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CORRECTION article

Front. Physiol., 24 January 2022
Sec. Computational Physiology and Medicine
Volume 12 - 2021 | https://doi.org/10.3389/fphys.2021.790998
This article is part of the Research Topic Machine Learning in Systems Quantitative Biology View all 4 articles

Corrigendum: Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

This article is a correction to:

  1. Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

    1. Read original article
\nMartina Milighetti,Martina Milighetti1,2John Shawe-TaylorJohn Shawe-Taylor3Benny Chain, Benny Chain1,3*
  • 1Division of Infection and Immunity, University College London, London, United Kingdom
  • 2Cancer Institute, University College London, London, United Kingdom
  • 3Department of Computer Science, University College London, London, United Kingdom

A Corrigendum on
Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes

by Milighetti, M., Shawe-Taylor, J., and Chain, B. (2021). Front. Physiol. 12:730908. doi: 10.3389/fphys.2021.730908

In the original article, there was a mistake in the provided supplementary material for the datasets Dash (Dash et al., 2017), 10X (10XGenomics, 2020), and newVdj (Bagaev et al., 2020) as published. The mistake derived from an imprecision in the method used to reconstruct the complete amino sequence for the TCRs from the V/J gene and CDR3 information, which impacted a small proportion of the sequences in these sets (between 0.5 and 7% depending on the set). The wrong sequences have now been either removed or corrected. The corrected version of these datasets is provided as supplementary material.

The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

10XGenomics (2020). A New Way of Exploring Immunity - Linking Highly Multiplexed Antigen Recognition to Immune Repertoire and Phenotype. 10XGenomics.

Bagaev, D. V., Vroomans, R. M. A., Samir, J., Stervbo, U., Rius, C., Dolton, G., et al. (2020). VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium. Nucleic Acids Res. 48, D1057–D1062. doi: 10.1093/nar/gkz874

PubMed Abstract | CrossRef Full Text | Google Scholar

Dash, P., Fiore-Gartland, A. J., Hertz, T., Wang, G. C., Sharma, S., Souquette, A., et al. (2017). Quantifiable predictive features define epitope-specific T cell receptor repertoires. Nature 547, 89–93. doi: 10.1038/nature22383

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: T cell receptor, antigen prediction, TCR-pMHC binding, homology modelling, T cell

Citation: Milighetti M, Shawe-Taylor J and Chain B (2022) Corrigendum: Predicting T Cell Receptor Antigen Specificity From Structural Features Derived From Homology Models of Receptor-Peptide-Major Histocompatibility Complexes. Front. Physiol. 12:790998. doi: 10.3389/fphys.2021.790998

Received: 07 October 2021; Accepted: 27 October 2021;
Published: 24 January 2022.

Edited and reviewed by: Tetsuya J. Kobayashi, The University of Tokyo, Japan

Copyright © 2022 Milighetti, Shawe-Taylor and Chain. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Benny Chain, b.chain@ucl.ac.uk

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.