Pires DEV, de Melo-Minardi RC, da Silveira CH, Campos FF, Meira W. aCSM: noise-free graph-based signatures to large-scale receptor-based ligand prediction.
ACTA ACUST UNITED AC 2013;
29:855-61. [PMID:
23396119 DOI:
10.1093/bioinformatics/btt058]
[Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
MOTIVATION
Receptor-ligand interactions are a central phenomenon in most biological systems. They are characterized by molecular recognition, a complex process mainly driven by physicochemical and structural properties of both receptor and ligand. Understanding and predicting these interactions are major steps towards protein ligand prediction, target identification, lead discovery and drug design.
RESULTS
We propose a novel graph-based-binding pocket signature called aCSM, which proved to be efficient and effective in handling large-scale protein ligand prediction tasks. We compare our results with those described in the literature and demonstrate that our algorithm overcomes the competitor's techniques. Finally, we predict novel ligands for proteins from Trypanosoma cruzi, the parasite responsible for Chagas disease, and validate them in silico via a docking protocol, showing the applicability of the method in suggesting ligands for pockets in a real-world scenario.
AVAILABILITY AND IMPLEMENTATION
Datasets and the source code are available at http://www.dcc.ufmg.br/∼dpires/acsm.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
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