Integration of kinetic data into affinity-based models for improved T cell specificity prediction

Area of Science:

• Immunology
• Biophysics
• Computational Biology

Background:

• T cell activation relies on complex T cell receptor (TCR) and peptide-major histocompatibility complex (pMHC) interactions.
• Existing affinity-based models sometimes fail to predict TCR-pMHC interactions with low binding affinity.

Purpose of the Study:

• To analyze TCR-pMHC systems with existing kinetic and affinity data where affinity-based predictions failed.
• To identify criteria for incorporating kinetic information to improve energy-based models.
• To refine TCR-pMHC interaction predictions and offer molecular insights into T cell activation.

Main Methods:

• Analysis of empirical kinetic and affinity data for specific TCR-pMHC systems.
• Development and application of a kinetic correction factor within energy-based models.
• Comparison of model predictions with and without the kinetic correction factor.

Main Results:

• Identification of specific criteria where a kinetic correction factor significantly improves energy-based model predictions for TCR-pMHC interactions.
• Demonstration that incorporating kinetic data refines predictions, particularly for low-affinity interactions.
• Validation of the kinetic correction factor's utility in systems where prior affinity-based models were insufficient.

Conclusions:

• A kinetic correction factor can enhance the predictive power of energy-based models for TCR-pMHC interactions.
• This approach refines understanding of T cell activation by integrating binding kinetics and affinity.
• The findings help reconcile conflicting reports on the roles of TCR-pMHC binding kinetics and affinity.