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Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2

T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recen...

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Detalles Bibliográficos
Autores principales: Zeng, Xincheng, Wang, Tianqun, Kang, Yue, Bai, Ganggang, Ma, Buyong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525649/
https://www.ncbi.nlm.nih.gov/pubmed/37753972
http://dx.doi.org/10.3390/antib12030058
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author Zeng, Xincheng
Wang, Tianqun
Kang, Yue
Bai, Ganggang
Ma, Buyong
author_facet Zeng, Xincheng
Wang, Tianqun
Kang, Yue
Bai, Ganggang
Ma, Buyong
author_sort Zeng, Xincheng
collection PubMed
description T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies.
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spelling pubmed-105256492023-09-28 Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2 Zeng, Xincheng Wang, Tianqun Kang, Yue Bai, Ganggang Ma, Buyong Antibodies (Basel) Article T cell receptor β-chain constant (TRBC) is a promising class of cancer targets consisting of two highly homologous proteins, TRBC1 and TRBC2. Developing targeted antibody therapeutics against TRBC1 or TRBC2 is expected to eradicate the malignant T cells and preserve half of the normal T cells. Recently, several antibody engineering strategies have been used to modulate the TRBC1 and TRBC2 specificity of antibodies. Here, we used molecular simulation and artificial intelligence methods to quantify the affinity difference in antibodies with various mutations for TRBC1 and TRBC2. The affinity of the existing mutants was verified by FEP calculations aided by the AI. We also performed long-time molecular dynamics simulations to reveal the dynamical antigen recognition mechanisms of the TRBC antibodies. MDPI 2023-09-17 /pmc/articles/PMC10525649/ /pubmed/37753972 http://dx.doi.org/10.3390/antib12030058 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zeng, Xincheng
Wang, Tianqun
Kang, Yue
Bai, Ganggang
Ma, Buyong
Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title_full Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title_fullStr Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title_full_unstemmed Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title_short Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies’ Recognition of TRBC1 and TRBC2
title_sort evaluation of molecular simulations and deep learning prediction of antibodies’ recognition of trbc1 and trbc2
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10525649/
https://www.ncbi.nlm.nih.gov/pubmed/37753972
http://dx.doi.org/10.3390/antib12030058
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