ABSTRACT
Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA (rCoRIA) and mixed-CoRIA (mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole (rCoRIA) and with individual active site residues in the receptor (mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r(2) (correlation coefficient) and r(2)(pred) (predictive r(2)). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.
