Table of Contents
Scholarly Research Exchange
Volume 2008, Article ID 360572, 12 pages
http://dx.doi.org/10.3814/2008/360572
Research Article

HomoSAR: An Integrated Approach Using Homology Modeling and Quantitative Structure-Activity Relationship for Activity Prediction of Peptides

Department of Pharmaceutical Chemistry, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400098, India

Received 24 March 2008; Revised 4 August 2008; Accepted 12 August 2008

Copyright © 2008 Raghuvir R. S. Pissurlenkar and Evans C. Coutinho. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

3D-QSAR of peptides is a daunting task. The difficulty in peptide QSAR arises due to the sheer number of conformational degrees of freedom for peptides that makes alignment in a 3D grid an overwhelming task. In this paper, we propose a method of QSAR where the alignment of peptides is shifted from 3D space to 1D space, making the alignment of peptides a very simple proposition. The method called HomoSAR, is based on an integrated approach that uses the principles of homology modeling in conjunction with the QSAR formalism to predict and design new peptide sequences. The peptides to be studied are subjected to a multiple sequence alignment which is followed by scoring every position in the peptide sequence against a reference peptide in the alignment, through calculation of similarity indices. The similarity indices obtained for each position (amino acid residue) in the peptide form the “descriptor” values (independent variables) which are then correlated to the biological activity of the peptide by G/PLS techniques. As an application, the methodology has been illustrated for the dataset of nonamer peptides that bind to the Class I major histocompatibility complex (MHC) molecule HLA-A0201 as this dataset has been extensively studied. The models generated have statistically significant correlation coefficients and predictive r2. The cross validated coefficients (q2) are in an acceptable range. The HomoSAR approach identifies amino acids and properties that are preferred or detrimental at every position in the peptide sequence. The approach is simple to use and is able to extract all information contained in the dataset to explain the underlying structure activity relationships. The approach is applicable to peptide sequences which are not all of uniform length.