Charting γ-secretase substrates by explainable AI

Breimann, Stephan; Kamp, Frits; Basset, Gabriele; Abou-Ajram, Claudia; Güner, Gökhan; Yanagida, Kanta; Okochi, Masayasu; Müller, Stephan A.; Lichtenthaler, Stefan F.; Langosch, Dieter; Frishman, Dmitrij; Steiner, Harald (2025): Charting γ-secretase substrates by explainable AI. Nature Communications, 16: 5428. ISSN 2041-1723

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.

• Dokumententyp: Artikel (LMU)
• Organisationseinheit (Fakultäten): 18 Chemie und Pharmazie > GenZentrum
• DFG-Fachsystematik der Wissenschaftsbereiche: Lebenswissenschaften
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 263531414
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 390857198
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 491502892