Nat. Commun. | AI赋能的癌细胞表面蛋白靶向微型结合蛋白发现与生化优化

TL;DR - A Nature Communications study presents an AI-guided pipeline for designing and experimentally optimizing minibinders against cancer cell-surface proteins. It shows that practical performance depends not only on predicted binding affinity but also on target-specific designability and non-interface biochemical properties such as isoelectric point.

• RFdiffusion, ProteinMPNN, and structure predictors were combined with cell-surface display, FACS, and sequencing to screen minibinders targeting PD-L1, CD276, and VTCN1.
• Design success was strongly target-dependent: PD-L1 yielded multiple validated binders, including a 2.12 nM candidate, whereas CD276 and especially VTCN1 were more difficult.
• Chai-1 ESM ipTM scores correlated better with experimental binding than the other evaluated structure scores and identified many disruptive interface mutations.
• Minibinders worked as PD-L1 detection reagents and CAR recognition domains, while optimizing non-interface residues toward a moderate pI improved CAR surface expression, activity, and target selectivity.