Journal Article

DZNE-2025-00769

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Charting γ-secretase substrates by explainable AI.

Breimann, S. (First author)DZNE* ; Kamp, F. ; Basset, G. ; Abou-Ajram, C. ; Güner, G.DZNE* ; Yanagida, K. ; Okochi, M. ; Müller, S. A.DZNE* ; Lichtenthaler, S. F.DZNE* ; Langosch, D. ; Frishman, D. ; Steiner, H. (Last author)DZNE*

2025
Springer Nature [London]

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Please use a persistent id in citations: doi:10.1038/s41467-025-60638-z

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.

Keyword(s): Amyloid Precursor Protein Secretases: metabolism (MeSH) ; Amyloid Precursor Protein Secretases: chemistry (MeSH) ; Amyloid Precursor Protein Secretases: genetics (MeSH) ; Humans (MeSH) ; Machine Learning (MeSH) ; Substrate Specificity (MeSH) ; Alzheimer Disease: metabolism (MeSH) ; Algorithms (MeSH) ; Protein Binding (MeSH) ; Amyloid Precursor Protein Secretases

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Contributing Institute(s):

1. Biochemistry of γ-Secretase (AG Steiner)
2. Neuroproteomics (AG Lichtenthaler)

Research Program(s):

1. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Appears in the scientific report 2025

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Database coverage:
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Linked articles:

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Software Breimann, S.DZNE*
Software: AAanalysis, v1.0.0
Zenodo (2025) [10.5281/ZENODO.15320204]2025 BibTeX | EndNote: XML, Text | RIS

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