Empirical prediction of peptide octanol-water partition coefficients

Peptides are of great therapeutic potential as vaccines and drugs. Knowledge of physicochemical descriptors, including the partition coefficient P (commonly expressed in logarithm form: logP), is useful for screening out unsuitable molecules and also for the development of predictive Quantitative Structure-Activity Relationships (QSARs). In this paper we develop a new approach to the prediction of LogP values for peptides based on an empirical relationship between global molecular properties and measured physical properties. Our method was successful in terms of peptide prediction (total r2 = 0.641). The final model consisted of 5 physicochemical descriptors (molecular weight, number of single bonds, 2D-VDW volume, 2D-VSA hydrophobic and 2D-VSA polar). The approach is peptide specific and its predictive accuracy was high. Overall, 67% of the peptides were able to be predicted within +/-0.5 log units from the experimental values. Our method thus represents a novel prediction method with proven predictive ability.

Modeling the peptide-T cell receptor interaction by the comparative molecular similarity indices analysis-soft independent modeling of class analogy technique

A set of 38 epitopes and 183 non-epitopes, which bind to alleles of the HLA-A3 supertype, was subjected to a combination of comparative molecular similarity indices analysis (CoMSIA) and soft independent modeling of class analogy (SIMCA). During the process of T cell recognition, T cell receptors (TCR) interact with the central section of the bound nonamer peptide; thus only positions 4−8 were considered in the study. The derived model distinguished 82% of the epitopes and 73% of the non-epitopes after cross-validation in five groups. The overall preference from the model is for polar amino acids with high electron density and the ability to form hydrogen bonds. These so-called “aggressive” amino acids are flanked by small-sized residues, which enable such residues to protrude from the binding cleft and take an active role in TCR-mediated T cell recognition. Combinations of “aggressive” and “passive” amino acids in the middle part of epitopes constitute a putative TCR binding motif