Literature DB >> 25280064

Docking ligands into flexible and solvated macromolecules. 7. Impact of protein flexibility and water molecules on docking-based virtual screening accuracy.

Eric Therrien1, Nathanael Weill, Anna Tomberg, Christopher R Corbeil, Devin Lee, Nicolas Moitessier.   

Abstract

The use of predictive computational methods in the drug discovery process is in a state of continual growth. Over the last two decades, an increasingly large number of docking tools have been developed to identify hits or optimize lead molecules through in-silico screening of chemical libraries to proteins. In recent years, the focus has been on implementing protein flexibility and water molecules. Our efforts led to the development of Fitted first reported in 2007 and further developed since then. In this study, we wished to evaluate the impact of protein flexibility and occurrence of water molecules on the accuracy of the Fitted docking program to discriminate active compounds from inactive compounds in virtual screening (VS) campaigns. For this purpose, a total of 171 proteins cocrystallized with small molecules representing 40 unique enzymes and receptors as well as sets of known ligands and decoys were selected from the Protein Data Bank (PDB) and the Directory of Useful Decoys (DUD), respectively. This study revealed that implementing displaceable crystallographic or computationally placed particle water molecules and protein flexibility can improve the enrichment in active compounds. In addition, an informed decision based on library diversity or research objectives (hit discovery vs lead optimization) on which implementation to use may lead to significant improvements.

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Year:  2014        PMID: 25280064     DOI: 10.1021/ci500299h

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


  7 in total

1.  Effect of Binding Pose and Modeled Structures on SVMGen and GlideScore Enrichment of Chemical Libraries.

Authors:  David Xu; Samy O Meroueh
Journal:  J Chem Inf Model       Date:  2016-05-24       Impact factor: 4.956

2.  Computational Strategy for Bound State Structure Prediction in Structure-Based Virtual Screening: A Case Study of Protein Tyrosine Phosphatase Receptor Type O Inhibitors.

Authors:  Xuben Hou; David Rooklin; Duxiao Yang; Xiao Liang; Kangshuai Li; Jianing Lu; Cheng Wang; Peng Xiao; Yingkai Zhang; Jin-Peng Sun; Hao Fang
Journal:  J Chem Inf Model       Date:  2018-10-19       Impact factor: 4.956

3.  Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets.

Authors:  Wei P Feinstein; Michal Brylinski
Journal:  J Cheminform       Date:  2015-05-15       Impact factor: 5.514

4.  In Silico Exploration for Novel Type-I Inhibitors of Tie-2/TEK: The Performance of Different Selection Strategy in Selecting Virtual Screening Candidates.

Authors:  Peichen Pan; Huiyong Sun; Hui Liu; Dan Li; Wenfang Zhou; Xiaotian Kong; Youyong Li; Huidong Yu; Tingjun Hou
Journal:  Sci Rep       Date:  2016-11-23       Impact factor: 4.379

5.  Protein Binding Pocket Optimization for Virtual High-Throughput Screening (vHTS) Drug Discovery.

Authors:  Dimitris Gazgalis; Mehreen Zaka; Bilal Haider Abbasi; Diomedes E Logothetis; Mihaly Mezei; Meng Cui
Journal:  ACS Omega       Date:  2020-06-10

6.  Multi-Body Interactions in Molecular Docking Program Devised with Key Water Molecules in Protein Binding Sites.

Authors:  Wei Xiao; Disha Wang; Zihao Shen; Shiliang Li; Honglin Li
Journal:  Molecules       Date:  2018-09-11       Impact factor: 4.411

7.  ALADDIN: Docking Approach Augmented by Machine Learning for Protein Structure Selection Yields Superior Virtual Screening Performance.

Authors:  Ningning Fan; Christoph A Bauer; Conrad Stork; Christina de Bruyn Kops; Johannes Kirchmair
Journal:  Mol Inform       Date:  2019-11-08       Impact factor: 3.353

  7 in total

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