Literature DB >> 28902511

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

Kai Liu1, Hironori Kokubo1.   

Abstract

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.

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Year:  2017        PMID: 28902511     DOI: 10.1021/acs.jcim.7b00412

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  28 in total

1.  Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations.

Authors:  Nathan M Lim; Meghan Osato; Gregory L Warren; David L Mobley
Journal:  J Chem Theory Comput       Date:  2020-03-27       Impact factor: 6.006

2.  Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Authors:  Tai-Sung Lee; Bryce K Allen; Timothy J Giese; Zhenyu Guo; Pengfei Li; Charles Lin; T Dwight McGee; David A Pearlman; Brian K Radak; Yujun Tao; Hsu-Chun Tsai; Huafeng Xu; Woody Sherman; Darrin M York
Journal:  J Chem Inf Model       Date:  2020-09-16       Impact factor: 4.956

3.  Fully Flexible Ligand Docking for the P2X7 Receptor Using ROSIE.

Authors:  Sudad Dayl; Ralf Schmid
Journal:  Methods Mol Biol       Date:  2022

4.  Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening.

Authors:  Aayush Gupta; Huan-Xiang Zhou
Journal:  J Chem Inf Model       Date:  2021-08-17       Impact factor: 6.162

5.  Battling BTK mutants with noncovalent inhibitors that overcome Cys481 and Thr474 mutations in Waldenström macroglobulinemia therapy: structural mechanistic insights on the role of fenebrutinib.

Authors:  Ghazi Elamin; Aimen Aljoundi; Mohamed Issa Alahmdi; Nader E Abo-Dya; Mahmoud E S Soliman
Journal:  J Mol Model       Date:  2022-10-12       Impact factor: 2.172

6.  Structural insights into binding of therapeutic channel blockers in NMDA receptors.

Authors:  Tsung-Han Chou; Max Epstein; Kevin Michalski; Eve Fine; Philip C Biggin; Hiro Furukawa
Journal:  Nat Struct Mol Biol       Date:  2022-05-30       Impact factor: 18.361

Review 7.  Molecular dynamics: a powerful tool for studying the medicinal chemistry of ion channel modulators.

Authors:  Daniel Şterbuleac
Journal:  RSC Med Chem       Date:  2021-07-22

8.  An in silico pipeline for the discovery of multitarget ligands: A case study for epi-polypharmacology based on DNMT1/HDAC2 inhibition.

Authors:  Fernando D Prieto-Martínez; Eli Fernández-de Gortari; José L Medina-Franco; L Michel Espinoza-Fonseca
Journal:  Artif Intell Life Sci       Date:  2021-09-12

9.  D3R Grand Challenge 4: ligand similarity and MM-GBSA-based pose prediction and affinity ranking for BACE-1 inhibitors.

Authors:  Sukanya Sasmal; Léa El Khoury; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2019-11-28       Impact factor: 3.686

10.  Reversibly Sampling Conformations and Binding Modes Using Molecular Darting.

Authors:  Samuel C Gill; David L Mobley
Journal:  J Chem Theory Comput       Date:  2020-12-08       Impact factor: 6.006

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