TCR & T-cell receptor — neoantigen vaccine coverage

AI / Methods

HLA micropolymorphisms confine neoantigen conformational adaptability and guide T cell receptor selectivity.

Research reveals that HLA micropolymorphisms confine neoantigen conformational adaptability, directly guiding T cell receptor selectivity. Specifically, micropolymorphisms in HLA-A*03:02 versus A*03:01 prevent TCR binding by altering the neoantigen's conformational ensemble rather than peptide binding, highlighting a critical mechanistic constraint for vaccine design that must be accounted for in antigen selection pipelines.

europepmc · brief 2026-06-03 · published 2026-06-01

AI / Methods

Healthy donor T cell receptors expand functional neoantigen recognition beyond patient vaccination - Science | AAAS

A Science publication reports that healthy donor T cell receptors can expand functional neoantigen recognition beyond patient vaccination. This finding suggests potential for off-the-shelf TCR therapies or broader immune monitoring strategies that do not rely solely on patient-specific vaccine responses.

news · brief 2026-06-02 · published 2026-04-17

AI / Methods

T-cell receptor specificity landscape revealed through de novo peptide design

Researchers introduced HERMES, a structure-based, physics-guided machine learning model that predicts TCR interactions with peptides on MHC class I alleles. Trained on the protein universe without direct TCR-pMHC data, HERMES achieved up to 0.72 correlation with experimental data and enables de novo design of immunogenic peptides.

arxiv · brief 2026-05-31 · published 2025-03-01

AI / Methods

Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity

A new attention-aware contrastive learning model, ATMTCR, improves the prediction of TCR-antigen binding specificity. By using Transformer encoders and masked contrastive views on large-scale TCR CDR3 sequences, the model extracts high-order semantic information to outperform existing algorithms in predicting neoantigen reactivity.

arxiv · brief 2026-05-31 · published 2022-05-17

AI / Methods

DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction

Researchers introduced DapPep, a domain-adaptive peptide-agnostic learning framework for universal TCR-antigen binding affinity prediction. Using a lightweight self-attention architecture combined with protein language models, DapPep outperforms existing tools in predicting binding for unseen peptides, addressing a key bottleneck in neoantigen vaccine design for data-scarce settings.

arxiv · brief 2026-05-30 · published 2024-11-26

AI / Methods

tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificity

Researchers introduce tcrLM, a lightweight masked language model for predicting TCR-antigen binding specificity. Pretrained on over 100 million TCR CDR3 sequences with virtual adversarial training, tcrLM outperforms existing methods in predicting binding, offering a computationally efficient tool for neoantigen vaccine design.

arxiv · brief 2026-05-30 · published 2024-06-24

AI / Methods

Contact map dependence of a T cell receptor binding repertoire

This study analyzes the contact map dependence of TCR binding repertoires using an affinity-based model. By weighting pairwise amino acid interactions via crystal structure-derived contact maps, the work provides insights into TCR-pMHC binding energy distributions and their influence on T cell recognition probability during negative selection.

arxiv · brief 2026-05-30 · published 2021-12-30

AI / Methods

Physicochemically Informed Dual-Conditioned Generative Model of T-Cell Receptor Variable Regions for Cellular Therapy

PhysicoGPTCR, a dual-conditioned generative protein Transformer, is introduced for designing TCR variable regions. Trained on TCR-peptide-HLA triples with physicochemical descriptors, the model improves binding-competent clone generation and sequence space exploration compared to baselines like GPTCR and VAEs, advancing computer-aided cellular therapy.

arxiv · brief 2026-05-30 · published 2025-10-07