MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction

Abstract

The precise prediction of Major Histocompatibility Complex (MHC)-peptide complex structures is pivotal for understanding cellular immune responses and advancing vaccine design. In this study, we enhanced AlphaFold's capabilities by fine-tuning it with a specialized dataset consisting of exclusively high-resolution Class I MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFold's original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of Class I MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora, as well as the AlphaFold multimer model. Our results demonstrate that our fine-tuned model outperforms others in terms of RMSD (median value for C-alpha atoms for peptides is 0.66 Å) and also provides enhanced predicted lDDT scores, offering a more reliable assessment of the predicted structures. These advances have substantial implications for computational immunology, potentially accelerating the development of novel therapeutics and vaccines by providing a more precise computational lens through which to view MHC-peptide interactions.