Bridging AI, molecular dynamics, and experiments for mechanism-aware TCR engineering in cancer immunotherapy

Abstract

T cell receptor engineering holds great promise for cancer immunotherapy, yet the identification and optimization of high-affinity, functionally effective TCRs remain challenging. While advances in artificial intelligence have enabled large-scale exploration of TCR sequence space, these approaches often lack mechanistic interpretability and generate extensive candidate pools that are difficult to prioritize. Conversely, physics-based methods such as molecular dynamics simulations provide detailed insight into binding energetics and conformational dynamics but are not suited for high-throughput screening. Here, we propose an integrated, mechanism-aware framework that bridges AI-driven design, MD-based refinement, and experimental validation. In this strategy, biologically informed selection of initial TCRs is followed by AI-driven generation of variants, while molecular dynamics simulations act as a low-throughput, high-information filter to uncover the molecular determinants of TCR-pMHC interactions, including allosteric communication. Importantly, MD is positioned not only as a selection tool but also as a generator of mutation hypotheses. Experimental validation using surface plasmon resonance and functional cellular assays such as RAPTR enables the identification of TCRs that are both high-affinity and functionally active. This integrated approach establishes a closed-loop, multi-scale design paradigm that combines exploration, mechanistic understanding, and functional validation, providing a promising strategy for the rational development of therapeutic TCRs.