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Devaurs D, Antunes DA, Hall-Swan S, Mitchell N, Moll M, Lizée G, Kavraki LE. Using parallelized incremental meta-docking can solve the conformational sampling issue when docking large ligands to proteins. BMC Mol Cell Biol 2019; 20:42. [PMID: 31488048 PMCID: PMC6729087 DOI: 10.1186/s12860-019-0218-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 08/08/2019] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Docking large ligands, and especially peptides, to protein receptors is still considered a challenge in computational structural biology. Besides the issue of accurately scoring the binding modes of a protein-ligand complex produced by a molecular docking tool, the conformational sampling of a large ligand is also often considered a challenge because of its underlying combinatorial complexity. In this study, we evaluate the impact of using parallelized and incremental paradigms on the accuracy and performance of conformational sampling when docking large ligands. We use five datasets of protein-ligand complexes involving ligands that could not be accurately docked by classical protein-ligand docking tools in previous similar studies. RESULTS Our computational evaluation shows that simply increasing the amount of conformational sampling performed by a protein-ligand docking tool, such as Vina, by running it for longer is rarely beneficial. Instead, it is more efficient and advantageous to run several short instances of this docking tool in parallel and group their results together, in a straightforward parallelized docking protocol. Even greater accuracy and efficiency are achieved by our parallelized incremental meta-docking tool, DINC, showing the additional benefits of its incremental paradigm. Using DINC, we could accurately reproduce the vast majority of the protein-ligand complexes we considered. CONCLUSIONS Our study suggests that, even when trying to dock large ligands to proteins, the conformational sampling of the ligand should no longer be considered an issue, as simple docking protocols using existing tools can solve it. Therefore, scoring should currently be regarded as the biggest unmet challenge in molecular docking.
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Affiliation(s)
- Didier Devaurs
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Dinler A Antunes
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Sarah Hall-Swan
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Nicole Mitchell
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Mark Moll
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
| | - Gregory Lizée
- Department of Melanoma Medical Oncology - Research, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX 77030 USA
| | - Lydia E Kavraki
- Department of Computer Science, Rice University, 6100 Main St, Houston, TX 77005 USA
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In Silico Repositioning of Cannabigerol as a Novel Inhibitor of the Enoyl Acyl Carrier Protein (ACP) Reductase (InhA). Molecules 2019; 24:molecules24142567. [PMID: 31311157 PMCID: PMC6680637 DOI: 10.3390/molecules24142567] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 07/08/2019] [Accepted: 07/13/2019] [Indexed: 12/22/2022] Open
Abstract
Cannabigerol (CBG) and cannabichromene (CBC) are non-psychoactive cannabinoids that have raised increasing interest in recent years. These compounds exhibit good tolerability and low toxicity, representing promising candidates for drug repositioning. To identify novel potential therapeutic targets for CBG and CBC, an integrated ligand-based and structure-based study was performed. The results of the analysis led to the identification of CBG as a low micromolar inhibitor of the Enoyl acyl carrier protein (ACP) reductase (InhA) enzyme.
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Antunes DA, Devaurs D, Kavraki LE. Understanding the challenges of protein flexibility in drug design. Expert Opin Drug Discov 2015; 10:1301-13. [DOI: 10.1517/17460441.2015.1094458] [Citation(s) in RCA: 77] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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De Paris R, Quevedo CV, Ruiz DDA, Norberto de Souza O. An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features. PLoS One 2015. [PMID: 26218832 PMCID: PMC4517875 DOI: 10.1371/journal.pone.0133172] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
Abstract
Protein receptor conformations, obtained from molecular dynamics (MD) simulations, have become a promising treatment of its explicit flexibility in molecular docking experiments applied to drug discovery and development. However, incorporating the entire ensemble of MD conformations in docking experiments to screen large candidate compound libraries is currently an unfeasible task. Clustering algorithms have been widely used as a means to reduce such ensembles to a manageable size. Most studies investigate different algorithms using pairwise Root-Mean Square Deviation (RMSD) values for all, or part of the MD conformations. Nevertheless, the RMSD only may not be the most appropriate gauge to cluster conformations when the target receptor has a plastic active site, since they are influenced by changes that occur on other parts of the structure. Hence, we have applied two partitioning methods (k-means and k-medoids) and four agglomerative hierarchical methods (Complete linkage, Ward's, Unweighted Pair Group Method and Weighted Pair Group Method) to analyze and compare the quality of partitions between a data set composed of properties from an enzyme receptor substrate-binding cavity and two data sets created using different RMSD approaches. Ensembles of representative MD conformations were generated by selecting a medoid of each group from all partitions analyzed. We investigated the performance of our new method for evaluating binding conformation of drug candidates to the InhA enzyme, which were performed by cross-docking experiments between a 20 ns MD trajectory and 20 different ligands. Statistical analyses showed that the novel ensemble, which is represented by only 0.48% of the MD conformations, was able to reproduce 75% of all dynamic behaviors within the binding cavity for the docking experiments performed. Moreover, this new approach not only outperforms the other two RMSD-clustering solutions, but it also shows to be a promising strategy to distill biologically relevant information from MD trajectories, especially for docking purposes.
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Affiliation(s)
- Renata De Paris
- Grupo de Pesquisa em Inteligência de Negócio—GPIN, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681-Prédio 32, sala 628, Porto Alegre, RS, Brasil
| | - Christian V. Quevedo
- Grupo de Pesquisa em Inteligência de Negócio—GPIN, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681-Prédio 32, sala 628, Porto Alegre, RS, Brasil
| | - Duncan D. A. Ruiz
- Grupo de Pesquisa em Inteligência de Negócio—GPIN, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681-Prédio 32, sala 628, Porto Alegre, RS, Brasil
- * E-mail: (DDAR); (ONS)
| | - Osmar Norberto de Souza
- Laboratório de Bioinformática, Modelagem e Simulação de Biossistemas—LABIO, Faculdade de Informática, PUCRS, Av. Ipiranga, 6681- Building 32, Room 602, Porto Alegre, RS, Brasil
- * E-mail: (DDAR); (ONS)
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Clustering molecular dynamics trajectories for optimizing docking experiments. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:916240. [PMID: 25873944 PMCID: PMC4385651 DOI: 10.1155/2015/916240] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 03/05/2015] [Indexed: 12/03/2022]
Abstract
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
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Winck AT, Machado KS, de Souza ON, Ruiz DD. Context-based preprocessing of molecular docking data. BMC Genomics 2014; 14 Suppl 6:S6. [PMID: 24564276 PMCID: PMC3909228 DOI: 10.1186/1471-2164-14-s6-s6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Data preprocessing is a major step in data mining. In data preprocessing, several known techniques can be applied, or new ones developed, to improve data quality such that the mining results become more accurate and intelligible. Bioinformatics is one area with a high demand for generation of comprehensive models from large datasets. In this article, we propose a context-based data preprocessing approach to mine data from molecular docking simulation results. The test cases used a fully-flexible receptor (FFR) model of Mycobacterium tuberculosis InhA enzyme (FFR_InhA) and four different ligands. Results We generated an initial set of attributes as well as their respective instances. To improve this initial set, we applied two selection strategies. The first was based on our context-based approach while the second used the CFS (Correlation-based Feature Selection) machine learning algorithm. Additionally, we produced an extra dataset containing features selected by combining our context strategy and the CFS algorithm. To demonstrate the effectiveness of the proposed method, we evaluated its performance based on various predictive (RMSE, MAE, Correlation, and Nodes) and context (Precision, Recall and FScore) measures. Conclusions Statistical analysis of the results shows that the proposed context-based data preprocessing approach significantly improves predictive and context measures and outperforms the CFS algorithm. Context-based data preprocessing improves mining results by producing superior interpretable models, which makes it well-suited for practical applications in molecular docking simulations using FFR models.
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Engel TA, Charão AS, Kirsch-Pinheiro M, Steffenel LA. Performance Improvement of Data Mining in Weka through GPU Acceleration. ACTA ACUST UNITED AC 2014. [DOI: 10.1016/j.procs.2014.05.402] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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wFReDoW: a cloud-based web environment to handle molecular docking simulations of a fully flexible receptor model. BIOMED RESEARCH INTERNATIONAL 2013; 2013:469363. [PMID: 23691504 PMCID: PMC3652109 DOI: 10.1155/2013/469363] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2012] [Revised: 02/28/2013] [Accepted: 03/06/2013] [Indexed: 11/18/2022]
Abstract
Molecular docking simulations of fully flexible protein receptor (FFR) models are coming of age. In our studies, an FFR model is represented by a series of different conformations derived from a molecular dynamic simulation trajectory of the receptor. For each conformation in the FFR model, a docking simulation is executed and analyzed. An important challenge is to perform virtual screening of millions of ligands using an FFR model in a sequential mode since it can become computationally very demanding. In this paper, we propose a cloud-based web environment, called web Flexible Receptor Docking Workflow (wFReDoW), which reduces the CPU time in the molecular docking simulations of FFR models to small molecules. It is based on the new workflow data pattern called self-adaptive multiple instances (P-SaMIs) and on a middleware built on Amazon EC2 instances. P-SaMI reduces the number of molecular docking simulations while the middleware speeds up the docking experiments using a High Performance Computing (HPC) environment on the cloud. The experimental results show a reduction in the total elapsed time of docking experiments and the quality of the new reduced receptor models produced by discarding the nonpromising conformations from an FFR model ruled by the P-SaMI data pattern.
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Yuriev E, Ramsland PA. Latest developments in molecular docking: 2010-2011 in review. J Mol Recognit 2013; 26:215-39. [PMID: 23526775 DOI: 10.1002/jmr.2266] [Citation(s) in RCA: 201] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2012] [Revised: 01/16/2013] [Accepted: 01/19/2013] [Indexed: 12/28/2022]
Affiliation(s)
- Elizabeth Yuriev
- Medicinal Chemistry, Monash Institute of Pharmaceutical Sciences; Monash University; Parkville; VIC; 3052; Australia
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Barros RC, Winck AT, Machado KS, Basgalupp MP, de Carvalho ACPLF, Ruiz DD, de Souza ON. Automatic design of decision-tree induction algorithms tailored to flexible-receptor docking data. BMC Bioinformatics 2012; 13:310. [PMID: 23171000 PMCID: PMC3534569 DOI: 10.1186/1471-2105-13-310] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2012] [Accepted: 10/29/2012] [Indexed: 11/29/2022] Open
Abstract
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.
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Matijssen C, Silva-Santisteban MC, Westwood IM, Siddique S, Choi V, Sheldrake P, van Montfort RL, Blagg J. Benzimidazole inhibitors of the protein kinase CHK2: clarification of the binding mode by flexible side chain docking and protein-ligand crystallography. Bioorg Med Chem 2012; 20:6630-9. [PMID: 23058106 PMCID: PMC3778940 DOI: 10.1016/j.bmc.2012.09.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2012] [Revised: 09/07/2012] [Accepted: 09/13/2012] [Indexed: 11/23/2022]
Abstract
Two closely related binding modes have previously been proposed for the ATP-competitive benzimidazole class of checkpoint kinase 2 (CHK2) inhibitors; however, neither binding mode is entirely consistent with the reported SAR. Unconstrained rigid docking of benzimidazole ligands into representative CHK2 protein crystal structures reveals an alternative binding mode involving a water-mediated interaction with the hinge region; docking which incorporates protein side chain flexibility for selected residues in the ATP binding site resulted in a refinement of the water-mediated hinge binding mode that is consistent with observed SAR. The flexible docking results are in good agreement with the crystal structures of four exemplar benzimidazole ligands bound to CHK2 which unambiguously confirmed the binding mode of these inhibitors, including the water-mediated interaction with the hinge region, and which is significantly different from binding modes previously postulated in the literature.
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Key Words
- adp, adenosine diphosphate
- atm, ataxia telangiectasia mutated
- atp, adenosine triphosphate
- chk2, checkpoint kinase 2
- gold, genetic optimisation for ligand docking
- gst, glutathione s-transferase
- kd, kinase domain
- moe, molecular operating environment
- parp, poly adp-ribose polymerase
- pdb, protein data bank
- plif, protein ligand interaction fingerprints
- sar, structure activity relationship
- sift, structural interaction fingerprints
- kinase
- chk2
- flexible docking
- crystallography
- inhibitor
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Affiliation(s)
- Cornelis Matijssen
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
| | - M. Cris Silva-Santisteban
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
- Division of Structural Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London SW3 6JB, UK
| | - Isaac M. Westwood
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
- Division of Structural Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London SW3 6JB, UK
| | - Samerene Siddique
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
| | - Vanessa Choi
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
- Division of Structural Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London SW3 6JB, UK
| | - Peter Sheldrake
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
| | - Rob L.M. van Montfort
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
- Division of Structural Biology, Institute of Cancer Research, Chester Beatty Laboratories, Chelsea, London SW3 6JB, UK
| | - Julian Blagg
- Cancer Research UK Cancer Therapeutics Unit, Division of Cancer Therapeutics, Institute of Cancer Research, Haddow Laboratories, Sutton, Surrey SM2 5NG, UK
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