Abstract: To address the limited efficacy of peptide vaccines caused by insufficient binding affinity between melanoma antigens and the major histocompatibility complex (MHC) proteins, this study proposes a free energy perturbation (FEP)-AI synergistic design framework for efficiently screening high-affinity candidate peptides. We focused on the PRAME190-198 peptide-HLA-A*11:01 complex, and employed FEP to accurately quantify the impact of antigenic amino acid mutations on binding free energy. The resulting precise physical data were used to calibrate the FEPaML model through multiple rounds of iterative optimization. The optimized framework was then applied to perform high-throughput prediction and ranking of large-scale combinatorial mutations, followed by validation using the FEP calculation. The FEPaML model, calibrated with high-precision physical data from FEP, enabled efficient and accurate antigen optimization design and successfully captured the synergistic effects of multi-site mutations. The identified optimal triple mutant, I7F-E8S-K9R, achieved a relative binding free energy of -24.43 kJ·mol-1, showing significantly improved binding affinity compared to the wild type. This framework enables efficient and precise optimization of antigen peptide affinity, substantially reducing screening cost and duration while providing robust theoretical and technical support for personalized melanoma vaccine design.

Key words: Molecular dynamics, Free energy perturbation, Machine learning, Antigen optimization