Sequence and structural determinants of efficacious de novo chimeric antigen receptors
Ontology highlight
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 variable generative protein design workflows to screen 1,487 novel protein binders targeting BCMA, CD22, and CD19 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.
ORGANISM(S): Homo sapiens
PROVIDER: GSE310399 | GEO | 2026/07/06
REPOSITORIES: GEO
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