Charting γ-secretase substrates by explainable AI
Nat Commun. 2025 Jul 1;16(1):5428.
| Authors/Editors: |
Breimann S Kamp F Basset G Abou-Ajram C Güner G Yanagida K Okochi M Müller SA Lichtenthaler SF Langosch D Frishman D Steiner H |
|---|---|
| Publication Date: | 2025 |
| Type of Publication: | Journal Article |
Abstract
Proteases recognize substrates by decoding sequence information-an essential cellular process elusive when recognition motifs are absent. Here, we unravel this problem for γ-secretase, an intramembrane-cleaving protease associated with Alzheimer's disease and cancer, by developing Comparative Physicochemical Profiling (CPP), a sequence-based algorithm for identifying interpretable physicochemical features. We show that CPP deciphers a γ-secretase substrate signature with single-residue resolution, which can explain the conformational transitions observed in substrates upon γ-secretase binding. Using machine learning, we predict the entire human γ-secretase substrate scope, revealing numerous previously unknown substrates. Our approach outperforms state-of-the-art protein language models, improving prediction accuracy from 60% to 90%, and achieves an 88% success rate in experimental validation. Building on these advancements, we identify pathways and diseases not linked before to γ-secretase. Generally, CPP decodes physicochemical signatures-a concept that extends beyond sequence motifs. We anticipate that our approach will be broadly applicable to diverse molecular recognition processes.
