Investigating the binding area of protein surface using MCL algorithm

Proteins combine with other materials to achieve a variety of functions, which will be similar if their active sites are similar. Thus we can infer a proteinpsilas function by identifying its binding area. This paper proposes a novel method to select a proteinpsilas binding area using the Markov Cluster (MCL) algorithm. A distance matrix is constructed from the surface residues distance on the protein, then transformed to the connectivity matrix for application of the MCL process, and finally evaluated by using Catalytic Site Atlas (CSA) data. In the experimental result using CSA data which comprised 94 selected single chain proteins, our algorithm detects 91 (97%) binding areas near the active site of each protein. We introduced new geometrical features with the aim of improving the prediction accuracy of the active site residues by selecting the residues near the active site.

[1]  Janet M. Thornton,et al.  The Catalytic Site Atlas: a resource of catalytic sites and residues identified in enzymes using structural data , 2004, Nucleic Acids Res..

[2]  Keun Ho Ryu,et al.  A Personalized Biological Data Management System Based on BSML , 2006, ICIC.

[3]  Robert B Russell,et al.  Finding functional sites in structural genomics proteins. , 2004, Structure.

[4]  Jie Liang,et al.  Inferring functional relationships of proteins from local sequence and spatial surface patterns. , 2003, Journal of molecular biology.

[5]  P. Babbitt.  Definitions of enzyme function for the structural genomics era. , 2003, Current opinion in chemical biology.

[6]  Robert D. Carr,et al.  101 optimal PDB structure alignments: a branch-and-cut algorithm for the maximum contact map overlap problem , 2001, RECOMB.

[7]  Keun Ho Ryu,et al.  Automatic Orthologous-Protein-Clustering from Multiple Complete-Genomes by the Best Reciprocal BLAST Hits , 2006, BioDM.

[8]  Oliviero Carugo,et al.  Prediction of protein‐protein interactions based on surface patch comparison , 2004, Proteomics.

[9]  M. Swindells,et al.  Protein clefts in molecular recognition and function. , 1996, Protein science : a publication of the Protein Society.

[10]  R. Laskowski.  SURFNET: a program for visualizing molecular surfaces, cavities, and intermolecular interactions. , 1995, Journal of molecular graphics.

[11]  Eytan Domany,et al.  Protein fold recognition and dynamics in the space of contact maps , 1996, Proteins.

[12]  S. Jones,et al.  Principles of protein-protein interactions. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[13]  J C Wootton,et al.  Dynamic contact maps of protein structures. , 1998, Journal of molecular graphics & modelling.

[14]  Robert D. Carr,et al.  Alignment Of Protein Structures With A Memetic Evolutionary Algorithm , 2002, GECCO.

[15]  S. vanDongen.  Graph Clustering by Flow Simulation , 2000 .

[16]  M Vendruscolo,et al.  Recovery of protein structure from contact maps. , 1997, Folding & design.

[17]  J. Thornton,et al.  A method for localizing ligand binding pockets in protein structures , 2005, Proteins.

[18]  A Valencia,et al.  Three-dimensional view of the surface motif associated with the P-loop structure: cis and trans cases of convergent evolution. , 2000, Journal of molecular biology.

[19]  Vasant Honavar,et al.  Predicting Protein-Protein Interaction Sites From Amino Acid Sequence , 2002 .

[20]  H. Villar,et al.  Deciphering cryptic similarities in protein binding sites. , 1998, Current opinion in biotechnology.

[21]  Anton J. Enright,et al.  An efficient algorithm for large-scale detection of protein families. , 2002, Nucleic acids research.

[22]  Gideon Schreiber,et al.  The molecular architecture of protein-protein binding sites. , 2005, Current opinion in structural biology.