Mapping the polymerization landscape of human serpins across genetic variation using Gaussian Processes
摘要
Proteins function on rugged energy landscapes that enable conformational plasticity but increase vulnerability to misfolding under genetic variation and stress. Serine protease inhibitors (serpins) are metastable proteins whose variants can polymerize and aggregate, causing diseases including emphysema, angioedema, thrombosis and dementia. Existing aggregation predictors underperform on serpin variants. Here, we present a Gaussian process (GP) framework with a sequence position-dependent spatial covariance (SCV) kernel that integrates residue position and structure-informed stability perturbations (ΔΔG) to infer residue-resolved polymerization landscapes across human serpins, together with posterior uncertainty. The model supports a stability-dependent, C-terminal polymerization mechanism across serpin family members and predicts previously uncharacterized human serpin variants with high polymerogenicity. Experimental testing in cells confirmed increased intracellular polymer burden for high-risk variants. By capturing this common, stability-dependent C-terminal polymerization mechanism based on genetic variation in the population, the GP-SCV framework suggests a potential unified strategy for variant prioritization and therapeutic development in serpinopathies.
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