MOTIVATION: Accurate structural modeling of peptide-major histocompatibility complex (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 peptide-MHC generator (PMGen), 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.

The identification of proteasome-generated spliced peptides (PSP) revealed a new unpredicted activity of the major cellular protease. However, so far characterization of PSP was entirely dependent on the availability of patient-derived cytotoxic CD8+ T lymphocytes (CTL) thus preventing a systematic investigation of proteasome-catalyzed peptide splicing (PCPS). For an unrestricted PSP identification we here developed SpliceMet, combining the computer-based algorithm ProteaJ with in vitro proteasomal degradation assays and mass spectrometry. By applying SpliceMet for the analysis of proteasomal processing products of four different substrate polypeptides, derived from human tumor as well as viral antigens, we identified fifteen new spliced peptides generated by PCPS either by cis or from two separate substrate molecules, i.e., by trans splicing. Our data suggest that 20S proteasomes represent a molecular machine that, due to its catalytic and structural properties, facilitates the generation of spliced peptides, thereby providing a pool of qualitatively new peptides from which functionally relevant products may be selected.