Prediction of promiscuous and high-affinity mutated MHC binders.

The identification of peptides in an antigenic sequence that can bind with high affinity to a wide range of MHC alleles is one of the challenges in subunit vaccine design. The mutation of natural peptides is an alternative to obtaining peptides that can bind to a wide range of MHC alleles with high affinity. A large number of experiments are typically necessary to identify mutations that define high-affinity binding peptides. Therefore there is a need to develop a computational method for detecting amino acid mutations in a peptide for making it high-affinity or promiscuous MHC binders. This report describes a high-throughput computer driven solution for the identification of promiscuous and high-affinity mutated binders of 47 MHC class I alleles by introducing mutations in an antigenic sequence. The method implements quantitative matrices for creating optimal mutations in an antigenic sequence. It has two major options: (i) prediction of promiscuous MHC binders and (ii) prediction of high-affinity binders. In case of prediction of promiscuous binders, the server allows a user to select (i) permissible mutations in a peptide; (ii) MHC alleles to whom it should bind; and (iii) positions at which mutation is allowed. In the case of prediction of high-affinity binders, the server allows users to specify the positions that should be conserved in the native protein. In both cases, the method computes the type of mutations and position of mutations in 9-mer peptides required to have the desired results. The web server MMBPred is available at www.imtech.res.in/raghava/mmbpred/.