De novo designed protein enables precise epitope-level control of Gremlin-1 antagonism
Adsersen, B. I.; Moeller-Jensen, C. M.; Jacobsen, C. P.; Domeyer, T.; Loeffler, J. R.; Fernandez-Quintero, M. L.; Tomberli, I.; Jensen, A. B.; Jakobsen, M. S.; Bangaru, S.; Ward, A.; Rivera-de-Torre, E.; Gunnarsson, S. B.; Hald, A.; Jenkins, T. P.
Show abstract
Growth-factor antagonists regulate key developmental and pathological processes, yet the molecular principles governing their control remain poorly understood. Gremlin-1 (GREM1) is a secreted antagonist of bone morphogenetic proteins (BMPs) whose dysregulation is implicated in fibrosis and cancer. Here, we report a de novo designed protein that binds GREM1 with sub-nanomolar affinity and selectively releases BMPs for downstream signalling. By integrating generative deep-learning-based protein design with molecular-dynamics-derived flexibility descriptors, we identify a predictive relationship between interface rigidity, desolvation energy, and binding success. The resulting binder, RF1-2, reproduces the native BMP-binding epitope on GREM1 at near-atomic precision, as confirmed by cryo-electron microscopy, and competitively blocks BMP-2 and BMP-4 association. These results establish interface rigidity as a key physical determinant of antagonist inhibition and demonstrate how AI-guided protein design can uncover molecular principles underlying extracellular signalling control.
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