01-01

Despite many environmental advantages of using alcohol as a fuel, there are still serious questions about its economical feasibility when compared with oil-based fuels. The bioethanol industry needs to be more competitive, and therefore, all stages of its production process must be simple, inexpensive, efficient, and "easy" to control. In recent years, there have been significant improvements in process design, such as in the purification technologies for ethanol dehydration (molecular sieves, pressure swing adsorption, pervaporation, etc.) and in genetic modifications of microbial strains. However, a lot of research effort is still required in optimization and control, where the first step is the development of suitable models of the process, which can be used as a simulated plant, as a soft sensor or as part of the control algorithm. Thus, toward developing good, reliable, and simple but highly predictive models that can be used in the future for optimization and process control applications, in this paper an unstructured and a cybernetic model are proposed and compared for the simultaneous saccharification-fermentation process (SSF) for the production of ethanol from starch by a recombinant Saccharomyces cerevisiae strain. The cybernetic model proposed is a new one that considers the degradation of starch not only into glucose but also into dextrins (reducing sugars) and takes into account the intracellular reactions occurring inside the cells, giving a more detailed description of the process. Furthermore, an identification procedure based on the Metropolis Monte Carlo optimization method coupled with a sensitivity analysis is proposed for the identification of the model's parameters, employing experimental data reported in the literature.

Relationship between protein molecular structural makeup and metabolizable protein supply to dairy cattle from new cool-season forage corn cultivars.

PubMed

Abeysekara, Saman; Khan, Nazir A; Yu, Peiqiang

2018-02-15

Protein solubility, ruminal degradation and intestinal digestibility are strongly related to their inherent molecular makeup. This study was designed to quantitatively evaluate protein digestion in the rumen and intestine of dairy cattle, and estimate the content of truly metabolizable protein (MP) in newly developed cool-season forage corn cultivars. The second objective was to quantify protein inherent molecular structural characteristics using advance molecular spectroscopic technique (FT/IR-ATR) and correlate it to protein metabolic characteristics. Six new cool-season corn cultivars, including 3 Pioneer (PNR) and 3 Hyland (HL), coded as PNR-7443R, PNR-P7213R, PNR-7535R, HL-SR06, HL-SR22, HL-BAXXOS-RR, were evaluated in the present study. The metabolic characteristics, MP supply to dairy cattle, and energy synchronization properties were modeled by two protein evaluation models, namely, the Dutch DVE/OEB system and the NRC-2001 model. Both models estimated significant (P<0.05) differences in contents of microbial protein (MCP) synthesis and truly absorbable rumen undegraded protein (ARUP) among the cultivars. The NRC-2001 model estimated significant (P<0.05) differences in MP content and degraded protein balance (DPB) among the cultivars. The contents MCP, ARUP and MP were higher (P<0.05) for cultivar HL-SR06, resulting in the lowest (P<0.05) DPB. However, none of the cultivars reached the optimal target hourly effective degradability ratio [25gNg/kg organic matter (OM)], demonstrating N deficiency in the rumen. There were non-significant differences among the cultivars in molecular-spectral intensities of protein. The amide I/II ratio had a significant correlation with ARUP (r=-0.469; P<0.001) and absorbable endogenous protein (AECP NRC ) (P<0.001; r=0.612). Similarly, amide-II area had a weak but significant correlation (r=0.299; P<0.001) with RUP and ARUP, and with AECP NRC (P<0.001; r=0.411). Except total digestible nutrients and AECP NRC , the amide-I area did not show significant correlations with DVE/OEB and NRC predicted protein fractions. This study shows that molecular spectroscopy can be potentially used as a rapid tool to quantify protein molecular makeup and screen the protein nutritive value of forage corn. Copyright © 2017 Elsevier B.V. All rights reserved.

Relationship between protein molecular structural makeup and metabolizable protein supply to dairy cattle from new cool-season forage corn cultivars

NASA Astrophysics Data System (ADS)

Abeysekara, Saman; Khan, Nazir A.; Yu, Peiqiang

2018-02-01

Protein solubility, ruminal degradation and intestinal digestibility are strongly related to their inherent molecular makeup. This study was designed to quantitatively evaluate protein digestion in the rumen and intestine of dairy cattle, and estimate the content of truly metabolizable protein (MP) in newly developed cool-season forage corn cultivars. The second objective was to quantify protein inherent molecular structural characteristics using advance molecular spectroscopic technique (FT/IR-ATR) and correlate it to protein metabolic characteristics. Six new cool-season corn cultivars, including 3 Pioneer (PNR) and 3 Hyland (HL), coded as PNR-7443R, PNR-P7213R, PNR-7535R, HL-SR06, HL-SR22, HL-BAXXOS-RR, were evaluated in the present study. The metabolic characteristics, MP supply to dairy cattle, and energy synchronization properties were modeled by two protein evaluation models, namely, the Dutch DVE/OEB system and the NRC-2001 model. Both models estimated significant (P < 0.05) differences in contents of microbial protein (MCP) synthesis and truly absorbable rumen undegraded protein (ARUP) among the cultivars. The NRC-2001 model estimated significant (P < 0.05) differences in MP content and degraded protein balance (DPB) among the cultivars. The contents MCP, ARUP and MP were higher (P < 0.05) for cultivar HL-SR06, resulting in the lowest (P < 0.05) DPB. However, none of the cultivars reached the optimal target hourly effective degradability ratio [25 g N g/kg organic matter (OM)], demonstrating N deficiency in the rumen. There were non-significant differences among the cultivars in molecular-spectral intensities of protein. The amide I/II ratio had a significant correlation with ARUP (r = - 0.469; P < 0.001) and absorbable endogenous protein (AECPNRC) (P < 0.001; r = 0.612). Similarly, amide-II area had a weak but significant correlation (r = 0.299; P < 0.001) with RUP and ARUP, and with AECPNRC (P < 0.001; r = 0.411). Except total digestible nutrients and AECPNRC, the amide-I area did not show significant correlations with DVE/OEB and NRC predicted protein fractions. This study shows that molecular spectroscopy can be potentially used as a rapid tool to quantify protein molecular makeup and screen the protein nutritive value of forage corn.

QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.

Excess electron localization in solvated DNA bases.

PubMed

Smyth, Maeve; Kohanoff, Jorge

2011-06-10

We present a first-principles molecular dynamics study of an excess electron in condensed phase models of solvated DNA bases. Calculations on increasingly large microsolvated clusters taken from liquid phase simulations show that adiabatic electron affinities increase systematically upon solvation, as for optimized gas-phase geometries. Dynamical simulations after vertical attachment indicate that the excess electron, which is initially found delocalized, localizes around the nucleobases within a 15 fs time scale. This transition requires small rearrangements in the geometry of the bases.

Excess Electron Localization in Solvated DNA Bases

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

Smyth, Maeve; Kohanoff, Jorge

2011-06-10

We present a first-principles molecular dynamics study of an excess electron in condensed phase models of solvated DNA bases. Calculations on increasingly large microsolvated clusters taken from liquid phase simulations show that adiabatic electron affinities increase systematically upon solvation, as for optimized gas-phase geometries. Dynamical simulations after vertical attachment indicate that the excess electron, which is initially found delocalized, localizes around the nucleobases within a 15 fs time scale. This transition requires small rearrangements in the geometry of the bases.

Collection of X-ray diffraction data from macromolecular crystals

PubMed Central

Dauter, Zbigniew

2017-01-01

Diffraction data acquisition is the final experimental stage of the crystal structure analysis. All subsequent steps involve mainly computer calculations. Optimally measured and accurate data make the structure solution and refinement easier and lead to more faithful interpretation of the final models. Here, the important factors in data collection from macromolecular crystals are discussed and strategies appropriate for various applications, such as molecular replacement, anomalous phasing, atomic-resolution refinement etc., are presented. Criteria useful for judging the diffraction data quality are also discussed. PMID:28573573

A study of MRI-guided diffuse fluorescence molecular tomography for monitoring PDT effects in pancreas cancer

NASA Astrophysics Data System (ADS)

Samkoe, Kimberley S.; Davis, Scott C.; Srinivasan, Subhadra; O'Hara, Julia A.; Hasan, Tayyaba; Pogue, Brian W.

2009-06-01

Over the last several decades little progress has been made in the therapy and treatment monitoring of pancreas adenocarcinoma, a devastating and aggressive form of cancer that has a 5-year patient survival rate of 3%. Currently, investigations for the use of interstitial Verteporfin photodynamic therapy (PDT) are being undertaken in both orthotopic xenograft mouse models and in human clinical trials. In the mouse models, magnetic resonance (MR) imaging has been used as a measure of surrogate response to Verteporfin PDT; however, MR imaging alone lacks the molecular information required to assess the metabolic function and growth rates of the tumor immediately after treatment. We propose the implementation of MR-guided fluorescence tomography in conjunction with a fluorescently labeled (IR-Dye 800 CW, LI-COR) epidermal growth factor (EGF) as a molecular measure of surrogate response. To demonstrate the effectiveness of MR-guided diffuse fluorescence tomography for molecular imaging, we have used the AsPC-1 (+EGFR) human pancreatic adenocarcinoma in an orthotopic mouse model. EGF IRDye 800CW was injected 48 hours prior to imaging. MR image sequences were collected simultaneously with the fluorescence data using a MR-coupled diffuse optical tomography system. Image reconstruction was performed multiple times with varying abdominal organ segmentation in order to obtain a optimal tomographic image. It is shown that diffuse fluorescence tomography of the orthotopic pancreas model is feasible, with consideration of confounding fluorescence signals from the multiple organs and tissues surrounding the pancreas. MR-guided diffuse fluorescence tomography will be used to monitor EGF response after photodynamic therapy. Additionally, it provide the opportunity to individualize subsequent therapies based on response to PDT as well as to evaluate the success of combination therapies, such as PDT with chemotherapy, antibody therapy or even radiation.

Elucidation of Enzymatic Mechanism of Phenazine Biosynthetic Protein PhzF Using QM/MM and MD Simulations

PubMed Central

Liu, Fei; Zhao, Yi-Lei; Wang, Xiaolei; Hu, Hongbo; Peng, Huasong; Wang, Wei; Wang, Jing-Fang; Zhang, Xuehong

2015-01-01

The phenazine biosynthetic pathway is of considerable importance for the pharmaceutical industry. The pathway produces two products: phenazine-1,6-dicarboxylic acid and phenazine-1-carboxylic acid. PhzF is an isomerase that catalyzes trans-2,3-dihydro-3-hydroxyanthranilic acid isomerization and plays an essential role in the phenazine biosynthetic pathway. Although the PhzF crystal structure has been determined recently, an understanding of the detailed catalytic mechanism and the roles of key catalytic residues are still lacking. In this study, a computational strategy using a combination of molecular modeling, molecular dynamics simulations, and quantum mechanics/molecular mechanics simulations was used to elucidate these important issues. The Apo enzyme, enzyme–substrate complexes with negatively charged Glu45, enzyme–transition state analog inhibitor complexes with neutral Glu45, and enzyme–product complexes with negatively charged Glu45 structures were optimized and modeled using a 200 ns molecular dynamics simulation. Residues such as Gly73, His74, Asp208, Gly212, Ser213, and water, which play important roles in ligand binding and the isomerization reaction, were comprehensively investigated. Our results suggest that the Glu45 residue at the active site of PhzF acts as a general base/acid catalyst during proton transfer. This study provides new insights into the detailed catalytic mechanism of PhzF and the results have important implications for PhzF modification. PMID:26414009

Toward theragnostics.

PubMed

Pene, Frédéric; Courtine, Emilie; Cariou, Alain; Mira, Jean-Paul

2009-01-01

Theragnostics is a treatment strategy that combines therapeutics with diagnostics. It associates both a diagnostic test that identifies patients most likely to be helped or harmed by a new medication, and targeted drug therapy based on the test results. Bioinformatics, genomics, proteomics, and functional genomics are molecular biology tools essential for the progress of molecular theragnostics. These tools generate the genetic and protein information required for the development of diagnostic assays. Theragnostics includes a wide range of subjects, including personalized medicine, pharmacogenomics, and molecular imaging to develop efficient new targeted therapies with adequate benefit/risk to patients and a better molecular understanding of how to optimize drug selection. Furthermore, theragnostics aims to monitor the response to the treatment, to increase drug efficacy and safety. In addition, theragnostics could eliminate the unnecessary treatment of patients for whom therapy is not appropriate, resulting in significant drug cost savings for the healthcare system. However, the introduction of theragnostic tests into routine health care requires both a demonstration of cost-effectiveness and the availability of appropriate accessible testing systems. This review reports validation studies in oncology and infectious diseases that have demonstrated the benefits of such approach in well-defined subpopulations of patients, moving the field from the drug development process toward clinical practice and routine application. Theragnostics may change the usual business model of pharmaceutical companies from the classic blockbuster model toward targeted therapies.

Atomistic models for free energy evaluation of drug binding to membrane proteins.

PubMed

Durdagi, S; Zhao, C; Cuervo, J E; Noskov, S Y

2011-01-01

The binding of various molecules to integral membrane proteins with optimal affinity and specificity is central to normal function of cell. While membrane proteins represent about one third of the whole cell proteome, they are a majority of common drug targets. The quest for the development of computational models capable of accurate evaluation of binding affinities, decomposition of the binding into its principal components and thus mapping molecular mechanisms of binding remains one of the main goals of modern computational biophysics and related drug development. The primary scope of this review will be on the recent extension of computational methods for the study of drug binding to membrane proteins. Several examples of such applications will be provided ranging from secondary transporters to voltage gated channels. In this mini-review, we will provide a short summary on the breadth of different methods for binding affinity evaluation. These methods include molecular docking with docking scoring functions, molecular dynamics (MD) simulations combined with post-processing analysis using Molecular Mechanics/Poisson Boltzmann (Generalized Born) Surface Area (MM/PB(GB)SA), as well as direct evaluation of free energies from Free Energy Perturbation (FEP) with constraining schemes, and Potential of Mean Force (PMF) computations. We will compare advantages and shortcomings of popular techniques and provide discussion on the integrative strategies for drug development aimed at targeting membrane proteins.

Role of the pulmonologist in ordering post-procedure molecular markers in non-small-cell lung cancer: implications for personalized medicine.

PubMed

Murgu, Septimiu; Colt, Henri

2013-11-01

In the growing era of personalized medicine for the treatment of non-small-cell lung cancer (NSCLC), it is becoming increasingly important that sufficient quality and quantity of tumor tissue are available for morphologic diagnosis and molecular analysis. As new treatment options emerge that might require more frequent and possibly higher volume biopsies, the role of the pulmonologist will expand, and it will be important for pulmonologists to work within a multidisciplinary team to provide optimal therapeutic management for patients with NSCLC. In this review, we discuss the rationale for individualized treatment decisions for patients with NSCLC, molecular pathways and specific molecular predictors relevant to personalized NSCLC therapy, assay technologies for molecular marker analysis, and specifics regarding tumor specimen selection, acquisition, and handling. Moreover, we briefly address issues regarding racial and socioeconomic disparities as they relate to molecular testing and treatment decisions, and cost considerations for molecular testing and targeted therapies in NSCLC. We also propose a model for an institution-based multidisciplinary team, including oncologists, pathologists, pulmonologists, interventional radiologists, and thoracic surgeons, to ensure adequate material is available for cytological and histological studies and to standardize methods of tumor specimen handling and processing in an effort to provide beneficial, individualized therapy for patients with NSCLC. Copyright © 2013 Elsevier Inc. All rights reserved.

Influence of N-H...O and C-H...O hydrogen bonds on the 17O NMR tensors in crystalline uracil: computational study.

PubMed

Ida, Ramsey; De Clerk, Maurice; Wu, Gang

2006-01-26

We report a computational study for the 17O NMR tensors (electric field gradient and chemical shielding tensors) in crystalline uracil. We found that N-H...O and C-H...O hydrogen bonds around the uracil molecule in the crystal lattice have quite different influences on the 17O NMR tensors for the two C=O groups. The computed 17O NMR tensors on O4, which is involved in two strong N-H...O hydrogen bonds, show remarkable sensitivity toward the choice of cluster model, whereas the 17O NMR tensors on O2, which is involved in two weak C-H...O hydrogen bonds, show much smaller improvement when the cluster model includes the C-H...O hydrogen bonds. Our results demonstrate that it is important to have accurate hydrogen atom positions in the molecular models used for 17O NMR tensor calculations. In the absence of low-temperature neutron diffraction data, an effective way to generate reliable hydrogen atom positions in the molecular cluster model is to employ partial geometry optimization for hydrogen atom positions using a cluster model that includes all neighboring hydrogen-bonded molecules. Using an optimized seven-molecule model (a total of 84 atoms), we were able to reproduce the experimental 17O NMR tensors to a reasonably good degree of accuracy. However, we also found that the accuracy for the calculated 17O NMR tensors at O2 is not as good as that found for the corresponding tensors at O4. In particular, at the B3LYP/6-311++G(d,p) level of theory, the individual 17O chemical shielding tensor components differ by less than 10 and 30 ppm from the experimental values for O4 and O2, respectively. For the 17O quadrupole coupling constant, the calculated values differ by 0.30 and 0.87 MHz from the experimental values for O4 and O2, respectively.

Top-down models in biology: explanation and control of complex living systems above the molecular level.

PubMed

Pezzulo, Giovanni; Levin, Michael

2016-11-01

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering. © 2016 The Author(s).

Top-down models in biology: explanation and control of complex living systems above the molecular level

PubMed Central

2016-01-01

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering. PMID:27807271

Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data

PubMed Central

2015-01-01

A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data. To construct the selected ion chromatogram (XIC), a clustering method, the density based spatial clustering of applications with noise (DBSCAN), is applied to all m/z values of an LC-MS data set to group the m/z values into each XIC. The DBSCAN constructs XICs without the need for a user defined m/z variation window. After the XIC construction, the peaks of molecular ions in each XIC are detected using both the first and the second derivative tests, followed by an optimized chromatographic peak model selection method for peak deconvolution. A total of six chromatographic peak models are considered, including Gaussian, log-normal, Poisson, gamma, exponentially modified Gaussian, and hybrid of exponential and Gaussian models. The abundant nonoverlapping peaks are chosen to find the optimal peak models that are both data- and retention-time-dependent. Analysis of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum deconvolution method outperforms the traditional method. On average, the DDPM approach not only detected 58 more chromatographic peaks from each of the testing LC-MS data but also improved the retention time and peak area 3% and 6%, respectively. PMID:24533635

Exact probability distribution functions for Parrondo's games

NASA Astrophysics Data System (ADS)

Zadourian, Rubina; Saakian, David B.; Klümper, Andreas

2016-12-01

We study the discrete time dynamics of Brownian ratchet models and Parrondo's games. Using the Fourier transform, we calculate the exact probability distribution functions for both the capital dependent and history dependent Parrondo's games. In certain cases we find strong oscillations near the maximum of the probability distribution with two limiting distributions for odd and even number of rounds of the game. Indications of such oscillations first appeared in the analysis of real financial data, but now we have found this phenomenon in model systems and a theoretical understanding of the phenomenon. The method of our work can be applied to Brownian ratchets, molecular motors, and portfolio optimization.

Exact probability distribution functions for Parrondo's games.

PubMed

Zadourian, Rubina; Saakian, David B; Klümper, Andreas

2016-12-01

We study the discrete time dynamics of Brownian ratchet models and Parrondo's games. Using the Fourier transform, we calculate the exact probability distribution functions for both the capital dependent and history dependent Parrondo's games. In certain cases we find strong oscillations near the maximum of the probability distribution with two limiting distributions for odd and even number of rounds of the game. Indications of such oscillations first appeared in the analysis of real financial data, but now we have found this phenomenon in model systems and a theoretical understanding of the phenomenon. The method of our work can be applied to Brownian ratchets, molecular motors, and portfolio optimization.

Atom-bond electronegativity equalization method fused into molecular mechanics. I. A seven-site fluctuating charge and flexible body water potential function for water clusters.

PubMed

Yang, Zhong-Zhi; Wu, Yang; Zhao, Dong-Xia

2004-02-08

Recently, experimental and theoretical studies on the water system are very active and noticeable. A transferable intermolecular potential seven points approach including fluctuation charges and flexible body (ABEEM-7P) based on a combination of the atom-bond electronegativity equalization and molecular mechanics (ABEEM/MM), and its application to small water clusters are explored and tested in this paper. The consistent combination of ABEEM and molecular mechanics (MM) is to take the ABEEM charges of atoms, bonds, and lone-pair electrons into the intermolecular electrostatic interaction term in molecular mechanics. To examine the charge transfer we have used two models coming from the charge constraint types: one is a charge neutrality constraint on whole water system and the other is on each water molecule. Compared with previous water force fields, the ABEEM-7P model has two characters: (1) the ABEEM-7P model not only presents the electrostatic interaction of atoms, bonds and lone-pair electrons and their changing in respond to different ambient environment but also introduces "the hydrogen bond interaction region" in which a new parameter k(lp,H)(R(lp,H)) is used to describe the electrostatic interaction of the lone-pair electron and the hydrogen atom which can form the hydrogen bond; (2) nonrigid but flexible water body permitting the vibration of the bond length and angle is allowed due to the combination of ABEEM and molecular mechanics, and for van der Waals interaction the ABEEM-7P model takes an all atom-atom interaction, i.e., oxygen-oxygen, hydrogen-hydrogen, oxygen-hydrogen interaction into account. The ABEEM-7P model based on ABEEM/MM gives quite accurate predictions for gas-phase state properties of the small water clusters (H(2)O)(n) (n=2-6), such as optimized geometries, monomer dipole moments, vibrational frequencies, and cluster interaction energies. Due to its explicit description of charges and the hydrogen bond, the ABEEM-7P model will be applied to discuss properties of liquid water, ice, aqueous solutions, and biological systems.

Multiscale Kinetic Modeling Reveals an Ensemble of Cl–/H+ Exchange Pathways in ClC-ec1 Antiporter

PubMed Central

2018-01-01

Despite several years of research, the ion exchange mechanisms in chloride/proton antiporters and many other coupled transporters are not yet understood at the molecular level. Here, we present a novel approach to kinetic modeling and apply it to ion exchange in ClC-ec1. Our multiscale kinetic model is developed by (1) calculating the state-to-state rate coefficients with reactive and polarizable molecular dynamics simulations, (2) optimizing these rates in a global kinetic network, and (3) predicting new electrophysiological results. The model shows that the robust Cl:H exchange ratio (2.2:1) can indeed arise from kinetic coupling without large protein conformational changes, indicating a possible facile evolutionary connection to chloride channels. The E148 amino acid residue is shown to couple chloride and proton transport through protonation-dependent blockage of the central anion binding site and an anion-dependent pKa value, which influences proton transport. The results demonstrate how an ensemble of different exchange pathways, as opposed to a single series of transitions, culminates in the macroscopic observables of the antiporter, such as transport rates, chloride/proton stoichiometry, and pH dependence. PMID:29332400

IADE: a system for intelligent automatic design of bioisosteric analogs

NASA Astrophysics Data System (ADS)

Ertl, Peter; Lewis, Richard

2012-11-01

IADE, a software system supporting molecular modellers through the automatic design of non-classical bioisosteric analogs, scaffold hopping and fragment growing, is presented. The program combines sophisticated cheminformatics functionalities for constructing novel analogs and filtering them based on their drug-likeness and synthetic accessibility using automatic structure-based design capabilities: the best candidates are selected according to their similarity to the template ligand and to their interactions with the protein binding site. IADE works in an iterative manner, improving the fitness of designed molecules in every generation until structures with optimal properties are identified. The program frees molecular modellers from routine, repetitive tasks, allowing them to focus on analysis and evaluation of the automatically designed analogs, considerably enhancing their work efficiency as well as the area of chemical space that can be covered. The performance of IADE is illustrated through a case study of the design of a nonclassical bioisosteric analog of a farnesyltransferase inhibitor—an analog that has won a recent "Design a Molecule" competition.

Synthesis, molecular modeling and biological evaluation of PSB as targeted antibiotics.

PubMed

Cheng, Kui; Zheng, Qing-Zhong; Hou, Jin; Zhou, Yang; Liu, Chang-Hong; Zhao, Jing; Zhu, Hai-Liang

2010-04-01

We described here the design, synthesis, molecular modeling, and biological evaluation of a series of peptide and Schiff bases (PSB) small molecules, inhibitors of Escherichia coli beta-Ketoacyl-acyl carrier protein synthase III (ecKAS III). The initial lead compound was reported by us previously, we continued to carry out structure-activity relationship studies and optimize the lead structure to potent inhibitors in this research. The results demonstrated that both N-(2-(3,5-dichloro-2-hydroxybenzylideneamino)propyl)-2-hydroxybenzamide (1f) and 2-hydroxy-N-(2-(2-hydroxy-5-iodobenzylideneamino)propyl)-4-methylbenzamide (3e) posses good ecKAS III inhibitory activity and well binding affinities by bonding Gly152/Gly209 of ecKAS III and fit into the mouth of the substrate tunnel, and can be as potential antibiotics agent, displaying minimal inhibitory concentration values in the range 0.20-3.13microg/mL and 0.39-3.13microg/mL against various bacteria. Copyright 2010 Elsevier Ltd. All rights reserved.

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

Ghosh, Arun K.; Takayama, Jun; Rao, Kalapala Venkateswar

The design, synthesis, X-ray crystal structure, molecular modeling, and biological evaluation of a series of new generation SARS-CoV PLpro inhibitors are described. A new lead compound 3 (6577871) was identified via high-throughput screening of a diverse chemical library. Subsequently, we carried out lead optimization and structure-activity studies to provide a series of improved inhibitors that show potent PLpro inhibition and antiviral activity against SARS-CoV infected Vero E6 cells. Interestingly, the (S)-Me inhibitor 15h (enzyme IC{sub 50} = 0.56 {mu}M; antiviral EC{sub 50} = 9.1 {mu}M) and the corresponding (R)-Me 15g (IC{sub 50} = 0.32 {mu}M; antiviral EC{sub 50} = 9.1more » {mu}M) are the most potent compounds in this series, with nearly equivalent enzymatic inhibition and antiviral activity. A protein-ligand X-ray structure of 15g-bound SARS-CoV PLpro and a corresponding model of 15h docked to PLpro provide intriguing molecular insight into the ligand-binding site interactions.« less

Preparation, in vitro evaluation and molecular modelling of pyridinium-quinolinium/isoquinolinium non-symmetrical bisquaternary cholinesterase inhibitors.

PubMed

Komloova, Marketa; Horova, Anna; Hrabinova, Martina; Jun, Daniel; Dolezal, Martin; Vinsova, Jarmila; Kuca, Kamil; Musilek, Kamil

2013-12-15

Two series of non-symmetrical bisquaternary pyridinium-quinolinium and pyridinium-isoquinolinium compounds were prepared as molecules potentially applicable in myasthenia gravis treatment. Their inhibitory ability towards human recombinant acetylcholinesterase and human plasmatic butyrylcholinesterase was determined and the results were compared to the known effective inhibitors such as ambenonium dichloride, edrophonium bromide and experimental compound BW284C51. Two compounds, 1-(10-(pyridinium-1-yl)decyl)quinolinium dibromide and 1-(12-(pyridinium-1-yl)dodecyl)quinolinium dibromide, showed very promising affinity for acetylcholinesterase with their IC50 values reaching nM inhibition of acetylcholinesterase. These most active compounds also showed satisfactory selectivity towards acetylcholinesterase and they seem to be very promising as leading structures for further modifications and optimization. Two of the most promising compounds were examined in the molecular modelling study in order to find the possible interactions between the ligand and tested enzyme. Copyright © 2013 Elsevier Ltd. All rights reserved.

IADE: a system for intelligent automatic design of bioisosteric analogs.

PubMed

Ertl, Peter; Lewis, Richard

2012-11-01

IADE, a software system supporting molecular modellers through the automatic design of non-classical bioisosteric analogs, scaffold hopping and fragment growing, is presented. The program combines sophisticated cheminformatics functionalities for constructing novel analogs and filtering them based on their drug-likeness and synthetic accessibility using automatic structure-based design capabilities: the best candidates are selected according to their similarity to the template ligand and to their interactions with the protein binding site. IADE works in an iterative manner, improving the fitness of designed molecules in every generation until structures with optimal properties are identified. The program frees molecular modellers from routine, repetitive tasks, allowing them to focus on analysis and evaluation of the automatically designed analogs, considerably enhancing their work efficiency as well as the area of chemical space that can be covered. The performance of IADE is illustrated through a case study of the design of a nonclassical bioisosteric analog of a farnesyltransferase inhibitor--an analog that has won a recent "Design a Molecule" competition.

Classical Optimal Control for Energy Minimization Based On Diffeomorphic Modulation under Observable-Response-Preserving Homotopy.

PubMed

Soley, Micheline B; Markmann, Andreas; Batista, Victor S

2018-06-12

We introduce the so-called "Classical Optimal Control Optimization" (COCO) method for global energy minimization based on the implementation of the diffeomorphic modulation under observable-response-preserving homotopy (DMORPH) gradient algorithm. A probe particle with time-dependent mass m( t;β) and dipole μ( r, t;β) is evolved classically on the potential energy surface V( r) coupled to an electric field E( t;β), as described by the time-dependent density of states represented on a grid, or otherwise as a linear combination of Gaussians generated by the k-means clustering algorithm. Control parameters β defining m( t;β), μ( r, t;β), and E( t;β) are optimized by following the gradients of the energy with respect to β, adapting them to steer the particle toward the global minimum energy configuration. We find that the resulting COCO algorithm is capable of resolving near-degenerate states separated by large energy barriers and successfully locates the global minima of golf potentials on flat and rugged surfaces, previously explored for testing quantum annealing methodologies and the quantum optimal control optimization (QuOCO) method. Preliminary results show successful energy minimization of multidimensional Lennard-Jones clusters. Beyond the analysis of energy minimization in the specific model systems investigated, we anticipate COCO should be valuable for solving minimization problems in general, including optimization of parameters in applications to machine learning and molecular structure determination.

Comparison of non-Gaussian and Gaussian diffusion models of diffusion weighted imaging of rectal cancer at 3.0 T MRI.

PubMed

Zhang, Guangwen; Wang, Shuangshuang; Wen, Didi; Zhang, Jing; Wei, Xiaocheng; Ma, Wanling; Zhao, Weiwei; Wang, Mian; Wu, Guosheng; Zhang, Jinsong

2016-12-09

Water molecular diffusion in vivo tissue is much more complicated. We aimed to compare non-Gaussian diffusion models of diffusion-weighted imaging (DWI) including intra-voxel incoherent motion (IVIM), stretched-exponential model (SEM) and Gaussian diffusion model at 3.0 T MRI in patients with rectal cancer, and to determine the optimal model for investigating the water diffusion properties and characterization of rectal carcinoma. Fifty-nine consecutive patients with pathologically confirmed rectal adenocarcinoma underwent DWI with 16 b-values at a 3.0 T MRI system. DWI signals were fitted to the mono-exponential and non-Gaussian diffusion models (IVIM-mono, IVIM-bi and SEM) on primary tumor and adjacent normal rectal tissue. Parameters of standard apparent diffusion coefficient (ADC), slow- and fast-ADC, fraction of fast ADC (f), α value and distributed diffusion coefficient (DDC) were generated and compared between the tumor and normal tissues. The SEM exhibited the best fitting results of actual DWI signal in rectal cancer and the normal rectal wall (R 2  = 0.998, 0.999 respectively). The DDC achieved relatively high area under the curve (AUC = 0.980) in differentiating tumor from normal rectal wall. Non-Gaussian diffusion models could assess tissue properties more accurately than the ADC derived Gaussian diffusion model. SEM may be used as a potential optimal model for characterization of rectal cancer.

An atomistic fingerprint algorithm for learning ab initio molecular force fields

NASA Astrophysics Data System (ADS)

Tang, Yu-Hang; Zhang, Dongkun; Karniadakis, George Em

2018-01-01

Molecular fingerprints, i.e., feature vectors describing atomistic neighborhood configurations, is an important abstraction and a key ingredient for data-driven modeling of potential energy surface and interatomic force. In this paper, we present the density-encoded canonically aligned fingerprint algorithm, which is robust and efficient, for fitting per-atom scalar and vector quantities. The fingerprint is essentially a continuous density field formed through the superimposition of smoothing kernels centered on the atoms. Rotational invariance of the fingerprint is achieved by aligning, for each fingerprint instance, the neighboring atoms onto a local canonical coordinate frame computed from a kernel minisum optimization procedure. We show that this approach is superior over principal components analysis-based methods especially when the atomistic neighborhood is sparse and/or contains symmetry. We propose that the "distance" between the density fields be measured using a volume integral of their pointwise difference. This can be efficiently computed using optimal quadrature rules, which only require discrete sampling at a small number of grid points. We also experiment on the choice of weight functions for constructing the density fields and characterize their performance for fitting interatomic potentials. The applicability of the fingerprint is demonstrated through a set of benchmark problems.

PuReMD-GPU: A reactive molecular dynamics simulation package for GPUs

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

Kylasa, S.B., E-mail: skylasa@purdue.edu; Aktulga, H.M., E-mail: hmaktulga@lbl.gov; Grama, A.Y., E-mail: ayg@cs.purdue.edu

2014-09-01

We present an efficient and highly accurate GP-GPU implementation of our community code, PuReMD, for reactive molecular dynamics simulations using the ReaxFF force field. PuReMD and its incorporation into LAMMPS (Reax/C) is used by a large number of research groups worldwide for simulating diverse systems ranging from biomembranes to explosives (RDX) at atomistic level of detail. The sub-femtosecond time-steps associated with ReaxFF strongly motivate significant improvements to per-timestep simulation time through effective use of GPUs. This paper presents, in detail, the design and implementation of PuReMD-GPU, which enables ReaxFF simulations on GPUs, as well as various performance optimization techniques wemore » developed to obtain high performance on state-of-the-art hardware. Comprehensive experiments on model systems (bulk water and amorphous silica) are presented to quantify the performance improvements achieved by PuReMD-GPU and to verify its accuracy. In particular, our experiments show up to 16× improvement in runtime compared to our highly optimized CPU-only single-core ReaxFF implementation. PuReMD-GPU is a unique production code, and is currently available on request from the authors.« less

Synthesis, crystal structures and spectroscopic properties of triazine-based hydrazone derivatives; a comparative experimental-theoretical study.

PubMed

Arshad, Muhammad Nadeem; Bibi, Aisha; Mahmood, Tariq; Asiri, Abdullah M; Ayub, Khurshid

2015-04-03

We report here a comparative theoretical and experimental study of four triazine-based hydrazone derivatives. The hydrazones are synthesized by a three step process from commercially available benzil and thiosemicarbazide. The structures of all compounds were determined by using the UV-Vis., FT-IR, NMR (1H and 13C) spectroscopic techniques and finally confirmed unequivocally by single crystal X-ray diffraction analysis. Experimental geometric parameters and spectroscopic properties of the triazine based hydrazones are compared with those obtained from density functional theory (DFT) studies. The model developed here comprises of geometry optimization at B3LYP/6-31G (d, p) level of DFT. Optimized geometric parameters of all four compounds showed excellent correlations with the results obtained from X-ray diffraction studies. The vibrational spectra show nice correlations with the experimental IR spectra. Moreover, the simulated absorption spectra also agree well with experimental results (within 10-20 nm). The molecular electrostatic potential (MEP) mapped over the entire stabilized geometries of the compounds indicated their chemical reactivates. Furthermore, frontier molecular orbital (electronic properties) and first hyperpolarizability (nonlinear optical response) were also computed at the B3LYP/6-31G (d, p) level of theory.

Dielectric Screening Meets Optimally Tuned Density Functionals.

PubMed

Kronik, Leeor; Kümmel, Stephan

2018-04-17

A short overview of recent attempts at merging two independently developed methods is presented. These are the optimal tuning of a range-separated hybrid (OT-RSH) functional, developed to provide an accurate first-principles description of the electronic structure and optical properties of gas-phase molecules, and the polarizable continuum model (PCM), developed to provide an approximate but computationally tractable description of a solvent in terms of an effective dielectric medium. After a brief overview of the OT-RSH approach, its combination with the PCM as a potentially accurate yet low-cost approach to the study of molecular assemblies and solids, particularly in the context of photocatalysis and photovoltaics, is discussed. First, solvated molecules are considered, with an emphasis on the challenge of balancing eigenvalue and total energy trends. Then, it is shown that the same merging of methods can also be used to study the electronic and optical properties of molecular solids, with a similar discussion of the pros and cons. Tuning of the effective scalar dielectric constant as one recent approach that mitigates some of the difficulties in merging the two approaches is considered. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Covalent addition of chitosan to graphene sheets: Density functional theory explorations of quadrupole coupling constants

NASA Astrophysics Data System (ADS)

Mokhtari, Ali; Harismah, Kun; Mirzaei, Mahmoud

2015-12-01

Density functional theory (DFT) calculations have been performed to detect the stabilities and properties of chitosan-functionalized graphene and graphene-oxide structures (G-Chit and GO-Chit). The model systems with two different sizes of sheets have been optimized and the molecular and atomic properties have been evaluated for them. The results indicated that investigated G-Chit and GO-Chit structures could be considered as stable structures but with different properties. The properties for GO and GO-Chit structures are almost similar; however, they are different from the original G and G-Chit structures. The results also indicated that the properties could be also size-dependent, in which different molecular and atomic properties have been observed for the investigate G sheets.

Parameterization of Ca+2-protein interactions for molecular dynamics simulations.

PubMed

Project, Elad; Nachliel, Esther; Gutman, Menachem

2008-05-01

Molecular dynamics simulations of Ca+2 ions near protein were performed with three force fields: GROMOS96, OPLS-AA, and CHARMM22. The simulations reveal major, force-field dependent, inconsistencies in the interaction between the Ca+2 ions with the protein. The variations are attributed to the nonbonded parameterizations of the Ca+2-carboxylates interactions. The simulations results were compared to experimental data, using the Ca+2-HCOO- equilibrium as a model. The OPLS-AA force field grossly overestimates the binding affinity of the Ca+2 ions to the carboxylate whereas the GROMOS96 and CHARMM22 force fields underestimate the stability of the complex. Optimization of the Lennard-Jones parameters for the Ca+2-carboxylate interactions were carried out, yielding new parameters which reproduce experimental data. Copyright 2007 Wiley Periodicals, Inc.

Electroporating Fields Target Oxidatively Damaged Areas in the Cell Membrane

PubMed Central

Vernier, P. Thomas; Levine, Zachary A.; Wu, Yu-Hsuan; Joubert, Vanessa; Ziegler, Matthew J.; Mir, Lluis M.; Tieleman, D. Peter

2009-01-01

Reversible electropermeabilization (electroporation) is widely used to facilitate the introduction of genetic material and pharmaceutical agents into living cells. Although considerable knowledge has been gained from the study of real and simulated model membranes in electric fields, efforts to optimize electroporation protocols are limited by a lack of detailed understanding of the molecular basis for the electropermeabilization of the complex biomolecular assembly that forms the plasma membrane. We show here, with results from both molecular dynamics simulations and experiments with living cells, that the oxidation of membrane components enhances the susceptibility of the membrane to electropermeabilization. Manipulation of the level of oxidative stress in cell suspensions and in tissues may lead to more efficient permeabilization procedures in the laboratory and in clinical applications such as electrochemotherapy and electrotransfection-mediated gene therapy. PMID:19956595

Anesthesia in Experimental Stroke Research

PubMed Central

Hoffmann, Ulrike; Sheng, Huaxin; Ayata, Cenk; Warner, David S.

2016-01-01

Anesthetics have enabled major advances in development of experimental models of human stroke. Yet their profound pharmacologic effects on neural function can confound the interpretation of experimental stroke research. Anesthetics have drug and dose-specific effects on cerebral blood flow and metabolism, neurovascular coupling, autoregulation, ischemic depolarizations, excitotoxicity, inflammation, neural networks, and numerous molecular pathways relevant for stroke outcome. Both pre- and post-conditioning properties have been described. Anesthetics also modulate systemic arterial blood pressure, lung ventilation, and thermoregulation, all of which may interact with the ischemic insult as well as the therapeutic interventions. These confounds present a dilemma. Here, we provide an overview of the anesthetic mechanisms of action and molecular and physiologic effects on factors relevant to stroke outcomes that can guide the choice and optimization of the anesthetic regimen in experimental stroke. PMID:27534542

Mechanical regulation of cardiac development

PubMed Central

Lindsey, Stephanie E.; Butcher, Jonathan T.; Yalcin, Huseyin C.

2014-01-01

Mechanical forces are essential contributors to and unavoidable components of cardiac formation, both inducing and orchestrating local and global molecular and cellular changes. Experimental animal studies have contributed substantially to understanding the mechanobiology of heart development. More recent integration of high-resolution imaging modalities with computational modeling has greatly improved our quantitative understanding of hemodynamic flow in heart development. Merging these latest experimental technologies with molecular and genetic signaling analysis will accelerate our understanding of the relationships integrating mechanical and biological signaling for proper cardiac formation. These advances will likely be essential for clinically translatable guidance for targeted interventions to rescue malforming hearts and/or reconfigure malformed circulations for optimal performance. This review summarizes our current understanding on the levels of mechanical signaling in the heart and their roles in orchestrating cardiac development. PMID:25191277