TPBTE: A model based on convolutional Transformer for predicting the binding of TCR to epitope

LightCTL: lightweight contrastive TCR-pMHC specificity learning with context-aware prompt

Identification of T cell receptor (TCR) specificities for antigens from large-scale single-cell or bulk TCR repertoire data plays a vital role in disease diagnosis and immunotherapy. In silico prediction models have emerged in recent years. However, the generalizability and transferability of current computational models remain significant hurdles in accurately predicting TCR-pMHC binding specificity, primarily due to the limited availability of experimental data and the vast diversity of TCR sequences. In this paper, we propose a lightweight contrastive TCR-pMHC learning with context-aware prompts, named LightCTL, to infer TCR-pMHC binding specificity. For each TCR and peptide-MHC sequence, we utilize a TCR encoding module and a pMHC encoding module to transform them into latent representations. Specifically, we introduce a contrastive TCR-pMHC learning paradigm to enhance the generalization ability of TCR-pMHC binding specificity prediction by learning the matching relationship between TCR-pMHC and MHC-peptide. We fuse the TCR and pMHC latent representations and employ a novel context-aware prompt module to consider the varying importance of different feature maps. Compared with existing methods, LightCTL substantially improves the accuracy of predicting TCR-pMHC binding specificity. Moreover, comparative experiments across eight independent datasets demonstrate the generalization ability of LightCTL, showing superior performance for predicting unknown TCR-pMHC pairs. Finally, we assess LightCTL's efficacy across different TCR sequence lengths and distinct unseen epitopes, as well as estimate cytomegalovirus-specific TCR diversity and clone frequency from peripheral TCR repertoire data. Overall, our findings highlight LightCTL as a versatile analytical method for advancing novel T-cell therapies and identifying novel biomarkers for disease diagnosis.

Enhanced prediction of antigen and antibody binding interface using ESM-2 and Bi-LSTM

The binding interface between antigens and antibodies is pivotal in humoral immune responses and provides crucial effective defense against pathogens and exogenous threats. Existing predictive computational methodologies, including structure-based and sequence-based approaches, offer valuable insights but face challenges such as unknown antigen structures and reliance on manually curated features. Most current methods primarily predict antigen epitope, often neglecting the specific molecular epitope-paratope interactions essential for immune efficacy. In this study, we introduce a novel approach EPP (Epitope-Paratope Predictor), using the ESM-2 protein language model as a feature encoder and a Bi-LSTM network to predict epitope-paratope interactions. Our method processes antigen and antibody sequences as inputs, leveraging a novel dataset strategy and encoding protein representations to enhance prediction accuracy. The results demonstrate a significant improvement in prediction accuracy compared to existing methods, highlighting the importance of protein feature encoder and temporal dependencies within sequences. The model's performance in different antigen clusters is analyzed, while those predictions are compared with that from AlphaFold3 and Dock method. Our method validation shows superior performance in recognizing distinctive epitopes of the same antigen when bound to different antibodies. This approach offers a new strategy for an in-depth understanding of antigen-antibody interactions, essential for an array of pioneer projects, such as structure-guided design and affinity maturation for precision antibodies targeting a given epitope.

A computational approach to matching multiple sclerosis-related, IGH CDR3s with a MBP epitope

In multiple sclerosis (MS), T-cell receptors (TCRs) and antibodies specifically target the main structural proteins of myelin, including myelin basic protein (MBP), especially a specific, canonical, immunoglobulin (IG)-targeted MBP epitope. Efficient computational analyses to diagnose or monitor autoimmune conditions, which could have broad applicability in clinical trials or in diagnoses, remains a challenge. As such, we considered the possibility that focusing on the immunoglobin heavy chain (IGH) complementarity determining region-3 (CDR3) amino acid sequences could support the development of an efficient, convenient, and user-friendly approach to detecting or assessing IGH targets in MS. Thus, we applied a chemical complementarity scoring algorithm, extensively benchmarked in many cancer settings, to assess the combined electrostatic and hydrophobic attractiveness of large numbers of (individual patient) IGH CDR3s and the canonical IG MBP epitope. Samples and controls were filtered to only include CDR3s above a baseline chemical complementarity score. Then, the frequency of each unique IGH CDR3 (with the minimum MBP epitope complementarity) in the MS samples was compared to the same parameter for the control sample. Specifically, a greater number of high frequency IGH CDR3s, with chemically complementary to the canonical MBP epitope, was detected in 47 out of 48 MS-control comparisons, in most cases representing a p < 0.0001. With continued development, this approach has the potential to lead to a user-friendly computational screening tool for patients at risk for developing MS. Additional results indicate that the methodology could also be applied to antigen epitope discovery.

Development and Clinical Applications of Therapeutic Cancer Vaccines with Individualized and Shared Neoantigens

Neoantigens, presented as peptides on the surfaces of cancer cells, have recently been proposed as optimal targets for immunotherapy in clinical practice. The promising outcomes of neoantigen-based cancer vaccines have inspired enthusiasm for their broader clinical applications. However, the individualized tumor-specific antigens (TSA) entail considerable costs and time due to the variable immunogenicity and response rates of these neoantigens-based vaccines, influenced by factors such as neoantigen response, vaccine types, and combination therapy. Given the crucial role of neoantigen efficacy, a number of bioinformatics algorithms and pipelines have been developed to improve the accuracy rate of prediction through considering a series of factors involving in HLA-peptide-TCR complex formation, including peptide presentation, HLA-peptide affinity, and TCR recognition. On the other hand, shared neoantigens, originating from driver mutations at hot mutation spots (e.g., KRASG12D), offer a promising and ideal target for the development of therapeutic cancer vaccines. A series of clinical practices have established the efficacy of these vaccines in patients with distinct HLA haplotypes. Moreover, increasing evidence demonstrated that a combination of tumor associated antigens (TAAs) and neoantigens can also improve the prognosis, thus expand the repertoire of shared neoantigens for cancer vaccines. In this review, we provide an overview of the complex process involved in identifying personalized neoantigens, their clinical applications, advances in vaccine technology, and explore the therapeutic potential of shared neoantigen strategies.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.