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Sequence and structural determinants of efficacious de novo chimeric antigen receptors

Chow, A.; Chu, H.; Li, R.; Nalbant, B.; Dozic, A.; Kida, L.; Lareau, C.

2025-12-13 bioengineering
10.64898/2025.12.12.694033 bioRxiv
Show abstract

Advances in generative protein design using artificial intelligence (AI) have enabled the rapid development of binders against heterogeneous targets, including tumor-associated antigens. Despite extensive biochemical characterization, these novel protein binders have had limited evaluation as agents in candidate therapeutics, including chimeric antigen receptor (CAR) T cells. Here, we synthesize generative protein design workflows to screen 1,589 novel protein binders targeting BCMA, CD19, and CD22 for efficacy in scalable protein-binding and T cell assays. We identify three main challenges that hinder the utility of de novo protein binders as CARs, including tonic signaling, occluded epitope engagement, and off-target activity. We develop computational and experimental heuristics to overcome these limitations, including screens of sequence variants for individual parental structures, that restore on-target CAR activation while mitigating liabilities. Together, our framework accelerates the development of AI-designed proteins for future preclinical therapeutic screening, helping enable a new generation of cellular therapies.

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