PMGen: From Peptide-MHC Structure Prediction to Peptide Generation

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ID: 317435
2026
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Abstract
MOTIVATION: Accurate structural modeling of peptide-MHC (pMHC) complexes is essential for structure-driven immunotherapy design, yet current prediction tools suffer from narrow class coverage, restricted peptide lengths, insufficient accuracy, and a lack of built-in structure-aware peptide sampling. Consequently, most mimotope and altered peptide ligand designs rely solely on sequence substitution, leaving spatial and biophysical insights from pMHC structures largely unexploited. RESULTS: We introduce PMGen (Peptide-MHC Generator), an integrated framework for structure prediction and structure-guided design of variable-length peptides across MHC class I and II. PMGen enforces anchor constraints within AlphaFold2 through two complementary strategies, Initial Guess and Template Engineering, achieving state-of-the-art structural fidelity without model fine-tuning. On a comprehensive benchmark, PMGen outperforms all existing methods, yielding median peptide-core Cα RMSDs of 0.62 Å for MHC-I and 0.33 Å for MHC-II. We show that PMGen can recover incorrectly predicted anchor positions and that AlphaFold pLDDT scores enable sequence-independent binding-core identification. Applied to a published neoantigen/wild-type pair, PMGen accurately captures mutation-induced conformational changes. Beyond structure prediction, we show that ProteinMPNN sampling on PMGen-predicted backbones yields higher-affinity peptides while preserving the parental 3D conformation. Using PMGen to generate 63,817 high-confidence pMHC structures as training data, we further improve ProteinMPNN's peptide sequence recovery from 0.14 to 0.64 on a test set of 85 unseen MHC-I alleles, highlighting the value of accurate predicted structures for downstream machine learning tasks. AVAILABILITY AND IMPLEMENTATION: PMGen is freely available at https://github.com/soedinglab/PMGen, with an interactive Colab notebook at https://colab.research.google.com/github/soedinglab/PMGen/blob/master/colab.ipynb. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164839354 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Amir H. Asgary, Amirreza Aleyasin, Jonas Arne Mehl, Salman Soleiman Fallah, Hasmig Aintablian, Burkhard Ludewig, Michele Mishto, Juliane Liepe, Johannes Söding
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag381
URL
https://doi.org/10.1093/bioinformatics/btag381
Keywords Keywords not found

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