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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...

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Autores principales: Papadopoulou, Iliana, Nguyen, An-Phi, Weber, Anna, Martínez, María Rodríguez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
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.
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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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