Predicting SARS-CoV-2 epitope-specific TCR recognition using pre-trained protein embeddings

  1. Youngmahn Han
  2. Aeri Lee

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Abstract

The COVID-19 pandemic is ongoing because of the high transmission rate and the emergence of SARS-CoV-2 variants. The P272L mutation in SARS-Cov-2 S -protein is known to be highly relevant to the viral escape associated with the second pandemic wave in Europe. Epitope-specific T-cell receptor (TCR) recognition is a key factor in determining the T-cell immunogenicity of a SARS-CoV-2 epitope. Although several data-driven methods for predicting epitope-specific TCR recognition have been proposed, they remain challenging owing to the enormous diversity of TCRs and the lack of available training data. Self-supervised transfer learning has recently been demonstrated to be powerful for extracting useful information from unlabeled protein sequences and increasing the predictive performance of the fine-tuned models in downstream tasks.

Here, we present a predictive model based on Bidirectional Encoder Representations from Transformers (BERT), employing self-supervised transfer learning, to predict SARS-CoV-2 T-cell epitope-specific TCR recognition. The fine-tuned model showed notably high predictive performance for independent evaluation using the SARS-CoV-2 epitope-specific TCR CDR3β sequence datasets. In particular, we found the proline at position 4 corresponding to the P272L mutation in the SARS-CoV-2 S -protein 269-277 epitope ( YLQPRTFLL ) may contribute substantially to TCR recognition of the epitope through interpreting the output attention weights of our model.

We anticipate that our findings will provide new directions for constructing a reliable data-driven model to predict the immunogenic T-cell epitopes using limited training data and help accelerate the development of an effective vaccine in response to SARS-CoV-2 variants.

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    SciScore for 10.1101/2021.11.17.468929: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Python source codes and all the datasets supporting this work can be downloaded from https://github.com/luseedbio/TCRBert.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

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  2. Version published to 10.1101/2021.11.17.468929v1 on bioRxiv