Literature DB >> 25266271

Improving docking results via reranking of ensembles of ligand poses in multiple X-ray protein conformations with MM-GBSA.

P A Greenidge1, C Kramer, J-C Mozziconacci, W Sherman.   

Abstract

There is a tendency in the literature to be critical of scoring functions when docking programs perform poorly. The assumption is that existing scoring functions need to be enhanced or new ones developed in order to improve the performance of docking programs for tasks such as pose prediction and virtual screening. However, failures can result from either sampling or scoring (or a combination of the two), although less emphasis tends to be given to the former. In this work, we use the programs GOLD and Glide on a high-quality data set to explore whether failures in pose prediction and binding affinity estimation can be attributable more to sampling or scoring. We show that identification of the correct pose (docking power) can be improved by incorporating ligand strain into the scoring function or rescoring an ensemble of diverse docking poses with MM-GBSA in a postprocessing step. We explore the use of nondefault docking settings and find that enhancing ligand sampling also improves docking power, again suggesting that sampling is more limiting than scoring for the docking programs investigated in this work. In cross-docking calculations (docking a ligand to a noncognate receptor structure) we observe a significant reduction in the accuracy of pose ranking, as expected and has been reported by others; however, we demonstrate that these alternate poses may in fact be more complementary between the ligand and the rigid receptor conformation, emphasizing that treating the receptor rigidly is an artificial constraint on the docking problem. We simulate protein flexibility by the use of multiple crystallographic conformations of a protein and demonstrate that docking results can be improved with this level of protein sampling. This work indicates the need for better sampling in docking programs, especially for the receptor. This study also highlights the variable descriptive value of RMSD as the sole arbiter of pose replication quality. It is shown that ligand poses within 2 Å of the crystallographic one can show dramatic differences in calculated relative protein-ligand energies. MM-GBSA rescoring of distinct poses overcomes some of the sensitivities of pose ranking experienced by the docking scoring functions due to protein preparation and binding site definition.

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Year:  2014        PMID: 25266271     DOI: 10.1021/ci5003735

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


  19 in total

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3.  Pharmacophore-based virtual screening, biological evaluation and binding mode analysis of a novel protease-activated receptor 2 antagonist.

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Journal:  J Comput Aided Mol Des       Date:  2016-09-06       Impact factor: 3.686

4.  Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.

Authors:  Léa El Khoury; Diogo Santos-Martins; Sukanya Sasmal; Jérôme Eberhardt; Giulia Bianco; Francesca Alessandra Ambrosio; Leonardo Solis-Vasquez; Andreas Koch; Stefano Forli; David L Mobley
Journal:  J Comput Aided Mol Des       Date:  2019-11-06       Impact factor: 3.686

5.  Ensemble docking to difficult targets in early-stage drug discovery: Methodology and application to fibroblast growth factor 23.

Authors:  Hector A Velazquez; Demian Riccardi; Zhousheng Xiao; Leigh Darryl Quarles; Charless Ryan Yates; Jerome Baudry; Jeremy C Smith
Journal:  Chem Biol Drug Des       Date:  2017-11-03       Impact factor: 2.817

6.  Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Authors:  Joel Ricci-Lopez; Sergio A Aguila; Michael K Gilson; Carlos A Brizuela
Journal:  J Chem Inf Model       Date:  2021-10-15       Impact factor: 4.956

7.  Ligand Strain Energy in Large Library Docking.

Authors:  Shuo Gu; Matthew S Smith; Ying Yang; John J Irwin; Brian K Shoichet
Journal:  J Chem Inf Model       Date:  2021-09-01       Impact factor: 6.162

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Review 9.  A practical guide to large-scale docking.

Authors:  Brian J Bender; Stefan Gahbauer; Andreas Luttens; Jiankun Lyu; Chase M Webb; Reed M Stein; Elissa A Fink; Trent E Balius; Jens Carlsson; John J Irwin; Brian K Shoichet
Journal:  Nat Protoc       Date:  2021-09-24       Impact factor: 17.021

10.  Discovery of the first chemical tools to regulate MKK3-mediated MYC activation in cancer.

Authors:  Xuan Yang; Dacheng Fan; Aidan Henry Troha; Hyunjun Max Ahn; Kun Qian; Bo Liang; Yuhong Du; Haian Fu; Andrey A Ivanov
Journal:  Bioorg Med Chem       Date:  2021-07-22       Impact factor: 3.461

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