A predictive multiscale framework for post-translational modification-dependent peptide-MHC class I binding.

Peptide-HLA II interaction prediction for post-translationally modified peptides

CD4+ T cells recognize peptides presented by human leukocyte antigen (HLA) II, implementing a fundamental mediation mechanism of the adaptive immune system. Although post-translational modifications (PTMs) alter immune responses, PTM-peptide-HLA interaction prediction remains challenging due to data scarcity resulting from substoichiometric levels of PTMs. To overcome this, we developed PepChem, a deep learning model utilizing novel, molecular-level peptide representations that enable predictions for sidechain modifications. Using monoallelic datasets that we reanalyze for PTMs of interest, we show accurate predictions on PTMs that were unseen during training. Furthermore, we introduce a novel training protocol that improves PTM-peptide generalization compared to conventional methods. We predict and experimentally validate citrullination-induced binding increase of rheumatoid arthritis (RA)-linked peptides to HLA II risk allele DRB1*04:01. This framework bridges the critical gap in PTM-aware immune recognition prediction, with immediate applications in autoimmunity, cancer, and infectious disease.

Ensembles of in silico structures enable T cell peptide-MHC binding prediction

Adaptive immunity relies on T-cell receptor (TCR) recognition of peptides presented by the major histocompatibility complex (pMHC). Accurate prediction of TCR:pMHC binding pairs from sequence data remains a longstanding challenge in computational immunology, limiting the development of precision immunotherapies like cancer vaccines and adoptive cell therapies. Here, we present enFoldX (ensemble of Folded compleXes), a structure-based approach leveraging biophysical characterization of AlphaFold3-generated ensembles to classify TCR:pMHC sequence pairs as cognate versus non-cognate. Unlike previous methods reliant on only sequence data or a single, static predicted structure, enFoldX extracts features from an entire generated ensemble with a custom focus on the biophysical binding interface. Our model distinguishes T cell reactivity between peptides differing by a single amino acid substitution, the resolution required for cancer neoantigens, and generalizes to unseen peptides, MHCs, and TCRs, a major objective for artificial intelligence (AI) in immunology. Our performance on these crucial tasks demonstrates that diverse, structural sampling of biophysical interactions over an ensemble is fundamental for accurate AI-driven binding predictions and offers lessons for efficient future data generation to improve models. Our findings therefore offer a scalable framework to accelerate therapeutic binder design, and we provide access to a publicly available code repository.

Peptide Loading Complex in Cancer: From Peptide Translocation, Editing, and Loading into MHC-I to a Potential Therapeutic Target

An overview of the PLC as a major determinant of tumor immune escape and a potential therapy target is provided and existing or evolving therapeutic approaches to recover/reprogram PLC functions are explored.

STRUMP-I: Structure-Based Machine Learning Approach to pMHC-I Binding Prediction Using Force Field Energy Features

The adaptive immune system monitors cellular integrity by recognizing short peptides from intracellular proteins presented on major histocompatibility complex class I (MHC-I) molecules, collectively termed peptide–MHC complexes (pMHC), enabling detection of foreign or mutated proteins. With the rising importance of immunotherapies targeting cancer neoantigens, accurately predicting which peptides bind to MHC alleles is critical. Current computational methods for pMHC-I binding prediction fall into sequence-based methods, which rely heavily on large training datasets, and structure-based methods that leverage structural modeling and pMHC binding energetics. Although sequence-based methods are widely used, their performance depends on the size and quality of the training data. Structure-based approaches, by contrast, can generalize better across diverse MHC alleles, but they traditionally depend on identifying a single global minimum-energy conformation, an assumption that may be inadequate for the promiscuous binding of MHC-I molecules. To address these limitations, we developed STRUMP-I (STRUcture-based pMHC Prediction for class I), a novel pMHC-I binding prediction tool that directly leverages a broad set of force-field-derived energy terms as machine learning features. In the standard benchmark set, STRUMP-I achieved performance comparable to state-of-the-art sequence-based models overall and showed a clear advantage for alleles with limited or imbalanced representation. Furthermore, STRUMP-I complemented sequence-based methods by removing method-specific false positives and improving precision, with a more favorable precision–recall tradeoff than AF-FT. These evaluations reinforced the value of STRUMP-I as a structure-informed prioritization method, particularly for underrepresented alleles and as a high-precision post-prediction filter.

A multi-scale structural and biophysical atlas of TCR-peptide-HLA recognition dynamics

The TG2/LRP1 Pathway for T Cell Activation by Post‐Translationally Modified Antigens

Post‐translational modifications (PTMs) can generate neo‐epitopes, modified peptides that evade immune tolerance and trigger immune responses. This review focuses on the TG2/LRP1 pathway as a new paradigm for coupling the formation of post‐translationally modified peptides with their effective presentation as T‐cell antigens by dendritic cells. Transglutaminase 2 (TG2) catalyzes the Gln → Glu conversion of specific residues in peptides derived from dietary gluten thereby enhancing their affinity for HLA‐DQ2, the principal genetic determinant of celiac disease. However, because such modified peptides are scarce in intestinal mucosa, an effective mechanism for lysosomal uptake by antigen‐presenting cells (APCs) is necessary. Protein–protein interaction between certain peptide‐bound TG2 complexes and the low‐density lipoprotein receptor‐related protein 1 (LRP1) results in efficient endocytosis of these antigenic peptides along with their concomitant release in the endo‐lysosomal compartment as deamidated products. An analogous PTM‐driven mechanism for antigen presentation may also operate in autoimmune conditions associated with peptidylarginine deiminase (PADI) activity, such as rheumatoid arthritis (RA). The exquisite cellular selectivity of the TG2/LRP1 pathway has implications not only for understanding its role in autoimmunity but also for its potential exploitation in vaccine design.