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DECODE: a computational pipeline to discover T cell receptor binding rules
MOTIVATION: Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-bas...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235487/ https://www.ncbi.nlm.nih.gov/pubmed/35758821 http://dx.doi.org/10.1093/bioinformatics/btac257 |
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author | Papadopoulou, Iliana Nguyen, An-Phi Weber, Anna Martínez, María Rodríguez |
author_facet | Papadopoulou, Iliana Nguyen, An-Phi Weber, Anna Martínez, María Rodríguez |
author_sort | Papadopoulou, Iliana |
collection | PubMed |
description | MOTIVATION: Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope. RESULTS: We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding. AVAILABILITY AND IMPLEMENTATION: Code is available publicly at https://github.com/phineasng/DECODE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. |
format | Online Article Text |
id | pubmed-9235487 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92354872022-06-29 DECODE: a computational pipeline to discover T cell receptor binding rules Papadopoulou, Iliana Nguyen, An-Phi Weber, Anna Martínez, María Rodríguez Bioinformatics ISCB/Ismb 2022 MOTIVATION: Understanding the mechanisms underlying T cell receptor (TCR) binding is of fundamental importance to understanding adaptive immune responses. A better understanding of the biochemical rules governing TCR binding can be used, e.g. to guide the design of more powerful and safer T cell-based therapies. Advances in repertoire sequencing technologies have made available millions of TCR sequences. Data abundance has, in turn, fueled the development of many computational models to predict the binding properties of TCRs from their sequences. Unfortunately, while many of these works have made great strides toward predicting TCR specificity using machine learning, the black-box nature of these models has resulted in a limited understanding of the rules that govern the binding of a TCR and an epitope. RESULTS: We present an easy-to-use and customizable computational pipeline, DECODE, to extract the binding rules from any black-box model designed to predict the TCR-epitope binding. DECODE offers a range of analytical and visualization tools to guide the user in the extraction of such rules. We demonstrate our pipeline on a recently published TCR-binding prediction model, TITAN, and show how to use the provided metrics to assess the quality of the computed rules. In conclusion, DECODE can lead to a better understanding of the sequence motifs that underlie TCR binding. Our pipeline can facilitate the investigation of current immunotherapeutic challenges, such as cross-reactive events due to off-target TCR binding. AVAILABILITY AND IMPLEMENTATION: Code is available publicly at https://github.com/phineasng/DECODE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2022-06-27 /pmc/articles/PMC9235487/ /pubmed/35758821 http://dx.doi.org/10.1093/bioinformatics/btac257 Text en © The Author(s) 2022. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | ISCB/Ismb 2022 Papadopoulou, Iliana Nguyen, An-Phi Weber, Anna Martínez, María Rodríguez DECODE: a computational pipeline to discover T cell receptor binding rules |
title | DECODE: a computational pipeline to discover T cell receptor binding rules |
title_full | DECODE: a computational pipeline to discover T cell receptor binding rules |
title_fullStr | DECODE: a computational pipeline to discover T cell receptor binding rules |
title_full_unstemmed | DECODE: a computational pipeline to discover T cell receptor binding rules |
title_short | DECODE: a computational pipeline to discover T cell receptor binding rules |
title_sort | decode: a computational pipeline to discover t cell receptor binding rules |
topic | ISCB/Ismb 2022 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9235487/ https://www.ncbi.nlm.nih.gov/pubmed/35758821 http://dx.doi.org/10.1093/bioinformatics/btac257 |
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