Protein-Protein Interactions in a Crowded Environment: An Analysis via Cross-Docking Simulations and Evolutionary Information

PubMed Central

Lopes, Anne; Sacquin-Mora, Sophie; Dimitrova, Viktoriya; Laine, Elodie; Ponty, Yann; Carbone, Alessandra

2013-01-01

Large-scale analyses of protein-protein interactions based on coarse-grain molecular docking simulations and binding site predictions resulting from evolutionary sequence analysis, are possible and realizable on hundreds of proteins with variate structures and interfaces. We demonstrated this on the 168 proteins of the Mintseris Benchmark 2.0. On the one hand, we evaluated the quality of the interaction signal and the contribution of docking information compared to evolutionary information showing that the combination of the two improves partner identification. On the other hand, since protein interactions usually occur in crowded environments with several competing partners, we realized a thorough analysis of the interactions of proteins with true partners but also with non-partners to evaluate whether proteins in the environment, competing with the true partner, affect its identification. We found three populations of proteins: strongly competing, never competing, and interacting with different levels of strength. Populations and levels of strength are numerically characterized and provide a signature for the behavior of a protein in the crowded environment. We showed that partner identification, to some extent, does not depend on the competing partners present in the environment, that certain biochemical classes of proteins are intrinsically easier to analyze than others, and that small proteins are not more promiscuous than large ones. Our approach brings to light that the knowledge of the binding site can be used to reduce the high computational cost of docking simulations with no consequence in the quality of the results, demonstrating the possibility to apply coarse-grain docking to datasets made of thousands of proteins. Comparison with all available large-scale analyses aimed to partner predictions is realized. We release the complete decoys set issued by coarse-grain docking simulations of both true and false interacting partners, and their evolutionary sequence analysis leading to binding site predictions. Download site: http://www.lgm.upmc.fr/CCDMintseris/ PMID:24339765

A systematic analysis of scoring functions in rigid-body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining.

PubMed

Barradas-Bautista, Didier; Moal, Iain H; Fernández-Recio, Juan

2017-07-01

Protein-protein interactions play fundamental roles in biological processes including signaling, metabolism, and trafficking. While the structure of a protein complex reveals crucial details about the interaction, it is often difficult to acquire this information experimentally. As the number of interactions discovered increases faster than they can be characterized, protein-protein docking calculations may be able to reduce this disparity by providing models of the interacting proteins. Rigid-body docking is a widely used docking approach, and is often capable of generating a pool of models within which a near-native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. Recently, more than 100 scoring functions from the CCharPPI server were evaluated for this task using decoy structures generated with SwarmDock. Here, we extend this analysis to identify the predictive success rates of the scoring functions on decoys from three rigid-body docking programs, ZDOCK, FTDock, and SDOCK, allowing us to assess the transferability of the functions. We also apply set-theoretic measure to test whether the scoring functions are capable of identifying near-native poses within different subsets of the benchmark. This information can provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts. Proteins 2017; 85:1287-1297. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

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

PubMed

Fukunishi, Yoshifumi

2010-01-01

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

Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

PubMed

Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

2015-01-01

Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

Molecular docking performance evaluated on the D3R Grand Challenge 2015 drug-like ligand datasets

NASA Astrophysics Data System (ADS)

Selwa, Edithe; Martiny, Virginie Y.; Iorga, Bogdan I.

2016-09-01

The D3R Grand Challenge 2015 was focused on two protein targets: Heat Shock Protein 90 (HSP90) and Mitogen-Activated Protein Kinase Kinase Kinase Kinase 4 (MAP4K4). We used a protocol involving a preliminary analysis of the available data in PDB and PubChem BioAssay, and then a docking/scoring step using more computationally demanding parameters that were required to provide more reliable predictions. We could evidence that different docking software and scoring functions can behave differently on individual ligand datasets, and that the flexibility of specific binding site residues is a crucial element to provide good predictions.

Text Mining for Protein Docking

PubMed Central

Badal, Varsha D.; Kundrotas, Petras J.; Vakser, Ilya A.

2015-01-01

The rapidly growing amount of publicly available information from biomedical research is readily accessible on the Internet, providing a powerful resource for predictive biomolecular modeling. The accumulated data on experimentally determined structures transformed structure prediction of proteins and protein complexes. Instead of exploring the enormous search space, predictive tools can simply proceed to the solution based on similarity to the existing, previously determined structures. A similar major paradigm shift is emerging due to the rapidly expanding amount of information, other than experimentally determined structures, which still can be used as constraints in biomolecular structure prediction. Automated text mining has been widely used in recreating protein interaction networks, as well as in detecting small ligand binding sites on protein structures. Combining and expanding these two well-developed areas of research, we applied the text mining to structural modeling of protein-protein complexes (protein docking). Protein docking can be significantly improved when constraints on the docking mode are available. We developed a procedure that retrieves published abstracts on a specific protein-protein interaction and extracts information relevant to docking. The procedure was assessed on protein complexes from Dockground (http://dockground.compbio.ku.edu). The results show that correct information on binding residues can be extracted for about half of the complexes. The amount of irrelevant information was reduced by conceptual analysis of a subset of the retrieved abstracts, based on the bag-of-words (features) approach. Support Vector Machine models were trained and validated on the subset. The remaining abstracts were filtered by the best-performing models, which decreased the irrelevant information for ~ 25% complexes in the dataset. The extracted constraints were incorporated in the docking protocol and tested on the Dockground unbound benchmark set, significantly increasing the docking success rate. PMID:26650466

A High Performance Cloud-Based Protein-Ligand Docking Prediction Algorithm

PubMed Central

Chen, Jui-Le; Yang, Chu-Sing

2013-01-01

The potential of predicting druggability for a particular disease by integrating biological and computer science technologies has witnessed success in recent years. Although the computer science technologies can be used to reduce the costs of the pharmaceutical research, the computation time of the structure-based protein-ligand docking prediction is still unsatisfied until now. Hence, in this paper, a novel docking prediction algorithm, named fast cloud-based protein-ligand docking prediction algorithm (FCPLDPA), is presented to accelerate the docking prediction algorithm. The proposed algorithm works by leveraging two high-performance operators: (1) the novel migration (information exchange) operator is designed specially for cloud-based environments to reduce the computation time; (2) the efficient operator is aimed at filtering out the worst search directions. Our simulation results illustrate that the proposed method outperforms the other docking algorithms compared in this paper in terms of both the computation time and the quality of the end result. PMID:23762864

Surflex-Dock: Docking benchmarks and real-world application

NASA Astrophysics Data System (ADS)

Spitzer, Russell; Jain, Ajay N.

2012-06-01

Benchmarks for molecular docking have historically focused on re-docking the cognate ligand of a well-determined protein-ligand complex to measure geometric pose prediction accuracy, and measurement of virtual screening performance has been focused on increasingly large and diverse sets of target protein structures, cognate ligands, and various types of decoy sets. Here, pose prediction is reported on the Astex Diverse set of 85 protein ligand complexes, and virtual screening performance is reported on the DUD set of 40 protein targets. In both cases, prepared structures of targets and ligands were provided by symposium organizers. The re-prepared data sets yielded results not significantly different than previous reports of Surflex-Dock on the two benchmarks. Minor changes to protein coordinates resulting from complex pre-optimization had large effects on observed performance, highlighting the limitations of cognate ligand re-docking for pose prediction assessment. Docking protocols developed for cross-docking, which address protein flexibility and produce discrete families of predicted poses, produced substantially better performance for pose prediction. Performance on virtual screening performance was shown to benefit by employing and combining multiple screening methods: docking, 2D molecular similarity, and 3D molecular similarity. In addition, use of multiple protein conformations significantly improved screening enrichment.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors.

PubMed

Sivan, Sree Kanth; Manga, Vijjulatha

2012-02-01

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

An Automated Strategy for Binding-Pose Selection and Docking Assessment in Structure-Based Drug Design.

PubMed

Ballante, Flavio; Marshall, Garland R

2016-01-25

Molecular docking is a widely used technique in drug design to predict the binding pose of a candidate compound in a defined therapeutic target. Numerous docking protocols are available, each characterized by different search methods and scoring functions, thus providing variable predictive capability on a same ligand-protein system. To validate a docking protocol, it is necessary to determine a priori the ability to reproduce the experimental binding pose (i.e., by determining the docking accuracy (DA)) in order to select the most appropriate docking procedure and thus estimate the rate of success in docking novel compounds. As common docking programs use generally different root-mean-square deviation (RMSD) formulas, scoring functions, and format results, it is both difficult and time-consuming to consistently determine and compare their predictive capabilities in order to identify the best protocol to use for the target of interest and to extrapolate the binding poses (i.e., best-docked (BD), best-cluster (BC), and best-fit (BF) poses) when applying a given docking program over thousands/millions of molecules during virtual screening. To reduce this difficulty, two new procedures called Clusterizer and DockAccessor have been developed and implemented for use with some common and "free-for-academics" programs such as AutoDock4, AutoDock4(Zn), AutoDock Vina, DOCK, MpSDockZn, PLANTS, and Surflex-Dock to automatically extrapolate BD, BC, and BF poses as well as to perform consistent cluster and DA analyses. Clusterizer and DockAccessor (code available over the Internet) represent two novel tools to collect computationally determined poses and detect the most predictive docking approach. Herein an application to human lysine deacetylase (hKDAC) inhibitors is illustrated.

SAMPL4 & DOCK3.7: lessons for automated docking procedures

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

Prediction of homoprotein and heteroprotein complexes by protein docking and template‐based modeling: A CASP‐CAPRI experiment

PubMed Central

Velankar, Sameer; Kryshtafovych, Andriy; Huang, Shen‐You; Schneidman‐Duhovny, Dina; Sali, Andrej; Segura, Joan; Fernandez‐Fuentes, Narcis; Viswanath, Shruthi; Elber, Ron; Grudinin, Sergei; Popov, Petr; Neveu, Emilie; Lee, Hasup; Baek, Minkyung; Park, Sangwoo; Heo, Lim; Rie Lee, Gyu; Seok, Chaok; Qin, Sanbo; Zhou, Huan‐Xiang; Ritchie, David W.; Maigret, Bernard; Devignes, Marie‐Dominique; Ghoorah, Anisah; Torchala, Mieczyslaw; Chaleil, Raphaël A.G.; Bates, Paul A.; Ben‐Zeev, Efrat; Eisenstein, Miriam; Negi, Surendra S.; Weng, Zhiping; Vreven, Thom; Pierce, Brian G.; Borrman, Tyler M.; Yu, Jinchao; Ochsenbein, Françoise; Guerois, Raphaël; Vangone, Anna; Rodrigues, João P.G.L.M.; van Zundert, Gydo; Nellen, Mehdi; Xue, Li; Karaca, Ezgi; Melquiond, Adrien S.J.; Visscher, Koen; Kastritis, Panagiotis L.; Bonvin, Alexandre M.J.J.; Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Li, Jilong; Ma, Zhiwei; Cheng, Jianlin; Zou, Xiaoqin; Shen, Yang; Peterson, Lenna X.; Kim, Hyung‐Rae; Roy, Amit; Han, Xusi; Esquivel‐Rodriguez, Juan; Kihara, Daisuke; Yu, Xiaofeng; Bruce, Neil J.; Fuller, Jonathan C.; Wade, Rebecca C.; Anishchenko, Ivan; Kundrotas, Petras J.; Vakser, Ilya A.; Imai, Kenichiro; Yamada, Kazunori; Oda, Toshiyuki; Nakamura, Tsukasa; Tomii, Kentaro; Pallara, Chiara; Romero‐Durana, Miguel; Jiménez‐García, Brian; Moal, Iain H.; Férnandez‐Recio, Juan; Joung, Jong Young; Kim, Jong Yun; Joo, Keehyoung; Lee, Jooyoung; Kozakov, Dima; Vajda, Sandor; Mottarella, Scott; Hall, David R.; Beglov, Dmitri; Mamonov, Artem; Xia, Bing; Bohnuud, Tanggis; Del Carpio, Carlos A.; Ichiishi, Eichiro; Marze, Nicholas; Kuroda, Daisuke; Roy Burman, Shourya S.; Gray, Jeffrey J.; Chermak, Edrisse; Cavallo, Luigi; Oliva, Romina; Tovchigrechko, Andrey

2016-01-01

ABSTRACT We present the results for CAPRI Round 30, the first joint CASP‐CAPRI experiment, which brought together experts from the protein structure prediction and protein–protein docking communities. The Round comprised 25 targets from amongst those submitted for the CASP11 prediction experiment of 2014. The targets included mostly homodimers, a few homotetramers, and two heterodimers, and comprised protein chains that could readily be modeled using templates from the Protein Data Bank. On average 24 CAPRI groups and 7 CASP groups submitted docking predictions for each target, and 12 CAPRI groups per target participated in the CAPRI scoring experiment. In total more than 9500 models were assessed against the 3D structures of the corresponding target complexes. Results show that the prediction of homodimer assemblies by homology modeling techniques and docking calculations is quite successful for targets featuring large enough subunit interfaces to represent stable associations. Targets with ambiguous or inaccurate oligomeric state assignments, often featuring crystal contact‐sized interfaces, represented a confounding factor. For those, a much poorer prediction performance was achieved, while nonetheless often providing helpful clues on the correct oligomeric state of the protein. The prediction performance was very poor for genuine tetrameric targets, where the inaccuracy of the homology‐built subunit models and the smaller pair‐wise interfaces severely limited the ability to derive the correct assembly mode. Our analysis also shows that docking procedures tend to perform better than standard homology modeling techniques and that highly accurate models of the protein components are not always required to identify their association modes with acceptable accuracy. Proteins 2016; 84(Suppl 1):323–348. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:27122118

Blind Pose Prediction, Scoring, and Affinity Ranking of the CSAR 2014 Dataset.

PubMed

Martiny, Virginie Y; Martz, François; Selwa, Edithe; Iorga, Bogdan I

2016-06-27

The 2014 CSAR Benchmark Exercise was focused on three protein targets: coagulation factor Xa, spleen tyrosine kinase, and bacterial tRNA methyltransferase. Our protocol involved a preliminary analysis of the structural information available in the Protein Data Bank for the protein targets, which allowed the identification of the most appropriate docking software and scoring functions to be used for the rescoring of several docking conformations datasets, as well as for pose prediction and affinity ranking. The two key points of this study were (i) the prior evaluation of molecular modeling tools that are most adapted for each target and (ii) the increased search efficiency during the docking process to better explore the conformational space of big and flexible ligands.

Multiple grid arrangement improves ligand docking with unknown binding sites: Application to the inverse docking problem.

PubMed

Ban, Tomohiro; Ohue, Masahito; Akiyama, Yutaka

2018-04-01

The identification of comprehensive drug-target interactions is important in drug discovery. Although numerous computational methods have been developed over the years, a gold standard technique has not been established. Computational ligand docking and structure-based drug design allow researchers to predict the binding affinity between a compound and a target protein, and thus, they are often used to virtually screen compound libraries. In addition, docking techniques have also been applied to the virtual screening of target proteins (inverse docking) to predict target proteins of a drug candidate. Nevertheless, a more accurate docking method is currently required. In this study, we proposed a method in which a predicted ligand-binding site is covered by multiple grids, termed multiple grid arrangement. Notably, multiple grid arrangement facilitates the conformational search for a grid-based ligand docking software and can be applied to the state-of-the-art commercial docking software Glide (Schrödinger, LLC). We validated the proposed method by re-docking with the Astex diverse benchmark dataset and blind binding site situations, which improved the correct prediction rate of the top scoring docking pose from 27.1% to 34.1%; however, only a slight improvement in target prediction accuracy was observed with inverse docking scenarios. These findings highlight the limitations and challenges of current scoring functions and the need for more accurate docking methods. The proposed multiple grid arrangement method was implemented in Glide by modifying a cross-docking script for Glide, xglide.py. The script of our method is freely available online at http://www.bi.cs.titech.ac.jp/mga_glide/. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

Computational analysis of the binding ability of heterocyclic and conformationally constrained epibatidine analogs in the neuronal nicotinic acetylcholine receptor.

PubMed

Soriano, Elena; Marco-Contelles, José; Colmena, Inés; Gandía, Luis

2010-05-01

One of the most critical issues on the study of ligand-receptor interactions in drug design is the knowledge of the bioactive conformation of the ligand. In this study, we describe a computational approach aimed at estimating the binding ability of epibatidine analogs to interact with the neuronal nicotinic acetylcholine receptor (nAChR) and get insights into the bioactive conformation. The protocol followed consists of a docking analysis and evaluation of pharmacophore parameters of the docked structures. On the basis of the biological data, the results have revealed that the docking analysis is able to predict active ligands, whereas further efforts are needed to develop a suitable and solid pharmacophore model.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions

DOE PAGES

Evangelista, Wilfredo; Weir, Rebecca; Ellingson, Sally; ...

2016-07-29

The use of ensemble-based docking for the exploration of biochemical pathways and toxicity prediction of drug candidates is described. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.

CDOCKER and lambda λ -dynamics for prospective prediction in D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Ding, Xinqiang; Hayes, Ryan L.; Vilseck, Jonah Z.; Charles, Murchtricia K.; Brooks, Charles L.

2018-01-01

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and λ-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 Å. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 Å for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For λ-dynamics techniques, including multisite λ-dynamics (MSλD), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.

PubMed

Ding, Xinqiang; Hayes, Ryan L; Vilseck, Jonah Z; Charles, Murchtricia K; Brooks, Charles L

2018-01-01

The opportunity to prospectively predict ligand bound poses and free energies of binding to the Farnesoid X Receptor in the D3R Grand Challenge 2 provided a useful exercise to evaluate CHARMM based docking (CDOCKER) and [Formula: see text]-dynamics methodologies for use in "real-world" applications in computer aided drug design. In addition to measuring their current performance, several recent methodological developments have been analyzed retrospectively to highlight best procedural practices in future applications. For pose prediction with CDOCKER, when the protein structure used for rigid receptor docking was close to the crystallographic holo structure, reliable poses were obtained. Benzimidazoles, with a known holo receptor structure, were successfully docked with an average RMSD of 0.97 [Formula: see text]. Other non-benzimidazole ligands displayed less accuracy largely because the receptor structures we chose for docking were too different from the experimental holo structures. However, retrospective analysis has shown that when these ligands were re-docked into their holo structures, the average RMSD dropped to 1.18 [Formula: see text] for all ligands. When sulfonamides and spiros were docked with the apo structure, which agrees more with their holo structure than the structures we chose, five out of six ligands were correctly docked. These docking results emphasize the need for flexible receptor docking approaches. For [Formula: see text]-dynamics techniques, including multisite [Formula: see text]-dynamics (MS[Formula: see text]D), reasonable agreement with experiment was observed for the 33 ligands investigated; root mean square errors of 2.08 and 1.67 kcal/mol were obtained for free energy sets 1 and 2, respectively. Retrospectively, soft-core potentials, adaptive landscape flattening, and biasing potential replica exchange (BP-REX) algorithms were critical to model large substituent perturbations with sufficient precision and within restrictive timeframes, such as was required with participation in Grand Challenge 2. These developments, their associated benefits, and proposed procedures for their use in future applications are discussed.

Binding site and affinity prediction of general anesthetics to protein targets using docking.

PubMed

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G

2012-05-01

The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC(50)) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC(50) values and octanol/water partition coefficients for the 6 general anesthetics. All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC(50) values for the 6 frequently used anesthetics in GLIC for the site identified in the experimental crystal data (P = 0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these 6 anesthetics' known experimental EC(50) values. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (AutoDock) for both water-soluble and membrane proteins. Correlation of predicted affinity and EC(50) for 6 frequently used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism.

Ensemble-based docking: From hit discovery to metabolism and toxicity predictions.

PubMed

Evangelista, Wilfredo; Weir, Rebecca L; Ellingson, Sally R; Harris, Jason B; Kapoor, Karan; Smith, Jeremy C; Baudry, Jerome

2016-10-15

This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

Protein-protein docking with binding site patch prediction and network-based terms enhanced combinatorial scoring.

PubMed

Gong, Xinqi; Wang, Panwen; Yang, Feng; Chang, Shan; Liu, Bin; He, Hongqiu; Cao, Libin; Xu, Xianjin; Li, Chunhua; Chen, Weizu; Wang, Cunxin

2010-11-15

Protein-protein docking has made much progress in recent years, but challenges still exist. Here we present the application of our docking approach HoDock in CAPRI. In this approach, a binding site prediction is implemented to reduce docking sampling space and filter out unreasonable docked structures, and a network-based enhanced combinatorial scoring function HPNCscore is used to evaluate the decoys. The experimental information was combined with the predicted binding site to pick out the most likely key binding site residues. We applied the HoDock method in the recent rounds of the CAPRI experiments, and got good results as predictors on targets 39, 40, and 41. We also got good results as scorers on targets 35, 37, 40, and 41. This indicates that our docking approach can contribute to the progress of protein-protein docking methods and to the understanding of the mechanism of protein-protein interactions. © 2010 Wiley-Liss, Inc.

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.

Synthesis of 4-aminophenyl substituted indole derivatives for the instrumental analysis and molecular docking evaluation studies

NASA Astrophysics Data System (ADS)

Singh, Navneet; Kumar, Keshav

2017-07-01

The Indole has been known to maintain celebrity status since so many decades and has been a centre point at the spectrum of pharmacological research. The present work stimulates an idea of generating a pool of library of lead compounds. The data collected can be used for the mapping of biologically active compounds. The reported derivatives of 4-aminophenyl substituted Indole were prepared by the methods of Fischer Indole synthesis and Vilsemeier reaction followed by screening for instrumental analysis and molecular docking studies. The synthesized compounds 4-(1-(2-phenylhydrazono)ethyl)aniline, 1, 4-(1H-indol-2-yl)aniline, 2 and 2-(4-aminophenyl)-1H-indole-3-carbaldehyde, 3 were found to have remarkable yield and instrumental data analysis and also showed remarkable docked characteristic. The molecular docking studies revealed that ligand (amino acids) of comp. 1, 2 and 3 had been docked successfully on the binding site of the 3JUS protein selected from PDB with H bonding. The molecular docking data showed that compound 1, would possess remarkable biological activity and compd. 2 and 3 would possess mild to moderate biological activity. Thus this research work paves the way to synthesize new derivatives and thus to develop new compounds in future with accurate prediction.

[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

PubMed

Qiao, Lian-Sheng; Jiang, Lu-di; Luo, Gang-Gang; Lu, Fang; Chen, Yan-Kun; Wang, Ling-Zhi; Li, Gong-Yu; Zhang, Yan-Ling

2017-02-01

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

PubMed Central

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G.

2012-01-01

Background The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explore whether a computational method, AutoDock, could serve as such a tool. Methods High-resolution crystal data of water soluble proteins (cytochrome C, apoferritin and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus, GLIC) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (https://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants are compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent co-crystallization data. Docking calculations for six general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known EC50 were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50s and octanol/water partition coefficients for the six general anesthetics. Results All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (p=0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC50s for the six commonly used anesthetics in GLIC for the site identified in the experimental crystal data (p=0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these six anesthetics’ known experimental EC50s. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. Conclusion We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (Autodock) for both water soluble and membrane proteins. Correlation of predicted affinity and EC50 for six commonly used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism. PMID:22392968

GPU.proton.DOCK: Genuine Protein Ultrafast proton equilibria consistent DOCKing.

PubMed

Kantardjiev, Alexander A

2011-07-01

GPU.proton.DOCK (Genuine Protein Ultrafast proton equilibria consistent DOCKing) is a state of the art service for in silico prediction of protein-protein interactions via rigorous and ultrafast docking code. It is unique in providing stringent account of electrostatic interactions self-consistency and proton equilibria mutual effects of docking partners. GPU.proton.DOCK is the first server offering such a crucial supplement to protein docking algorithms--a step toward more reliable and high accuracy docking results. The code (especially the Fast Fourier Transform bottleneck and electrostatic fields computation) is parallelized to run on a GPU supercomputer. The high performance will be of use for large-scale structural bioinformatics and systems biology projects, thus bridging physics of the interactions with analysis of molecular networks. We propose workflows for exploring in silico charge mutagenesis effects. Special emphasis is given to the interface-intuitive and user-friendly. The input is comprised of the atomic coordinate files in PDB format. The advanced user is provided with a special input section for addition of non-polypeptide charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. The output is comprised of docked complexes in PDB format as well as interactive visualization in a molecular viewer. GPU.proton.DOCK server can be accessed at http://gpudock.orgchm.bas.bg/.

Molecular design of new aggrecanases-2 inhibitors.

PubMed

Shan, Zhi Jie; Zhai, Hong Lin; Huang, Xiao Yan; Li, Li Na; Zhang, Xiao Yun

2013-10-01

Aggrecanases-2 is a very important potential drug target for the treatment of osteoarthritis. In this study, a series of known aggrecanases-2 inhibitors was analyzed by the technologies of three-dimensional quantitative structure-activity relationships (3D-QSAR) and molecular docking. Two 3D-QSAR models, which based on comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) methods, were established. Molecular docking was employed to explore the details of the interaction between inhibitors and aggrecanases-2 protein. According to the analyses for these models, several new potential inhibitors with higher activity predicted were designed, and were supported by the simulation of molecular docking. This work propose the fast and effective approach to design and prediction for new potential inhibitors, and the study of the interaction mechanism provide a better understanding for the inhibitors binding into the target protein, which will be useful for the structure-based drug design and modifications. Copyright © 2013 Elsevier Ltd. All rights reserved.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

DockBench as docking selector tool: the lesson learned from D3R Grand Challenge 2015.

PubMed

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2016-09-01

Structure-based drug design (SBDD) has matured within the last two decades as a valuable tool for the optimization of low molecular weight lead compounds to highly potent drugs. The key step in SBDD requires knowledge of the three-dimensional structure of the target-ligand complex, which is usually determined by X-ray crystallography. In the absence of structural information for the complex, SBDD relies on the generation of plausible molecular docking models. However, molecular docking protocols suffer from inaccuracies in the description of the interaction energies between the ligand and the target molecule, and often fail in the prediction of the correct binding mode. In this context, the appropriate selection of the most accurate docking protocol is absolutely relevant for the final molecular docking result, even if addressing this point is absolutely not a trivial task. D3R Grand Challenge 2015 has represented a precious opportunity to test the performance of DockBench, an integrate informatics platform to automatically compare RMDS-based molecular docking performances of different docking/scoring methods. The overall performance resulted in the blind prediction are encouraging in particular for the pose prediction task, in which several complex were predicted with a sufficient accuracy for medicinal chemistry purposes.

Extracellular domains play different roles in gap junction formation and docking compatibility.

PubMed

Bai, Donglin; Wang, Ao Hong

2014-02-15

GJ (gap junction) channels mediate direct intercellular communication and play an important role in many physiological processes. Six connexins oligomerize to form a hemichannel and two hemichannels dock together end-to-end to form a GJ channel. Connexin extracellular domains (E1 and E2) have been shown to be important for the docking, but the molecular mechanisms behind the docking and formation of GJ channels are not clear. Recent developments in atomic GJ structure and functional studies on a series of connexin mutants revealed that E1 and E2 are likely to play different roles in the docking. Non-covalent interactions at the docking interface, including hydrogen bonds, are predicted to form between interdocked extracellular domains. Protein sequence alignment analysis on the docking compatible/incompatible connexins indicate that the E1 domain is important for the formation of the GJ channel and the E2 domain is important in the docking compatibility in heterotypic channels. Interestingly, the hydrogen-bond forming or equivalent residues in both E1 and E2 domains are mutational hot spots for connexin-linked human diseases. Understanding the molecular mechanisms of GJ docking can assist us to develop novel strategies in rescuing the disease-linked connexin mutants.

Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking.

PubMed

Yan, Yumeng; Wen, Zeyu; Wang, Xinxiang; Huang, Sheng-You

2017-03-01

Protein-protein docking is an important computational tool for predicting protein-protein interactions. With the rapid development of proteomics projects, more and more experimental binding information ranging from mutagenesis data to three-dimensional structures of protein complexes are becoming available. Therefore, how to appropriately incorporate the biological information into traditional ab initio docking has been an important issue and challenge in the field of protein-protein docking. To address these challenges, we have developed a Hybrid DOCKing protocol of template-based and template-free approaches, referred to as HDOCK. The basic procedure of HDOCK is to model the structures of individual components based on the template complex by a template-based method if a template is available; otherwise, the component structures will be modeled based on monomer proteins by regular homology modeling. Then, the complex structure of the component models is predicted by traditional protein-protein docking. With the HDOCK protocol, we have participated in the CPARI experiment for rounds 28-35. Out of the 25 CASP-CAPRI targets for oligomer modeling, our HDOCK protocol predicted correct models for 16 targets, ranking one of the top algorithms in this challenge. Our docking method also made correct predictions on other CAPRI challenges such as protein-peptide binding for 6 out of 8 targets and water predictions for 2 out of 2 targets. The advantage of our hybrid docking approach over pure template-based docking was further confirmed by a comparative evaluation on 20 CASP-CAPRI targets. Proteins 2017; 85:497-512. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Combining self- and cross-docking as benchmark tools: the performance of DockBench in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Salmaso, Veronica; Sturlese, Mattia; Cuzzolin, Alberto; Moro, Stefano

2018-01-01

Molecular docking is a powerful tool in the field of computer-aided molecular design. In particular, it is the technique of choice for the prediction of a ligand pose within its target binding site. A multitude of docking methods is available nowadays, whose performance may vary depending on the data set. Therefore, some non-trivial choices should be made before starting a docking simulation. In the same framework, the selection of the target structure to use could be challenging, since the number of available experimental structures is increasing. Both issues have been explored within this work. The pose prediction of a pool of 36 compounds provided by D3R Grand Challenge 2 organizers was preceded by a pipeline to choose the best protein/docking-method couple for each blind ligand. An integrated benchmark approach including ligand shape comparison and cross-docking evaluations was implemented inside our DockBench software. The results are encouraging and show that bringing attention to the choice of the docking simulation fundamental components improves the results of the binding mode predictions.

pyDockWEB: a web server for rigid-body protein-protein docking using electrostatics and desolvation scoring.

PubMed

Jiménez-García, Brian; Pons, Carles; Fernández-Recio, Juan

2013-07-01

pyDockWEB is a web server for the rigid-body docking prediction of protein-protein complex structures using a new version of the pyDock scoring algorithm. We use here a new custom parallel FTDock implementation, with adjusted grid size for optimal FFT calculations, and a new version of pyDock, which dramatically speeds up calculations while keeping the same predictive accuracy. Given the 3D coordinates of two interacting proteins, pyDockWEB returns the best docking orientations as scored mainly by electrostatics and desolvation energy. The server does not require registration by the user and is freely accessible for academics at http://life.bsc.es/servlet/pydock. Supplementary data are available at Bioinformatics online.

Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

PubMed

Tyagi, Sadhna; Pleiss, Juergen

2006-06-25

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

A strategy based on gas chromatography-mass spectrometry and virtual molecular docking for analysis and prediction of bioactive composition in natural product essential oil.

PubMed

Wang, Haiyang; Gu, Dongyu; Wang, Miao; Guo, Hong; Wu, Huijuan; Tian, Guangliang; Li, Qian; Yang, Yi; Tian, Jing

2017-06-09

The discovery of leads from medicinal plants is crucial to drug development. The present study presents a strategy based on GC-MS coupled with molecular docking for analysis, identification and prediction of protein tyrosine phosphatase 1B inhibitors in the essential oil from Himalayan Cedar (HC). The essential oil with IC 50 value of 120.71±0.26μg/mL exhibited potential activity against protein tyrosine phosphatase 1B (PTP1B) in vitro. After GC-MS analysis, 35 compounds were identified from this oil. The identified compounds were individually docked with PTP1B. Caryophyllene oxide with the lowest binding energy of -6.28kcal/mol was completely wrapped by the active site of PTP1B. The docking results indicated that caryophyllene oxide has potential PTP1B inhibitory activity and may be responsible for the PTP1B inhibitory activity of the essential oil. Caryophyllene oxide in the essential oil of Himalayan Cedar was isolated by HSCCC and the PTP1B inhibitory activity of this compound was then evaluated; the IC 50 value was 31.32±0.38μM. The result revealed that the present strategy can effectively discover the active composition from the complex mixture of medicinal plants. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-12-09

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

Conformational Heterogeneity of Unbound Proteins Enhances Recognition in Protein-Protein Encounters.

PubMed

Pallara, Chiara; Rueda, Manuel; Abagyan, Ruben; Fernández-Recio, Juan

2016-07-12

To understand cellular processes at the molecular level we need to improve our knowledge of protein-protein interactions, from a structural, mechanistic, and energetic point of view. Current theoretical studies and computational docking simulations show that protein dynamics plays a key role in protein association and support the need for including protein flexibility in modeling protein interactions. Assuming the conformational selection binding mechanism, in which the unbound state can sample bound conformers, one possible strategy to include flexibility in docking predictions would be the use of conformational ensembles originated from unbound protein structures. Here we present an exhaustive computational study about the use of precomputed unbound ensembles in the context of protein docking, performed on a set of 124 cases of the Protein-Protein Docking Benchmark 3.0. Conformational ensembles were generated by conformational optimization and refinement with MODELLER and by short molecular dynamics trajectories with AMBER. We identified those conformers providing optimal binding and investigated the role of protein conformational heterogeneity in protein-protein recognition. Our results show that a restricted conformational refinement can generate conformers with better binding properties and improve docking encounters in medium-flexible cases. For more flexible cases, a more extended conformational sampling based on Normal Mode Analysis was proven helpful. We found that successful conformers provide better energetic complementarity to the docking partners, which is compatible with recent views of binding association. In addition to the mechanistic considerations, these findings could be exploited for practical docking predictions of improved efficiency.

InterEvDock: a docking server to predict the structure of protein–protein interactions using evolutionary information

PubMed Central

Yu, Jinchao; Vavrusa, Marek; Andreani, Jessica; Rey, Julien; Tufféry, Pierre; Guerois, Raphaël

2016-01-01

The structural modeling of protein–protein interactions is key in understanding how cell machineries cross-talk with each other. Molecular docking simulations provide efficient means to explore how two unbound protein structures interact. InterEvDock is a server for protein docking based on a free rigid-body docking strategy. A systematic rigid-body docking search is performed using the FRODOCK program and the resulting models are re-scored with InterEvScore and SOAP-PP statistical potentials. The InterEvScore potential was specifically designed to integrate co-evolutionary information in the docking process. InterEvDock server is thus particularly well suited in case homologous sequences are available for both binding partners. The server returns 10 structures of the most likely consensus models together with 10 predicted residues most likely involved in the interface. In 91% of all complexes tested in the benchmark, at least one residue out of the 10 predicted is involved in the interface, providing useful guidelines for mutagenesis. InterEvDock is able to identify a correct model among the top10 models for 49% of the rigid-body cases with evolutionary information, making it a unique and efficient tool to explore structural interactomes under an evolutionary perspective. The InterEvDock web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock/. PMID:27131368

PharmDock: a pharmacophore-based docking program

PubMed Central

2014-01-01

Background Protein-based pharmacophore models are enriched with the information of potential interactions between ligands and the protein target. We have shown in a previous study that protein-based pharmacophore models can be applied for ligand pose prediction and pose ranking. In this publication, we present a new pharmacophore-based docking program PharmDock that combines pose sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. Results Tests of PharmDock on ligand pose prediction, binding affinity estimation, compound ranking and virtual screening yielded comparable or better performance to existing and widely used docking programs. The docking program comes with an easy-to-use GUI within PyMOL. Two features have been incorporated in the program suite that allow for user-defined guidance of the docking process based on previous experimental data. Docking with those features demonstrated superior performance compared to unbiased docking. Conclusion A protein pharmacophore-based docking program, PharmDock, has been made available with a PyMOL plugin. PharmDock and the PyMOL plugin are freely available from http://people.pharmacy.purdue.edu/~mlill/software/pharmdock. PMID:24739488

Prediction of protein-peptide interactions: application of the XPairIt API to anthrax lethal factor and substrates

NASA Astrophysics Data System (ADS)

Hurley, Margaret M.; Sellers, Michael S.

2013-05-01

As software and methodology develop, key aspects of molecular interactions such as detailed energetics and flexibility are continuously better represented in docking simulations. In the latest iteration of the XPairIt API and Docking Protocol, we perform a blind dock of a peptide into the cleavage site of the Anthrax lethal factor (LF) metalloprotein. Molecular structures are prepared from RCSB:1JKY and we demonstrate a reasonably accurate docked peptide through analysis of protein motion and, using NCI Plot, visualize and characterize the forces leading to binding. We compare our docked structure to the 1JKY crystal structure and the more recent 1PWV structure, and discuss both captured and overlooked interactions. Our results offer a more detailed look at secondary contact and show that both van der Waals and electrostatic interactions from peptide residues further from the enzyme's catalytic site are significant.

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

PubMed

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

2018-02-01

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

A cross docking pipeline for improving pose prediction and virtual screening performance

NASA Astrophysics Data System (ADS)

Kumar, Ashutosh; Zhang, Kam Y. J.

2018-01-01

Pose prediction and virtual screening performance of a molecular docking method depend on the choice of protein structures used for docking. Multiple structures for a target protein are often used to take into account the receptor flexibility and problems associated with a single receptor structure. However, the use of multiple receptor structures is computationally expensive when docking a large library of small molecules. Here, we propose a new cross-docking pipeline suitable to dock a large library of molecules while taking advantage of multiple target protein structures. Our method involves the selection of a suitable receptor for each ligand in a screening library utilizing ligand 3D shape similarity with crystallographic ligands. We have prospectively evaluated our method in D3R Grand Challenge 2 and demonstrated that our cross-docking pipeline can achieve similar or better performance than using either single or multiple-receptor structures. Moreover, our method displayed not only decent pose prediction performance but also better virtual screening performance over several other methods.

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

PubMed

Ramírez, David; Caballero, Julio

2018-04-28

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

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

NASA Astrophysics Data System (ADS)

Sippl, Wolfgang

2000-08-01

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

Nonlinear scoring functions for similarity-based ligand docking and binding affinity prediction.

PubMed

Brylinski, Michal

2013-11-25

A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors with promising leads selected based on favorable intermolecular interactions. Despite a continuous progress in the modeling of protein-ligand interactions for pharmaceutical design, important challenges still remain, thus the development of novel techniques is required. In this communication, we describe eSimDock, a new approach to ligand docking and binding affinity prediction. eSimDock employs nonlinear machine learning-based scoring functions to improve the accuracy of ligand ranking and similarity-based binding pose prediction, and to increase the tolerance to structural imperfections in the target structures. In large-scale benchmarking using the Astex/CCDC data set, we show that 53.9% (67.9%) of the predicted ligand poses have RMSD of <2 Å (<3 Å). Moreover, using binding sites predicted by recently developed eFindSite, eSimDock models ligand binding poses with an RMSD of 4 Å for 50.0-39.7% of the complexes at the protein homology level limited to 80-40%. Simulations against non-native receptor structures, whose mean backbone rearrangements vary from 0.5 to 5.0 Å Cα-RMSD, show that the ratio of docking accuracy and the estimated upper bound is at a constant level of ∼0.65. Pearson correlation coefficient between experimental and predicted by eSimDock Ki values for a large data set of the crystal structures of protein-ligand complexes from BindingDB is 0.58, which decreases only to 0.46 when target structures distorted to 3.0 Å Cα-RMSD are used. Finally, two case studies demonstrate that eSimDock can be customized to specific applications as well. These encouraging results show that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models. eSimDock is freely available to the academic community as a Web server at http://www.brylinski.org/esimdock .

Role of binding entropy in the refinement of protein-ligand docking predictions: analysis based on the use of 11 scoring functions.

PubMed

Ruvinsky, Anatoly M

2007-06-01

We present results of testing the ability of eleven popular scoring functions to predict native docked positions using a recently developed method (Ruvinsky and Kozintsev, J Comput Chem 2005, 26, 1089) for estimation the entropy contributions of relative motions to protein-ligand binding affinity. The method is based on the integration of the configurational integral over clusters obtained from multiple docked positions. We use a test set of 100 PDB protein-ligand complexes and ensembles of 101 docked positions generated by (Wang et al. J Med Chem 2003, 46, 2287) for each ligand in the test set. To test the suggested method we compared the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10-21% when used in conjunction with the AutoDock scoring function, by 2-25% with G-Score, by 7-41% with D-Score, by 0-8% with LigScore, by 1-6% with PLP, by 0-12% with LUDI, by 2-8% with F-Score, by 7-29% with ChemScore, by 0-9% with X-Score, by 2-19% with PMF, and by 1-7% with DrugScore. We also compared the performance of the suggested method with the method based on ranking by cluster occupancy only. We analyze how the choice of a clustering-RMSD and a low bound of dense clusters impacts on docking accuracy of the scoring methods. We derive optimal intervals of the clustering-RMSD for 11 scoring functions.

Quantitative Structure-Activity Relationship Modeling Coupled with Molecular Docking Analysis in Screening of Angiotensin I-Converting Enzyme Inhibitory Peptides from Qula Casein Hydrolysates Obtained by Two-Enzyme Combination Hydrolysis.

PubMed

Lin, Kai; Zhang, Lanwei; Han, Xue; Meng, Zhaoxu; Zhang, Jianming; Wu, Yifan; Cheng, Dayou

2018-03-28

In this study, Qula casein derived from yak milk casein was hydrolyzed using a two-enzyme combination approach, and high angiotensin I-converting enzyme (ACE) inhibitory activity peptides were screened by quantitative structure-activity relationship (QSAR) modeling integrated with molecular docking analysis. Hydrolysates (<3 kDa) derived from combinations of thermolysin + alcalase and thermolysin + proteinase K demonstrated high ACE inhibitory activities. Peptide sequences in hydrolysates derived from these two combinations were identified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). On the basis of the QSAR modeling prediction, a total of 16 peptides were selected for molecular docking analysis. The docking study revealed that four of the peptides (KFPQY, MPFPKYP, MFPPQ, and QWQVL) bound the active site of ACE. These four novel peptides were chemically synthesized, and their IC 50 was determined. Among these peptides, KFPQY showed the highest ACE inhibitory activity (IC 50 = 12.37 ± 0.43 μM). Our study indicated that Qula casein presents an excellent source to produce ACE inhibitory peptides.

Lessons in molecular recognition. 2. Assessing and improving cross-docking accuracy.

PubMed

Sutherland, Jeffrey J; Nandigam, Ravi K; Erickson, Jon A; Vieth, Michal

2007-01-01

Docking methods are used to predict the manner in which a ligand binds to a protein receptor. Many studies have assessed the success rate of programs in self-docking tests, whereby a ligand is docked into the protein structure from which it was extracted. Cross-docking, or using a protein structure from a complex containing a different ligand, provides a more realistic assessment of a docking program's ability to reproduce X-ray results. In this work, cross-docking was performed with CDocker, Fred, and Rocs using multiple X-ray structures for eight proteins (two kinases, one nuclear hormone receptor, one serine protease, two metalloproteases, and two phosphodiesterases). While average cross-docking accuracy is not encouraging, it is shown that using the protein structure from the complex that contains the bound ligand most similar to the docked ligand increases docking accuracy for all methods ("similarity selection"). Identifying the most successful protein conformer ("best selection") and similarity selection substantially reduce the difference between self-docking and average cross-docking accuracy. We identify universal predictors of docking accuracy (i.e., showing consistent behavior across most protein-method combinations), and show that models for predicting docking accuracy built using these parameters can be used to select the most appropriate docking method.

Combined 3D-QSAR modeling and molecular docking study on azacycles CCR5 antagonists

NASA Astrophysics Data System (ADS)

Ji, Yongjun; Shu, Mao; Lin, Yong; Wang, Yuanqiang; Wang, Rui; Hu, Yong; Lin, Zhihua

2013-08-01

The beta chemokine receptor 5 (CCR5) is an attractive target for pharmaceutical industry in the HIV-1, inflammation and cancer therapeutic areas. In this study, we have developed quantitative structure activity relationship (QSAR) models for a series of 41 azacycles CCR5 antagonists using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and Topomer CoMFA methods. The cross-validated coefficient q2 values of 3D-QASR (CoMFA, CoMSIA, and Topomer CoMFA) methods were 0.630, 0.758, and 0.852, respectively, the non-cross-validated R2 values were 0.979, 0.978, and 0.990, respectively. Docking studies were also employed to determine the most probable binding mode. 3D contour maps and docking results suggested that bulky groups and electron-withdrawing groups on the core part would decrease antiviral activity. Furthermore, docking results indicated that H-bonds and π bonds were favorable for antiviral activities. Finally, a set of novel derivatives with predicted activities were designed.

Exploring the Therapeutic Mechanism of Desmodium styracifolium on Oxalate Crystal-Induced Kidney Injuries Using Comprehensive Approaches Based on Proteomics and Network Pharmacology.

PubMed

Hou, Jiebin; Chen, Wei; Lu, Hongtao; Zhao, Hongxia; Gao, Songyan; Liu, Wenrui; Dong, Xin; Guo, Zhiyong

2018-01-01

Purpose: As a Chinese medicinal herb, Desmodium styracifolium (Osb.) Merr (DS) has been applied clinically to alleviate crystal-induced kidney injuries, but its effective components and their specific mechanisms still need further exploration. This research first combined the methods of network pharmacology and proteomics to explore the therapeutic protein targets of DS on oxalate crystal-induced kidney injuries to provide a reference for relevant clinical use. Methods: Oxalate-induced kidney injury mouse, rat, and HK-2 cell models were established. Proteins differentially expressed between the oxalate and control groups were respectively screened using iTRAQ combined with MALDI-TOF-MS. The common differential proteins of the three models were further analyzed by molecular docking with DS compounds to acquire differential targets. The inverse docking targets of DS were predicted through the platform of PharmMapper. The protein-protein interaction (PPI) relationship between the inverse docking targets and the differential proteins was established by STRING. Potential targets were further validated by western blot based on a mouse model with DS treatment. The effects of constituent compounds, including luteolin, apigenin, and genistein, were investigated based on an oxalate-stimulated HK-2 cell model. Results: Thirty-six common differentially expressed proteins were identified by proteomic analysis. According to previous research, the 3D structures of 15 major constituents of DS were acquired. Nineteen differential targets, including cathepsin D (CTSD), were found using molecular docking, and the component-differential target network was established. Inverse-docking targets including p38 MAPK and CDK-2 were found, and the network of component-reverse docking target was established. Through PPI analysis, 17 inverse-docking targets were linked to differential proteins. The combined network of component-inverse docking target-differential proteins was then constructed. The expressions of CTSD, p-p38 MAPK, and p-CDK-2 were shown to be increased in the oxalate group and decreased in kidney tissue by the DS treatment. Luteolin, apigenin, and genistein could protect oxalate-stimulated tubular cells as active components of DS. Conclusion: The potential targets including the CTSD, p38 MAPK, and CDK2 of DS in oxalate-induced kidney injuries and the active components (luteolin, apigenin, and genistein) of DS were successfully identified in this study by combining proteomics analysis, network pharmacology prediction, and experimental validation.

Isolation of anticancer drug TAXOL from Pestalotiopsis breviseta with apoptosis and B-Cell lymphoma protein docking studies.

PubMed

Kathiravan, G; Sureban, Sripathi M; Sree, Harsha N; Bhuvaneshwari, V; Kramony, Evelin

2012-12-01

Extraction and investigation of TAXOL from Pestalotiopsis breviseta (Sacc.) using protein docking, which is a computational technique that samples conformations of small molecules in protein-binding sites. Scoring functions are used to assess which of these conformations best complements the protein binding site and active site prediction. Coelomycetous fungi P. breviseta (Sacc.) Steyaert was screened for the production of TAXOL, an anticancer drug. TAXOL PRODUCTION WAS CONFIRMED BY THE FOLLOWING METHODS: Ultraviolet (UV) spectroscopic analysis, Infrared analysis, High performance liquid chromatography analysis (HPLC), and Liquid chromatography mass spectrum (LC-MASS). TAXOL produced by the fungi was compared with authentic TAXOL, and protein docking studies were performed. The BCL2 protein of human origin showed a higher affinity toward the compound paclitaxel. It has the binding energy value of -13.0061 (KJ/Mol) with four hydrogen bonds.

Isolation of anticancer drug TAXOL from Pestalotiopsis breviseta with apoptosis and B-Cell lymphoma protein docking studies

PubMed Central

Kathiravan, G.; Sureban, Sripathi M.; Sree, Harsha N.; Bhuvaneshwari, V.; Kramony, Evelin

2012-01-01

Background: Extraction and investigation of TAXOL from Pestalotiopsis breviseta (Sacc.) using protein docking, which is a computational technique that samples conformations of small molecules in protein-binding sites. Scoring functions are used to assess which of these conformations best complements the protein binding site and active site prediction. Materials and Methods: Coelomycetous fungi P. breviseta (Sacc.) Steyaert was screened for the production of TAXOL, an anticancer drug. Results: TAXOL production was confirmed by the following methods: Ultraviolet (UV) spectroscopic analysis, Infrared analysis, High performance liquid chromatography analysis (HPLC), and Liquid chromatography mass spectrum (LC-MASS). TAXOL produced by the fungi was compared with authentic TAXOL, and protein docking studies were performed. Conclusion: The BCL2 protein of human origin showed a higher affinity toward the compound paclitaxel. It has the binding energy value of −13.0061 (KJ/Mol) with four hydrogen bonds. PMID:24808664

Lessons from (co-)evolution in the docking of proteins and peptides for CAPRI Rounds 28-35.

PubMed

Yu, Jinchao; Andreani, Jessica; Ochsenbein, Françoise; Guerois, Raphaël

2017-03-01

Computational protein-protein docking is of great importance for understanding protein interactions at the structural level. Critical assessment of prediction of interactions (CAPRI) experiments provide the protein docking community with a unique opportunity to blindly test methods based on real-life cases and help accelerate methodology development. For CAPRI Rounds 28-35, we used an automatic docking pipeline integrating the coarse-grained co-evolution-based potential InterEvScore. This score was developed to exploit the information contained in the multiple sequence alignments of binding partners and selectively recognize co-evolved interfaces. Together with Zdock/Frodock for rigid-body docking, SOAP-PP for atomic potential and Rosetta applications for structural refinement, this pipeline reached high performance on a majority of targets. For protein-peptide docking and interfacial water position predictions, we also explored different means of taking evolutionary information into account. Overall, our group ranked 1 st by correctly predicting 10 targets, composed of 1 High, 7 Medium and 2 Acceptable predictions. Excellent and Outstanding levels of accuracy were reached for each of the two water prediction targets, respectively. Altogether, in 15 out of 18 targets in total, evolutionary information, either through co-evolution or conservation analyses, could provide key constraints to guide modeling towards the most likely assemblies. These results open promising perspectives regarding the way evolutionary information can be valuable to improve docking prediction accuracy. Proteins 2017; 85:378-390. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Immunoinformatic Analysis of Crimean Congo Hemorrhagic Fever Virus Glycoproteins and Epitope Prediction for Synthetic Peptide Vaccine.

PubMed

Tipu, Hamid Nawaz

2016-02-01

To determine the Crimean Congo Hemorrhagic Fever (CCHF) virus M segement glycoprotein's immunoinformatic parameters, and identify Human Leukocyte Antigen (HLA) class I binders as candidates for synthetic peptide vaccines. Cross-sectional study. Combined Military Hospital, Khuzdar Cantt, in May 2015. Data acquisition, antigenicity prediction, secondary and tertiary structure prediction, residue analysis were done using immunoinformatics tools. HLAclass I binders in glycoprotein's sequence were identified at nanomer length using NetMHC 3.4 and mapped onto tertiary structure. Docking was done for strongest binder against its corresponding allele with CABS-dock. HLAA*0101, 0201, 0301, 2402, 2601 and B*0702, 0801, 2705, 3901, 4001, 5801, 1501 were analyzed against two glycoprotein components of the virus. Atotal of 35 nanomers from GP1, and 3 from GP2 were identified. HLAB*0702 bound maximum number of peptides (6), while HLAB*4001 showed strongest binding affinity. HLAspecific glycoproteins epitope prediction can help identify synthetic peptide vaccine candidates.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

PubMed

Kilambi, Krishna Praneeth; Pacella, Michael S; Xu, Jianqing; Labonte, Jason W; Porter, Justin R; Muthu, Pravin; Drew, Kevin; Kuroda, Daisuke; Schueler-Furman, Ora; Bonneau, Richard; Gray, Jeffrey J

2013-12-01

Rounds 20-27 of the Critical Assessment of PRotein Interactions (CAPRI) provided a testing platform for computational methods designed to address a wide range of challenges. The diverse targets drove the creation of and new combinations of computational tools. In this study, RosettaDock and other novel Rosetta protocols were used to successfully predict four of the 10 blind targets. For example, for DNase domain of Colicin E2-Im2 immunity protein, RosettaDock and RosettaLigand were used to predict the positions of water molecules at the interface, recovering 46% of the native water-mediated contacts. For α-repeat Rep4-Rep2 and g-type lysozyme-PliG inhibitor complexes, homology models were built and standard and pH-sensitive docking algorithms were used to generate structures with interface RMSD values of 3.3 Å and 2.0 Å, respectively. A novel flexible sugar-protein docking protocol was also developed and used for structure prediction of the BT4661-heparin-like saccharide complex, recovering 71% of the native contacts. Challenges remain in the generation of accurate homology models for protein mutants and sampling during global docking. On proteins designed to bind influenza hemagglutinin, only about half of the mutations were identified that affect binding (T55: 54%; T56: 48%). The prediction of the structure of the xylanase complex involving homology modeling and multidomain docking pushed the limits of global conformational sampling and did not result in any successful prediction. The diversity of problems at hand requires computational algorithms to be versatile; the recent additions to the Rosetta suite expand the capabilities to encompass more biologically realistic docking problems. Copyright © 2013 Wiley Periodicals, Inc.

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.

PubMed

Xu, Weijun; Lucke, Andrew J; Fairlie, David P

2015-04-01

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches. Copyright © 2015 Elsevier Inc. All rights reserved.

HDOCK: a web server for protein–protein and protein–DNA/RNA docking based on a hybrid strategy

PubMed Central

Yan, Yumeng; Zhang, Di; Zhou, Pei; Li, Botong

2017-01-01

Abstract Protein–protein and protein–DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein–protein and protein–DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10–20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein–protein and protein–DNA benchmarks and performed better than template-based modeling on the three protein–RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/. PMID:28521030

Building protein-protein interaction networks for Leishmania species through protein structural information.

PubMed

Dos Santos Vasconcelos, Crhisllane Rafaele; de Lima Campos, Túlio; Rezende, Antonio Mauro

2018-03-06

Systematic analysis of a parasite interactome is a key approach to understand different biological processes. It makes possible to elucidate disease mechanisms, to predict protein functions and to select promising targets for drug development. Currently, several approaches for protein interaction prediction for non-model species incorporate only small fractions of the entire proteomes and their interactions. Based on this perspective, this study presents an integration of computational methodologies, protein network predictions and comparative analysis of the protozoan species Leishmania braziliensis and Leishmania infantum. These parasites cause Leishmaniasis, a worldwide distributed and neglected disease, with limited treatment options using currently available drugs. The predicted interactions were obtained from a meta-approach, applying rigid body docking tests and template-based docking on protein structures predicted by different comparative modeling techniques. In addition, we trained a machine-learning algorithm (Gradient Boosting) using docking information performed on a curated set of positive and negative protein interaction data. Our final model obtained an AUC = 0.88, with recall = 0.69, specificity = 0.88 and precision = 0.83. Using this approach, it was possible to confidently predict 681 protein structures and 6198 protein interactions for L. braziliensis, and 708 protein structures and 7391 protein interactions for L. infantum. The predicted networks were integrated to protein interaction data already available, analyzed using several topological features and used to classify proteins as essential for network stability. The present study allowed to demonstrate the importance of integrating different methodologies of interaction prediction to increase the coverage of the protein interaction of the studied protocols, besides it made available protein structures and interactions not previously reported.

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

PubMed

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

2017-11-01

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

Protein-protein interaction specificity is captured by contact preferences and interface composition.

PubMed

Nadalin, Francesca; Carbone, Alessandra

2018-02-01

Large-scale computational docking will be increasingly used in future years to discriminate protein-protein interactions at the residue resolution. Complete cross-docking experiments make in silico reconstruction of protein-protein interaction networks a feasible goal. They ask for efficient and accurate screening of the millions structural conformations issued by the calculations. We propose CIPS (Combined Interface Propensity for decoy Scoring), a new pair potential combining interface composition with residue-residue contact preference. CIPS outperforms several other methods on screening docking solutions obtained either with all-atom or with coarse-grain rigid docking. Further testing on 28 CAPRI targets corroborates CIPS predictive power over existing methods. By combining CIPS with atomic potentials, discrimination of correct conformations in all-atom structures reaches optimal accuracy. The drastic reduction of candidate solutions produced by thousands of proteins docked against each other makes large-scale docking accessible to analysis. CIPS source code is freely available at http://www.lcqb.upmc.fr/CIPS. alessandra.carbone@lip6.fr. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

ClusPro: an automated docking and discrimination method for the prediction of protein complexes.

PubMed

Comeau, Stephen R; Gatchell, David W; Vajda, Sandor; Camacho, Carlos J

2004-01-01

Predicting protein interactions is one of the most challenging problems in functional genomics. Given two proteins known to interact, current docking methods evaluate billions of docked conformations by simple scoring functions, and in addition to near-native structures yield many false positives, i.e. structures with good surface complementarity but far from the native. We have developed a fast algorithm for filtering docked conformations with good surface complementarity, and ranking them based on their clustering properties. The free energy filters select complexes with lowest desolvation and electrostatic energies. Clustering is then used to smooth the local minima and to select the ones with the broadest energy wells-a property associated with the free energy at the binding site. The robustness of the method was tested on sets of 2000 docked conformations generated for 48 pairs of interacting proteins. In 31 of these cases, the top 10 predictions include at least one near-native complex, with an average RMSD of 5 A from the native structure. The docking and discrimination method also provides good results for a number of complexes that were used as targets in the Critical Assessment of PRedictions of Interactions experiment. The fully automated docking and discrimination server ClusPro can be found at http://structure.bu.edu

PaFlexPepDock: parallel ab-initio docking of peptides onto their receptors with full flexibility based on Rosetta.

PubMed

Li, Haiou; Lu, Liyao; Chen, Rong; Quan, Lijun; Xia, Xiaoyan; Lü, Qiang

2014-01-01

Structural information related to protein-peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein-peptide interactions explored by experimental ways. Protein-peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein-peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein-peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions.

Vibrational spectroscopic, molecular docking and quantum chemical studies on 6-aminonicotinamide

NASA Astrophysics Data System (ADS)

Mohamed Asath, R.; Premkumar, S.; Mathavan, T.; Milton Franklin Benial, A.

2017-04-01

The most stable molecular structure of 6-aminonicotinamide (ANA) molecule was predicted by conformational analysis and vibrational spectral analysis was carried out by experimental and theoretical methods. The calculated and experimentally observed vibrational frequencies were assigned and compared. The π→π* electronic transition of the molecule was predicted by theoretically calculated ultraviolet-visible spectra in gas and liquid phase and further validated experimentally using ethanol as a solvent. Frontier molecular orbitals analysis was carried out to probe the reactive nature of the ANA molecule and further the site selectivity to specific chemical reactions were effectively analyzed by Fukui function calculation. The molecular electrostatic potential surface was simulated to confirm the reactive sites of the molecule. The natural bond orbital analysis was also performed to understand the intra molecular interactions, which confirms the bioactivity of the ANA molecule. Neuroprotective nature of the ANA molecule was analyzed by molecular docking analysis and the ANA molecule was identified as a good inhibitor against Alzheimer's disease.

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

NASA Astrophysics Data System (ADS)

Isvoran, Adriana

2016-03-01

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

HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy.

PubMed

Yan, Yumeng; Zhang, Di; Zhou, Pei; Li, Botong; Huang, Sheng-You

2017-07-03

Protein-protein and protein-DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein-protein and protein-DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10-20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein-protein and protein-DNA benchmarks and performed better than template-based modeling on the three protein-RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/. © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

InterEvDock2: an expanded server for protein docking using evolutionary and biological information from homology models and multimeric inputs.

PubMed

Quignot, Chloé; Rey, Julien; Yu, Jinchao; Tufféry, Pierre; Guerois, Raphaël; Andreani, Jessica

2018-05-08

Computational protein docking is a powerful strategy to predict structures of protein-protein interactions and provides crucial insights for the functional characterization of macromolecular cross-talks. We previously developed InterEvDock, a server for ab initio protein docking based on rigid-body sampling followed by consensus scoring using physics-based and statistical potentials, including the InterEvScore function specifically developed to incorporate co-evolutionary information in docking. InterEvDock2 is a major evolution of InterEvDock which allows users to submit input sequences - not only structures - and multimeric inputs and to specify constraints for the pairwise docking process based on previous knowledge about the interaction. For this purpose, we added modules in InterEvDock2 for automatic template search and comparative modeling of the input proteins. The InterEvDock2 pipeline was benchmarked on 812 complexes for which unbound homology models of the two partners and co-evolutionary information are available in the PPI4DOCK database. InterEvDock2 identified a correct model among the top 10 consensus in 29% of these cases (compared to 15-24% for individual scoring functions) and at least one correct interface residue among 10 predicted in 91% of these cases. InterEvDock2 is thus a unique protein docking server, designed to be useful for the experimental biology community. The InterEvDock2 web interface is available at http://bioserv.rpbs.univ-paris-diderot.fr/services/InterEvDock2/.

Predicting receptor functionality of signaling lymphocyte activation molecule for measles virus hemagglutinin by docking simulation.

PubMed

Suzuki, Yoshiyuki

2017-05-01

Predicting susceptibility of various species to a virus assists assessment of risk of interspecies transmission. Evaluation of receptor functionality may be useful in screening for susceptibility. In this study, docking simulation was conducted for measles virus hemagglutinin (MV-H) and immunoglobulin-like variable domain of signaling lymphocyte activation molecule (SLAM-V). It was observed that the docking scores for MV-H and SLAM-V correlated with the activity of SLAM as an MV receptor. These results suggest that the receptor functionality may be predicted from the docking scores of virion surface proteins and cellular receptor molecules. © 2017 The Societies and John Wiley & Sons Australia, Ltd.

3D-QSAR modeling and molecular docking studies on a series of 2,5 disubstituted 1,3,4-oxadiazoles

NASA Astrophysics Data System (ADS)

Ghaleb, Adib; Aouidate, Adnane; Ghamali, Mounir; Sbai, Abdelouahid; Bouachrine, Mohammed; Lakhlifi, Tahar

2017-10-01

3D-QSAR (comparative molecular field analysis (CoMFA)) and comparative molecular similarity indices analysis (CoMSIA) were performed on novel 2,5 disubstituted 1,3,4-oxadiazoles analogues as anti-fungal agents. The CoMFA and CoMSIA models using 13 compounds in the training set gives Q2 values of 0.52 and 0.51 respectively, while R2 values of 0.92. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to determine a three-dimensional quantitative structure-activity relationship. Based on this study a set of new molecules with high predicted activities were designed. Surflex-docking confirmed the stability of predicted molecules in the receptor.

CoMSIA and Docking Study of Rhenium Based Estrogen Receptor Ligand Analogs

PubMed Central

Wolohan, Peter; Reichert, David E.

2007-01-01

OPLS all atom force field parameters were developed in order to model a diverse set of novel rhenium based estrogen receptor ligands whose relative binding affinities (RBA) to the estrogen receptor alpha isoform (ERα) with respect to 17β-Estradiol were available. The binding properties of these novel rhenium based organometallic complexes were studied with a combination of Comparative Molecular Similarity Indices Analysis (CoMSIA) and docking. A total of 29 estrogen receptor ligands consisting of 11 rhenium complexes and 18 organic ligands were docked inside the ligand-binding domain (LBD) of ERα utilizing the program Gold. The top ranked pose was used to construct CoMSIA models from a training set of 22 of the estrogen receptor ligands which were selected at random. In addition scoring functions from the docking runs and the polar volume (PV) were also studied to investigate their ability to predict RBA ERα. A partial least-squares analysis consisting of the CoMSIA steric, electrostatic and hydrophobic indices together with the polar volume proved sufficiently predictive having a correlation coefficient, r2, of 0.94 and a cross-validated correlation coefficient, q2, utilizing the leave one out method of 0.68. Analysis of the scoring functions from Gold showed particularly poor correlation to RBA ERα which did not improve when the rhenium complexes were extracted to leave the organic ligands. The combined CoMSIA and polar volume model ranked correctly the ligands in order of increasing RBA ERα, illustrating the utility of this method as a prescreening tool in the development of novel rhenium based estrogen receptor ligands. PMID:17280694

Protein-Protein Docking with F2Dock 2.0 and GB-Rerank

PubMed Central

Chowdhury, Rezaul; Rasheed, Muhibur; Keidel, Donald; Moussalem, Maysam; Olson, Arthur; Sanner, Michel; Bajaj, Chandrajit

2013-01-01

Motivation Computational simulation of protein-protein docking can expedite the process of molecular modeling and drug discovery. This paper reports on our new F2 Dock protocol which improves the state of the art in initial stage rigid body exhaustive docking search, scoring and ranking by introducing improvements in the shape-complementarity and electrostatics affinity functions, a new knowledge-based interface propensity term with FFT formulation, a set of novel knowledge-based filters and finally a solvation energy (GBSA) based reranking technique. Our algorithms are based on highly efficient data structures including the dynamic packing grids and octrees which significantly speed up the computations and also provide guaranteed bounds on approximation error. Results The improved affinity functions show superior performance compared to their traditional counterparts in finding correct docking poses at higher ranks. We found that the new filters and the GBSA based reranking individually and in combination significantly improve the accuracy of docking predictions with only minor increase in computation time. We compared F2 Dock 2.0 with ZDock 3.0.2 and found improvements over it, specifically among 176 complexes in ZLab Benchmark 4.0, F2 Dock 2.0 finds a near-native solution as the top prediction for 22 complexes; where ZDock 3.0.2 does so for 13 complexes. F2 Dock 2.0 finds a near-native solution within the top 1000 predictions for 106 complexes as opposed to 104 complexes for ZDock 3.0.2. However, there are 17 and 15 complexes where F2 Dock 2.0 finds a solution but ZDock 3.0.2 does not and vice versa; which indicates that the two docking protocols can also complement each other. Availability The docking protocol has been implemented as a server with a graphical client (TexMol) which allows the user to manage multiple docking jobs, and visualize the docked poses and interfaces. Both the server and client are available for download. Server: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dock.shtml. Client: http://www.cs.utexas.edu/~bajaj/cvc/software/f2dockclient.shtml. PMID:23483883

Protein-protein docking using region-based 3D Zernike descriptors

PubMed Central

2009-01-01

Background Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. Results We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-αRMSD ≤ 2.5 Å) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. Conclusion We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods. PMID:20003235

Protein-protein docking using region-based 3D Zernike descriptors.

PubMed

Venkatraman, Vishwesh; Yang, Yifeng D; Sael, Lee; Kihara, Daisuke

2009-12-09

Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-alphaRMSD < or = 2.5 A) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

PubMed Central

Leong, Max K.; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

2017-01-01

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928–0.988,  = 0.894–0.954, RMSE = 0.002–0.412, s = 0.001–0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery. PMID:28059133

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme.

PubMed

Leong, Max K; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

2017-01-06

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r 2  = 0.928-0.988,  = 0.894-0.954, RMSE = 0.002-0.412, s = 0.001-0.214), and the predicted pK i values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r 2  = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q 2  = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

Prediction of N-Methyl-D-Aspartate Receptor GluN1-Ligand Binding Affinity by a Novel SVM-Pose/SVM-Score Combinatorial Ensemble Docking Scheme

NASA Astrophysics Data System (ADS)

Leong, Max K.; Syu, Ren-Guei; Ding, Yi-Lung; Weng, Ching-Feng

2017-01-01

The glycine-binding site of the N-methyl-D-aspartate receptor (NMDAR) subunit GluN1 is a potential pharmacological target for neurodegenerative disorders. A novel combinatorial ensemble docking scheme using ligand and protein conformation ensembles and customized support vector machine (SVM)-based models to select the docked pose and to predict the docking score was generated for predicting the NMDAR GluN1-ligand binding affinity. The predicted root mean square deviation (RMSD) values in pose by SVM-Pose models were found to be in good agreement with the observed values (n = 30, r2 = 0.928-0.988,  = 0.894-0.954, RMSE = 0.002-0.412, s = 0.001-0.214), and the predicted pKi values by SVM-Score were found to be in good agreement with the observed values for the training samples (n = 24, r2 = 0.967,  = 0.899, RMSE = 0.295, s = 0.170) and test samples (n = 13, q2 = 0.894, RMSE = 0.437, s = 0.202). When subjected to various statistical validations, the developed SVM-Pose and SVM-Score models consistently met the most stringent criteria. A mock test asserted the predictivity of this novel docking scheme. Collectively, this accurate novel combinatorial ensemble docking scheme can be used to predict the NMDAR GluN1-ligand binding affinity for facilitating drug discovery.

Study on the activity of non-purine xanthine oxidase inhibitor by 3D-QSAR modeling and molecular docking

NASA Astrophysics Data System (ADS)

Li, Peizhen; Tian, Yueli; Zhai, Honglin; Deng, Fangfang; Xie, Meihong; Zhang, Xiaoyun

2013-11-01

Non-purine derivatives have been shown to be promising novel drug candidates as xanthine oxidase inhibitors. Based on three-dimensional quantitative structure-activity relationship (3D-QSAR) methods including comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA), two 3D-QSAR models for a series of non-purine xanthine oxidase (XO) inhibitors were established, and their reliability was supported by statistical parameters. Combined 3D-QSAR modeling and the results of molecular docking between non-purine xanthine oxidase inhibitors and XO, the main factors that influenced activity of inhibitors were investigated, and the obtained results could explain known experimental facts. Furthermore, several new potential inhibitors with higher activity predicted were designed, which based on our analyses, and were supported by the simulation of molecular docking. This study provided some useful information for the development of non-purine xanthine oxidase inhibitors with novel structures.

Predicting bioactive conformations and binding modes of macrocycles

NASA Astrophysics Data System (ADS)

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

2016-10-01

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

Docking screens: right for the right reasons?

PubMed

Kolb, Peter; Irwin, John J

2009-01-01

Whereas docking screens have emerged as the most practical way to use protein structure for ligand discovery, an inconsistent track record raises questions about how well docking actually works. In its favor, a growing number of publications report the successful discovery of new ligands, often supported by experimental affinity data and controls for artifacts. Few reports, however, actually test the underlying structural hypotheses that docking makes. To be successful and not just lucky, prospective docking must not only rank a true ligand among the top scoring compounds, it must also correctly orient the ligand so the score it receives is biophysically sound. If the correct binding pose is not predicted, a skeptic might well infer that the discovery was serendipitous. Surveying over 15 years of the docking literature, we were surprised to discover how rarely sufficient evidence is presented to establish whether docking actually worked for the right reasons. The paucity of experimental tests of theoretically predicted poses undermines confidence in a technique that has otherwise become widely accepted. Of course, solving a crystal structure is not always possible, and even when it is, it can be a lot of work, and is not readily accessible to all groups. Even when a structure can be determined, investigators may prefer to gloss over an erroneous structural prediction to better focus on their discovery. Still, the absence of a direct test of theory by experiment is a loss for method developers seeking to understand and improve docking methods. We hope this review will motivate investigators to solve structures and compare them with their predictions whenever possible, to advance the field.

PEPSI-Dock: a detailed data-driven protein-protein interaction potential accelerated by polar Fourier correlation.

PubMed

Neveu, Emilie; Ritchie, David W; Popov, Petr; Grudinin, Sergei

2016-09-01

Docking prediction algorithms aim to find the native conformation of a complex of proteins from knowledge of their unbound structures. They rely on a combination of sampling and scoring methods, adapted to different scales. Polynomial Expansion of Protein Structures and Interactions for Docking (PEPSI-Dock) improves the accuracy of the first stage of the docking pipeline, which will sharpen up the final predictions. Indeed, PEPSI-Dock benefits from the precision of a very detailed data-driven model of the binding free energy used with a global and exhaustive rigid-body search space. As well as being accurate, our computations are among the fastest by virtue of the sparse representation of the pre-computed potentials and FFT-accelerated sampling techniques. Overall, this is the first demonstration of a FFT-accelerated docking method coupled with an arbitrary-shaped distance-dependent interaction potential. First, we present a novel learning process to compute data-driven distant-dependent pairwise potentials, adapted from our previous method used for rescoring of putative protein-protein binding poses. The potential coefficients are learned by combining machine-learning techniques with physically interpretable descriptors. Then, we describe the integration of the deduced potentials into a FFT-accelerated spherical sampling provided by the Hex library. Overall, on a training set of 163 heterodimers, PEPSI-Dock achieves a success rate of 91% mid-quality predictions in the top-10 solutions. On a subset of the protein docking benchmark v5, it achieves 44.4% mid-quality predictions in the top-10 solutions when starting from bound structures and 20.5% when starting from unbound structures. The method runs in 5-15 min on a modern laptop and can easily be extended to other types of interactions. https://team.inria.fr/nano-d/software/PEPSI-Dock sergei.grudinin@inria.fr. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Accuracy of binding mode prediction with a cascadic stochastic tunneling method.

PubMed

Fischer, Bernhard; Basili, Serena; Merlitz, Holger; Wenzel, Wolfgang

2007-07-01

We investigate the accuracy of the binding modes predicted for 83 complexes of the high-resolution subset of the ASTEX/CCDC receptor-ligand database using the atomistic FlexScreen approach with a simple forcefield-based scoring function. The median RMS deviation between experimental and predicted binding mode was just 0.83 A. Over 80% of the ligands dock within 2 A of the experimental binding mode, for 60 complexes the docking protocol locates the correct binding mode in all of ten independent simulations. Most docking failures arise because (a) the experimental structure clashed in our forcefield and is thus unattainable in the docking process or (b) because the ligand is stabilized by crystal water. 2007 Wiley-Liss, Inc.

Potential interaction of natural dietary bioactive compounds with COX-2.

PubMed

Maldonado-Rojas, Wilson; Olivero-Verbel, Jesus

2011-09-01

Bioactive natural products present in the diet play an important role in several biological processes, and many have been involved in the alleviation and control of inflammation-related diseases. These actions have been linked to both gene expression modulation of pro-inflammatory enzymes, such as cyclooxygenase 2 (COX-2), and to an action involving a direct inhibitory binding on this protein. In this study, several food-related compounds with known gene regulatory action on inflammation have been examined in silico as COX-2 ligands, utilizing AutoDock Vina, GOLD and Surflex-Dock (SYBYL) as docking protocols. Curcumin and all-trans retinoic acid presented the maximum absolute AutoDock Vina-derived binding affinities (9.3 kcal/mol), but genistein, apigenin, cyanidin, kaempferol, and docosahexaenoic acid, were close to this value. AutoDock Vina affinities and GOLD scores for several known COX-2 inhibitors significatively correlated with reported median inhibitory concentrations (R² = 0.462, P < 0.001 and R² = 0.238, P = 0.029, respectively), supporting the computational reliability of the predictions made by our docking simulations. Moreover, docking analysis insinuate the synergistic action of curcumin on celecoxib-induced inhibition of COX-2 may occur allosterically, as this natural compound docks to a place different from the inhibitor binding site. These results suggest that the anti-inflammatory properties of some food-derived molecules could be the result of their direct binding capabilities to COX-2, and this process can be modeled using protein-ligand docking methodologies. Copyright © 2011 Elsevier Inc. All rights reserved.

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.

First report on 3D-QSAR and molecular dynamics based docking studies of GCPII inhibitors for targeted drug delivery applications

NASA Astrophysics Data System (ADS)

Pandit, Amit; Sengupta, Sagnik; Krishnan, Mena Asha; Reddy, Ramesh B.; Sharma, Rajesh; Venkatesh, Chelvam

2018-05-01

Prostate Specific Membrane Antigen (PSMA) or Glutamate carboxypeptidase II (GCPII) has been identified as an important target in diagnosis and therapy of prostate cancer. Among several types of inhibitors, urea based inhibitors are the most common and widely employed in preclinical and clinical studies. Computational studies have been carried out to uncover active sites and interaction of PSMA inhibitors with the protein by modifying the core structure of the ligand. Analysis of the literature, however, show lack of 3-D quantitative structure activity relationship (QSAR) and molecular dynamics based molecular docking study to identify structural modifications responsible for better GCPII inhibitory activity. The present study aims to fulfil this gap by analysing well known PSMA inhibitors reported in the literature with known experimental PSMA inhibition constants. Also in order to validate the in silico study, a new GCPII inhibitor 7 was designed, synthesized and experimental PSMA enzyme inhibition was evaluated by using freshly isolated PSMA protein from human cancer cell line derived from lymph node, LNCaP. 3D-QSAR CoMFA models on 58 urea based GCPII inhibitors were generated, and the best correlation was obtained in Gast-Huck charge assigning method with q2, r2 and predictive r2 values as 0.592, 0.995 and 0.842 respectively. Moreover, steric, electrostatic, and hydrogen bond donor field contribution analysis provided best statistical values from CoMSIA model (q2, r2 and predictive r2 as 0.527, 0.981 and 0.713 respectively). Contour maps study revealed that electrostatic field contribution is the major factor for discovering better binding affinity ligands. Further molecular dynamic assisted molecular docking was also performed on GCPII receptor (PDB ID 4NGM) and most active GCPII inhibitor, DCIBzL. 4NGM co-crystallised ligand, JB7 was used to validate the docking procedure and the amino acid interactions present in JB7 are compared with DCIBzL. The results suggest that Arg210, Asn257, Gly518, Tyr552, Lys699, and Tyr700 amino acid residues may play a crucial role in GCPII inhibition. Molecular Dynamics Simulation provides information about docked pose stability of DCIBzL. By combination of CoMFA-CoMSIA field analysis and docking interaction analysis studies, conclusive SAR was generated for urea based derivatives based on which GCPII inhibitor 7 was designed and chemically synthesized in our laboratory. Evaluation of GCPII inhibitory activity of 7 by performing NAALADase assay provided IC50 value of 113 nM which is in close agreement with in silico predicted value (119 nM). Thus we have successfully validated our 3D-QSAR and molecular docking based designing of GCPII inhibitors methodology through biological experiments. This conclusive SAR would be helpful to generate novel and more potent GCPII inhibitors for drug delivery applications.

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

PubMed

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

2006-01-01

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

Rigid-Docking Approaches to Explore Protein-Protein Interaction Space.

PubMed

Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ohue, Masahito; Akiyama, Yutaka

Protein-protein interactions play core roles in living cells, especially in the regulatory systems. As information on proteins has rapidly accumulated on publicly available databases, much effort has been made to obtain a better picture of protein-protein interaction networks using protein tertiary structure data. Predicting relevant interacting partners from their tertiary structure is a challenging task and computer science methods have the potential to assist with this. Protein-protein rigid docking has been utilized by several projects, docking-based approaches having the advantages that they can suggest binding poses of predicted binding partners which would help in understanding the interaction mechanisms and that comparing docking results of both non-binders and binders can lead to understanding the specificity of protein-protein interactions from structural viewpoints. In this review we focus on explaining current computational prediction methods to predict pairwise direct protein-protein interactions that form protein complexes.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm.

PubMed

Zhou, Pei; Jin, Bowen; Li, Hao; Huang, Sheng-You

2018-05-09

Protein-peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein-peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein-peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

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.

Molecular Docking and Molecular Dynamics to Identify a Novel Human Immunodeficiency Virus Inhibitor from Alkaloids of Toddalia asiatica.

PubMed

Priya, R; Sumitha, Rajendrarao; Doss, C George Priya; Rajasekaran, C; Babu, S; Seenivasan, R; Siva, R

2015-10-01

Acquired immunodeficiency syndrome caused by human immunodeficiency virus (HIV) is an immunosuppressive disease. Over the past decades, it has plagued human health due to the grave consequences in its harness. For this reason, anti-HIV agents are imperative, and the search for the same from natural resources would assure the safety. In this investigation we have performed molecular docking, molecular property prediction, drug-likeness score, and molecular dynamics (MD) simulation to develop a novel anti-HIV drug. We have screened 12 alkaloids from a medicinal plant Toddalia asiatica for its probabilistic binding with the active site of the HIV-1-reverse transcriptase (HIV-1-RT) domain (the major contributor to the onset of the disease). The docking results were evaluated based on free energies of binding (ΔG), and the results suggested toddanol, toddanone, and toddalenone to be potent inhibitors of HIV-1-RT. In addition, the alkaloids were subjected to molecular property prediction analysis. Toddanol and toddanone with more rotatable bonds were found to have a drug-likeness score of 0.23 and 0.11, respectively. These scores were comparable with the standard anti-HIV drug zidovudine with a model score 0.28. Finally, two characteristic protein-ligand complexes were exposed to MD simulation to determine the stability of the predicted conformations. The toddanol-RT complex showed higher stability and stronger H-bonds than toddanone-RT complex. Based on these observations, we firmly believe that the alkaloid toddanol could aid in efficient HIV-1 drug discovery. In the present study, the molecular docking and MD simulations are performed to explore the possible binding mode of HIV 1 RT with 12 alkaloids of T. asiatica. Molecular docking by AutoDock4 revealed three alkaloids toddanol, toddanone, and toddalenone with highest binding affinity towards HIV 1 RT. The drug likeness model score revealed a positive score for toddanol and toddanone which is comparable to the drug likeness score of the standard anti HIV drug zidovudine. Results from simulation analysis revealed that toddanol RT complex is more stable than toddanone RT complex inferring toddanol as a potential anti HIV drug molecule. Abbreviations used: HIV: Human immunodeficiency virus, HIV 1 RT: HIV 1 reverse transcriptase, RNase H: Ribonuclease H, MD: Molecular dynamics, PDB: Protein databank, RMSD: Root mean square deviation, RMSF: Root mean square fluctuation.

Performance of MDockPP in CAPRI rounds 28-29 and 31-35 including the prediction of water-mediated interactions.

PubMed

Xu, Xianjin; Qiu, Liming; Yan, Chengfei; Ma, Zhiwei; Grinter, Sam Z; Zou, Xiaoqin

2017-03-01

Protein-protein interactions are either through direct contacts between two binding partners or mediated by structural waters. Both direct contacts and water-mediated interactions are crucial to the formation of a protein-protein complex. During the recent CAPRI rounds, a novel parallel searching strategy for predicting water-mediated interactions is introduced into our protein-protein docking method, MDockPP. Briefly, a FFT-based docking algorithm is employed in generating putative binding modes, and an iteratively derived statistical potential-based scoring function, ITScorePP, in conjunction with biological information is used to assess and rank the binding modes. Up to 10 binding modes are selected as the initial protein-protein complex structures for MD simulations in explicit solvent. Water molecules near the interface are clustered based on the snapshots extracted from independent equilibrated trajectories. Then, protein-ligand docking is employed for a parallel search for water molecules near the protein-protein interface. The water molecules generated by ligand docking and the clustered water molecules generated by MD simulations are merged, referred to as the predicted structural water molecules. Here, we report the performance of this protocol for CAPRI rounds 28-29 and 31-35 containing 20 valid docking targets and 11 scoring targets. In the docking experiments, we predicted correct binding modes for nine targets, including one high-accuracy, two medium-accuracy, and six acceptable predictions. Regarding the two targets for the prediction of water-mediated interactions, we achieved models ranked as "excellent" in accordance with the CAPRI evaluation criteria; one of these two targets is considered as a difficult target for structural water prediction. Proteins 2017; 85:424-434. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Computer-aided molecular modeling techniques for predicting the stability of drug cyclodextrin inclusion complexes in aqueous solutions

NASA Astrophysics Data System (ADS)

Faucci, Maria Teresa; Melani, Fabrizio; Mura, Paola

2002-06-01

Molecular modeling was used to investigate factors influencing complex formation between cyclodextrins and guest molecules and predict their stability through a theoretical model based on the search for a correlation between experimental stability constants ( Ks) and some theoretical parameters describing complexation (docking energy, host-guest contact surfaces, intermolecular interaction fields) calculated from complex structures at a minimum conformational energy, obtained through stochastic methods based on molecular dynamic simulations. Naproxen, ibuprofen, ketoprofen and ibuproxam were used as model drug molecules. Multiple Regression Analysis allowed identification of the significant factors for the complex stability. A mathematical model ( r=0.897) related log Ks with complex docking energy and lipophilic molecular fields of cyclodextrin and drug.

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

PubMed Central

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

2014-01-01

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

IFACEwat: the interfacial water-implemented re-ranking algorithm to improve the discrimination of near native structures for protein rigid docking.

PubMed

Su, Chinh; Nguyen, Thuy-Diem; Zheng, Jie; Kwoh, Chee-Keong

2014-01-01

Protein-protein docking is an in silico method to predict the formation of protein complexes. Due to limited computational resources, the protein-protein docking approach has been developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations and water contribution is ignored or implicitly presented. Despite obtaining a number of acceptable complex predictions, it seems to-date that most initial rigid docking algorithms still find it difficult or even fail to discriminate successfully the correct predictions from the other incorrect or false positive ones. To improve the rigid docking results, re-ranking is one of the effective methods that help re-locate the correct predictions in top high ranks, discriminating them from the other incorrect ones. Our results showed that the IFACEwat increased both the numbers of the near-native structures and improved their ranks as compared to the initial rigid docking ZDOCK3.0.2. In fact, the IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method F2Dock, the IFACEwat performed equivalently well or even better for several Antigen/Antibody complexes. With the inclusion of interfacial water, the IFACEwat improves mostly results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly taking into account the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near native structures found. As our implementation so far targeted to improve the results of ZDOCK3.0.2, and particularly for the Antigen/Antibody complexes, it is expected in the near future that more implementations will be conducted to be applicable for other initial rigid docking algorithms.

istar: a web platform for large-scale protein-ligand docking.

PubMed

Li, Hongjian; Leung, Kwong-Sak; Ballester, Pedro J; Wong, Man-Hon

2014-01-01

Protein-ligand docking is a key computational method in the design of starting points for the drug discovery process. We are motivated by the desire to automate large-scale docking using our popular docking engine idock and thus have developed a publicly-accessible web platform called istar. Without tedious software installation, users can submit jobs using our website. Our istar website supports 1) filtering ligands by desired molecular properties and previewing the number of ligands to dock, 2) monitoring job progress in real time, and 3) visualizing ligand conformations and outputting free energy and ligand efficiency predicted by idock, binding affinity predicted by RF-Score, putative hydrogen bonds, and supplier information for easy purchase, three useful features commonly lacked on other online docking platforms like DOCK Blaster or iScreen. We have collected 17,224,424 ligands from the All Clean subset of the ZINC database, and revamped our docking engine idock to version 2.0, further improving docking speed and accuracy, and integrating RF-Score as an alternative rescoring function. To compare idock 2.0 with the state-of-the-art AutoDock Vina 1.1.2, we have carried out a rescoring benchmark and a redocking benchmark on the 2,897 and 343 protein-ligand complexes of PDBbind v2012 refined set and CSAR NRC HiQ Set 24Sept2010 respectively, and an execution time benchmark on 12 diverse proteins and 3,000 ligands of different molecular weight. Results show that, under various scenarios, idock achieves comparable success rates while outperforming AutoDock Vina in terms of docking speed by at least 8.69 times and at most 37.51 times. When evaluated on the PDBbind v2012 core set, our istar platform combining with RF-Score manages to reproduce Pearson's correlation coefficient and Spearman's correlation coefficient of as high as 0.855 and 0.859 respectively between the experimental binding affinity and the predicted binding affinity of the docked conformation. istar is freely available at http://istar.cse.cuhk.edu.hk/idock.

Protein tyrosine phosphatases: Ligand interaction analysis and optimisation of virtual screening.

PubMed

Ghattas, Mohammad A; Atatreh, Noor; Bichenkova, Elena V; Bryce, Richard A

2014-07-01

Docking-based virtual screening is an established component of structure-based drug discovery. Nevertheless, scoring and ranking of computationally docked ligand libraries still suffer from many false positives. Identifying optimal docking parameters for a target protein prior to virtual screening can improve experimental hit rates. Here, we examine protocols for virtual screening against the important but challenging class of drug target, protein tyrosine phosphatases. In this study, common interaction features were identified from analysis of protein-ligand binding geometries of more than 50 complexed phosphatase crystal structures. It was found that two interactions were consistently formed across all phosphatase inhibitors: (1) a polar contact with the conserved arginine residue, and (2) at least one interaction with the P-loop backbone amide. In order to investigate the significance of these features on phosphatase-ligand binding, a series of seeded virtual screening experiments were conducted on three phosphatase enzymes, PTP1B, Cdc25b and IF2. It was observed that when the conserved arginine and P-loop amide interactions were used as pharmacophoric constraints during docking, enrichment of the virtual screen significantly increased in the three studied phosphatases, by up to a factor of two in some cases. Additionally, the use of such pharmacophoric constraints considerably improved the ability of docking to predict the inhibitor's bound pose, decreasing RMSD to the crystallographic geometry by 43% on average. Constrained docking improved enrichment of screens against both open and closed conformations of PTP1B. Incorporation of an ordered water molecule in PTP1B screening was also found to generally improve enrichment. The knowledge-based computational strategies explored here can potentially inform structure-based design of new phosphatase inhibitors using docking-based virtual screening. Copyright © 2014 Elsevier Inc. All rights reserved.

Structure-based design and evaluation of novel N-phenyl-1H-indol-2-amine derivatives for fat mass and obesity-associated (FTO) protein inhibition.

PubMed

Padariya, Monikaben; Kalathiya, Umesh

2016-10-01

Fat mass and obesity-associated (FTO) protein contributes to non-syndromic human obesity which refers to excessive fat accumulation in human body and results in health risk. FTO protein has become a promising target for anti-obesity medicines as there is an immense need for the rational design of potent inhibitors to treat obesity. In our study, a new scaffold N-phenyl-1H-indol-2-amine was selected as a base for FTO protein inhibitors by applying scaffold hopping approach. Using this novel scaffold, different derivatives were designed by extending scaffold structure with potential functional groups. Molecular docking simulations were carried out by using two different docking algorithm implemented in CDOCKER (flexible docking) and AutoDock programs (rigid docking). Analyzing results of rigid and flexible docking, compound MU06 was selected based on different properties and predicted binding affinities for further analysis. Molecular dynamics simulation of FTO/MU06 complex was performed to characterize structure rationale and binding stability. Certainly, Arg96 and His231 residue of FTO protein showed stable interaction with inhibitor MU06 throughout the production dynamics phase. Three residues of FTO protein (Arg96, Asp233, and His231) were found common in making H-bond interactions with MU06 during molecular dynamics simulation and CDOCKER docking. Copyright © 2016 Elsevier Ltd. All rights reserved.

PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking.

PubMed

Ng, Marcus C K; Fong, Simon; Siu, Shirley W I

2015-06-01

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Structural insights into transient receptor potential vanilloid type 1 (TRPV1) from homology modeling, flexible docking, and mutational studies.

PubMed

Lee, Jin Hee; Lee, Yoonji; Ryu, HyungChul; Kang, Dong Wook; Lee, Jeewoo; Lazar, Jozsef; Pearce, Larry V; Pavlyukovets, Vladimir A; Blumberg, Peter M; Choi, Sun

2011-04-01

The transient receptor potential vanilloid subtype 1 (TRPV1) is a non-selective cation channel composed of four monomers with six transmembrane helices (TM1-TM6). TRPV1 is found in the central and peripheral nervous system, and it is an important therapeutic target for pain relief. We describe here the construction of a tetrameric homology model of rat TRPV1 (rTRPV1). We experimentally evaluated by mutational analysis the contribution of residues of rTRPV1 contributing to ligand binding by the prototypical TRPV1 agonists, capsaicin and resiniferatoxin (RTX). We then performed docking analysis using our homology model. The docking results with capsaicin and RTX showed that our homology model was reliable, affording good agreement with our mutation data. Additionally, the binding mode of a simplified RTX (sRTX) ligand as predicted by the modeling agreed well with those of capsaicin and RTX, accounting for the high binding affinity of the sRTX ligand for TRPV1. Through the homology modeling, docking and mutational studies, we obtained important insights into the ligand-receptor interactions at the molecular level which should prove of value in the design of novel TRPV1 ligands.

Structural insights into transient receptor potential vanilloid type 1 (TRPV1) from homology modeling, flexible docking, and mutational studies

NASA Astrophysics Data System (ADS)

Lee, Jin Hee; Lee, Yoonji; Ryu, HyungChul; Kang, Dong Wook; Lee, Jeewoo; Lazar, Jozsef; Pearce, Larry V.; Pavlyukovets, Vladimir A.; Blumberg, Peter M.; Choi, Sun

2011-04-01

The transient receptor potential vanilloid subtype 1 (TRPV1) is a non-selective cation channel composed of four monomers with six transmembrane helices (TM1-TM6). TRPV1 is found in the central and peripheral nervous system, and it is an important therapeutic target for pain relief. We describe here the construction of a tetrameric homology model of rat TRPV1 (rTRPV1). We experimentally evaluated by mutational analysis the contribution of residues of rTRPV1 contributing to ligand binding by the prototypical TRPV1 agonists, capsaicin and resiniferatoxin (RTX). We then performed docking analysis using our homology model. The docking results with capsaicin and RTX showed that our homology model was reliable, affording good agreement with our mutation data. Additionally, the binding mode of a simplified RTX (sRTX) ligand as predicted by the modeling agreed well with those of capsaicin and RTX, accounting for the high binding affinity of the sRTX ligand for TRPV1. Through the homology modeling, docking and mutational studies, we obtained important insights into the ligand-receptor interactions at the molecular level which should prove of value in the design of novel TRPV1 ligands.

Combined 3D-QSAR Modeling and Molecular Docking Studies on Pyrrole-Indolin-2-ones as Aurora A Kinase Inhibitors

PubMed Central

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2011-01-01

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r2cv values of 0.726 and 0.566, and r2 values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed. PMID:21673910

Combined 3D-QSAR modeling and molecular docking studies on pyrrole-indolin-2-ones as Aurora A kinase inhibitors.

PubMed

Ai, Yong; Wang, Shao-Teng; Sun, Ping-Hua; Song, Fa-Jun

2011-01-01

Aurora kinases have emerged as attractive targets for the design of anticancer drugs. 3D-QSAR (comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA)) and Surflex-docking studies were performed on a series of pyrrole-indoline-2-ones as Aurora A inhibitors. The CoMFA and CoMSIA models using 25 inhibitors in the training set gave r(2) (cv) values of 0.726 and 0.566, and r(2) values of 0.972 and 0.984, respectively. The adapted alignment method with the suitable parameters resulted in reliable models. The contour maps produced by the CoMFA and CoMSIA models were employed to rationalize the key structural requirements responsible for the activity. Surflex-docking studies revealed that the sulfo group, secondary amine group on indolin-2-one, and carbonyl of 6,7-dihydro-1H-indol-4(5H)-one groups were significant for binding to the receptor, and some essential features were also identified. Based on the 3D-QSAR and docking results, a set of new molecules with high predicted activities were designed.

HSYMDOCK: a docking web server for predicting the structure of protein homo-oligomers with Cn or Dn symmetry.

PubMed

Yan, Yumeng; Tao, Huanyu; Huang, Sheng-You

2018-05-26

A major subclass of protein-protein interactions is formed by homo-oligomers with certain symmetry. Therefore, computational modeling of the symmetric protein complexes is important for understanding the molecular mechanism of related biological processes. Although several symmetric docking algorithms have been developed for Cn symmetry, few docking servers have been proposed for Dn symmetry. Here, we present HSYMDOCK, a web server of our hierarchical symmetric docking algorithm that supports both Cn and Dn symmetry. The HSYMDOCK server was extensively evaluated on three benchmarks of symmetric protein complexes, including the 20 CASP11-CAPRI30 homo-oligomer targets, the symmetric docking benchmark of 213 Cn targets and 35 Dn targets, and a nonredundant test set of 55 transmembrane proteins. It was shown that HSYMDOCK obtained a significantly better performance than other similar docking algorithms. The server supports both sequence and structure inputs for the monomer/subunit. Users have an option to provide the symmetry type of the complex, or the server can predict the symmetry type automatically. The docking process is fast and on average consumes 10∼20 min for a docking job. The HSYMDOCK web server is available at http://huanglab.phys.hust.edu.cn/hsymdock/.

GRID and docking analyses reveal a molecular basis for flavonoid inhibition of Src family kinase activity.

PubMed

Wright, Bernice; Watson, Kimberly A; McGuffin, Liam J; Lovegrove, Julie A; Gibbins, Jonathan M

2015-11-01

Flavonoids reduce cardiovascular disease risk through anti-inflammatory, anti-coagulant and anti-platelet actions. One key flavonoid inhibitory mechanism is blocking kinase activity that drives these processes. Flavonoids attenuate activities of kinases including phosphoinositide-3-kinase, Fyn, Lyn, Src, Syk, PKC, PIM1/2, ERK, JNK and PKA. X-ray crystallographic analyses of kinase-flavonoid complexes show that flavonoid ring systems and their hydroxyl substitutions are important structural features for their binding to kinases. A clearer understanding of structural interactions of flavonoids with kinases is necessary to allow construction of more potent and selective counterparts. We examined flavonoid (quercetin, apigenin and catechin) interactions with Src family kinases (Lyn, Fyn and Hck) applying the Sybyl docking algorithm and GRID. A homology model (Lyn) was used in our analyses to demonstrate that high-quality predicted kinase structures are suitable for flavonoid computational studies. Our docking results revealed potential hydrogen bond contacts between flavonoid hydroxyls and kinase catalytic site residues. Identification of plausible contacts indicated that quercetin formed the most energetically stable interactions, apigenin lacked hydroxyl groups necessary for important contacts and the non-planar structure of catechin could not support predicted hydrogen bonding patterns. GRID analysis using a hydroxyl functional group supported docking results. Based on these findings, we predicted that quercetin would inhibit activities of Src family kinases with greater potency than apigenin and catechin. We validated this prediction using in vitro kinase assays. We conclude that our study can be used as a basis to construct virtual flavonoid interaction libraries to guide drug discovery using these compounds as molecular templates. Crown Copyright © 2015. Published by Elsevier Inc. All rights reserved.

In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors.

PubMed

Nongonierma, Alice B; Mooney, Catherine; Shields, Denis C; FitzGerald, Richard J

2014-07-01

Molecular docking of a library of all 8000 possible tripeptides to the active site of DPP-IV was used to determine their binding potential. A number of tripeptides were selected for experimental testing, however, there was no direct correlation between the Vina score and their in vitro DPP-IV inhibitory properties. While Trp-Trp-Trp, the peptide with the best docking score, was a moderate DPP-IV inhibitor (IC50 216μM), Lineweaver and Burk analysis revealed its action to be non-competitive. This suggested that it may not bind to the active site of DPP-IV as assumed in the docking prediction. Furthermore, there was no significant link between DPP-IV inhibition and the physicochemical properties of the peptides (molecular mass, hydrophobicity, hydrophobic moment (μH), isoelectric point (pI) and charge). LIGPLOTs indicated that competitive inhibitory peptides were predicted to have both hydrophobic and hydrogen bond interactions with the active site of DPP-IV. DPP-IV inhibitory peptides generally had a hydrophobic or aromatic amino acid at the N-terminus, preferentially a Trp for non-competitive inhibitors and a broader range of residues for competitive inhibitors (Ile, Leu, Val, Phe, Trp or Tyr). Two of the potent DPP-IV inhibitors, Ile-Pro-Ile and Trp-Pro (IC50 values of 3.5 and 44.2μM, respectively), were predicted to be gastrointestinally/intestinally stable. This work highlights the needs to test the assumptions (i.e. competitive binding) of any integrated strategy of computational and experimental screening, in optimizing screening. Future strategies targeting allosteric mechanisms may need to rely more on structure-activity relationship modeling, rather than on docking, in computationally selecting peptides for screening. Copyright © 2014 Elsevier Inc. All rights reserved.

Novel pyrrolopyridinone derivatives as anticancer inhibitors towards Cdc7: QSAR studies based on dockings by solvation score approach.

PubMed

Wu, Xiangxiang; Zeng, Huahui; Zhu, Xin; Ma, Qiujuan; Hou, Yimin; Wu, Xuefen

2013-11-20

A series of pyrrolopyridinone derivatives as specific inhibitors towards the cell division cycle 7 (Cdc7) was taken into account, and the efficacy of these compounds was analyzed by QSAR and docking approaches to gain deeper insights into the interaction mechanism and ligands selectivity for Cdc7. By regression analysis the prediction models based on Grid score and Zou-GB/SA score were found, respectively with good quality of fits (r(2)=0.748, 0.951; r(cv)(2)=0.712, 0.839). The accuracy of the models was validated by test set and the deviation of the predicted values in validation set using Zou-GB/SA score was smaller than that using Grid score, suggesting that the model based on Zou-GB/SA score provides a more effective method for predicting potencies of Cdc7 inhibitors. Copyright © 2013 Elsevier B.V. All rights reserved.

Virtual screening of ABCC1 transporter nucleotidebinding domains as a therapeutic target in multidrug resistant cancer

PubMed Central

Rungsardthong, Kanin; Mares- Sámano, Sergio; Penny, Jeffrey

2012-01-01

ABCC1 is a member of the ATP-binding Cassette super family of transporters, actively effluxes xenobiotics from cells. Clinically, ABCC1 expression is linked to cancer multidrug resistance. Substrate efflux is energised by ATP binding and hydrolysis at the nucleotide-binding domains (NBDs) and inhibition of these events may help combat drug resistance. The aim of this study is to identify potential inhibitors of ABCC1 through virtual screening of National Cancer Institute (NCI) compounds. A threedimensional model of ABCC1 NBD2 was generated using MODELLER whilst the X-ray crystal structure of ABCC1 NBD1 was retrieved from the Protein Data Bank. A pharmacophore hypothesis was generated based on flavonoids known to bind at the NBDs using PHASE, and used to screen the NCI database. GLIDE was employed in molecular docking studies for all hit compounds identified by pharmacophore screening. The best potential inhibitors were identified as compounds possessing predicted binding affinities greater than ATP. Approximately 5% (13/265) of the hit compounds possessed lower docking scores than ATP in ABCC1 NBD1 (NSC93033, NSC662377, NSC319661, NSC333748, NSC683893, NSC226639, NSC94231, NSC55979, NSC169121, NSC166574, NSC73380, NSC127738, NSC115534), whereas approximately 7% (7/104) of docked NCI compounds were predicted to possess lower docking scores than ATP in ABCC1 NBD2 (NSC91789, NSC529483, NSC211168, NSC318214, NSC116519, NSC372332, NSC526974). Analyses of docking orientations revealed P-loop residues of each NBD and the aromatic amino acids Trp653 (NBD1) and Tyr1302 (NBD2) were key in interacting with high-affinity compounds. On the basis of docked orientation and docking score the compounds identified may be potential inhibitors of ABCC1 and require further pharmacological analysis. Abbreviations ABC - ATP-binding cassette, DHS - dehydrosilybin, MDR - multidrug resistance, NBD - nucleotide-binding domain, PDB - protein data bank. PMID:23144549

Sulfonamide-containing PTP 1B inhibitors: Docking studies, synthesis and model validation

NASA Astrophysics Data System (ADS)

Niu, Enli; Gan, Qiang; Chen, Xi; Feng, Changgen

2017-01-01

PTP 1B plays an important role in regulating insulin signaling pathway and is regarded as a valid target for curing diabetes and obesity. In this paper, two novel sulfonamide-containing PTP 1B inhibitors were designed, synthesized in mild condition, and characterized by FT-IR, 1H NMR, 13C NMR and elemental analysis. The single crystal of compounds 7 and 8 were obtained and their structures were determined by X-ray single crystal diffraction analysis. In addition, their inhibitory activity were predicted by genetic algorithm, and carried on in vitro enzyme activity test. Of which compound 8 showed good inhibitory activity, in consistent with docking studies.

POSE Algorithms for Automated Docking

NASA Technical Reports Server (NTRS)

Heaton, Andrew F.; Howard, Richard T.

2011-01-01

POSE (relative position and attitude) can be computed in many different ways. Given a sensor that measures bearing to a finite number of spots corresponding to known features (such as a target) of a spacecraft, a number of different algorithms can be used to compute the POSE. NASA has sponsored the development of a flash LIDAR proximity sensor called the Vision Navigation Sensor (VNS) for use by the Orion capsule in future docking missions. This sensor generates data that can be used by a variety of algorithms to compute POSE solutions inside of 15 meters, including at the critical docking range of approximately 1-2 meters. Previously NASA participated in a DARPA program called Orbital Express that achieved the first automated docking for the American space program. During this mission a large set of high quality mated sensor data was obtained at what is essentially the docking distance. This data set is perhaps the most accurate truth data in existence for docking proximity sensors in orbit. In this paper, the flight data from Orbital Express is used to test POSE algorithms at 1.22 meters range. Two different POSE algorithms are tested for two different Fields-of-View (FOVs) and two different pixel noise levels. The results of the analysis are used to predict future performance of the POSE algorithms with VNS data.

ProSelection: A Novel Algorithm to Select Proper Protein Structure Subsets for in Silico Target Identification and Drug Discovery Research.

PubMed

Wang, Nanyi; Wang, Lirong; Xie, Xiang-Qun

2017-11-27

Molecular docking is widely applied to computer-aided drug design and has become relatively mature in the recent decades. Application of docking in modeling varies from single lead compound optimization to large-scale virtual screening. The performance of molecular docking is highly dependent on the protein structures selected. It is especially challenging for large-scale target prediction research when multiple structures are available for a single target. Therefore, we have established ProSelection, a docking preferred-protein selection algorithm, in order to generate the proper structure subset(s). By the ProSelection algorithm, protein structures of "weak selectors" are filtered out whereas structures of "strong selectors" are kept. Specifically, the structure which has a good statistical performance of distinguishing active ligands from inactive ligands is defined as a strong selector. In this study, 249 protein structures of 14 autophagy-related targets are investigated. Surflex-dock was used as the docking engine to distinguish active and inactive compounds against these protein structures. Both t test and Mann-Whitney U test were used to distinguish the strong from the weak selectors based on the normality of the docking score distribution. The suggested docking score threshold for active ligands (SDA) was generated for each strong selector structure according to the receiver operating characteristic (ROC) curve. The performance of ProSelection was further validated by predicting the potential off-targets of 43 U.S. Federal Drug Administration approved small molecule antineoplastic drugs. Overall, ProSelection will accelerate the computational work in protein structure selection and could be a useful tool for molecular docking, target prediction, and protein-chemical database establishment research.

Molecular docking.

PubMed

Morris, Garrett M; Lim-Wilby, Marguerita

2008-01-01

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analyzing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software.

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

NASA Astrophysics Data System (ADS)

Baumgartner, Matthew P.; Evans, David A.

2018-01-01

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

Modeling and docking antibody structures with Rosetta

PubMed Central

Weitzner, Brian D.; Jeliazkov, Jeliazko R.; Lyskov, Sergey; Marze, Nicholas; Kuroda, Daisuke; Frick, Rahel; Adolf-Bryfogle, Jared; Biswas, Naireeta; Dunbrack, Roland L.; Gray, Jeffrey J.

2017-01-01

We describe Rosetta-based computational protocols for predicting the three-dimensional structure of an antibody from sequence (RosettaAntibody) and then docking the antibody to protein antigens (SnugDock). Antibody modeling leverages canonical loop conformations to graft large segments from experimentally-determined structures as well as (1) energetic calculations to minimize loops, (2) docking methodology to refine the VL–VH relative orientation, and (3) de novo prediction of the elusive complementarity determining region (CDR) H3 loop. To alleviate model uncertainty, antibody–antigen docking resamples CDR loop conformations and can use multiple models to represent an ensemble of conformations for the antibody, the antigen or both. These protocols can be run fully-automated via the ROSIE web server (http://rosie.rosettacommons.org/) or manually on a computer with user control of individual steps. For best results, the protocol requires roughly 1,000 CPU-hours for antibody modeling and 250 CPU-hours for antibody–antigen docking. Tasks can be completed in under a day by using public supercomputers. PMID:28125104

Molecular docking studies to map the binding site of squalene synthase inhibitors on dehydrosqualene synthase of Staphylococcus aureus.

PubMed

Kahlon, Amandeep Kaur; Roy, Sudeep; Sharma, Ashok

2010-10-01

Dehydrosqualene synthase of Staphylococcus aureus is involved in the synthesis of golden carotenoid pigment staphyloxanthin. This pigment of S. aureus provides the antioxidant property to this bacterium to survive inside the host cell. Dehydrosqualene synthase (CrtM) is having structural similarity with the human squalene synthase enzyme which is involved in the cholesterol synthesis pathway in humans (Liu et al., 2008). Cholesterol lowering drugs were found to have inhibitory effect on dehydrosqualene synthase enzyme of S. aureus. The present study attempts to focus on squalene synthase inhibitors, lapaquistat acetate and squalestatins reported as cholesterol lowering agents in vitro and in vivo but not studied in context to dehydrosqualene synthase of S. aureus. Mode of binding of lapaquistat acetate and squalestatin analogs on dehydrosqualene synthase (CrtM) enzyme of S. aureus was identified by performing docking analysis with Scigress Explorer Ultra 7.7 docking software. Based on the molecular docking analysis, it was found that the His18, Arg45, Asp48, Asp52, Tyr129, Gln165, Asn168 and Asp172 residues interacted with comparatively high frequency with the inhibitors studied. Comparative docking study with Discovery studio 2.0 also confirmed the involvement of these residues of dehydrosqualene synthase enzyme with the inhibitors studied. This further confirms the importance of these residues in the enzyme function. In silico ADMET analysis was done to predict the ADMET properties of the standard drugs and test compounds. This might provide insights to develop new drugs to target the virulence factor, dehydrosqualene synthase of S. aureus.

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

PubMed

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

2016-10-01

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

Tertiary structure-based analysis of microRNA–target interactions

PubMed Central

Gan, Hin Hark; Gunsalus, Kristin C.

2013-01-01

Current computational analysis of microRNA interactions is based largely on primary and secondary structure analysis. Computationally efficient tertiary structure-based methods are needed to enable more realistic modeling of the molecular interactions underlying miRNA-mediated translational repression. We incorporate algorithms for predicting duplex RNA structures, ionic strength effects, duplex entropy and free energy, and docking of duplex–Argonaute protein complexes into a pipeline to model and predict miRNA–target duplex binding energies. To ensure modeling accuracy and computational efficiency, we use an all-atom description of RNA and a continuum description of ionic interactions using the Poisson–Boltzmann equation. Our method predicts the conformations of two constructs of Caenorhabditis elegans let-7 miRNA–target duplexes to an accuracy of ∼3.8 Å root mean square distance of their NMR structures. We also show that the computed duplex formation enthalpies, entropies, and free energies for eight miRNA–target duplexes agree with titration calorimetry data. Analysis of duplex–Argonaute docking shows that structural distortions arising from single-base-pair mismatches in the seed region influence the activity of the complex by destabilizing both duplex hybridization and its association with Argonaute. Collectively, these results demonstrate that tertiary structure-based modeling of miRNA interactions can reveal structural mechanisms not accessible with current secondary structure-based methods. PMID:23417009

DOCKTITE-a highly versatile step-by-step workflow for covalent docking and virtual screening in the molecular operating environment.

PubMed

Scholz, Christoph; Knorr, Sabine; Hamacher, Kay; Schmidt, Boris

2015-02-23

The formation of a covalent bond with the target is essential for a number of successful drugs, yet tools for covalent docking without significant restrictions regarding warhead or receptor classes are rare and limited in use. In this work we present DOCKTITE, a highly versatile workflow for covalent docking in the Molecular Operating Environment (MOE) combining automated warhead screening, nucleophilic side chain attachment, pharmacophore-based docking, and a novel consensus scoring approach. The comprehensive validation study includes pose predictions of 35 protein/ligand complexes which resulted in a mean RMSD of 1.74 Å and a prediction rate of 71.4% with an RMSD below 2 Å, a virtual screening with an area under the curve (AUC) for the receiver operating characteristics (ROC) of 0.81, and a significant correlation between predicted and experimental binding affinities (ρ = 0.806, R(2) = 0.649, p < 0.005).

Predicting protein interactions by Brownian dynamics simulations.

PubMed

Meng, Xuan-Yu; Xu, Yu; Zhang, Hong-Xing; Mezei, Mihaly; Cui, Meng

2012-01-01

We present a newly adapted Brownian-Dynamics (BD)-based protein docking method for predicting native protein complexes. The approach includes global BD conformational sampling, compact complex selection, and local energy minimization. In order to reduce the computational costs for energy evaluations, a shell-based grid force field was developed to represent the receptor protein and solvation effects. The performance of this BD protein docking approach has been evaluated on a test set of 24 crystal protein complexes. Reproduction of experimental structures in the test set indicates the adequate conformational sampling and accurate scoring of this BD protein docking approach. Furthermore, we have developed an approach to account for the flexibility of proteins, which has been successfully applied to reproduce the experimental complex structure from the structure of two unbounded proteins. These results indicate that this adapted BD protein docking approach can be useful for the prediction of protein-protein interactions.

A combined pharmacophore modeling, 3D-QSAR and molecular docking study of substituted bicyclo-[3.3.0]oct-2-enes as liver receptor homolog-1 (LRH-1) agonists

NASA Astrophysics Data System (ADS)

Lalit, Manisha; Gangwal, Rahul P.; Dhoke, Gaurao V.; Damre, Mangesh V.; Khandelwal, Kanchan; Sangamwar, Abhay T.

2013-10-01

A combined pharmacophore modelling, 3D-QSAR and molecular docking approach was employed to reveal structural and chemical features essential for the development of small molecules as LRH-1 agonists. The best HypoGen pharmacophore hypothesis (Hypo1) consists of one hydrogen-bond donor (HBD), two general hydrophobic (H), one hydrophobic aromatic (HYAr) and one hydrophobic aliphatic (HYA) feature. It has exhibited high correlation coefficient of 0.927, cost difference of 85.178 bit and low RMS value of 1.411. This pharmacophore hypothesis was cross-validated using test set, decoy set and Cat-Scramble methodology. Subsequently, validated pharmacophore hypothesis was used in the screening of small chemical databases. Further, 3D-QSAR models were developed based on the alignment obtained using substructure alignment. The best CoMFA and CoMSIA model has exhibited excellent rncv2 values of 0.991 and 0.987, and rcv2 values of 0.767 and 0.703, respectively. CoMFA predicted rpred2 of 0.87 and CoMSIA predicted rpred2 of 0.78 showed that the predicted values were in good agreement with the experimental values. Molecular docking analysis reveals that π-π interaction with His390 and hydrogen bond interaction with His390/Arg393 is essential for LRH-1 agonistic activity. The results from pharmacophore modelling, 3D-QSAR and molecular docking are complementary to each other and could serve as a powerful tool for the discovery of potent small molecules as LRH-1 agonists.

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

PubMed

Bohari, Mohammed H; Sastry, G Narahari

2012-09-01

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Improved performance in CAPRI round 37 using LZerD docking and template-based modeling with combined scoring functions.

PubMed

Peterson, Lenna X; Shin, Woong-Hee; Kim, Hyungrae; Kihara, Daisuke

2018-03-01

We report our group's performance for protein-protein complex structure prediction and scoring in Round 37 of the Critical Assessment of PRediction of Interactions (CAPRI), an objective assessment of protein-protein complex modeling. We demonstrated noticeable improvement in both prediction and scoring compared to previous rounds of CAPRI, with our human predictor group near the top of the rankings and our server scorer group at the top. This is the first time in CAPRI that a server has been the top scorer group. To predict protein-protein complex structures, we used both multi-chain template-based modeling (TBM) and our protein-protein docking program, LZerD. LZerD represents protein surfaces using 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. Because 3DZD are a soft representation of the protein surface, LZerD is tolerant to small conformational changes, making it well suited to docking unbound and TBM structures. The key to our improved performance in CAPRI Round 37 was to combine multi-chain TBM and docking. As opposed to our previous strategy of performing docking for all target complexes, we used TBM when multi-chain templates were available and docking otherwise. We also describe the combination of multiple scoring functions used by our server scorer group, which achieved the top rank for the scorer phase. © 2017 Wiley Periodicals, Inc.

Combined HQSAR, topomer CoMFA, homology modeling and docking studies on triazole derivatives as SGLT2 inhibitors.

PubMed

Yu, Shuling; Yuan, Jintao; Zhang, Yi; Gao, Shufang; Gan, Ying; Han, Meng; Chen, Yuewen; Zhou, Qiaoqiao; Shi, Jiahua

2017-06-01

Sodium-glucose cotransporter 2 (SGLT2) is a promising target for diabetes therapy. We aimed to develop computational approaches to identify structural features for more potential SGLT2 inhibitors. In this work, 46 triazole derivatives as SGLT2 inhibitors were studied using a combination of several approaches, including hologram quantitative structure-activity relationships (HQSAR), topomer comparative molecular field analysis (CoMFA), homology modeling, and molecular docking. HQSAR and topomer CoMFA were used to construct models. Molecular docking was conducted to investigate the interaction of triazole derivatives and homology modeling of SGLT2, as well as to validate the results of the HQSAR and topomer CoMFA models. The most effective HQSAR and topomer CoMFA models exhibited noncross-validated correlation coefficients of 0.928 and 0.891 for the training set, respectively. External predictions were made successfully on a test set and then compared with previously reported models. The graphical results of HQSAR and topomer CoMFA were proven to be consistent with the binding mode of the inhibitors and SGLT2 from molecular docking. The models and docking provided important insights into the design of potent inhibitors for SGLT2.

Computational Exploration of a Protein Receptor Binding Space with Student Proposed Peptide Ligands

PubMed Central

King, Matthew D.; Phillips, Paul; Turner, Matthew W.; Katz, Michael; Lew, Sarah; Bradburn, Sarah; Andersen, Tim; Mcdougal, Owen M.

2017-01-01

Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results. PMID:26537635

Predicting the Accuracy of Protein–Ligand Docking on Homology Models

PubMed Central

BORDOGNA, ANNALISA; PANDINI, ALESSANDRO; BONATI, LAURA

2011-01-01

Ligand–protein docking is increasingly used in Drug Discovery. The initial limitations imposed by a reduced availability of target protein structures have been overcome by the use of theoretical models, especially those derived by homology modeling techniques. While this greatly extended the use of docking simulations, it also introduced the need for general and robust criteria to estimate the reliability of docking results given the model quality. To this end, a large-scale experiment was performed on a diverse set including experimental structures and homology models for a group of representative ligand–protein complexes. A wide spectrum of model quality was sampled using templates at different evolutionary distances and different strategies for target–template alignment and modeling. The obtained models were scored by a selection of the most used model quality indices. The binding geometries were generated using AutoDock, one of the most common docking programs. An important result of this study is that indeed quantitative and robust correlations exist between the accuracy of docking results and the model quality, especially in the binding site. Moreover, state-of-the-art indices for model quality assessment are already an effective tool for an a priori prediction of the accuracy of docking experiments in the context of groups of proteins with conserved structural characteristics. PMID:20607693

Exploring the Stability of Ligand Binding Modes to Proteins by Molecular Dynamics Simulations: A Cross-docking Study.

PubMed

Liu, Kai; Kokubo, Hironori

2017-10-23

Docking has become an indispensable approach in drug discovery research to predict the binding mode of a ligand. One great challenge in docking is to efficiently refine the correct pose from various putative docking poses through scoring functions. We recently examined the stability of self-docking poses under molecular dynamics (MD) simulations and showed that equilibrium MD simulations have some capability to discriminate between correct and decoy poses. Here, we have extended our previous work to cross-docking studies for practical applications. Three target proteins (thrombin, heat shock protein 90-alpha, and cyclin-dependent kinase 2) of pharmaceutical interest were selected. Three comparable poses (one correct pose and two decoys) for each ligand were then selected from the docking poses. To obtain the docking poses for the three target proteins, we used three different protocols, namely: normal docking, induced fit docking (IFD), and IFD against the homology model. Finally, five parallel MD equilibrium runs were performed on each pose for the statistical analysis. The results showed that the correct poses were generally more stable than the decoy poses under MD. The discrimination capability of MD depends on the strategy. The safest way was to judge a pose as being stable if any one run among five parallel runs was stable under MD. In this case, 95% of the correct poses were retained under MD, and about 25-44% of the decoys could be excluded by the simulations for all cases. On the other hand, if we judge a pose as being stable when any two or three runs were stable, with the risk of incorrectly excluding some correct poses, approximately 31-53% or 39-56% of the two decoys could be excluded by MD, respectively. Our results suggest that simple equilibrium simulations can serve as an effective filter to exclude decoy poses that cannot be distinguished by docking scores from the computationally expensive free-energy calculations.

Multi-Layer Identification of Highly-Potent ABCA1 Up-Regulators Targeting LXRβ Using Multiple QSAR Modeling, Structural Similarity Analysis, and Molecular Docking.

PubMed

Chen, Meimei; Yang, Fafu; Kang, Jie; Yang, Xuemei; Lai, Xinmei; Gao, Yuxing

2016-11-29

In this study, in silico approaches, including multiple QSAR modeling, structural similarity analysis, and molecular docking, were applied to develop QSAR classification models as a fast screening tool for identifying highly-potent ABCA1 up-regulators targeting LXRβ based on a series of new flavonoids. Initially, four modeling approaches, including linear discriminant analysis, support vector machine, radial basis function neural network, and classification and regression trees, were applied to construct different QSAR classification models. The statistics results indicated that these four kinds of QSAR models were powerful tools for screening highly potent ABCA1 up-regulators. Then, a consensus QSAR model was developed by combining the predictions from these four models. To discover new ABCA1 up-regulators at maximum accuracy, the compounds in the ZINC database that fulfilled the requirement of structural similarity of 0.7 compared to known potent ABCA1 up-regulator were subjected to the consensus QSAR model, which led to the discovery of 50 compounds. Finally, they were docked into the LXRβ binding site to understand their role in up-regulating ABCA1 expression. The excellent binding modes and docking scores of 10 hit compounds suggested they were highly-potent ABCA1 up-regulators targeting LXRβ. Overall, this study provided an effective strategy to discover highly potent ABCA1 up-regulators.

IFACEwat: the interfacial water-implemented re-ranking algorithm to improve the discrimination of near native structures for protein rigid docking

PubMed Central

2014-01-01

Background Protein-protein docking is an in silico method to predict the formation of protein complexes. Due to limited computational resources, the protein-protein docking approach has been developed under the assumption of rigid docking, in which one of the two protein partners remains rigid during the protein associations and water contribution is ignored or implicitly presented. Despite obtaining a number of acceptable complex predictions, it seems to-date that most initial rigid docking algorithms still find it difficult or even fail to discriminate successfully the correct predictions from the other incorrect or false positive ones. To improve the rigid docking results, re-ranking is one of the effective methods that help re-locate the correct predictions in top high ranks, discriminating them from the other incorrect ones. In this paper, we propose a new re-ranking technique using a new energy-based scoring function, namely IFACEwat - a combined Interface Atomic Contact Energy (IFACE) and water effect. The IFACEwat aims to further improve the discrimination of the near-native structures of the initial rigid docking algorithm ZDOCK3.0.2. Unlike other re-ranking techniques, the IFACEwat explicitly implements interfacial water into the protein interfaces to account for the water-mediated contacts during the protein interactions. Results Our results showed that the IFACEwat increased both the numbers of the near-native structures and improved their ranks as compared to the initial rigid docking ZDOCK3.0.2. In fact, the IFACEwat achieved a success rate of 83.8% for Antigen/Antibody complexes, which is 10% better than ZDOCK3.0.2. As compared to another re-ranking technique ZRANK, the IFACEwat obtains success rates of 92.3% (8% better) and 90% (5% better) respectively for medium and difficult cases. When comparing with the latest published re-ranking method F2Dock, the IFACEwat performed equivalently well or even better for several Antigen/Antibody complexes. Conclusions With the inclusion of interfacial water, the IFACEwat improves mostly results of the initial rigid docking, especially for Antigen/Antibody complexes. The improvement is achieved by explicitly taking into account the contribution of water during the protein interactions, which was ignored or not fully presented by the initial rigid docking and other re-ranking techniques. In addition, the IFACEwat maintains sufficient computational efficiency of the initial docking algorithm, yet improves the ranks as well as the number of the near native structures found. As our implementation so far targeted to improve the results of ZDOCK3.0.2, and particularly for the Antigen/Antibody complexes, it is expected in the near future that more implementations will be conducted to be applicable for other initial rigid docking algorithms. PMID:25521441

High performance transcription factor-DNA docking with GPU computing

PubMed Central

2012-01-01

Background Protein-DNA docking is a very challenging problem in structural bioinformatics and has important implications in a number of applications, such as structure-based prediction of transcription factor binding sites and rational drug design. Protein-DNA docking is very computational demanding due to the high cost of energy calculation and the statistical nature of conformational sampling algorithms. More importantly, experiments show that the docking quality depends on the coverage of the conformational sampling space. It is therefore desirable to accelerate the computation of the docking algorithm, not only to reduce computing time, but also to improve docking quality. Methods In an attempt to accelerate the sampling process and to improve the docking performance, we developed a graphics processing unit (GPU)-based protein-DNA docking algorithm. The algorithm employs a potential-based energy function to describe the binding affinity of a protein-DNA pair, and integrates Monte-Carlo simulation and a simulated annealing method to search through the conformational space. Algorithmic techniques were developed to improve the computation efficiency and scalability on GPU-based high performance computing systems. Results The effectiveness of our approach is tested on a non-redundant set of 75 TF-DNA complexes and a newly developed TF-DNA docking benchmark. We demonstrated that the GPU-based docking algorithm can significantly accelerate the simulation process and thereby improving the chance of finding near-native TF-DNA complex structures. This study also suggests that further improvement in protein-DNA docking research would require efforts from two integral aspects: improvement in computation efficiency and energy function design. Conclusions We present a high performance computing approach for improving the prediction accuracy of protein-DNA docking. The GPU-based docking algorithm accelerates the search of the conformational space and thus increases the chance of finding more near-native structures. To the best of our knowledge, this is the first ad hoc effort of applying GPU or GPU clusters to the protein-DNA docking problem. PMID:22759575

Docking and scoring protein interactions: CAPRI 2009.

PubMed

Lensink, Marc F; Wodak, Shoshana J

2010-11-15

Protein docking algorithms are assessed by evaluating blind predictions performed during 2007-2009 in Rounds 13-19 of the community-wide experiment on critical assessment of predicted interactions (CAPRI). We evaluated the ability of these algorithms to sample docking poses and to single out specific association modes in 14 targets, representing 11 distinct protein complexes. These complexes play important biological roles in RNA maturation, G-protein signal processing, and enzyme inhibition and function. One target involved protein-RNA interactions not previously considered in CAPRI, several others were hetero-oligomers, or featured multiple interfaces between the same protein pair. For most targets, predictions started from the experimentally determined structures of the free (unbound) components, or from models built from known structures of related or similar proteins. To succeed they therefore needed to account for conformational changes and model inaccuracies. In total, 64 groups and 12 web-servers submitted docking predictions of which 4420 were evaluated. Overall our assessment reveals that 67% of the groups, more than ever before, produced acceptable models or better for at least one target, with many groups submitting multiple high- and medium-accuracy models for two to six targets. Forty-one groups including four web-servers participated in the scoring experiment with 1296 evaluated models. Scoring predictions also show signs of progress evidenced from the large proportion of correct models submitted. But singling out the best models remains a challenge, which also adversely affects the ability to correctly rank docking models. With the increased interest in translating abstract protein interaction networks into realistic models of protein assemblies, the growing CAPRI community is actively developing more efficient and reliable docking and scoring methods for everyone to use. © 2010 Wiley-Liss, Inc.

Molecular Docking and Molecular Dynamics to Identify a Novel Human Immunodeficiency Virus Inhibitor from Alkaloids of Toddalia asiatica

PubMed Central

Priya, R.; Sumitha, Rajendrarao; Doss, C. George Priya; Rajasekaran, C.; Babu, S.; Seenivasan, R.; Siva, R.

2015-01-01

Background: Acquired immunodeficiency syndrome caused by human immunodeficiency virus (HIV) is an immunosuppressive disease. Over the past decades, it has plagued human health due to the grave consequences in its harness. Objective: For this reason, anti-HIV agents are imperative, and the search for the same from natural resources would assure the safety. Materials and Methods: In this investigation we have performed molecular docking, molecular property prediction, drug-likeness score, and molecular dynamics (MD) simulation to develop a novel anti-HIV drug. We have screened 12 alkaloids from a medicinal plant Toddalia asiatica for its probabilistic binding with the active site of the HIV-1-reverse transcriptase (HIV-1-RT) domain (the major contributor to the onset of the disease). Results: The docking results were evaluated based on free energies of binding (ΔG), and the results suggested toddanol, toddanone, and toddalenone to be potent inhibitors of HIV-1-RT. In addition, the alkaloids were subjected to molecular property prediction analysis. Toddanol and toddanone with more rotatable bonds were found to have a drug-likeness score of 0.23 and 0.11, respectively. These scores were comparable with the standard anti-HIV drug zidovudine with a model score 0.28. Finally, two characteristic protein-ligand complexes were exposed to MD simulation to determine the stability of the predicted conformations. Conclusion: The toddanol-RT complex showed higher stability and stronger H-bonds than toddanone-RT complex. Based on these observations, we firmly believe that the alkaloid toddanol could aid in efficient HIV-1 drug discovery. SUMMARY In the present study, the molecular docking and MD simulations are performed to explore the possible binding mode of HIV 1 RT with 12 alkaloids of T. asiatica. Molecular docking by AutoDock4 revealed three alkaloids toddanol, toddanone, and toddalenone with highest binding affinity towards HIV 1 RT. The drug likeness model score revealed a positive score for toddanol and toddanone which is comparable to the drug likeness score of the standard anti HIV drug zidovudine. Results from simulation analysis revealed that toddanol RT complex is more stable than toddanone RT complex inferring toddanol as a potential anti HIV drug molecule. Abbreviations used: HIV: Human immunodeficiency virus, HIV 1 RT: HIV 1 reverse transcriptase, RNase H: Ribonuclease H, MD: Molecular dynamics, PDB: Protein databank, RMSD: Root mean square deviation, RMSF: Root mean square fluctuation. PMID:26929575

Docking and QSAR comparative studies of polycyclic aromatic hydrocarbons and other procarcinogen interactions with cytochromes P450 1A1 and 1B1.

PubMed

Gonzalez, J; Marchand-Geneste, N; Giraudel, J L; Shimada, T

2012-01-01

To obtain chemical clues on the process of bioactivation by cytochromes P450 1A1 and 1B1, some QSAR studies were carried out based on cellular experiments of the metabolic activation of polycyclic aromatic hydrocarbons and heterocyclic aromatic compounds by those enzymes. Firstly, the 3D structures of cytochromes 1A1 and 1B1 were built using homology modelling with a cytochrome 1A2 template. Using these structures, 32 ligands including heterocyclic aromatic compounds, polycyclic aromatic hydrocarbons and corresponding diols, were docked with LigandFit and CDOCKER algorithms. Binding mode analysis highlighted the importance of hydrophobic interactions and the hydrogen bonding network between cytochrome amino acids and docked molecules. Finally, for each enzyme, multilinear regression and artificial neural network QSAR models were developed and compared. These statistical models highlighted the importance of electronic, structural and energetic descriptors in metabolic activation process, and could be used for virtual screening of ligand databases. In the case of P450 1A1, the best model was obtained with artificial neural network analysis and gave an r (2) of 0.66 and an external prediction [Formula: see text] of 0.73. Concerning P450 1B1, artificial neural network analysis gave a much more robust model, associated with an r (2) value of 0.73 and an external prediction [Formula: see text] of 0.59.

Six degree of freedom FORTRAN program, ASTP docking dynamics, users guide

NASA Technical Reports Server (NTRS)

Mount, G. O., Jr.; Mikhalkin, B.

1974-01-01

The digital program ASTP Docking Dynamics as outlined is intended to aid the engineer using the program to determine the docking system loads and attendant vehicular motion resulting from docking two vehicles that have an androgynous, six-hydraulic-attenuator, guide ring, docking interface similar to that designed for the Apollo/Soyuz Test Project (ASTP). This program is set up to analyze two different vehicle combinations: the Apollo CSM docking to Soyuz and the shuttle orbiter docking to another orbiter. The subroutine modifies the vehicle control systems to describe one or the other vehicle combinations; the rest of the vehicle characteristics are changed by input data. To date, the program has been used to predict and correlate ASTP docking loads and performance with docking test program results from dynamic testing. The program modified for use on IBM 360 computers. Parts of the original docking system equations in the areas of hydraulic damping and capture latches are modified to better describe the detail design of the ASTP docking system.

Replica Exchange Improves Sampling in Low-Resolution Docking Stage of RosettaDock

PubMed Central

Zhang, Zhe; Lange, Oliver F.

2013-01-01

Many protein-protein docking protocols are based on a shotgun approach, in which thousands of independent random-start trajectories minimize the rigid-body degrees of freedom. Another strategy is enumerative sampling as used in ZDOCK. Here, we introduce an alternative strategy, ReplicaDock, using a small number of long trajectories of temperature replica exchange. We compare replica exchange sampling as low-resolution stage of RosettaDock with RosettaDock's original shotgun sampling as well as with ZDOCK. A benchmark of 30 complexes starting from structures of the unbound binding partners shows improved performance for ReplicaDock and ZDOCK when compared to shotgun sampling at equal or less computational expense. ReplicaDock and ZDOCK consistently reach lower energies and generate significantly more near-native conformations than shotgun sampling. Accordingly, they both improve typical metrics of prediction quality of complex structures after refinement. Additionally, the refined ReplicaDock ensembles reach significantly lower interface energies and many previously hidden features of the docking energy landscape become visible when ReplicaDock is applied. PMID:24009670

A benchmark testing ground for integrating homology modeling and protein docking.

PubMed

Bohnuud, Tanggis; Luo, Lingqi; Wodak, Shoshana J; Bonvin, Alexandre M J J; Weng, Zhiping; Vajda, Sandor; Schueler-Furman, Ora; Kozakov, Dima

2017-01-01

Protein docking procedures carry out the task of predicting the structure of a protein-protein complex starting from the known structures of the individual protein components. More often than not, however, the structure of one or both components is not known, but can be derived by homology modeling on the basis of known structures of related proteins deposited in the Protein Data Bank (PDB). Thus, the problem is to develop methods that optimally integrate homology modeling and docking with the goal of predicting the structure of a complex directly from the amino acid sequences of its component proteins. One possibility is to use the best available homology modeling and docking methods. However, the models built for the individual subunits often differ to a significant degree from the bound conformation in the complex, often much more so than the differences observed between free and bound structures of the same protein, and therefore additional conformational adjustments, both at the backbone and side chain levels need to be modeled to achieve an accurate docking prediction. In particular, even homology models of overall good accuracy frequently include localized errors that unfavorably impact docking results. The predicted reliability of the different regions in the model can also serve as a useful input for the docking calculations. Here we present a benchmark dataset that should help to explore and solve combined modeling and docking problems. This dataset comprises a subset of the experimentally solved 'target' complexes from the widely used Docking Benchmark from the Weng Lab (excluding antibody-antigen complexes). This subset is extended to include the structures from the PDB related to those of the individual components of each complex, and hence represent potential templates for investigating and benchmarking integrated homology modeling and docking approaches. Template sets can be dynamically customized by specifying ranges in sequence similarity and in PDB release dates, or using other filtering options, such as excluding sets of specific structures from the template list. Multiple sequence alignments, as well as structural alignments of the templates to their corresponding subunits in the target are also provided. The resource is accessible online or can be downloaded at http://cluspro.org/benchmark, and is updated on a weekly basis in synchrony with new PDB releases. Proteins 2016; 85:10-16. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

PubMed Central

2015-01-01

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

Quantum.Ligand.Dock: protein-ligand docking with quantum entanglement refinement on a GPU system.

PubMed

Kantardjiev, Alexander A

2012-07-01

Quantum.Ligand.Dock (protein-ligand docking with graphic processing unit (GPU) quantum entanglement refinement on a GPU system) is an original modern method for in silico prediction of protein-ligand interactions via high-performance docking code. The main flavour of our approach is a combination of fast search with a special account for overlooked physical interactions. On the one hand, we take care of self-consistency and proton equilibria mutual effects of docking partners. On the other hand, Quantum.Ligand.Dock is the the only docking server offering such a subtle supplement to protein docking algorithms as quantum entanglement contributions. The motivation for development and proposition of the method to the community hinges upon two arguments-the fundamental importance of quantum entanglement contribution in molecular interaction and the realistic possibility to implement it by the availability of supercomputing power. The implementation of sophisticated quantum methods is made possible by parallelization at several bottlenecks on a GPU supercomputer. The high-performance implementation will be of use for large-scale virtual screening projects, structural bioinformatics, systems biology and fundamental research in understanding protein-ligand recognition. The design of the interface is focused on feasibility and ease of use. Protein and ligand molecule structures are supposed to be submitted as atomic coordinate files in PDB format. A customization section is offered for addition of user-specified charges, extra ionogenic groups with intrinsic pK(a) values or fixed ions. Final predicted complexes are ranked according to obtained scores and provided in PDB format as well as interactive visualization in a molecular viewer. Quantum.Ligand.Dock server can be accessed at http://87.116.85.141/LigandDock.html.

A python-based docking program utilizing a receptor bound ligand shape: PythDock.

PubMed

Chung, Jae Yoon; Cho, Seung Joo; Hah, Jung-Mi

2011-09-01

PythDock is a heuristic docking program that uses Python programming language with a simple scoring function and a population based search engine. The scoring function considers electrostatic and dispersion/repulsion terms. The search engine utilizes a particle swarm optimization algorithm. A grid potential map is generated using the shape information of a bound ligand within the active site. Therefore, the searching area is more relevant to the ligand binding. To evaluate the docking performance of PythDock, two well-known docking programs (AutoDock and DOCK) were also used with the same data. The accuracy of docked results were measured by the difference of the ligand structure between x-ray structure, and docked pose, i.e., average root mean squared deviation values of the bound ligand were compared for fourteen protein-ligand complexes. Since the number of ligands' rotational flexibility is an important factor affecting the accuracy of a docking, the data set was chosen to have various degrees of flexibility. Although PythDock has a scoring function simpler than those of other programs (AutoDock and DOCK), our results showed that PythDock predicted more accurate poses than both AutoDock4.2 and DOCK6.2. This indicates that PythDock could be a useful tool to study ligand-receptor interactions and could also be beneficial in structure based drug design.

Rapid and accurate prediction and scoring of water molecules in protein binding sites.

PubMed

Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

2012-01-01

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

DOCLASP - Docking ligands to target proteins using spatial and electrostatic congruence extracted from a known holoenzyme and applying simple geometrical transformations.

PubMed

Chakraborty, Sandeep

2014-01-01

The ability to accurately and effectively predict the interaction between proteins and small drug-like compounds has long intrigued researchers for pedagogic, humanitarian and economic reasons. Protein docking methods (AutoDock, GOLD, DOCK, FlexX and Glide to name a few) rank a large number of possible conformations of protein-ligand complexes using fast algorithms. Previously, it has been shown that structural congruence leading to the same enzymatic function necessitates the congruence of electrostatic properties (CLASP). The current work presents a methodology for docking a ligand into a target protein, provided that there is at least one known holoenzyme with ligand bound - DOCLASP (Docking using CLASP). The contact points of the ligand in the holoenzyme defines a motif, which is used to query the target enzyme using CLASP. If there are significant matches, the holoenzyme and the target protein are superimposed based on congruent atoms. The same linear and rotational transformations are also applied to the ligand, thus creating a unified coordinate framework having the holoenzyme, the ligand and the target enzyme. In the current work, the dipeptidyl peptidase-IV inhibitor vildagliptin was docked to the PI-PLC structure complexed with myo-inositol using DOCLASP. Also, corroboration of the docking of phenylthiourea to the modelled structure of polyphenol oxidase (JrPPO1) from walnut is provided based on the subsequently solved structure of JrPPO1 (PDBid:5CE9). Analysis of the binding of the antitrypanosomial drug suramin to nine non-homologous proteins in the PDB database shows a diverse set of binding motifs, and multiple binding sites in the phospholipase A2-likeproteins from the Bothrops genus of pitvipers. The conformational changes in the suramin molecule on binding highlights the challenges in docking flexible ligands into an already 'plastic' binding site. Thus, DOCLASP presents a method for 'soft docking' ligands to proteins with low computational requirements.

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.

Molecular docking study, synthesis and biological evaluation of Mannich bases as Hsp90 inhibitors.

PubMed

Gupta, Sayan Dutta; Bommaka, Manish Kumar; Mazaira, Gisela I; Galigniana, Mario D; Subrahmanyam, Chavali Venkata Satya; Gowrishankar, Naryanasamy Lachmana; Raghavendra, Nulgumnalli Manjunathaiah

2015-09-01

The ubiquitously expressed heat shock protein 90 is an encouraging target for the development of novel anticancer agents. In a program directed towards uncovering novel chemical scaffolds against Hsp90, we performed molecular docking studies using Tripos-Sybyl drug designing software by including the required conserved water molecules. The results of the docking studies predicted Mannich bases derived from 2,4-dihydroxy acetophenone/5-chloro 2,4-dihydroxy acetophenone as potential Hsp90 inhibitors. Subsequently, a few of them were synthesized (1-6) and characterized by IR, (1)H NMR, (13)C NMR and mass spectral analysis. The synthesized Mannich compounds were evaluated for their potential to suppress Hsp90 ATPase activity by the colorimetric Malachite green assay. Subsequently, the molecules were screened for their antiproilferative effect against PC3 pancreatic carcinoma cells by adopting the 3-(4,5-dimethythiazol- 2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay method. The activity profile of the identified derivatives correlated well with their docking results. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimization of protein-protein docking for predicting Fc-protein interactions.

PubMed

Agostino, Mark; Mancera, Ricardo L; Ramsland, Paul A; Fernández-Recio, Juan

2016-11-01

The antibody crystallizable fragment (Fc) is recognized by effector proteins as part of the immune system. Pathogens produce proteins that bind Fc in order to subvert or evade the immune response. The structural characterization of the determinants of Fc-protein association is essential to improve our understanding of the immune system at the molecular level and to develop new therapeutic agents. Furthermore, Fc-binding peptides and proteins are frequently used to purify therapeutic antibodies. Although several structures of Fc-protein complexes are available, numerous others have not yet been determined. Protein-protein docking could be used to investigate Fc-protein complexes; however, improved approaches are necessary to efficiently model such cases. In this study, a docking-based structural bioinformatics approach is developed for predicting the structures of Fc-protein complexes. Based on the available set of X-ray structures of Fc-protein complexes, three regions of the Fc, loosely corresponding to three turns within the structure, were defined as containing the essential features for protein recognition and used as restraints to filter the initial docking search. Rescoring the filtered poses with an optimal scoring strategy provided a success rate of approximately 80% of the test cases examined within the top ranked 20 poses, compared to approximately 20% by the initial unrestrained docking. The developed docking protocol provides a significant improvement over the initial unrestrained docking and will be valuable for predicting the structures of currently undetermined Fc-protein complexes, as well as in the design of peptides and proteins that target Fc. Copyright © 2016 John Wiley & Sons, Ltd.

Computational exploration of a protein receptor binding space with student proposed peptide ligands.

PubMed

King, Matthew D; Phillips, Paul; Turner, Matthew W; Katz, Michael; Lew, Sarah; Bradburn, Sarah; Andersen, Tim; McDougal, Owen M

2016-01-01

Computational molecular docking is a fast and effective in silico method for the analysis of binding between a protein receptor model and a ligand. The visualization and manipulation of protein to ligand binding in three-dimensional space represents a powerful tool in the biochemistry curriculum to enhance student learning. The DockoMatic tutorial described herein provides a framework by which instructors can guide students through a drug screening exercise. Using receptor models derived from readily available protein crystal structures, docking programs have the ability to predict ligand binding properties, such as preferential binding orientations and binding affinities. The use of computational studies can significantly enhance complimentary wet chemical experimentation by providing insight into the important molecular interactions within the system of interest, as well as guide the design of new candidate ligands based on observed binding motifs and energetics. In this laboratory tutorial, the graphical user interface, DockoMatic, facilitates docking job submissions to the docking engine, AutoDock 4.2. The purpose of this exercise is to successfully dock a 17-amino acid peptide, α-conotoxin TxIA, to the acetylcholine binding protein from Aplysia californica-AChBP to determine the most stable binding configuration. Each student will then propose two specific amino acid substitutions of α-conotoxin TxIA to enhance peptide binding affinity, create the mutant in DockoMatic, and perform docking calculations to compare their results with the class. Students will also compare intermolecular forces, binding energy, and geometric orientation of their prepared analog to their initial α-conotoxin TxIA docking results. © 2015 The International Union of Biochemistry and Molecular Biology.

Template-Based Modeling of Protein-RNA Interactions.

PubMed

Zheng, Jinfang; Kundrotas, Petras J; Vakser, Ilya A; Liu, Shiyong

2016-09-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes.

Comprehensive insight into the binding of sunitinib, a multi-targeted anticancer drug to human serum albumin

NASA Astrophysics Data System (ADS)

Kabir, Md. Zahirul; Tee, Wei-Ven; Mohamad, Saharuddin B.; Alias, Zazali; Tayyab, Saad

2017-06-01

Binding studies between a multi-targeted anticancer drug, sunitinib (SU) and human serum albumin (HSA) were made using fluorescence, UV-vis absorption, circular dichroism (CD) and molecular docking analysis. Both fluorescence quenching data and UV-vis absorption results suggested formation of SU-HSA complex. Moderate binding affinity between SU and HSA was evident from the value of the binding constant (3.04 × 104 M-1), obtained at 298 K. Involvement of hydrophobic interactions and hydrogen bonds as the leading intermolecular forces in the formation of SU-HSA complex was predicted from the thermodynamic data of the binding reaction. These results were in good agreement with the molecular docking analysis. Microenvironmental perturbations around Tyr and Trp residues as well as secondary and tertiary structural changes in HSA upon SU binding were evident from the three-dimensional fluorescence and circular dichroism results. SU binding to HSA also improved the thermal stability of the protein. Competitive displacement results and molecular docking analysis revealed the binding locus of SU to HSA in subdomain IIA (Sudlow's site I). The influence of a few common ions on the binding constant of SU-HSA complex was also noticed.

Encompassing receptor flexibility in virtual screening using ensemble docking-based hybrid QSAR: discovery of novel phytochemicals for BACE1 inhibition.

PubMed

Chakraborty, Sandipan; Ramachandran, Balaji; Basu, Soumalee

2014-10-01

Mimicking receptor flexibility during receptor-ligand binding is a challenging task in computational drug design since it is associated with a large increase in the conformational search space. In the present study, we have devised an in silico design strategy incorporating receptor flexibility in virtual screening to identify potential lead compounds as inhibitors for flexible proteins. We have considered BACE1 (β-secretase), a key target protease from a therapeutic perspective for Alzheimer's disease, as the highly flexible receptor. The protein undergoes significant conformational transitions from open to closed form upon ligand binding, which makes it a difficult target for inhibitor design. We have designed a hybrid structure-activity model containing both ligand based descriptors and energetic descriptors obtained from molecular docking based on a dataset of structurally diverse BACE1 inhibitors. An ensemble of receptor conformations have been used in the docking study, further improving the prediction ability of the model. The designed model that shows significant prediction ability judged by several statistical parameters has been used to screen an in house developed 3-D structural library of 731 phytochemicals. 24 highly potent, novel BACE1 inhibitors with predicted activity (Ki) ≤ 50 nM have been identified. Detailed analysis reveals pharmacophoric features of these novel inhibitors required to inhibit BACE1.

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

PubMed

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

2013-10-01

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

Hot-spot analysis for drug discovery targeting protein-protein interactions.

PubMed

Rosell, Mireia; Fernández-Recio, Juan

2018-04-01

Protein-protein interactions are important for biological processes and pathological situations, and are attractive targets for drug discovery. However, rational drug design targeting protein-protein interactions is still highly challenging. Hot-spot residues are seen as the best option to target such interactions, but their identification requires detailed structural and energetic characterization, which is only available for a tiny fraction of protein interactions. Areas covered: In this review, the authors cover a variety of computational methods that have been reported for the energetic analysis of protein-protein interfaces in search of hot-spots, and the structural modeling of protein-protein complexes by docking. This can help to rationalize the discovery of small-molecule inhibitors of protein-protein interfaces of therapeutic interest. Computational analysis and docking can help to locate the interface, molecular dynamics can be used to find suitable cavities, and hot-spot predictions can focus the search for inhibitors of protein-protein interactions. Expert opinion: A major difficulty for applying rational drug design methods to protein-protein interactions is that in the majority of cases the complex structure is not available. Fortunately, computational docking can complement experimental data. An interesting aspect to explore in the future is the integration of these strategies for targeting PPIs with large-scale mutational analysis.

AnchorDock: Blind and Flexible Anchor-Driven Peptide Docking.

PubMed

Ben-Shimon, Avraham; Niv, Masha Y

2015-05-05

The huge conformational space stemming from the inherent flexibility of peptides is among the main obstacles to successful and efficient computational modeling of protein-peptide interactions. Current peptide docking methods typically overcome this challenge using prior knowledge from the structure of the complex. Here we introduce AnchorDock, a peptide docking approach, which automatically targets the docking search to the most relevant parts of the conformational space. This is done by precomputing the free peptide's structure and by computationally identifying anchoring spots on the protein surface. Next, a free peptide conformation undergoes anchor-driven simulated annealing molecular dynamics simulations around the predicted anchoring spots. In the challenging task of a completely blind docking test, AnchorDock produced exceptionally good results (backbone root-mean-square deviation ≤ 2.2Å, rank ≤15) for 10 of 13 unbound cases tested. The impressive performance of AnchorDock supports a molecular recognition pathway that is driven via pre-existing local structural elements. Copyright © 2015 Elsevier Ltd. All rights reserved.

Improving binding mode and binding affinity predictions of docking by ligand-based search of protein conformations: evaluation in D3R grand challenge 2015

NASA Astrophysics Data System (ADS)

Xu, Xianjin; Yan, Chengfei; Zou, Xiaoqin

2017-08-01

The growing number of protein-ligand complex structures, particularly the structures of proteins co-bound with different ligands, in the Protein Data Bank helps us tackle two major challenges in molecular docking studies: the protein flexibility and the scoring function. Here, we introduced a systematic strategy by using the information embedded in the known protein-ligand complex structures to improve both binding mode and binding affinity predictions. Specifically, a ligand similarity calculation method was employed to search a receptor structure with a bound ligand sharing high similarity with the query ligand for the docking use. The strategy was applied to the two datasets (HSP90 and MAP4K4) in recent D3R Grand Challenge 2015. In addition, for the HSP90 dataset, a system-specific scoring function (ITScore2_hsp90) was generated by recalibrating our statistical potential-based scoring function (ITScore2) using the known protein-ligand complex structures and the statistical mechanics-based iterative method. For the HSP90 dataset, better performances were achieved for both binding mode and binding affinity predictions comparing with the original ITScore2 and with ensemble docking. For the MAP4K4 dataset, although there were only eight known protein-ligand complex structures, our docking strategy achieved a comparable performance with ensemble docking. Our method for receptor conformational selection and iterative method for the development of system-specific statistical potential-based scoring functions can be easily applied to other protein targets that have a number of protein-ligand complex structures available to improve predictions on binding.

Assessment of CAPRI predictions in rounds 3-5 shows progress in docking procedures.

PubMed

Méndez, Raúl; Leplae, Raphaël; Lensink, Marc F; Wodak, Shoshana J

2005-08-01

The current status of docking procedures for predicting protein-protein interactions starting from their three-dimensional (3D) structure is reassessed by evaluating blind predictions, performed during 2003-2004 as part of Rounds 3-5 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). Ten newly determined structures of protein-protein complexes were used as targets for these rounds. They comprised 2 enzyme-inhibitor complexes, 2 antigen-antibody complexes, 2 complexes involved in cellular signaling, 2 homo-oligomers, and a complex between 2 components of the bacterial cellulosome. For most targets, the predictors were given the experimental structures of 1 unbound and 1 bound component, with the latter in a random orientation. For some, the structure of the free component was derived from that of a related protein, requiring the use of homology modeling. In some of the targets, significant differences in conformation were displayed between the bound and unbound components, representing a major challenge for the docking procedures. For 1 target, predictions could not go to completion. In total, 1866 predictions submitted by 30 groups were evaluated. Over one-third of these groups applied completely novel docking algorithms and scoring functions, with several of them specifically addressing the challenge of dealing with side-chain and backbone flexibility. The quality of the predicted interactions was evaluated by comparison to the experimental structures of the targets, made available for the evaluation, using the well-agreed-upon criteria used previously. Twenty-four groups, which for the first time included an automatic Web server, produced predictions ranking from acceptable to highly accurate for all targets, including those where the structures of the bound and unbound forms differed substantially. These results and a brief survey of the methods used by participants of CAPRI Rounds 3-5 suggest that genuine progress in the performance of docking methods is being achieved, with CAPRI acting as the catalyst.

GENIUS In Silico Screening Technology for HCV Drug Discovery.

PubMed

Patil, Vaishali M; Masand, Neeraj; Gupta, Satya P

2016-01-01

The various reported in silico screening protocols such as molecular docking are associated with various drawbacks as well as benefits. In molecular docking, on interaction with ligand, the protein or receptor molecule gets activated by adopting conformational changes. These conformational changes cannot be utilized to predict the 3D structure of a protein-ligand complex from unbound protein conformations rigid docking, which necessitates the demand for understanding protein flexibility. Therefore, efficiency and accuracy of docking should be achieved and various available/developed protocols may be adopted. One such protocol is GENIUS induced-fit docking and it is used effectively for the development of anti-HCV NS3-4A serine protease inhibitors. The present review elaborates the GENIUS docking protocol along with its benefits and drawbacks.

Homology modeling, molecular docking and electrostatic potential analysis of MurF ligase from Klebsiella pneumonia

PubMed Central

Sivaramakrishnan, Venkatabalasubramanian; Thiyagarajan, Chinnaiyan; Kalaivanan, Sivakumaran; Selvakumar, Raj; Anusuyadevi, Muthuswamy; Jayachandran, Kesavan Swaminathan

2012-01-01

In spite of availability of moderately protective vaccine and antibiotics, new antibacterial agents are urgently needed to decrease the global incidence of Klebsiella pneumonia infections. MurF ligase, a key enzyme, which participates in the bacterial cell wall assembly, is indispensable to existence of K. pneumonia. MurF ligase lack mammalian vis-à-vis and have high specificity, uniqueness, and occurrence only in eubacteria, epitomizing them as promising therapeutic targets for intervention. In this study, we present a unified approach involving homology modeling and molecular docking studies on MurF ligase enzyme. As part of this study, a homology model of K. pneumonia (MurF ligase) enzyme was predicted for the first time in order to carry out structurebased drug design. The accuracy of the model was further validated using different computational approaches. The comparative molecular docking study on this enzyme was undertaken using different phyto-ligands from Desmodium sp. and a known antibiotic Ciprofloxacin. The docking analysis indicated the importance of hotspots (HIS 281 and ASN 282) within the MurF binding pocket. The Lipinski's rule of five was analyzed for all ligands considered for this study by calculating the ADME/Tox, drug likeliness using Qikprop simulation. Only ten ligands were found to comply with the Lipinski rule of five. Based on the molecular docking results and Lipinki values 6-Methyltetrapterol A was confirmed as a promising lead compound. The present study should therefore play a guiding role in the experimental design and development of 6-Methyltetrapterol A as a bactericidal agent. PMID:22715301

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K as anti-malarial agents

NASA Astrophysics Data System (ADS)

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-06-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents.

PubMed

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-01-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme ( Pf LDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The Pf LDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of Pf LDH, we have used Discovery studio to perform molecular docking in the active binding pocket of Pf LDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q 2 of 0.516. The model has predicted r 2 of 0.91 showing that predicted IC 50 values are in good agreement with experimental IC 50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors.

Docking Based 3D-QSAR Study of Tricyclic Guanidine Analogues of Batzelladine K As Anti-Malarial Agents

PubMed Central

Ahmed, Nafees; Anwar, Sirajudheen; Thet Htar, Thet

2017-01-01

The Plasmodium falciparum Lactate Dehydrogenase enzyme (PfLDH) catalyzes inter-conversion of pyruvate to lactate during glycolysis producing the energy required for parasitic growth. The PfLDH has been studied as a potential molecular target for development of anti-malarial agents. In an attempt to find the potent inhibitor of PfLDH, we have used Discovery studio to perform molecular docking in the active binding pocket of PfLDH by CDOCKER, followed by three-dimensional quantitative structure-activity relationship (3D-QSAR) studies of tricyclic guanidine batzelladine compounds, which were previously synthesized in our laboratory. Docking studies showed that there is a very strong correlation between in silico and in vitro results. Based on docking results, a highly predictive 3D-QSAR model was developed with q2 of 0.516. The model has predicted r2 of 0.91 showing that predicted IC50 values are in good agreement with experimental IC50 values. The results obtained from this study revealed the developed model can be used to design new anti-malarial compounds based on tricyclic guanidine derivatives and to predict activities of new inhibitors. PMID:28664157

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

NASA Astrophysics Data System (ADS)

Virgili-Llop, Josep; Zagaris, Costantinos; Park, Hyeongjun; Zappulla, Richard; Romano, Marcello

2018-03-01

An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a ground-based air-bearing test bed, using spacecraft simulators that float over a granite table.

Intuitive, but not simple: including explicit water molecules in protein-protein docking simulations improves model quality.

PubMed

Parikh, Hardik I; Kellogg, Glen E

2014-06-01

Characterizing the nature of interaction between proteins that have not been experimentally cocrystallized requires a computational docking approach that can successfully predict the spatial conformation adopted in the complex. In this work, the Hydropathic INTeractions (HINT) force field model was used for scoring docked models in a data set of 30 high-resolution crystallographically characterized "dry" protein-protein complexes and was shown to reliably identify native-like models. However, most current protein-protein docking algorithms fail to explicitly account for water molecules involved in bridging interactions that mediate and stabilize the association of the protein partners, so we used HINT to illuminate the physical and chemical properties of bridging waters and account for their energetic stabilizing contributions. The HINT water Relevance metric identified the "truly" bridging waters at the 30 protein-protein interfaces and we utilized them in "solvated" docking by manually inserting them into the input files for the rigid body ZDOCK program. By accounting for these interfacial waters, a statistically significant improvement of ∼24% in the average hit-count within the top-10 predictions the protein-protein dataset was seen, compared to standard "dry" docking. The results also show scoring improvement, with medium and high accuracy models ranking much better than incorrect ones. These improvements can be attributed to the physical presence of water molecules that alter surface properties and better represent native shape and hydropathic complementarity between interacting partners, with concomitantly more accurate native-like structure predictions. © 2013 Wiley Periodicals, Inc.

Combining molecular docking and QSAR studies for modeling the anti-tyrosinase activity of aromatic heterocycle thiosemicarbazone analogues

NASA Astrophysics Data System (ADS)

Dong, Huanhuan; Liu, Jing; Liu, Xiaoru; Yu, Yanying; Cao, Shuwen

2018-01-01

A collection of thirty-six aromatic heterocycle thiosemicarbazone analogues presented a broad span of anti-tyrosinase activities were designed and obtained. A robust and reliable two-dimensional quantitative structure-activity relationship model, as evidenced by the high q2 and r2 values (0.848 and 0.893, respectively), was gained based on the analogues to predict the quantitative chemical-biological relationship and the new modifier direction. Inhibitory activities of the compounds were found to greatly depend on molecular shape and orbital energy. Substituents brought out large ovality and high highest-occupied molecular orbital energy values helped to improve the activity of these analogues. The molecular docking results provided visual evidence for QSAR analysis and inhibition mechanism. Based on these, two novel tyrosinase inhibitors O04 and O05 with predicted IC50 of 0.5384 and 0.8752 nM were designed and suggested for further research.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library.

PubMed

Elam, Christopher; Lape, Michael; Deye, Joel; Zultowsky, Jodie; Stanton, David T; Paula, Stefan

2011-05-01

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

NASA Astrophysics Data System (ADS)

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

2018-01-01

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

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

Li, Xiaolin; Ye, Li; Wang, Xiaoxiang

2012-12-15

Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 andmore » non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.« less

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.

3D-QSAR and docking studies of flavonoids as potent Escherichia coli inhibitors

PubMed Central

Fang, Yajing; Lu, Yulin; Zang, Xixi; Wu, Ting; Qi, XiaoJuan; Pan, Siyi; Xu, Xiaoyun

2016-01-01

Flavonoids are potential antibacterial agents. However, key substituents and mechanism for their antibacterial activity have not been fully investigated. The quantitative structure-activity relationship (QSAR) and molecular docking of flavonoids relating to potent anti-Escherichia coli agents were investigated. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were developed by using the pIC50 values of flavonoids. The cross-validated coefficient (q2) values for CoMFA (0.743) and for CoMSIA (0.708) were achieved, illustrating high predictive capabilities. Selected descriptors for the CoMFA model were ClogP (logarithm of the octanol/water partition coefficient), steric and electrostatic fields, while, ClogP, electrostatic and hydrogen bond donor fields were used for the CoMSIA model. Molecular docking results confirmed that half of the tested flavonoids inhibited DNA gyrase B (GyrB) by interacting with adenosine-triphosphate (ATP) pocket in a same orientation. Polymethoxyl flavones, flavonoid glycosides, isoflavonoids changed their orientation, resulting in a decrease of inhibitory activity. Moreover, docking results showed that 3-hydroxyl, 5-hydroxyl, 7-hydroxyl and 4-carbonyl groups were found to be crucial active substituents of flavonoids by interacting with key residues of GyrB, which were in agreement with the QSAR study results. These results provide valuable information for structure requirements of flavonoids as antibacterial agents. PMID:27049530

Computational Design of Apolipoprotein E4 Inhibitors for Alzheimer's Disease Therapy from Traditional Chinese Medicine

PubMed Central

Huang, Hung-Jin; Chen, Hsin-Yi; Lee, Cheng-Chun

2014-01-01

Apolipoprotein E4 (Apo E4) is the major genetic risk factor in the causation of Alzheimer's disease (AD). In this study we utilize virtual screening of the world's largest traditional Chinese medicine (TCM) database and investigate potential compounds for the inhibition of ApoE4. We present the top three TCM candidates: Solapalmitine, Isodesacetyluvaricin, and Budmunchiamine L5 for further investigation. Dynamics analysis and molecular dynamics (MD) simulation were used to simulate protein-ligand complexes for observing the interactions and protein variations. Budmunchiamine L5 did not have the highest score from virtual screening; however, the dynamics pose is similar to the initial docking pose after MD simulation. Trajectory analysis reveals that Budmunchiamine L5 was stable over all simulation times. The migration distance of Budmunchiamine L5 illustrates that docked ligands are not variable from the initial docked site. Interestingly, Arg158 was observed to form H-bonds with Budmunchiamine L5 in the docking pose and MD snapshot, which indicates that the TCM compounds could stably bind to ApoE4. Our results show that Budmunchiamine L5 has good absorption, blood brain barrier (BBB) penetration, and less toxicity according to absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction and could, therefore, be safely used for developing novel ApoE4 inhibitors. PMID:24967370

Naringenin and quercetin--potential anti-HCV agents for NS2 protease targets.

PubMed

Lulu, S Sajitha; Thabitha, A; Vino, S; Priya, A Mohana; Rout, Madhusmita

2016-01-01

Nonstructural proteins of hepatitis C virus had drawn much attention for the scientific fraternity in drug discovery due to its important role in the disease. 3D structure of the protein was predicted using molecular modelling protocol. Docking studies of 10 medicinal plant compounds and three drugs available in the market (control) with NS2 protease were employed by using rigid docking approach of AutoDock 4.2. Among the molecules tested for docking study, naringenin and quercetin revealed minimum binding energy of - 7.97 and - 7.95 kcal/mol with NS2 protease. All the ligands were docked deeply within the binding pocket region of the protein. The docking study results showed that these compounds are potential inhibitors of the target; and also all these docked compounds have good inhibition constant, vdW+Hbond+desolv energy with best RMSD value.

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

PubMed

LaBute, Montiago X; Zhang, Xiaohua; Lenderman, Jason; Bennion, Brian J; Wong, Sergio E; Lightstone, Felice C

2014-01-01

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60-0.69 and 0.61-0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

The use of docking-based comparative intermolecular contacts analysis to identify optimal docking conditions within glucokinase and to discover of new GK activators

NASA Astrophysics Data System (ADS)

Taha, Mutasem O.; Habash, Maha; Khanfar, Mohammad A.

2014-05-01

Glucokinase (GK) is involved in normal glucose homeostasis and therefore it is a valid target for drug design and discovery efforts. GK activators (GKAs) have excellent potential as treatments of hyperglycemia and diabetes. The combined recent interest in GKAs, together with docking limitations and shortages of docking validation methods prompted us to use our new 3D-QSAR analysis, namely, docking-based comparative intermolecular contacts analysis (dbCICA), to validate docking configurations performed on a group of GKAs within GK binding site. dbCICA assesses the consistency of docking by assessing the correlation between ligands' affinities and their contacts with binding site spots. Optimal dbCICA models were validated by receiver operating characteristic curve analysis and comparative molecular field analysis. dbCICA models were also converted into valid pharmacophores that were used as search queries to mine 3D structural databases for new GKAs. The search yielded several potent bioactivators that experimentally increased GK bioactivity up to 7.5-folds at 10 μM.

Chemical system biology based molecular interactions to identify inhibitors against Q151M mutant of HIV-1 reverse transcriptase.

PubMed

Pandey, Rajan Kumar; Sharma, Drista; Ojha, Rupal; Bhatt, Tarun Kumar; Prajapati, Vijay Kumar

2018-05-09

The emergence of mutations leading to drug resistance is the main cause of therapeutic failure in the human HIV infection. Chemical system biology approach has drawn great attention to discover new antiretroviral hits with high efficacy and negligible toxicity, which can be used as a prerequisite for HIV drug resistance global action plan 2017-21. To discover potential hits, we docked 49 antiretroviral analogs (n = 6294) against HIV-1 reverse transcriptase Q151M mutant & its wild-type form and narrow downed their number in three sequential modes of docking using Schrödinger suite. Later on, 80 ligands having better docking score than reference ligands (tenofovir and lamivudine) were screened for ADME, toxicity prediction, and binding energy estimation. Simultaneously, the area under the curve (AUC) was estimated using receiver operating characteristics (ROC) curve analysis to validate docking protocols. Finally, single point energy and molecular dynamics simulation approaches were performed for best two ligands (L3 and L14). This study reveals the antiretroviral efficacy of obtained two best ligands and delivers the hits against HIV-1 reverse transcriptase Q151M mutant. Copyright © 2018 Elsevier B.V. All rights reserved.

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/.

Protein social behavior makes a stronger signal for partner identification than surface geometry

PubMed Central

Laine, Elodie

2016-01-01

ABSTRACT Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico‐chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross‐docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S‐index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface‐based (ranking) score to discriminate partners from non‐interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137–154. © 2016 Wiley Periodicals, Inc. PMID:27802579

Protein social behavior makes a stronger signal for partner identification than surface geometry.

PubMed

Laine, Elodie; Carbone, Alessandra

2017-01-01

Cells are interactive living systems where proteins movements, interactions and regulation are substantially free from centralized management. How protein physico-chemical and geometrical properties determine who interact with whom remains far from fully understood. We show that characterizing how a protein behaves with many potential interactors in a complete cross-docking study leads to a sharp identification of its cellular/true/native partner(s). We define a sociability index, or S-index, reflecting whether a protein likes or not to pair with other proteins. Formally, we propose a suitable normalization function that accounts for protein sociability and we combine it with a simple interface-based (ranking) score to discriminate partners from non-interactors. We show that sociability is an important factor and that the normalization permits to reach a much higher discriminative power than shape complementarity docking scores. The social effect is also observed with more sophisticated docking algorithms. Docking conformations are evaluated using experimental binding sites. These latter approximate in the best possible way binding sites predictions, which have reached high accuracy in recent years. This makes our analysis helpful for a global understanding of partner identification and for suggesting discriminating strategies. These results contradict previous findings claiming the partner identification problem being solvable solely with geometrical docking. Proteins 2016; 85:137-154. © 2016 Wiley Periodicals, Inc. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

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

PubMed

Gerek, Z Nevin; Ozkan, S Banu

2010-05-01

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

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

PubMed Central

Gerek, Z Nevin; Ozkan, S Banu

2010-01-01

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

Docking Analysis and Multidimensional Hybrid QSAR Model of 1,4-Benzodiazepine-2,5-Diones as HDM2 Antagonists

PubMed Central

Dai, Yujie; Chen, Nan; Wang, Qiang; Zheng, Heng; Zhang, Xiuli; Jia, Shiru; Dong, Lilong; Feng, Dacheng

2012-01-01

The inhibitors of p53-HDM2 interaction are attractive molecules for the treatment of wild-type p53 tumors. In order to search more potent HDM2 inhibitors, docking operation with CDOCKER protocol in Discovery Studio 2.1 (DS2.1) and multidimensional hybrid quantitative structure-activity relationship (QSAR) studies through the physiochemical properties obtained from DS2.1 and E-Dragon 1.0 as descriptors, have been performed on 59 1,4-benzodiazepine- 2,5-diones which have p53-HDM2 interaction inhibitory activities. The docking results indicate that π-π interaction between the imidazole group in HIS96 and the aryl ring at 4-N of 1,4-benzodiazepine-2,5-dione may be one of the key factors for the combination of ligands with HDM2. Two QSAR models were obtained using genetic function approximation (GFA) and genetic partial least squares (G/PLS) based on the descriptors obtained from DS2.1 and E-dragon 1.0, respectively. The best model can explain 85.5% of the variance (R 2adj ) while it could predict 81.7% of the variance (R 2 cv ). With this model, the bioactivities of some new compounds were predicted. PMID:24250508

Docking Analysis and Multidimensional Hybrid QSAR Model of 1,4-Benzodiazepine-2,5-Diones as HDM2 Antagonists.

PubMed

Dai, Yujie; Chen, Nan; Wang, Qiang; Zheng, Heng; Zhang, Xiuli; Jia, Shiru; Dong, Lilong; Feng, Dacheng

2012-01-01

The inhibitors of p53-HDM2 interaction are attractive molecules for the treatment of wild-type p53 tumors. In order to search more potent HDM2 inhibitors, docking operation with CDOCKER protocol in Discovery Studio 2.1 (DS2.1) and multidimensional hybrid quantitative structure-activity relationship (QSAR) studies through the physiochemical properties obtained from DS2.1 and E-Dragon 1.0 as descriptors, have been performed on 59 1,4-benzodiazepine- 2,5-diones which have p53-HDM2 interaction inhibitory activities. The docking results indicate that π-π interaction between the imidazole group in HIS96 and the aryl ring at 4-N of 1,4-benzodiazepine-2,5-dione may be one of the key factors for the combination of ligands with HDM2. Two QSAR models were obtained using genetic function approximation (GFA) and genetic partial least squares (G/PLS) based on the descriptors obtained from DS2.1 and E-dragon 1.0, respectively. The best model can explain 85.5% of the variance (R (2) adj ) while it could predict 81.7% of the variance (R (2) cv ). With this model, the bioactivities of some new compounds were predicted.

Template-Based Modeling of Protein-RNA Interactions

PubMed Central

Zheng, Jinfang; Kundrotas, Petras J.; Vakser, Ilya A.

2016-01-01

Protein-RNA complexes formed by specific recognition between RNA and RNA-binding proteins play an important role in biological processes. More than a thousand of such proteins in human are curated and many novel RNA-binding proteins are to be discovered. Due to limitations of experimental approaches, computational techniques are needed for characterization of protein-RNA interactions. Although much progress has been made, adequate methodologies reliably providing atomic resolution structural details are still lacking. Although protein-RNA free docking approaches proved to be useful, in general, the template-based approaches provide higher quality of predictions. Templates are key to building a high quality model. Sequence/structure relationships were studied based on a representative set of binary protein-RNA complexes from PDB. Several approaches were tested for pairwise target/template alignment. The analysis revealed a transition point between random and correct binding modes. The results showed that structural alignment is better than sequence alignment in identifying good templates, suitable for generating protein-RNA complexes close to the native structure, and outperforms free docking, successfully predicting complexes where the free docking fails, including cases of significant conformational change upon binding. A template-based protein-RNA interaction modeling protocol PRIME was developed and benchmarked on a representative set of complexes. PMID:27662342

Spectroscopic and molecular docking studies on N,N-di-tert-butoxycarbonyl (Boc)-2-amino pyridine: A potential bioactive agent for lung cancer treatment

NASA Astrophysics Data System (ADS)

Mohamed Asath, R.; Premkumar, R.; Mathavan, T.; Milton Franklin Benial, A.

2017-09-01

Potential energy surface scan was performed and the most stable molecular structure of the N,N-di-tert-butoxycarbonyl (Boc)-2-amino pyridine (DBAP) molecule was predicted. The most stable molecular structure of the molecule was optimized using B3LYP method with cc-pVTZ basis set. Anticancer activity of the DBAP molecule was evaluated by molecular docking analysis. The structural parameters and vibrational wavenumbers were calculated for the optimized molecular structure. The experimental and theoretical wavenumbers were assigned and compared. Ultraviolet-Visible spectrum was simulated and validated experimentally. The molecular electrostatic potential surface was simulated and Fukui function calculations were also carried out to investigate the reactive nature of the DBAP molecule. The natural bond orbital analysis was also performed to probe the intramolecular interactions and confirm the bioactivity of the DBAP molecule. The molecular docking analysis reveals the better inhibitory nature of the DBAP molecule against the epidermal growth factor receptor (EGFR) protein which causes lung cancer. Hence, the present study unveils the structural and bioactive nature of the title molecule. The DBAP molecule was identified as a potential inhibitor against the lung cancer which may be useful in further development of drug designing in the treatment of lung cancer.

Prediction and analysis of promiscuous T cell-epitopes derived from the vaccine candidate antigens of Leishmania donovani binding to MHC class-II alleles using in silico approach.

PubMed

Kashyap, Manju; Jaiswal, Varun; Farooq, Umar

2017-09-01

Visceral leishmaniasis is a dreadful infectious disease and caused by the intracellular protozoan parasites, Leishmania donovani and Leishmania infantum. Despite extensive efforts for developing effective prophylactic vaccine, still no vaccine is available against leishmaniasis. However, advancement in immunoinformatics methods generated new dimension in peptide based vaccine development. The present study was aimed to identify T-cell epitopes from the vaccine candidate antigens like Lipophosphogylcan-3(LPG-3) and Nucleoside hydrolase (NH) from the L. donovani using in silico methods. Available best tools were used for the identification of promiscuous peptides for MHC class-II alleles. A total of 34 promiscuous peptides from LPG-3, 3 from NH were identified on the basis of their 100% binding affinity towards all six HLA alleles, taken in this study. These peptides were further checked computationally to know their IFN-γ and IL4 inducing potential and nine peptides were identified. Peptide binding interactions with predominant HLA alleles were done by docking. Out of nine docked promiscuous peptides, only two peptides (QESRILRVIKKKLVR, RILRVIKKKLVRKTL), from LPG-3 and one peptide (FDKFWCLVIDALKRI) from NH showed lowest binding energy with all six alleles. These promiscuous T-cell epitopes were predicted on the basis of their antigenicity, hydrophobicity, potential immune response and docking scores. The immunogenicity of predicted promiscuous peptides might be used for subunit vaccine development with immune-modulating adjuvants. Copyright © 2017 Elsevier B.V. All rights reserved.

Binding Site and Potency Prediction of Teixobactin and other Lipid II Ligands by Statistical Base Scoring of Conformational Space Maps.

PubMed

Lungu, Claudiu N; Diudea, Mircea V

2018-01-01

Lipid II, a peptidoglycan, is a precursor in bacterial cell synthesis. It has both hydrophilic and lipophilic properties. The molecule translocates a bacterial membrane to deliver and incorporate "building blocks" from disaccharide-pentapeptide into the peptidoglican wall. Lipid II is a valid antibiotic target. A receptor binding pocket may be occupied by a ligand in various plausible conformations, among which only few ones are energetically related to a biological activity in the physiological efficiency domain. This paper reports the mapping of the conformational space of Lipid II in its interaction with Teixobactin and other Lipid II ligands. In order to study computationally the complex between Lipid II and ligands, a docking study was first carried on. Docking site was retrieved form literature. After docking, 5 ligand conformations and further 5 complexes (denoted 00 to 04) for each molecule were taken into account. For each structure, conformational studies were performed. Statistical analysis, conformational analysis and molecular dynamics based clustering were used to predict the potency of these compounds. A score for potency prediction was developed. Appling lipid II classification according to Lipid II conformational energy, a conformation of Teixobactin proved to be energetically favorable, followed by Oritravicin, Dalbavycin, Telvanicin, Teicoplamin and Vancomycin, respectively. Scoring of molecules according to cluster band and PCA produced the same result. Molecules classified according to standard deviations showed Dalbavycin as the most favorable conformation, followed by Teicoplamin, Telvanicin, Teixobactin, Oritravicin and Vancomycin, respectively. Total score showing best energetic efficiency of complex formation shows Teixobactin to have the best conformation (a score of 15 points) followed by Dalbavycin (14 points), Oritravicin (12v points), Telvanicin (10 points), Teicoplamin (9 points), Vancomycin (3 points). Statistical analysis of conformations can be used to predict the efficiency of ligand - target interaction and consecutively to find insight regarding ligand potency and postulate about favorable conformation of ligand and binding site. In this study it was shown that Teixobactin is more efficient in binding with Lipid II compared to Vancomycin, results confirmed by experimental data reported in literature. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Probing the human estrogen receptor-α binding requirements for phenolic mono- and di-hydroxyl compounds: A combined synthesis, binding and docking study

PubMed Central

McCullough, Christopher; Neumann, Terrence S.; Gone, Jayapal Reddy; He, Zhengjie; Herrild, Christian; Wondergem, Julie; Pandey, Rajesh K.; Donaldson, William A.; Sem, Daniel S.

2014-01-01

Various estrogen analogs were synthesized and tested for binding to human ERα using a fluorescence polarization displacement assay. Binding affinity and orientation were also predicted using docking calculations. Docking was able to accurately predict relative binding affinity and orientation for estradiol, but only if a tightly bound water molecule bridging Arg394/Glu353 is present. Di-hydroxyl compounds sometimes bind in two orientations, which are flipped in terms of relative positioning of their hydroxyl groups. Di-hydroxyl compounds were predicted to bind with their aliphatic hydroxyl group interacting with His524 in ERα. One nonsteroid-based dihdroxyl compound was 1000-fold specific for ERβ over ERα, and was also 25-fold specific for agonist ERβ versus antagonist activity. Docking predictions suggest this specificity may be due to interaction of the aliphatic hydroxyl with His475 in the agonist form of ERβ, versus with Thr299 in the antagonist form. But, the presence of this aliphatic hydroxyl is not required in all compounds, since mono-hydroxyl (phenolic) compounds bind ERα with high affinity, via hydroxyl hydrogen bonding interactions with the ERα Arg394/Glu353/water triad, and van der Waals interactions with the rest of the molecule. PMID:24315190

Building macromolecular assemblies by information-driven docking: introducing the HADDOCK multibody docking server.

PubMed

Karaca, Ezgi; Melquiond, Adrien S J; de Vries, Sjoerd J; Kastritis, Panagiotis L; Bonvin, Alexandre M J J

2010-08-01

Over the last years, large scale proteomics studies have generated a wealth of information of biomolecular complexes. Adding the structural dimension to the resulting interactomes represents a major challenge that classical structural experimental methods alone will have difficulties to confront. To meet this challenge, complementary modeling techniques such as docking are thus needed. Among the current docking methods, HADDOCK (High Ambiguity-Driven DOCKing) distinguishes itself from others by the use of experimental and/or bioinformatics data to drive the modeling process and has shown a strong performance in the critical assessment of prediction of interactions (CAPRI), a blind experiment for the prediction of interactions. Although most docking programs are limited to binary complexes, HADDOCK can deal with multiple molecules (up to six), a capability that will be required to build large macromolecular assemblies. We present here a novel web interface of HADDOCK that allows the user to dock up to six biomolecules simultaneously. This interface allows the inclusion of a large variety of both experimental and/or bioinformatics data and supports several types of cyclic and dihedral symmetries in the docking of multibody assemblies. The server was tested on a benchmark of six cases, containing five symmetric homo-oligomeric protein complexes and one symmetric protein-DNA complex. Our results reveal that, in the presence of either bioinformatics and/or experimental data, HADDOCK shows an excellent performance: in all cases, HADDOCK was able to generate good to high quality solutions and ranked them at the top, demonstrating its ability to model symmetric multicomponent assemblies. Docking methods can thus play an important role in adding the structural dimension to interactomes. However, although the current docking methodologies were successful for a vast range of cases, considering the variety and complexity of macromolecular assemblies, inclusion of some kind of experimental information (e.g. from mass spectrometry, nuclear magnetic resonance, cryoelectron microscopy, etc.) will remain highly desirable to obtain reliable results.

A Critical Assessment of Combined Ligand-based and Structure-based Approaches to hERG Channel Blocker Modeling

PubMed Central

Du-Cuny, Lei; Chen, Lu; Zhang, Shuxing

2014-01-01

Blockade of hERG channel prolongs the duration of the cardiac action potential and is a common reason for drug failure in preclinical safety trials. Therefore, it is of great importance to develop robust in silico tools to predict potential hERG blockers in the early stages of drug discovery and development. Herein we described comprehensive approaches to assess the discrimination of hERG-active and -inactive compounds by combining QSAR modeling, pharmacophore analysis, and molecular docking. Our consensus models demonstrated high predictive capacity and improved enrichment, and they could correctly classify 91.8% of 147 hERG blockers from 351 inactives. To further enhance our modeling effort, hERG homology models were constructed and molecular docking studies were conducted, resulting in high correlations (R2=0.81) between predicted and experimental binding affinities. We expect our unique models can be applied to efficient screening for hERG blockades, and our extensive understanding of the hERG-inhibitor interactions will facilitate the rational design of drugs devoid of hERG channel activity and hence with reduced cardiac toxicities. PMID:21902220

Synthesis, biological evaluation, QSAR study and molecular docking of novel N-(4-amino carbonylpiperazinyl) (thio)phosphoramide derivatives as cholinesterase inhibitors.

PubMed

Gholivand, Khodayar; Ebrahimi Valmoozi, Ali Asghar; Bonsaii, Mahyar

2014-06-01

Novel (thio)phosphoramidate derivatives based on piperidincarboxamide with the general formula of (NH2-C(O)-C5H9N)-P(X=O,S)R1R2 (1-5) and (NH2-C(O)-C5H9N)2-P(O)R (6-9) were synthesized and characterized by (31)P, (13)C, (1)H NMR, IR spectroscopy. Furthermore, the crystal structure of compound (NH2-C(O)-C5H9N)2-P(O)(OC6H5) (6) was investigated. The activities of derivatives on cholinesterases (ChE) were determined using a modified Ellman's method. Also the mixed-type mechanisms of these compounds were evaluated by Lineweaver-Burk plots. Molecular docking and quantitative structure-activity relationship (QSAR) were used to understand the relationship between molecular structural features and anti-ChE activity, and to predict the binding affinity of phosphoramido-piperidinecarboxamides (PAPCAs) to ChE receptors. From molecular docking analysis, noncovalent interactions especially hydrogen bonding as well as hydrophobic was found between PAPCAs and ChE. Based on the docking results, appropriate molecular structural parameters were adopted to develop a QSAR model. DFT-QSAR models for ChE enzymes demonstrated the importance of electrophilicity parameter in describing the anti-AChE and anti-BChE activities of the synthesized compounds. The correlation matrix of QSAR models and docking analysis confirmed that electrophilicity descriptor can control the influence of the hydrophobic properties of P=(O, S) and CO functional groups of PAPCA derivatives in the inhibition of human ChE enzymes. Copyright © 2014 Elsevier Inc. All rights reserved.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

PubMed

Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

NASA Astrophysics Data System (ADS)

Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

2014-04-01

The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

Molecular docking study, synthesis and biological evaluation of Schiff bases as Hsp90 inhibitors.

PubMed

Dutta Gupta, Sayan; Snigdha, D; Mazaira, Gisela I; Galigniana, Mario D; Subrahmanyam, C V S; Gowrishankar, N L; Raghavendra, N M

2014-04-01

Heat shock protein 90 (Hsp90) is an emerging attractive target for the discovery of novel cancer therapeutic agents. Docking methods are powerful in silico tools for lead generation and optimization. In our mission to rationally develop novel effective small molecules against Hsp90, we predicted the potency of our designed compounds by Sybyl surflex Geom X docking method. The results of the above studies revealed that Schiff bases derived from 2,4-dihydroxy benzaldehyde/5-chloro-2,4-dihydroxy benzaldehyde demonstrated effective binding with the protein. Subsequently, a few of them were synthesized (1-10) and characterized by IR, (1)HNMR and mass spectral analysis. The synthesized molecules were evaluated for their potential to suppress Hsp90 ATPase activity by Malachite green assay. The anticancer studies were performed by 3-(4,5-dimethythiazol- 2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay method. The software generated results was in satisfactory agreement with the evaluated biological activity. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

3D-QSAR and molecular docking studies on HIV protease inhibitors

NASA Astrophysics Data System (ADS)

Tong, Jianbo; Wu, Yingji; Bai, Min; Zhan, Pei

2017-02-01

In order to well understand the chemical-biological interactions governing their activities toward HIV protease activity, QSAR models of 34 cyclic-urea derivatives with inhibitory HIV were developed. The quantitative structure activity relationship (QSAR) model was built by using comparative molecular similarity indices analysis (CoMSIA) technique. And the best CoMSIA model has rcv2, rncv2 values of 0.586 and 0.931 for cross-validated and non-cross-validated. The predictive ability of CoMSIA model was further validated by a test set of 7 compounds, giving rpred2 value of 0.973. Docking studies were used to find the actual conformations of chemicals in active site of HIV protease, as well as the binding mode pattern to the binding site in protease enzyme. The information provided by 3D-QSAR model and molecular docking may lead to a better understanding of the structural requirements of 34 cyclic-urea derivatives and help to design potential anti-HIV protease molecules.

Virtual screening studies to design potent CDK2-cyclin A inhibitors.

PubMed

Vadivelan, S; Sinha, Barij Nayan; Irudayam, Sheeba Jem; Jagarlapudi, Sarma A R P

2007-01-01

The cell division cycle is controlled by cyclin-dependent kinases (CDK), which consist of a catalytic subunit (CDK1-CDK8) and a regulatory subunit (cyclin A-H). Pharmacophore analysis indicates that the best inhibitor model consists of (1) two hydrogen bond acceptors, (2) one hydrogen bond donor, and (3) one hydrophobic feature. The HypoRefine pharmacophore model gave an enrichment factor of 1.31 and goodness of fit score of 0.76. Docking studies were carried out to explore the structural requirements for the CDK2-cyclin A inhibitors and to construct highly predictive models for the design of new inhibitors. Docking studies demonstrate the important role of hydrogen bond and hydrophobic interactions in determining the inhibitor-receptor binding affinity. The validated pharmacophore model is further used for retrieving the most active hits/lead from a virtual library of molecules. Subsequently, docking studies were performed on the hits, and novel series of potent leads were suggested based on the interaction energy between CDK2-cyclin A and the putative inhibitors.

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins.

PubMed

Ashtawy, Hossam M; Mahapatra, Nihar R

2015-01-01

Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein's binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark dataset on both diverse and homogeneous (protein-family-specific) test sets. Further, we perform a systematic analysis of the performance of the proposed SFs in identifying native poses of ligands that are docked to novel protein targets. We find that the best performing ML SF has a success rate of 80% in identifying poses that are within 1 Å root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70% achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. In addition, the proposed ML SFs perform better on novel proteins that they were never trained on before. We also observed steady gains in the performance of these scoring functions as the training set size and number of features were increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex.

Machine-learning scoring functions for identifying native poses of ligands docked to known and novel proteins

PubMed Central

2015-01-01

Background Molecular docking is a widely-employed method in structure-based drug design. An essential component of molecular docking programs is a scoring function (SF) that can be used to identify the most stable binding pose of a ligand, when bound to a receptor protein, from among a large set of candidate poses. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited docking power (or ability to successfully identify the correct pose) has been a major impediment to cost-effective drug discovery. Therefore, in this work, we explore a range of novel SFs employing different machine-learning (ML) approaches in conjunction with physicochemical and geometrical features characterizing protein-ligand complexes to predict the native or near-native pose of a ligand docked to a receptor protein's binding site. We assess the docking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark dataset on both diverse and homogeneous (protein-family-specific) test sets. Further, we perform a systematic analysis of the performance of the proposed SFs in identifying native poses of ligands that are docked to novel protein targets. Results and conclusion We find that the best performing ML SF has a success rate of 80% in identifying poses that are within 1 Å root-mean-square deviation from the native poses of 65 different protein families. This is in comparison to a success rate of only 70% achieved by the best conventional SF, ASP, employed in the commercial docking software GOLD. In addition, the proposed ML SFs perform better on novel proteins that they were never trained on before. We also observed steady gains in the performance of these scoring functions as the training set size and number of features were increased by considering more protein-ligand complexes and/or more computationally-generated poses for each complex. PMID:25916860

Towards the development of universal, fast and highly accurate docking/scoring methods: a long way to go

PubMed Central

Moitessier, N; Englebienne, P; Lee, D; Lawandi, J; Corbeil, C R

2008-01-01

Accelerating the drug discovery process requires predictive computational protocols capable of reducing or simplifying the synthetic and/or combinatorial challenge. Docking-based virtual screening methods have been developed and successfully applied to a number of pharmaceutical targets. In this review, we first present the current status of docking and scoring methods, with exhaustive lists of these. We next discuss reported comparative studies, outlining criteria for their interpretation. In the final section, we describe some of the remaining developments that would potentially lead to a universally applicable docking/scoring method. PMID:18037925

Protein-Protein Docking in Drug Design and Discovery.

PubMed

Kaczor, Agnieszka A; Bartuzi, Damian; Stępniewski, Tomasz Maciej; Matosiuk, Dariusz; Selent, Jana

2018-01-01

Protein-protein interactions (PPIs) are responsible for a number of key physiological processes in the living cells and underlie the pathomechanism of many diseases. Nowadays, along with the concept of so-called "hot spots" in protein-protein interactions, which are well-defined interface regions responsible for most of the binding energy, these interfaces can be targeted with modulators. In order to apply structure-based design techniques to design PPIs modulators, a three-dimensional structure of protein complex has to be available. In this context in silico approaches, in particular protein-protein docking, are a valuable complement to experimental methods for elucidating 3D structure of protein complexes. Protein-protein docking is easy to use and does not require significant computer resources and time (in contrast to molecular dynamics) and it results in 3D structure of a protein complex (in contrast to sequence-based methods of predicting binding interfaces). However, protein-protein docking cannot address all the aspects of protein dynamics, in particular the global conformational changes during protein complex formation. In spite of this fact, protein-protein docking is widely used to model complexes of water-soluble proteins and less commonly to predict structures of transmembrane protein assemblies, including dimers and oligomers of G protein-coupled receptors (GPCRs). In this chapter we review the principles of protein-protein docking, available algorithms and software and discuss the recent examples, benefits, and drawbacks of protein-protein docking application to water-soluble proteins, membrane anchoring and transmembrane proteins, including GPCRs.

Multilevel Parallelization of AutoDock 4.2.

PubMed

Norgan, Andrew P; Coffman, Paul K; Kocher, Jean-Pierre A; Katzmann, David J; Sosa, Carlos P

2011-04-28

Virtual (computational) screening is an increasingly important tool for drug discovery. AutoDock is a popular open-source application for performing molecular docking, the prediction of ligand-receptor interactions. AutoDock is a serial application, though several previous efforts have parallelized various aspects of the program. In this paper, we report on a multi-level parallelization of AutoDock 4.2 (mpAD4). Using MPI and OpenMP, AutoDock 4.2 was parallelized for use on MPI-enabled systems and to multithread the execution of individual docking jobs. In addition, code was implemented to reduce input/output (I/O) traffic by reusing grid maps at each node from docking to docking. Performance of mpAD4 was examined on two multiprocessor computers. Using MPI with OpenMP multithreading, mpAD4 scales with near linearity on the multiprocessor systems tested. In situations where I/O is limiting, reuse of grid maps reduces both system I/O and overall screening time. Multithreading of AutoDock's Lamarkian Genetic Algorithm with OpenMP increases the speed of execution of individual docking jobs, and when combined with MPI parallelization can significantly reduce the execution time of virtual screens. This work is significant in that mpAD4 speeds the execution of certain molecular docking workloads and allows the user to optimize the degree of system-level (MPI) and node-level (OpenMP) parallelization to best fit both workloads and computational resources.

Analyses of the dynamic docking test system for advanced mission docking system test programs. [Apollo Soyuz Test Project

NASA Technical Reports Server (NTRS)

Gates, R. M.; Williams, J. E.

1974-01-01

Results are given of analytical studies performed in support of the design, implementation, checkout and use of NASA's dynamic docking test system (DDTS). Included are analyses of simulator components, a list of detailed operational test procedures, a summary of simulator performance, and an analysis and comparison of docking dynamics and loads obtained by test and analysis.

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0

NASA Astrophysics Data System (ADS)

Vistoli, Giulio; Pedretti, Alessandro; Mazzolari, Angelica; Testa, Bernard

2010-09-01

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (≅73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r 2 = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pKm values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.

Molecular Docking for Prediction and Interpretation of Adverse Drug Reactions.

PubMed

Luo, Heng; Fokoue-Nkoutche, Achille; Singh, Nalini; Yang, Lun; Hu, Jianying; Zhang, Ping

2018-05-23

Adverse drug reactions (ADRs) present a major burden for patients and the healthcare industry. Various computational methods have been developed to predict ADRs for drug molecules. However, many of these methods require experimental or surveillance data and cannot be used when only structural information is available. We collected 1,231 small molecule drugs and 600 human proteins and utilized molecular docking to generate binding features among them. We developed machine learning models that use these docking features to make predictions for 1,533 ADRs. These models obtain an overall area under the receiver operating characteristic curve (AUROC) of 0.843 and an overall area under the precision-recall curve (AUPR) of 0.395, outperforming seven structural fingerprint-based prediction models. Using the method, we predicted skin striae for fluticasone propionate, dermatitis acneiform for mometasone, and decreased libido for irinotecan, as demonstrations. Furthermore, we analyzed the top binding proteins associated with some of the ADRs, which can help to understand and/or generate hypotheses for underlying mechanisms of ADRs. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

CoMFA analyses of C-2 position salvinorin A analogs at the kappa-opioid receptor provides insights into epimer selectivity.

PubMed

McGovern, Donna L; Mosier, Philip D; Roth, Bryan L; Westkaemper, Richard B

2010-04-01

The highly potent and kappa-opioid (KOP) receptor-selective hallucinogen Salvinorin A and selected analogs have been analyzed using the 3D quantitative structure-affinity relationship technique Comparative Molecular Field Analysis (CoMFA) in an effort to derive a statistically significant and predictive model of salvinorin affinity at the KOP receptor and to provide additional statistical support for the validity of previously proposed structure-based interaction models. Two CoMFA models of Salvinorin A analogs substituted at the C-2 position are presented. Separate models were developed based on the radioligand used in the kappa-opioid binding assay, [(3)H]diprenorphine or [(125)I]6 beta-iodo-3,14-dihydroxy-17-cyclopropylmethyl-4,5 alpha-epoxymorphinan ([(125)I]IOXY). For each dataset, three methods of alignment were employed: a receptor-docked alignment derived from the structure-based docking algorithm GOLD, another from the ligand-based alignment algorithm FlexS, and a rigid realignment of the poses from the receptor-docked alignment. The receptor-docked alignment produced statistically superior results compared to either the FlexS alignment or the realignment in both datasets. The [(125)I]IOXY set (Model 1) and [(3)H]diprenorphine set (Model 2) gave q(2) values of 0.592 and 0.620, respectively, using the receptor-docked alignment, and both models produced similar CoMFA contour maps that reflected the stereoelectronic features of the receptor model from which they were derived. Each model gave significantly predictive CoMFA statistics (Model 1 PSET r(2)=0.833; Model 2 PSET r(2)=0.813). Based on the CoMFA contour maps, a binding mode was proposed for amine-containing Salvinorin A analogs that provides a rationale for the observation that the beta-epimers (R-configuration) of protonated amines at the C-2 position have a higher affinity than the corresponding alpha-epimers (S-configuration). (c) 2010. Published by Elsevier Inc.

Molecular docking and QSAR study on steroidal compounds as aromatase inhibitors.

PubMed

Dai, Yujie; Wang, Qiang; Zhang, Xiuli; Jia, Shiru; Zheng, Heng; Feng, Dacheng; Yu, Peng

2010-12-01

In order to develop more potent, selective and less toxic steroidal aromatase (AR) inhibitors, molecular docking, 2D and 3D hybrid quantitative structure-activity relationship (QSAR) study have been conducted using topological, molecular shape, spatial, structural and thermodynamic descriptors on 32 steroidal compounds. The molecular docking study shows that one or more hydrogen bonds with MET374 are one of the essential requirements for the optimum binding of ligands. The QSAR model obtained indicates that the aromatase inhibitory activity can be enhanced by increasing SIC, SC_3_C, Jurs_WNSA_1, Jurs_WPSA_1 and decreasing CDOCKER interaction energy (ECD), IAC_Total and Shadow_XZfrac. The predicted results shows that this model has a comparatively good predictive power which can be used in prediction of activity of new steroidal aromatase inhibitors. Copyright © 2010 Elsevier Masson SAS. All rights reserved.

A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

PubMed Central

Trellet, Mikael; Melquiond, Adrien S. J.; Bonvin, Alexandre M. J. J.

2013-01-01

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking. PMID:23516555

A unified conformational selection and induced fit approach to protein-peptide docking.

PubMed

Trellet, Mikael; Melquiond, Adrien S J; Bonvin, Alexandre M J J

2013-01-01

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

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

PubMed

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

2012-11-21

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

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

PubMed Central

2012-01-01

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

Docking analysis of verteporfin with YAP WW domain.

PubMed

Kandoussi, Ilham; Lakhlili, Wiame; Taoufik, Jamal; Ibrahimi, Azeddine

2017-01-01

The YAP oncogene is a known cancer target. Therefore, it is of interest to understand the molecular docking interaction of verteporfin (a derivative of benzo-porphyrin) with the WW domain of YAP (clinically used for photo-dynamic therapy in macular degeneration) as a potential WW domain-ligand modulator by inhibition. A homology protein SWISS MODEL of the human YAP protein was constructed to dock (using AutoDock vina) with the PubChem verteporfin structure for interaction analysis. The docking result shows the possibilities of verteporfin interaction with the oncogenic transcription cofactor YAP having WW1 and WW2 domains. Thus, the ability of verteporfin to bind with the YAP WW domain having modulator activity is implied in this analysis.

Morning glory resin glycosides as α-glucosidase inhibitors: In vitro and in silico analysis.

PubMed

Rosas-Ramírez, Daniel; Escandón-Rivera, Sonia; Pereda-Miranda, Rogelio

2018-04-01

Twenty-seven individual resin glycosides from the morning glory family (Convolvulaceae) were evaluated for their α-glucosidase inhibitory potential. Four of these compounds displayed an inhibitory activity comparable to acarbose, which was used as a positive control. Molecular modeling studies performed by docking analysis were accomplished to predict that the active compounds and acarbose bind to the α-1,4-glucosidase enzyme catalytic site of MAL12 from the yeast Saccharomyces cerevisiae through stable hydrogen bonds primarily with the amino acid residues HIS279 and GLN322. Docking studies with the human maltase-glucoamylase (MGAM) also identified binding modes for resin glycosides inside the catalytic site in the proximity of TYR1251. These results postulate that resin glycosides may be a source of phytotherapeutic agents with antihyperglycemic properties for the prophylaxis and treatment of non-insulin-dependent type 2 diabetes mellitus. Copyright © 2018 Elsevier Ltd. All rights reserved.

Accessible high-throughput virtual screening molecular docking software for students and educators.

PubMed

Jacob, Reed B; Andersen, Tim; McDougal, Owen M

2012-05-01

We survey low cost high-throughput virtual screening (HTVS) computer programs for instructors who wish to demonstrate molecular docking in their courses. Since HTVS programs are a useful adjunct to the time consuming and expensive wet bench experiments necessary to discover new drug therapies, the topic of molecular docking is core to the instruction of biochemistry and molecular biology. The availability of HTVS programs coupled with decreasing costs and advances in computer hardware have made computational approaches to drug discovery possible at institutional and non-profit budgets. This paper focuses on HTVS programs with graphical user interfaces (GUIs) that use either DOCK or AutoDock for the prediction of DockoMatic, PyRx, DockingServer, and MOLA since their utility has been proven by the research community, they are free or affordable, and the programs operate on a range of computer platforms.

Fully Flexible Docking of Medium Sized Ligand Libraries with RosettaLigand

PubMed Central

DeLuca, Samuel; Khar, Karen; Meiler, Jens

2015-01-01

RosettaLigand has been successfully used to predict binding poses in protein-small molecule complexes. However, the RosettaLigand docking protocol is comparatively slow in identifying an initial starting pose for the small molecule (ligand) making it unfeasible for use in virtual High Throughput Screening (vHTS). To overcome this limitation, we developed a new sampling approach for placing the ligand in the protein binding site during the initial ‘low-resolution’ docking step. It combines the translational and rotational adjustments to the ligand pose in a single transformation step. The new algorithm is both more accurate and more time-efficient. The docking success rate is improved by 10–15% in a benchmark set of 43 protein/ligand complexes, reducing the number of models that typically need to be generated from 1000 to 150. The average time to generate a model is reduced from 50 seconds to 10 seconds. As a result we observe an effective 30-fold speed increase, making RosettaLigand appropriate for docking medium sized ligand libraries. We demonstrate that this improved initial placement of the ligand is critical for successful prediction of an accurate binding position in the ‘high-resolution’ full atom refinement step. PMID:26207742

Computational Prediction and Experimental Verification of New MAP Kinase Docking Sites and Substrates Including Gli Transcription Factors

PubMed Central

Whisenant, Thomas C.; Ho, David T.; Benz, Ryan W.; Rogers, Jeffrey S.; Kaake, Robyn M.; Gordon, Elizabeth A.; Huang, Lan; Baldi, Pierre; Bardwell, Lee

2010-01-01

In order to fully understand protein kinase networks, new methods are needed to identify regulators and substrates of kinases, especially for weakly expressed proteins. Here we have developed a hybrid computational search algorithm that combines machine learning and expert knowledge to identify kinase docking sites, and used this algorithm to search the human genome for novel MAP kinase substrates and regulators focused on the JNK family of MAP kinases. Predictions were tested by peptide array followed by rigorous biochemical verification with in vitro binding and kinase assays on wild-type and mutant proteins. Using this procedure, we found new ‘D-site’ class docking sites in previously known JNK substrates (hnRNP-K, PPM1J/PP2Czeta), as well as new JNK-interacting proteins (MLL4, NEIL1). Finally, we identified new D-site-dependent MAPK substrates, including the hedgehog-regulated transcription factors Gli1 and Gli3, suggesting that a direct connection between MAP kinase and hedgehog signaling may occur at the level of these key regulators. These results demonstrate that a genome-wide search for MAP kinase docking sites can be used to find new docking sites and substrates. PMID:20865152

Homology modeling, active site prediction, and targeting the anti hypertension activity through molecular docking on endothelin – B receptor domain

PubMed Central

Rayalu, Daddam Jayasimha; Selvaraj, Chandrabose; Singh, Sanjeev Kumar; Ganeshan, Ramakrishan; Kumar, Nagapatla Udaya; Seshapani, Panthangi

2012-01-01

In cardiovascular system, activation of Endothelin receptors causes vasoconstriction which leads to Pulmonary Arterial Hypertension (PAH). Endothelin receptor antagonism has emerged as an important therapeutic strategy in pulmonary arterial hypertension. Bosentan is intended to affect vasoconstriction, hypertrophic and fibrotic effects by blocking the actions of receptors ETA and ETB. In this study we identified the action of Bosentan on endothelin B receptor using docking studies with homology modeled endothelin B receptor. Through the modeled protein, the flexible Docking study was performed with Bosentan and its derivatives with theoretically predicted active sites. The results indicated that amino acid ARG82, ARG84 and HIS197 present in endothelin B receptor are core important for binding activities and these residues are having strong hydrogen bond interactions with Bosentan. We have investigated the Bosentan and its derivatives interactions and scoring parameters using gold docking package. Among the docked compounds, one of the Bosentan derivatives BD6 shows better interaction than Bosentan with endothelin B receptor. Our results may be helpful for further investigations in both in vivo and in vitro conditions. PMID:22359440

The connection characteristics of flux pinned docking interface

NASA Astrophysics Data System (ADS)

Zhang, Mingliang; Han, Yanjun; Guo, Xing; Zhao, Cunbao; Deng, Feiyue

2017-03-01

This paper presents the mechanism and potential advantages of flux pinned docking interface mainly composed of a high temperature superconductor and an electromagnet. In order to readily assess the connection characteristics of flux pinned docking interface, the force between a high temperature superconductor and an electromagnet needs to be investigated. Based on the magnetic dipole method and the Ampere law method, the force between two current coils can be compared, which shows that the Ampere law method has the higher calculated accuracy. Based on the improved frozen image model and the Ampere law method, the force between high temperature superconductor bulk and permanent magnet can be calculated, which is validated experimentally. Moreover, the force between high temperature superconductor and electromagnet applied to flux pinned docking interface is able to be predicted and analyzed. The connection stiffness between high temperature superconductor and permanent magnet can be calculated based on the improved frozen image model and Hooke's law. The relationship between the connection stiffness and field cooling height is analyzed. Furthermore, the connection stiffness of the flux pinned docking interface is predicted and optimized, and its effective working range is defined and analyzed in case of some different parameters.

[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.

Application of 3D-QSAR, Pharmacophore, and Molecular Docking in the Molecular Design of Diarylpyrimidine Derivatives as HIV-1 Nonnucleoside Reverse Transcriptase Inhibitors.

PubMed

Liu, Genyan; Wang, Wenjie; Wan, Youlan; Ju, Xiulian; Gu, Shuangxi

2018-05-11

Diarylpyrimidines (DAPYs), acting as HIV-1 nonnucleoside reverse transcriptase inhibitors (NNRTIs), have been considered to be one of the most potent drug families in the fight against acquired immunodeficiency syndrome (AIDS). To better understand the structural requirements of HIV-1 NNRTIs, three-dimensional quantitative structure⁻activity relationship (3D-QSAR), pharmacophore, and molecular docking studies were performed on 52 DAPY analogues that were synthesized in our previous studies. The internal and external validation parameters indicated that the generated 3D-QSAR models, including comparative molecular field analysis (CoMFA, q 2 = 0.679, R 2 = 0.983, and r pred 2 = 0.884) and comparative molecular similarity indices analysis (CoMSIA, q 2 = 0.734, R 2 = 0.985, and r pred 2 = 0.891), exhibited good predictive abilities and significant statistical reliability. The docking results demonstrated that the phenyl ring at the C₄-position of the pyrimidine ring was better than the cycloalkanes for the activity, as the phenyl group was able to participate in π⁻π stacking interactions with the aromatic residues of the binding site, whereas the cycloalkanes were not. The pharmacophore model and 3D-QSAR contour maps provided significant insights into the key structural features of DAPYs that were responsible for the activity. On the basis of the obtained information, a series of novel DAPY analogues of HIV-1 NNRTIs with potentially higher predicted activity was designed. This work might provide useful information for guiding the rational design of potential HIV-1 NNRTI DAPYs.

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.

Rapid Design of Knowledge-Based Scoring Potentials for Enrichment of Near-Native Geometries in Protein-Protein Docking.

PubMed

Sasse, Alexander; de Vries, Sjoerd J; Schindler, Christina E M; de Beauchêne, Isaure Chauvot; Zacharias, Martin

2017-01-01

Protein-protein docking protocols aim to predict the structures of protein-protein complexes based on the structure of individual partners. Docking protocols usually include several steps of sampling, clustering, refinement and re-scoring. The scoring step is one of the bottlenecks in the performance of many state-of-the-art protocols. The performance of scoring functions depends on the quality of the generated structures and its coupling to the sampling algorithm. A tool kit, GRADSCOPT (GRid Accelerated Directly SCoring OPTimizing), was designed to allow rapid development and optimization of different knowledge-based scoring potentials for specific objectives in protein-protein docking. Different atomistic and coarse-grained potentials can be created by a grid-accelerated directly scoring dependent Monte-Carlo annealing or by a linear regression optimization. We demonstrate that the scoring functions generated by our approach are similar to or even outperform state-of-the-art scoring functions for predicting near-native solutions. Of additional importance, we find that potentials specifically trained to identify the native bound complex perform rather poorly on identifying acceptable or medium quality (near-native) solutions. In contrast, atomistic long-range contact potentials can increase the average fraction of near-native poses by up to a factor 2.5 in the best scored 1% decoys (compared to existing scoring), emphasizing the need of specific docking potentials for different steps in the docking protocol.

QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.

PubMed

Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon

2012-01-01

Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.

A systematic study of chemogenomics of carbohydrates.

PubMed

Gu, Jiangyong; Luo, Fang; Chen, Lirong; Yuan, Gu; Xu, Xiaojie

2014-03-04

Chemogenomics focuses on the interactions between biologically active molecules and protein targets for drug discovery. Carbohydrates are the most abundant compounds in natural products. Compared with other drugs, the carbohydrate drugs show weaker side effects. Searching for multi-target carbohydrate drugs can be regarded as a solution to improve therapeutic efficacy and safety. In this work, we collected 60 344 carbohydrates from the Universal Natural Products Database (UNPD) and explored the chemical space of carbohydrates by principal component analysis. We found that there is a large quantity of potential lead compounds among carbohydrates. Then we explored the potential of carbohydrates in drug discovery by using a network-based multi-target computational approach. All carbohydrates were docked to 2389 target proteins. The most potential carbohydrates for drug discovery and their indications were predicted based on a docking score-weighted prediction model. We also explored the interactions between carbohydrates and target proteins to find the pathological networks, potential drug candidates and new indications.

Synthesis of a novel methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene) sulfonamido]methyl}-3-(2-methoxyphenyl)prop-2-enoate: Molecular structure, spectral, antimicrobial, molecular docking and DFT computational approaches

NASA Astrophysics Data System (ADS)

Murugavel, S.; Vetri velan, V.; Kannan, Damodharan; Bakthadoss, Manickam

2017-01-01

The title compound methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene)sulfonamido] methyl}-3-(2-methoxyphenyl)prop-2-enoate (MFMSM) has been synthesized and single crystals were grown by slow evaporation solution growth technique at room temperature. XRD, FT-IR and NMR spectra of MFMSM in the solid phase were recorded and analyzed. The optimized geometry and vibrational wave numbers were computed using DFT method. The NLO, Mulliken, MEP, HOMO-LUMO energy gap and thermodynamic properties were theoretically predicted. The NBO analysis explained the intramolecular hydrogen bonding. The global chemical reactivity descriptors are calculated for MFMSM and used to predict their relative stability and reactivity. All the calculations were carried out by B3LYP/6-311G (d,p) method. MFMSM has been screened for its antimicrobial activity and found to exhibit antifungal and antibacterial effects. Docking simulation has been performed.

Discovery of new enzymes and metabolic pathways by using structure and genome context.

PubMed

Zhao, Suwen; Kumar, Ritesh; Sakai, Ayano; Vetting, Matthew W; Wood, B McKay; Brown, Shoshana; Bonanno, Jeffery B; Hillerich, Brandan S; Seidel, Ronald D; Babbitt, Patricia C; Almo, Steven C; Sweedler, Jonathan V; Gerlt, John A; Cronan, John E; Jacobson, Matthew P

2013-10-31

Assigning valid functions to proteins identified in genome projects is challenging: overprediction and database annotation errors are the principal concerns. We and others are developing computation-guided strategies for functional discovery with 'metabolite docking' to experimentally derived or homology-based three-dimensional structures. Bacterial metabolic pathways often are encoded by 'genome neighbourhoods' (gene clusters and/or operons), which can provide important clues for functional assignment. We recently demonstrated the synergy of docking and pathway context by 'predicting' the intermediates in the glycolytic pathway in Escherichia coli. Metabolite docking to multiple binding proteins and enzymes in the same pathway increases the reliability of in silico predictions of substrate specificities because the pathway intermediates are structurally similar. Here we report that structure-guided approaches for predicting the substrate specificities of several enzymes encoded by a bacterial gene cluster allowed the correct prediction of the in vitro activity of a structurally characterized enzyme of unknown function (PDB 2PMQ), 2-epimerization of trans-4-hydroxy-L-proline betaine (tHyp-B) and cis-4-hydroxy-D-proline betaine (cHyp-B), and also the correct identification of the catabolic pathway in which Hyp-B 2-epimerase participates. The substrate-liganded pose predicted by virtual library screening (docking) was confirmed experimentally. The enzymatic activities in the predicted pathway were confirmed by in vitro assays and genetic analyses; the intermediates were identified by metabolomics; and repression of the genes encoding the pathway by high salt concentrations was established by transcriptomics, confirming the osmolyte role of tHyp-B. This study establishes the utility of structure-guided functional predictions to enable the discovery of new metabolic pathways.

Predicting protein complex geometries with a neural network.

PubMed

Chae, Myong-Ho; Krull, Florian; Lorenzen, Stephan; Knapp, Ernst-Walter

2010-03-01

A major challenge of the protein docking problem is to define scoring functions that can distinguish near-native protein complex geometries from a large number of non-native geometries (decoys) generated with noncomplexed protein structures (unbound docking). In this study, we have constructed a neural network that employs the information from atom-pair distance distributions of a large number of decoys to predict protein complex geometries. We found that docking prediction can be significantly improved using two different types of polar hydrogen atoms. To train the neural network, 2000 near-native decoys of even distance distribution were used for each of the 185 considered protein complexes. The neural network normalizes the information from different protein complexes using an additional protein complex identity input neuron for each complex. The parameters of the neural network were determined such that they mimic a scoring funnel in the neighborhood of the native complex structure. The neural network approach avoids the reference state problem, which occurs in deriving knowledge-based energy functions for scoring. We show that a distance-dependent atom pair potential performs much better than a simple atom-pair contact potential. We have compared the performance of our scoring function with other empirical and knowledge-based scoring functions such as ZDOCK 3.0, ZRANK, ITScore-PP, EMPIRE, and RosettaDock. In spite of the simplicity of the method and its functional form, our neural network-based scoring function achieves a reasonable performance in rigid-body unbound docking of proteins. Proteins 2010. (c) 2009 Wiley-Liss, Inc.

Prediction of the binding sites of huperzine A in acetylcholinesterase by docking studies

NASA Astrophysics Data System (ADS)

Pang, Yuan-Ping; Kozikowski, Alan P.

1994-12-01

We have performed docking studies with the SYSDOC program on acetylcholinesterase (AChE) to predict the binding sites in AChE of huperzine A (HA), which is a potent and selective, reversible inhibitor of AChE. The unique aspects of our docking studies include the following: (i) Molecular flexibility of the guest and the host is taken into account, which permits both to change their conformations upon binding. (ii) The binding energy is evaluated by a sum of energies of steric, electrostatic and hydrogen bonding interactions. In the energy calculation no grid approximation is used, and all hydrogen atoms of the system are treated explicitly. (iii) The energy of cation-π interactions between the guest and the host, which is important in the binding of AChE, is included in the calculated binding energy. (iv) Docking is performed in all regions of the host's binding cavity. Based on our docking studies and the pharmacological results reported for HA and its analogs, we predict that HA binds to the bottom of the binding cavity of AChE (the gorge) with its ammonium group interacting with Trp84, Phe330, Glu199 and Asp72 (catalytic site). At the the opening of the gorge with its ammonium group partially interacting with Trp279 (peripheral site). At the catalytic site, three partially overlapping subsites of HA were identified which might provide a dynamic view of binding of HA to the catalytic site.

Application of binding free energy calculations to prediction of binding modes and affinities of MDM2 and MDMX inhibitors.

PubMed

Lee, Hui Sun; Jo, Sunhwan; Lim, Hyun-Suk; Im, Wonpil

2012-07-23

Molecular docking is widely used to obtain binding modes and binding affinities of a molecule to a given target protein. Despite considerable efforts, however, prediction of both properties by docking remains challenging mainly due to protein's structural flexibility and inaccuracy of scoring functions. Here, an integrated approach has been developed to improve the accuracy of binding mode and affinity prediction and tested for small molecule MDM2 and MDMX antagonists. In this approach, initial candidate models selected from docking are subjected to equilibration MD simulations to further filter the models. Free energy perturbation molecular dynamics (FEP/MD) simulations are then applied to the filtered ligand models to enhance the ability in predicting the near-native ligand conformation. The calculated binding free energies for MDM2 complexes are overestimated compared to experimental measurements mainly due to the difficulties in sampling highly flexible apo-MDM2. Nonetheless, the FEP/MD binding free energy calculations are more promising for discriminating binders from nonbinders than docking scores. In particular, the comparison between the MDM2 and MDMX results suggests that apo-MDMX has lower flexibility than apo-MDM2. In addition, the FEP/MD calculations provide detailed information on the different energetic contributions to ligand binding, leading to a better understanding of the sensitivity and specificity of protein-ligand interactions.

Homology modeling, molecular dynamics and inhibitor binding study on MurD ligase of Mycobacterium tuberculosis.

PubMed

Arvind, Akanksha; Kumar, Vivek; Saravanan, Parameswaran; Mohan, C Gopi

2012-09-01

The cell wall of mycobacterium offers well validated targets which can be exploited for discovery of new lead compounds. MurC-MurF ligases catalyze a series of irreversible steps in the biosynthesis of peptidoglycan precursor, i.e. MurD catalyzes the ligation of D-glutamate to the nucleotide precursor UMA. The three dimensional structure of Mtb-MurD is not known and was predicted by us for the first time using comparative homology modeling technique. The accuracy and stability of the predicted Mtb-MurD structure was validated using Procheck and molecular dynamics simulation. Key interactions in Mtb-MurD were studied using docking analysis of available transition state inhibitors of E.coli-MurD. The docking analysis revealed that analogues of both L and D forms of glutamic acid have similar interaction profiles with Mtb-MurD. Further, residues His192, Arg382, Ser463, and Tyr470 are proposed to be important for inhibitor-(Mtb-MurD) interactions. We also identified few pharmacophoric features essential for Mtb-MurD ligase inhibitory activity and which can further been utilized for the discovery of putative antitubercular chemotherapy.

3D-QSAR, homology modeling, and molecular docking studies on spiropiperidines analogues as agonists of nociceptin/orphanin FQ receptor.

PubMed

Liu, Ming; He, Lin; Hu, Xiaopeng; Liu, Peiqing; Luo, Hai-Bin

2010-12-01

The nociceptin/orphanin FQ receptor (NOP) has been implicated in a wide range of biological functions, including pain, anxiety, depression and drug abuse. Especially, its agonists have a great potential to be developed into anxiolytics. However, the crystal structure of NOP is still not available. In the present work, both structure-based and ligand-based modeling methods have been used to achieve a comprehensive understanding on 67N-substituted spiropiperidine analogues as NOP agonists. The comparative molecular-field analysis method was performed to formulate a reasonable 3D-QSAR model (cross-validated coefficient q(2)=0.819 and conventional r(2)=0.950), whose robustness and predictability were further verified by leave-eight-out, Y-randomization, and external test-set validations. The excellent performance of CoMFA to the affinity differences among these compounds was attributed to the contributions of electrostatic/hydrogen-bonding and steric/hydrophobic interactions, which was supported by the Surflex-Dock and CDOCKER molecular-docking simulations based on the 3D model of NOP built by the homology modeling method. The CoMFA contour maps and the molecular docking simulations were integrated to propose a binding mode for the spiropiperidine analogues at the binding site of NOP. Copyright © 2010 Elsevier Ltd. All rights reserved.

[Regression analysis to select native-like structures from decoys of antigen-antibody docking].

PubMed

Chen, Zhengshan; Chi, Xiangyang; Fan, Pengfei; Zhang, Guanying; Wang, Meirong; Yu, Changming; Chen, Wei

2018-06-25

Given the increasing exploitation of antibodies in different contexts such as molecular diagnostics and therapeutics, it would be beneficial to unravel properties of antigen-antibody interaction with modeling of computational protein-protein docking, especially, in the absence of a cocrystal structure. However, obtaining a native-like antigen-antibody structure remains challenging due in part to failing to reliably discriminate accurate from inaccurate structures among tens of thousands of decoys after computational docking with existing scoring function. We hypothesized that some important physicochemical and energetic features could be used to describe antigen-antibody interfaces and identify native-like antigen-antibody structure. We prepared a dataset, a subset of Protein-Protein Docking Benchmark Version 4.0, comprising 37 nonredundant 3D structures of antigen-antibody complexes, and used it to train and test multivariate logistic regression equation which took several important physicochemical and energetic features of decoys as dependent variables. Our results indicate that the ability to identify native-like structures of our method is superior to ZRANK and ZDOCK score for the subset of antigen-antibody complexes. And then, we use our method in workflow of predicting epitope of anti-Ebola glycoprotein monoclonal antibody-4G7 and identify three accurate residues in its epitope.

Docking analysis of verteporfin with YAP WW domain

PubMed Central

Kandoussi, Ilham; Lakhlili, Wiame; Taoufik, Jamal; Ibrahimi, Azeddine

2017-01-01

The YAP oncogene is a known cancer target. Therefore, it is of interest to understand the molecular docking interaction of verteporfin (a derivative of benzo-porphyrin) with the WW domain of YAP (clinically used for photo-dynamic therapy in macular degeneration) as a potential WW domain-ligand modulator by inhibition. A homology protein SWISS MODEL of the human YAP protein was constructed to dock (using AutoDock vina) with the PubChem verteporfin structure for interaction analysis. The docking result shows the possibilities of verteporfin interaction with the oncogenic transcription cofactor YAP having WW1 and WW2 domains. Thus, the ability of verteporfin to bind with the YAP WW domain having modulator activity is implied in this analysis. PMID:28943729

Molecular docking studies of 3-bromopyruvate and its derivatives to metabolic regulatory enzymes: Implication in designing of novel anticancer therapeutic strategies

PubMed Central

Yadav, Saveg; Pandey, Shrish Kumar; Singh, Vinay Kumar; Goel, Yugal; Kumar, Ajay

2017-01-01

Altered metabolism is an emerging hallmark of cancer, as malignant cells display a mammoth up-regulation of enzymes responsible for steering their bioenergetic and biosynthetic machinery. Thus, the recent anticancer therapeutic strategies focus on the targeting of metabolic enzymes, which has led to the identification of specific metabolic inhibitors. One of such inhibitors is 3-bromopyruvate (3-BP), with broad spectrum of anticancer activity due to its ability to inhibit multiple metabolic enzymes. However, the molecular characterization of its binding to the wide spectrum of target enzymes remains largely elusive. Therefore, in the present study we undertook in silico investigations to decipher the molecular nature of the docking of 3-BP with key target enzymes of glycolysis and TCA cycle by PatchDock and YASARA docking tools. Additionally, derivatives of 3-BP, dibromopyruvate (DBPA) and propionic acid (PA), with reported biological activity, were also investigated for docking to important target metabolic enzymes of 3-BP, in order to predict their therapeutic efficacy versus that of 3-BP. A comparison of the docking scores with respect to 3-BP indicated that both of these derivatives display a better binding strength to metabolic enzymes. Further, analysis of the drug likeness of 3-BP, DBPA and PA by Lipinski filter, admetSAR and FAF Drug3 indicated that all of these agents showed desirable drug-like criteria. The outcome of this investigation sheds light on the molecular characteristics of the binding of 3-BP and its derivatives with metabolic enzymes and thus may significantly contribute in designing and optimizing therapeutic strategies against cancer by using these agents. PMID:28463978

Molecular docking studies of 3-bromopyruvate and its derivatives to metabolic regulatory enzymes: Implication in designing of novel anticancer therapeutic strategies.

PubMed

Yadav, Saveg; Pandey, Shrish Kumar; Singh, Vinay Kumar; Goel, Yugal; Kumar, Ajay; Singh, Sukh Mahendra

2017-01-01

Altered metabolism is an emerging hallmark of cancer, as malignant cells display a mammoth up-regulation of enzymes responsible for steering their bioenergetic and biosynthetic machinery. Thus, the recent anticancer therapeutic strategies focus on the targeting of metabolic enzymes, which has led to the identification of specific metabolic inhibitors. One of such inhibitors is 3-bromopyruvate (3-BP), with broad spectrum of anticancer activity due to its ability to inhibit multiple metabolic enzymes. However, the molecular characterization of its binding to the wide spectrum of target enzymes remains largely elusive. Therefore, in the present study we undertook in silico investigations to decipher the molecular nature of the docking of 3-BP with key target enzymes of glycolysis and TCA cycle by PatchDock and YASARA docking tools. Additionally, derivatives of 3-BP, dibromopyruvate (DBPA) and propionic acid (PA), with reported biological activity, were also investigated for docking to important target metabolic enzymes of 3-BP, in order to predict their therapeutic efficacy versus that of 3-BP. A comparison of the docking scores with respect to 3-BP indicated that both of these derivatives display a better binding strength to metabolic enzymes. Further, analysis of the drug likeness of 3-BP, DBPA and PA by Lipinski filter, admetSAR and FAF Drug3 indicated that all of these agents showed desirable drug-like criteria. The outcome of this investigation sheds light on the molecular characteristics of the binding of 3-BP and its derivatives with metabolic enzymes and thus may significantly contribute in designing and optimizing therapeutic strategies against cancer by using these agents.

ConsDock: A new program for the consensus analysis of protein-ligand interactions.

PubMed

Paul, Nicodème; Rognan, Didier

2002-06-01

Protein-based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein-bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein-ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top-ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 A RMSD of the X-ray structure. It can be applied as a postprocessing filter to either single- or multiple-docking programs to prioritize three-dimensional guided lead optimization from the most likely docking solution. Copyright 2002 Wiley-Liss, Inc.

Silencing of dedicator of cytokinesis (DOCK180) obliterates pregnancy by interfering with decidualization due to blockage of nuclear entry of autoimmune regulator (AIRE).

PubMed

Mohan, Jasna Jagan; Narayan, Prashanth; Padmanabhan, Renjini Ambika; Joseph, Selin; Kumar, Pradeep G; Laloraya, Malini

2018-07-01

Dedicator of cytokinesis (DOCK 180) involved in cytoskeletal reorganization is primarily a cytosolic molecule. It is recently shown to be nuclear in HeLa cells but its nuclear function is not known. The spatiotemporal distribution of DOCK180 in uterus was studied in uterine cytoplasmic and nuclear compartments during the "window of implantation." The functional significance of nuclear DOCK180 was explored by homology modeling, co-immunoprecipitation assays, and mass spectrometric analysis. Dock180's role in early pregnancy was ascertained by Dock 180 silencing and subsequent quantitative real-time PCR and Western blotting analysis. Our study shows a nuclear DOCK180 in the uterus during "window of implantation." Estrogen and progesterone mediate expression and nuclear translocation of DOCK180. The nuclear function of DOCK180 is attributed to its ability to import autoimmune regulator (AIRE) into the nucleus. Silencing of Dock180 inhibited AIRE nuclear shuttling which influenced its downstream targets, thereby affecting decidualization with AIRE and HOXA-10 as the major players as well as lack of implantation site formation due to impact on angiogenesis-associated genes. DOCK180 has an indispensable role in pregnancy establishment as knocking down Dock180 abrogates pregnancy by a consolidated impact on decidualization and angiogenesis by regulating AIRE nuclear entry. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Machine Vision for Relative Spacecraft Navigation During Approach to Docking

NASA Technical Reports Server (NTRS)

Chien, Chiun-Hong; Baker, Kenneth

2011-01-01

This paper describes a machine vision system for relative spacecraft navigation during the terminal phase of approach to docking that: 1) matches high contrast image features of the target vehicle, as seen by a camera that is bore-sighted to the docking adapter on the chase vehicle, to the corresponding features in a 3d model of the docking adapter on the target vehicle and 2) is robust to on-orbit lighting. An implementation is provided for the case of the Space Shuttle Orbiter docking to the International Space Station (ISS) with quantitative test results using a full scale, medium fidelity mock-up of the ISS docking adapter mounted on a 6-DOF motion platform at the NASA Marshall Spaceflight Center Flight Robotics Laboratory and qualitative test results using recorded video from the Orbiter Docking System Camera (ODSC) during multiple orbiter to ISS docking missions. The Natural Feature Image Registration (NFIR) system consists of two modules: 1) Tracking which tracks the target object from image to image and estimates the position and orientation (pose) of the docking camera relative to the target object and 2) Acquisition which recognizes the target object if it is in the docking camera Field-of-View and provides an approximate pose that is used to initialize tracking. Detected image edges are matched to the 3d model edges whose predicted location, based on the pose estimate and its first time derivative from the previous frame, is closest to the detected edge1 . Mismatches are eliminated using a rigid motion constraint. The remaining 2d image to 3d model matches are used to make a least squares estimate of the change in relative pose from the previous image to the current image. The changes in position and in attitude are used as data for two Kalman filters whose outputs are smoothed estimate of position and velocity plus attitude and attitude rate that are then used to predict the location of the 3d model features in the next image.

Agonist Binding to Chemosensory Receptors: A Systematic Bioinformatics Analysis

PubMed Central

Fierro, Fabrizio; Suku, Eda; Alfonso-Prieto, Mercedes; Giorgetti, Alejandro; Cichon, Sven; Carloni, Paolo

2017-01-01

Human G-protein coupled receptors (hGPCRs) constitute a large and highly pharmaceutically relevant membrane receptor superfamily. About half of the hGPCRs' family members are chemosensory receptors, involved in bitter taste and olfaction, along with a variety of other physiological processes. Hence these receptors constitute promising targets for pharmaceutical intervention. Molecular modeling has been so far the most important tool to get insights on agonist binding and receptor activation. Here we investigate both aspects by bioinformatics-based predictions across all bitter taste and odorant receptors for which site-directed mutagenesis data are available. First, we observe that state-of-the-art homology modeling combined with previously used docking procedures turned out to reproduce only a limited fraction of ligand/receptor interactions inferred by experiments. This is most probably caused by the low sequence identity with available structural templates, which limits the accuracy of the protein model and in particular of the side-chains' orientations. Methods which transcend the limited sampling of the conformational space of docking may improve the predictions. As an example corroborating this, we review here multi-scale simulations from our lab and show that, for the three complexes studied so far, they significantly enhance the predictive power of the computational approach. Second, our bioinformatics analysis provides support to previous claims that several residues, including those at positions 1.50, 2.50, and 7.52, are involved in receptor activation. PMID:28932739

Automated large-scale file preparation, docking, and scoring: evaluation of ITScore and STScore using the 2012 Community Structure-Activity Resource benchmark.

PubMed

Grinter, Sam Z; Yan, Chengfei; Huang, Sheng-You; Jiang, Lin; Zou, Xiaoqin

2013-08-26

In this study, we use the recently released 2012 Community Structure-Activity Resource (CSAR) data set to evaluate two knowledge-based scoring functions, ITScore and STScore, and a simple force-field-based potential (VDWScore). The CSAR data set contains 757 compounds, most with known affinities, and 57 crystal structures. With the help of the script files for docking preparation, we use the full CSAR data set to evaluate the performances of the scoring functions on binding affinity prediction and active/inactive compound discrimination. The CSAR subset that includes crystal structures is used as well, to evaluate the performances of the scoring functions on binding mode and affinity predictions. Within this structure subset, we investigate the importance of accurate ligand and protein conformational sampling and find that the binding affinity predictions are less sensitive to non-native ligand and protein conformations than the binding mode predictions. We also find the full CSAR data set to be more challenging in making binding mode predictions than the subset with structures. The script files used for preparing the CSAR data set for docking, including scripts for canonicalization of the ligand atoms, are offered freely to the academic community.

GalaxyRefineComplex: Refinement of protein-protein complex model structures driven by interface repacking.

PubMed

Heo, Lim; Lee, Hasup; Seok, Chaok

2016-08-18

Protein-protein docking methods have been widely used to gain an atomic-level understanding of protein interactions. However, docking methods that employ low-resolution energy functions are popular because of computational efficiency. Low-resolution docking tends to generate protein complex structures that are not fully optimized. GalaxyRefineComplex takes such low-resolution docking structures and refines them to improve model accuracy in terms of both interface contact and inter-protein orientation. This refinement method allows flexibility at the protein interface and in the overall docking structure to capture conformational changes that occur upon binding. Symmetric refinement is also provided for symmetric homo-complexes. This method was validated by refining models produced by available docking programs, including ZDOCK and M-ZDOCK, and was successfully applied to CAPRI targets in a blind fashion. An example of using the refinement method with an existing docking method for ligand binding mode prediction of a drug target is also presented. A web server that implements the method is freely available at http://galaxy.seoklab.org/refinecomplex.

A Hadoop-based Molecular Docking System

NASA Astrophysics Data System (ADS)

Dong, Yueli; Guo, Quan; Sun, Bin

2017-10-01

Molecular docking always faces the challenge of managing tens of TB datasets. It is necessary to improve the efficiency of the storage and docking. We proposed the molecular docking platform based on Hadoop for virtual screening, it provides the preprocessing of ligand datasets and the analysis function of the docking results. A molecular cloud database that supports mass data management is constructed. Through this platform, the docking time is reduced, the data storage is efficient, and the management of the ligand datasets is convenient.

Probing ligand binding modes of Mycobacterium tuberculosis MurC ligase by molecular modeling, dynamics simulation and docking.

PubMed

Anuradha, C M; Mulakayala, Chaitanya; Babajan, Banaganapalli; Naveen, M; Rajasekhar, Chikati; Kumar, Chitta Suresh

2010-01-01

Multi drug resistance capacity for Mycobacterium tuberculosis (MDR-Mtb) demands the profound need for developing new anti-tuberculosis drugs. The present work is on Mtb-MurC ligase, which is an enzyme involved in biosynthesis of peptidoglycan, a component of Mtb cell wall. In this paper the 3-D structure of Mtb-MurC has been constructed using the templates 1GQQ and 1P31. Structural refinement and energy minimization of the predicted Mtb-MurC ligase model has been carried out by molecular dynamics. The streochemical check failures in the energy minimized model have been evaluated through Procheck, Whatif ProSA, and Verify 3D. Further torsion angles for the side chains of amino acid residues of the developed model were determined using Predictor. Docking analysis of Mtb-MurC model with ligands and natural substrates enabled us to identify specific residues viz. Gly125, Lys126, Arg331, and Arg332, within the Mtb-MurC binding pocket to play an important role in ligand and substrate binding affinity and selectivity. The availability of Mtb-MurC ligase built model, together with insights gained from docking analysis will promote the rational design of potent and selective Mtb-MurC ligase inhibitors as antituberculosis therapeutics.

3D-QSAR (CoMFA, CoMSIA), molecular docking and molecular dynamics simulations study of 6-aryl-5-cyano-pyrimidine derivatives to explore the structure requirements of LSD1 inhibitors.

PubMed

Ding, Lina; Wang, Zhi-Zheng; Sun, Xu-Dong; Yang, Jing; Ma, Chao-Ya; Li, Wen; Liu, Hong-Min

2017-08-01

Recently, Histone Lysine Specific Demethylase 1 (LSD1) was regarded as a promising anticancer target for the novel drug discovery. And several small molecules as LSD1 inhibitors in different structures have been reported. In this work, we carried out a molecular modeling study on the 6-aryl-5-cyano-pyrimidine fragment LSD1 inhibitors using three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking and molecular dynamics simulations. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used to generate 3D-QSAR models. The results show that the best CoMFA model has q 2 =0.802, r 2 ncv =0.979, and the best CoMSIA model has q 2 =0.799, r 2 ncv =0.982. The electrostatic, hydrophobic and H-bond donor fields play important roles in the models. Molecular docking studies predict the binding mode and the interactions between the ligand and the receptor protein. Molecular dynamics simulations results reveal that the complex of the ligand and the receptor protein are stable at 300K. All the results can provide us more useful information for our further drug design. Copyright © 2017. Published by Elsevier Ltd.

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

PubMed

Camacho, Carlos J

2005-08-01

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

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 Docking Studies of Flavonoids Derivatives on the Flavonoid 3- O-Glucosyltransferase.

PubMed

Harsa, Alexandra M; Harsa, Teodora E; Diudea, Mircea V; Janezic, Dusanka

2015-01-01

A study of 30 flavonoid derivatives, taken from PubChem database and docked on flavonoid 3-O-glucosyltransferase 3HBF, next submitted to a QSAR study, performed within a hypermolecule frame, to model their LD50 values, is reported. The initial set of molecules was split into a training set and the test set (taken from the best scored molecules in the docking test); the predicted LD50 values, computed on similarity clusters, built up for each of the molecules of the test set, surpassed in accuracy the best model. The binding energies to 3HBF protein, provided by the docking step, are not related to the LD50 of these flavonoids, more protein targets are to be investigated in this respect. However, the docking step was useful in choosing the test set of molecules.

CovalentDock Cloud: a web server for automated covalent docking.

PubMed

Ouyang, Xuchang; Zhou, Shuo; Ge, Zemei; Li, Runtao; Kwoh, Chee Keong

2013-07-01

Covalent binding is an important mechanism for many drugs to gain its function. We developed a computational algorithm to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online without any local installation and configuration. It provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis. This web server is free and open to all users at http://docking.sce.ntu.edu.sg/.

CovalentDock Cloud: a web server for automated covalent docking

PubMed Central

Ouyang, Xuchang; Zhou, Shuo; Ge, Zemei; Li, Runtao; Kwoh, Chee Keong

2013-01-01

Covalent binding is an important mechanism for many drugs to gain its function. We developed a computational algorithm to model this chemical event and extended it to a web server, the CovalentDock Cloud, to make it accessible directly online without any local installation and configuration. It provides a simple yet user-friendly web interface to perform covalent docking experiments and analysis online. The web server accepts the structures of both the ligand and the receptor uploaded by the user or retrieved from online databases with valid access id. It identifies the potential covalent binding patterns, carries out the covalent docking experiments and provides visualization of the result for user analysis. This web server is free and open to all users at http://docking.sce.ntu.edu.sg/. PMID:23677616

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

PubMed

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

2004-04-30

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

MEGADOCK: An All-to-All Protein-Protein Interaction Prediction System Using Tertiary Structure Data

PubMed Central

Ohue, Masahito; Matsuzaki, Yuri; Uchikoga, Nobuyuki; Ishida, Takashi; Akiyama, Yutaka

2014-01-01

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock. PMID:23855673

Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility.

PubMed

Tian, Sheng; Sun, Huiyong; Pan, Peichen; Li, Dan; Zhen, Xuechu; Li, Youyong; Hou, Tingjun

2014-10-27

In this study, to accommodate receptor flexibility, based on multiple receptor conformations, a novel ensemble docking protocol was developed by using the naïve Bayesian classification technique, and it was evaluated in terms of the prediction accuracy of docking-based virtual screening (VS) of three important targets in the kinase family: ALK, CDK2, and VEGFR2. First, for each target, the representative crystal structures were selected by structural clustering, and the capability of molecular docking based on each representative structure to discriminate inhibitors from non-inhibitors was examined. Then, for each target, 50 ns molecular dynamics (MD) simulations were carried out to generate an ensemble of the conformations, and multiple representative structures/snapshots were extracted from each MD trajectory by structural clustering. On average, the representative crystal structures outperform the representative structures extracted from MD simulations in terms of the capabilities to separate inhibitors from non-inhibitors. Finally, by using the naïve Bayesian classification technique, an integrated VS strategy was developed to combine the prediction results of molecular docking based on different representative conformations chosen from crystal structures and MD trajectories. It was encouraging to observe that the integrated VS strategy yields better performance than the docking-based VS based on any single rigid conformation. This novel protocol may provide an improvement over existing strategies to search for more diverse and promising active compounds for a target of interest.

DockTrina: docking triangular protein trimers.

PubMed

Popov, Petr; Ritchie, David W; Grudinin, Sergei

2014-01-01

In spite of the abundance of oligomeric proteins within a cell, the structural characterization of protein-protein interactions is still a challenging task. In particular, many of these interactions involve heteromeric complexes, which are relatively difficult to determine experimentally. Hence there is growing interest in using computational techniques to model such complexes. However, assembling large heteromeric complexes computationally is a highly combinatorial problem. Nonetheless the problem can be simplified greatly by considering interactions between protein trimers. After dimers and monomers, triangular trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) are the most frequently observed quaternary structural motifs according to the three-dimensional (3D) complex database. This article presents DockTrina, a novel protein docking method for modeling the 3D structures of nonsymmetrical triangular trimers. The method takes as input pair-wise contact predictions from a rigid body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation test. Finally, it ranks the predictions using a scoring function which combines triples of pair-wise contact terms and a geometric clash penalty term. The overall approach takes less than 2 min per complex on a modern desktop computer. The method is tested and validated using a benchmark set of 220 bound and seven unbound protein trimer structures. DockTrina will be made available at http://nano-d.inrialpes.fr/software/docktrina. Copyright © 2013 Wiley Periodicals, Inc.

From Molecular Docking to 3D-Quantitative Structure-Activity Relationships (3D-QSAR): Insights into the Binding Mode of 5-Lipoxygenase Inhibitors.

PubMed

Eren, Gokcen; Macchiarulo, Antonio; Banoglu, Erden

2012-02-01

Pharmacological intervention with 5-Lipoxygenase (5-LO) is a promising strategy for treatment of inflammatory and allergic ailments, including asthma. With the aim of developing predictive models of 5-LO affinity and gaining insights into the molecular basis of ligand-target interaction, we herein describe QSAR studies of 59 diverse nonredox-competitive 5-LO inhibitors based on the use of molecular shape descriptors and docking experiments. These studies have successfully yielded a predictive model able to explain much of the variance in the activity of the training set compounds while predicting satisfactorily the 5-LO inhibitory activity of an external test set of compounds. The inspection of the selected variables in the QSAR equation unveils the importance of specific interactions which are observed from docking experiments. Collectively, these results may be used to design novel potent and selective nonredox 5-LO inhibitors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

In Silico Analysis for the Study of Botulinum Toxin Structure

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2010-01-01

Protein-protein interactions play many important roles in biological function. Knowledge of protein-protein complex structure is required for understanding the function. The determination of protein-protein complex structure by experimental studies remains difficult, therefore computational prediction of protein structures by structure modeling and docking studies is valuable method. In addition, MD simulation is also one of the most popular methods for protein structure modeling and characteristics. Here, we attempt to predict protein-protein complex structure and property using some of bioinformatic methods, and we focus botulinum toxin complex as target structure.

Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

PubMed

Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

2014-10-01

Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

Comprehensive Mechanistic Analysis of Hits from High-Throughput and Docking Screens against β-Lactamase

PubMed Central

Babaoglu, Kerim; Simeonov, Anton; Irwin, John J.; Nelson, Michael E.; Feng, Brian; Thomas, Craig J.; Cancian, Laura; Costi, M. Paola; Maltby, David A.; Jadhav, Ajit; Inglese, James; Austin, Christopher P.; Shoichet, Brian K.

2009-01-01

High-throughput screening (HTS) is widely used in drug discovery. Especially for screens of unbiased libraries, false positives can dominate “hit lists”; their origins are much debated. Here we determine the mechanism of every active hit from a screen of 70,563 unbiased molecules against β-lactamase using quantitative HTS (qHTS). Of the 1274 initial inhibitors, 95% were detergent-sensitive and were classified as aggregators. Among the 70 remaining were 25 potent, covalent-acting β-lactams. Mass spectra, counter-screens, and crystallography identified 12 as promiscuous covalent inhibitors. The remaining 33 were either aggregators or irreproducible. No specific reversible inhibitors were found. We turned to molecular docking to prioritize molecules from the same library for testing at higher concentrations. Of 16 tested, 2 were modest inhibitors. Subsequent X-ray structures corresponded to the docking prediction. Analog synthesis improved affinity to 8 µM. These results suggest that it may be the physical behavior of organic molecules, not their reactivity, that accounts for most screening artifacts. Structure-based methods may prioritize weak-but-novel chemotypes in unbiased library screens. PMID:18333608

Semi-automted analysis of high-resolution aerial images to quantify docks in Upper Midwest glacial lakes

USGS Publications Warehouse

Beck, Marcus W.; Vondracek, Bruce C.; Hatch, Lorin K.; Vinje, Jason

2013-01-01

Lake resources can be negatively affected by environmental stressors originating from multiple sources and different spatial scales. Shoreline development, in particular, can negatively affect lake resources through decline in habitat quality, physical disturbance, and impacts on fisheries. The development of remote sensing techniques that efficiently characterize shoreline development in a regional context could greatly improve management approaches for protecting and restoring lake resources. The goal of this study was to develop an approach using high-resolution aerial photographs to quantify and assess docks as indicators of shoreline development. First, we describe a dock analysis workflow that can be used to quantify the spatial extent of docks using aerial images. Our approach incorporates pixel-based classifiers with object-based techniques to effectively analyze high-resolution digital imagery. Second, we apply the analysis workflow to quantify docks for 4261 lakes managed by the Minnesota Department of Natural Resources. Overall accuracy of the analysis results was 98.4% (87.7% based on ) after manual post-processing. The analysis workflow was also 74% more efficient than the time required for manual digitization of docks. These analyses have immediate relevance for resource planning in Minnesota, whereas the dock analysis workflow could be used to quantify shoreline development in other regions with comparable imagery. These data can also be used to better understand the effects of shoreline development on aquatic resources and to evaluate the effects of shoreline development relative to other stressors.

Chemical and protein structural basis for biological crosstalk between PPAR α and COX enzymes

NASA Astrophysics Data System (ADS)

Cleves, Ann E.; Jain, Ajay N.

2015-02-01

We have previously validated a probabilistic framework that combined computational approaches for predicting the biological activities of small molecule drugs. Molecule comparison methods included molecular structural similarity metrics and similarity computed from lexical analysis of text in drug package inserts. Here we present an analysis of novel drug/target predictions, focusing on those that were not obvious based on known pharmacological crosstalk. Considering those cases where the predicted target was an enzyme with known 3D structure allowed incorporation of information from molecular docking and protein binding pocket similarity in addition to ligand-based comparisons. Taken together, the combination of orthogonal information sources led to investigation of a surprising predicted relationship between a transcription factor and an enzyme, specifically, PPAR α and the cyclooxygenase enzymes. These predictions were confirmed by direct biochemical experiments which validate the approach and show for the first time that PPAR α agonists are cyclooxygenase inhibitors.

AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility

PubMed Central

Ravindranath, Pradeep Anand; Forli, Stefano; Goodsell, David S.; Olson, Arthur J.; Sanner, Michel F.

2015-01-01

Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design. While modeling receptor flexibility is important for correctly predicting ligand binding, it still remains challenging. This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori. The challenges of this approach include the: 1) exponential growth of the search space, demanding more efficient search methods; and 2) increased number of false positives, calling for scoring functions tailored for flexible receptor docking. We present AutoDockFR–AutoDock for Flexible Receptors (ADFR), a new docking engine based on the AutoDock4 scoring function, which addresses the aforementioned challenges with a new Genetic Algorithm (GA) and customized scoring function. We validate ADFR using the Astex Diverse Set, demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4. We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding. These cross-docking experiments are based on two datasets: 1) SEQ17 –a receptor diversity set containing 17 pairs of apo-holo structures; and 2) CDK2 –a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors. We show that, when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains, ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70.6% vs. 35.3% systems on SEQ17, and 76.9% vs. 61.5% on CDK2. ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets. Furthermore, we show that correctly docked CDK2 complexes re-create on average 79.8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes. Finally, we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added. PMID:26629955

Deciphering the pharmacological mechanism of the Chinese formula Huanglian-Jie-Du decoction in the treatment of ischemic stroke using a systems biology-based strategy

PubMed Central

Zhang, Yan-qiong; Wang, Song-song; Zhu, Wei-liang; Ma, Yan; Zhang, Fang-bo; Liang, Ri-xin; Xu, Hai-yu; Yang, Hong-jun

2015-01-01

Aim: Huanglian-Jie-Du decoction (HLJDD) is an important multiherb remedy in TCM, which is recently demonstrated to be effective to treat ischemic stroke. Here, we aimed to investigate the pharmacological mechanisms of HLJDD in the treatment of ischemic stroke using systems biology approaches. Methods: Putative targets of HLJDD were predicted using MetaDrug. An interaction network of putative HLJDD targets and known therapeutic targets for the treatment of ischemic stroke was then constructed, and candidate HLJDD targets were identified by calculating topological features, including 'Degree', 'Node-betweenness', 'Closeness', and 'K-coreness'. The binding efficiencies of the candidate HLJDD targets with the corresponding compositive compounds were further validated by a molecular docking simulation. Results: A total of 809 putative targets were obtained for 168 compositive compounds in HLJDD. Additionally, 39 putative targets were common to all four herbs of HLJDD. Next, 49 major nodes were identified as candidate HLJDD targets due to their network topological importance. The enrichment analysis based on the Gene Ontology (GO) annotation system and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway demonstrated that candidate HLJDD targets were more frequently involved in G-protein-coupled receptor signaling pathways, neuroactive ligand-receptor interactions and gap junctions, which all played important roles in the progression of ischemic stroke. Finally, the molecular docking simulation showed that 170 pairs of chemical components and candidate HLJDD targets had strong binding efficiencies. Conclusion: This study has developed for the first time a comprehensive systems approach integrating drug target prediction, network analysis and molecular docking simulation to reveal the relationships between the herbs contained in HLJDD and their putative targets and ischemic stroke-related pathways. PMID:25937634

3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

NASA Astrophysics Data System (ADS)

Assefa, Haregewein; Kamath, Shantaram; Buolamwini, John K.

2003-08-01

The overexpression and/or mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase has been observed in many human solid tumors, and is under intense investigation as a novel anticancer molecular target. Comparative 3D-QSAR analyses using different alignments were undertaken employing comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) for 122 anilinoquinazoline and 50 anilinoquinoline inhibitors of EGFR kinase. The SYBYL multifit alignment rule was applied to three different conformational templates, two obtained from a MacroModel Monte Carlo conformational search, and one from the bound conformation of erlotinib in complex with EGFR in the X-ray crystal structure. In addition, a flexible ligand docking alignment obtained with the GOLD docking program, and a novel flexible receptor-guided consensus dynamics alignment obtained with the DISCOVER program in the INSIGHTII modeling package were also investigated. 3D-QSAR models with q2 values up to 0.70 and r2 values up to 0.97 were obtained. Among the 4-anilinoquinazoline set, the q2 values were similar, but the ability of the different conformational models to predict the activities of an external test set varied considerably. In this regard, the model derived using the X-ray crystallographically determined bioactive conformation of erlotinib afforded the best predictive model. Electrostatic, hydrophobic and H-bond donor descriptors contributed the most to the QSAR models of the 4-anilinoquinazolines, whereas electrostatic, hydrophobic and H-bond acceptor descriptors contributed the most to the 4-anilinoquinoline QSAR, particularly the H-bond acceptor descriptor. A novel receptor-guided consensus dynamics alignment has also been introduced for 3D-QSAR studies. This new alignment method may incorporate to some extent ligand-receptor induced fit effects into 3D-QSAR models.

Molecular Docking of Enzyme Inhibitors: A Computational Tool for Structure-Based Drug Design

ERIC Educational Resources Information Center

Rudnitskaya, Aleksandra; Torok, Bela; Torok, Marianna

2010-01-01

Molecular docking is a frequently used method in structure-based rational drug design. It is used for evaluating the complex formation of small ligands with large biomolecules, predicting the strength of the bonding forces and finding the best geometrical arrangements. The major goal of this advanced undergraduate biochemistry laboratory exercise…

Sedimentation problems in a lateral dock on the Paraná River

NASA Astrophysics Data System (ADS)

Latessa, Gaston; Sabarots Gerbec, Martin; Arecco, Pablo

2017-04-01

The Paraná River is one of the largest water courses in the world and along its reach in the Argentine territory, it receives a large load of sediments from the Pilcomayo and Bermejo Rivers, through the Paraguay River, in the upper basin at the North of Argentina and South of Bolivia. The suspended sediment load is estimated in 100 Million ton/year. This unique characteristic drives the Paraná River morphology downstream, as well as the Paraná delta morphodynamics. On top of its natural behaviour, the Paraná-Paraguay river system is an important inland waterway transport corridor, with a significant amount of sea going vessels and inland barges navigating throughout stretches of more than 3000 Km. Consequently, there are numerous port complexes and terminals along the river banks. The typical wet infrastructure of these terminals is usually composed by jetties and quay walls, and occasionally with side or lateral docks. Whereas, the case included within this study presents all these components. This study presents a hydrodynamic and sedimentology 3D model to predict the velocity fields and the associated shear stresses that will drive morphological processes in the lateral dock. The terminal layout, side dock configuration, and sedimentation issues will be analyzed from multidisciplinary point of view, under different hydrological events and considering the correlated sediment loads. Recent bathymetry studies had been carried out and this set of data will be implemented to build the domain geometry. The flow series is as well extended with the up to date gauged flows and levels, to carry out statistical analysis and identify the design flows for different probabilities. The main objective of this analysis will be to understand and identify the scour and deposition processes and the possible problems to the structures safety and the operation of the docks, and introduce variations to the baseline design, if necessary. Results will be contrasted and validated with empirical formulae and criteria.

Docking glycosaminoglycans to proteins: analysis of solvent inclusion

NASA Astrophysics Data System (ADS)

Samsonov, Sergey A.; Teyra, Joan; Pisabarro, M. Teresa

2011-05-01

Glycosaminoglycans (GAGs) are anionic polysaccharides, which participate in key processes in the extracellular matrix by interactions with protein targets. Due to their charged nature, accurate consideration of electrostatic and water-mediated interactions is indispensable for understanding GAGs binding properties. However, solvent is often overlooked in molecular recognition studies. Here we analyze the abundance of solvent in GAG-protein interfaces and investigate the challenges of adding explicit solvent in GAG-protein docking experiments. We observe PDB GAG-protein interfaces being significantly more hydrated than protein-protein interfaces. Furthermore, by applying molecular dynamics approaches we estimate that about half of GAG-protein interactions are water-mediated. With a dataset of eleven GAG-protein complexes we analyze how solvent inclusion affects Autodock 3, eHiTs, MOE and FlexX docking. We develop an approach to de novo place explicit solvent into the binding site prior to docking, which uses the GRID program to predict positions of waters and to locate possible areas of solvent displacement upon ligand binding. To investigate how solvent placement affects docking performance, we compare these results with those obtained by taking into account information about the solvent position in the crystal structure. In general, we observe that inclusion of solvent improves the results obtained with these methods. Our data show that Autodock 3 performs best, though it experiences difficulties to quantitatively reproduce experimental data on specificity of heparin/heparan sulfate disaccharides binding to IL-8. Our work highlights the current challenges of introducing solvent in protein-GAGs recognition studies, which is crucial for exploiting the full potential of these molecules for rational engineering.

The Importance of Ligand Conformational Energies in Carbohydrate Docking: Sorting the Wheat from the Chaff

PubMed Central

Nivedha, Anita K.; Makeneni, Spandana; Foley, B. Lachele; Tessier, Matthew B.; Woods, Robert J.

2014-01-01

Docking algorithms that aim to be applicable to a broad range of ligands suffer reduced accuracy because they are unable to incorporate ligand-specific conformational energies. Here, we develop internal energy functions, Carbohydrate Intrinsic (CHI), to account for the rotational preferences of the glycosidic torsion angles in carbohydrates. The relative energies predicted by the CHI energy functions mirror the conformational distributions of glycosidic linkages determined from a survey of oligosaccharide-protein complexes in the Protein Data Bank. Addition of CHI energies to the standard docking scores in Autodock 3, 4.2, and Vina consistently improves pose ranking of oligosaccharides docked to a set of anti-carbohydrate antibodies. The CHI energy functions are also independent of docking algorithm, and with minor modifications, may be incorporated into both theoretical modeling methods, and experimental NMR or X-ray structure refinement programs. PMID:24375430

Structural Interface Parameters Are Discriminatory in Recognising Near-Native Poses of Protein-Protein Interactions

PubMed Central

Malhotra, Sony; Sankar, Kannan; Sowdhamini, Ramanathan

2014-01-01

Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets. PMID:24498255

Structural interface parameters are discriminatory in recognising near-native poses of protein-protein interactions.

PubMed

Malhotra, Sony; Sankar, Kannan; Sowdhamini, Ramanathan

2014-01-01

Interactions at the molecular level in the cellular environment play a very crucial role in maintaining the physiological functioning of the cell. These molecular interactions exist at varied levels viz. protein-protein interactions, protein-nucleic acid interactions or protein-small molecules interactions. Presently in the field, these interactions and their mechanisms mark intensively studied areas. Molecular interactions can also be studied computationally using the approach named as Molecular Docking. Molecular docking employs search algorithms to predict the possible conformations for interacting partners and then calculates interaction energies. However, docking proposes number of solutions as different docked poses and hence offers a serious challenge to identify the native (or near native) structures from the pool of these docked poses. Here, we propose a rigorous scoring scheme called DockScore which can be used to rank the docked poses and identify the best docked pose out of many as proposed by docking algorithm employed. The scoring identifies the optimal interactions between the two protein partners utilising various features of the putative interface like area, short contacts, conservation, spatial clustering and the presence of positively charged and hydrophobic residues. DockScore was first trained on a set of 30 protein-protein complexes to determine the weights for different parameters. Subsequently, we tested the scoring scheme on 30 different protein-protein complexes and native or near-native structure were assigned the top rank from a pool of docked poses in 26 of the tested cases. We tested the ability of DockScore to discriminate likely dimer interactions that differ substantially within a homologous family and also demonstrate that DOCKSCORE can distinguish correct pose for all 10 recent CAPRI targets.

Evaluation of the respiratory health of dock workers who load grain cargoes in British Columbia.

PubMed Central

Dimich-Ward, H D; Kennedy, S M; Dittrick, M A; DyBuncio, A; Chan-Yeung, M

1995-01-01

OBJECTIVES--To investigate the respiratory health of dock workers who load grain cargoes. METHODS--The respiratory health of 118 dock workers who load grain cargoes in the ports of Vancouver and Prince Rupert was compared with that of 555 grain elevator workers from the same regions. 128 civic workers were used as an unexposed control group. RESULTS--The prevalences of chronic cough and phlegm were at least as high in dock workers as those found in the elevator workers, and when adjusted for differences in duration of employment and smoking, dock workers had an eightfold higher risk of developing chronic phlegm than did civic workers. Symptoms of eye and skin irritation that were experienced at least monthly were highest for dock workers. Average percentage of the predicted FEV1 and FVC for dock workers (mean 100.6% and 105.3% respectively) were similar to the civic workers but significantly higher than those found for elevator workers. Higher subjective estimates of duration of exposure to grain dust (hours/day) were associated with lower values of FEV1. CONCLUSIONS--The more intermittent grain dust exposure patterns of dock workers may have allowed for some recovery of lung function, but chronic respiratory symptoms were less labile. PMID:7795744

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase.

PubMed

Singh, Nidhi; Shah, Priyanka; Dwivedi, Hemlata; Mishra, Shikha; Tripathi, Renu; Sahasrabuddhe, Amogh A; Siddiqi, Mohammad Imran

2016-11-15

N-Myristoyltransferase (NMT) catalyzes the transfer of myristate to the amino-terminal glycine of a subset of proteins, a co-translational modification involved in trafficking substrate proteins to membrane locations, stabilization and protein-protein interactions. It is a studied and validated pre-clinical drug target for fungal and parasitic infections. In the present study, a machine learning approach, docking studies and CoMFA analysis have been integrated with the objective of translation of knowledge into a pipelined workflow towards the identification of putative hits through the screening of large compound libraries. In the proposed pipeline, the reported parasitic NMT inhibitors have been used to develop predictive machine learning classification models. Simultaneously, a TbNMT complex model was generated to establish the relationship between the binding mode of the inhibitors for LmNMT and TbNMT through molecular dynamics simulation studies. A 3D-QSAR model was developed and used to predict the activity of the proposed hits in the subsequent step. The hits classified as active based on the machine learning model were assessed as the potential anti-trypanosomal NMT inhibitors through molecular docking studies, predicted activity using a QSAR model and visual inspection. In the final step, the proposed pipeline was validated through in vitro experiments. A total of seven hits have been proposed and tested in vitro for evaluation of dual inhibitory activity against Leishmania donovani and Trypanosoma brucei. Out of these five compounds showed significant inhibition against both of the organisms. The common topmost active compound SEW04173 belongs to a pyrazole carboxylate scaffold and is anticipated to enrich the chemical space with enhanced potency through optimization.

Detailed analysis of grid-based molecular docking: A case study of CDOCKER-A CHARMm-based MD docking algorithm.

PubMed

Wu, Guosheng; Robertson, Daniel H; Brooks, Charles L; Vieth, Michal

2003-10-01

The influence of various factors on the accuracy of protein-ligand docking is examined. The factors investigated include the role of a grid representation of protein-ligand interactions, the initial ligand conformation and orientation, the sampling rate of the energy hyper-surface, and the final minimization. A representative docking method is used to study these factors, namely, CDOCKER, a molecular dynamics (MD) simulated-annealing-based algorithm. A major emphasis in these studies is to compare the relative performance and accuracy of various grid-based approximations to explicit all-atom force field calculations. In these docking studies, the protein is kept rigid while the ligands are treated as fully flexible and a final minimization step is used to refine the docked poses. A docking success rate of 74% is observed when an explicit all-atom representation of the protein (full force field) is used, while a lower accuracy of 66-76% is observed for grid-based methods. All docking experiments considered a 41-member protein-ligand validation set. A significant improvement in accuracy (76 vs. 66%) for the grid-based docking is achieved if the explicit all-atom force field is used in a final minimization step to refine the docking poses. Statistical analysis shows that even lower-accuracy grid-based energy representations can be effectively used when followed with full force field minimization. The results of these grid-based protocols are statistically indistinguishable from the detailed atomic dockings and provide up to a sixfold reduction in computation time. For the test case examined here, improving the docking accuracy did not necessarily enhance the ability to estimate binding affinities using the docked structures. Copyright 2003 Wiley Periodicals, Inc.

In silico studies and fluorescence binding assays of potential anti-prion compounds reveal an important binding site for prion inhibition from PrP(C) to PrP(Sc).

PubMed

Pagadala, Nataraj S; Perez-Pineiro, Rolando; Wishart, David S; Tuszynski, Jack A

2015-02-16

To understand the pharmacophore properties of 2-aminothiazoles and design novel inhibitors against the prion protein, a highly predictive 3D quantitative structure-activity relationship (QSAR) has been developed by performing comparative molecular field analysis (CoMFA) and comparative similarity analysis (CoMSIA). Both CoMFA and CoMSIA maps reveal the presence of the oxymethyl groups in meta and para positions on the phenyl ring of compound 17 (N-[4-(3,4-dimethoxyphenyl)-1,3-thiazol-2-yl]quinolin-2-amine), is necessary for activity while electro-negative nitrogen of quinoline is highly favorable to enhance activity. The blind docking results for these compounds show that the compound with quinoline binds with higher affinity than isoquinoline and naphthalene groups. Out of 150 novel compounds retrieved using finger print analysis by pharmacophoric model predicted based on five test sets of compounds, five compounds with diverse scaffolds were selected for biological evaluation as possible PrP inhibitors. Molecular docking combined with fluorescence quenching studies show that these compounds bind to pocket-D of SHaPrP near Trp145. The new antiprion compounds 3 and 6, which bind with the interaction energies of -12.1 and -13.2 kcal/mol, respectively, show fluorescence quenching with binding constant (Kd) values of 15.5 and 44.14 μM, respectively. Further fluorescence binding assays with compound 5, which is similar to 2-aminothiazole as a positive control, also show that the molecule binds to the pocket-D with the binding constant (Kd) value of 84.7 μM. Finally, both molecular docking and a fluorescence binding assay of noscapine as a negative control reveals the same binding site on the surface of pocket-A near a rigid loop between β2 and α2 interacting with Arg164. This high level of correlation between molecular docking and fluorescence quenching studies confirm that these five compounds are likely to act as inhibitors for prion propagation while noscapine might act as a prion accelerator from PrP(C) to PrP(Sc). Copyright © 2014 Elsevier Masson SAS. All rights reserved.

Activation of a camptothecin prodrug by specific carboxylesterases as predicted by quantitative structure-activity relationship and molecular docking studies.

PubMed

Yoon, Kyoung Jin P; Krull, Erik J; Morton, Christopher L; Bornmann, William G; Lee, Richard E; Potter, Philip M; Danks, Mary K

2003-11-01

7-Ethyl-10-[4-(1-piperidino)-1-piperidino]carbonyloxycamptothecin (irinotecan, CPT-11) is a camptothecin prodrug that is metabolized by carboxylesterases (CE) to the active metabolite 7-ethyl-10-hydroxycamptothecin (SN-38), a topoisomerase I inhibitor. CPT-11 has shown encouraging antitumor activity against a broad spectrum of tumor types in early clinical trials, but hematopoietic and gastrointestinal toxicity limit its administration. To increase the therapeutic index of CPT-11 and to develop other prodrug analogues for enzyme/prodrug gene therapy applications, our laboratories propose to develop camptothecin prodrugs that will be activated by specific CEs. Specific analogues might then be predicted to be activated, for example, predominantly by human liver CE(hCE1), by human intestinal CE (hiCE), or in gene therapy approaches using a rabbit liver CE (rCE). This study describes a molecular modeling approach to relate the structure of rCE-activated camptothecin prodrugs with their biological activation. Comparative molecular field analysis, comparative molecular similarity index analysis, and docking studies were used to predict the biological activity of a 4-benzylpiperazine derivative of CPT-11 [7-ethyl-10-[4-(1-benzyl)-1-piperazino]carbonyloxycamptothecin (BP-CPT)] in U373MG glioma cell lines transfected with plasmids encoding rCE or hiCE. BP-CPT has been reported to be activated more efficiently than CPT-11 by a rat serum esterase activity; however, three-dimensional quantitative structure-activity relationship studies predicted that rCE would activate BP-CPT less efficiently than CPT-11. This was confirmed by both growth inhibition experiments and kinetic studies. The method is being used to design camptothecin prodrugs predicted to be activated by specific CEs.

Cellulase enzyme: Homology modeling, binding site identification and molecular docking

NASA Astrophysics Data System (ADS)

Selvam, K.; Senbagam, D.; Selvankumar, T.; Sudhakar, C.; Kamala-Kannan, S.; Senthilkumar, B.; Govarthanan, M.

2017-12-01

Cellulase is an enzyme that degrades the linear polysaccharide like cellulose into glucose by breaking the β-1,4- glycosidic bonds. These enzymes are the third largest enzymes with a great potential towards the ethanol production and play a vital role in degrading the biomass. The production of ethanol depends upon the ability of the cellulose to utilize the wide range of substrates. In this study, the 3D structure of cellulase from Acinetobacter sp. was modeled by using Modeler 9v9 and validated by Ramachandran plot. The accuracy of the predicted 3D structure was checked using Ramachandran plot analysis showed that 81.1% in the favored region, compatibility of an atomic model (3D) with amino acid sequence (1D) for the model was observed as 78.21% and 49.395% for Verify 3D and ERRAT at SAVES server. As the binding efficacy with the substrate might suggests the choice of the substrate as carbon and nitrogen sources, the cellobiose, cellotetraose, cellotetriose and laminaribiose were employed in the docking studies. The docking of cellobiose, cellotetraose, cellotetriose and laminaribiose with cellulase exhibited the binding energy of -6.1523 kJ/mol, -7.8759 kJ/mol,-6.1590 kJ/mol and -6.7185 kJ/mol, respectively. These docking studies revealed that cellulase has the greater potential towards the cellotetraose as a substrate for the high yield of ethanol.

Cross-Link Guided Molecular Modeling with ROSETTA

PubMed Central

Leitner, Alexander; Rosenberger, George; Aebersold, Ruedi; Malmström, Lars

2013-01-01

Chemical cross-links identified by mass spectrometry generate distance restraints that reveal low-resolution structural information on proteins and protein complexes. The technology to reliably generate such data has become mature and robust enough to shift the focus to the question of how these distance restraints can be best integrated into molecular modeling calculations. Here, we introduce three workflows for incorporating distance restraints generated by chemical cross-linking and mass spectrometry into ROSETTA protocols for comparative and de novo modeling and protein-protein docking. We demonstrate that the cross-link validation and visualization software Xwalk facilitates successful cross-link data integration. Besides the protocols we introduce XLdb, a database of chemical cross-links from 14 different publications with 506 intra-protein and 62 inter-protein cross-links, where each cross-link can be mapped on an experimental structure from the Protein Data Bank. Finally, we demonstrate on a protein-protein docking reference data set the impact of virtual cross-links on protein docking calculations and show that an inter-protein cross-link can reduce on average the RMSD of a docking prediction by 5.0 Å. The methods and results presented here provide guidelines for the effective integration of chemical cross-link data in molecular modeling calculations and should advance the structural analysis of particularly large and transient protein complexes via hybrid structural biology methods. PMID:24069194

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery.

PubMed

Catana, Cornel; Stouten, Pieter F W

2007-01-01

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.

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

PubMed

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

2018-06-21

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

Important Aspects of Post-Prandial Antidiabetic Drug, Acarbose.

PubMed

Singla, Rajeev Kumar; Singh, Radha; Dubey, Ashok Kumar

2016-01-01

Acarbose, a well known and efficacious α-amylase and α-glucosidase inhibitor, is a postprandial acting antidiabetic drug. DNS-based α-amylase inhibitory assays showed that use of acarbose at concentrations above 125 µg/ml resulted in release of reducing sugar in the reaction, an unexpected observation. Objective of the present study was to design experimental strategies to address this unusual finding. Acarbose was found to be susceptible to thermo-lysis. Further, besides being an inhibitor, it could also be hydrolyzed by porcine pancreatic α-amylase, but had weaker affinity for α - amylase compared to starch. GRIP docking was done for the mechanistic analysis of the active site in the enzyme for substrate, inhibitor and, inhibitor's metabolite (K2). Interaction between acarbose and α-amylase involved significant hydrogen binding compared to that of starch, producing a stronger enzyme-inhibitor complex. Further, docking analysis led us to predict the site on α-amylase where the inhibitor (acarbose) bound more tightly, which possibly affected the binding and hydrolysis of starch exerting its effective anti-diabetic function.

Synthesis, crystal structure analysis, molecular docking studies and density functional theory predictions of the local reactive properties and degradation properties of a novel halochalcone

NASA Astrophysics Data System (ADS)

Arshad, Suhana; Pillai, Renjith Raveendran; Zainuri, Dian Alwani; Khalib, Nuridayanti Che; Razak, Ibrahim Abdul; Armaković, Stevan; Armaković, Sanja J.

2017-09-01

In the present study, single crystals of E)-3-(3,5-dichlorophenyl)-1-(4-fluorophenyl)prop-2-en-1-one, were prepared and structurally characterized by single crystal X-ray diffraction analysis. The molecular structure crystallized in monoclinic crystal system with P21/c space group. Sensitivity of the title molecule towards electrophilic attacks has been examined by calculations of average localized ionization energies (ALIE) and their mapping to electron density surface. Further determination of atoms that could be important reactive centres has been performed by calculations of Fukui functions. Sensitivity of title molecule towards autoxidation and hydrolysis mechanisms has been assessed by calculations of bond dissociation energies and radial distribution functions (RDF), respectively. Also, in order to explore possible binding mode of the title compound towards Dihydrofolate reductase enzyme, we have utilized in silico molecular docking to explore possible binding modes of the title compound with the DHFR enzyme.

Spectroscopic, quantum chemical calculation and molecular docking of dipfluzine

NASA Astrophysics Data System (ADS)

Srivastava, Karnica; Srivastava, Anubha; Tandon, Poonam; Sinha, Kirti; Wang, Jing

2016-12-01

Molecular structure and vibrational analysis of dipfluzine (C27H29FN2O) were presented using FT-IR and FT-Raman spectroscopy and quantum chemical calculations. The theoretical ground state geometry and electronic structure of dipfluzine are optimized by the DFT/B3LYP/6-311++G (d,p) method and compared with those of the crystal data. The 1D potential energy scan was performed by varying the dihedral angle using B3LYP functional at 6-31G(d,p) level of theory and thus the most stable conformer of the compound were determined. Molecular electrostatic potential surface (MEPS), frontier orbital analysis and electronic reactivity descriptor were used to predict the chemical reactivity of molecule. Energies of intra- and inter-molecular hydrogen bonds in molecule and their electronic aspects were investigated by natural bond orbital (NBO). To find out the anti-apoptotic activity of the title compound molecular docking studies have been performed against protein Fas.

New antimicrobial peptides against foodborne pathogens: From in silico design to experimental evidence.

PubMed

Palmieri, Gianna; Balestrieri, Marco; Proroga, Yolande T R; Falcigno, Lucia; Facchiano, Angelo; Riccio, Alessia; Capuano, Federico; Marrone, Raffaele; Neglia, Gianluca; Anastasio, Aniello

2016-11-15

Recently there has been growing interest in the discovery of new antimicrobial agents to increase safety and shelf-life of food products. Here, we developed an innovative approach by introducing the concept that mitochondrial targeting peptides (MTP) can interact and disrupt bacterial membranes, acting as antimicrobial agents. As proof-of-principle, we used a multidisciplinary strategy by combining in silico predictions, docking simulations and antimicrobial assays, to identify two peptides, MTP1 and MTP2, which were structurally and functionally characterized. Both compounds appeared effective against Listeria monocytogenes, one of the most important foodborne pathogens. Specifically, a significant bactericidal activity was evidenced with EC50 values of 16.8±1.2μM for MTP1 and 109±7.0μM for MTP2. Finally, NMR structure determinations suggested that MTP1 would be oriented into the membrane bilayer, while the molecular shape of MTP2 could indicate porin-mediated antimicrobial mechanisms, as predicted using molecular docking analysis. Therefore, MTPs represent alternative sources to design new potential bio-preservatives. Copyright © 2016 Elsevier Ltd. All rights reserved.

Binding site exploration of CCR5 using in silico methodologies: a 3D-QSAR approach.

PubMed

Gadhe, Changdev G; Kothandan, Gugan; Cho, Seung Joo

2013-01-01

Chemokine receptor 5 (CCR5) is an important receptor used by human immunodeficiency virus type 1 (HIV-1) to gain viral entry into host cell. In this study, we used a combined approach of comparative modeling, molecular docking, and three dimensional quantitative structure activity relationship (3D-QSAR) analyses to elucidate detailed interaction of CCR5 with their inhibitors. Docking study of the most potent inhibitor from a series of compounds was done to derive the bioactive conformation. Parameters such as random selection, rational selection, different charges and grid spacing were utilized in the model development to check their performance on the model predictivity. Final comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were chosen based on the rational selection method, Gasteiger-Hückel charges and a grid spacing of 0.5 Å. Rational model for CoMFA (q(2) = 0.722, r(2) = 0.884, Q(2) = 0.669) and CoMSIA (q(2) = 0.712, r(2) = 0.825, Q(2) = 0.522) was obtained with good statistics. Mapping of contour maps onto CCR5 interface led us to better understand of the ligand-protein interaction. Docking analysis revealed that the Glu283 is crucial for interaction. Two new amino acid residues, Tyr89 and Thr167 were identified as important in ligand-protein interaction. No site directed mutagenesis studies on these residues have been reported.

Virtual Screening of Novel Glucosamine-6-Phosphate Synthase Inhibitors.

PubMed

Lather, Amit; Sharma, Sunil; Khatkar, Anurag

2018-01-01

Infections caused by microorganisms are the major cause of death today. The tremendous and improper use of antimicrobial agents leads to antimicrobial resistance. Various currently available antimicrobial drugs are inadequate to control the infections and lead to various adverse drug reactions. Efforts based on computer-aided drug design (CADD) can excavate a large number of databases to generate new, potent hits and minimize the requirement of time as well as money for the discovery of newer antimicrobials. Pharmaceutical sciences also have made development with advances in drug designing concepts. The current research article focuses on the study of various G-6-P synthase inhibitors from literature cited molecular database. Docking analysis was conducted and ADMET data of various molecules was evaluated by Schrodinger Glide and PreADMET software, respectively. Here, the results presented efficacy of various inhibitors towards enzyme G-6-P synthase. Docking scores, binding energy and ADMET data of various molecules showed good inhibitory potential toward G-6-P synthase as compared to standard antibiotics. This novel antimicrobial drug target G-6-P synthase has not so extensively been explored for its application in antimicrobial therapy, so the work done so far proved highly essential. This article has helped the drug researchers and scientists to intensively explore about this wonderful antimicrobial drug target. The Schrodinger, Inc. (New York, USA) software was utilized to carry out the computational calculations and docking studies. The hardware configuration was Intel® core (TM) i5-4210U CPU @ 2.40GHz, RAM memory 4.0 GB under 64-bit window operating system. The ADMET data was calculated by using the PreADMET tool (PreADMET ver. 2.0). All the computational work was completed in the Laboratory for Enzyme Inhibition Studies, Department of Pharmaceutical Sciences, M.D. University, Rohtak, INDIA. Molecular docking studies were carried out to identify the binding affinities and interaction between the inhibitors and the target proteins (G-6-P synthase) by using Glide software (Schrodinger Inc. U.S.A.-Maestro version 10.2). Grid-based Ligand Docking with Energetic (Glide) is one of the most accurate docking softwares available for ligand-protein, protein-protein binding studies. A library of hundreds of available ligands was docked against targeted proteins G-6-P synthase having PDB ID 1moq. Results of docking are shown in Table 1 and Table 2. Results of G-6-P synthase docking showed that some compounds were found to have comparable docking score and binding energy (kj/mol) as compared to standard antibiotics. Many of the ligands showed hydrogen bond interaction, hydrophobic interactions, electrostatic interactions, ionic interactions and π- π stacking with the various amino acid residues in the binding pockets of G-6-P synthase. The docking study estimated free energy of binding, binding pose andglide score and all these parameters provide a promising tool for the discovery of new potent natural inhibitors of G-6-P synthase. These G-6-P synthase inhibitors could further be used as antimicrobials. Here, a detailed binding analysis and new insights of inhibitors from various classes of molecules were docked in binding cavity of G-6-P synthase. ADME and toxicity prediction of these compounds will further accentuate us to study these compounds in vivo. This information will possibly present further expansion of effective antimicrobials against several microbial infections. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Use of Natural Products as Chemical Library for Drug Discovery and Network Pharmacology

PubMed Central

Gu, Jiangyong; Gui, Yuanshen; Chen, Lirong; Yuan, Gu; Lu, Hui-Zhe; Xu, Xiaojie

2013-01-01

Background Natural products have been an important source of lead compounds for drug discovery. How to find and evaluate bioactive natural products is critical to the achievement of drug/lead discovery from natural products. Methodology We collected 19,7201 natural products structures, reported biological activities and virtual screening results. Principal component analysis was employed to explore the chemical space, and we found that there was a large portion of overlap between natural products and FDA-approved drugs in the chemical space, which indicated that natural products had large quantity of potential lead compounds. We also explored the network properties of natural product-target networks and found that polypharmacology was greatly enriched to those compounds with large degree and high betweenness centrality. In order to make up for a lack of experimental data, high throughput virtual screening was employed. All natural products were docked to 332 target proteins of FDA-approved drugs. The most potential natural products for drug discovery and their indications were predicted based on a docking score-weighted prediction model. Conclusions Analysis of molecular descriptors, distribution in chemical space and biological activities of natural products was conducted in this article. Natural products have vast chemical diversity, good drug-like properties and can interact with multiple cellular target proteins. PMID:23638153

Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

PubMed

Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

2018-02-01

Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

A Computational Approach to Finding Novel Targets for Existing Drugs

PubMed Central

Li, Yvonne Y.; An, Jianghong; Jones, Steven J. M.

2011-01-01

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects. PMID:21909252

Molecular Docking and Prediction of Pharmacokinetic Properties of Dual Mechanism Drugs that Block MAO-B and Adenosine A2A Receptors for the Treatment of Parkinson's Disease

PubMed Central

Azam, Faizul; Madi, Arwa M.; Ali, Hamed I.

2012-01-01

Monoamine oxidase B (MAO-B) inhibitory potential of adenosine A2A receptor (AA2AR) antagonists has raised the possibility of designing dual-target–directed drugs that may provide enhanced symptomatic relief and that may also slow the progression of Parkinson's disease (PD) by protecting against further neurodegeneration. To explain the dual inhibition of MAO-B and AA2AR at the molecular level, molecular docking technique was employed. Lamarckian genetic algorithm methodology was used for flexible ligand docking studies. A good correlation (R2= 0.524 and 0.627 for MAO-B and AA2AR, respectively) was established between docking predicted and experimental Ki values, which confirms that the molecular docking approach is reliable to study the mechanism of dual interaction of caffeinyl analogs with MAO-B and AA2AR. Parameters for Lipinski's “Rule-of-Five” were also calculated to estimate the pharmacokinetic properties of dual-target–directed drugs where both MAO-B inhibition and AA2AR antagonism exhibited a positive correlation with calculated LogP having a correlation coefficient R2 of 0.535 and 0.607, respectively. These results provide some beneficial clues in structural modification for designing new inhibitors as dual-target–directed drugs with desired pharmacokinetic properties for the treatment of PD. PMID:23112538

Efficient Relaxation of Protein-Protein Interfaces by Discrete Molecular Dynamics Simulations.

PubMed

Emperador, Agusti; Solernou, Albert; Sfriso, Pedro; Pons, Carles; Gelpi, Josep Lluis; Fernandez-Recio, Juan; Orozco, Modesto

2013-02-12

Protein-protein interactions are responsible for the transfer of information inside the cell and represent one of the most interesting research fields in structural biology. Unfortunately, after decades of intense research, experimental approaches still have difficulties in providing 3D structures for the hundreds of thousands of interactions formed between the different proteins in a living organism. The use of theoretical approaches like docking aims to complement experimental efforts to represent the structure of the protein interactome. However, we cannot ignore that current methods have limitations due to problems of sampling of the protein-protein conformational space and the lack of accuracy of available force fields. Cases that are especially difficult for prediction are those in which complex formation implies a non-negligible change in the conformation of the interacting proteins, i.e., those cases where protein flexibility plays a key role in protein-protein docking. In this work, we present a new approach to treat flexibility in docking by global structural relaxation based on ultrafast discrete molecular dynamics. On a standard benchmark of protein complexes, the method provides a general improvement over the results obtained by rigid docking. The method is especially efficient in cases with large conformational changes upon binding, in which structure relaxation with discrete molecular dynamics leads to a predictive success rate double that obtained with state-of-the-art rigid-body docking.

Identifying the binding mode of a molecular scaffold

NASA Astrophysics Data System (ADS)

Chema, Doron; Eren, Doron; Yayon, Avner; Goldblum, Amiram; Zaliani, Andrea

2004-01-01

We describe a method for docking of a scaffold-based series and present its advantages over docking of individual ligands, for determining the binding mode of a molecular scaffold in a binding site. The method has been applied to eight different scaffolds of protein kinase inhibitors (PKI). A single analog of each of these eight scaffolds was previously crystallized with different protein kinases. We have used FlexX to dock a set of molecules that share the same scaffold, rather than docking a single molecule. The main mode of binding is determined by the mode of binding of the largest cluster among the docked molecules that share a scaffold. Clustering is based on our `nearest single neighbor' method [J. Chem. Inf. Comput. Sci., 43 (2003) 208-217]. Additional criteria are applied in those cases in which more than one significant binding mode is found. Using the proposed method, most of the crystallographic binding modes of these scaffolds were reconstructed. Alternative modes, that have not been detected yet by experiments, could also be identified. The method was applied to predict the binding mode of an additional molecular scaffold that was not yet reported and the predicted binding mode has been found to be very similar to experimental results for a closely related scaffold. We suggest that this approach be used as a virtual screening tool for scaffold-based design processes.

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

NASA Astrophysics Data System (ADS)

Logean, Antoine; Rognan, Didier

2002-04-01

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

Infrared spectrum, NBO, HOMO-LUMO, MEP and molecular docking studies (2E)-3-(3-nitrophenyl)-1-[4-piperidin-1-yl]prop-2-en-1-one.

PubMed

Panicker, C Yohannan; Varghese, Hema Tresa; Nayak, Prakash S; Narayana, B; Sarojini, B K; Fun, H K; War, Javeed Ahamad; Srivastava, S K; Van Alsenoy, C

2015-09-05

FT-IR spectrum of (2E)-3-(3-nitrophenyl)-1-[4-piperidin-1-yl]prop-2-en-1-one was recorded and analyzed. The vibrational wavenumbers were computed using HF and DFT quantum chemical calculations. The data obtained from wavenumber calculations are used to assign IR bands. Potential energy distribution was done using GAR2PED software. The geometrical parameters of the title compound are in agreement with the XRD results. NBO analysis, HOMO-LUMO, first and second hyperpolarizability and molecular electrostatic potential results are also reported. The possible electrophile attacking sites of the title molecule is identified using MEP surface plot study. Molecular docking results predicted the anti-leishmanic activity for the compound. Copyright © 2015. Published by Elsevier B.V.

Synthesis, spectroscopic investigations, DFT studies, molecular docking and antimicrobial potential of certain new indole-isatin molecular hybrids: Experimental and theoretical approaches

NASA Astrophysics Data System (ADS)

Almutairi, Maha S.; Zakaria, Azza S.; Ignasius, P. Primsa; Al-Wabli, Reem I.; Joe, Isaac Hubert; Attia, Mohamed I.

2018-02-01

Indole-isatin molecular hybrids 5a-i have been synthesized and characterized by different spectroscopic methods to be evaluated as new antimicrobial agents against a panel of Gram positive bacteria, Gram negative bacteria, and moulds. Compound 5h was selected as a representative example of the prepared compounds 5a-i to perform computational investigations. Its vibrational properties have been studied using FT-IR and FT-Raman with the aid of density functional theory approach. The natural bond orbital analysis as well as HOMO and LUMO molecular orbitals investigations of compound 5h were carried out to explore its possible intermolecular delocalization or hyperconjugation and its possible interactions with the target protein. Molecular docking of compound 5h predicted its binding mode with the fungal target protein.

3D-QSAR and docking studies of 3-Pyridine heterocyclic derivatives as potent PI3K/mTOR inhibitors

NASA Astrophysics Data System (ADS)

Yang, Wenjuan; Shu, Mao; Wang, Yuanqiang; Wang, Rui; Hu, Yong; Meng, Lingxin; Lin, Zhihua

2013-12-01

Phosphoinosmde-3-kinase/ mammalian target of rapamycin (PI3K/mTOR) dual inhibitors have attracted a great deal of interest as antitumor drugs research. In order to design and optimize these dual inhibitors, two types of 3D-quantitative structure-activity relationship (3D-QSAR) studies based on the ligand alignment and receptor alignment were applied using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). In the study based on ligands alignment, models of PI3K (CoMFA with r2, 0.770; q2, 0.622; CoMSIA with r2, 0.945; q2, 0.748) and mTOR (CoMFA with r2, 0.850; q2, 0.654; CoMSIA with r2, 0.983; q2, 0.676) have good predictability. And in the study based on receptor alignment, models of PI3K (CoMFA with r2, 0.745; q2, 0.538; CoMSIA with r2, 0.938; q2, 0.630) and mTOR (CoMFA with r2, 0.977; q2, 0.825; CoMSIA with r2, 0.985; q2, 0.728) also have good predictability. 3D contour maps and docking results suggested different groups on the core parts of the compounds could enhance the biological activities. Finally, ten derivatives as potential candidates of PI3K/mTOR inhibitors with good predicted activities were designed.

Molecular docking studies of curcumin natural derivatives with DNA topoisomerase I and II-DNA complexes.

PubMed

Kumar, Anil; Bora, Utpal

2014-12-01

DNA topoisomerase I (topo I) and II (topo II) are essential enzymes that solve the topological problems of DNA by allowing DNA strands or double helices to pass through each other during cellular processes such as replication, transcription, recombination, and chromatin remodeling. Their critical roles make topoisomerases an attractive drug target against cancer. The present molecular docking study provides insights into the inhibition of topo I and II by curcumin natural derivatives. The binding modes suggested that curcumin natural derivatives docked at the site of DNA cleavage parallel to the axis of DNA base pairing. Cyclocurcumin and curcumin sulphate were predicted to be the most potent inhibitors amongst all the curcumin natural derivatives docked. The binding modes of cyclocurcumin and curcumin sulphate were similar to known inhibitors of topo I and II. Residues like Arg364, Asn722 and base A113 (when docked to topo I-DNA complex) and residues Asp479, Gln778 and base T9 (when docked to topo II-DNA complex) seem to play important role in the binding of curcumin natural derivatives at the site of DNA cleavage.

Assessing the binding of cholinesterase inhibitors by docking and molecular dynamics studies.

PubMed

Ali, M Rejwan; Sadoqi, Mostafa; Møller, Simon G; Boutajangout, Allal; Mezei, Mihaly

2017-09-01

In this report we assessed by docking and molecular dynamics the binding mechanisms of three FDA-approved Alzheimer drugs, inhibitors of the enzyme acetylcholinesterase (AChE): donepezil, galantamine and rivastigmine. Dockings by the softwares Autodock-Vina, PatchDock and Plant reproduced the docked conformations of the inhibitor-enzyme complexes within 2Å of RMSD of the X-ray structure. Free-energy scores show strong affinity of the inhibitors for the enzyme binding pocket. Three independent Molecular Dynamics simulation runs indicated general stability of donepezil, galantamine and rivastigmine in their respective enzyme binding pocket (also referred to as gorge) as well as the tendency to form hydrogen bonds with the water molecules. The binding of rivastigmine in the Torpedo California AChE binding pocket is interesting as it eventually undergoes carbamylation and breaks apart according to the X-ray structure of the complex. Similarity search in the ZINC database and targeted docking on the gorge region of the AChE enzyme gave new putative inhibitor molecules with high predicted binding affinity, suitable for potential biophysical and biological assessments. Copyright © 2017 Elsevier Inc. All rights reserved.

Empirical scoring functions for advanced protein-ligand docking with PLANTS.

PubMed

Korb, Oliver; Stützle, Thomas; Exner, Thomas E

2009-01-01

In this paper we present two empirical scoring functions, PLANTS(CHEMPLP) and PLANTS(PLP), designed for our docking algorithm PLANTS (Protein-Ligand ANT System), which is based on ant colony optimization (ACO). They are related, regarding their functional form, to parts of already published scoring functions and force fields. The parametrization procedure described here was able to identify several parameter settings showing an excellent performance for the task of pose prediction on two test sets comprising 298 complexes in total. Up to 87% of the complexes of the Astex diverse set and 77% of the CCDC/Astex clean listnc (noncovalently bound complexes of the clean list) could be reproduced with root-mean-square deviations of less than 2 A with respect to the experimentally determined structures. A comparison with the state-of-the-art docking tool GOLD clearly shows that this is, especially for the druglike Astex diverse set, an improvement in pose prediction performance. Additionally, optimized parameter settings for the search algorithm were identified, which can be used to balance pose prediction reliability and search speed.

A Molecular docking study to predict enantioseparation of some chiral carboxylic acid derivatives by methyl-β-cyclodextrin

NASA Astrophysics Data System (ADS)

Nurhidayah, E. S.; Ivansyah, A. L.; Martoprawiro, M. A.; Zulfikar, M. A.

2018-05-01

A molecular docking study, using molecular mechanics calculations with Arguslab, was used to help predict the enantioseparation of some guest molecules of chiral carboxylic acid derivatives by heptakis-2,6-di-O-methyl-β-cyclodextrin (DIMEB) and heptakis-2,3,6-tri-O-methyl-β-cyclodextrin (TRIMEB) as host molecules. The small differences in the binding free energy values (ΔΔG) obtained from Arguslab did not indicate any significant enantioseparation. From the molecular docking simulation results, it is predicted that in the case of DIMEB as host molecule, R-enantiomer of Etodolac, Fenoprofen, Indoprofen, Ketorolac, and Naproxen will be eluted first than S-enantiomer; However, S-enantiomer of Carprofen, Flurbiprofen, Ketoprofen, Pirprofen, Proglumide, Sulindac, Surprofen, and Zaltoprofen will be eluted first than R-enantiomer by DIMEB as host molecule. When TRIMEB is used as a host molecule, R-enantiomer of Carprofen, Flurbiprofen, Indoprofen, Ketoprofen, Naproxen, Pirprofen, and Surprofen will be eluted first than S-enantiomer; However, S-enantiomer of Etodolac, Fenoprofen, Ketorolac, Proglumide, Sulindac and Zaltoprofen will be eluted first than R-enantiomer by TRIMEB as host molecule.

A ligand-based comparative molecular field analysis (CoMFA) and homology model based molecular docking studies on 3', 4'-dihydroxyflavones as rat 5-lipoxygenase inhibitors: Design of new inhibitors.

PubMed

Ahamed, T K Shameera; Muraleedharan, K

2017-12-01

In this study, ligand based comparative molecular field analysis (CoMFA) with five principal components was performed on class of 3', 4'-dihydroxyflavone derivatives for potent rat 5-LOX inhibitors. The percentage contributions in building of CoMFA model were 91.36% for steric field and 8.6% for electrostatic field. R 2 values for training and test sets were found to be 0.9320 and 0.8259, respectively. In case of LOO, LTO and LMO cross validation test, q 2 values were 0.6587, 0.6479 and 0.5547, respectively. These results indicate that the model has high statistical reliability and good predictive power. The extracted contour maps were used to identify the important regions where the modification was necessary to design a new molecule with improved activity. The study has developed a homology model for rat 5-LOX and recognized the key residues at the binding site. Docking of most active molecule to the binding site of 5-LOX confirmed the stability and rationality of CoMFA model. Based on molecular docking results and CoMFA contour plots, new inhibitors with higher activity with respect to the most active compound in data set were designed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Automated Docking with Protein Flexibility in the Design of Femtomolar “Click Chemistry” Inhibitors of Acetylcholinesterase

PubMed Central

Morris, Garrett M.; Green, Luke G.; Radić, Zoran; Taylor, Palmer; Sharpless, K. Barry; Olson, Arthur J.; Grynszpan, Flavio

2013-01-01

The use of computer-aided structure-based drug design prior to synthesis has proven to be generally valuable in suggesting improved binding analogues of existing ligands.1 Here we describe the application of the program AutoDock2 to the design of a focused library that was used in the “click chemistry in-situ” generation of the most potent non-covalent inhibitor of the enzyme acetylcholinesterase (AChE) yet developed (Kd = ~100 fM).3 AutoDock version 3.0.5 has been widely distributed and successfully used to predict bound conformations of flexible ligands. Here, we also used a version of AutoDock which permits additional conformational flexibility in selected amino acid sidechains of the target protein. PMID:23451944

A Message Passing Approach to Side Chain Positioning with Applications in Protein Docking Refinement *

PubMed Central

Moghadasi, Mohammad; Kozakov, Dima; Mamonov, Artem B.; Vakili, Pirooz; Vajda, Sandor; Paschalidis, Ioannis Ch.

2013-01-01

We introduce a message-passing algorithm to solve the Side Chain Positioning (SCP) problem. SCP is a crucial component of protein docking refinement, which is a key step of an important class of problems in computational structural biology called protein docking. We model SCP as a combinatorial optimization problem and formulate it as a Maximum Weighted Independent Set (MWIS) problem. We then employ a modified and convergent belief-propagation algorithm to solve a relaxation of MWIS and develop randomized estimation heuristics that use the relaxed solution to obtain an effective MWIS feasible solution. Using a benchmark set of protein complexes we demonstrate that our approach leads to more accurate docking predictions compared to a baseline algorithm that does not solve the SCP. PMID:23515575

Molecular docking and simulation studies of gustatory receptor of Aedes aegypti: A potent drug target to distract host-seeking behaviour in mosquitoes.

PubMed

Gupta, Krishna Kant; Sethi, Guneswar; Jayaraman, Manikandan

2016-01-01

It is well reported that exhaled CO 2 and skin odour from human being assist female mosquitoes to locate human host. Basically, the receptors for this activity are expressed in cpA neurons. In both Aedes aegypti and Anopheles gambiae, this CO 2-sensitive olfactory neuron detects myriad number of chemicals present in human skin. Therefore, manipulation of gustatory receptors housing these neurons may serve as important targets for behavioural intervention. The study was aimed towards virtual screening of small molecules in the analyzed conserved active site residues of gustatory receptor and molecular dynamics simulation study of optimum protein-ligand complex to identify a suitable lead molecule for distracting host-seeking behaviour of mosquitoes. The conserved residue analysis of gustatory receptor (GR) of Ae. aegypti and An. gambiae was performed. The structure of GR protein from Ae. aegypti was modeled and validated, and then molecular docking was performed to screen 2903 small molecules against the predicted active residues of GR. Further, simulation studies were also carried out to prove protein-ligand stability. The glutamine 154 residue of GR was found to be highly conserved in Ae. aegypti and An. gambiae. Docking results indicated that the dodecanoic acid, 1,2,3-propanetriyl ester (dynasan 112) was interacting with this residue, as it showed better LibDock score than previously reported ethyl acetate used as mosquito repellant. Simulation studies indicated the structural instability of GR protein in docked form with dynasan 112 suggesting its involvement in structural changes. Based on the interaction energies and stability, this compound has been proposed to be used in mosquitoes' repellant. A novel effective odorant acting as inhibitor of GR is proposed based on its stability, docking score, interactions and RMSD, considering ethyl pyruvate as a standard inhibitor. Host preference and host-seeking ability of mosquito vectors play key roles in disease transmission, a clear understanding of these aspects is essential for preventing the spread of the disease.

Molecular docking, 3D QSAR and dynamics simulation studies of imidazo-pyrrolopyridines as janus kinase 1 (JAK 1) inhibitors.

PubMed

Itteboina, Ramesh; Ballu, Srilata; Sivan, Sree Kanth; Manga, Vijjulatha

2016-10-01

Janus kinase 1 (JAK 1) plays a critical role in initiating responses to cytokines by the JAK-signal transducer and activator of transcription (JAK-STAT). This controls survival, proliferation and differentiation of a variety of cells. Docking, 3D quantitative structure activity relationship (3D-QSAR) and molecular dynamics (MD) studies were performed on a series of Imidazo-pyrrolopyridine derivatives reported as JAK 1 inhibitors. QSAR model was generated using 30 molecules in the training set; developed model showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of this model was determined using a test set of 13 molecules that gave acceptable predictive correlation (r 2 Pred ) values. Finally, molecular dynamics simulation was performed to validate docking results and MM/GBSA calculations. This facilitated us to compare binding free energies of cocrystal ligand and newly designed molecule R1. The good concordance between the docking results and CoMFA/CoMSIA contour maps afforded obliging clues for the rational modification of molecules to design more potent JAK 1 inhibitors. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Prediction of Botulinum Toxin Structure Based on in Silico and in Vitro Analysis

NASA Astrophysics Data System (ADS)

Suzuki, Tomonori; Miyazaki, Satoru

2011-01-01

Many of biological system mediated through protein-protein interactions. Knowledge of protein-protein complex structure is required for understanding the function. The determination of huge size and flexible protein-protein complex structure by experimental studies remains difficult, costly and five-consuming, therefore computational prediction of protein structures by homolog modeling and docking studies is valuable method. In addition, MD simulation is also one of the most powerful methods allowing to see the real dynamics of proteins. Here, we predict protein-protein complex structure of botulinum toxin to analyze its property. These bioinformatics methods are useful to report the relation between the flexibility of backbone structure and the activity.

Progress in protein-protein docking: atomic resolution predictions in the CAPRI experiment using RosettaDock with an improved treatment of side-chain flexibility.

PubMed

Schueler-Furman, Ora; Wang, Chu; Baker, David

2005-08-01

RosettaDock uses real-space Monte Carlo minimization (MCM) on both rigid-body and side-chain degrees of freedom to identify the lowest free energy docked arrangement of 2 protein structures. An improved version of the method that uses gradient-based minimization for off-rotamer side-chain optimization and includes information from unbound structures was used to create predictions for Rounds 4 and 5 of CAPRI. First, large numbers of independent MCM trajectories were carried out and the lowest free energy docked configurations identified. Second, new trajectories were started from these lowest energy structures to thoroughly sample the surrounding conformation space, and the lowest energy configurations were submitted as predictions. For all cases in which there were no significant backbone conformational changes, a small number of very low-energy configurations were identified in the first, global search and subsequently found to be close to the center of the basin of attraction in the free energy landscape in the second, local search. Following the release of the experimental coordinates, it was found that the centers of these free energy minima were remarkably close to the native structures in not only the rigid-body orientation but also the detailed conformations of the side-chains. Out of 8 targets, the lowest energy models had interface root-mean-square deviations (RMSDs) less than 1.1 A from the correct structures for 6 targets, and interface RMSDs less than 0.4 A for 3 targets. The predictions were top submissions to CAPRI for Targets 11, 12, 14, 15, and 19. The close correspondence of the lowest free energy structures found in our searches to the experimental structures suggests that our free energy function is a reasonable representation of the physical chemistry, and that the real space search with full side-chain flexibility to some extent solves the protein-protein docking problem in the absence of significant backbone conformational changes. On the other hand, the approach fails when there are significant backbone conformational changes as the steric complementarity of the 2 proteins cannot be modeled without incorporating backbone flexibility, and this is the major goal of our current work.

Enthalpy-Driven RNA Folding: Single-Molecule Thermodynamics of Tetraloop–Receptor Tertiary Interaction†

PubMed Central

Fiore, Julie L.; Kraemer, Benedikt; Koberling, Felix; Edmann, Rainer; Nesbitt, David J.

2010-01-01

RNA folding thermodynamics are crucial for structure prediction, which requires characterization of both enthalpic and entropic contributions of tertiary motifs to conformational stability. We explore the temperature dependence of RNA folding due to the ubiquitous GAAA tetraloop–receptor docking interaction, exploiting immobilized and freely diffusing single-molecule fluorescence resonance energy transfer (smFRET) methods. The equilibrium constant for intramolecular docking is obtained as a function of temperature (T = 21–47 °C), from which a van’t Hoff analysis yields the enthalpy (ΔH°) and entropy (ΔS°) of docking. Tetraloop–receptor docking is significantly exothermic and entropically unfavorable in 1 mM MgCl2 and 100 mM NaCl, with excellent agreement between immobilized (ΔH° = −17.4 ± 1.6 kcal/mol, and ΔS° = −56.2 ± 5.4 cal mol−1 K−1) and freely diffusing (ΔH° = −17.2 ± 1.6 kcal/mol, and ΔS° = −55.9 ± 5.2 cal mol−1 K−1) species. Kinetic heterogeneity in the tetraloop–receptor construct is unaffected over the temperature range investigated, indicating a large energy barrier for interconversion between the actively docking and nondocking subpopulations. Formation of the tetraloop–receptor interaction can account for ~60% of the ΔH° and ΔS° of P4–P6 domain folding in the Tetrahymena ribozyme, suggesting that it may act as a thermodynamic clamp for the domain. Comparison of the isolated tetraloop–receptor and other tertiary folding thermodynamics supports a theme that enthalpy- versus entropy-driven folding is determined by the number of hydrogen bonding and base stacking interactions. PMID:19186984

Probing the dynamic nature of water molecules and their influences on ligand binding in a model binding site.

PubMed

Cappel, Daniel; Wahlström, Rickard; Brenk, Ruth; Sotriffer, Christoph A

2011-10-24

The model binding site of the cytochrome c peroxidase (CCP) W191G mutant is used to investigate the structural and dynamic properties of the water network at the buried cavity using computational methods supported by crystallographic analysis. In particular, the differences of the hydration pattern between the uncomplexed state and various complexed forms are analyzed as well as the differences between five complexes of CCP W191G with structurally closely related ligands. The ability of docking programs to correctly handle the water molecules in these systems is studied in detail. It is found that fully automated prediction of water replacement or retention upon docking works well if some additional preselection is carried out but not necessarily if the entire water network in the cavity is used as input. On the other hand, molecular interaction fields for water calculated from static crystal structures and hydration density maps obtained from molecular dynamics simulations agree very well with crystallographically observed water positions. For one complex, the docking and MD results sensitively depend on the quality of the starting structure, and agreement is obtained only after redetermination of the crystal structure and refinement at higher resolution.

SRMS Assisted Docking and Undocking for the Orbiter Repair Maneuver

NASA Technical Reports Server (NTRS)

Quiocho, Leslie J.; Briscoe, Timothy J.; Schliesing, John A.; Braman, Julia M.

2005-01-01

As part of the Orbiter Repair Maneuver (ORM) planned for Return to Flight (RTF) operations, the Shuttle Remote Manipulator System (SRMS) must undock the Orbiter, maneuver it through a complex trajectory at extremely low rates, present it to an EVA crewman at the end of the Space Station Remote Manipulator System to perform the Thermal Protection System (TPS) repair, and then retrace back through the trajectory to dock the Orbiter with the Orbiter Docking System (ODs). The initial and final segments of this operation involve the interaction between the SRMS, ISS, Orbiter and ODs. This paper first provides an overview of the Monte-Carlo screening analysis for the installation (both nominal and contingency), including the variation of separation distance, misalignment conditions, SRMS joint/brake parameter characteristics, and PRCS jet combinations and corresponding thrust durations. The resulting 'optimum' solution is presented based on trade studies between predicted capture success and integrated system loads. This paper then discusses the upgrades to the APAS math model associated with the new SRMS assisted undocking technique and reviews simulation results for various options investigated for either the active and passive separation of the ISS from the Orbiter.

Developing a Comparative Docking Protocol for the Prediction of Peptide Selectivity Profiles: Investigation of Potassium Channel Toxins

PubMed Central

Chen, Po-Chia; Kuyucak, Serdar

2012-01-01

During the development of selective peptides against highly homologous targets, a reliable tool is sought that can predict information on both mechanisms of binding and relative affinities. These tools must first be tested on known profiles before application on novel therapeutic candidates. We therefore present a comparative docking protocol in HADDOCK using critical motifs, and use it to “predict” the various selectivity profiles of several major αKTX scorpion toxin families versus Kv1.1, Kv1.2 and Kv1.3. By correlating results across toxins of similar profiles, a comprehensive set of functional residues can be identified. Reasonable models of channel-toxin interactions can be then drawn that are consistent with known affinity and mutagenesis. Without biological information on the interaction, HADDOCK reproduces mechanisms underlying the universal binding of αKTX-2 toxins, and Kv1.3 selectivity of αKTX-3 toxins. The addition of constraints encouraging the critical lysine insertion confirms these findings, and gives analogous explanations for other families, including models of partial pore-block in αKTX-6. While qualitatively informative, the HADDOCK scoring function is not yet sufficient for accurate affinity-ranking. False minima in low-affinity complexes often resemble true binding in high-affinity complexes, despite steric/conformational penalties apparent from visual inspection. This contamination significantly complicates energetic analysis, although it is usually possible to obtain correct ranking via careful interpretation of binding-well characteristics and elimination of false positives. Aside from adaptations to the broader potassium channel family, we suggest that this strategy of comparative docking can be extended to other channels of interest with known structure, especially in cases where a critical motif exists to improve docking effectiveness. PMID:22474570

Modeling complexes of modeled proteins.

PubMed

Anishchenko, Ivan; Kundrotas, Petras J; Vakser, Ilya A

2017-03-01

Structural characterization of proteins is essential for understanding life processes at the molecular level. However, only a fraction of known proteins have experimentally determined structures. This fraction is even smaller for protein-protein complexes. Thus, structural modeling of protein-protein interactions (docking) primarily has to rely on modeled structures of the individual proteins, which typically are less accurate than the experimentally determined ones. Such "double" modeling is the Grand Challenge of structural reconstruction of the interactome. Yet it remains so far largely untested in a systematic way. We present a comprehensive validation of template-based and free docking on a set of 165 complexes, where each protein model has six levels of structural accuracy, from 1 to 6 Å C α RMSD. Many template-based docking predictions fall into acceptable quality category, according to the CAPRI criteria, even for highly inaccurate proteins (5-6 Å RMSD), although the number of such models (and, consequently, the docking success rate) drops significantly for models with RMSD > 4 Å. The results show that the existing docking methodologies can be successfully applied to protein models with a broad range of structural accuracy, and the template-based docking is much less sensitive to inaccuracies of protein models than the free docking. Proteins 2017; 85:470-478. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling

NASA Astrophysics Data System (ADS)

Bhakat, Soumendranath; Åberg, Emil; Söderhjelm, Pär

2018-01-01

Advanced molecular docking methods often aim at capturing the flexibility of the protein upon binding to the ligand. In this study, we investigate whether instead a simple rigid docking method can be applied, if combined with multiple target structures to model the backbone flexibility and molecular dynamics simulations to model the sidechain and ligand flexibility. The methods are tested for the binding of 35 ligands to FXR as part of the first stage of the Drug Design Data Resource (D3R) Grand Challenge 2 blind challenge. The results show that the multiple-target docking protocol performs surprisingly well, with correct poses found for 21 of the ligands. MD simulations started on the docked structures are remarkably stable, but show almost no tendency of refining the structure closer to the experimentally found binding pose. Reconnaissance metadynamics enhances the exploration of new binding poses, but additional collective variables involving the protein are needed to exploit the full potential of the method.

Prediction of binding poses to FXR using multi-targeted docking combined with molecular dynamics and enhanced sampling.

PubMed

Bhakat, Soumendranath; Åberg, Emil; Söderhjelm, Pär

2018-01-01

Advanced molecular docking methods often aim at capturing the flexibility of the protein upon binding to the ligand. In this study, we investigate whether instead a simple rigid docking method can be applied, if combined with multiple target structures to model the backbone flexibility and molecular dynamics simulations to model the sidechain and ligand flexibility. The methods are tested for the binding of 35 ligands to FXR as part of the first stage of the Drug Design Data Resource (D3R) Grand Challenge 2 blind challenge. The results show that the multiple-target docking protocol performs surprisingly well, with correct poses found for 21 of the ligands. MD simulations started on the docked structures are remarkably stable, but show almost no tendency of refining the structure closer to the experimentally found binding pose. Reconnaissance metadynamics enhances the exploration of new binding poses, but additional collective variables involving the protein are needed to exploit the full potential of the method.

FlexAID: Revisiting Docking on Non-Native-Complex Structures.

PubMed

Gaudreault, Francis; Najmanovich, Rafael J

2015-07-27

Small-molecule protein docking is an essential tool in drug design and to understand molecular recognition. In the present work we introduce FlexAID, a small-molecule docking algorithm that accounts for target side-chain flexibility and utilizes a soft scoring function, i.e. one that is not highly dependent on specific geometric criteria, based on surface complementarity. The pairwise energy parameters were derived from a large dataset of true positive poses and negative decoys from the PDBbind database through an iterative process using Monte Carlo simulations. The prediction of binding poses is tested using the widely used Astex dataset as well as the HAP2 dataset, while performance in virtual screening is evaluated using a subset of the DUD dataset. We compare FlexAID to AutoDock Vina, FlexX, and rDock in an extensive number of scenarios to understand the strengths and limitations of the different programs as well as to reported results for Glide, GOLD, and DOCK6 where applicable. The most relevant among these scenarios is that of docking on flexible non-native-complex structures where as is the case in reality, the target conformation in the bound form is not known a priori. We demonstrate that FlexAID, unlike other programs, is robust against increasing structural variability. FlexAID obtains equivalent sampling success as GOLD and performs better than AutoDock Vina or FlexX in all scenarios against non-native-complex structures. FlexAID is better than rDock when there is at least one critical side-chain movement required upon ligand binding. In virtual screening, FlexAID results are lower on average than those of AutoDock Vina and rDock. The higher accuracy in flexible targets where critical movements are required, intuitive PyMOL-integrated graphical user interface and free source code as well as precompiled executables for Windows, Linux, and Mac OS make FlexAID a welcome addition to the arsenal of existing small-molecule protein docking methods.

DockQ: A Quality Measure for Protein-Protein Docking Models

PubMed Central

Basu, Sankar

2016-01-01

The state-of-the-art to assess the structural quality of docking models is currently based on three related yet independent quality measures: Fnat, LRMS, and iRMS as proposed and standardized by CAPRI. These quality measures quantify different aspects of the quality of a particular docking model and need to be viewed together to reveal the true quality, e.g. a model with relatively poor LRMS (>10Å) might still qualify as 'acceptable' with a descent Fnat (>0.50) and iRMS (<3.0Å). This is also the reason why the so called CAPRI criteria for assessing the quality of docking models is defined by applying various ad-hoc cutoffs on these measures to classify a docking model into the four classes: Incorrect, Acceptable, Medium, or High quality. This classification has been useful in CAPRI, but since models are grouped in only four bins it is also rather limiting, making it difficult to rank models, correlate with scoring functions or use it as target function in machine learning algorithms. Here, we present DockQ, a continuous protein-protein docking model quality measure derived by combining Fnat, LRMS, and iRMS to a single score in the range [0, 1] that can be used to assess the quality of protein docking models. By using DockQ on CAPRI models it is possible to almost completely reproduce the original CAPRI classification into Incorrect, Acceptable, Medium and High quality. An average PPV of 94% at 90% Recall demonstrating that there is no need to apply predefined ad-hoc cutoffs to classify docking models. Since DockQ recapitulates the CAPRI classification almost perfectly, it can be viewed as a higher resolution version of the CAPRI classification, making it possible to estimate model quality in a more quantitative way using Z-scores or sum of top ranked models, which has been so valuable for the CASP community. The possibility to directly correlate a quality measure to a scoring function has been crucial for the development of scoring functions for protein structure prediction, and DockQ should be useful in a similar development in the protein docking field. DockQ is available at http://github.com/bjornwallner/DockQ/ PMID:27560519

Testing inhomogeneous solvation theory in structure-based ligand discovery.

PubMed

Balius, Trent E; Fischer, Marcus; Stein, Reed M; Adler, Thomas B; Nguyen, Crystal N; Cruz, Anthony; Gilson, Michael K; Kurtzman, Tom; Shoichet, Brian K

2017-08-15

Binding-site water is often displaced upon ligand recognition, but is commonly neglected in structure-based ligand discovery. Inhomogeneous solvation theory (IST) has become popular for treating this effect, but it has not been tested in controlled experiments at atomic resolution. To do so, we turned to a grid-based version of this method, GIST, readily implemented in molecular docking. Whereas the term only improves docking modestly in retrospective ligand enrichment, it could be added without disrupting performance. We thus turned to prospective docking of large libraries to investigate GIST's impact on ligand discovery, geometry, and water structure in a model cavity site well-suited to exploring these terms. Although top-ranked docked molecules with and without the GIST term often overlapped, many ligands were meaningfully prioritized or deprioritized; some of these were selected for testing. Experimentally, 13/14 molecules prioritized by GIST did bind, whereas none of the molecules that it deprioritized were observed to bind. Nine crystal complexes were determined. In six, the ligand geometry corresponded to that predicted by GIST, for one of these the pose without the GIST term was wrong, and three crystallographic poses differed from both predictions. Notably, in one structure, an ordered water molecule with a high GIST displacement penalty was observed to stay in place. Inclusion of this water-displacement term can substantially improve the hit rates and ligand geometries from docking screens, although the magnitude of its effects can be small and its impact in drug binding sites merits further controlled studies.

Computational predictive models for P-glycoprotein inhibition of in-house chalcone derivatives and drug-bank compounds.

PubMed

Ngo, Trieu-Du; Tran, Thanh-Dao; Le, Minh-Tri; Thai, Khac-Minh

2016-11-01

The human P-glycoprotein (P-gp) efflux pump is of great interest for medicinal chemists because of its important role in multidrug resistance (MDR). Because of the high polyspecificity as well as the unavailability of high-resolution X-ray crystal structures of this transmembrane protein, ligand-based, and structure-based approaches which were machine learning, homology modeling, and molecular docking were combined for this study. In ligand-based approach, individual two-dimensional quantitative structure-activity relationship models were developed using different machine learning algorithms and subsequently combined into the Ensemble model which showed good performance on both the diverse training set and the validation sets. The applicability domain and the prediction quality of the developed models were also judged using the state-of-the-art methods and tools. In our structure-based approach, the P-gp structure and its binding region were predicted for a docking study to determine possible interactions between the ligands and the receptor. Based on these in silico tools, hit compounds for reversing MDR were discovered from the in-house and DrugBank databases through virtual screening using prediction models and molecular docking in an attempt to restore cancer cell sensitivity to cytotoxic drugs.

Identification of 1H-indene-(1,3,5,6)-tetrol derivatives as potent pancreatic lipase inhibitors using molecular docking and molecular dynamics approach.

PubMed

Kalathiya, Umesh; Padariya, M; Baginski, M

2016-11-01

Pancreatic lipase is a potential therapeutic target to treat diet-induced obesity in humans, as obesity-related diseases continue to be a global problem. Despite intensive research on finding potential inhibitors, very few compounds have been introduced to clinical studies. In this work, new chemical scaffold 1H-indene-(1,3,5,6)-tetrol was proposed using knowledge-based approach, and 36 inhibitors were derived by modifying its functional groups at different positions in scaffold. To explore binding affinity and interactions of ligands with protein, CDOCKER and AutoDock programs were used for molecular docking studies. Analyzing results of rigid and flexible docking algorithms, inhibitors C_12, C_24, and C_36 were selected based on different properties and high predicted binding affinities for further analysis. These three inhibitors have different moieties placed at different functional groups in scaffold, and to characterize structural rationales for inhibitory activities of compounds, molecular dynamics simulations were performed (500 nSec). It has been shown through simulations that two structural fragments (indene and indole) in inhibitor can be treated as isosteric structures and their position at binding cleft can be replaced by each other. Taking into account these information, two lines of inhibitors can further be developed, each line based on a different core scaffold, that is, indene/indole. © 2015 International Union of Biochemistry and Molecular Biology, Inc.

Imperfect duplicate insertions type of mutations in plasmepsin V modulates binding properties of PEXEL motifs of export proteins in Indian Plasmodium vivax.

PubMed

Rawat, Manmeet; Vijay, Sonam; Gupta, Yash; Tiwari, Pramod Kumar; Sharma, Arun

2013-01-01

Plasmepsin V (PM-V) have functionally conserved orthologues across the Plasmodium genus who's binding and antigenic processing at the PEXEL motifs for export about 200-300 essential proteins is important for the virulence and viability of the causative Plasmodium species. This study was undertaken to determine P. vivax plasmepsin V Ind (PvPM-V-Ind) PEXEL motif export pathway for pathogenicity-related proteins/antigens export thereby altering plasmodium exportome during erythrocytic stages. We identify and characterize Plasmodium vivax plasmepsin-V-Ind (mutant) gene by cloning, sequence analysis, in silico bioinformatic protocols and structural modeling predictions based on docking studies on binding capacity with PEXEL motifs processing in terms of binding and accessibility of export proteins. Cloning and sequence analysis for genetic diversity demonstrates PvPM-V-Ind (mutant) gene is highly conserved among all isolates from different geographical regions of India. Imperfect duplicate insertion types of mutations (SVSE from 246-249 AA and SLSE from 266-269 AA) were identified among all Indian isolates in comparison to P.vivax Sal-1 (PvPM-V-Sal 1) isolate. In silico bioinformatics interaction studies of PEXEL peptide and active enzyme reveal that PvPM-V-Ind (mutant) is only active in endoplasmic reticulum lumen and membrane embedding is essential for activation of plasmepsin V. Structural modeling predictions based on docking studies with PEXEL motif show significant variation in substrate protein binding of these imperfect mutations with data mined PEXEL sequences. The predicted variation in the docking score and interacting amino acids of PvPM-V-Ind (mutant) proteins with PEXEL and lopinavir suggests a modulation in the activity of PvPM-V in terms of binding and accessibility at these sites. Our functional modeled validation of PvPM-V-Ind (mutant) imperfect duplicate insertions with data mined PEXEL sequences leading to altered binding and substrate accessibility of the enzyme makes it a plausible target to investigate export mechanisms for in silico virtual screening and novel pharmacophore designing.

Imperfect Duplicate Insertions Type of Mutations in Plasmepsin V Modulates Binding Properties of PEXEL Motifs of Export Proteins in Indian Plasmodium vivax

PubMed Central

Rawat, Manmeet; Vijay, Sonam; Gupta, Yash; Tiwari, Pramod Kumar; Sharma, Arun

2013-01-01

Introduction Plasmepsin V (PM-V) have functionally conserved orthologues across the Plasmodium genus who's binding and antigenic processing at the PEXEL motifs for export about 200–300 essential proteins is important for the virulence and viability of the causative Plasmodium species. This study was undertaken to determine P. vivax plasmepsin V Ind (PvPM-V-Ind) PEXEL motif export pathway for pathogenicity-related proteins/antigens export thereby altering plasmodium exportome during erythrocytic stages. Method We identify and characterize Plasmodium vivax plasmepsin-V-Ind (mutant) gene by cloning, sequence analysis, in silico bioinformatic protocols and structural modeling predictions based on docking studies on binding capacity with PEXEL motifs processing in terms of binding and accessibility of export proteins. Results Cloning and sequence analysis for genetic diversity demonstrates PvPM-V-Ind (mutant) gene is highly conserved among all isolates from different geographical regions of India. Imperfect duplicate insertion types of mutations (SVSE from 246–249 AA and SLSE from 266–269 AA) were identified among all Indian isolates in comparison to P.vivax Sal-1 (PvPM-V-Sal 1) isolate. In silico bioinformatics interaction studies of PEXEL peptide and active enzyme reveal that PvPM-V-Ind (mutant) is only active in endoplasmic reticulum lumen and membrane embedding is essential for activation of plasmepsin V. Structural modeling predictions based on docking studies with PEXEL motif show significant variation in substrate protein binding of these imperfect mutations with data mined PEXEL sequences. The predicted variation in the docking score and interacting amino acids of PvPM-V-Ind (mutant) proteins with PEXEL and lopinavir suggests a modulation in the activity of PvPM-V in terms of binding and accessibility at these sites. Conclusion/Significance Our functional modeled validation of PvPM-V-Ind (mutant) imperfect duplicate insertions with data mined PEXEL sequences leading to altered binding and substrate accessibility of the enzyme makes it a plausible target to investigate export mechanisms for in silico virtual screening and novel pharmacophore designing. PMID:23555891

Structure-Based Predictions of Activity Cliffs

PubMed Central

Husby, Jarmila; Bottegoni, Giovanni; Kufareva, Irina; Abagyan, Ruben; Cavalli, Andrea

2015-01-01

In drug discovery, it is generally accepted that neighboring molecules in a given descriptors' space display similar activities. However, even in regions that provide strong predictability, structurally similar molecules can occasionally display large differences in potency. In QSAR jargon, these discontinuities in the activity landscape are known as ‘activity cliffs’. In this study, we assessed the reliability of ligand docking and virtual ligand screening schemes in predicting activity cliffs. We performed our calculations on a diverse, independently collected database of cliff-forming co-crystals. Starting from ideal situations, which allowed us to establish our baseline, we progressively moved toward simulating more realistic scenarios. Ensemble- and template-docking achieved a significant level of accuracy, suggesting that, despite the well-known limitations of empirical scoring schemes, activity cliffs can be accurately predicted by advanced structure-based methods. PMID:25918827

Safety in earth orbit study. Volume 2: Analysis of hazardous payloads, docking, on-board survivability

NASA Technical Reports Server (NTRS)

1972-01-01

Detailed and supporting analyses are presented of the hazardous payloads, docking, and on-board survivability aspects connected with earth orbital operations of the space shuttle program. The hazards resulting from delivery, deployment, and retrieval of hazardous payloads, and from handling and transport of cargo between orbiter, sortie modules, and space station are identified and analyzed. The safety aspects of shuttle orbiter to modular space station docking includes docking for assembly of space station, normal resupply docking, and emergency docking. Personnel traffic patterns, escape routes, and on-board survivability are analyzed for orbiter with crew and passenger, sortie modules, and modular space station, under normal, emergency, and EVA and IVA operations.

Drosophila photoreceptor axon guidance and targeting requires the dreadlocks SH2/SH3 adapter protein.

PubMed

Garrity, P A; Rao, Y; Salecker, I; McGlade, J; Pawson, T; Zipursky, S L

1996-05-31

Mutations in the Drosophila gene dreadlocks (dock) disrupt photoreceptor cell (R cell) axon guidance and targeting. Genetic mosaic analysis and cell-type-specific expression of dock transgenes demonstrate dock is required in R cells for proper innervation. Dock protein contains one SH2 and three SH3 domains, implicating it in tyrosine kinase signaling, and is highly related to the human proto-oncogene Nck. Dock expression is detected in R cell growth cones in the target region. We propose Dock transmits signals in the growth cone in response to guidance and targeting cues. These findings provide an important step for dissection of signaling pathways regulating growth cone motility.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics.

PubMed

Armen, Roger S; Chen, Jianhan; Brooks, Charles L

2009-10-13

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and "noise" that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds.

An Evaluation of Explicit Receptor Flexibility in Molecular Docking Using Molecular Dynamics and Torsion Angle Molecular Dynamics

PubMed Central

Armen, Roger S.; Chen, Jianhan; Brooks, Charles L.

2009-01-01

Incorporating receptor flexibility into molecular docking should improve results for flexible proteins. However, the incorporation of explicit all-atom flexibility with molecular dynamics for the entire protein chain may also introduce significant error and “noise” that could decrease docking accuracy and deteriorate the ability of a scoring function to rank native-like poses. We address this apparent paradox by comparing the success of several flexible receptor models in cross-docking and multiple receptor ensemble docking for p38α mitogen-activated protein (MAP) kinase. Explicit all-atom receptor flexibility has been incorporated into a CHARMM-based molecular docking method (CDOCKER) using both molecular dynamics (MD) and torsion angle molecular dynamics (TAMD) for the refinement of predicted protein-ligand binding geometries. These flexible receptor models have been evaluated, and the accuracy and efficiency of TAMD sampling is directly compared to MD sampling. Several flexible receptor models are compared, encompassing flexible side chains, flexible loops, multiple flexible backbone segments, and treatment of the entire chain as flexible. We find that although including side chain and some backbone flexibility is required for improved docking accuracy as expected, docking accuracy also diminishes as additional and unnecessary receptor flexibility is included into the conformational search space. Ensemble docking results demonstrate that including protein flexibility leads to to improved agreement with binding data for 227 active compounds. This comparison also demonstrates that a flexible receptor model enriches high affinity compound identification without significantly increasing the number of false positives from low affinity compounds. PMID:20160879

Casual Dock Work: Profile of Diseases and Injuries and Perception of Influence on Health

PubMed Central

Cezar-Vaz, Marta Regina; de Almeida, Marlise Capa Verde; Bonow, Clarice Alves; Rocha, Laurelize Pereira; Borges, Anelise Miritz; Piexak, Diéssica Roggia

2014-01-01

The present study aimed to identify the profile of diseases and injuries that affect casual dock workers and identify casual dock workers’ perceptions of positive and negative work influences on their health. This study consisted of two phases. The first phase was a quantitative study composed of a retrospective analysis, conducted with 953 medical records. The second phase of the research is a non-random sample with 51 casual dock workers. Data analysis was performed with SPSS 19.0. The average age of the casual dock workers was 48.7. Concerning working time, the majority had more than 19.6 years of dock work experience. In the first phase, 527 pathologic diagnoses were identified. The diagnoses that affected the musculoskeletal system (15.8%, N = 152; p < 0.01) were highlighted. Consequences to physical health produced by accidents stood out, with fracture registration predominating (12.8%, N = 122; p < 0.05). Significant differences were found for positive work influence on the cardiovascular system and family health. It was concluded that the diagnoses obtained are related to the influence of dock work perception and have motivated an introduction of preventive measures. PMID:24557521

Synthesis, antimalarial activity, heme binding and docking studies of N-substituted 4-aminoquinoline-pyrimidine molecular hybrids.

PubMed

Maurya, Shiv Shyam; Khan, Shabana I; Bahuguna, Aparna; Kumar, Deepak; Rawat, Diwan S

2017-03-31

A series of novel N-substituted 4-aminoquinoline-pyrimidine hybrids have been synthesized via simple and economic route and evaluated for their antimalarial activity. Most compounds showed potent antimalarial activity against both CQ-sensitive and CQ-resistant strains with high selectivity index. All the compounds were found to be non-toxic to the mammalian cell lines. The most active compound 7b was analysed for heme binding activity using UV-spectrophotometer. Compound was found to interact with heme and a complex formation between compound and heme in a 1:1 stoichiometry ratio was determined using job plots. The interaction of these hybrids was also investigated by the molecular docking studies in the binding site of wild type Pf-DHFR-TS and quadruple mutant Pf-DHFR-TS. The pharmacokinetic property analysis of best active compounds was also studied by ADMET prediction. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

4-Aminoquinoline-pyrimidine hybrids: synthesis, antimalarial activity, heme binding and docking studies.

PubMed

Kumar, Deepak; Khan, Shabana I; Tekwani, Babu L; Ponnan, Prija; Rawat, Diwan S

2015-01-07

A series of novel 4-aminoquinoline-pyrimidine hybrids has been synthesized and evaluated for their antimalarial activity. Several compounds showed promising in vitro antimalarial activity against both CQ-sensitive and CQ-resistant strains with high selectivity index. All the compounds were found to be non-toxic to the mammalian cell lines. Selected compound 7g exhibited significant suppression of parasitemia in the in vivo assay. The heme binding studies were conducted to determine the mode of action of these hybrid molecules. These compounds form a stable 1:1 complex with hematin suggesting that heme may be one of the possible targets of these hybrids. The interaction of these conjugate hybrids was also investigated by the molecular docking studies in the binding site of PfDHFR. The pharmacokinetic property analysis of best active compounds was also studied using ADMET prediction. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina.

PubMed

Di Muzio, Elena; Toti, Daniele; Polticelli, Fabio

2017-02-01

Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

DockingApp: a user friendly interface for facilitated docking simulations with AutoDock Vina

NASA Astrophysics Data System (ADS)

Di Muzio, Elena; Toti, Daniele; Polticelli, Fabio

2017-02-01

Molecular docking is a powerful technique that helps uncover the structural and energetic bases of the interaction between macromolecules and substrates, endogenous and exogenous ligands, and inhibitors. Moreover, this technique plays a pivotal role in accelerating the screening of large libraries of compounds for drug development purposes. The need to promote community-driven drug development efforts, especially as far as neglected diseases are concerned, calls for user-friendly tools to allow non-expert users to exploit the full potential of molecular docking. Along this path, here is described the implementation of DockingApp, a freely available, extremely user-friendly, platform-independent application for performing docking simulations and virtual screening tasks using AutoDock Vina. DockingApp sports an intuitive graphical user interface which greatly facilitates both the input phase and the analysis of the results, which can be visualized in graphical form using the embedded JMol applet. The application comes with the DrugBank set of more than 1400 ready-to-dock, FDA-approved drugs, to facilitate virtual screening and drug repurposing initiatives. Furthermore, other databases of compounds such as ZINC, available also in AutoDock format, can be readily and easily plugged in.

Docking and free energy simulations to predict conformational domains involved in hCG-LH receptor interactions using recombinant antibodies.

PubMed

Majumdar, Ritankar; Railkar, Reema; Dighe, Rajan R

2011-11-01

Single chain fragment variables (ScFvs) have been extensively employed in studying the protein-protein interactions. ScFvs derived from phage display libraries have an additional advantage of being generated against a native antigen, circumventing loss of information on conformational epitopes. In the present study, an attempt has been made to elucidate human chorionic gonadotropin (hCG)-luteinizing hormone (LH) receptor interactions by using a neutral and two inhibitory ScFvs against hCG. The objective was to dock a computationally derived model of these ScFvs onto the crystal structure of hCG and understand the differential roles of the mapped epitopes in hCG-LH receptor interactions. An anti-hCG ScFv, whose epitope was mapped previously using biochemical tools, served as the positive control for assessing the quality of docking analysis. To evaluate the role of specific side chains at the hCG-ScFv interface, binding free energy as well as residue interaction energies of complexes in solution were calculated using molecular mechanics Poisson-Boltzmann/surface area method after performing the molecular dynamic simulations on the selected hCG-ScFv models and validated using biochemical and SPR analysis. The robustness of these calculations was demonstrated by comparing the theoretically determined binding energies with the experimentally obtained kinetic parameters for hCG-ScFv complexes. Superimposition of hCG-ScFv model onto a model of hCG complexed with the 51-266 residues of LH receptor revealed importance of the residues previously thought to be unimportant for hormone binding and response. This analysis provides an alternate tool for understanding the structure-function analysis of ligand-receptor interactions. Copyright © 2011 Wiley-Liss, Inc.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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

PubMed Central

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

2015-01-01

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

Analysis and Selection of a Remote Docking Simulation Visual Display System

NASA Technical Reports Server (NTRS)

Shields, N., Jr.; Fagg, M. F.

1984-01-01

The development of a remote docking simulation visual display system is examined. Video system and operator performance are discussed as well as operator command and control requirements and a design analysis of the reconfigurable work station.

Structure and Sequence Search on Aptamer-Protein Docking

NASA Astrophysics Data System (ADS)

Xiao, Jiajie; Bonin, Keith; Guthold, Martin; Salsbury, Freddie

2015-03-01

Interactions between proteins and deoxyribonucleic acid (DNA) play a significant role in the living systems, especially through gene regulation. However, short nucleic acids sequences (aptamers) with specific binding affinity to specific proteins exhibit clinical potential as therapeutics. Our capillary and gel electrophoresis selection experiments show that specific sequences of aptamers can be selected that bind specific proteins. Computationally, given the experimentally-determined structure and sequence of a thrombin-binding aptamer, we can successfully dock the aptamer onto thrombin in agreement with experimental structures of the complex. In order to further study the conformational flexibility of this thrombin-binding aptamer and to potentially develop a predictive computational model of aptamer-binding, we use GPU-enabled molecular dynamics simulations to both examine the conformational flexibility of the aptamer in the absence of binding to thrombin, and to determine our ability to fold an aptamer. This study should help further de-novo predictions of aptamer sequences by enabling the study of structural and sequence-dependent effects on aptamer-protein docking specificity.

Imidazole-containing farnesyltransferase inhibitors: 3D quantitative structure-activity relationships and molecular docking

NASA Astrophysics Data System (ADS)

Xie, Aihua; Odde, Srinivas; Prasanna, Sivaprakasam; Doerksen, Robert J.

2009-07-01

One of the most promising anticancer and recent antimalarial targets is the heterodimeric zinc-containing protein farnesyltransferase (FT). In this work, we studied a highly diverse series of 192 Abbott-initiated imidazole-containing compounds and their FT inhibitory activities using 3D-QSAR and docking, in order to gain understanding of the interaction of these inhibitors with FT to aid development of a rational strategy for further lead optimization. We report several highly significant and predictive CoMFA and CoMSIA models. The best model, composed of CoMFA steric and electrostatic fields combined with CoMSIA hydrophobic and H-bond acceptor fields, had r 2 = 0.878, q 2 = 0.630, and r pred 2 = 0.614. Docking studies on the statistical outliers revealed that some of them had a different binding mode in the FT active site based on steric bulk and available active site space, explaining why the predicted activities differed from the experimental activities.

Docking and scoring with ICM: the benchmarking results and strategies for improvement

PubMed Central

Neves, Marco A. C.; Totrov, Maxim; Abagyan, Ruben

2012-01-01

Flexible docking and scoring using the Internal Coordinate Mechanics software (ICM) was benchmarked for ligand binding mode prediction against the 85 co-crystal structures in the modified Astex data set. The ICM virtual ligand screening was tested against the 40 DUD target benchmarks and 11-target WOMBAT sets. The self-docking accuracy was evaluated for the top 1 and top 3 scoring poses at each ligand binding site with near native conformations below 2 Å RMSD found in 91% and 95% of the predictions, respectively. The virtual ligand screening using single rigid pocket conformations provided the median area under the ROC curves equal to 69.4 with 22.0% true positives recovered at 2% false positive rate. Significant improvements up to ROC AUC= 82.2 and ROC(2%)= 45.2 were achieved following our best practices for flexible pocket refinement and out-of-pocket binding rescore. The virtual screening can be further improved by considering multiple conformations of the target. PMID:22569591

New mathematic model for predicting chiral separation using molecular docking: mechanism of chiral recognition of triadimenol analogues.

PubMed

Zhang, Guoqing; Sun, Qingyan; Hou, Ying; Hong, Zhanying; Zhang, Jun; Zhao, Liang; Zhang, Hai; Chai, Yifeng

2009-07-01

The purpose of this paper was to study the enantioseparation mechanism of triadimenol compounds by carboxymethylated (CM)-beta-CD mediated CE. All the enantiomers were separated under the same experimental conditions to study the chiral recognition mechanism using a 30 mM sodium dihydrogen phosphate buffer at pH 2.2 adjusted by phosphoric acid. The inclusion courses between CM-beta-CD and enantiomers were investigated by the means of molecular docking technique. It was found that there were at least three points (one hydrophobic bond and two hydrogen bonds) involved in the interaction of each enantiomer with the chiral selectors. A new mathematic model has been built up based on the results of molecular mechanics calculations, which could analyze the relationship between the resolution of enantioseparation and the interaction energy in the docking area. Comparing the results of the separation by CE, the established mathematic model demonstrated good capability to predict chiral separation of triadimenol enantiomers using CM-beta-CD mediated CE.

Performance of a docking/molecular dynamics protocol for virtual screening of nutlin-class inhibitors of Mdmx.

PubMed

Bharatham, Nagakumar; Finch, Kristin E; Min, Jaeki; Mayasundari, Anand; Dyer, Michael A; Guy, R Kiplin; Bashford, Donald

2017-06-01

A virtual screening protocol involving docking and molecular dynamics has been tested against the results of fluorescence polarization assays testing the potency of a series of compounds of the nutlin class for inhibition of the interaction between p53 and Mdmx, an interaction identified as a driver of certain cancers. The protocol uses a standard docking method (AutoDock) with a cutoff based on the AutoDock score (ADscore), followed by molecular dynamics simulation with a cutoff based on root-mean-square-deviation (RMSD) from the docked pose. An analysis of the experimental and computational results shows modest performance of ADscore alone, but dramatically improved performance when RMSD is also used. Published by Elsevier Inc.

New phthalimide-appended Schiff bases: Studies of DNA binding, molecular docking and antioxidant activities.

PubMed

Nayab, Pattan Sirajuddin; Akrema; Ansari, Istikhar A; Shahid, Mohammad; Rahisuddin

2017-08-01

Herein, we investigated new phthalimide-based Schiff base molecules as promising DNA-binding and free radical scavenging agents. Physicochemical properties of these molecules were demonstrated on the basis of elemental analysis, ultraviolet-visible (UV-Vis), infra-red (IR), 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopy. All spectral data are agreed well with the proposed Schiff base framework. The DNA-binding potential of synthesized compounds were investigated by means of UV-visible, fluorescence, iodide quenching, circular dichroism, viscosity and thermal denaturation studies. The intrinsic binding constants (K b ) were calculated from absorption studies were found to be 1.1 × 10 4 and 1.0 × 10 4  M -1 for compounds 2a and 2b suggesting that compound 2a binding abilities with DNA were stronger than the compound 2b. Our studies showed that the presented compounds interact with DNA through groove binding. Molecular docking studies were carried out to predict the binding between Ct-DNA and test compounds. Interestingly, in silico predictions were corroborated with in vitro DNA-binding conclusions. Furthermore, the title compounds displayed remarkable antioxidant activity compared with reference standard. Copyright © 2016 John Wiley & Sons, Ltd.

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

PubMed

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

2017-01-01

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

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting.

PubMed

Radifar, Muhammad; Yuniarti, Nunung; Istyastono, Enade Perdana

2013-01-01

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα). PyPLIF is freely available at http://code.google.com/p/pyplif.

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

PubMed

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

2017-05-05

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

Analysis of hydrophobic interactions of antagonists with the beta2-adrenergic receptor.

PubMed

Novoseletsky, V N; Pyrkov, T V; Efremov, R G

2010-01-01

The adrenergic receptors mediate a wide variety of physiological responses, including vasodilatation and vasoconstriction, heart rate modulation, and others. Beta-adrenergic antagonists ('beta-blockers') thus constitute a widely used class of drugs in cardiovascular medicine as well as in management of anxiety, migraine, and glaucoma. The importance of the hydrophobic effect has been evidenced for a wide range of beta-blocker properties. To better understand the role of the hydrophobic effect in recognition of beta-blockers by their receptor, we carried out a molecular docking study combined with an original approach to estimate receptor-ligand hydrophobic interactions. The proposed method is based on automatic detection of molecular fragments in ligands and the analysis of their interactions with receptors separately. A series of beta-blockers, based on phenylethanolamines and phenoxypropanolamines, were docked to the beta2-adrenoceptor binding site in the crystal structure. Hydrophobic complementarity between the ligand and the receptor was calculated using the PLATINUM web-server (http://model.nmr.ru/platinum). Based on the analysis of the hydrophobic match for molecular fragments of beta-blockers, we have developed a new scoring function which efficiently predicts dissociation constant (pKd) with strong correlations (r(2) approximately 0.8) with experimental data.

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

PubMed

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

2015-01-01

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

Exploration of enzyme-ligand interactions in CYP2D6 & 3A4 homology models and crystal structures using a novel computational approach.

PubMed

Kjellander, Britta; Masimirembwa, Collen M; Zamora, Ismael

2007-01-01

New crystal structures of human CYP2D6 and CYP3A4 have recently been reported, and in this study, we wanted to compare them with previously used homology models with respect to predictions of site of metabolism and ligand-enzyme interactions. The data set consisted of a family of synthetic opioid analgesics with the aim to cover both CYP2D6 and CYP3A4, as most of these compounds are metabolized by both isoforms. The program MetaSite was used for the site of metabolism predictions, and the results were validated by experimental assessment of the major metabolites formed with recombinant CYP450s. This was made on a selection of 14 compounds in the data set. The prediction rates for MetaSite were 79-100% except for the CYP3A4 homology model, which picked the correct site in half of the cases. Despite differences in orientation of some important amino acids in the active sites, the MetaSite-predicted sites were the same for the different structures, with the exception of the CYP3A4 homology model. Further exploration of interactions with ligands was done by docking substrates/inhibitors in the different structures with the docking program GLUE. To address the challenge in interpreting patterns of enzyme-ligand interactions for the large number of different docking poses, a new computational tool to handle the results from the dockings was developed, in which the output highlights the relative importance of amino acids in CYP450-substrate/inhibitor interactions. The method is based on calculations of the interaction energies for each pose with the surrounding amino acids. For the CYP3A4 structures, this method was compared with consensus principal component analysis (CPCA), a commonly used method for structural comparison to evaluate the usefulness of the new method. The results from the two methods were comparable with each other, and the highlighted amino acids resemble those that were identified to have a different orientation in the compared structures. The new method has clear advantages over CPCA in that it is far simpler to interpret and there is no need for protein alignment. The methodology enables structural comparison but also gives insights on important amino acid substrate/inhibitor interactions and can therefore be very useful when suggesting modifications of new chemical entities to improve their metabolic profiles.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.

A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339

Single-Point Mutation with a Rotamer Library Toolkit: Toward Protein Engineering.

PubMed

Pottel, Joshua; Moitessier, Nicolas

2015-12-28

Protein engineers have long been hard at work to harness biocatalysts as a natural source of regio-, stereo-, and chemoselectivity in order to carry out chemistry (reactions and/or substrates) not previously achieved with these enzymes. The extreme labor demands and exponential number of mutation combinations have induced computational advances in this domain. The first step in our virtual approach is to predict the correct conformations upon mutation of residues (i.e., rebuilding side chains). For this purpose, we opted for a combination of molecular mechanics and statistical data. In this work, we have developed automated computational tools to extract protein structural information and created conformational libraries for each amino acid dependent on a variable number of parameters (e.g., resolution, flexibility, secondary structure). We have also developed the necessary tool to apply the mutation and optimize the conformation accordingly. For side-chain conformation prediction, we obtained overall average root-mean-square deviations (RMSDs) of 0.91 and 1.01 Å for the 18 flexible natural amino acids within two distinct sets of over 3000 and 1500 side-chain residues, respectively. The commonly used dihedral angle differences were also evaluated and performed worse than the state of the art. These two metrics are also compared. Furthermore, we generated a family-specific library for kinases that produced an average 2% lower RMSD upon side-chain reconstruction and a residue-specific library that yielded a 17% improvement. Ultimately, since our protein engineering outlook involves using our docking software, Fitted/Impacts, we applied our mutation protocol to a benchmarked data set for self- and cross-docking. Our side-chain reconstruction does not hinder our docking software, demonstrating differences in pose prediction accuracy of approximately 2% (RMSD cutoff metric) for a set of over 200 protein/ligand structures. Similarly, when docking to a set of over 100 kinases, side-chain reconstruction (using both general and biased conformation libraries) had minimal detriment to the docking accuracy.

[Anti-tumor target prediction and activity verification of Ganoderma lucidum triterpenoids].

PubMed

Du, Guo-Hua; Wang, Hong-Xu; Yan, Zheng; Liu, Li-Ying; Chen, Ruo-Yun

2017-02-01

It has reported that Ganoderma lucidum triterpenoids had anti-tumor activity. However, the anti-tumor target is still unclear. The present study was designed to investigate the anti-tumor activity of G. lucidum triterpenoids on different tumor cells, and predict their potential targets by virtual screening. In this experiment, molecular docking was used to simulate the interactions of 26 triterpenoids isolated from G. lucidum and 11 target proteins by LibDock module of Discovery Studio2016 software, then the anti-tumor targets of triterpenoids were predicted. In addition, the in vitro anti-tumor effects of triterpenoids were evaluated by MTT assay by determining the inhibition of proliferation in 5 tumor cell lines. The docking results showed that the poses were greater than five, and Libdock Scores higher than 100, which can be used to determine whether compounds were activity. Eight triterpenoids might have anti-tumor activity as a result of good docking, five of which had multiple targets. MTT experiments demonstrated that the ganoderic acid Y had a certain inhibitory activity on lung cancer cell H460, with IC₅₀ of 22.4 μmol•L ⁻¹, followed by 7-oxo-ganoderic acid Z2, with IC₅₀ of 43.1 μmol•L ⁻¹. However, the other triterpenoids had no anti-tumor activity in the detected tumor cell lines. Taking together, molecular docking approach established here can be used for preliminary screening of anti-tumor activity of G.lucidum ingredients. Through this screening method, combined with the MTT assay, we can conclude that ganoderic acid Y had antitumor activity, especially anti-lung cancer, and 7-oxo-ganoderic acid Z2 as well as ganoderon B, to a certain extent, had anti-tumor activity. These findings can provide basis for the development of anti-tumor drugs. However, the anti-tumor mechanisms need to be further studied. Copyright© by the Chinese Pharmaceutical Association.

WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand-Receptor Docking.

PubMed

Murphy, Robert B; Repasky, Matthew P; Greenwood, Jeremy R; Tubert-Brohman, Ivan; Jerome, Steven; Annabhimoju, Ramakrishna; Boyles, Nicholas A; Schmitz, Christopher D; Abel, Robert; Farid, Ramy; Friesner, Richard A

2016-05-12

We have developed a new methodology for protein-ligand docking and scoring, WScore, incorporating a flexible description of explicit water molecules. The locations and thermodynamics of the waters are derived from a WaterMap molecular dynamics simulation. The water structure is employed to provide an atomic level description of ligand and protein desolvation. WScore also contains a detailed model for localized ligand and protein strain energy and integrates an MM-GBSA scoring component with these terms to assess delocalized strain of the complex. Ensemble docking is used to take into account induced fit effects on the receptor conformation, and protein reorganization free energies are assigned via fitting to experimental data. The performance of the method is evaluated for pose prediction, rank ordering of self-docked complexes, and enrichment in virtual screening, using a large data set of PDB complexes and compared with the Glide SP and Glide XP models; significant improvements are obtained.

DOT2: Macromolecular Docking With Improved Biophysical Models

PubMed Central

Roberts, Victoria A.; Thompson, Elaine E.; Pique, Michael E.; Perez, Martin S.; Eyck, Lynn Ten

2015-01-01

Computational docking is a useful tool for predicting macromolecular complexes, which are often difficult to determine experimentally. Here we present the DOT2 software suite, an updated version of the DOT intermolecular docking program. DOT2 provides straightforward, automated construction of improved biophysical models based on molecular coordinates, offering checkpoints that guide the user to include critical features. DOT has been updated to run more quickly, allow flexibility in grid size and spacing, and generate a complete list of favorable candidate configu-rations. Output can be filtered by experimental data and rescored by the sum of electrostatic and atomic desolvation energies. We show that this rescoring method improves the ranking of correct complexes for a wide range of macromolecular interactions, and demonstrate that biologically relevant models are essential for biologically relevant results. The flexibility and versatility of DOT2 accommodate realistic models of complex biological systems, improving the likelihood of a successful docking outcome. PMID:23695987

Application of the docking program SOL for CSAR benchmark.

PubMed

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

2013-08-26

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

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

PubMed

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

2017-10-02

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

Analysis of protein-protein docking decoys using interaction fingerprints: application to the reconstruction of CaM-ligand complexes.

PubMed

Uchikoga, Nobuyuki; Hirokawa, Takatsugu

2010-05-11

Protein-protein docking for proteins with large conformational changes was analyzed by using interaction fingerprints, one of the scales for measuring similarities among complex structures, utilized especially for searching near-native protein-ligand or protein-protein complex structures. Here, we have proposed a combined method for analyzing protein-protein docking by taking large conformational changes into consideration. This combined method consists of ensemble soft docking with multiple protein structures, refinement of complexes, and cluster analysis using interaction fingerprints and energy profiles. To test for the applicability of this combined method, various CaM-ligand complexes were reconstructed from the NMR structures of unbound CaM. For the purpose of reconstruction, we used three known CaM-ligands, namely, the CaM-binding peptides of cyclic nucleotide gateway (CNG), CaM kinase kinase (CaMKK) and the plasma membrane Ca2+ ATPase pump (PMCA), and thirty-one structurally diverse CaM conformations. For each ligand, 62000 CaM-ligand complexes were generated in the docking step and the relationship between their energy profiles and structural similarities to the native complex were analyzed using interaction fingerprint and RMSD. Near-native clusters were obtained in the case of CNG and CaMKK. The interaction fingerprint method discriminated near-native structures better than the RMSD method in cluster analysis. We showed that a combined method that includes the interaction fingerprint is very useful for protein-protein docking analysis of certain cases.

Active sites prediction and binding analysis E1-E2 protein human papillomavirus with biphenylsulfonacetic acid

NASA Astrophysics Data System (ADS)

Iryani, I.; Amelia, F.; Iswendi, I.

2018-04-01

Cervix cancer triggered by Human papillomavirus infection is the second cause to woman death in worldwide. The binding site of E1-E2 protein of HPV 16 is not known from a 3-D structure yet, so in this study we address this issue to study the structure of E1-E2 protein from Human papillomavirus type 16 and to find its potential binding sites using biphenylsulfonacetic acid as inhibitor. Swiss model was used for 3D structure prediction and PDB: 2V9P (E1 protein) and 2NNU (E2 protein) having 52.32% and 100% identity respectively was selected as a template. The 3D model structure developed of E1 and E2 in the core and allowed regions were 99.2% and 99.5%. The ligand binding sites were predicted using online server meta pocket 2.0 and MOE 2009.10 was used for docking. E1-and E2 protein of HPV-16 has three potential binding site that can interact with the inhibitors. The Docking biphenylsulfonacetic acid using these binding sites shows that ligand interact with the protein through hydrogen bonds on Lys 403, Arg 410, His 551 in the first pocket, on Tyr 32, Leu 99 in the second pocket, and Lys 558m Lys 517 in the third pocket.

3D-QSAR and molecular docking studies on designing inhibitors of the hepatitis C virus NS5B polymerase

NASA Astrophysics Data System (ADS)

Li, Wenlian; Si, Hongzong; Li, Yang; Ge, Cuizhu; Song, Fucheng; Ma, Xiuting; Duan, Yunbo; Zhai, Honglin

2016-08-01

Viral hepatitis C infection is one of the main causes of the hepatitis after blood transfusion and hepatitis C virus (HCV) infection is a global health threat. The HCV NS5B polymerase, an RNA dependent RNA polymerase (RdRp) and an essential role in the replication of the virus, has no functional equivalent in mammalian cells. So the research and development of efficient NS5B polymerase inhibitors provides a great strategy for antiviral therapy against HCV. A combined three-dimensional quantitative structure-activity relationship (QSAR) modeling was accomplished to profoundly understand the structure-activity correlation of a train of indole-based inhibitors of the HCV NS5B polymerase to against HCV. A comparative molecular similarity indices analysis (COMSIA) model as the foundation of the maximum common substructure alignment was developed. The optimum model exhibited statistically significant results: the cross-validated correlation coefficient q2 was 0.627 and non-cross-validated r2 value was 0.943. In addition, the results of internal validations of bootstrapping and Y-randomization confirmed the rationality and good predictive ability of the model, as well as external validation (the external predictive correlation coefficient rext2 = 0.629). The information obtained from the COMSIA contour maps enables the interpretation of their structure-activity relationship. Furthermore, the molecular docking study of the compounds for 3TYV as the protein target revealed important interactions between active compounds and amino acids, and several new potential inhibitors with higher activity predicted were designed basis on our analyses and supported by the simulation of molecular docking. Meanwhile, the OSIRIS Property Explorer was introduced to help select more satisfactory compounds. The satisfactory results from this study may lay a reliable theoretical base for drug development of hepatitis C virus NS5B polymerase inhibitors.

Network pharmacology-based strategy for predicting active ingredients and potential targets of Yangxinshi tablet for treating heart failure.

PubMed

Chen, Langdong; Cao, Yan; Zhang, Hai; Lv, Diya; Zhao, Yahong; Liu, Yanjun; Ye, Guan; Chai, Yifeng

2018-01-31

Yangxinshi tablet (YXST) is an effective treatment for heart failure and myocardial infarction; it consists of 13 herbal medicines formulated according to traditional Chinese Medicine (TCM) practices. It has been used for the treatment of cardiovascular disease for many years in China. In this study, a network pharmacology-based strategy was used to elucidate the mechanism of action of YXST for the treatment of heart failure. Cardiovascular disease-related protein target and compound databases were constructed for YXST. A molecular docking platform was used to predict the protein targets of YXST. The affinity between proteins and ingredients was determined using surface plasmon resonance (SPR) assays. The action modes between targets and representative ingredients were calculated using Glide docking, and the related pathways were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. A protein target database containing 924 proteins was constructed; 179 compounds in YXST were identified, and 48 compounds with high relevance to the proteins were defined as representative ingredients. Thirty-four protein targets of the 48 representative ingredients were analyzed and classified into two categories: immune and cardiovascular systems. The SPR assay and molecular docking partly validated the interplay between protein targets and representative ingredients. Moreover, 28 pathways related to heart failure were identified, which provided directions for further research on YXST. This study demonstrated that the cardiovascular protective effect of YXST mainly involved the immune and cardiovascular systems. Through the research strategy based on network pharmacology, we analysis the complex system of YXST and found 48 representative compounds, 34 proteins and 28 related pathways of YXST, which could help us understand the underlying mechanism of YSXT's anti-heart failure effect. The network-based investigation could help researchers simplify the complex system of YXSY. It may also offer a feasible approach to decipher the chemical and pharmacological bases of other TCM formulas. Copyright © 2018 Elsevier B.V. All rights reserved.

Conformational analysis, X-ray crystallographic, FT-IR, FT-Raman, DFT, MEP and molecular docking studies on 1-(1-(3-methoxyphenyl) ethylidene) thiosemicarbazide

NASA Astrophysics Data System (ADS)

Saravanan, R. R.; Seshadri, S.; Gunasekaran, S.; Mendoza-Meroño, R.; Garcia-Granda, S.

2015-03-01

Conformational analysis, X-ray crystallographic, FT-IR, FT-Raman, DFT, MEP and molecular docking studies on 1-(1-(3-methoxyphenyl) ethylidene) thiosemicarbazide (MPET) are investigated. From conformational analysis the examination of the positions of a molecule taken and the energy changes is observed. The docking studies of the ligand MPET with target protein showed that this is a good molecule which docks well with target related to HMG-CoA. Hence MPET can be considered for developing into a potent anti-cholesterol drug. MEP assists in optimization of electrostatic interactions between the protein and the ligand. The MEP surface displays the molecular shape, size and electrostatic potential values. The optimized geometry of the compound was calculated from the DFT-B3LYP gradient calculations employing 6-31G (d, p) basis set and calculated vibrational frequencies are evaluated via comparison with experimental values.

Exploration of crystal simulation potential by fluconazole isomorphism and its application in improvement of pharmaceutical properties

NASA Astrophysics Data System (ADS)

Thakur, Amitha; Kumar, Dinesh; Thipparaboina, Rajesh; Shastri, Nalini R.

2014-11-01

Control of crystal morphology during crystallization is a paramount challenge in pharmaceutical processing. Hence, there is need to introduce computational methods for morphology prediction to manage production cost of drugs and improve related pharmaceutical and biopharmaceutical properties. Layer docking approach with molecular dynamics opens a new avenue for crystal habit prediction in presence of solvent. In the present study, attempts were made to correlate predicted and experimental crystal habits of fluconazole considering solvent interactions using layer docking approach. Simulated results from layer docking approach with methanol as solvent gave two dominant facets (0 1 1) and (1 0 1) with a surface area 22.43% and 19.82% respectively, which were in agreement with the experimental results. Experimentally grown modified crystal habit of fluconazole in methanol showed enhanced dissolution rate (p<0.05) when compared to plain drug. This was attributed to the increased surface area on the specified facets caused by interactions with the solvent. Furthermore, Differential Scanning Calorimetry, Fourier Transform Infrared (FTIR) Spectroscopy and powder X-ray Diffraction of recrystallized samples confirmed only a habit change and absence of any polymorphs, hydrates or solvates. Flow and compressibility of fluconazole recrystallized in methanol was significantly improved when compared to plain drug. This study demonstrates a methodical approach using computational tools for prediction and modification of crystal habit, to enhance dissolution of poorly soluble drugs, for future pharmaceutical applications.

Target specific proteochemometric model development for BACE1 - protein flexibility and structural water are critical in virtual screening.

PubMed

Manoharan, Prabu; Chennoju, Kiranmai; Ghoshal, Nanda

2015-07-01

BACE1 is an attractive target in Alzheimer's disease (AD) treatment. A rational drug design effort for the inhibition of BACE1 is actively pursued by researchers in both academic and pharmaceutical industries. This continued effort led to the steady accumulation of BACE1 crystal structures, co-complexed with different classes of inhibitors. This wealth of information is used in this study to develop target specific proteochemometric models and these models are exploited for predicting the prospective BACE1 inhibitors. The models developed in this study have performed excellently in predicting the computationally generated poses, separately obtained from single and ensemble docking approaches. The simple protein-ligand contact (SPLC) model outperforms other sophisticated high end models, in virtual screening performance, developed during this study. In an attempt to account for BACE1 protein active site flexibility information in predictive models, we included the change in the area of solvent accessible surface and the change in the volume of solvent accessible surface in our models. The ensemble and single receptor docking results obtained from this study indicate that the structural water mediated interactions improve the virtual screening results. Also, these waters are essential for recapitulating bioactive conformation during docking study. The proteochemometric models developed in this study can be used for the prediction of BACE1 inhibitors, during the early stage of AD drug discovery.

Docking and scoring protein complexes: CAPRI 3rd Edition.

PubMed

Lensink, Marc F; Méndez, Raúl; Wodak, Shoshana J

2007-12-01

The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI). These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions. These groups scored the uploaded set and submitted their own best models for assessment. The structures of nine protein complexes including one homodimer were used as targets. These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance. For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association. In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here. Fifteen groups submitted 305 models for five targets in the scoring experiment. Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer. In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge. In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models. But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough. With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future. (c) 2007 Wiley-Liss, Inc.

Exploring the novel heterocyclic derivatives as lead molecules for design and development of potent anticancer agents.

PubMed

Azad, Iqbal; Nasibullah, Malik; Khan, Tahmeena; Hassan, Firoj; Akhter, Yusuf

2018-05-01

This paper deals with in silico evaluation of newly proposed heterocyclic derivatives in search of potential anticancer activity. Best possible drug candidates have been proposed using a rational approach employing a pipeline of computational techniques namely MetaPrint2D prediction, molinspiration, cheminformatics, Osiris Data warrior, AutoDock and iGEMDOCK. Lazar toxicity prediction, AdmetSAR predictions, and targeted docking studies were also performed. 27 heterocyclic derivatives were selected for bioactivity prediction and drug likeness score on the basis of Lipinski's rule, Viber rule, Ghose filter, leadlikeness and Pan Assay Interference Compounds (PAINS) rule. Bufuralol, Sunitinib, and Doxorubicin were selected as reference standard drug for the comparison of molecular descriptors and docking. Bufuralol is a known non-selective adreno-receptor blocking agent. Studies showed that beta blockers are also used against different types of cancers. Sunitinib is well known Food and Drug administration (FDA) approved pyrrole containing tyrosine kinase inhibitor and our proposed molecules possess similarities with both drug and doxorubicin is another moiety having anticancer activity. All heterocyclic derivatives were found to obey the drug filters except standard drug Doxorubicin. Bioactivity score of the compounds was predicted for drug targets including enzymes, nuclear receptors, kinase inhibitors, G protein-coupled receptor (GPCR) ligands and ion channel modulators. Absorption, distribution, metabolism and toxicity (ADMET) prediction of all proposed compound showed good Blood-brain barrier (BBB) penetration, Human intestinal absorption (HIA), Caco-2 cell permeability except compound-11 and was found to have no AdmetSAR toxicity as well as carcinogenic effect. Compounds 1-9 were slightly mutagenic while compound 2, 11, 20 and 21 showed carcinogenic effect according to Lazar toxicity prediction. Rests of the compounds were predicted to have no side effect. Molecular docking was performed with vascular endothelial growth factor receptor-2(VEGFR2) and glutathione S-transferase-1 (GSTP1) because both are common cancer causing proteins. Sunitinib and Doxorubicin possess great affinity to inhibit these cancers causing protein. Self-organizing map (SOM) was used to depict data in a simple 2D presentation. Our studies justify that good oral bioavailability and therapeutic efficacy of 10, 12-19 and 22-27 compounds can be considered as potential anticancer agents. Copyright © 2018 Elsevier Inc. All rights reserved.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

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

PubMed

Liu, Kai; Watanabe, Etsurou; Kokubo, Hironori

2017-02-01

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

Shared control on lunar spacecraft teleoperation rendezvous operations with large time delay

NASA Astrophysics Data System (ADS)

Ya-kun, Zhang; Hai-yang, Li; Rui-xue, Huang; Jiang-hui, Liu

2017-08-01

Teleoperation could be used in space on-orbit serving missions, such as object deorbits, spacecraft approaches, and automatic rendezvous and docking back-up systems. Teleoperation rendezvous and docking in lunar orbit may encounter bottlenecks for the inherent time delay in the communication link and the limited measurement accuracy of sensors. Moreover, human intervention is unsuitable in view of the partial communication coverage problem. To solve these problems, a shared control strategy for teleoperation rendezvous and docking is detailed. The control authority in lunar orbital maneuvers that involves two spacecraft as rendezvous and docking in the final phase was discussed in this paper. The predictive display model based on the relative dynamic equations is established to overcome the influence of the large time delay in communication link. We discuss and attempt to prove via consistent, ground-based simulations the relative merits of fully autonomous control mode (i.e., onboard computer-based), fully manual control (i.e., human-driven at the ground station) and shared control mode. The simulation experiments were conducted on the nine-degrees-of-freedom teleoperation rendezvous and docking simulation platform. Simulation results indicated that the shared control methods can overcome the influence of time delay effects. In addition, the docking success probability of shared control method was enhanced compared with automatic and manual modes.

MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.

PubMed

Hayashi, Takanori; Matsuzaki, Yuri; Yanagisawa, Keisuke; Ohue, Masahito; Akiyama, Yutaka

2018-05-08

Protein-protein interactions (PPIs) play several roles in living cells, and computational PPI prediction is a major focus of many researchers. The three-dimensional (3D) structure and binding surface are important for the design of PPI inhibitors. Therefore, rigid body protein-protein docking calculations for two protein structures are expected to allow elucidation of PPIs different from known complexes in terms of 3D structures because known PPI information is not explicitly required. We have developed rapid PPI prediction software based on protein-protein docking, called MEGADOCK. In order to fully utilize the benefits of computational PPI predictions, it is necessary to construct a comprehensive database to gather prediction results and their predicted 3D complex structures and to make them easily accessible. Although several databases exist that provide predicted PPIs, the previous databases do not contain a sufficient number of entries for the purpose of discovering novel PPIs. In this study, we constructed an integrated database of MEGADOCK PPI predictions, named MEGADOCK-Web. MEGADOCK-Web provides more than 10 times the number of PPI predictions than previous databases and enables users to conduct PPI predictions that cannot be found in conventional PPI prediction databases. In MEGADOCK-Web, there are 7528 protein chains and 28,331,628 predicted PPIs from all possible combinations of those proteins. Each protein structure is annotated with PDB ID, chain ID, UniProt AC, related KEGG pathway IDs, and known PPI pairs. Additionally, MEGADOCK-Web provides four powerful functions: 1) searching precalculated PPI predictions, 2) providing annotations for each predicted protein pair with an experimentally known PPI, 3) visualizing candidates that may interact with the query protein on biochemical pathways, and 4) visualizing predicted complex structures through a 3D molecular viewer. MEGADOCK-Web provides a huge amount of comprehensive PPI predictions based on docking calculations with biochemical pathways and enables users to easily and quickly assess PPI feasibilities by archiving PPI predictions. MEGADOCK-Web also promotes the discovery of new PPIs and protein functions and is freely available for use at http://www.bi.cs.titech.ac.jp/megadock-web/ .

In silico characterization and analysis of RTBP1 and NgTRF1 protein through MD simulation and molecular docking - A comparative study.

PubMed

Mukherjee, Koel; Pandey, Dev Mani; Vidyarthi, Ambarish Saran

2015-02-06

Gaining access to sequence and structure information of telomere binding proteins helps in understanding the essential biological processes involve in conserved sequence specific interaction between DNA and the proteins. Rice telomere binding protein (RTBP1) and Nicotiana glutinosa telomere repeat binding factor (NgTRF1) are helix turn helix motif type of proteins that plays role in telomeric DNA protection and length regulation. Both the proteins share same type of domain but till now there is very less communication on the in silico studies of these complete proteins.Here we intend to do a comparative study between two proteins through modeling of the complete proteins, physiochemical characterization, MD simulation and DNA-protein docking. I-TASSER and CLC protein work bench was performed to find out the protein 3D structure as well as the different parameters to characterize the proteins. MD simulation was completed by GROMOS forcefield of GROMACS for 10 ns of time stretch. The simulated 3D structures were docked with template DNA (3D DNA modeled through 3D-DART) of TTTAGGG conserved sequence motif using HADDOCK web server.Digging up all the facts about the proteins it was reveled that around 120 amino acids in the tail part was showing a good sequence similarity between the proteins. Molecular modeling, sequence characterization and secondary structure prediction also indicates the similarity between the protein's structure and sequence. The result of MD simulation highlights on the RMSD, RMSF, Rg, PCA and Energy plots which also conveys the similar type of motional behavior between them. The best complex formation for both the proteins in docking result also indicates for the first interaction site which is mainly the helix3 region of the DNA binding domain. The overall computational analysis reveals that RTBP1 and NgTRF1 proteins display good amount of similarity in their physicochemical properties, structure, dynamics and binding mode.

In Silico Characterization and Analysis of RTBP1 and NgTRF1 Protein Through MD Simulation and Molecular Docking: A Comparative Study.

PubMed

Mukherjee, Koel; Pandey, Dev Mani; Vidyarthi, Ambarish Saran

2015-09-01

Gaining access to sequence and structure information of telomere-binding proteins helps in understanding the essential biological processes involve in conserved sequence-specific interaction between DNA and the proteins. Rice telomere-binding protein (RTBP1) and Nicotiana glutinosa telomere repeat binding factor (NgTRF1) are helix-turn-helix motif type of proteins that plays role in telomeric DNA protection and length regulation. Both the proteins share same type of domain, but till now there is very less communication on the in silico studies of these complete proteins. Here we intend to do a comparative study between two proteins through modeling of the complete proteins, physiochemical characterization, MD simulation and DNA-protein docking. I-TASSER and CLC protein work bench was performed to find out the protein 3D structure as well as the different parameters to characterize the proteins. MD simulation was completed by GROMOS forcefield of GROMACS for 10 ns of time stretch. The simulated 3D structures were docked with template DNA (3D DNA modeled through 3D-DART) of TTTAGGG conserved sequence motif using HADDOCK Web server. By digging up all the facts about the proteins, it was revealed that around 120 amino acids in the tail part were showing a good sequence similarity between the proteins. Molecular modeling, sequence characterization and secondary structure prediction also indicate the similarity between the protein's structure and sequence. The result of MD simulation highlights on the RMSD, RMSF, Rg, PCA and energy plots which also conveys the similar type of motional behavior between them. The best complex formation for both the proteins in docking result also indicates for the first interaction site which is mainly the helix3 region of the DNA-binding domain. The overall computational analysis reveals that RTBP1 and NgTRF1 proteins display good amount of similarity in their physicochemical properties, structure, dynamics and binding mode.

Synthesis, spectroscopic analyses (FT-IR and NMR), vibrational study, chemical reactivity and molecular docking study and anti-tubercular activity of condensed oxadiazole and pyrazine derivatives

NASA Astrophysics Data System (ADS)

El-Azab, Adel S.; Mary, Y. Sheena; Abdel-Aziz, Alaa A. M.; Miniyar, Pankaj B.; Armaković, Stevan; Armaković, Sanja J.

2018-03-01

The Fourier transform infrared spectra of the compounds 2-(5-phenyl-1,3,4-oxadiazol-2-yl)pyrazine (PHOXPY), 2-(5-styryl-1,3,4-oxadiazol-2-yl)pyrazine (STOXPY) and 2-(5-(furan-2-yl)-1,3,4-oxadiazol-2-yl)pyrazine (FUOXPY) have been recorded and the wavenumbers are computed at the density functional theory level. The assignments of all the fundamental bands of each molecule are made using potential energy distribution. The computed values of dipole moment, polarizability and hyperpolarizability values indicate that the title molecules exhibit NLO properties. The HOMO and LUMO energies demonstrate the chemical stability of the molecules and NBO analysis is made to study the stability of molecules arising from hyper conjugative interactions and charge delocalization. Detailed computational analysis and spectroscopic characterization has been performed for three newly synthesized oxadiazole derivatives. Obtained computational and experimental results have been mutually compared in order to understand the influence of structural parts specific for each derivative. From the MIC determination, MTb H37Rv was found to be sensitive to compounds, PHOXPY, STOXPY and FUOXPY. The results obtained from anti-TB activity are more promising as the compounds were found to be more potent than reference standards, streptomycin and pyrazinamide. Efforts were made in order to predict both global and local reactive properties of the title oxadiazole derivatives, including their sensitivity towards autoxidation mechanism and influence of water. The results obtained from anti-TB activity are more promising for the title compounds. Interaction with representative protein Pterindeaminase inhibitor asricin A was also investigated using the molecular docking procedure. The docked ligands form stable complexes with the receptor ricin A and the docking results suggest that these compounds can be developed as new anti-cancer drugs.

DOCK2 regulates cell proliferation through Rac and ERK activation in B cell lymphoma

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

Wang, Lei; Nishihara, Hiroshi, E-mail: nisihara@patho2.med.hokudai.ac.jp; Kimura, Taichi

2010-04-23

DOCK2; a member of the CDM protein family, regulates cell motility and cytokine production through the activation of Rac in mammalian hematopoietic cells and plays a pivotal role in the modulation of the immune system. Here we demonstrated the alternative function of DOCK2 in hematopoietic tumor cells, especially in terms of its association with the tumor progression. Immunostaining for DOCK2 in 20 cases of human B cell lymphoma tissue specimens including diffuse large B cell lymphoma and follicular lymphoma revealed the prominent expression of DOCK2 in all of the lymphoma cells. DOCK2-knockdown (KD) of the B cell lymphoma cell lines,more » Ramos and Raji, using the lentiviral shRNA system presented decreased cell proliferation compared to the control cells. Furthermore, the tumor formation of DOCK2-KD Ramos cell in nude mice was significantly abrogated. Western blotting analysis and pull-down assay using GST-PAK-RBD kimeric protein suggested the presence of DOCK2-Rac-ERK pathway regulating the cell proliferation of these lymphoma cells. This is the first report to clarify the prominent role of DOCK2 in hematopoietic malignancy.« less

Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines.

PubMed

Zhang, Xiaohua; Wong, Sergio E; Lightstone, Felice C

2013-04-30

A mixed parallel scheme that combines message passing interface (MPI) and multithreading was implemented in the AutoDock Vina molecular docking program. The resulting program, named VinaLC, was tested on the petascale high performance computing (HPC) machines at Lawrence Livermore National Laboratory. To exploit the typical cluster-type supercomputers, thousands of docking calculations were dispatched by the master process to run simultaneously on thousands of slave processes, where each docking calculation takes one slave process on one node, and within the node each docking calculation runs via multithreading on multiple CPU cores and shared memory. Input and output of the program and the data handling within the program were carefully designed to deal with large databases and ultimately achieve HPC on a large number of CPU cores. Parallel performance analysis of the VinaLC program shows that the code scales up to more than 15K CPUs with a very low overhead cost of 3.94%. One million flexible compound docking calculations took only 1.4 h to finish on about 15K CPUs. The docking accuracy of VinaLC has been validated against the DUD data set by the re-docking of X-ray ligands and an enrichment study, 64.4% of the top scoring poses have RMSD values under 2.0 Å. The program has been demonstrated to have good enrichment performance on 70% of the targets in the DUD data set. An analysis of the enrichment factors calculated at various percentages of the screening database indicates VinaLC has very good early recovery of actives. Copyright © 2013 Wiley Periodicals, Inc.

Robust scoring functions for protein-ligand interactions with quantum chemical charge models.

PubMed

Wang, Jui-Chih; Lin, Jung-Hsin; Chen, Chung-Ming; Perryman, Alex L; Olson, Arthur J

2011-10-24

Ordinary least-squares (OLS) regression has been used widely for constructing the scoring functions for protein-ligand interactions. However, OLS is very sensitive to the existence of outliers, and models constructed using it are easily affected by the outliers or even the choice of the data set. On the other hand, determination of atomic charges is regarded as of central importance, because the electrostatic interaction is known to be a key contributing factor for biomolecular association. In the development of the AutoDock4 scoring function, only OLS was conducted, and the simple Gasteiger method was adopted. It is therefore of considerable interest to see whether more rigorous charge models could improve the statistical performance of the AutoDock4 scoring function. In this study, we have employed two well-established quantum chemical approaches, namely the restrained electrostatic potential (RESP) and the Austin-model 1-bond charge correction (AM1-BCC) methods, to obtain atomic partial charges, and we have compared how different charge models affect the performance of AutoDock4 scoring functions. In combination with robust regression analysis and outlier exclusion, our new protein-ligand free energy regression model with AM1-BCC charges for ligands and Amber99SB charges for proteins achieve lowest root-mean-squared error of 1.637 kcal/mol for the training set of 147 complexes and 2.176 kcal/mol for the external test set of 1427 complexes. The assessment for binding pose prediction with the 100 external decoy sets indicates very high success rate of 87% with the criteria of predicted root-mean-squared deviation of less than 2 Å. The success rates and statistical performance of our robust scoring functions are only weakly class-dependent (hydrophobic, hydrophilic, or mixed).

Pharmacophore-based virtual screening, biological evaluation and binding mode analysis of a novel protease-activated receptor 2 antagonist

NASA Astrophysics Data System (ADS)

Cho, Nam-Chul; Seo, Seoung-Hwan; Kim, Dohee; Shin, Ji-Sun; Ju, Jeongmin; Seong, Jihye; Seo, Seon Hee; Lee, Iiyoun; Lee, Kyung-Tae; Kim, Yun Kyung; No, Kyoung Tai; Pae, Ae Nim

2016-08-01

Protease-activated receptor 2 (PAR2) is a G protein-coupled receptor, mediating inflammation and pain signaling in neurons, thus it is considered to be a potential therapeutic target for inflammatory diseases. In this study, we performed a ligand-based virtual screening of 1.6 million compounds by employing a common-feature pharmacophore model and two-dimensional similarity search to identify a new PAR2 antagonist. The common-feature pharmacophore model was established based on the biological screening results of our in-house library. The initial virtual screening yielded a total number of 47 hits, and additional biological activity tests including PAR2 antagonism and anti-inflammatory effects resulted in a promising candidate, compound 43, which demonstrated an IC50 value of 8.22 µM against PAR2. In next step, a PAR2 homology model was constructed using the crystal structure of the PAR1 as a template to explore the binding mode of the identified ligands. A molecular docking method was optimized by comparing the binding modes of a known PAR2 agonist GB110 and antagonist GB83, and applied to predict the binding mode of our hit compound 43. In-depth docking analyses revealed that the hydrophobic interaction with Phe2435.39 is crucial for PAR2 ligands to exert antagonistic activity. MD simulation results supported the predicted docking poses that PAR2 antagonist blocked a conformational rearrangement of Na+ allosteric site in contrast to PAR2 agonist that showed Na+ relocation upon GPCR activation. In conclusion, we identified new a PAR2 antagonist together with its binding mode, which provides useful insights for the design and development of PAR2 ligands.

Fragment-based docking: development of the CHARMMing Web user interface as a platform for computer-aided drug design.

PubMed

Pevzner, Yuri; Frugier, Emilie; Schalk, Vinushka; Caflisch, Amedeo; Woodcock, H Lee

2014-09-22

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser. One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing's capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of "re-dockings" with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing's docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening.

Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling.

PubMed

Rockey, William M; Hernandez, Frank J; Huang, Sheng-You; Cao, Song; Howell, Craig A; Thomas, Gregory S; Liu, Xiu Ying; Lapteva, Natalia; Spencer, David M; McNamara, James O; Zou, Xiaoqin; Chen, Shi-Jie; Giangrande, Paloma H

2011-10-01

RNA aptamers represent an emerging class of pharmaceuticals with great potential for targeted cancer diagnostics and therapy. Several RNA aptamers that bind cancer cell-surface antigens with high affinity and specificity have been described. However, their clinical potential has yet to be realized. A significant obstacle to the clinical adoption of RNA aptamers is the high cost of manufacturing long RNA sequences through chemical synthesis. Therapeutic aptamers are often truncated postselection by using a trial-and-error process, which is time consuming and inefficient. Here, we used a "rational truncation" approach guided by RNA structural prediction and protein/RNA docking algorithms that enabled us to substantially truncateA9, an RNA aptamer to prostate-specific membrane antigen (PSMA),with great potential for targeted therapeutics. This truncated PSMA aptamer (A9L; 41mer) retains binding activity, functionality, and is amenable to large-scale chemical synthesis for future clinical applications. In addition, the modeled RNA tertiary structure and protein/RNA docking predictions revealed key nucleotides within the aptamer critical for binding to PSMA and inhibiting its enzymatic activity. Finally, this work highlights the utility of existing RNA structural prediction and protein docking techniques that may be generally applicable to developing RNA aptamers optimized for therapeutic use.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.

PubMed

Pérez, Germán M; Salomón, Luis A; Montero-Cabrera, Luis A; de la Vega, José M García; Mascini, Marcello

2016-05-01

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

A Systems Biology-Based Approach to Uncovering Molecular Mechanisms Underlying Effects of Traditional Chinese Medicine Qingdai in Chronic Myelogenous Leukemia, Involving Integration of Network Pharmacology and Molecular Docking Technology.

PubMed

Zhou, Chao; Liu, LiJuan; Zhuang, Jing; Wei, JunYu; Zhang, TingTing; Gao, ChunDi; Liu, Cun; Li, HuaYao; Si, HongZong; Sun, ChangGang

2018-06-23

BACKGROUND The method of multiple targets overall control is increasingly used to predict the main active ingredient and potential target group of Chinese traditional medicines and to determine the mechanisms involved in their curative effects. Qingdai is the main traditional Chinese medicine used in the treatment of chronic myelogenous leukemia (CML), but the complex active ingredients and antitumor targets in treatment of CML have not been clearly defined in previous studies. MATERIAL AND METHODS We constructed a protein-protein interaction network diagram of CML with 638 nodes (proteins) and 1830 edges, based on the biological function of chronic myelocytic leukemia by use of Cytoscape, and we determined 19 key gene nodes in the CML molecule by network topological properties analysis in a data bank. Then, we used the Surflex-dock plugin in SYBYL7.3 docking and acquired the protein crystal structures of key genes involved in CML from the chemical composition of the traditional Chinese medicine Qingdai with key proteins in CML networks. RESULTS According to the score and the spatial structure, the pharmacodynamically active ingredients of Qingdai are Isdirubin, Isoindigo, N-phenyl-2-naphthylamine, and Isatin, among which Isdirubin is the most important. We further screened the most effective activity key protein structures of CML to find the best pharmacodynamically active ingredients of Qingdai, according to the binding interactions of the inhibitors at the catalytic site performed in best docking combinations. CONCLUSIONS The results suggest that Isdirubin plays a role in resistance to CML by altering the expressions of PIK3CA, MYC, JAK2, and TP53 target proteins. Network pharmacology and molecular docking technology can be used to search for possible reactive molecules in traditional chinese medicines (TCM) and to elucidate their molecular mechanisms.

Molecular Docking Studies to Explore Potential Binding Pockets and Inhibitors for Chikungunya Virus Envelope Glycoproteins.

PubMed

Nguyen, Phuong T V; Yu, Haibo; Keller, Paul A

2017-03-11

The chikungunya virus (CHIKV) envelope glycoproteins are considered important potential targets for anti-CHIKV drug discovery due to their crucial roles in virus attachment and virus entry. In this study, using two available crystal structures of the immature and mature forms of envelope glycoproteins, virtual screenings based on blind dockings and focused dockings were carried out to identify potential binding pockets and hit compounds for the virus. The chemical library database of compounds, NCI Diversity Set II, was used in these docking studies. In addition to reproducing previously reported examples, new binding pockets were identified, e.g., Pocket 2 in the 3N40, and Pocket 2 and Pocket 3 in the 3N42. Convergences in conformational sampling in docking using AutoDock Vina were evaluated. An analysis of docking results was carried out to understand interactions of the envelope glycoproteins complexes. Some key residues for interactions, for example Gly91 and His230, are identified as possessing important roles in the fusion process.

EMI from Spacecraft Docking Systems Spacecraft Charging - Plasma Contact Potentials

NASA Technical Reports Server (NTRS)

Norgard, John D.; Scully, Robert; Musselman, Randall

2012-01-01

The plasma contact potential of a visiting vehicle (VV), such as the Orion Service Module (SM), is determined while docking at the Orion Crew Exploration Vehicle (CEV). Due to spacecraft charging effects on-orbit, the potential difference between the CEV and the VV can be large at docking, and an electrostatic discharge (ESD) could occur at capture, which could degrade, disrupt, damage, or destroy sensitive electronic equipment on the CEV and/or VV. Analytical and numerical models of the CEV are simulated to predict the worst-case potential difference between the CEV and the VV when the CEV is unbiased (solar panels unlit: eclipsed in the dark and inactive) or biased (solar panels sunlit: in the light and active).

Plasticity of the Binding Site of Renin: Optimized Selection of Protein Structures for Ensemble Docking.

PubMed

Strecker, Claas; Meyer, Bernd

2018-05-29

Protein flexibility poses a major challenge to docking of potential ligands in that the binding site can adopt different shapes. Docking algorithms usually keep the protein rigid and only allow the ligand to be treated as flexible. However, a wrong assessment of the shape of the binding pocket can prevent a ligand from adapting a correct pose. Ensemble docking is a simple yet promising method to solve this problem: Ligands are docked into multiple structures, and the results are subsequently merged. Selection of protein structures is a significant factor for this approach. In this work we perform a comprehensive and comparative study evaluating the impact of structure selection on ensemble docking. We perform ensemble docking with several crystal structures and with structures derived from molecular dynamics simulations of renin, an attractive target for antihypertensive drugs. Here, 500 ns of MD simulations revealed binding site shapes not found in any available crystal structure. We evaluate the importance of structure selection for ensemble docking by comparing binding pose prediction, ability to rank actives above nonactives (screening utility), and scoring accuracy. As a result, for ensemble definition k-means clustering appears to be better suited than hierarchical clustering with average linkage. The best performing ensemble consists of four crystal structures and is able to reproduce the native ligand poses better than any individual crystal structure. Moreover this ensemble outperforms 88% of all individual crystal structures in terms of screening utility as well as scoring accuracy. Similarly, ensembles of MD-derived structures perform on average better than 75% of any individual crystal structure in terms of scoring accuracy at all inspected ensembles sizes.

New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies.

PubMed

Farid, Ramy; Day, Tyler; Friesner, Richard A; Pearlstein, Robert A

2006-05-01

We created a homology model of the homo-tetrameric pore domain of HERG using the crystal structure of the bacterial potassium channel, KvAP, as a template. We docked a set of known blockers with well-characterized effects on channel function into the lumen of the pore between the selectivity filter and extracellular entrance using a novel docking and refinement procedure incorporating Glide and Prime. Key aromatic groups of the blockers are predicted to form multiple simultaneous ring stacking and hydrophobic interactions among the eight aromatic residues lining the pore. Furthermore, each blocker can achieve these interactions via multiple docking configurations. To further interpret the docking results, we mapped hydrophobic and hydrophilic potentials within the lumen of each refined docked complex. Hydrophilic iso-potential contours define a 'propeller-shaped' volume at the selectivity filter entrance. Hydrophobic contours define a hollow 'crown-shaped' volume located above the 'propeller', whose hydrophobic 'rim' extends along the pore axis between Tyr652 and Phe656. Blockers adopt conformations/binding orientations that closely mimic the shapes and properties of these contours. Blocker basic groups are localized in the hydrophilic 'propeller', forming electrostatic interactions with Ser624 rather than a generally accepted pi-cation interaction with Tyr652. Terfenadine, cisapride, sertindole, ibutilide, and clofilium adopt similar docked poses, in which their N-substituents bridge radially across the hollow interior of the 'crown' (analogous to the hub and spokes of a wheel), and project aromatic/hydrophobic portions into the hydrophobic 'rim'. MK-499 docks with its longitudinal axis parallel to the axis of the pore and 'crown', and its hydrophobic groups buried within the hydrophobic 'rim'.

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

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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

PubMed

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

2016-09-01

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

An Exploratory Exercise in Taguchi Analysis of Design Parameters: Application to a Shuttle-to-space Station Automated Approach Control System

NASA Technical Reports Server (NTRS)

Deal, Don E.

1991-01-01

The chief goals of the summer project have been twofold - first, for my host group and myself to learn as much of the working details of Taguchi analysis as possible in the time allotted, and, secondly, to apply the methodology to a design problem with the intention of establishing a preliminary set of near-optimal (in the sense of producing a desired response) design parameter values from among a large number of candidate factor combinations. The selected problem is concerned with determining design factor settings for an automated approach program which is to have the capability of guiding the Shuttle into the docking port of the Space Station under controlled conditions so as to meet and/or optimize certain target criteria. The candidate design parameters under study were glide path (i.e., approach) angle, path intercept and approach gains, and minimum impulse bit mode (a parameter which defines how Shuttle jets shall be fired). Several performance criteria were of concern: terminal relative velocity at the instant the two spacecraft are mated; docking offset; number of Shuttle jet firings in certain specified directions (of interest due to possible plume impingement on the Station's solar arrays), and total RCS (a measure of the energy expended in performing the approach/docking maneuver). In the material discussed here, we have focused on single performance criteria - total RCS. An analysis of the possibility of employing a multiobjective function composed of a weighted sum of the various individual criteria has been undertaken, but is, at this writing, incomplete. Results from the Taguchi statistical analysis indicate that only three of the original four posited factors are significant in affecting RCS response. A comparison of model simulation output (via Monte Carlo) with predictions based on estimated factor effects inferred through the Taguchi experiment array data suggested acceptable or close agreement between the two except at the predicted optimum point, where a difference outside a rule-of-thumb bound was observed. We have concluded that there is most likely an interaction effect not provided for in the original orthogonal array selected as the basis for our experimental design. However, we feel that the data indicates that this interaction is a mild one and that inclusion of its effect will not alter the location of the optimum.

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.

Synthesis, crystal structure analysis, spectral investigations, DFT computations, Biological activities and molecular docking of methyl(2E)-2-{[N-(2-formylphenyl)(4-methylbenzene) sulfonamido]methyl}-3-(4-fluorophenyl)prop-2-enoate, a potential bioactive agent

NASA Astrophysics Data System (ADS)

Murugavel, S.; Vetri Velan, V.; Kannan, Damodharan; Bakthadoss, Manickam

2016-03-01

The title compound methyl(2E)-2-{[N-(2-formylphenyl) (4-methylbenzene)sulfonamido]methyl}-3-(4-fluorophenyl) prop-2-enoate (MFMSF) has been synthesized and single crystals were grown by slow evaporation solution growth technique at room temperature. The grown crystals were characterized by FTIR, 1H NMR, 13C NMR, and single crystal X-ray diffraction. In the crystal, molecules are linked by intermolecular C-H…O hydrogen bonds forming a two-dimensional supramolecular network along [110] direction. The molecular geometry was also optimized using density functional theory (DFT/B3LYP) method with the 6-311G (d,p) basis set in ground state and compared with the experimental data. The entire vibrational assignments of wave numbers were made on the basis of potential energy distribution (PED) by VEDA 4 programme. Stability of the molecule arising from hyper conjugative interactions, charge delocalization has been analyzed using natural bond orbital (NBO) analysis. In addition, NLO, MEP, Mulliken, thermodynamic properties, HOMO and LUMO energy gap were theoretically predicted. The global chemical reactivity descriptors are calculated for MFMSF and used to predict their relative stability and reactivity. The antibacterial activity of the compound was also tested against various pathogens. The molecular docking studies concede that title compound may exhibit PBP-2X inhibitor activity.

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions

NASA Astrophysics Data System (ADS)

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A.; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2016-09-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

D3R Grand Challenge 2015: Evaluation of Protein-Ligand Pose and Affinity Predictions

PubMed Central

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2017-01-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (i) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (ii) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor. PMID:27696240

An integrated molecular docking and rescoring method for predicting the sensitivity spectrum of various serine hydrolases to organophosphorus pesticides.

PubMed

Yang, Ling-Ling; Yang, Xiao; Li, Guo-Bo; Fan, Kai-Ge; Yin, Peng-Fei; Chen, Xiang-Gui

2016-04-01

The enzymatic chemistry method is currently the most widely used method for the rapid detection of organophosphorus (OP) pesticides, but the enzymes used, such as cholinesterases, lack sufficient sensitivity to detect low concentrations of OP pesticides present in given samples. Serine hydrolase is considered an ideal enzyme source in seeking high-sensitivity enzymes used for OP pesticide detection. However, it is difficult to systematically evaluate sensitivities of various serine hydrolases to OP pesticides by in vitro experiments. This study aimed to establish an in silico method to predict the sensitivity spectrum of various serine hydrolases to OP pesticides. A serine hydrolase database containing 219 representative serine hydrolases was constructed. Based on this database, an integrated molecular docking and rescoring method was established, in which the AutoDock Vina program was used to produce the binding poses of OP pesticides to various serine hydrolases and the ID-Score method developed recently by us was adopted as a rescoring method to predict their binding affinities. In retrospective case studies, this method showed good performance in predicting the sensitivities of known serine hydrolases to two OP pesticides: paraoxon and diisopropyl fluorophosphate. The sensitivity spectrum of the 219 collected serine hydrolases to 37 commonly used OP pesticides was finally obtained using this method. Overall, this study presented a promising in silico tool to predict the sensitivity spectrum of various serine hydrolases to OP pesticides, which will help in finding high-sensitivity serine hydrolases for OP pesticide detection. © 2015 Society of Chemical Industry.

Identification of Histone Deacetylase (HDAC) as a drug target against MRSA via interolog method of protein-protein interaction prediction.

PubMed

Uddin, Reaz; Tariq, Syeda Sumayya; Azam, Syed Sikander; Wadood, Abdul; Moin, Syed Tarique

2017-08-30

Patently, Protein-Protein Interactions (PPIs) lie at the core of significant biological functions and make the foundation of host-pathogen relationships. Hence, the current study is aimed to use computational biology techniques to predict host-pathogen Protein-Protein Interactions (HP-PPIs) between MRSA and Humans as potential drug targets ultimately proposing new possible inhibitors against them. As a matter of fact this study is based on the Interolog method which implies that homologous proteins retain their ability to interact. A distant homolog approach based on Interolog method was employed to speculate MRSA protein homologs in Humans using PSI-BLAST. In addition the protein interaction partners of these homologs as listed in Database of Interacting Proteins (DIP) were predicted to interact with MRSA as well. Moreover, a direct approach using BLAST was also applied so as to attain further confidence in the strategy. Consequently, the common HP-PPIs predicted by both approaches are suggested as potential drug targets (22%) whereas, the unique HP-PPIs estimated only through distant homolog approach are presented as novel drug targets (12%). Furthermore, the most repeated entry in our results was found to be MRSA Histone Deacetylase (HDAC) which was then modeled using SWISS-MODEL. Eventually, small molecules from ZINC, selected randomly, were docked against HDAC using Auto Dock and are suggested as potential binders (inhibitors) based on their energetic profiles. Thus the current study provides basis for further in-depth analysis of such data which not only include MRSA but other deadly pathogens as well. Copyright © 2017 Elsevier B.V. All rights reserved.

Finding a Potential Dipeptidyl Peptidase-4 (DPP-4) Inhibitor for Type-2 Diabetes Treatment Based on Molecular Docking, Pharmacophore Generation, and Molecular Dynamics Simulation

PubMed Central

Meduru, Harika; Wang, Yeng-Tseng; Tsai, Jeffrey J. P.; Chen, Yu-Ching

2016-01-01

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme that is responsible for inactivating intestinal peptides glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decline in blood glucose levels. The aim of this study was to explore the inhibition activity of small molecule inhibitors to DPP-4 following a computational strategy based on docking studies and molecular dynamics simulations. The thorough docking protocol we applied allowed us to derive good correlation parameters between the predicted binding affinities (pKi) of the DPP-4 inhibitors and the experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out in order to identify the binding modes of structurally diverse compounds in the receptor active site. Consideration of the permanence and flexibility of DPP-4 inhibitor complexes by means of molecular dynamics (MD) simulation specified that the inhibitors maintained the binding mode observed in the docking study. The present study helps generate new information for further structural optimization and can influence the development of new DPP-4 inhibitors discoveries in the treatment of type-2 diabetes. PMID:27304951

Finding a Potential Dipeptidyl Peptidase-4 (DPP-4) Inhibitor for Type-2 Diabetes Treatment Based on Molecular Docking, Pharmacophore Generation, and Molecular Dynamics Simulation.

PubMed

Meduru, Harika; Wang, Yeng-Tseng; Tsai, Jeffrey J P; Chen, Yu-Ching

2016-06-13

Dipeptidyl peptidase-4 (DPP-4) is the vital enzyme that is responsible for inactivating intestinal peptides glucagon like peptide-1 (GLP-1) and Gastric inhibitory polypeptide (GIP), which stimulates a decline in blood glucose levels. The aim of this study was to explore the inhibition activity of small molecule inhibitors to DPP-4 following a computational strategy based on docking studies and molecular dynamics simulations. The thorough docking protocol we applied allowed us to derive good correlation parameters between the predicted binding affinities (pKi) of the DPP-4 inhibitors and the experimental activity values (pIC50). Based on molecular docking receptor-ligand interactions, pharmacophore generation was carried out in order to identify the binding modes of structurally diverse compounds in the receptor active site. Consideration of the permanence and flexibility of DPP-4 inhibitor complexes by means of molecular dynamics (MD) simulation specified that the inhibitors maintained the binding mode observed in the docking study. The present study helps generate new information for further structural optimization and can influence the development of new DPP-4 inhibitors discoveries in the treatment of type-2 diabetes.

A combinatorial approach to protein docking with flexible side chains.

PubMed

Althaus, Ernst; Kohlbacher, Oliver; Lenhof, Hans-Peter; Müller, Peter

2002-01-01

Rigid-body docking approaches are not sufficient to predict the structure of a protein complex from the unbound (native) structures of the two proteins. Accounting for side chain flexibility is an important step towards fully flexible protein docking. This work describes an approach that allows conformational flexibility for the side chains while keeping the protein backbone rigid. Starting from candidates created by a rigid-docking algorithm, we demangle the side chains of the docking site, thus creating reasonable approximations of the true complex structure. These structures are ranked with respect to the binding free energy. We present two new techniques for side chain demangling. Both approaches are based on a discrete representation of the side chain conformational space by the use of a rotamer library. This leads to a combinatorial optimization problem. For the solution of this problem, we propose a fast heuristic approach and an exact, albeit slower, method that uses branch-and-cut techniques. As a test set, we use the unbound structures of three proteases and the corresponding protein inhibitors. For each of the examples, the highest-ranking conformation produced was a good approximation of the true complex structure.

Exploring details about structure requirements based on novel CGRP receptor antagonists urethanamide, aspartate, succinate and pyridine derivatives by in silico methods

NASA Astrophysics Data System (ADS)

Li, Yan; He, Haoran; Wang, Jinghui; Han, Chunxiao; Feng, Jiaqi; Zhang, Shuwei; Yang, Ling

2014-09-01

The migraine never fails to afflict individuals in the world that knows no lack of such cases. CGRP (calcitonin gene-related peptide) is found closely related to migraine and olcegepant (BIBN4096) is effective in alleviating the pain. In our work, the combination of ligand- and receptor-based three-dimensional quantitative structure-activity relationship (3D-QSAR) studies along with molecular docking was applied to provide us insights about how urethanamide, pyridine and aspartate and succinate derivatives (novel CGRP receptor antagonists) play a part in inhibiting the activity of CGRP receptor. The optimal CoMSIA model shows the Q2 of 0.505, R2ncv of 0.992 and its accurate predictive ability was confirmed by checking out an independent test set which gave R2pred value of 0.885. Besides, the 3D contour maps help us identify how different groups affect the antagonist activity while connecting to some key positions. In addition, the docking analysis shows the binding site emerging as the distorted “V” shape and including two binding pockets: one of them is hydrophobic, fixing the structural part 3 of compound 80, the other anchors the part 1 of compound 80. The docking analysis also shows the interaction mechanism between compound 80 and CGRP receptor, similar to the interaction between olcegepant and CGRP receptor. The findings derived from this work reveal the mechanism of related antagonists and facilitate the future rational design of novel antagonists with higher potency.

Quantum mechanical and spectroscopic (FT-IR, FT-Raman) study, NBO analysis, HOMO-LUMO, first order hyperpolarizability and molecular docking study of methyl[(3R)-3-(2-methylphenoxy)-3-phenylpropyl]amine by density functional method

NASA Astrophysics Data System (ADS)

Kuruvilla, Tintu K.; Prasana, Johanan Christian; Muthu, S.; George, Jacob; Mathew, Sheril Ann

2018-01-01

Quantum chemical techniques such as density functional theory (DFT) have become a powerful tool in the investigation of the molecular structure and vibrational spectrum and are finding increasing use in application related to biological systems. The Fourier transform infrared (FT-IR) and Fourier transform Raman (FT-Raman) techniques are employed to characterize the title compound. The vibrational frequencies were obtained by DFT/B3LYP calculations with 6-31G(d,p) and 6-311 ++G(d,p) as basis sets. The geometry of the title compound was optimized. The vibrational assignments and the calculation of Potential Energy Distribution (PED) were carried out using the Vibrational Energy Distribution Analysis (VEDA) software. Molecular electrostatic potential was calculated for the title compound to predict the reactive sites for electrophilic and nucleophilic attack. In addition, the first-order hyperpolarizability, HOMO and LUMO energies, Fukui function and NBO were computed. The thermodynamic properties of the title compound were calculated at different temperatures, revealing the correlations between heat capacity (C), entropy (S) and enthalpy changes (H) with temperatures. Molecular docking studies were also conducted as part of this study. The paper further explains the experimental results which are in line with the theoretical calculations and provide optimistic evidence through molecular docking that the title compound can act as a good antidepressant. It also provides sufficient justification for the title compound to be selected as a good candidate for further studies related to NLO properties.

Combinatorial multispectral, thermodynamics, docking and site-directed mutagenesis reveal the cognitive characteristics of honey bee chemosensory protein to plant semiochemical.

PubMed

Tan, Jing; Song, Xinmi; Fu, Xiaobin; Wu, Fan; Hu, Fuliang; Li, Hongliang

2018-08-05

In the chemoreceptive system of insects, there are always some soluble binding proteins, such as some antennal-specific chemosensory proteins (CSPs), which are abundantly distributed in the chemosensory sensillar lymph. The antennal-specific CSPs usually have strong capability to bind diverse semiochemicals, while the detailed interaction between CSPs and the semiochemicals remain unclear. Here, by means of the combinatorial multispectral, thermodynamics, docking and site-directed mutagenesis, we detailedly interpreted a binding interaction between a plant semiochemical β-ionone and antennal-specific CSP1 from the worker honey bee. Thermodynamic parameters (ΔH < 0, ΔS > 0) indicate that the interaction is mainly driven by hydrophobic forces and electrostatic interactions. Docking prediction results showed that there are two key amino acids, Phe44 and Gln63, may be involved in the interacting process of CSP1 to β-ionone. In order to confirm the two key amino acids, site-directed mutagenesis were performed and the binding constant (K A ) for two CSP1 mutant proteins was reduced by 60.82% and 46.80% compared to wild-type CSP1. The thermodynamic analysis of mutant proteins furtherly verified that Phe44 maintained an electrostatic interaction and Gln63 contributes hydrophobic and electrostatic forces. Our investigation initially elucidates the physicochemical mechanism of the interaction between antennal-special CSPs in insects including bees to plant semiochemicals, as well as the development of twice thermodynamic analysis (wild type and mutant proteins) combined with multispectral and site-directed mutagenesis methods. Copyright © 2018 Elsevier B.V. All rights reserved.

PTools: an opensource molecular docking library

PubMed Central

Saladin, Adrien; Fiorucci, Sébastien; Poulain, Pierre; Prévost, Chantal; Zacharias, Martin

2009-01-01

Background Macromolecular docking is a challenging field of bioinformatics. Developing new algorithms is a slow process generally involving routine tasks that should be found in a robust library and not programmed from scratch for every new software application. Results We present an object-oriented Python/C++ library to help the development of new docking methods. This library contains low-level routines like PDB-format manipulation functions as well as high-level tools for docking and analyzing results. We also illustrate the ease of use of this library with the detailed implementation of a 3-body docking procedure. Conclusion The PTools library can handle molecules at coarse-grained or atomic resolution and allows users to rapidly develop new software. The library is already in use for protein-protein and protein-DNA docking with the ATTRACT program and for simulation analysis. This library is freely available under the GNU GPL license, together with detailed documentation. PMID:19409097

PTools: an opensource molecular docking library.

PubMed

Saladin, Adrien; Fiorucci, Sébastien; Poulain, Pierre; Prévost, Chantal; Zacharias, Martin

2009-05-01

Macromolecular docking is a challenging field of bioinformatics. Developing new algorithms is a slow process generally involving routine tasks that should be found in a robust library and not programmed from scratch for every new software application. We present an object-oriented Python/C++ library to help the development of new docking methods. This library contains low-level routines like PDB-format manipulation functions as well as high-level tools for docking and analyzing results. We also illustrate the ease of use of this library with the detailed implementation of a 3-body docking procedure. The PTools library can handle molecules at coarse-grained or atomic resolution and allows users to rapidly develop new software. The library is already in use for protein-protein and protein-DNA docking with the ATTRACT program and for simulation analysis. This library is freely available under the GNU GPL license, together with detailed documentation.

Shipbuilding Docks as Experimental Systems for Realistic Assessments of Anthropogenic Stressors on Marine Organisms

PubMed Central

Harding, Harry R.; Bunce, Tom; Birch, Fiona; Lister, Jessica; Spiga, Ilaria; Benson, Tom; Rossington, Kate; Jones, Diane; Tyler, Charles R.; Simpson, Stephen D.

2017-01-01

Abstract Empirical investigations of the impacts of anthropogenic stressors on marine organisms are typically performed under controlled laboratory conditions, onshore mesocosms, or via offshore experiments with realistic (but uncontrolled) environmental variation. These approaches have merits, but onshore setups are generally small sized and fail to recreate natural stressor fields, whereas offshore studies are often compromised by confounding factors. We suggest the use of flooded shipbuilding docks to allow studying realistic exposure to stressors and their impacts on the intra- and interspecific responses of animals. Shipbuilding docks permit the careful study of groups of known animals, including the evaluation of their behavioral interactions, while enabling full control of the stressor and many environmental conditions. We propose that this approach could be used for assessing the impacts of prominent anthropogenic stressors, including chemicals, ocean warming, and sound. Results from shipbuilding-dock studies could allow improved parameterization of predictive models relating to the environmental risks and population consequences of anthropogenic stressors. PMID:29599545

Accounting for observed small angle X-ray scattering profile in the protein-protein docking server ClusPro.

PubMed

Xia, Bing; Mamonov, Artem; Leysen, Seppe; Allen, Karen N; Strelkov, Sergei V; Paschalidis, Ioannis Ch; Vajda, Sandor; Kozakov, Dima

2015-07-30

The protein-protein docking server ClusPro is used by thousands of laboratories, and models built by the server have been reported in over 300 publications. Although the structures generated by the docking include near-native ones for many proteins, selecting the best model is difficult due to the uncertainty in scoring. Small angle X-ray scattering (SAXS) is an experimental technique for obtaining low resolution structural information in solution. While not sufficient on its own to uniquely predict complex structures, accounting for SAXS data improves the ranking of models and facilitates the identification of the most accurate structure. Although SAXS profiles are currently available only for a small number of complexes, due to its simplicity the method is becoming increasingly popular. Since combining docking with SAXS experiments will provide a viable strategy for fairly high-throughput determination of protein complex structures, the option of using SAXS restraints is added to the ClusPro server. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Shipbuilding Docks as Experimental Systems for Realistic Assessments of Anthropogenic Stressors on Marine Organisms.

PubMed

Bruintjes, Rick; Harding, Harry R; Bunce, Tom; Birch, Fiona; Lister, Jessica; Spiga, Ilaria; Benson, Tom; Rossington, Kate; Jones, Diane; Tyler, Charles R; Radford, Andrew N; Simpson, Stephen D

2017-09-01

Empirical investigations of the impacts of anthropogenic stressors on marine organisms are typically performed under controlled laboratory conditions, onshore mesocosms, or via offshore experiments with realistic (but uncontrolled) environmental variation. These approaches have merits, but onshore setups are generally small sized and fail to recreate natural stressor fields, whereas offshore studies are often compromised by confounding factors. We suggest the use of flooded shipbuilding docks to allow studying realistic exposure to stressors and their impacts on the intra- and interspecific responses of animals. Shipbuilding docks permit the careful study of groups of known animals, including the evaluation of their behavioral interactions, while enabling full control of the stressor and many environmental conditions. We propose that this approach could be used for assessing the impacts of prominent anthropogenic stressors, including chemicals, ocean warming, and sound. Results from shipbuilding-dock studies could allow improved parameterization of predictive models relating to the environmental risks and population consequences of anthropogenic stressors.

CoMFA, LeapFrog and blind docking studies on sulfonanilide derivatives acting as selective aromatase expression regulators.

PubMed

Gueto, Carlos; Torres, Juan; Vivas-Reyes, Ricardo

2009-09-01

Aromatase, the enzyme responsible for estrogen biosynthesis, is an attractive target in the treatment of hormone-dependent breast cancer. In this manuscript, the structure-based drug design approach of sulfonanilide analogues as potential selective aromatase expression regulators (SAERs) is described. Receptor-independent CoMFA (Comparative Molecular Field Analysis) maps were employed for generating a pseudocavity for LeapFrog calculation. A robust model, using 45 and 10 molecules in the training and test sets, respectively, was developed producing statistically significant results with cross-validated and conventional correlation coefficients of 0.656 and 0.956, respectively. This model was used to predict the activity of newly proposed molecules as SAERs candidates being two magnitude orders more potent than the previously reported compounds. Also in the present study, the computational blind docking method using eHiTS is tested on molecules study group and COX-2 enzyme. Future perspectives of the method in the screening of SAERs candidates with no COX-2 inhibitory activity are discussed.

Discovery of potent α-glucosidase inhibitor flavonols: Insights into mechanism of action through inhibition kinetics and docking simulations.

PubMed

Şöhretoğlu, Didem; Sari, Suat; Barut, Burak; Özel, Arzu

2018-05-17

Beside other pharmaceutical benefits, flavonoids are known for their potent α-glucosidase inhibition. In the present study, we investigated α-glucosidase inhibitory effects of structurally related 11 flavonols, among which quercetin-3-O-(3″-O-galloyl)-β-galactopyranoside (8) and quercetin 3-O-(6″-O-galloyl)-β-glucopyranoside (9) showed significant inhibition compared to the positive control, acarbose, with IC 50 values of 0.97 ± 0.02 and 1.35 ± 0.06 µM, respectively. It was found that while sugar substitution to C3-OH of C ring reduced the α-glucosidase inhibitory effect, galloyl substitution to these sugar units increased it. An enzyme kinetics analysis revealed that 7 was competitive, whereas 1, 2, 8, and 9 were uncompetitive inhibitors. In the light of these findings, we performed molecular docking studies to predict their inhibition mechanisms at atomic level. Copyright © 2018 Elsevier Inc. All rights reserved.

The role of protein plasticity in computational rationalization studies on regioselectivity in testosterone hydroxylation by cytochrome P450 BM3 mutants.

PubMed

de Beer, Stephanie B A; van Bergen, Laura A H; Keijzer, Karlijn; Rea, Vanina; Venkataraman, Harini; Guerra, Celia Fonseca; Bickelhaupt, F Matthias; Vermeulen, Nico P E; Commandeur, Jan N M; Geerke, Daan P

2012-02-01

Recently, it was found that mutations in the binding cavity of drug-metabolizing Cytochrome P450 BM3 mutants can result in major changes in regioselectivity in testosterone (TES) hydroxylation. In the current work, we report the intrinsic reactivity of TES' C-H bonds and our attempts to rationalize experimentally observed changes in TES hydroxylation using a protein structure-based in silico approach, by setting up and employing a combined Molecular Dynamics (MD) and ligand docking approach to account for the flexibility and plasticity of BM3 mutants. Using this approach, about 100,000 TES binding poses were obtained per mutant. The predicted regioselectivity in TES hydroxylation by the mutants was found to be in disagreement with experiment. As revealed in a detailed structural analysis of the obtained docking poses, this disagreement is due to limitations in correctly scoring hydrogen-bonding and steric interactions with specific active-site residues, which could explain the experimentally observed trends in regioselectivity in TES hydroxylation.

Comparative docking and CoMFA analysis of curcumine derivatives as HIV-1 integrase inhibitors.

PubMed

Gupta, Pawan; Garg, Prabha; Roy, Nilanjan

2011-08-01

The docking studies and comparative molecular field analysis (CoMFA) were performed on highly active molecules of curcumine derivatives against 3' processing activity of HIV-1 integrase (IN) enzyme. The optimum CoMFA model was selected with statistically significant cross-validated r(2) value of 0.815 and non-cross validated r (2) value of 0.99. The common pharmacophore of highly active molecules was used for screening of HIV-1 IN inhibitors. The high contribution of polar interactions in pharmacophore mapping is well supported by docking and CoMFA results. The results of docking, CoMFA, and pharmacophore mapping give structural insights as well as important binding features of curcumine derivatives as HIV-1 IN inhibitors which can provide guidance for the rational design of novel HIV-1 IN inhibitors.

Docking and 3-D QSAR studies on indolyl aryl sulfones. Binding mode exploration at the HIV-1 reverse transcriptase non-nucleoside binding site and design of highly active N-(2-hydroxyethyl)carboxamide and N-(2-hydroxyethyl)carbohydrazide derivatives.

PubMed

Ragno, Rino; Artico, Marino; De Martino, Gabriella; La Regina, Giuseppe; Coluccia, Antonio; Di Pasquali, Alessandra; Silvestri, Romano

2005-01-13

Three-dimensional quantitative structure-activity relationship (3-D QSAR) studies and docking simulations were developed on indolyl aryl sulfones (IASs), a class of novel HIV-1 non-nucleoside reverse transcriptase (RT) inhibitors (Silvestri, et al. J. Med. Chem. 2003, 46, 2482-2493) highly active against wild type and some clinically relevant resistant strains (Y181C, the double mutant K103N-Y181C, and the K103R-V179D-P225H strain, highly resistant to efavirenz). Predictive 3-D QSAR models using the combination of GRID and GOLPE programs were obtained using a receptor-based alignment by means of docking IASs into the non-nucleoside binding site (NNBS) of RT. The derived 3-D QSAR models showed conventional correlation (r(2)) and cross-validated (q(2)) coefficients values ranging from 0.79 to 0.93 and from 0.59 to 0.84, respectively. All described models were validated by an external test set compiled from previously reported pyrryl aryl sulfones (Artico, et al. J. Med. Chem. 1996, 39, 522-530). The most predictive 3-D QSAR model was then used to predict the activity of novel untested IASs. The synthesis of six designed derivatives (prediction set) allowed disclosure of new IASs endowed with high anti-HIV-1 activities.

Similarities among receptor pockets and among compounds: analysis and application to in silico ligand screening.

PubMed

Fukunishi, Yoshifumi; Mikami, Yoshiaki; Nakamura, Haruki

2005-09-01

We developed a new method to evaluate the distances and similarities between receptor pockets or chemical compounds based on a multi-receptor versus multi-ligand docking affinity matrix. The receptors were classified by a cluster analysis based on calculations of the distance between receptor pockets. A set of low homologous receptors that bind a similar compound could be classified into one cluster. Based on this line of reasoning, we proposed a new in silico screening method. According to this method, compounds in a database were docked to multiple targets. The new docking score was a slightly modified version of the multiple active site correction (MASC) score. Receptors that were at a set distance from the target receptor were not included in the analysis, and the modified MASC scores were calculated for the selected receptors. The choice of the receptors is important to achieve a good screening result, and our clustering of receptors is useful to this purpose. This method was applied to the analysis of a set of 132 receptors and 132 compounds, and the results demonstrated that this method achieves a high hit ratio, as compared to that of a uniform sampling, using a receptor-ligand docking program, Sievgene, which was newly developed with a good docking performance yielding 50.8% of the reconstructed complexes at a distance of less than 2 A RMSD.

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

QSAR and docking based semi-synthesis and in vitro evaluation of 18 β-glycyrrhetinic acid derivatives against human lung cancer cell line A-549.

PubMed

Yadav, Dharmendra Kumar; Kalani, Komal; Khan, Feroz; Srivastava, Santosh Kumar

2013-12-01

For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r(2)) and prediction accuracy (rCV(2)) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

Hybrid Steered Molecular Dynamics-Docking: An Efficient Solution to the Problem of Ranking Inhibitor Affinities Against a Flexible Drug Target.

PubMed

Whalen, Katie L; Chang, Kevin M; Spies, M Ashley

2011-05-16

Existing techniques which attempt to predict the affinity of protein-ligand interactions have demonstrated a direct relationship between computational cost and prediction accuracy. We present here the first application of a hybrid ensemble docking and steered molecular dynamics scheme (with a minimized computational cost), which achieves a binding affinity rank-ordering of ligands with a Spearman correlation coefficient of 0.79 and an RMS error of 0.7 kcal/mol. The scheme, termed Flexible Enzyme Receptor Method by Steered Molecular Dynamics (FERM-SMD), is applied to an in-house collection of 17 validated ligands of glutamate racemase. The resulting improved accuracy in affinity prediction allows elucidation of the key structural components of a heretofore unreported glutamate racemase inhibitor (K(i) = 9 µM), a promising new lead in the development of antibacterial therapeutics.

3D QSAR studies on protein tyrosine phosphatase 1B inhibitors: comparison of the quality and predictivity among 3D QSAR models obtained from different conformer-based alignments.

PubMed

Pandey, Gyanendra; Saxena, Anil K

2006-01-01

A set of 65 flexible peptidomimetic competitive inhibitors (52 in the training set and 13 in the test set) of protein tyrosine phosphatase 1B (PTP1B) has been used to compare the quality and predictive power of 3D quantitative structure-activity relationship (QSAR) comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models for the three most commonly used conformer-based alignments, namely, cocrystallized conformer-based alignment (CCBA), docked conformer-based alignment (DCBA), and global minima energy conformer-based alignment (GMCBA). These three conformers of 5-[(2S)-2-({(2S)-2-[(tert-butoxycarbonyl)amino]-3-phenylpropanoyl}amino)3-oxo-3-pentylamino)propyl]-2-(carboxymethoxy)benzoic acid (compound number 66) were obtained from the X-ray structure of its cocrystallized complex with PTP1B (PDB ID: 1JF7), its docking studies, and its global minima by simulated annealing. Among the 3D QSAR models developed using the above three alignments, the CCBA provided the optimal predictive CoMFA model for the training set with cross-validated r2 (q2)=0.708, non-cross-validated r2=0.902, standard error of estimate (s)=0.165, and F=202.553 and the optimal CoMSIA model with q2=0.440, r2=0.799, s=0.192, and F=117.782. These models also showed the best test set prediction for the 13 compounds with predictive r2 values of 0.706 and 0.683, respectively. Though the QSAR models derived using the other two alignments also produced statistically acceptable models in the order DCBA>GMCBA in terms of the values of q2, r2, and predictive r2, they were inferior to the corresponding models derived using CCBA. Thus, the order of preference for the alignment selection for 3D QSAR model development may be CCBA>DCBA>GMCBA, and the information obtained from the CoMFA and CoMSIA contour maps may be useful in designing specific PTP1B inhibitors.

Molecular dynamics modeling the synthetic and biological polymers interactions pre-studied via docking

NASA Astrophysics Data System (ADS)

Tsvetkov, Vladimir B.; Serbin, Alexander V.

2014-06-01

In previous works we reported the design, synthesis and in vitro evaluations of synthetic anionic polymers modified by alicyclic pendant groups (hydrophobic anchors), as a novel class of inhibitors of the human immunodeficiency virus type 1 ( HIV-1) entry into human cells. Recently, these synthetic polymers interactions with key mediator of HIV-1 entry-fusion, the tri-helix core of the first heptad repeat regions [ HR1]3 of viral envelope protein gp41, were pre-studied via docking in terms of newly formulated algorithm for stepwise approximation from fragments of polymeric backbone and side-group models toward real polymeric chains. In the present article the docking results were verified under molecular dynamics ( MD) modeling. In contrast with limited capabilities of the docking, the MD allowed of using much more large models of the polymeric ligands, considering flexibility of both ligand and target simultaneously. Among the synthesized polymers the dinorbornen anchors containing alternating copolymers of maleic acid were selected as the most representative ligands (possessing the top anti-HIV activity in vitro in correlation with the highest binding energy in the docking). To verify the probability of binding of the polymers with the [HR1]3 in the sites defined via docking, various starting positions of polymer chains were tried. The MD simulations confirmed the main docking-predicted priority for binding sites, and possibilities for axial and belting modes of the ligands-target interactions. Some newly MD-discovered aspects of the ligand's backbone and anchor units dynamic cooperation in binding the viral target clarify mechanisms of the synthetic polymers anti-HIV activity and drug resistance prevention.

Application of Shape Similarity in Pose Selection and Virtual Screening in CSARdock2014 Exercise.

PubMed

Kumar, Ashutosh; Zhang, Kam Y J

2016-06-27

To evaluate the applicability of shape similarity in docking-based pose selection and virtual screening, we participated in the CSARdock2014 benchmark exercise for identifying the correct docking pose of inhibitors targeting factor XA, spleen tyrosine kinase, and tRNA methyltransferase. This exercise provides a valuable opportunity for researchers to test their docking programs, methods, and protocols in a blind testing environment. In the CSARdock2014 benchmark exercise, we have implemented an approach that uses ligand 3D shape similarity to facilitate docking-based pose selection and virtual screening. We showed here that ligand 3D shape similarity between bound poses could be used to identify the native-like pose from an ensemble of docking-generated poses. Our method correctly identified the native pose as the top-ranking pose for 73% of test cases in a blind testing environment. Moreover, the pose selection results also revealed an excellent correlation between ligand 3D shape similarity scores and RMSD to X-ray crystal structure ligand. In the virtual screening exercise, the average RMSD for our pose prediction was found to be 1.02 Å, and it was one of the top performances achieved in CSARdock2014 benchmark exercise. Furthermore, the inclusion of shape similarity improved virtual screening performance of docking-based scoring and ranking. The coefficient of determination (r(2)) between experimental activities and docking scores for 276 spleen tyrosine kinase inhibitors was found to be 0.365 but reached 0.614 when the ligand 3D shape similarity was included.

Molecular structure, FT-IR, FT-Raman, NBO, HOMO and LUMO, MEP, NLO and molecular docking study of 2-[(E)-2-(2-bromophenyl)ethenyl]quinoline-6-carboxylic acid.

PubMed

Ulahannan, Rajeev T; Panicker, C Yohannan; Varghese, Hema Tresa; Musiol, Robert; Jampilek, Josef; Van Alsenoy, Christian; War, Javeed Ahmad; Srivastava, S K

2015-01-01

The optimized molecular structure, vibrational frequencies, corresponding vibrational assignments of 2-[(E)-2-(2-bromophenyl)ethenyl]quinoline-6-carboxylic acid have been investigated experimentally and theoretically using Gaussian09 software package. Potential energy distribution of the normal modes of vibrations was done using GAR2PED program. (1)H NMR chemical shifts calculations were carried out by using B3LYP functional with SDD basis set. The HOMO and LUMO analysis is used to determine the charge transfer within the molecule. The stability of the molecule arising from hyper-conjugative interaction and charge delocalization has been analyzed using NBO analysis. MEP was performed by the DFT method and the predicted infrared intensities and Raman activities have also been reported. The calculated geometrical parameters are in agreement with that of similar derivatives. The title compound forms a stable complex with PknB as is evident from the binding affinity values and the molecular docking results suggest that the compound might exhibit inhibitory activity against PknB and this may result in development of new anti-tuberculostic agents. Copyright © 2015 Elsevier B.V. All rights reserved.

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors

PubMed Central

Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

2015-01-01

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q2 = 0.621, r2pred = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained. PMID:26035757

A Combined Pharmacophore Modeling, 3D QSAR and Virtual Screening Studies on Imidazopyridines as B-Raf Inhibitors.

PubMed

Xie, Huiding; Chen, Lijun; Zhang, Jianqiang; Xie, Xiaoguang; Qiu, Kaixiong; Fu, Jijun

2015-05-29

B-Raf kinase is an important target in treatment of cancers. In order to design and find potent B-Raf inhibitors (BRIs), 3D pharmacophore models were created using the Genetic Algorithm with Linear Assignment of Hypermolecular Alignment of Database (GALAHAD). The best pharmacophore model obtained which was used in effective alignment of the data set contains two acceptor atoms, three donor atoms and three hydrophobes. In succession, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on 39 imidazopyridine BRIs to build three dimensional quantitative structure-activity relationship (3D QSAR) models based on both pharmacophore and docking alignments. The CoMSIA model based on the pharmacophore alignment shows the best result (q(2) = 0.621, r(2)(pred) = 0.885). This 3D QSAR approach provides significant insights that are useful for designing potent BRIs. In addition, the obtained best pharmacophore model was used for virtual screening against the NCI2000 database. The hit compounds were further filtered with molecular docking, and their biological activities were predicted using the CoMSIA model, and three potential BRIs with new skeletons were obtained.

Predicted Biological Activity of Purchasable Chemical Space

PubMed Central

2017-01-01

Whereas 400 million distinct compounds are now purchasable within the span of a few weeks, the biological activities of most are unknown. To facilitate access to new chemistry for biology, we have combined the Similarity Ensemble Approach (SEA) with the maximum Tanimoto similarity to the nearest bioactive to predict activity for every commercially available molecule in ZINC. This method, which we label SEA+TC, outperforms both SEA and a naïve-Bayesian classifier via predictive performance on a 5-fold cross-validation of ChEMBL’s bioactivity data set (version 21). Using this method, predictions for over 40% of compounds (>160 million) have either high significance (pSEA ≥ 40), high similarity (ECFP4MaxTc ≥ 0.4), or both, for one or more of 1382 targets well described by ligands in the literature. Using a further 1347 less-well-described targets, we predict activities for an additional 11 million compounds. To gauge whether these predictions are sensible, we investigate 75 predictions for 50 drugs lacking a binding affinity annotation in ChEMBL. The 535 million predictions for over 171 million compounds at 2629 targets are linked to purchasing information and evidence to support each prediction and are freely available via https://zinc15.docking.org and https://files.docking.org. PMID:29193970

Fragment-Based Docking: Development of the CHARMMing Web User Interface as a Platform for Computer-Aided Drug Design

PubMed Central

2015-01-01

Web-based user interfaces to scientific applications are important tools that allow researchers to utilize a broad range of software packages with just an Internet connection and a browser.1 One such interface, CHARMMing (CHARMM interface and graphics), facilitates access to the powerful and widely used molecular software package CHARMM. CHARMMing incorporates tasks such as molecular structure analysis, dynamics, multiscale modeling, and other techniques commonly used by computational life scientists. We have extended CHARMMing’s capabilities to include a fragment-based docking protocol that allows users to perform molecular docking and virtual screening calculations either directly via the CHARMMing Web server or on computing resources using the self-contained job scripts generated via the Web interface. The docking protocol was evaluated by performing a series of “re-dockings” with direct comparison to top commercial docking software. Results of this evaluation showed that CHARMMing’s docking implementation is comparable to many widely used software packages and validates the use of the new CHARMM generalized force field for docking and virtual screening. PMID:25151852

Fragment-based drug discovery and molecular docking in drug design.

PubMed

Wang, Tao; Wu, Mian-Bin; Chen, Zheng-Jie; Chen, Hua; Lin, Jian-Ping; Yang, Li-Rong

2015-01-01

Fragment-based drug discovery (FBDD) has caused a revolution in the process of drug discovery and design, with many FBDD leads being developed into clinical trials or approved in the past few years. Compared with traditional high-throughput screening, it displays obvious advantages such as efficiently covering chemical space, achieving higher hit rates, and so forth. In this review, we focus on the most recent developments of FBDD for improving drug discovery, illustrating the process and the importance of FBDD. In particular, the computational strategies applied in the process of FBDD and molecular-docking programs are highlighted elaborately. In most cases, docking is used for predicting the ligand-receptor interaction modes and hit identification by structurebased virtual screening. The successful cases of typical significance and the hits identified most recently are discussed.

First Principles Predictions of the Structure and Function of G-Protein-Coupled Receptors: Validation for Bovine Rhodopsin

PubMed Central

Trabanino, Rene J.; Hall, Spencer E.; Vaidehi, Nagarajan; Floriano, Wely B.; Kam, Victor W. T.; Goddard, William A.

2004-01-01

G-protein-coupled receptors (GPCRs) are involved in cell communication processes and with mediating such senses as vision, smell, taste, and pain. They constitute a prominent superfamily of drug targets, but an atomic-level structure is available for only one GPCR, bovine rhodopsin, making it difficult to use structure-based methods to design receptor-specific drugs. We have developed the MembStruk first principles computational method for predicting the three-dimensional structure of GPCRs. In this article we validate the MembStruk procedure by comparing its predictions with the high-resolution crystal structure of bovine rhodopsin. The crystal structure of bovine rhodopsin has the second extracellular (EC-II) loop closed over the transmembrane regions by making a disulfide linkage between Cys-110 and Cys-187, but we speculate that opening this loop may play a role in the activation process of the receptor through the cysteine linkage with helix 3. Consequently we predicted two structures for bovine rhodopsin from the primary sequence (with no input from the crystal structure)—one with the EC-II loop closed as in the crystal structure, and the other with the EC-II loop open. The MembStruk-predicted structure of bovine rhodopsin with the closed EC-II loop deviates from the crystal by 2.84 Å coordinate root mean-square (CRMS) in the transmembrane region main-chain atoms. The predicted three-dimensional structures for other GPCRs can be validated only by predicting binding sites and energies for various ligands. For such predictions we developed the HierDock first principles computational method. We validate HierDock by predicting the binding site of 11-cis-retinal in the crystal structure of bovine rhodopsin. Scanning the whole protein without using any prior knowledge of the binding site, we find that the best scoring conformation in rhodopsin is 1.1 Å CRMS from the crystal structure for the ligand atoms. This predicted conformation has the carbonyl O only 2.82 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 0.62 Å CRMS from the crystal structure. We also used HierDock to predict the binding site of 11-cis-retinal in the MembStruk-predicted structure of bovine rhodopsin (closed loop). Scanning the whole protein structure leads to a structure in which the carbonyl O is only 2.85 Å from the N of Lys-296. Making this Schiff base bond and minimizing leads to a final conformation only 2.92 Å CRMS from the crystal structure. The good agreement of the ab initio-predicted protein structures and ligand binding site with experiment validates the use of the MembStruk and HierDock first principles' methods. Since these methods are generic and applicable to any GPCR, they should be useful in predicting the structures of other GPCRs and the binding site of ligands to these proteins. PMID:15041637

An in silico high-throughput screen identifies potential selective inhibitors for the non-receptor tyrosine kinase Pyk2

PubMed Central

Meirson, Tomer; Samson, Abraham O; Gil-Henn, Hava

2017-01-01

The non-receptor tyrosine kinase proline-rich tyrosine kinase 2 (Pyk2) is a critical mediator of signaling from cell surface growth factor and adhesion receptors to cell migration, proliferation, and survival. Emerging evidence indicates that signaling by Pyk2 regulates hematopoietic cell response, bone density, neuronal degeneration, angiogenesis, and cancer. These physiological and pathological roles of Pyk2 warrant it as a valuable therapeutic target for invasive cancers, osteoporosis, Alzheimer’s disease, and inflammatory cellular response. Despite its potential as a therapeutic target, no potent and selective inhibitor of Pyk2 is available at present. As a first step toward discovering specific potential inhibitors of Pyk2, we used an in silico high-throughput screening approach. A virtual library of six million lead-like compounds was docked against four different high-resolution Pyk2 kinase domain crystal structures and further selected for predicted potency and ligand efficiency. Ligand selectivity for Pyk2 over focal adhesion kinase (FAK) was evaluated by comparative docking of ligands and measurement of binding free energy so as to obtain 40 potential candidates. Finally, the structural flexibility of a subset of the docking complexes was evaluated by molecular dynamics simulation, followed by intermolecular interaction analysis. These compounds may be considered as promising leads for further development of highly selective Pyk2 inhibitors. PMID:28572720

Revealing the functionality of hypothetical protein KPN00728 from Klebsiella pneumoniae MGH78578: molecular dynamics simulation approaches

PubMed Central

2011-01-01

Background Previously, the hypothetical protein, KPN00728 from Klebsiella pneumoniae MGH78578 was the Succinate dehydrogenase (SDH) chain C subunit via structural prediction and molecular docking simulation studies. However, due to limitation in docking simulation, an in-depth understanding of how SDH interaction occurs across the transmembrane of mitochondria could not be provided. Results In this present study, molecular dynamics (MD) simulation of KPN00728 and SDH chain D in a membrane was performed in order to gain a deeper insight into its molecular role as SDH. Structural stability was successfully obtained in the calculation for area per lipid, tail order parameter, thickness of lipid and secondary structural properties. Interestingly, water molecules were found to be highly possible in mediating the interaction between Ubiquinone (UQ) and SDH chain C via interaction with Ser27 and Arg31 residues as compared with earlier docking study. Polar residues such as Asp95 and Glu101 (KPN00728), Asp15 and Glu78 (SDH chain D) might have contributed in the creation of a polar environment which is essential for electron transport chain in Krebs cycle. Conclusions As a conclusion, a part from the structural stability comparability, the dynamic of the interacting residues and hydrogen bonding analysis had further proved that the interaction of KPN00728 as SDH is preserved and well agreed with our postulation earlier. PMID:22372825

Perturbation Theory/Machine Learning Model of ChEMBL Data for Dopamine Targets: Docking, Synthesis, and Assay of New l-Prolyl-l-leucyl-glycinamide Peptidomimetics.

PubMed

Ferreira da Costa, Joana; Silva, David; Caamaño, Olga; Brea, José M; Loza, Maria Isabel; Munteanu, Cristian R; Pazos, Alejandro; García-Mera, Xerardo; González-Díaz, Humbert

2018-06-25

Predicting drug-protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning (ML) models for one specific protein. Unfortunately, these models fail to account for large and complex big data sets of preclinical assays reported in public databases. This includes multiple conditions of assays, such as different experimental parameters, biological assays, target proteins, cell lines, organism of the target, or organism of assay. On the other hand, perturbation theory (PT) models allow us to predict the properties of a query compound or molecular system in experimental assays with multiple boundary conditions based on a previously known case of reference. In this work, we report the first PTML (PT + ML) study of a large ChEMBL data set of preclinical assays of compounds targeting dopamine pathway proteins. The best PTML model found predicts 50000 cases with accuracy of 70-91% in training and external validation series. We also compared the linear PTML model with alternative PTML models trained with multiple nonlinear methods (artificial neural network (ANN), Random Forest, Deep Learning, etc.). Some of the nonlinear methods outperform the linear model but at the cost of a notable increment of the complexity of the model. We illustrated the practical use of the new model with a proof-of-concept theoretical-experimental study. We reported for the first time the organic synthesis, chemical characterization, and pharmacological assay of a new series of l-prolyl-l-leucyl-glycinamide (PLG) peptidomimetic compounds. In addition, we performed a molecular docking study for some of these compounds with the software Vina AutoDock. The work ends with a PTML model predictive study of the outcomes of the new compounds in a large number of assays. Therefore, this study offers a new computational methodology for predicting the outcome for any compound in new assays. This PTML method focuses on the prediction with a simple linear model of multiple pharmacological parameters (IC 50 , EC 50 , K i , etc.) for compounds in assays involving different cell lines used, organisms of the protein target, or organism of assay for proteins in the dopamine pathway.

How to benchmark methods for structure-based virtual screening of large compound libraries.

PubMed

Christofferson, Andrew J; Huang, Niu

2012-01-01

Structure-based virtual screening is a useful computational technique for ligand discovery. To systematically evaluate different docking approaches, it is important to have a consistent benchmarking protocol that is both relevant and unbiased. Here, we describe the designing of a benchmarking data set for docking screen assessment, a standard docking screening process, and the analysis and presentation of the enrichment of annotated ligands among a background decoy database.

Design new P-glycoprotein modulators based on molecular docking and CoMFA study of α, β-unsaturated carbonyl-based compounds and oxime analogs as anticancer agents

NASA Astrophysics Data System (ADS)

Sepehri, Bakhtyar; Ghavami, Raouf

2017-02-01

In this research, molecular docking and CoMFA were used to determine interactions of α, β-unsaturated carbonyl-based compounds and oxime analogs with P-glycoprotein and prediction of their activity. Molecular docking study shown these molecules establish strong Van der Waals interactions with side chain of PHE-332, PHE-728 and PHE-974. Based on the effect of component numbers on squared correlation coefficient for cross validation tests (including leave-one-out and leave-many-out), CoMFA models with five components were built to predict pIC50 of molecules in seven cancer cell lines (including Panc-1 (pancreas cancer cell line), PaCa-2 (pancreatic carcinoma cell line), MCF-7 (breast cancer cell line), A-549 (epithelial), HT-29 (colon cancer cell line), H-460 (lung cancer cell line), PC-3 (prostate cancer cell line)). R2 values for training and test sets were in the range of 0.94-0.97 and 0.84 to 0.92, respectively, and for LOO and LMO cross validation test, q2 values were in the range of 0.75-0.82 and 0.65 to 0.73, respectively. Based on molecular docking results and extracted steric and electrostatic contour maps for CoMFA models, four new molecules with higher activity with respect to the most active compound in data set were designed.

Studies on molecular structure, vibrational spectra and molecular docking analysis of 3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl 4-aminobenzoate

NASA Astrophysics Data System (ADS)

Suresh, D. M.; Amalanathan, M.; Hubert Joe, I.; Bena Jothy, V.; Diao, Yun-Peng

2014-09-01

The molecular structure, vibrational analysis and molecular docking analysis of the 3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl 4-aminobenzoate (MDDNAB) molecule have been carried out using FT-IR and FT-Raman spectroscopic techniques and DFT method. The equilibrium geometry, harmonic vibrational wave numbers, various bonding features have been computed using density functional method. The calculated molecular geometry has been compared with experimental data. The detailed interpretation of the vibrational spectra has been carried out by using VEDA program. The hyper-conjugative interactions and charge delocalization have been analyzed using natural bond orbital (NBO) analysis. The simulated FT-IR and FT-Raman spectra satisfactorily coincide with the experimental spectra. The PES and charge analysis have been made. The molecular docking was done to identify the binding energy and the Hydrogen bonding with the cancer protein molecule.

Docking-based classification models for exploratory toxicology ...

EPA Pesticide Factsheets

Background: Exploratory toxicology is a new emerging research area whose ultimate mission is that of protecting human health and environment from risks posed by chemicals. In this regard, the ethical and practical limitation of animal testing has encouraged the promotion of computational methods for the fast screening of huge collections of chemicals available on the market. Results: We derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals having high quality experimental data, kindly provided by the U.S. Environmental Protection Agency (EPA). The predictive power of our docking-based models was supported by values of AUC, EF1% (EFmax = 7.1), -LR (at SE = 0.75) and +LR (at SE = 0.25) ranging from 0.63 to 0.72, from 2.5 to 6.2, from 0.35 to 0.67 and from 2.05 to 9.84, respectively. In addition, external predictions were successfully made on some representative known estrogenic chemicals. Conclusion: We show how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Importantly, these methods enable one to employ the physicochemical information contained in the X-ray solved biological target and to screen structurally-unrelated chemicals. Shows how structure-based methods, widely applied to drug discovery programs, can be adapted to meet the conditions of the regulatory context. Evaluation of 24 reliable dockin

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

PubMed

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

2017-01-01

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

Proposing Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of Their Therapeutic Activity, Pharmacokinetic and Toxicity Properties.

PubMed

Is, Yusuf Serhat; Durdagi, Serdar; Aksoydan, Busecan; Yurtsever, Mine

2018-05-07

Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure-activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand 1, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.

Docking, molecular dynamics and quantitative structure-activity relationship studies for HEPTs and DABOs as HIV-1 reverse transcriptase inhibitors.

PubMed

Mao, Yating; Li, Yan; Hao, Ming; Zhang, Shuwei; Ai, Chunzhi

2012-05-01

As a key component in combination therapy for acquired immunodeficiency syndrome (AIDS), non-nucleoside reverse transcriptase inhibitors (NNRTIs) have been proven to be an essential way in stopping HIV-1 replication. In the present work, in silico studies were conducted on a series of 119 NNRTIs, including 1-(2-hydroxyethoxymethyl)-6-(phenylthio)thymine (HEPT) and dihydroalkoxybenzyloxopyrimidine (DABO) derivatives by using the comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), docking simulations and molecular dynamics (MD). The statistical results of the optimal model, the ligand-based CoMSIA one (Q(2) = 0.48, R(ncv)(2) =0.847, R(pre)(2) = 0.745) validates its satisfactory predictive capacity both internally and externally. The contour maps, docking and MD results correlate well with each other, drawing conclusions as follows: 1) Compounds with bulky substituents in position-6 of ring A, hydrophobic groups around position- 1, 2, 6 are preferable to the biological activities; 2) Two hydrogen bonds between RT inhibitor and the Tyr 318, Lys 101 residues, respectively, and a π-π bond between the inhibitor and Trp 188 are formed and crucial to the orientation of the active conformation of the molecules; 3) The binding pocket is essentially hydrophobic, which are determined by residues such as Trp 229, Tyr 318, Val 179, Tyr 188 and Val 108, and hydrophobic substituents may bring an improvement to the biological activity; 4) DABO and HEPT derivatives have different structures but take a similar mechanism to inhibit RT. The potency difference between two isomers in HEPTs can be explained by the distinct locations of the 6-naphthylmethyl substituent and the reasons are explained in details. All these results could be employed to alter the structural scaffold in order to develop new HIV-1 RT inhibitors that have an improved biological property. To the best of our knowledge, this is the first report on 3D-QSAR modeling of this series of HEPT and DABO NNRTs. The QSAR model and the information derived, we hope, will be of great help in presenting clear guidelines and accurate activity predictions for newly designed HIV-1 reverse transcriptase (RT) inhibitor.

BiGGER: a new (soft) docking algorithm for predicting protein interactions.

PubMed

Palma, P N; Krippahl, L; Wampler, J E; Moura, J J

2000-06-01

A new computationally efficient and automated "soft docking" algorithm is described to assist the prediction of the mode of binding between two proteins, using the three-dimensional structures of the unbound molecules. The method is implemented in a software package called BiGGER (Bimolecular Complex Generation with Global Evaluation and Ranking) and works in two sequential steps: first, the complete 6-dimensional binding spaces of both molecules is systematically searched. A population of candidate protein-protein docked geometries is thus generated and selected on the basis of the geometric complementarity and amino acid pairwise affinities between the two molecular surfaces. Most of the conformational changes observed during protein association are treated in an implicit way and test results are equally satisfactory, regardless of starting from the bound or the unbound forms of known structures of the interacting proteins. In contrast to other methods, the entire molecular surfaces are searched during the simulation, using absolutely no additional information regarding the binding sites. In a second step, an interaction scoring function is used to rank the putative docked structures. The function incorporates interaction terms that are thought to be relevant to the stabilization of protein complexes. These include: geometric complementarity of the surfaces, explicit electrostatic interactions, desolvation energy, and pairwise propensities of the amino acid side chains to contact across the molecular interface. The relative functional contribution of each of these interaction terms to the global scoring function has been empirically adjusted through a neural network optimizer using a learning set of 25 protein-protein complexes of known crystallographic structures. In 22 out of 25 protein-protein complexes tested, near-native docked geometries were found with C(alpha) RMS deviations < or =4.0 A from the experimental structures, of which 14 were found within the 20 top ranking solutions. The program works on widely available personal computers and takes 2 to 8 hours of CPU time to run any of the docking tests herein presented. Finally, the value and limitations of the method for the study of macromolecular interactions, not yet revealed by experimental techniques, are discussed.

Screening of Toxic Effects of Bisphenol A and Products of Its Degradation: Zebrafish (Danio rerio) Embryo Test and Molecular Docking.

PubMed

Makarova, Katerina; Siudem, Pawel; Zawada, Katarzyna; Kurkowiak, Justyna

2016-10-01

Bisphenol A (BPA) acts as an endocrine-disrupting compound even at a low concentration. Degradation of BPA could lead to the formation of toxic products. In this study, we compare the toxicity of BPA and seven intermediate products of its degradation. The accuracy of three molecular docking programs (Surflex, Autodock, and Autodock Vina) in predicting the binding affinities of selected compounds to human (ERα, ERβ, and ERRγ) and zebrafish (ERα, ERRγA, and ERRγB) estrogen and estrogen-related receptors was evaluated. The docking experiments showed that 4-isopropylphenol could have similar toxicity to that of BPA due to its high affinity to ERRγ and ERRγB and high octanol-water partitioning coefficient. The least toxic compounds were hydroquinone and phenol. Those compounds as well as BPA were screened in the zebrafish (Danio rerio) embryo test. 4-isopropylphenol had the strongest toxic effect on zebrafish embryos and caused 100% lethality shortly after exposure. BPA caused the delay in development, multiple deformations, and low heartbeats (30 bps), whereas hydroquinone had no impact on the development of the zebrafish embryo. Thus, the results of zebrafish screening are in good agreement with our docking experiment. The molecular docking could be used to screen the toxicity of other xenoestrogens and their products of degradation.

Structural prediction and comparative docking studies of psychrophilic β- Galactosidase with lactose, ONPG and PNPG against its counter parts of mesophilic and thermophilic enzymes.

PubMed

Kumar, Ponnada Suresh; Pulicherla, Kk; Ghosh, Mrinmoy; Kumar, Anmol; Rao, Krs Sambasiva

2011-01-01

Enzymes from psychrophiles catalyze the reactions at low temperatures with higher specific activity. Among all the psychrophilic enzymes produced, cold active β-galactosidase from marine psychrophiles revalorizes a new arena in numerous areas at industrial level. The hydrolysis of lactose in to glucose and galactose by cold active β-galactosidase offers a new promising approach in removal of lactose from milk to overcome the problem of lactose intolerance. Herein we propose, a 3D structure of cold active β-galactosidase enzyme sourced from Pseudoalteromonas haloplanktis by using Modeler 9v8 and best model was developed having 88% of favourable region in ramachandran plot. Modelling was followed by docking studies with the help of Auto dock 4.0 against the three substrates lactose, ONPG and PNPG. In addition, comparative docking studies were also performed for the 3D model of psychrophilic β-galactosidase with mesophilic and thermophilic enzymes. Docking studies revealed that binding affinity of enzyme towards the three different substrates is more for psychrophilic enzyme when compared with mesophilic and thermophilic enzymes. It indicates that the enzyme has high specific activity at low temperature when compared with mesophilic and thermophilic enzymes.

In-Silico Analysis of Amotosalen Hydrochloride Binding to CD-61 of Platelets.

PubMed

Chaudhary, Hammad Tufail

2016-11-01

To determine the docking of Amotosalen hydrochloride (AH) at CD-61 of platelets, and to suggest the cause of bleeding in AH treated platelets transfusion. Descriptive study. Medical College, Taif University, Taif, Saudi Arabia, from October 2014 to May 2015. The study was carried out in-silico. PDB (protein data bank) code of Tirofiban bound to CD-61 was 2vdm. CD-61 was docked with Tirofiban using online docking tools, i.e. Patchdock and Firedock. Then, Amotosalen hydrochloride and CD-61 were also docked. Best docking poses to active sites of 2vdm were found. Ligplot of interactions of ligands and CD-61 were obtained. Then comparison of hydrogen bonds, hydrogen bond lengths, and hydrophobic bonds of 2vdm molecule and best poses of docking results were done. Patchdock and Firedock results of best poses were also analysed using SPSS version 16. More amino acids were involved in hydrogen and hydrophobic bonds in Patchdock and Firedock docking of Amotosalen hydrochloride with CD-61 than Patchdock and Firedock docking of CD-61 with Tirofiban. The binding energy was more in latter than former. Amotosalen hydrochloride binds to the active site of CD-61 with weaker binding force. Haemorrhage seen in Amotosalen hydrochloride-treated platelets might be due to binding of Amotosalen hydrochloride to CD-61.

Molecular dynamics, flexible docking, virtual screening, ADMET predictions, and molecular interaction field studies to design novel potential MAO-B inhibitors.

PubMed

Braun, Glaucia H; Jorge, Daniel M M; Ramos, Henrique P; Alves, Raquel M; da Silva, Vinicius B; Giuliatti, Silvana; Sampaio, Suley Vilela; Taft, Carlton A; Silva, Carlos H T P

2008-02-01

Monoamine oxidase is a flavoenzyme bound to the mitochondrial outer membranes of the cells, which is responsible for the oxidative deamination of neurotransmitter and dietary amines. It has two distinct isozymic forms, designated MAO-A and MAO-B, each displaying different substrate and inhibitor specificities. They are the well-known targets for antidepressant, Parkinson's disease, and neuroprotective drugs. Elucidation of the x-ray crystallographic structure of MAO-B has opened the way for the molecular modeling studies. In this work we have used molecular modeling, density functional theory with correlation, virtual screening, flexible docking, molecular dynamics, ADMET predictions, and molecular interaction field studies in order to design new molecules with potential higher selectivity and enzymatic inhibitory activity over MAO-B.

Space Tug Docking Study. Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

1976-01-01

Results of a detailed systems analysis of the entire rendezvous and docking operation to be performed by the all-up space tug are presented. Specific areas investigated include: generating of operational requirements and a data base of candidate operational techniques and subsystem mechanizations; selection and ranking of integrated system designs capable of meeting the requirements generated; and definition of this simulation/demonstration program required to select and prove the most effective manual, autonomous, and hybrid rendezvous and docking systems.

Meteoroid and Orbital Debris Threats to NASA's Docking Seals: Initial Assessment and Methodology

NASA Technical Reports Server (NTRS)

deGroh, Henry C., III; Nahra, Henry K.

2009-01-01

The Crew Exploration Vehicle (CEV) will be exposed to the Micrometeoroid Orbital Debris (MMOD) environment in Low Earth Orbit (LEO) during missions to the International Space Station (ISS) and to the micrometeoroid environment during lunar missions. The CEV will be equipped with a docking system which enables it to connect to ISS and the lunar module known as Altair; this docking system includes a hatch that opens so crew and supplies can pass between the spacecrafts. This docking system is known as the Low Impact Docking System (LIDS) and uses a silicone rubber seal to seal in cabin air. The rubber seal on LIDS presses against a metal flange on ISS (or Altair). All of these mating surfaces are exposed to the space environment prior to docking. The effects of atomic oxygen, ultraviolet and ionizing radiation, and MMOD have been estimated using ground based facilities. This work presents an initial methodology to predict meteoroid and orbital debris threats to candidate docking seals being considered for LIDS. The methodology integrates the results of ground based hypervelocity impacts on silicone rubber seals and aluminum sheets, risk assessments of the MMOD environment for a variety of mission scenarios, and candidate failure criteria. The experimental effort that addressed the effects of projectile incidence angle, speed, mass, and density, relations between projectile size and resulting crater size, and relations between crater size and the leak rate of candidate seals has culminated in a definition of the seal/flange failure criteria. The risk assessment performed with the BUMPER code used the failure criteria to determine the probability of failure of the seal/flange system and compared the risk to the allotted risk dictated by NASA's program requirements.

The Drosophila DOCK family protein Sponge is required for development of the air sac primordium

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

Morishita, Kazushge; Anh Suong, Dang Ngoc; Yoshida, Hideki

Dedicator of cytokinesis (DOCK) family genes are known as DOCK1-DOCK11 in mammals. DOCK family proteins mainly regulate actin filament polymerization and/or depolymerization and are GEF proteins, which contribute to cellular signaling events by activating small G proteins. Sponge (Spg) is a Drosophila counterpart to mammalian DOCK3/DOCK4, and plays a role in embryonic central nervous system development, R7 photoreceptor cell differentiation, and adult thorax development. In order to conduct further functional analyses on Spg in vivo, we examined its localization in third instar larval wing imaginal discs. Immunostaining with purified anti-Spg IgG revealed that Spg mainly localized in the air sacmore » primordium (ASP) in wing imaginal discs. Spg is therefore predicted to play an important role in the ASP. The specific knockdown of Spg by the breathless-GAL4 driver in tracheal cells induced lethality accompanied with a defect in ASP development and the induction of apoptosis. The monitoring of ERK signaling activity in wing imaginal discs by immunostaining with anti-diphospho-ERK IgG revealed reductions in the ERK signal cascade in Spg knockdown clones. Furthermore, the overexpression of D-raf suppressed defects in survival and the proliferation of cells in the ASP induced by the knockdown of Spg. Collectively, these results indicate that Spg plays a critical role in ASP development and tracheal cell viability that is mediated by the ERK signaling pathway. - Highlights: • Spg mainly localizes in the air sac primordium in wing imaginal discs. • Spg plays a critical role in air sac primordium development. • Spg positively regulates the ERK signal cascade.« less

Molecular dynamics modeling the synthetic and biological polymers interactions pre-studied via docking: anchors modified polyanions interference with the HIV-1 fusion mediator.

PubMed

Tsvetkov, Vladimir B; Serbin, Alexander V

2014-06-01

In previous works we reported the design, synthesis and in vitro evaluations of synthetic anionic polymers modified by alicyclic pendant groups (hydrophobic anchors), as a novel class of inhibitors of the human immunodeficiency virus type 1 (HIV-1) entry into human cells. Recently, these synthetic polymers interactions with key mediator of HIV-1 entry-fusion, the tri-helix core of the first heptad repeat regions [HR1]3 of viral envelope protein gp41, were pre-studied via docking in terms of newly formulated algorithm for stepwise approximation from fragments of polymeric backbone and side-group models toward real polymeric chains. In the present article the docking results were verified under molecular dynamics (MD) modeling. In contrast with limited capabilities of the docking, the MD allowed of using much more large models of the polymeric ligands, considering flexibility of both ligand and target simultaneously. Among the synthesized polymers the dinorbornen anchors containing alternating copolymers of maleic acid were selected as the most representative ligands (possessing the top anti-HIV activity in vitro in correlation with the highest binding energy in the docking). To verify the probability of binding of the polymers with the [HR1]3 in the sites defined via docking, various starting positions of polymer chains were tried. The MD simulations confirmed the main docking-predicted priority for binding sites, and possibilities for axial and belting modes of the ligands-target interactions. Some newly MD-discovered aspects of the ligand's backbone and anchor units dynamic cooperation in binding the viral target clarify mechanisms of the synthetic polymers anti-HIV activity and drug resistance prevention.

Meteoroid and Orbital Debris Threats to NASA's Docking Seals: Initial Assessment and Methodology

NASA Technical Reports Server (NTRS)

deGroh, Henry C., III; Gallo, Christopher A.; Nahra, Henry K.

2009-01-01

The Crew Exploration Vehicle (CEV) will be exposed to the Micrometeoroid Orbital Debris (MMOD) environment in Low Earth Orbit (LEO) during missions to the International Space Station (ISS) and to the micrometeoroid environment during lunar missions. The CEV will be equipped with a docking system which enables it to connect to ISS and the lunar module known as Altair; this docking system includes a hatch that opens so crew and supplies can pass between the spacecrafts. This docking system is known as the Low Impact Docking System (LIDS) and uses a silicone rubber seal to seal in cabin air. The rubber seal on LIDS presses against a metal flange on ISS (or Altair). All of these mating surfaces are exposed to the space environment prior to docking. The effects of atomic oxygen, ultraviolet and ionizing radiation, and MMOD have been estimated using ground based facilities. This work presents an initial methodology to predict meteoroid and orbital debris threats to candidate docking seals being considered for LIDS. The methodology integrates the results of ground based hypervelocity impacts on silicone rubber seals and aluminum sheets, risk assessments of the MMOD environment for a variety of mission scenarios, and candidate failure criteria. The experimental effort that addressed the effects of projectile incidence angle, speed, mass, and density, relations between projectile size and resulting crater size, and relations between crater size and the leak rate of candidate seals has culminated in a definition of the seal/flange failure criteria. The risk assessment performed with the BUMPER code used the failure criteria to determine the probability of failure of the seal/flange system and compared the risk to the allotted risk dictated by NASA s program requirements.

Spectroscopic and theoretical investigation of oxali-palladium interactions with β-lactoglobulin.

PubMed

Ghalandari, Behafarid; Divsalar, Adeleh; Saboury, Ali Akbar; Haertlé, Thomas; Parivar, Kazem; Bazl, Roya; Eslami-Moghadam, Mahbube; Amanlou, Massoud

2014-01-24

The possibility of using a small cheap dairy protein, β-lactoglobulin (β-LG), as a carrier for oxali-palladium for drug delivery was studied. Their binding in an aqueous solution at two temperatures of 25 and 37°C was investigated using spectroscopic techniques in combination with a molecular docking study. Fluorescence intensity changes showed combined static and dynamic quenching during β-LG oxali-palladium binding, with the static mode being predominant in the quenching mechanism. The binding and thermodynamic parameters were determined by analyzing the results of quenching and those of the van't Hoff equation. According to obtained results the binding constants at two temperatures of 25 and 37°C are 3.3×10(9) M(-1) and 18.4×10(6) M(-1) respectively. Fluorescence resonance energy transfer (FRET) showed that the experimental results and the molecular docking results were coherent. An absence change of β-LG secondary structure was confirmed by the CD results. Molecular docking results agreed fully with the experimental results since the fluorescence studies also revealed the presence of two binding sites with a negative value for the Gibbs free energy of binding of oxali-palladium to β-LG. Furthermore, molecular docking and experimental results suggest that the hydrophobic effect plays a critical role in the formation of the oxali-palladium complex with β-LG. This agreement between molecular docking and experimental results implies that docking studies may be a suitable method for predicting and confirming experimental results, as shown in this study. Hence, the combination of molecular docking and spectroscopy methods is an effective innovative approach for binding studies, particularly for pharmacophores. Copyright © 2013 Elsevier B.V. All rights reserved.

Differential Regulation of Synaptic Vesicle Tethering and Docking by UNC-18 and TOM-1.

PubMed

Gracheva, Elena O; Maryon, Ed B; Berthelot-Grosjean, Martine; Richmond, Janet E

2010-01-01

The assembly of SNARE complexes between syntaxin, SNAP-25 and synaptobrevin is required to prime synaptic vesicles for fusion. Since Munc18 and tomosyn compete for syntaxin interactions, the interplay between these proteins is predicted to be important in regulating synaptic transmission. We explored this possibility, by examining genetic interactions between C. elegans unc-18(Munc18), unc-64(syntaxin) and tom-1(tomosyn). We have previously demonstrated that unc-18 mutants have reduced synaptic transmission, whereas tom-1 mutants exhibit enhanced release. Here we show that the unc-18 mutant release defect is associated with loss of two morphologically distinct vesicle pools; those tethered within 25 nm of the plasma membrane and those docked with the plasma membrane. In contrast, priming defective unc-13 mutants accumulate tethered vesicles, while docked vesicles are greatly reduced, indicating tethering is UNC-18-dependent and occurs in the absence of priming. C. elegans unc-64 mutants phenocopy unc-18 mutants, losing both tethered and docked vesicles, whereas overexpression of open syntaxin preferentially increases vesicle docking, suggesting UNC-18/closed syntaxin interactions are responsible for vesicle tethering. Given the competition between vertebrate tomosyn and Munc18, for syntaxin binding, we hypothesized that C. elegans TOM-1 may inhibit both UNC-18-dependent vesicle targeting steps. Consistent with this hypothesis, tom-1 mutants exhibit enhanced UNC-18 plasma membrane localization and a concomitant increase in both tethered and docked synaptic vesicles. Furthermore, in tom-1;unc-18 double mutants the docked, primed vesicle pool is preferentially rescued relative to unc-18 single mutants. Together these data provide evidence for the differential regulation of two vesicle targeting steps by UNC-18 and TOM-1 through competitive interactions with syntaxin.

Prediction of the binding site of 1-benzyl-4-[(5,6-dimethoxy-1-indanon-2-yl)methyl]piperidine in acetylcholinesterase by docking studies with the SYSDOC program

NASA Astrophysics Data System (ADS)

Pang, Yuan-Ping; Kozikowski, Alan P.

1994-12-01

In the preceding paper we reported on a docking study with the SYSDOC program for predicting the binding sites of huperzine A in acetylcholinesterase (AChE) [Pang, Y.-P. and Kozikowski, A.P., J. Comput.-Aided Mol. Design, 8 (1994) 669]. Here we present a prediction of the binding sites of 1-benzyl-4-[(5,6-dimethoxy-1-indanon-2-yl)methyl]piperidine (E2020) in AChE by the same method. E2020 is one of the most potent and selective reversible inhibitors of AChE, and this molecule has puzzled researchers, partly due to its flexible structure, in understanding how it binds to AChE. Based on the results of docking 1320 different conformers of E2020 into 69 different conformers of AChE and on the pharmacological data reported for E2020 and its analogs, we predict that both the R- and the S-isomer of E2020 span the whole binding cavity of AChE, with the ammonium group interacting mainly with Trp84, Phe330 and Asp72, the phenyl group interacting mainly with Trp84 and Phe330, and the indanone moiety interacting mainly with Tyr70 and Trp279. The topography of the calculated E2020 binding sites provides insights into understanding the high potency of E2020 in the inhibition of AChE and provides hints as to possible structural modifications for identifying improved AChE inhibitors as potential therapeutics for the palliative treatment of Alzheimer's disease.

Confirming therapeutic target of protopine using immobilized β2 -adrenoceptor coupled with site-directed molecular docking and the target-drug interaction by frontal analysis and injection amount-dependent method.

PubMed

Liu, Guangxin; Wang, Pei; Li, Chan; Wang, Jing; Sun, Zhenyu; Zhao, Xinfeng; Zheng, Xiaohui

2017-07-01

Drug-protein interaction analysis is pregnant in designing new leads during drug discovery. We prepared the stationary phase containing immobilized β 2 -adrenoceptor (β 2 -AR) by linkage of the receptor on macroporous silica gel surface through N,N'-carbonyldiimidazole method. The stationary phase was applied in identifying antiasthmatic target of protopine guided by the prediction of site-directed molecular docking. Subsequent application of immobilized β 2 -AR in exploring the binding of protopine to the receptor was realized by frontal analysis and injection amount-dependent method. The association constants of protopine to β 2 -AR by the 2 methods were (1.00 ± 0.06) × 10 5 M -1 and (1.52 ± 0.14) × 10 4 M -1 . The numbers of binding sites were (1.23 ± 0.07) × 10 -7 M and (9.09 ± 0.06) × 10 -7 M, respectively. These results indicated that β 2 -AR is the specific target for therapeutic action of protopine in vivo. The target-drug binding occurred on Ser 169 in crystal structure of the receptor. Compared with frontal analysis, injection amount-dependent method is advantageous to drug saving, improvement of sampling efficiency, and performing speed. It has grave potential in high-throughput drug-receptor interaction analysis. Copyright © 2017 John Wiley & Sons, Ltd.

Inhibition of acetylcholinesterase by two genistein derivatives: kinetic analysis, molecular docking and molecular dynamics simulation.

PubMed

Fang, Jiansong; Wu, Ping; Yang, Ranyao; Gao, Li; Li, Chao; Wang, Dongmei; Wu, Song; Liu, Ai-Lin; Du, Guan-Hua

2014-12-01

In this study two genistein derivatives (G1 and G2) are reported as inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE), and differences in the inhibition of AChE are described. Although they differ in structure by a single methyl group, the inhibitory effect of G1 (IC50=264 nmol/L) on AChE was 80 times stronger than that of G2 (IC50=21,210 nmol/L). Enzyme-kinetic analysis, molecular docking and molecular dynamics (MD) simulations were conducted to better understand the molecular basis for this difference. The results obtained by kinetic analysis demonstrated that G1 can interact with both the catalytic active site and peripheral anionic site of AChE. The predicted binding free energies of two complexes calculated by the molecular mechanics/generalized born surface area (MM/GBSA) method were consistent with the experimental data. The analysis of the individual energy terms suggested that a difference between the net electrostatic contributions (ΔE ele+ΔG GB) was responsible for the binding affinities of these two inhibitors. Additionally, analysis of the molecular mechanics and MM/GBSA free energy decomposition revealed that the difference between G1 and G2 originated from interactions with Tyr124, Glu292, Val294 and Phe338 of AChE. In conclusion, the results reveal significant differences at the molecular level in the mechanism of inhibition of AChE by these structurally related compounds.

Ribonucleotide reductase as a drug target against drug resistance Mycobacterium leprae: A molecular docking study.

PubMed

Mohanty, Partha Sarathi; Bansal, Avi Kumar; Naaz, Farah; Gupta, Umesh Datta; Dwivedi, Vivek Dhar; Yadava, Umesh

2018-06-01

Leprosy is a chronic infection of skin and nerve caused by Mycobacterium leprae. The treatment is based on standard multi drug therapy consisting of dapsone, rifampicin and clofazamine. The use of rifampicin alone or with dapsone led to the emergence of rifampicin-resistant Mycobacterium leprae strains. The emergence of drug-resistant leprosy put a hurdle in the leprosy eradication programme. The present study aimed to predict the molecular model of ribonucleotide reductase (RNR), the enzyme responsible for biosynthesis of nucleotides, to screen new drugs for treatment of drug-resistant leprosy. The study was conducted by retrieving RNR of M. leprae from GenBank. A molecular 3D model of M. leprae was predicted using homology modelling and validated. A total of 325 characters were included in the analysis. The predicted 3D model of RNR showed that the ϕ and φ angles of 251 (96.9%) residues were positioned in the most favoured regions. It was also conferred that 18 α-helices, 6 β turns, 2 γ turns and 48 helix-helix interactions contributed to the predicted 3D structure. Virtual screening of Food and Drug Administration approved drug molecules recovered 1829 drugs of which three molecules, viz., lincomycin, novobiocin and telithromycin, were taken for the docking study. It was observed that the selected drug molecules had a strong affinity towards the modelled protein RNR. This was evident from the binding energy of the drug molecules towards the modelled protein RNR (-6.10, -6.25 and -7.10). Three FDA-approved drugs, viz., lincomycin, novobiocin and telithromycin, could be taken for further clinical studies to find their efficacy against drug resistant leprosy. Copyright © 2018 Elsevier B.V. All rights reserved.

Discovery of binding proteins for a protein target using protein-protein docking-based virtual screening.

PubMed

Zhang, Changsheng; Tang, Bo; Wang, Qian; Lai, Luhua

2014-10-01

Target structure-based virtual screening, which employs protein-small molecule docking to identify potential ligands, has been widely used in small-molecule drug discovery. In the present study, we used a protein-protein docking program to identify proteins that bind to a specific target protein. In the testing phase, an all-to-all protein-protein docking run on a large dataset was performed. The three-dimensional rigid docking program SDOCK was used to examine protein-protein docking on all protein pairs in the dataset. Both the binding affinity and features of the binding energy landscape were considered in the scoring function in order to distinguish positive binding pairs from negative binding pairs. Thus, the lowest docking score, the average Z-score, and convergency of the low-score solutions were incorporated in the analysis. The hybrid scoring function was optimized in the all-to-all docking test. The docking method and the hybrid scoring function were then used to screen for proteins that bind to tumor necrosis factor-α (TNFα), which is a well-known therapeutic target for rheumatoid arthritis and other autoimmune diseases. A protein library containing 677 proteins was used for the screen. Proteins with scores among the top 20% were further examined. Sixteen proteins from the top-ranking 67 proteins were selected for experimental study. Two of these proteins showed significant binding to TNFα in an in vitro binding study. The results of the present study demonstrate the power and potential application of protein-protein docking for the discovery of novel binding proteins for specific protein targets. © 2014 Wiley Periodicals, Inc.

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.

Quantum mechanical and spectroscopic (FT-IR, FT-Raman) study, NBO analysis, HOMO-LUMO, first order hyperpolarizability and molecular docking study of methyl[(3R)-3-(2-methylphenoxy)-3-phenylpropyl]amine by density functional method.

PubMed

Kuruvilla, Tintu K; Prasana, Johanan Christian; Muthu, S; George, Jacob; Mathew, Sheril Ann

2018-01-05

Quantum chemical techniques such as density functional theory (DFT) have become a powerful tool in the investigation of the molecular structure and vibrational spectrum and are finding increasing use in application related to biological systems. The Fourier transform infrared (FT-IR) and Fourier transform Raman (FT-Raman) techniques are employed to characterize the title compound. The vibrational frequencies were obtained by DFT/B3LYP calculations with 6-31G(d,p) and 6-311++G(d,p) as basis sets. The geometry of the title compound was optimized. The vibrational assignments and the calculation of Potential Energy Distribution (PED) were carried out using the Vibrational Energy Distribution Analysis (VEDA) software. Molecular electrostatic potential was calculated for the title compound to predict the reactive sites for electrophilic and nucleophilic attack. In addition, the first-order hyperpolarizability, HOMO and LUMO energies, Fukui function and NBO were computed. The thermodynamic properties of the title compound were calculated at different temperatures, revealing the correlations between heat capacity (C), entropy (S) and enthalpy changes (H) with temperatures. Molecular docking studies were also conducted as part of this study. The paper further explains the experimental results which are in line with the theoretical calculations and provide optimistic evidence through molecular docking that the title compound can act as a good antidepressant. It also provides sufficient justification for the title compound to be selected as a good candidate for further studies related to NLO properties. Copyright © 2017. Published by Elsevier B.V.

Investigation of Control System and Display Variations on Spacecraft Handling Qualities for Docking with Stationary and Rotating Targets

NASA Technical Reports Server (NTRS)

Jackson, E. Bruce; Goodrich, Kenneth H.; Bailey, Randall E.; Barnes, James R.; Ragsdale, William A.; Neuhaus, Jason R.

2010-01-01

This paper documents the investigation into the manual docking of a preliminary version of the Crew Exploration Vehicle with stationary and rotating targets in Low Earth Orbit. The investigation was conducted at NASA Langley Research Center in the summer of 2008 in a repurposed fixed-base transport aircraft cockpit and involved nine evaluation astronauts and research pilots. The investigation quantified the benefits of a feed-forward reaction control system thruster mixing scheme to reduce translation-into-rotation coupling, despite unmodeled variations in individual thruster force levels and off-axis center of mass locations up to 12 inches. A reduced rate dead-band in the phase-plane attitude controller also showed some promise. Candidate predictive symbology overlaid on a docking ring centerline camera image did not improve handling qualities, but an innovative attitude status indicator symbol was beneficial. The investigation also showed high workload and handling quality problems when manual dockings were performed with a rotating target. These concerns indicate achieving satisfactory handling quality ratings with a vehicle configuration similar to the nominal Crew Exploration Vehicle may require additional automation.

How to Deal with Low-Resolution Target Structures: Using SAR, Ensemble Docking, Hydropathic Analysis, and 3D-QSAR to Definitively Map the αβ-Tubulin Colchicine Site

PubMed Central

Da, Chenxiao; Mooberry, Susan L.; Gupton, John T.; Kellogg, Glen E.

2013-01-01

αβ-tubulin colchicine site inhibitors (CSIs) from four scaffolds that we previously tested for antiproliferative activity were modeled to better understand their effect on microtubules. Docking models, constructed by exploiting the SAR of a pyrrole subset and HINT scoring, guided ensemble docking of all 59 compounds. This conformation set and two variants having progressively less structure knowledge were subjected to CoMFA, CoMFA+HINT, and CoMSIA 3D-QSAR analyses. The CoMFA+HINT model (docked alignment) showed the best statistics: leave-one-out q2 of 0.616, r2 of 0.949 and r2pred (internal test set) of 0.755. An external (tested in other laboratories) collection of 24 CSIs from eight scaffolds were evaluated with the 3D-QSAR models, which correctly ranked their activity trends in 7/8 scaffolds for CoMFA+HINT (8/8 for CoMFA). The combination of SAR, ensemble docking, hydropathic analysis and 3D-QSAR provides an atomic-scale colchicine site model more consistent with a target structure resolution much higher than the ~3.6 Å available for αβ-tubulin. PMID:23961916

The 3D Structure of the Binding Pocket of the Human Oxytocin Receptor for Benzoxazine Antagonists, Determined by Molecular Docking, Scoring Functions and 3D-QSAR Methods

NASA Astrophysics Data System (ADS)

Jójárt, Balázs; Martinek, Tamás A.; Márki, Árpád

2005-05-01

Molecular docking and 3D-QSAR studies were performed to determine the binding mode for a series of benzoxazine oxytocin antagonists taken from the literature. Structural hypotheses were generated by docking the most active molecule to the rigid receptor by means of AutoDock 3.05. The cluster analysis yielded seven possible binding conformations. These structures were refined by using constrained simulated annealing, and the further ligands were aligned in the refined receptor by molecular docking. A good correlation was found between the estimated Δ G bind and the p K i values for complex F. The Connolly-surface analysis, CoMFA and CoMSIA models q CoMFA 2 = 0.653, q CoMSA 2 = 0.630 and r pred,CoMFA 2 = 0.852 , r pred,CoMSIA 2 = 0.815) confirmed the scoring function results. The structural features of the receptor-ligand complex and the CoMFA and CoMSIA fields are in closely connected. These results suggest that receptor-ligand complex F is the most likely binding hypothesis for the studied benzoxazine analogs.

Improved pose and affinity predictions using different protocols tailored on the basis of data availability

NASA Astrophysics Data System (ADS)

Prathipati, Philip; Nagao, Chioko; Ahmad, Shandar; Mizuguchi, Kenji

2016-09-01

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Synthesis, crystal structures, molecular docking and urease inhibition studies of Ni(II) and Cu(II) Schiff base complexes

NASA Astrophysics Data System (ADS)

Sangeeta, S.; Ahmad, K.; Noorussabah, N.; Bharti, S.; Mishra, M. K.; Sharma, S. R.; Choudhary, M.

2018-03-01

[Ni(L)2] 1 and [Cu(L)2] 2 [HL = 2-((E)-(2-methoxyphenylimino)methyl)-4,6-dichlorophenol] Schiff base complexes have been successfully synthesized and were characterized by FT-IR, UV-Vis, fluorescence spectroscopy and thermogravimetric analysis. The crystal structures of the two complexes were determined through X-ray crystallography. Its inhibitory activity against Helicobacter pylori urease was evaluated in vitro and showed strong inhibitory activity against H. pylori urease compared with acetohydroxamic acid (IC50 = 42.12 μmolL-1), which is a positive reference. A docking analysis using the AutoDock 4.0 program could explain the inhibitory activity of the complex against urease.

Mapping multiple potential ATP binding sites on the matrix side of the bovine ADP/ATP carrier by the combined use of MD simulation and docking.

PubMed

Di Marino, Daniele; Oteri, Francesco; della Rocca, Blasco Morozzo; D'Annessa, Ilda; Falconi, Mattia

2012-06-01

The mitochondrial adenosine diphosphate/adenosine triphosphate (ADP/ATP) carrier-AAC-was crystallized in complex with its specific inhibitor carboxyatractyloside (CATR). The protein consists of a six-transmembrane helix bundle that defines the nucleotide translocation pathway, which is closed towards the matrix side due to sharp kinks in the odd-numbered helices. In this paper, we describe the interaction between the matrix side of the AAC transporter and the ATP(4-) molecule using carrier structures obtained through classical molecular dynamics simulation (MD) and a protein-ligand docking procedure. Fifteen structures were extracted from a previously published MD trajectory through clustering analysis, and 50 docking runs were carried out for each carrier conformation, for a total of 750 runs ("MD docking"). The results were compared to those from 750 docking runs performed on the X-ray structure ("X docking"). The docking procedure indicated the presence of a single interaction site in the X-ray structure that was conserved in the structures extracted from the MD trajectory. MD docking showed the presence of a second binding site that was not found in the X docking. The interaction strategy between the AAC transporter and the ATP(4-) molecule was analyzed by investigating the composition and 3D arrangement of the interaction pockets, together with the orientations of the substrate inside them. A relationship between sequence repeats and the ATP(4-) binding sites in the AAC carrier structure is proposed.

CircDOCK1 suppresses cell apoptosis via inhibition of miR-196a-5p by targeting BIRC3 in OSCC

PubMed Central

Wang, Liping; Wei, Yongxiang; Yan, Yongyong; Wang, Haiyan; Yang, Jiantin; Zheng, Zhichao; Zha, Jun; Bo, Peng; Tang, Yinghua; Guo, Xueqi; Chen, Weihong; Zhu, Xinxin; Ge, Linhu

2018-01-01

Oral squamous cell carcinoma (OSCC) is the most frequent oral cancer in the world, accounting for more than 90% of all oral cancer diagnosis. Circular RNAs (circRNAs) are large types of non-coding RNAs, demonstrating a great capacity of regulating the expression of genes. However, most of the functions of circRNAs are still unknown. Recent research revealed that circRNAs could serve as a miRNA-sponge, consequently regulating the expression of target genes indirectly, including oncogenes. In this study, we built an apoptotic model with TNF-α, and then we confirmed a circRNA associated with the apoptosis of OSCC cells, circDOCK1 by comparing the expression profile of circRNAs in an apoptotic model with that in untreated OSCC cells. We ascertained the presence of circDOCK1 with qRT-PCR and circRNA sequencing. The knockdown of the expression of circDOCK1 led to the increase of apoptosis. Utilizing multiple bioinformatics methods, we predicted the interactions among circRNAs, miRNAs and genes, and built the circDOCK1/miR-196a-5p/BIRC3 axis. Both the silencing of circDOCK1 with small interfering RNA and the upregulation of the expression of miR-196a-5p with mimics led OSCC cells to increase apoptosis and decrease BIRC3 formation. We further confirmed this outcome by comparing the expression of circDOCK1, miR-196a-5p and BIRC3 in oral squamous carcinoma tissue with those in para-carcinoma tissue, and examining the expression profile of circRNAs in oral squamous carcinoma tissue and para-carcinoma tissue with microarray. Our results demonstrated that circDOCK1 regulated BIRC3 expression by functioning as a competing endogenous RNA (ceRNA) and participated in the process of OSCC apoptosis. Thus, we propose that circDOCK1 could represent a novel potential biomarker and therapeutic target of OSCC. PMID:29286141

Energy Fluctuations Shape Free Energy of Nonspecific Biomolecular Interactions

NASA Astrophysics Data System (ADS)

Elkin, Michael; Andre, Ingemar; Lukatsky, David B.

2012-01-01

Understanding design principles of biomolecular recognition is a key question of molecular biology. Yet the enormous complexity and diversity of biological molecules hamper the efforts to gain a predictive ability for the free energy of protein-protein, protein-DNA, and protein-RNA binding. Here, using a variant of the Derrida model, we predict that for a large class of biomolecular interactions, it is possible to accurately estimate the relative free energy of binding based on the fluctuation properties of their energy spectra, even if a finite number of the energy levels is known. We show that the free energy of the system possessing a wider binding energy spectrum is almost surely lower compared with the system possessing a narrower energy spectrum. Our predictions imply that low-affinity binding scores, usually wasted in protein-protein and protein-DNA docking algorithms, can be efficiently utilized to compute the free energy. Using the results of Rosetta docking simulations of protein-protein interactions from Andre et al. (Proc. Natl. Acad. Sci. USA 105:16148, 2008), we demonstrate the power of our predictions.

Why are most EU pigs tail docked? Economic and ethical analysis of four pig housing and management scenarios in the light of EU legislation and animal welfare outcomes.

PubMed

D'Eath, R B; Niemi, J K; Vosough Ahmadi, B; Rutherford, K M D; Ison, S H; Turner, S P; Anker, H T; Jensen, T; Busch, M E; Jensen, K K; Lawrence, A B; Sandøe, P

2016-04-01

To limit tail biting incidence, most pig producers in Europe tail dock their piglets. This is despite EU Council Directive 2008/120/EC banning routine tail docking and allowing it only as a last resort. The paper aims to understand what it takes to fulfil the intentions of the Directive by examining economic results of four management and housing scenarios, and by discussing their consequences for animal welfare in the light of legal and ethical considerations. The four scenarios compared are: 'Standard Docked', a conventional housing scenario with tail docking meeting the recommendations for Danish production (0.7 m2/pig); 'Standard Undocked', which is the same as 'Standard Docked' but with no tail docking, 'Efficient Undocked' and 'Enhanced Undocked', which have increased solid floor area (0.9 and 1.0 m2/pig, respectively) provision of loose manipulable materials (100 and 200 g/straw per pig per day) and no tail docking. A decision tree model based on data from Danish and Finnish pig production suggests that Standard Docked provides the highest economic gross margin with the least tail biting. Given our assumptions, Enhanced Undocked is the least economic, although Efficient Undocked is better economically and both result in a lower incidence of tail biting than Standard Undocked but higher than Standard Docked. For a pig, being bitten is worse for welfare (repeated pain, risk of infections) than being docked, but to compare welfare consequences at a farm level means considering the number of affected pigs. Because of the high levels of biting in Standard Undocked, it has on average inferior welfare to Standard Docked, whereas the comparison of Standard Docked and Enhanced (or Efficient) Undocked is more difficult. In Enhanced (or Efficient) Undocked, more pigs than in Standard Docked suffer from being tail bitten, whereas all the pigs avoid the acute pain of docking endured by the pigs in Standard Docked. We illustrate and discuss this ethical balance using numbers derived from the above-mentioned data. We discuss our results in the light of the EU Directive and its adoption and enforcement by Member States. Widespread use of tail docking seems to be accepted, mainly because the alternative steps that producers are required to take before resorting to it are not specified in detail. By tail docking, producers are acting in their own best interests. We suggest that for the practice of tail docking to be terminated in a way that benefits animal welfare, changes in the way pigs are housed and managed may first be required.

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

PubMed

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-14

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

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

NASA Astrophysics Data System (ADS)

Takemura, Kazuhiro; Matubayasi, Nobuyuki; Kitao, Akio

2018-03-01

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

Lessons learned from participating in D3R 2016 Grand Challenge 2: compounds targeting the farnesoid X receptor

NASA Astrophysics Data System (ADS)

Duan, Rui; Xu, Xianjin; Zou, Xiaoqin

2018-01-01

D3R 2016 Grand Challenge 2 focused on predictions of binding modes and affinities for 102 compounds against the farnesoid X receptor (FXR). In this challenge, two distinct methods, a docking-based method and a template-based method, were employed by our team for the binding mode prediction. For the new template-based method, 3D ligand similarities were calculated for each query compound against the ligands in the co-crystal structures of FXR available in Protein Data Bank. The binding mode was predicted based on the co-crystal protein structure containing the ligand with the best ligand similarity score against the query compound. For the FXR dataset, the template-based method achieved a better performance than the docking-based method on the binding mode prediction. For the binding affinity prediction, an in-house knowledge-based scoring function ITScore2 and MM/PBSA approach were employed. Good performance was achieved for MM/PBSA, whereas the performance of ITScore2 was sensitive to ligand composition, e.g. the percentage of carbon atoms in the compounds. The sensitivity to ligand composition could be a clue for the further improvement of our knowledge-based scoring function.

Novel Design Strategy for Checkpoint Kinase 2 Inhibitors Using Pharmacophore Modeling, Combinatorial Fusion, and Virtual Screening

PubMed Central

Wang, Yen-Ling

2014-01-01

Checkpoint kinase 2 (Chk2) has a great effect on DNA-damage and plays an important role in response to DNA double-strand breaks and related lesions. In this study, we will concentrate on Chk2 and the purpose is to find the potential inhibitors by the pharmacophore hypotheses (PhModels), combinatorial fusion, and virtual screening techniques. Applying combinatorial fusion into PhModels and virtual screening techniques is a novel design strategy for drug design. We used combinatorial fusion to analyze the prediction results and then obtained the best correlation coefficient of the testing set (r test) with the value 0.816 by combining the BesttrainBesttest and FasttrainFasttest prediction results. The potential inhibitors were selected from NCI database by screening according to BesttrainBesttest + FasttrainFasttest prediction results and molecular docking with CDOCKER docking program. Finally, the selected compounds have high interaction energy between a ligand and a receptor. Through these approaches, 23 potential inhibitors for Chk2 are retrieved for further study. PMID:24864236

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

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity

DOE PAGES

Zhou, Yuchen; McGillick, Brian E.; Teng, Yu-Han Gary; ...

2016-07-18

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A–G) identified thus far at least 4 can cause death in humans. Here, the goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, ‘footprint similarity’ (FPS) scoring was used to identify compounds that could potentially mimic features on themore » known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Finally, several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement.« less

Computational analysis of EBNA1 ``druggability'' suggests novel insights for Epstein-Barr virus inhibitor design

NASA Astrophysics Data System (ADS)

Gianti, Eleonora; Messick, Troy E.; Lieberman, Paul M.; Zauhar, Randy J.

2016-04-01

The Epstein-Barr Nuclear Antigen 1 (EBNA1) is a critical protein encoded by the Epstein-Barr Virus (EBV). During latent infection, EBNA1 is essential for DNA replication and transcription initiation of viral and cellular genes and is necessary to immortalize primary B-lymphocytes. Nonetheless, the concept of EBNA1 as drug target is novel. Two EBNA1 crystal structures are publicly available and the first small-molecule EBNA1 inhibitors were recently discovered. However, no systematic studies have been reported on the structural details of EBNA1 "druggable" binding sites. We conducted computational identification and structural characterization of EBNA1 binding pockets, likely to accommodate ligand molecules (i.e. "druggable" binding sites). Then, we validated our predictions by docking against a set of compounds previously tested in vitro for EBNA1 inhibition (PubChem AID-2381). Finally, we supported assessments of pocket druggability by performing induced fit docking and molecular dynamics simulations paired with binding affinity predictions by Molecular Mechanics Generalized Born Surface Area calculations for a number of hits belonging to druggable binding sites. Our results establish EBNA1 as a target for drug discovery, and provide the computational evidence that active AID-2381 hits disrupt EBNA1:DNA binding upon interacting at individual sites. Lastly, structural properties of top scoring hits are proposed to support the rational design of the next generation of EBNA1 inhibitors.

One-pot synthesis, biological evaluation, and docking study of new chromeno-annulated thiopyrano[2,3-c]pyrazoles.

PubMed

Parmar, Bhagyashri D; Sutariya, Tushar R; Brahmbhatt, Gaurangkumar C; Parmar, Narsidas J; Kant, Rajni; Gupta, Vivek K; Murumkar, Prashant R; Sharma, Mayank Kumar; Yadav, Mange Ram

2016-08-01

A one-pot synthesis of new chromeno-annulated thiopyrano[2,3-c]pyrazoles has been achieved through a domino-Knoevenagel-hetero-Diels-Alder reaction after combining various pyrazol-5-thiones with O-alkenyloxy/alkynyloxy-salicylaldehydes/naphthaldehydes in a Brønsted acidic ionic liquid, [Hmim]HSO[Formula: see text], methylimidazolium hydrogen sulphate, under microwave irradiation. The method is simple and in many cases the isolated products did not require further purification. The central pyranothiopyranyl cis-fusion was confirmed by 2D NMR NOESY and single-crystal X-ray analysis suggesting that the endo-E-Syn transition state would be the most favored pathway of the reaction. Many heterocycles of this new series were found active against six bacterial and two fungal strains. In addition, all the compounds possess good anti-oxidant activity with the ferric reducing anti-oxidant power value [Formula: see text]. All new structures were docked into active site of angiotensin I converting enzyme (ACE), assuming that the compounds possessed the anti-hypertensive activity potential on the basis of prediction of activity spectra of substances prediction results. Pyranyl ring oxygen in compound 9a forms two hydrogen bonds with HIS353 and HIS513 residues in the active site of the ACE having good G score ([Formula: see text]) of this compound, comparable to that of the reference drug captopril ([Formula: see text]).

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity.

PubMed

Zhou, Yuchen; McGillick, Brian E; Teng, Yu-Han Gary; Haranahalli, Krupanandan; Ojima, Iwao; Swaminathan, Subramanyam; Rizzo, Robert C

2016-10-15

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A-G) identified thus far at least 4 can cause death in humans. The goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, 'footprint similarity' (FPS) scoring was used to identify compounds that could potentially mimic features on the known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement. Copyright © 2016 Elsevier Ltd. All rights reserved.

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity

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

Zhou, Yuchen; McGillick, Brian E.; Teng, Yu-Han Gary

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A–G) identified thus far at least 4 can cause death in humans. Here, the goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, ‘footprint similarity’ (FPS) scoring was used to identify compounds that could potentially mimic features on themore » known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Finally, several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement.« less

Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

PubMed

Yan, Yumeng; Wen, Zeyu; Zhang, Di; Huang, Sheng-You

2018-05-18

RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.

In-Silico Screening of Ligand Based Pharmacophore, Database Mining and Molecular Docking on 2, 5-Diaminopyrimidines Azapurines as Potential Inhibitors of Glycogen Synthase Kinase-3β.

PubMed

Mishra, Pooja; Kesar, Seema; Paliwal, Sarvesh K; Chauhan, Monika; Madan, Kirtika

2018-05-29

Glycogen synthase kinase-3β plays a significant role in the regulation of various pathological pathways relating to central nervous system (CNS). Dysregulation of Glycogen synthase kinase 3 (GSK-3) activity gives a rise to numerous neuroinflammation and neurodegenerative related disorders that affect the whole central nervous system. By the sequential application of in-silico tools, efforts have been attempted to design the novel GSK-3β inhibitors. Owing to the potential role of GSK-3β in nervous disorders, we have attempted to develop the quantitative four featured pharmacophore model comprising two hydrogen bond acceptors (HBA), one ring aromatic (RA), and one hydrophobe (HY), which were further affirmed by cost-function analysis, rm2 matrices, internal and external test set validation and Güner-Henry (GH) scoring analysis. Validated pharmacophoric model was used for virtual screening and out of 345 compounds, two potential virtual hits were finalized that were on the basis of fit value, estimated activity and Lipinski's violation. The chosen compounds were subjected to dock within the active site of GSK-3β Result: Four essential features, i.e., two hydrogen bond acceptors(HBA), one ring aromatic(RA), and one hydrophobe(HY), were subjected to build the pharmacophoric model and showed good correlation coefficient, RMSD and cost difference values of 0.91, 0.94 and 42.9 respectively and further model was validated employing cost-function analysis, rm2-matrices, internal and external test set prediction with r2 value of 0.77 and 0.84. Docked conformations showed potential interactions in between the features of the identified hits (NCI 4296, NCI 3034) and the amino acids present in the active site. In line with the overhead discussion, and through our stepwise computational approaches, we have identified novel, structurally diverse glycogen synthase kinase inhibitors. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

3D-QSAR and Molecular Docking Studies on Derivatives of MK-0457, GSK1070916 and SNS-314 as Inhibitors against Aurora B Kinase

PubMed Central

Zhang, Baidong; Li, Yan; Zhang, Huixiao; Ai, Chunzhi

2010-01-01

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q2 = 0.605, r2pred = 0.826), (q2 = 0.52, r2pred = 0.798) and (q2 = 0.582, r2pred = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors. PMID:21151441

3D-QSAR and molecular docking studies on derivatives of MK-0457, GSK1070916 and SNS-314 as inhibitors against Aurora B kinase.

PubMed

Zhang, Baidong; Li, Yan; Zhang, Huixiao; Ai, Chunzhi

2010-11-02

Development of anticancer drugs targeting Aurora B, an important member of the serine/threonine kinases family, has been extensively focused on in recent years. In this work, by applying an integrated computational method, including comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), homology modeling and molecular docking, we investigated the structural determinants of Aurora B inhibitors based on three different series of derivatives of 108 molecules. The resultant optimum 3D-QSAR models exhibited (q(2) = 0.605, r(2) (pred) = 0.826), (q(2) = 0.52, r(2) (pred) = 0.798) and (q(2) = 0.582, r(2) (pred) = 0.971) for MK-0457, GSK1070916 and SNS-314 classes, respectively, and the 3D contour maps generated from these models were analyzed individually. The contour map analysis for the MK-0457 model revealed the relative importance of steric and electrostatic effects for Aurora B inhibition, whereas, the electronegative groups with hydrogen bond donating capacity showed a great impact on the inhibitory activity for the derivatives of GSK1070916. Additionally, the predictive model of the SNS-314 class revealed the great importance of hydrophobic favorable contour, since hydrophobic favorable substituents added to this region bind to a deep and narrow hydrophobic pocket composed of residues that are hydrophobic in nature and thus enhanced the inhibitory activity. Moreover, based on the docking study, a further comparison of the binding modes was accomplished to identify a set of critical residues that play a key role in stabilizing the drug-target interactions. Overall, the high level of consistency between the 3D contour maps and the topographical features of binding sites led to our identification of several key structural requirements for more potency inhibitors. Taken together, the results will serve as a basis for future drug development of inhibitors against Aurora B kinase for various tumors.

Synthesis, spectroscopic analyses, chemical reactivity and molecular docking study and anti-tubercular activity of pyrazine and condensed oxadiazole derivatives

NASA Astrophysics Data System (ADS)

Al-Tamimi, Abdul-Malek S.; Mary, Y. Sheena; Miniyar, Pankaj B.; Al-Wahaibi, Lamya H.; El-Emam, Ali A.; Armaković, Stevan; Armaković, Sanja J.

2018-07-01

The FT-IR spectral analysis and theoretical calculations of the wavenumbers of three oxadiazole derivatives, 2-(5-(2-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (ORTHOPHPZ), 2-(5-(3-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (METAPHPZ) and 2-(5-(4-chlorophenyl)-1,3,4-oxadiazol-2-yl)pyrazine (PARAPHPZ) were reported in the present work. The theoretically predicted values of polarizability give the nonlinear behaviour of the compounds. The frontier molecular orbital analysis show the chemical stability of the title compounds and the NBO analysis gives the interactions in the molecular systems. Understanding of reactivity of newly synthetiszed oxadiazole derivatives in this study has been achieved thanks to combination of density functional theory (DFT) calculations, molecular dynamics (MD) simulations and molecular docking procedures. New oxadiazole derivatives have also been characterized experimentally through FT-IR and NMR approaches, thanks to which detailed structural properties have been understood. Both global and local reactivity properties have been investigated by calculations of quantum molecular descriptors such as molecular electrostatic potential (MEP), local average ionization energy (ALIE), Fukui functions, bond dissociation energies for hydrogen abstraction (H-BDE), radial distribution functions and binding energies of ligand against selected protein. The first hyperpolarizabilities of ORTHOPHPZ, METAPHPZ and PARAPHPZ are respectively, 84.62, 94.71 and 184.10 times that of urea. The docked ligands form stable complexes with the receptor 1-phosphatidylinositol phosphodiesterase and the results suggest that these compounds can be developed as new anti-cancer drugs. The anti-TB activity of PM series against M. tuberculosis H37RV strain was performed by Middlebrooke 7H-9 method. The compounds, ORTHOPHPZ, METAPHPZ and PARAPHPZ were moderately active between 25 and 50 μg/ml concentration as compared with the standard anti-TB agents and the -log MIC activity was found in the range of 1.011-1.274 as compared with isoniazid (INH) (1.137) and pyrazinamide (PZA) (1.115) standard anti-TB agents.

Distinct docking and stabilization steps of the Pseudopilus conformational transition path suggest rotational assembly of type IV pilus-like fibers.

PubMed

Nivaskumar, Mangayarkarasi; Bouvier, Guillaume; Campos, Manuel; Nadeau, Nathalie; Yu, Xiong; Egelman, Edward H; Nilges, Michael; Francetic, Olivera

2014-05-06

The closely related bacterial type II secretion (T2S) and type IV pilus (T4P) systems are sophisticated machines that assemble dynamic fibers promoting protein transport, motility, or adhesion. Despite their essential role in virulence, the molecular mechanisms underlying helical fiber assembly remain unknown. Here, we use electron microscopy and flexible modeling to study conformational changes of PulG pili assembled by the Klebsiella oxytoca T2SS. Neural network analysis of 3,900 pilus models suggested a transition path toward low-energy conformations driven by progressive increase in fiber helical twist. Detailed predictions of interprotomer contacts along this path were tested by site-directed mutagenesis, pilus assembly, and protein secretion analyses. We demonstrate that electrostatic interactions between adjacent protomers (P-P+1) in the membrane drive pseudopilin docking, while P-P+3 and P-P+4 contacts determine downstream fiber stabilization steps. These results support a model of a spool-like assembly mechanism for fibers of the T2SS-T4P superfamily. Copyright © 2014 Elsevier Ltd. All rights reserved.

Distinct docking and stabilization steps of the pseudopilus conformational transition path suggest rotational assembly of type IV pilus-like fibers

PubMed Central

Nivaskumar, Mangayarkarasi; Bouvier, Guillaume; Campos, Manuel; Nadeau, Nathalie; Yu, Xiong; Egelman, Edward H.; Nilges, Michael; Francetic, Olivera

2014-01-01

SUMMARY The closely related bacterial type II secretion (T2S) and type IV pilus (T4P) systems are sophisticated machines that assemble dynamic fibers promoting protein transport, motility or adhesion. Despite their essential role in virulence, the molecular mechanisms underlying helical fiber assembly remain unknown. Here we use electron microscopy and flexible modeling to study conformational changes of PulG pili assembled by the Klebsiella oxytoca T2SS. Neural network analysis of 3900 pilus models suggested a transition path towards low-energy conformations driven by progressive increase in fiber helical twist. Detailed predictions of inter-protomer contacts along this path were tested by site-directed mutagenesis, pilus assembly and protein secretion analyses. We demonstrate that electrostatic interactions between adjacent protomers (P-P+1) in the membrane drive pseudopilin docking, while P-P+3 and P-P+4 contacts determine downstream fiber stabilization steps. These results support a new model of a spool-like assembly mechanism for fibers of the T2SS-T4P superfamily. PMID:24685147

Studies on molecular structure, vibrational spectra and molecular docking analysis of 3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl 4-aminobenzoate.

PubMed

Suresh, D M; Amalanathan, M; Joe, I Hubert; Jothy, V Bena; Diao, Yun-Peng

2014-09-15

The molecular structure, vibrational analysis and molecular docking analysis of the 3-Methyl-1,4-dioxo-1,4-dihydronaphthalen-2-yl 4-aminobenzoate (MDDNAB) molecule have been carried out using FT-IR and FT-Raman spectroscopic techniques and DFT method. The equilibrium geometry, harmonic vibrational wave numbers, various bonding features have been computed using density functional method. The calculated molecular geometry has been compared with experimental data. The detailed interpretation of the vibrational spectra has been carried out by using VEDA program. The hyper-conjugative interactions and charge delocalization have been analyzed using natural bond orbital (NBO) analysis. The simulated FT-IR and FT-Raman spectra satisfactorily coincide with the experimental spectra. The PES and charge analysis have been made. The molecular docking was done to identify the binding energy and the Hydrogen bonding with the cancer protein molecule. Copyright © 2014 Elsevier B.V. All rights reserved.

Implementation of the Hungarian Algorithm to Account for Ligand Symmetry and Similarity in Structure-Based Design

PubMed Central

2015-01-01

False negative docking outcomes for highly symmetric molecules are a barrier to the accurate evaluation of docking programs, scoring functions, and protocols. This work describes an implementation of a symmetry-corrected root-mean-square deviation (RMSD) method into the program DOCK based on the Hungarian algorithm for solving the minimum assignment problem, which dynamically assigns atom correspondence in molecules with symmetry. The algorithm adds only a trivial amount of computation time to the RMSD calculations and is shown to increase the reported overall docking success rate by approximately 5% when tested over 1043 receptor–ligand systems. For some families of protein systems the results are even more dramatic, with success rate increases up to 16.7%. Several additional applications of the method are also presented including as a pairwise similarity metric to compare molecules during de novo design, as a scoring function to rank-order virtual screening results, and for the analysis of trajectories from molecular dynamics simulation. The new method, including source code, is available to registered users of DOCK6 (http://dock.compbio.ucsf.edu). PMID:24410429

Comparative modeling and docking studies of p16ink4/cyclin D1/Rb pathway genes in lung cancer revealed functionally interactive residue of RB1 and its functional partner E2F1.

PubMed

Naqsh e Zahra, Syeda; Khattak, Naureen Aslam; Mir, Asif

2013-01-01

Lung cancer is the major cause of mortality worldwide. Major signalling pathways that could play significant role in lung cancer therapy include (1) Growth promoting pathways (Epidermal Growth Factor Receptor/Ras/ PhosphatidylInositol 3-Kinase) (2) Growth inhibitory pathways (p53/Rb/P14ARF, STK11) (3) Apoptotic pathways (Bcl-2/Bax/Fas/FasL). Insilico strategy was implemented to solve the mystery behind selected lung cancer pathway by applying comparative modeling and molecular docking studies. YASARA [v 12.4.1] was utilized to predict structural models of P16-INK4 and RB1 genes using template 4ELJ-A and 1MX6-B respectively. WHAT CHECK evaluation tool demonstrated overall quality of predicted P16-INK4 and RB1 with Z-score of -0.132 and -0.007 respectively which showed a strong indication of reliable structure prediction. Protein-protein interactions were explored by utilizing STRING server, illustrated that CDK4 and E2F1 showed strong interaction with P16-INK4 and RB1 based on confidence score of 0.999 and 0.999 respectively. In order to facilitate a comprehensive understanding of the complex interactions between candidate genes with their functional interactors, GRAMM-X server was used. Protein-protein docking investigation of P16-INK4 revealed four ionic bonds illustrating Arg47, Arg80,Cys72 and Met1 residues as actively participating in interactions with CDK4 while docking results of RB1 showed four hydrogen bonds involving Glu864, Ser567, Asp36 and Arg861 residues which interact strongly with its respective functional interactor E2F1. This research may provide a basis for understanding biological insights of P16-INK4 and RB1 proteins which will be helpful in future to design a suitable drug to inhibit the disease pathogenesis as we have determined the interacting amino acids which can be targeted in order to design a ligand in-vitro to propose a drug for clinical trials. Protein -protein docking of candidate genes and their important interacting residues likely to be provide a gateway for developing computer aided drug designing.

Uncovering potential anti-neuroinflammatory components of Modified Wuziyanzong Prescription through a target-directed molecular docking fingerprint strategy.

PubMed

Chen, Jinfeng; Wang, Jinlong; Lu, Yingyuan; Zhao, Shaoyang; Yu, Qian; Wang, Xuemei; Tu, Pengfei; Zeng, Kewu; Jiang, Yong

2018-05-01

Neuroinflammation is a main factor in the pathogenesis of neurodegenerative diseases, such as Alzheimer disease. Our previous studies indicated that the modified Wuziyanzong Prescription (MWP) can suppress neuroinflammatory responses via nuclear factor-kappa B (NF-κB) and mitogen-activated protein kinases (MAPKs) signaling pathways. However, the anti-neuroinflammatory components of MWP remain unclear. Herein, a target-directed molecular docking fingerprint (TMDF) strategy, via integrating the chemical profiling and molecular docking approaches, was developed to identify the potential anti-neuroinflammatory components of MWP. First, as many as 120 possible structures, including 49 flavonoids, 28 phenylpropionic acids, 18 amides, 10 carotenoids, eight phenylethanoid glycosides, four lignans, two iridoids, and one triterpenoid were deduced by the source attribution and structural classification-assisted strategy. Then, their geometries were docked against five major targets of the NF-κB and MAPKs signaling cascades, including p38-α, IKKβ, ERK1, ERK2, and TRAF6. The docking results revealed diverse contributions of different components towards the protein targets. Collectively, prenylated flavonoids showed intensive or moderate anti-neuroinflammatory activities, while phenylpropanoids, amides, phenylethanoid glycosides, lignans, and triterpenoids exhibited moderate or weak anti-neuroinflammatory effects. The anti-neuroinflammatory activities of four retrieved prenylated flavonoids were tested by Western blotting assay, and the results mostly agreed with those predicted by the docking method. These gained information demonstrates that the established TMDF strategy could be a rapid and feasible methodology to investigate the potential active components in herbal compound prescriptions. Copyright © 2018 Elsevier B.V. All rights reserved.

Molecular Docking, Molecular Dynamics Simulations, Computational Screening to Design Quorum Sensing Inhibitors Targeting LuxP of Vibrio harveyi and Its Biological Evaluation.

PubMed

Rajamanikandan, Sundaraj; Jeyakanthan, Jeyaraman; Srinivasan, Pappu

2017-01-01

Quorum sensing (QS) plays an important role in the biofilm formation, production of virulence factors and stress responses in Vibrio harveyi. Therefore, interrupting QS is a possible approach to modulate bacterial behavior. In the present study, three docking protocols, such as Rigid Receptor Docking (RRD), Induced Fit Docking (IFD), and Quantum Polarized Ligand Docking (QPLD) were used to elucidate the binding mode of boronic acid derivatives into the binding pocket of LuxP protein in V. harveyi. Among the three docking protocols, IFD accurately predicted the correct binding mode of the studied inhibitors. Molecular dynamics (MD) simulations of the protein-ligand complexes indicates that the inter-molecular hydrogen bonds formed between the protein and ligand complex remains stable during the simulation time. Pharmacophore and shape-based virtual screening were performed to find selective and potent compounds from ChemBridge database. Five hit compounds were selected and subjected to IFD and MD simulations to validate the binding mode. In addition, enrichment calculation was performed to discriminate and separate active compounds from the inactive compounds. Based on the computational studies, the potent Bicyclo [2.2.1] hept-5-ene-2,3-dicarboxylic acid-2,6-dimethylpyridine 1-oxide (ChemBridge_5144368) was selected for in vitro assays. The compound exhibited dose dependent inhibition in bioluminescence and also inhibits biofilm formation in V. harveyi to the level of 64.25 %. The result from the study suggests that ChemBridge_5144368 could serve as an anti-quorum sensing molecule for V. harveyi.

Advances in the treatment of explicit water molecules in docking and binding free energy calculations.

PubMed

Hu, Xiao; Maffucci, Irene; Contini, Alessandro

2018-05-13

The inclusion of direct effects mediated by water during the ligand-receptor recognition is a hot-topic of modern computational chemistry applied to drug discovery and development. Docking or virtual screening with explicit hydration is still debatable, despite the successful cases that have been presented in the last years. Indeed, how to select the water molecules that will be included in the docking process or how the included waters should be treated remain open questions. In this review, we will discuss some of the most recent methods that can be used in computational drug discovery and drug development when the effect of a single water, or of a small network of interacting waters, needs to be explicitly considered. Here, we analyse software to aid the selection, or to predict the position, of water molecules that are going to be explicitly considered in later docking studies. We also present software and protocols able to efficiently treat flexible water molecules during docking, including examples of applications. Finally, we discuss methods based on molecular dynamics simulations that can be used to integrate docking studies or to reliably and efficiently compute binding energies of ligands in presence of interfacial or bridging water molecules. Software applications aiding the design of new drugs that exploit water molecules, either as displaceable residues or as bridges to the receptor, are constantly being developed. Although further validation is needed, workflows that explicitly consider water will probably become a standard for computational drug discovery soon. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Screening of photosynthetic pigments for herbicidal activity with a new computational molecular approach.

PubMed

Krishnaraj, R Navanietha; Chandran, Saravanan; Pal, Parimal; Berchmans, Sheela

2013-12-01

There is an immense interest among the researchers to identify new herbicides which are effective against the herbs without affecting the environment. In this work, photosynthetic pigments are used as the ligands to predict their herbicidal activity. The enzyme 5-enolpyruvylshikimate-3-phosphate (EPSP) synthase is a good target for the herbicides. Homology modeling of the target enzyme is done using Modeler 9.11 and the model is validated. Docking studies were performed with AutoDock Vina algorithm to predict the binding of the natural pigments such as β-carotene, chlorophyll a, chlorophyll b, phycoerythrin and phycocyanin to the target. β-carotene, phycoerythrin and phycocyanin have higher binding energies indicating the herbicidal activity of the pigments. This work reports a procedure to screen herbicides with computational molecular approach. These pigments will serve as potential bioherbicides in the future.

Design, synthesis, anticancer screening, docking studies and in silico ADME prediction of some β-carboline derivatives.

PubMed

Abdelsalam, Mohamed A; AboulWafa, Omaima M; M Badawey, El-Sayed A; El-Shoukrofy, Mai S; El-Miligy, Mostafa M; Gouda, Noha; Elaasser, Mahmoud M

2018-05-22

Medicinal interest has focused on β-carbolines as anticancer agents. Several β-carbolines were designed, synthesized and evaluated for their cytotoxic activity against MCF-7 and A-549 cancer cell lines using MTT assay. Compounds 13a, 13c, 13d and 20a were the most promising showing high selectivity indices. Compounds 13c and 20a showed potent inhibition of topoisomerase (topo-I) and kinesin spindle protein (KSP/Eg5 ATPase) which was confirmed by their docking results into the active site of both enzymes. In silico physicochemical calculations predicted that compounds 13a, 13d and 20a obeyed Lipinski's rule of five. Compounds 13c and 20a are multitarget anticancer leads that act as potent inhibitors for both topo-I and/or KSP ATPase.

A Novel Terpenoid from Elephantopus Scaber – Antibacterial Activity on Staphylococcus Aureus: A Substantiate Computational Approach

PubMed Central

Daisy, P.; Mathew, Salu; Suveena, S.; Rayan, Nirmala A.

2008-01-01

Staphylococcus aureus has gained much attention in the last decade as it is a major cause of the Urinary Tract Infection in Diabetic patients. The Extended Spectrum β-Lactamases (ESβL) producers are highly resistant to several conventional antibiotics. This limits the therapeutic options.Hence efforts are now taken to screen few medicinal plants, which are both economic and less toxic. Among the several plants screened, we have chosen the acetone extract of Elephantopus scaber from which we purified a new terpenoid for our study. Its structure was generated using CHEMSKETCH software and the activity prediction was done using PASS PREDICTION software. We have confirmed the mechanism of anti-bacterial effect of terpenoid using Computer – Aided Drug Design (CADD) with computational methods to simulate drug – receptor interactions. The Protein-Ligand interaction plays a significant role in the structural based drug designing. In this present study we have taken the Autolysin, the bacteriolytic enzyme, that digest the cell wall peptidoglycon. The autolysin and terpenoid were docked using HEX docking software and the docking score with minimum energy value of -209.54 was calculated. It infers that the terpenoid can inhibit the activity of autolysin by forming a strong atomic interaction with the active site residues. Hence the terpenoid can act as a drug for bacterial infections. Further investigations can be carried out to predict the activity of terpeniod on other targets. PMID:23675090

"Soft docking": matching of molecular surface cubes.

PubMed

Jiang, F; Kim, S H

1991-05-05

Molecular recognition is achieved through the complementarity of molecular surface structures and energetics with, most commonly, associated minor conformational changes. This complementarity can take many forms: charge-charge interaction, hydrogen bonding, van der Waals' interaction, and the size and shape of surfaces. We describe a method that exploits these features to predict the sites of interactions between two cognate molecules given their three-dimensional structures. We have developed a "cube representation" of molecular surface and volume which enables us not only to design a simple algorithm for a six-dimensional search but also to allow implicitly the effects of the conformational changes caused by complex formation. The present molecular docking procedure may be divided into two stages. The first is the selection of a population of complexes by geometric "soft docking", in which surface structures of two interacting molecules are matched with each other, allowing minor conformational changes implicitly, on the basis of complementarity in size and shape, close packing, and the absence of steric hindrance. The second is a screening process to identify a subpopulation with many favorable energetic interactions between the buried surface areas. Once the size of the subpopulation is small, one may further screen to find the correct complex based on other criteria or constraints obtained from biochemical, genetic, and theoretical studies, including visual inspection. We have tested the present method in two ways. First is a control test in which we docked the components of a molecular complex of known crystal structure available in the Protein Data Bank (PDB). Two molecular complexes were used: (1) a ternary complex of dihydrofolate reductase, NADPH and methotrexate (3DFR in PDB) and (2) a binary complex of trypsin and trypsin inhibitor (2PTC in PDB). The components of each complex were taken apart at an arbitrary relative orientation and then docked together again. The results show that the geometric docking alone is sufficient to determine the correct docking solutions in these ideal cases, and that the cube representation of the molecules does not degrade the docking process in the search for the correct solution. The second is the more realistic experiment in which we docked the crystal structures of uncomplexed molecules and then compared the structures of docked complexes with the crystal structures of the corresponding complexes. This is to test the capability of our method in accommodating the effects of the conformational changes in the binding sites of the molecules in docking.(ABSTRACT TRUNCATED AT 400 WORDS)

A knowledge-based approach for identification of drugs against vivapain-2 protein of Plasmodium vivax through pharmacophore-based virtual screening with comparative modelling.

PubMed

Yadav, Manoj Kumar; Singh, Amisha; Swati, D

2014-08-01

Malaria is one of the most infectious diseases in the world. Plasmodium vivax, the pathogen causing endemic malaria in humans worldwide, is responsible for extensive disease morbidity. Due to the emergence of resistance to common anti-malarial drugs, there is a continuous need to develop a new class of drugs for this pathogen. P. vivax cysteine protease, also known as vivapain-2, plays an important role in haemoglobin hydrolysis and is considered essential for the survival of the parasite. The three-dimensional (3D) structure of vivapain-2 is not predicted experimentally, so its structure is modelled by using comparative modelling approach and further validated by Qualitative Model Energy Analysis (QMEAN) and RAMPAGE tools. The potential binding site of selected vivapain-2 structure has been detected by grid-based function prediction method. Drug targets and their respective drugs similar to vivapain-2 have been identified using three publicly available databases: STITCH 3.1, DrugBank and Therapeutic Target Database (TTD). The second approach of this work focuses on docking study of selected drug E-64 against vivapain-2 protein. Docking reveals crucial information about key residues (Asn281, Cys283, Val396 and Asp398) that are responsible for holding the ligand in the active site. The similarity-search criterion is used for the preparation of our in-house database of drugs, obtained from filtering the drugs from the DrugBank database. A five-point 3D pharmacophore model is generated for the docked complex of vivapain-2 with E-64. This study of 3D pharmacophore-based virtual screening results in identifying three new drugs, amongst which one is approved and the other two are experimentally proved. The ADMET properties of these drugs are found to be in the desired range. These drugs with novel scaffolds may act as potent drugs for treating malaria caused by P. vivax.

Molecular structure, spectroscopic and docking analysis of 1,3-diphenylpyrazole-4-propionic acid: A good prostaglandin reductase inhibitor

NASA Astrophysics Data System (ADS)

Kavitha, T.; Velraj, G.

2018-03-01

The molecule 1,3-diphenylpyrazole-4-propionic acid (DPPA) was optimized to its minimum energy level using density functional theory (DFT) calculations. The vibrational frequencies of DPPA were calculated along with their potential energy distribution (PED) and the obtained values are validated with the help of experimental calculations. The reactivity nature of the molecule was investigated with the aid of various DFT methods such as global reactivity descriptors, local reactivity descriptors, molecular electrostatic potential (MEP), natural bond orbitals (NBOs), etc. The prediction of activity spectra for substances (PASS) result forecast that, DPPA can be more active as a prostaglandin (PG) reductase inhibitor. The PGs are biologically synthesized by the cyclooxygenase (COX) enzyme which exists in COX1 and COX2 forms. The PGs produced by COX2 enzyme induces inflammation and fungal infections and hence the inhibition of COX2 enzyme is indispensable in anti-inflammation and anti-fungal activities. The docking analysis of DPPA with COX enzymes (both COX1 and COX2) were carried out and eventually, it was found that DPPA can selectively inhibit COX2 enzyme and can serve as a PG reductase inhibitor thereby acting as a lead compound for the treatment of inflammation and fungal diseases.

Understanding the polypharmacological anticancer effects of Xiao Chai Hu Tang via a computational pharmacological model

PubMed Central

ZHENG, CHUN-SONG; WU, YIN-SHENG; BAO, HONG-JUAN; XU, XIAO-JIE; CHEN, XING-QIANG; YE, HONG-ZHI; WU, GUANG-WEN; XU, HUI-FENG; LI, XI-HAI; CHEN, JIA-SHOU; LIU, XIAN-XIANG

2014-01-01

Xiao Chai Hu Tang (XCHT), a traditional herbal formula, is widely administered as a cancer treatment. However, the underlying molecular mechanisms of its anticancer effects are not fully understood. In the present study, a computational pharmacological model that combined chemical space mapping, molecular docking and network analysis was employed to predict which chemical compounds in XCHT are potential inhibitors of cancer-associated targets, and to establish a compound-target (C-T) network and compound-compound (C-C) association network. The identified compounds from XCHT demonstrated diversity in chemical space. Furthermore, they occupied regions of chemical space that were the same, or close to, those occupied by drug or drug-like compounds that are associated with cancer, according to the Therapeutic Targets Database. The analysis of the molecular docking and the C-T network demonstrated that the potential inhibitors possessed the properties of promiscuous drugs and combination therapies. The C-C network was classified into four clusters and the different clusters contained various multi-compound combinations that acted on different targets. The study indicated that XCHT has a polypharmacological role in treating cancer and the potential inhibitory components of XCHT require further investigation as potential therapeutic strategies for cancer patients. PMID:24926384