MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction

Biophys J. 2024 Sep 3;123(17):2902-2909. doi: 10.1016/j.bpj.2024.05.011. Epub 2024 May 15.

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 root-mean-square deviation (median value for Cα atoms for peptides is 0.66 Å) and also provides enhanced predicted local distance difference test 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.

MeSH terms

  • Histocompatibility Antigens Class I / chemistry
  • Histocompatibility Antigens Class I / immunology
  • Histocompatibility Antigens Class I / metabolism
  • Major Histocompatibility Complex
  • Models, Molecular*
  • Peptides* / chemistry
  • Peptides* / metabolism
  • Protein Binding

Substances

  • Peptides
  • Histocompatibility Antigens Class I