LIBRA: LIgand Binding site Recognition Application

MOTIVATION In recent years, structural genomics and ab initio molecular modeling activities are leading to the availability of a large number of structural models of proteins whose biochemical function is not known. The aim of this study was the development of a novel software tool that, given a protein's structural model, predicts the presence and identity of active sites and/or ligand binding sites. RESULTS The algorithm implemented by ligand binding site recognition application (LIBRA) is based on a graph theory approach to find the largest subset of similar residues between an input protein and a collection of known functional sites. The algorithm makes use of two predefined databases for active sites and ligand binding sites, respectively, derived from the Catalytic Site Atlas and the Protein Data Bank. Tests indicate that LIBRA is able to identify the correct binding/active site in 90% of the cases analyzed, 90% of which feature the identified site as ranking first. As far as ligand binding site recognition is concerned, LIBRA outperforms other structure-based ligand binding sites detection tools with which it has been compared. AVAILABILITY AND IMPLEMENTATION The application, developed in Java SE 7 with a Swing GUI embedding a JMol applet, can be run on any OS equipped with a suitable Java Virtual Machine (JVM), and is available at the following URL: http://www.computationalbiology.it/software/LIBRAv1.zip.

[1]  Janet M. Thornton,et al.  ProFunc: a server for predicting protein function from 3D structure , 2005, Nucleic Acids Res..

[2]  Janet M. Thornton,et al.  The Catalytic Site Atlas 2.0: cataloging catalytic sites and residues identified in enzymes , 2013, Nucleic Acids Res..

[3]  Janet M Thornton,et al.  Protein function prediction using local 3D templates. , 2005, Journal of molecular biology.

[4]  Jeffrey Skolnick,et al.  A Comprehensive Survey of Small-Molecule Binding Pockets in Proteins , 2013, PLoS Comput. Biol..

[5]  Kengo Kinoshita,et al.  Exhaustive comparison and classification of ligand-binding surfaces in proteins , 2013, Protein science : a publication of the Protein Society.

[6]  José Ignacio Garzón,et al.  Template-based prediction of protein function. , 2015, Current opinion in structural biology.

[7]  Robert M. Hanson,et al.  Jmol – a paradigm shift in crystallographic visualization , 2010 .

[8]  Frank Harary,et al.  Graph Theory , 2016 .

[9]  Shoshana J. Wodak,et al.  LigASite—a database of biologically relevant binding sites in proteins with known apo-structures , 2007, Nucleic Acids Res..

[10]  T. N. Bhat,et al.  The Protein Data Bank , 2000, Nucleic Acids Res..

[11]  P. Pardalos,et al.  An exact algorithm for the maximum clique problem , 1990 .

[12]  Fabio Polticelli,et al.  ASSIST: a fast versatile local structural comparison tool , 2014, Bioinform..

[13]  Hui Sun Lee,et al.  Ligand Binding Site Detection by Local Structure Alignment and Its Performance Complementarity , 2013, J. Chem. Inf. Model..