Quantitative approaches to computational vaccinology.

Abstract

This article reviews the newly released JenPep database and two new powerful techniques for T-cell epitope prediction: (i) the additive method; and (ii) a 3D-Quantitative Structure Activity Relationships (3D-QSAR) method, based on Comparative Molecular Similarity Indices Analysis (CoMSIA). The JenPep database is a family of relational databases supporting the growing need of immunoinformaticians for quantitative data on peptide binding to major histocompatibility complexes and to the Transporters associated with Antigen Processing (TAP). It also contains an annotated list of T-cell epitopes. The database is available free via the Internet (http://www.jenner.ac.uk/JenPep). The additive prediction method is based on the assumption that the binding affinity of a peptide depends on the contributions from each amino acid as well as on the interactions between the adjacent and every second side-chain. In the 3D-QSAR approach, the influence of five physicochemical properties (steric bulk, electrostatic potential, local hydrophobicity, hydrogen-bond donor and hydrogen-bond acceptor abilities) on the affinity of peptides binding to MHC molecules were considered. Both methods were exemplified through their application to the well-studied problem of peptides binding to the human class I MHC molecule HLA-A*0201.