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

Autor: Xincheng Zeng, Tianqun Wang, Yue Kang, Ganggang Bai, Buyong Ma
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Antibodies, Vol 12, Iss 3, p 58 (2023)
Druh dokumentu: article
ISSN: 12030058
2073-4468
DOI: 10.3390/antib12030058
Popis: 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.
Databáze: Directory of Open Access Journals
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