A cell-based kinetic framework enables TCR specificity prediction

Area of Science:

• Immunology
• Computational Biology
• Biophysics

Background:

• Predicting T-cell receptor (TCR) specificity is vital for advancing immunotherapy and vaccine development.
• Current predictive models are limited by reliance on non-representative signals and equilibrium binding assays, hindering generalizability.

Purpose of the Study:

• To address limitations in TCR specificity prediction by developing a mechanistically grounded and scalable approach.
• To establish a clear biophysical definition of TCR specificity and improve computational modeling.

Main Methods:

• Developed a cell-based assay for quantitative measurement of TCR-pMHC binding kinetics in a physiological context.
• Introduced a mechanistic framework, including the TCR cycle model, to interpret binding data.
• Generated multiplexed, high-throughput datasets integrating mechanistic modeling with machine learning.

Main Results:

• Demonstrated the insufficiency of the traditional reversible ligand-receptor model for general TCR recognition.
• Established a novel TCR cycle model as a minimal systems-level description for TCR-pMHC interactions.
• Created a foundation for mechanistically informed and scalable TCR specificity prediction.

Conclusions:

• The proposed framework and methods provide a more accurate and generalizable approach to predicting TCR specificity.
• This work paves the way for improved immunotherapy and vaccine design by enhancing our understanding of immune recognition.