Charting γ-secretase substrates by explainable AI

Stephan Breimann, Frits Kamp, Gabriele Basset, Claudia Abou-Ajram, Gökhan Güner, Kanta Yanagida, Masayasu Okochi, Stephan A. Müller, Stefan F. Lichtenthaler, Dieter Langosch, Dmitrij Frishman () and Harald Steiner ()
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Stephan Breimann: LMU Munich
Frits Kamp: LMU Munich
Gabriele Basset: LMU Munich
Claudia Abou-Ajram: LMU Munich
Gökhan Güner: DZNE Munich
Kanta Yanagida: Osaka Medical and Pharmaceutical University
Masayasu Okochi: Osaka University Graduate School of Medicine
Stephan A. Müller: DZNE Munich
Stefan F. Lichtenthaler: DZNE Munich
Dieter Langosch: TUM
Dmitrij Frishman: Technical University of Munich (TUM)
Harald Steiner: LMU Munich

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-60638-z

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