Constructing patch-based ligand-binding pocket database for predicting function of proteins

BMC Bioinformatics

Proceedings

Lee Sael¹  Daisuke Kihara¹ 

[1] Department of Biological Sciences, Purdue University, 47907, West Lafayette, IN, USA;Department of Computer Science, Purdue University, 47907, West Lafayette, IN, USA;

关键词: Enrichment Factor;    Ligand Molecule;    Flavin Adenine Dinucleotide;    Nicotinamide Adenine Dinucleotide;    Surface Patch;   

DOI  :  10.1186/1471-2105-13-S2-S7

来源: Springer

【 摘 要 】

BackgroundMany of solved tertiary structures of unknown functions do not have global sequence and structural similarities to proteins of known function. Often functional clues of unknown proteins can be obtained by predicting small ligand molecules that bind to the proteins.MethodsIn our previous work, we have developed an alignment free local surface-based pocket comparison method, named Patch-Surfer, which predicts ligand molecules that are likely to bind to a protein of interest. Given a query pocket in a protein, Patch-Surfer searches a database of known pockets and finds similar ones to the query. Here, we have extended the database of ligand binding pockets for Patch-Surfer to cover diverse types of binding ligands.Results and conclusionWe selected 9393 representative pockets with 2707 different ligand types from the Protein Data Bank. We tested Patch-Surfer on the extended pocket database to predict binding ligand of 75 non-homologous proteins that bind one of seven different ligands. Patch-Surfer achieved the average enrichment factor at 0.1 percent of over 20.0. The results did not depend on the sequence similarity of the query protein to proteins in the database, indicating that Patch-Surfer can identify correct pockets even in the absence of known homologous structures in the database.

【 授权许可】

CC BY   
© Sael and Kihara; licensee BioMed Central Ltd. 2012