Computational design of HLA class I superbinders for broad T cell immunogenicity

Abstract

Human leukocyte antigen (HLA) class I molecules are highly polymorphic, restricting peptide binding to narrow sequence subsets. Designing peptides that bind multiple HLA supertypes—termed superbinders—offers a promising strategy for broad-spectrum T cell vaccines and immunotherapies. Here, we present superHLA, a computational framework that combines Markov Chain Monte Carlo optimization with state-of-the-art major histocompatibility complex binding predictors to design synthetic 9-mer peptides with broad HLA-binding profiles. Using superHLA, we generated over 190,000 candidate superbinders predicted to bind 8 to 12 HLA class I alleles across distinct supertypes. A multitier filtering pipeline—incorporating sequence clustering, synthesis feasibility, cross-predictor validation, and self-peptidome exclusion—yielded a final panel of 100 peptides for experimental testing. Of these, 21 bound 4 to 9 supertypes in vitro. Superbinders displayed distinct anchor residue preferences and showed minimal similarity to human peptides. These results suggest that HLA superbinders are more abundant than previously recognized and can be rationally designed at scale. This approach supports development of pan-HLA immunogens with broad population coverage and may inform applications in vaccine research, neoantigen discovery, and immunotherapy.