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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences

Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
doi: https://doi.org/10.1101/2021.08.02.454701
Inyoung Kim
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
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  • For correspondence: iykim@snu.ac.kr
Sang Yoon Byun
2Department of Computer Science, Grinnell College, Grinnell, IA, United States of America
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Sangyeup Kim
3Department of Applied Biology and Chemistry, Seoul National University, Seoul, Republic of Korea
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Sangyoon Choi
4College of Veterinary Medicine, Seoul National University, Seoul, Republic of Korea
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Jinsung Noh
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
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Junho Chung
6Department of Biomedical Science, Seoul National University College of Medicine, Seoul, Republic of Korea
7Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, Republic of Korea
8Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
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Byung Gee Kim
1Artificial Intelligence Institute, Seoul National University, Seoul, Republic of Korea
5School of Chemical and Biological Engineering, Seoul National University, Seoul, Republic of Korea
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Abstract

Analyzing B cell receptor (BCR) repertoires is immensely useful in evaluating one’s immunological status. Conventionally, repertoire analysis methods have focused on comprehensive assessments of clonal compositions, including V(D)J segment usage, nucleotide insertions/deletions, and amino acid distributions. Here, we introduce a novel computational approach that applies deep-learning-based protein embedding techniques to analyze BCR repertoires. By selecting the most frequently occurring BCR sequences in a given repertoire and computing the sum of the vector representations of these sequences, we represent an entire repertoire as a 100-dimensional vector and eventually as a single data point in vector space. We demonstrate that this new approach enables us to not only accurately cluster BCR repertoires of coronavirus disease 2019 (COVID-19) patients and healthy subjects but also efficiently track minute changes in immune status over time as patients undergo treatment. Furthermore, using the distributed representations, we successfully trained an XGBoost classification model that achieved a mean accuracy rate of over 87% given a repertoire of CDR3 sequences.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The manuscript was edited; author affiliation changed

  • https://data.mendeley.com/datasets/37tz3dkzkv/1

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted November 29, 2021.
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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences
Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
bioRxiv 2021.08.02.454701; doi: https://doi.org/10.1101/2021.08.02.454701
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Computational analysis of B cell receptor repertoires in COVID-19 patients using deep embedded representations of protein sequences
Inyoung Kim, Sang Yoon Byun, Sangyeup Kim, Sangyoon Choi, Jinsung Noh, Junho Chung, Byung Gee Kim
bioRxiv 2021.08.02.454701; doi: https://doi.org/10.1101/2021.08.02.454701

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