ArticleBiophysical journal2024
MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.
Article in Biophysical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
12 citing papers in PubMed.
- PMGen: from peptide-MHC structure prediction to peptide generation.Bioinformatics (Oxford, England) · 2026Article
- Peptide-protein docking: from physics-based models to generative intelligence.Chemical communications (Cambridge, England) · 2026Review
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Modeling Protein-Protein and Protein-Ligand Interactions by the ClusPro Team in CASP16.Proteins · 2026Article
- Predicted peptide scaffolds for drug screening in endometrial cancer organoids.Scientific reports · 2025Article
- Structural Study of a Peptide Epitope Bearing Multiple Post-Translational Modifications in Rheumatoid Arthritis.International journal of molecular sciences · 2025Article
- A structure-guided approach to predict MHC-I restriction of T cell receptors for public antigens.Structure (London, England : 1993) · 2025Article
- Machine-guided dual-objective protein engineering for deimmunization and therapeutic functions.Cell systems · 2025Article
- TCR repertoire of human cytotoxic CD4 T cells responding to betaherpesviruses HHV-6B and HCMV.Frontiers in immunology · 2025Article
- A platform for mapping reactive cysteines within the immunopeptidome.Nature communications · 2024Article
- Machine learning tools advance biophysics.Biophysical journal · 2024Article
- Enhanced Protein-Protein Interaction Discovery via AlphaFold-Multimer.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
13 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.