A deep learning-generated hASIC1a miniprotein inhibitor confers neuroprotection in stroke

Sormann, J.; Epitropaki, K.; Vicino, M. F.; Fjorbak, C. L.; Pugh, C. F.; Lang, B.; Beyer, E. K.; Comaposada-Baro, R.; Hink, F.; Friis, S.; Usher, S. G.; Cai, C.; Autzen, H. E.; Rogers, J. M.; Pless, S.

doi:10.64898/2026.10.04.755611

bioRxiv · 10.64898/2026.10.04.755611

A deep learning-generated hASIC1a miniprotein inhibitor confers neuroprotection in stroke

Sormann, J.·Epitropaki, K.·Vicino, M. F.·Fjorbak, C. L.·Pugh, C. F.·Lang, B.·Beyer, E. K.·Comaposada-Baro, R.·Hink, F.·Friis, S.·Usher, S. G.·Cai, C.·Autzen, H. E.·Rogers, J. M.·Pless, S.

View original source ↗Export metadata ↓

Abstract

Ion channels remain notoriously recalcitrant drug targets despite their extensive pathophysiological significance. Acid-sensing ion channels (ASICs) are proton-gated cation channels that cause neuronal death when excessively activated, such as during ischaemic stroke. This acidosis-driven injury pathway remains unchallenged by existing therapeutics. Here we generate two de novo designed hASIC1a-specific miniproteins by developing a platform that integrates computational miniprotein design, high-throughput automated patch clamp screening, biophysical and structural characterisation, and in vivo validation. We identify a subtype-selective hASIC1a inhibitor, Denasin1, which achieves near-complete inhibition with nanomolar potency. Denasin1 is disordered in solution but adopts the computationally predicted helix-turn-helix fold when bound to the extracellular acidic pocket of hASIC1a. Crucially, Denasin1 reduces infarct volume by > 50% in a murine model of ischaemic stroke. We thereby establish our de novo design platform as a route to develop potent and selective ion channel modulators.

Explore related subjects

biophysics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Sormann, J., Epitropaki, K., Vicino, M. F., Fjorbak, C. L., Pugh, C. F., Lang, B., Beyer, E. K., Comaposada-Baro, R., Hink, F., Friis, S., Usher, S. G., Cai, C., Autzen, H. E., Rogers, J. M., Pless, S.. 2026-10-09. A deep learning-generated hASIC1a miniprotein inhibitor confers neuroprotection in stroke. https://doi.org/10.64898/2026.10.04.755611

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Cancers relax selection on charge at the mitochondrial complex I quinone cavity

Mitochondrial complex I translocates protons through hydrated channels whose connectivity and electrostatics are both defined by structure, but whether they are distinctly constrained is unknown. From a 2.39 cryo-EM structure modelling 2,945 ordered waters, we built the proton wire of the seven mtDNA-encoded ND subunits and scored all 40,166 missense substitutions on two near-orthogonal axes (r = 0.17): electrostatic shift at proton-loading sites, and topological disruption of the wire. Across 858 germline variant sites, loading sites (0.51) and cut vertices (0.67) are both depleted. In tumours only loading sites are enriched (1.42 TCGA, 1.67 PCAWG, 1.29 MSK-IMPACT), a 3.06-fold difference tested as a wire class tissue-type interaction (P = 3.6 10-). It survives adjustment for conservation, burial and coverage, and is concentrated in ND1 within 15 of the quinone cavity, not spread along the proton path. Selection separates charge from connectivity, without establishing a fitness benefit.

biophysics↗

bioRxiv2026

Rugnux: from diffraction images to structure at synchrotrons and XFELs in one program

Rising data rates in macromolecular crystallography outpace multi-program pipelines on computing clusters. Rugnux, a single GPU-accelerated program, turns rotation or serial still diffraction images into merged data and an initial structure. At synchrotrons, results arrive seconds after the last image; on one workstation, 207 public datasets from 36 sources took a median of 15 s each, with deposition-quality. Serial XFEL data were processed faster than the collection rate, enabling real-time structure determination.

biophysics↗

bioRxiv2026

Phase Separation of Intracellular Proteins Influences Synaptic Bistability

Synaptic bistability at the post synaptic density has been proposed to be the result of the nucleation and evaporation of receptor clusters. The exact biophysical mechanism underlying these inter-receptor attractions that can trigger nucleation remains to be elucidated. Emerging evidence demonstrates that numerous postsynaptic density submembrane proteins, that interact with various membrane receptors, can undergo phase separation, necessitating a revised conceptual model. By employing Langevin dynamics simulations of coarse-grained scaffold proteins and interacting membrane receptors, we study how phase transitions of scaffold proteins alter receptor clustering at the membrane. In our model, scaffold macromolecules can undergo phase separation and interact with receptor molecules at the model membranes. We observed that the presence of scaffold polymers across the membrane creates an attractive interaction between otherwise purely repulsive receptors. By systematically varying the number of receptors at the membrane, we dissected the coupling between receptor clustering and the phase behavior of scaffold macromolecules. We show that the propensity for receptor clustering is enhanced by the condensation of scaffold proteins. We then used two different scaffolds and systematically varied the interaction between them, to change the spatial organization of two condensed phases w.r.t. one another. Interestingly, we observed that receptor clustering can be reduced by co-phase separation of scaffold macromolecules, indicating the role of competing interactions in receptor clustering. Our work advances understanding of receptor-mediated signaling and phase separation, providing insights into cellular compartmentalization and biochemical regulation at the synapse.

biophysics↗