In silico prediction of peptide binding affinity to class I mouse major histocompatibility complexes: a comparative molecular similarity index analysis (CoMSIA) study.
Current methods for the in silico identification of T cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate prediction of peptide-major histocompatibility complex (MHC) affinity. A three-dimensional quantitative structure-activity relationship (3D-QSAR) for the prediction of peptide binding to class I MHC molecules was established using the comparative molecular similarity index analysis (CoMSIA) method. Three MHC alleles were studied: H2-D(b), H2-K(b), and H2-K(k). Models were produced for each allele. Each model consisted of five physicochemical descriptors-steric bulk, electrostatic potentials, hydrophobic interactions, and hydrogen-bond donor and hydrogen-bond acceptor abilities. The models have an acceptable level of predictivity: cross-validation leave-one-out statistical terms q2 and SEP (standard error of prediction) ranged between 0.490 and 0.679 and between 0.525 and 0.889, respectively. The non-cross-validated statistical terms r2 and SEE (standard error of estimate) ranged between 0.913 and 0.979 and between 0.167 and 0.248, respectively. The use of coefficient contour maps, which indicate favored and disfavored areas for each position of the MHC-bound peptides, allowed the binding specificity of each allele to be identified, visualized, and understood. The present study demonstrates the effectiveness of CoMSIA as a method for studying peptide-MHC interactions. The peptides used in this study are available on the Internet (http://www.jenner.ac.uk/AntiJen). The partial least-squares method is available commercially in the SYBYL molecular modeling software package.