AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins
- Bianca Broske
- Benjamin A. McEnroe
- Sophie C. Frechen
- Tim N. Kempchen
- Caroline I. Fandrey
- Elisabeth Tan
- Dominic Ferber
- Michelle C. R. Yong
- Marie Kleinert
- Julia M. Messmer
- Peter Konopka
- Alexander Hoch
- Katja Blumenstock
- Jan M. P. Tödtmann
- Johannes Oldenburg
- Heiko Rühl
- Alexander Semaan
- Marieta I. Toma
- Kristina Markova
- Sebastian Kobold
- Tim Rollenske
- Matthias Geyer
- Stephan Menzel
- Tobias Bald
- Jonathan L. Schmid-Burgk
- Gregor Hagelueken
- Michael Hölzel
2026-08-20
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.