Predicting Peptide Binders of Flexible Lengths with Genetic Annealing Algorithm

Prediction of peptides that bind to major histocompatibility complex class II (MHC-II) molecules is vital for drug discovery and vaccine development. Prediction of peptides binding to MHC-II molecules is complicated because of the broad range of their lengths. Peptides bind to the molecules at an ungapped motif present at the binding site. Obtaining an alignment of binding sites of binding proteins facilitates determining of the binding motif. However, multiple sequence alignment often fails on peptides. In this paper, we propose a genetic annealing algorithm (GAA) to identify an alignment for binding peptides that can subsequently be used to predict binding peptides. Our approach is demonstrated with a dataset having difficulty in finding a consensus motif through experimental means and using existing motif detection methods. GAA based approach outperformed Gibbs motif sampler and RANKPEP approaches in predicting peptides binding to MHC II molecules.