Sequence and structural determinants of efficacious de novo chimaeric antigen receptors
TL;DR
This framework synthesizes generative protein design workflows to screen 1,758 newly designed protein binders targeting BCMA, CD19 and CD22 for efficacy in scalable protein-binding, T-cell activation and in vivo killing assays, and develops computational and experimental heuristics to overcome limitations.
Abstract
Advances in generative protein design using artificial intelligence (AI) have enabled the rapid development of binders against heterogeneous targets, including tumour-associated antigens. Despite extensive biochemical characterization, these novel protein binders have had limited evaluation in candidate therapeutics, including chimaeric antigen receptor (CAR) T cells. Here we synthesize generative protein design workflows to screen 1,758 newly designed protein binders targeting BCMA, CD19 and CD22 for efficacy in scalable protein-binding, T-cell activation and in vivo killing assays. We characterize three main challenges that hinder the utility of de novo protein binders as CARs, including tonic signalling, occluded epitope engagement and off-target activity. We develop computational and experimental heuristics to overcome these limitations, including screens of sequence variants of individual parental structures, that retain 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.