ArticleProtein science : a publication of the Protein Society2026
Engineering selective amyloid precursor protein inhibitors by machine learning and deep mutational scanning.
Article in Protein science : a publication of the Protein Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Deep mutational scanning (DMS) has proven effective for mapping protein-protein interactions (PPIs), but it cannot provide complete coverage of the mutation landscape, particularly for multi-mutant variants. To address this limitation, we trained machine-learning (ML) models on previously generated DMS data for a stabilized amyloid precursor protein inhibitor (APPI) binding to either of two serine proteases, mesotrypsin and kallikrein-6 (KLK6), which are implicated in various human disorders. We combined the models to accurately predict the binding selectivity of APPI variants, including double-mutant variants, for the two serine proteases. We achieved a Pearson correlation of 0.937 between predicted log
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