TSCC: Two-Stage Combinatorial Clustering for virtual screening using protein-ligand interactions and physicochemical features

BMC Genomics

Proceedings

Cheng-Neng Ko¹  Yen-Fu Chen¹  Jinn-Moon Yang²  Daniel L Clinciu³  Chi-Chun Lo⁴ 

[1] Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 75 Bo Ai Street, 30050, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 75 Bo Ai Street, 30050, Hsinchu, Taiwan;Department of Biological Science and Technology, National Chiao Tung University, 75 Bo Ai Street, 30050, Hsinchu, Taiwan;Core Facility for Structural Bioinformatics, National Chiao Tung University, 75 Bo Ai Street, 30050, Hsinchu, Taiwan;Institute of Bioinformatics and Systems Biology, National Chiao Tung University, 75 Bo Ai Street, 30050, Hsinchu, Taiwan;Institute of Information Management, National Chiao Tung University, 1001 University Road, 30010, Hsinchu, Taiwan;Institute of Information Management, National Chiao Tung University, 1001 University Road, 30010, Hsinchu, Taiwan;

关键词: Root Mean Square Deviation;    Thymidine Kinase;    Virtual Screening;    Reference Threshold;    Average Root Mean Square Deviation;   

DOI  :  10.1186/1471-2164-11-S4-S26

来源: Springer

【 摘 要 】

BackgroundThe increasing numbers of 3D compounds and protein complexes stored in databases contribute greatly to current advances in biotechnology, being employed in several pharmaceutical and industrial applications. However, screening and retrieving appropriate candidates as well as handling false positives presents a challenge for all post-screening analysis methods employed in retrieving therapeutic and industrial targets.ResultsUsing the TSCC method, virtually screened compounds were clustered based on their protein-ligand interactions, followed by structure clustering employing physicochemical features, to retrieve the final compounds. Based on the protein-ligand interaction profile (first stage), docked compounds can be clustered into groups with distinct binding interactions. Structure clustering (second stage) grouped similar compounds obtained from the first stage into clusters of similar structures; the lowest energy compound from each cluster being selected as a final candidate.ConclusionBy representing interactions at the atomic-level and including measures of interaction strength, better descriptions of protein-ligand interactions and a more specific analysis of virtual screening was achieved. The two-stage clustering approach enhanced our post-screening analysis resulting in accurate performances in clustering, mining and visualizing compound candidates, thus, improving virtual screening enrichment.

【 授权许可】

Unknown   
© Clinciu et al; licensee BioMed Central Ltd. 2010. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.