Computational prediction of the interaction of T cell receptors (TCRs) and
their ligands is a grand challenge in immunology. Despite advances in
high-throughput assays, specificity-labelled TCR data remains sparse. In other
domains, the pre-training of language models on unlabelled data has been
successfully used to address data bottlenecks. However, it is unclear how to
best pre-train protein language models for TCR specificity prediction. Here we
introduce a TCR language model called SCEPTR (Simple Contrastive Embedding of
the Primary sequence of T cell Receptors), capable of data-efficient transfer
learning. Through our model, we introduce a novel pre-training strategy
combining autocontrastive learning and masked-language modelling, which enables
SCEPTR to achieve its state-of-the-art performance. In contrast, existing
protein language models and a variant of SCEPTR pre-trained without
autocontrastive learning are outperformed by sequence alignment-based methods.
We anticipate that contrastive learning will be a useful paradigm to decode the
rules of TCR specificity.

通过简单的对比编码 T 细胞受体（TCR）的主要序列，提出了一种名为 SCEPTR 的 TCR 语言模型，它能够进行高效的数据转移学习，并且通过独特的预训练策略结合自对比学习和掩码语言建模的方法，取得了最先进的性能，从而解码了 TCR 特异性规则。

T 细胞受体表征的对比学习

Contrastive learning of T cell receptor representations

T cell receptors (TCRs) are critical components of adaptive immune systems,
responsible for responding to threats by recognizing epitope sequences
presented on host cell surface. Computational prediction of binding affinity
between TCRs and epitope sequences using machine/deep learning has attracted
intense attention recently. However, its success is hindered by the lack of
large collections of annotated TCR-epitope pairs. Annotating their binding
affinity requires expensive and time-consuming wet-lab evaluation. To reduce
annotation cost, we present ActiveTCR, a framework that incorporates active
learning and TCR-epitope binding affinity prediction models. Starting with a
small set of labeled training pairs, ActiveTCR iteratively searches for
unlabeled TCR-epitope pairs that are ''worth'' for annotation. It aims to
maximize performance gains while minimizing the cost of annotation. We compared
four query strategies with a random sampling baseline and demonstrated that
ActiveTCR reduces annotation costs by approximately 40%. Furthermore, we showed
that providing ground truth labels of TCR-epitope pairs to query strategies can
help identify and reduce more than 40% redundancy among already annotated pairs
without compromising model performance, enabling users to train equally
powerful prediction models with less training data. Our work is the first
systematic investigation of data optimization for TCR-epitope binding affinity
prediction.

T 细胞受体（TCR）是适应性免疫系统的关键组成部分，负责通过识别寄主细胞表面上呈现的表位序列来响应威胁。本研究提出了 ActiveTCR，这是一个结合主动学习和 TCR - 表位结合亲和力预测模型的框架，旨在通过减少注释成本来提高性能，并展示了首次对 TCR - 表位结合亲和力预测数据优化的系统性研究。

TCR - 表位结合亲和力预测的成本效益主动学习框架

Active Learning Framework for Cost-Effective TCR-Epitope Binding  Affinity Prediction

Cancer is a complex disease characterized by uncontrolled cell growth and
proliferation. T cell receptors (TCRs) are essential proteins for the adaptive
immune system, and their specific recognition of antigens plays a crucial role
in the immune response against diseases, including cancer. The diversity and
specificity of TCRs make them ideal for targeting cancer cells, and recent
advancements in sequencing technologies have enabled the comprehensive
profiling of TCR repertoires. This has led to the discovery of TCRs with potent
anti-cancer activity and the development of TCR-based immunotherapies. In this
study, we investigate the use of sparse coding for the multi-class
classification of TCR protein sequences with cancer categories as target
labels. Sparse coding is a popular technique in machine learning that enables
the representation of data with a set of informative features and can capture
complex relationships between amino acids and identify subtle patterns in the
sequence that might be missed by low-dimensional methods. We first compute the
k-mers from the TCR sequences and then apply sparse coding to capture the
essential features of the data. To improve the predictive performance of the
final embeddings, we integrate domain knowledge regarding different types of
cancer properties. We then train different machine learning (linear and
non-linear) classifiers on the embeddings of TCR sequences for the purpose of
supervised analysis. Our proposed embedding method on a benchmark dataset of
TCR sequences significantly outperforms the baselines in terms of predictive
performance, achieving an accuracy of 99.8\%. Our study highlights the
potential of sparse coding for the analysis of TCR protein sequences in cancer
research and other related fields.

本研究使用稀疏编码对 TCR 蛋白序列进行多类别分类以在癌症研究和其他相关领域中分析 TCR 蛋白序列，结果实现了 99.8％的准确率，并突显了稀疏编码在肿瘤治疗中的潜力。