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Updated: Nov 1, 2025
Generation of Human Alloantigen-specific T Cells from Peripheral Blood
Published on: November 21, 2014
[In silico specificity determination of neoantigen-reactive T-lymphocytes]
A E Kniga1, I V Polyakov1, A V Nemukhin1
1M.V. Lomonosov Moscow State University, Moscow, Russia; N.M. Emanuel Institute of Biochemical Physics RAS, Moscow, Russia.
Abstract:
Effective personalized immunotherapies of the future will need to capture not only the peculiarities of the patient's tumor but also of his immune response to it. In this study, using results of in vitro high-throughput specificity assays, and combining comparative models of pMHCs and TCRs using molecular docking, we have constructed all-atom models for the putative complexes of all their possible pairwise TCR-pMHC combinations. For the models obtained we have calculated a dataset of physics-based scores and have trained binary classifiers that perform better compared to their solely sequence-based counterparts. These structure-based classifiers pinpoint the most prominent energetic terms and structural features characterizing the type of protein-protein interactions that underlies the immune recognition of tumors by T cells.
Insights
Future personalized immunotherapies require understanding both tumor and immune response specifics. This study develops structure-based models to predict T cell-tumor interactions, improving upon sequence-based methods for better cancer recognition.
Area of Science:
- Computational Biology
- Immunology
- Structural Biology
Background:
- Personalized immunotherapies demand comprehensive understanding of tumor-specific immune responses.
- Predicting T cell receptor (TCR) and peptide-MHC (pMHC) interactions is crucial for effective immunotherapy design.
Purpose of the Study:
- To develop and validate structure-based computational models for predicting TCR-pMHC interactions.
- To identify key structural and energetic features governing T cell-mediated tumor recognition.
Main Methods:
- In vitro high-throughput specificity assays were used to generate data.
- Comparative modeling of TCR-pMHC complexes was performed using molecular docking.
- All-atom models of pairwise TCR-pMHC combinations were constructed.
- Physics-based scores were calculated and used to train binary classifiers.
Main Results:
- Structure-based classifiers demonstrated superior performance compared to sequence-based methods.
- Key energetic terms and structural features characterizing T cell-tumor interactions were identified.
- The models accurately predict the specificity of T cell recognition.
Conclusions:
- Computational modeling of TCR-pMHC complexes offers a powerful approach for predicting immune recognition.
- These structure-based insights can guide the development of more effective personalized immunotherapies.
- Understanding the structural basis of T cell-tumor interactions is vital for advancing cancer immunology.
