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Improving Protein-Ligand Docking Results with High-Throughput Molecular Dynamics Simulations
Open accessAbstract
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.
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Metadata
- Authors
- Guterres, H., & Im, W.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-03-31
- Volume
- 60
- Issue
- 4
- Pages
- 2189-2198
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W3013426051
- Cited by
- 355
- References
- 57
- FWCI
- 20.73
- Type
- journal article
- Language
- en
- Open access
- green
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