Dataset Information

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment.

ABSTRACT: Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to conventional systematic parallel virtual screening methods in two ways. Firstly, it automates the process of creating and launching multiple independent dockings on a high performance computing cluster. Secondly, it uses a N?i?ve Bayes scoring function to calculate binding energy of un-docked ligands to identify and preferentially dock (Autodock predicted) better binders. The application was tested on four proteins using a library of 10,573 ligands. In all the experiments, (i). 200 of the 1,000 best binders are identified after docking only ~14 percent of the chemical library, (ii). 9 or 10 best-binders are identified after docking only ~19 percent of the chemical library, and (iii). no significant enrichment is observed after docking ~70 percent of the chemical library. The results show significant increase in enrichment of potential drug leads in early rounds of virtual screening.

SUBMITTER: Pradeep P

PROVIDER: S-EPMC4784258 | biostudies-literature | 2015 Nov-Dec

REPOSITORIES: biostudies-literature

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A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment.

Pradeep Prachi P Struble Craig C Neumann Terrence T Sem Daniel S DS Merrill Stephen J SJ

IEEE/ACM transactions on computational biology and bioinformatics 20151101 6

Molecular docking is a computational technique which predicts the binding energy and the preferred binding mode of a ligand to a protein target. Virtual screening is a tool which uses docking to investigate large chemical libraries to identify ligands that bind favorably to a protein target. We have developed a novel scoring based distributed protein docking application to improve enrichment in virtual screening. The application addresses the issue of time and cost of screening in contrast to co ...[more]

PMID: 26671816

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Project description:MOTIVATION:Protein complexes play critical roles in many aspects of biological functions. Three-dimensional (3D) structures of protein complexes are critical for gaining insights into structural bases of interactions and their roles in the biomolecular pathways that orchestrate key cellular processes. Because of the expense and effort associated with experimental determinations of 3D protein complex structures, computational docking has evolved as a valuable tool to predict 3D structures of biomolecular complexes. Despite recent progress, reliably distinguishing near-native docking conformations from a large number of candidate conformations, the so-called scoring problem, remains a major challenge. RESULTS:Here we present iScore, a novel approach to scoring docked conformations that combines HADDOCK energy terms with a score obtained using a graph representation of the protein-protein interfaces and a measure of evolutionary conservation. It achieves a scoring performance competitive with, or superior to, that of state-of-the-art scoring functions on two independent datasets: (i) Docking software-specific models and (ii) the CAPRI score set generated by a wide variety of docking approaches (i.e. docking software-non-specific). iScore ranks among the top scoring approaches on the CAPRI score set (13 targets) when compared with the 37 scoring groups in CAPRI. The results demonstrate the utility of combining evolutionary, topological and energetic information for scoring docked conformations. This work represents the first successful demonstration of graph kernels to protein interfaces for effective discrimination of near-native and non-native conformations of protein complexes. AVAILABILITY AND IMPLEMENTATION:The iScore code is freely available from Github: https://github.com/DeepRank/iScore (DOI: 10.5281/zenodo.2630567). And the docking models used are available from SBGrid: https://data.sbgrid.org/dataset/684). SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

Dataset Information

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment.

Publications

A Novel Scoring Based Distributed Protein Docking Application to Improve Enrichment.

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