MMBPred

MMBPred predicts amino-acid substitutions that enhance peptide binding affinity or promiscuity to Major Histocompatibility Complex (MHC) class I alleles for immunogen design.

Key Features:

• High-throughput computational screening: Identifies mutations within antigenic sequences to enhance binding to multiple MHC class I alleles using automated, large-scale computation.
• Quantitative matrices: Utilizes quantitative matrices to determine optimal amino-acid substitutions that improve peptide-MHC binding characteristics.
• Promiscuous MHC binder prediction: Accepts specified permissible mutations, selected target MHC alleles, and designated mutable positions to identify peptides predicted to bind a broad array of MHC class I alleles.
• High-affinity binder prediction: Allows specification of conserved positions within the native sequence and computes required mutations and their positions in 9-mer peptides to achieve high-affinity binding to chosen MHC class I alleles.

Scientific Applications:

• Subunit vaccine design: Guides selection and optimization of antigenic peptides for subunit vaccines by enhancing MHC class I binding across diverse alleles.
• Immunotherapy and epitope selection: Supports identification of high-affinity or promiscuous epitopes for immunotherapy development and population-wide epitope coverage analysis.

Methodology:

Performs high-throughput computational screening using quantitative matrices to compute amino-acid substitutions and their positions in 9-mer peptides for promiscuous or high-affinity binding to selected MHC class I alleles.