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

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.