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Computing Ligands Bound to Proteins Using MELD-Accelerated MD.

ABSTRACT: Predicting the poses of small-molecule ligands in protein binding sites is often done by virtual screening algorithms such as DOCK. In principle, molecular dynamics (MD) using atomistic force fields could give better free-energy-based pose selection, but MD is computationally expensive. Here, we ask if modeling employing limited data (MELD)-accelerated MD (MELD × MD) can pick out the best DOCK poses taken as input. We study 30 different ligand-protein pairs. MELD × MD finds native poses, based on best free energies, in 23 out of the 30 cases, 20 of which were previously known DOCK failures. We conclude that MELD × MD can add value for predicting accurate poses of small molecules bound to proteins.

SUBMITTER: Liu C

PROVIDER: S-EPMC7572789 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Computing Ligands Bound to Proteins Using MELD-Accelerated MD.

Liu Cong C Brini Emiliano E Perez Alberto A Dill Ken A KA

Journal of chemical theory and computation 20200923 10

Predicting the poses of small-molecule ligands in protein binding sites is often done by virtual screening algorithms such as DOCK. In principle, molecular dynamics (MD) using atomistic force fields could give better free-energy-based pose selection, but MD is computationally expensive. Here, we ask if modeling employing limited data (MELD)-accelerated MD (MELD × MD) can pick out the best DOCK poses taken as input. We study 30 different ligand-protein pairs. MELD × MD finds native poses, based o ...[more]

PMID: 32910647

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