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

Glukhov, E.; Kalitin, D.; Stepanenko, D.; Zhu, Y.; Nguen, T.; Jones, G.; Simmerling, C.; Mitchell, J. C.; Vajda, S.; Dill, K. A.; Padhorny, D.; Kozakov, D.

doi:10.1101/2023.11.29.569310

bioRxiv · 10.1101/2023.11.29.569310

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

Glukhov, E.·Kalitin, D.·Stepanenko, D.·Zhu, Y.·Nguen, T.·Jones, G.·Simmerling, C.·Mitchell, J. C.·Vajda, S.·Dill, K. A.·Padhorny, D.·Kozakov, D.

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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 AlphaFolds capabilities by fine-tuning it with a specialized dataset comprised by exclusively high-resolution MHC-peptide crystal structures. This tailored approach aimed to address the generalist nature of AlphaFolds original training, which, while broad-ranging, lacked the granularity necessary for the high-precision demands of MHC-peptide interaction prediction. A comparative analysis was conducted against the homology-modeling-based method Pandora [13], as well as the AlphaFold multimer model [8]. Our results demonstrate that our fine-tuned model outperforms both in terms of RMSD (median value is 0.65 [A]) but 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.

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Glukhov, E., Kalitin, D., Stepanenko, D., Zhu, Y., Nguen, T., Jones, G., Simmerling, C., Mitchell, J. C., Vajda, S., Dill, K. A., Padhorny, D., Kozakov, D.. 2023-12-01. MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction. https://doi.org/10.1101/2023.11.29.569310

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