Neoantigen-based immunotherapy has emerged as a promising approach for cancer treatment. One key strategy in neoantigen-based vaccine design is to alter known neoantigens into enhanced mimotopes that elicit more robust immune responses. However, screening mimotopes presents challenges in both diversity and precision. While machine learning (ML) models facilitate high-throughput screening of immunogenic candidates, they struggle to distinguish mimotopes from original neoantigens (i.e. identify mimotopes with higher binding affinities, rather than solely distinguish between binding and nonbinding peptides). In contrast, alchemical methods such as free energy perturbation (FEP) provide quantitative binding free-energy differences between mimotopes and neoantigens but are computationally intensive. To leverage the strengths of both approaches, we propose an FEP-assisted ML (FEPaML) strategy that employs Bayesian optimization to iteratively refine knowledge-based predictions with physics-based evaluations, thereby progressively achieving locally optimized, precise, and robust outcomes. Our FEPaML strategy is then applied to screen mimotopes for several representative neoantigens. It has demonstrated excellent predictive precisions (exceeding 0.9) with a relatively small number of FEP samplings, significantly outperforming existing ML models.
Keywords: Bayesian optimization; free-energy perturbation; machine learning; mimotope; neoantigen-based vaccine.
© The Author(s) 2025. Published by Oxford University Press.