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Evaluation of Molecular Simulations and Deep Learning Prediction of Antibodies' Recognition of TRBC1 and TRBC2
Xincheng Zeng1, Tianqun Wang2, Yue Kang2
1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China.
Abstract:
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.
Insights
This study quantifies antibody affinity differences for T cell receptor β-chain constant (TRBC) targets TRBC1 and TRBC2 using AI and molecular simulations. Findings aid in developing precise cancer therapies that selectively eliminate malignant T cells.
Area of Science:
- Immunology
- Computational Biology
- Structural Biology
Background:
- T cell receptor β-chain constant (TRBC) proteins, TRBC1 and TRBC2, are homologous cancer targets.
- Targeted antibody therapeutics aim to eradicate malignant T cells while preserving normal T cells.
Purpose of the Study:
- To quantify affinity differences in mutated antibodies against TRBC1 and TRBC2.
- To explore antibody-antigen recognition mechanisms using advanced computational methods.
Main Methods:
- Utilized molecular simulations and artificial intelligence (AI) to assess antibody affinity.
- Employed Free Energy Perturbation (FEP) calculations for affinity verification.
- Conducted long-time molecular dynamics simulations to analyze antigen recognition dynamics.
Main Results:
- Quantified affinity variations in antibodies with specific mutations for TRBC1 and TRBC2.
- Validated AI-predicted affinities using FEP calculations.
- Revealed dynamic mechanisms of TRBC antibody-antigen interactions.
Conclusions:
- AI and molecular simulations effectively quantify antibody affinity for TRBC targets.
- Understanding dynamic recognition mechanisms can guide the design of improved TRBC-targeted antibody therapies for cancer.
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