AI-enabled discovery and biochemical optimization of minibinders targeting cancer cell-surface proteins
Year: 2026
Type: article
Abstract: 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.
Source: Nature Communications
Authors: 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
Institutions: University of Bonn, University Hospital Bonn, Saarland University, Charité - Universitätsmedizin Berlin, German Cancer Research Center, Helmholtz Munich, LMU Klinikum, German Center for Lung Research, Deutsches Konsortium für Translationale Krebsforschung, Ludwig-Maximilians-Universität München
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,990
OpenAlex ID: W7203818877