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Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations.

Abstract：

:Structure-based virtual screening relies on classical scoring functions that often fail to reliably discriminate binders from nonbinders. In this work, we present a high-throughput protein-ligand complex molecular dynamics (MD) simulation that uses the output from AutoDock Vina to improve docking results in distinguishing active from decoy ligands in a directory of useful decoy-enhanced (DUD-E) dataset. MD trajectories are processed by evaluating ligand-binding stability using root-mean-square deviations. We select 56 protein targets (of 7 different protein classes) and 560 ligands (280 actives, 280 decoys) and show 22% improvement in ROC AUC (area under the curve, receiver operating characteristics curve), from an initial value of 0.68 (AutoDock Vina) to a final value of 0.83. The MD simulation demonstrates a robust performance across all seven different protein classes. In addition, some predicted ligand-binding modes are moderately refined during MD simulations. These results systematically validate the reliability of a physics-based approach to evaluate protein-ligand binding interactions.

journal_name

J Chem Inf Model

journal_title

Journal of chemical information and modeling

authors

Guterres H,Im W

doi

10.1021/acs.jcim.0c00057

subject

Has Abstract

pub_date

2020-04-27 00:00:00

pages

2189-2198

issue

4

eissn

1549-9596

issn

1549-960X

journal_volume

60

pub_type

杂志文章

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