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AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins

ABSTRACT: AI-based protein design can rapidly generate novel protein binders, but experimental validation and functional optimization remain bottlenecks. We present a scalable workflow to develop of AI-designed minibinders against cancer-associated surface proteins. Screening thousands of designs using mammalian cell-surface display identifies several high-affinity PD-L1 minibinders but far fewer for CD276 (B7-H3) and VTCN1 (B7-H4), highlighting substantial target dependence. Interface predicted template modeling (ipTM) scores generated by Chai-1 with ESM embeddings correlate with binding success and capture deleterious effects of interface mutations. Fluorophore-labeled AI-minibinders enable flow-cytometric staining comparable to conventional antibodies. However, as chimeric antigen receptors (CARs), some show poor cell-surface trafficking and limited CAR-T cell functionality. Redesign through a genetic algorithm-based diversification strategy that preserves the binding interface while changing non-binding surfaces reveals experimentally a defined pI window that improves CAR expression and enhances target-selective tumor cell killing while reducing off-target cytotoxicity. Our findings establish biochemical optimization beyond the binding interface as critical requirement for translating AI-minibinders into functional applications.

ORGANISM(S): synthetic construct

PROVIDER: GSE338543 | GEO | 2026/07/27

REPOSITORIES: GEO

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