UC Davis

T cells discriminate peptide-major histocompatibility complexes (pMHCs) expressed on antigen presenting cells (APCs) via their T Cell Receptors (TCRs). The adaptability of the human immune system is demonstrated, in part, by the ability to generate a stunningly large number of possible TCRs (>1020-1061). However, matching TCRs with specific pMHC targets to invoke an appropriate and targeted immune response remains challenging and has created a need for a deeper understanding of how the TCR engages its antigen to induce an immunogenic response. Utilizing experimental observation and molecular dynamics, we propose a force-dependent kinetic proofreading discrimination model whereby the TCR must sustain and form transient bonds under load for sufficient time to initiate biochemical signaling. This computational model is utilized to construct the building blocks of TCR design by (1) machine learning the physiochemical determinants of TCR dissociation kinetics, (2) homology modelling patient-specific TCRs to a target pMHC, and (3) predicting TCR function to a target pMHC from primary amino acid sequence. The creation of highly immunogenic, tumor-specific TCRs will require rapid and efficient screening of TCR information space. The success of these techniques will be measured by the ability to accurately predict in vitro T cell immunogenicity and will depend on the generation of high-fidelity datasets. Moreover, additional biological complexity may need to be integrated into this computational framework to augment predictive power. Hence, we investigate the effects of glycosylation, coreceptors (CD3 and CD4), and the phospholipid bilayer on the TCR interaction with the pMHC. In addition, methodology and software is developed to analyze the non-equilibrium receptor-ligand kinetics in a microfluidic flow chamber. The methods proposed in this work are suggested to provide an architecture that may inform the design of novel TCRs for immunotherapies.