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

Experimental validation and functional optimization remain bottlenecks in AI-based protein design. We present a scalable workflow for developing 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, when incorporated into chimeric antigen receptors (CAR), some show poor cell-surface trafficking and limited functionality. Redesign through a genetic algorithm-based diversification strategy that preserves the binding interface while changing non-binding surfaces experimentally reveals an isoelectric point (pI) window that improves CAR expression and enhances target-selective tumor cell killing. Our findings identify biochemical optimization beyond the binding interface as a critical requirement for translating AI-minibinders into functional applications.

• Broske, B.
• McEnroe, B. A.
• Frechen, S. C.
• Kempchen, T. N.
• Fandrey, C. I.
• Tan, E.
• Ferber, D.
• Yong, M. C. R.
• Kleinert, M.
• Messmer, J. M.
• Konopka, P.
• Hoch, A.
• Blumenstock, K.
• Todtmann, J. M. P.
• Oldenburg, J.
• Ruhl, H.
• Semaan, A.
• Toma, M. I.
• Markova, K.
• Kobold, S.
• Rollenske, T.
• Geyer, M.
• Menzel, S.
• Bald, T.
• Schmid-Burgk, J. L.
• Hagelueken, G.
• Holzel, M.