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MHC-Fine: Fine-tuned AlphaFold for Precise MHC-Peptide Complex Prediction
Open accessPreprint — published as 10.1016/j.bpj.2024.05.011
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 comprised by exclusively high-resolution 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 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 Å) 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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Cites
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764
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Metadata
- Authors
- Glukhov, E., Kalitin, D., Stepanenko, D., Zhu, Y., Nguyen, T., Jones, G., Simmerling, C., Mitchell, J. C., Vajda, S., Dill, K. A., Padhorny, D., & Kozakov, D.
- Affiliations
- Stony Brook University (US)
- Ukrainian Catholic University (UA)
- Oak Ridge National Laboratory (US)
- Stony Brook School (US)
- Corresponding author
- Padhorny, Dzmitry
- Kozakov, Dima
- ORCID
- Venue
- Biophysical Journal
- Publisher
- openRxiv
- Published
- 2023-12-01
- Volume
- 123
- Issue
- 17
- Pages
- 2902-2909
- ISSN
- 0006-3495 (ISSN-L)
- PMC
- PMC11393670
- OpenAlex
- W4396934049
- Cited by
- 22
- References
- 1
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 7
- FWCI
- 2.99
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Biophysical Journal (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion, cc-by)
- Funders
- National Science Foundation (DEAC05- 00OR22725, 2054251, 1664644)
- U.S. Department of Energy (AC05-00OR22725, DE--AC05--00OR22725, DE-AC05?, 00OR22725)
- National Institutes of Health (DE-AC05-00OR22725, RM1135136)
- Office of Science (DEAC05- 00OR22725, AC05-00OR22725)
- National Institute of General Medical Sciences (R01GM140098, R35GM118078)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Keywords
- MHC class I, Pandora, protein structure prediction, vaccine design, immunoinformatics
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