A generalized framework for computational design and mutational scanning of T-cell receptor binding interfaces
Timothy P Riley1, Cory M Ayres1, Lance M Hellman1
1Department of Chemistry & Biochemistry and the Harper Cancer Research Institute, University of Notre Dame, 251 Nieuwland Science Hall, Notre Dame, IN 46556, USA.
Abstract:
T-cell receptors (TCRs) have emerged as a new class of therapeutics, most prominently for cancer where they are the key components of new cellular therapies as well as soluble biologics. Many studies have generated high affinity TCRs in order to enhance sensitivity. Recent outcomes, however, have suggested that fine manipulation of TCR binding, with an emphasis on specificity may be more valuable than large affinity increments. Structure-guided design is ideally suited for this role, and here we studied the generality of structure-guided design as applied to TCRs. We found that a previous approach, which successfully optimized the binding of a therapeutic TCR, had poor accuracy when applied to a broader set of TCR interfaces. We thus sought to develop a more general purpose TCR design framework. After assembling a large dataset of experimental data spanning multiple interfaces, we trained a new scoring function that accounted for unique features of each interface. Together with other improvements, such as explicit inclusion of molecular flexibility, this permitted the design new affinity-enhancing mutations in multiple TCRs, including those not used in training. Our approach also captured the impacts of mutations and substitutions in the peptide/MHC ligand, and recapitulated recent findings regarding TCR specificity, indicating utility in more general mutational scanning of TCR-pMHC interfaces.
Insights
Developing a new computational framework for T-cell receptor (TCR) design enhances therapeutic specificity. This structure-guided approach improves TCR binding affinity and specificity for improved cancer therapies.
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.
