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

arXiv: https://arxiv.org/abs/2411.17798 · HTML: https://mlsanity.com/paper/2411.17798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 45 references that are in the map.
- First posted on arXiv 26 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CVT-SLR: Contrastive Visual-Textual Transformation for Sign Language Recognition with Variational Alignment](https://mlsanity.com/paper/2303.05725/) (2023)
- [Protein Language Models and Structure Prediction: Connection and Progression](https://mlsanity.com/paper/2211.16742/) (2022)
- [TITAN: T Cell Receptor Specificity Prediction with Bimodal Attention Networks](https://mlsanity.com/paper/2105.03323/) (2021)
- [Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction](https://mlsanity.com/paper/2310.11466/) (2023)
- [Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings](https://mlsanity.com/paper/2210.16848/) (2022)
- [Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy](https://mlsanity.com/paper/2302.10888/) (2023)
- [Progressive Multi-Modality Learning for Inverse Protein Folding](https://mlsanity.com/paper/2312.06297/) (2023)
- [Leveraging Graph-based Cross-modal Information Fusion for Neural Sign Language Translation](https://mlsanity.com/paper/2211.00526/) (2022)
- [MetaEnzyme: Meta Pan-Enzyme Learning for Task-Adaptive Redesign](https://mlsanity.com/paper/2408.10247/) (2024)

## Abstract (arXiv)

Identifying T-cell receptors (TCRs) that interact with antigenic peptides provides the technical basis for developing vaccines and immunotherapies. The emergent deep learning methods excel at learning antigen binding patterns from known TCRs but struggle with novel or sparsely represented antigens. However, binding specificity for unseen antigens or exogenous peptides is critical. We introduce a domain-adaptive peptide-agnostic learning framework DapPep for universal TCR-antigen binding affinity prediction to address this challenge. The lightweight self-attention architecture combines a pre-trained protein language model with an inner-loop self-supervised regime to enable robust TCR-peptide representations. Extensive experiments on various benchmarks demonstrate that DapPep consistently outperforms existing tools, showcasing robust generalization capability, especially for data-scarce settings and unseen peptides. Moreover, DapPep proves effective in challenging clinical tasks such as sorting reactive T cells in tumor neoantigen therapy and identifying key positions in 3D structures.