A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces

Area of Science:

• Immunology
• Structural Biology
• Computational Biology

Background:

• T-cell receptors (TCRs) are crucial for cellular therapies and biologics, particularly in cancer treatment.
• High-affinity TCRs have been pursued to increase therapeutic sensitivity.
• Recent findings suggest TCR specificity is more critical than solely increasing binding affinity.

Purpose of the Study:

• To evaluate the general applicability of structure-guided design for T-cell receptors (TCRs).
• To develop a more versatile TCR design framework that improves upon existing methods.
• To enable precise manipulation of TCR binding for enhanced specificity and affinity.

Main Methods:

• Assembled a comprehensive dataset of experimental data across multiple TCR interfaces.
• Trained a novel scoring function incorporating unique interface features and molecular flexibility.
• Validated the framework's ability to design affinity-enhancing mutations in various TCRs.

Main Results:

• Identified limitations in previous TCR optimization approaches for broader applications.
• Developed and validated a new, general-purpose TCR design framework.
• Successfully designed affinity-enhancing mutations in TCRs, including those not used in training.
• The framework accurately predicted the effects of mutations on TCR-peptide/MHC interactions and specificity.

Conclusions:

• Structure-guided design offers a powerful strategy for optimizing T-cell receptor (TCR) therapeutics.
• The developed computational framework provides a more general and accurate approach for TCR engineering.
• This work advances the design of TCR-based therapies with improved specificity and efficacy for cancer treatment.