Determining epitope specificity of T cell receptors with TCRGP
Jokinen, E.; Huuhtanen, J.; Mustjoki, S.; Heinonen, M.; Lähdesmäki, H.
Show abstract
T cell receptors (TCRs) can recognize various pathogens and consequently start immune responses. TCRs can be sequenced from individuals and methods analyzing the specificity of the TCRs can help us better understand individuals immune status in different diseases. We have developed TCRGP, a novel Gaussian process method to predict if TCRs recognize certain epitopes. This method can utilize CDR sequences from TCR and TCR{beta} chains and learn which CDRs are important in recognizing different epitopes. We have experimented with with epitope-specific data against 29 epitopes and performed a comprehensive evaluation with existing prediction methods. On this data, TCRGP outperforms other state-of-the-art methods in epitope-specificity predictions. We also propose a novel analysis approach for combined single-cell RNA and TCR{beta} (scRNA+TCR{beta}) sequencing data by quantifying epitope-specific TCRs with TCRGP in phenotypes identified from scRNA-seq data. With this approach, we find HBV-epitope specific T cells and their transcriptomic states in hepatocellular carcinoma patients.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- EPIC-TRACE: predicting TCR binding to unseen epitopes using attention and contextualized embeddings 97%
- HAMdetector: A Bayesian regression model that integrates information to detect HLA-associated mutations 96%
- BERTrand - peptide:TCR binding prediction using Bidirectional Encoder Representations from Transformers augmented with random TCR pairing 96%
Similar papers in this journal
- Interpretable deep learning to uncover the molecular binding patterns determining TCR-epitope interactions 96%
- Guiding a language-model based protein design method towards MHC Class-I immune-visibility profiles for vaccines and therapeutics 95%
- DoggifAI: a transformer based approach for antibodycaninisation 95%
Similar papers in this journal
- Somatic hypermutation analysis for improved identification of B cell clonal families from next-generation sequencing data 95%
- Evaluating the utility of amino acid similarity-aware kmers to represent TCR repertoires for classification 94%
- Neural Network Models for Sequence-Based TCR and HLA Association Prediction 94%
Similar papers in this journal
- Current challenges for epitope-agnostic TCR interaction prediction and a new perspective derived from image classification 94%
- PRIEST - Predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information 94%
- TEINet: a deep learning framework for prediction of TCR-epitope binding specificity 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.