A selective method for optimizing ensemble docking-based experiments on an InhA Fully-Flexible receptor model

BackgroundIn the rational drug design process, an ensemble of conformations obtained from a molecular dynamics simulation plays a crucial role in docking experiments. Some studies have found that Fully-Flexible Receptor (FFR) models predict realistic binding energy accurately and improve scoring to enhance selectiveness. At the same time, methods have been proposed to reduce the high computational costs involved in considering the explicit flexibility of proteins in receptor-ligand docking. This study introduces a novel method to optimize ensemble docking-based experiments by reducing the size of an InhA FFR model at docking runtime and scaling docking workflow invocations on cloud virtual machines.ResultsFirst, in order to find the most affordable cost-benefit pool of virtual machines, we evaluated the performance of the docking workflow invocations in different configurations of Azure instances. Second, we validated the gains obtained by the proposed method based on the quality of the Reduced Fully-Flexible Receptor (RFFR) models produced using AutoDock4.2. The analyses show that the proposed method reduced the model size by approximately 50% while covering at least 86% of the best docking results from the 74 ligands tested. Third, we tested our novel method using AutoDock Vina, a different docking software, and showed the positive accuracy achieved in the resulting RFFR models. Finally, our results demonstrated that the method proposed optimized ensemble docking experiments and is applicable to different docking software. In addition, it detected new binding modes, which would be unreachable if employing only the rigid structure used to generate the InhA FFR model.ConclusionsOur results showed that the selective method is a valuable strategy for optimizing ensemble docking-based experiments using different docking software. The RFFR models produced by discarding non-promising snapshots from the original model are accurately shaped for a larger number of ligands, and the elapsed time spent in the ensemble docking experiments are considerably reduced.

[1]  Xiaohua Zhang,et al.  Message passing interface and multithreading hybrid for parallel molecular docking of large databases on petascale high performance computing machines , 2013, J. Comput. Chem..

[2]  M. Mezei,et al.  Molecular docking: a powerful approach for structure-based drug discovery. , 2011, Current computer-aided drug design.

[3]  Gábor Terstyánszky,et al.  Large‐scale virtual screening experiments on Windows Azure‐based cloud resources , 2014, Concurr. Comput. Pract. Exp..

[4]  Jeremy C. Smith,et al.  Task‐parallel message passing interface implementation of Autodock4 for docking of very large databases of compounds using high‐performance super‐computers , 2011, J. Comput. Chem..

[5]  L. Kavraki,et al.  Understanding the challenges of protein flexibility in drug design , 2015, Expert opinion on drug discovery.

[6]  C. Vilchèze,et al.  Crystal Structure of the Mycobacterium tuberculosis Enoyl-ACP Reductase, InhA, in Complex with NAD+ and a C16 Fatty Acyl Substrate* , 1999, The Journal of Biological Chemistry.

[7]  M. Maccarrone,et al.  Differences in the Endocannabinoid System of Sperm from Fertile and Infertile Men , 2012, PloS one.

[8]  Rommie E. Amaro,et al.  An improved relaxed complex scheme for receptor flexibility in computer-aided drug design , 2008, J. Comput. Aided Mol. Des..

[9]  Paul Watson,et al.  Developing cloud applications using the e-Science Central platform , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[10]  Holger Gohlke,et al.  Target flexibility: an emerging consideration in drug discovery and design. , 2008, Journal of medicinal chemistry.

[11]  I. Kuntz.  Structure-Based Strategies for Drug Design and Discovery , 1992, Science.

[12]  S. Cole,et al.  Towards a new tuberculosis drug: pyridomycin – nature's isoniazid , 2012, EMBO molecular medicine.

[13]  Peter J Tonge,et al.  High affinity InhA inhibitors with activity against drug-resistant strains of Mycobacterium tuberculosis. , 2006, ACS chemical biology.

[14]  C. Wiesmann,et al.  The discovery of first-in-class drugs: origins and evolution , 2014, Nature Reviews Drug Discovery.

[15]  David S. Goodsell,et al.  A semiempirical free energy force field with charge‐based desolvation , 2007, J. Comput. Chem..

[16]  David Ryan Koes,et al.  ZINCPharmer: pharmacophore search of the ZINC database , 2012, Nucleic Acids Res..

[17]  Duncan D. A. Ruiz,et al.  An Effective Approach for Clustering InhA Molecular Dynamics Trajectory Using Substrate-Binding Cavity Features , 2015, PloS one.

[18]  Alexander D. MacKerell,et al.  Identification and validation of human DNA ligase inhibitors using computer-aided drug design. , 2008, Journal of medicinal chemistry.

[19]  I. Kuntz,et al.  DOCK 6: combining techniques to model RNA-small molecule complexes. , 2009, RNA.

[20]  Kristina Chodorow,et al.  MongoDB: The Definitive Guide , 2010 .

[21]  David S. Goodsell,et al.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility , 2009, J. Comput. Chem..

[22]  Sharangdhar S. Phatak,et al.  From laptop to benchtop to bedside: structure-based drug design on protein targets. , 2012, Current pharmaceutical design.

[23]  Charles C. Persinger,et al.  How to improve R&D productivity: the pharmaceutical industry's grand challenge , 2010, Nature Reviews Drug Discovery.

[24]  X. Zou,et al.  Ensemble docking of multiple protein structures: Considering protein structural variations in molecular docking , 2006, Proteins.

[25]  Matteo Masetti,et al.  Protein Flexibility in Drug Discovery: From Theory to Computation , 2015, ChemMedChem.

[26]  Daniel M Zuckerman,et al.  Ensemble-based convergence analysis of biomolecular trajectories. , 2006, Biophysical journal.

[27]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[28]  Sally R. Ellingson,et al.  High‐throughput virtual molecular docking with AutoDockCloud , 2014, Concurr. Comput. Pract. Exp..

[29]  Wolfgang Wenzel,et al.  Modeling loop backbone flexibility in receptor‐ligand docking simulations , 2012, J. Comput. Chem..

[30]  Paul Watson,et al.  Cloud computing for fast prediction of chemical activity , 2013, Future Gener. Comput. Syst..

[31]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[32]  Osmar Norberto de Souza,et al.  A strategic solution to optimize molecular docking simulations using Fully-Flexible Receptor models , 2014, Expert Syst. Appl..

[33]  António J. M. Ribeiro,et al.  Protein-ligand docking in the new millennium--a retrospective of 10 years in the field. , 2013, Current medicinal chemistry.

[34]  Ola Spjuth,et al.  Large-scale virtual screening on public cloud resources with Apache Spark , 2017, Journal of Cheminformatics.

[35]  Yi Zheng,et al.  Rational Drug Design , 2012, Methods in Molecular Biology.

[36]  Ruth Huey,et al.  Computational protein–ligand docking and virtual drug screening with the AutoDock suite , 2016, Nature Protocols.

[37]  Kohei Ichikawa,et al.  Deployment of a Multi-site Cloud Environment for Molecular Virtual Screenings , 2015, 2015 IEEE 11th International Conference on e-Science.

[38]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[39]  J. Gready,et al.  Combining docking and molecular dynamic simulations in drug design , 2006, Medicinal research reviews.

[40]  Osmar Norberto de Souza,et al.  P-SaMI: a data-flow pattern to perform massively-parallel molecular docking experiments using a fully-flexible receptor model , 2015, SAC.

[41]  J. Sacchettini,et al.  Crystal structure and function of the isoniazid target of Mycobacterium tuberculosis , 1995, Science.

[42]  Duncan D. A. Ruiz,et al.  wFReDoW: A Cloud-Based Web Environment to Handle Molecular Docking Simulations of a Fully Flexible Receptor Model , 2013, BioMed research international.

[43]  Rodrigo C. Barros,et al.  Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments , 2015, Comput. Intell. Neurosci..

[44]  Gurmit Singh,et al.  Mitochondria and Cancer , 2013, BioMed research international.

[45]  J. Mccammon,et al.  Exploring the role of receptor flexibility in structure-based drug discovery. , 2014, Biophysical chemistry.

[46]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[47]  Marta Mattoso,et al.  Exploring Large Scale Receptor-Ligand Pairs in Molecular Docking Workflows in HPC Clouds , 2014, 2014 IEEE International Parallel & Distributed Processing Symposium Workshops.

[48]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[49]  Ryan G. Coleman,et al.  ZINC: A Free Tool to Discover Chemistry for Biology , 2012, J. Chem. Inf. Model..

[50]  Osmar Norberto de Souza,et al.  Mining flexible‐receptor molecular docking data , 2011, WIREs Data Mining Knowl. Discov..

[51]  Martin Zacharias,et al.  ReFlexIn: A Flexible Receptor Protein-Ligand Docking Scheme Evaluated on HIV-1 Protease , 2012, PloS one.

[52]  Yoonseok Shin,et al.  Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects , 2015, Comput. Intell. Neurosci..

[53]  I M Kapetanovic,et al.  Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. , 2008, Chemico-biological interactions.