TY  - JOUR
AU  - Breimann, Stephan
AU  - Kamp, Frits
AU  - Basset, Gabriele
AU  - Abou-Ajram, Claudia
AU  - Güner, Gökhan
AU  - Yanagida, Kanta
AU  - Okochi, Masayasu
AU  - Müller, Stephan A
AU  - Lichtenthaler, Stefan F
AU  - Langosch, Dieter
AU  - Frishman, Dmitrij
AU  - Steiner, Harald
TI  - Charting γ-secretase substrates by explainable AI.
JO  - Nature Communications
VL  - 16
IS  - 1
SN  - 2041-1723
CY  - [London]
PB  - Springer Nature
M1  - DZNE-2025-00769
SP  - 5428
PY  - 2025
AB  - 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
KW  - Amyloid Precursor Protein Secretases: metabolism
KW  - Amyloid Precursor Protein Secretases: chemistry
KW  - Amyloid Precursor Protein Secretases: genetics
KW  - Humans
KW  - Machine Learning
KW  - Substrate Specificity
KW  - Alzheimer Disease: metabolism
KW  - Algorithms
KW  - Protein Binding
KW  - Amyloid Precursor Protein Secretases (NLM Chemicals)
LB  - PUB:(DE-HGF)16
C6  - pmid:40593564
C2  - pmc:PMC12219630
DO  - DOI:10.1038/s41467-025-60638-z
UR  - https://pub.dzne.de/record/279438
ER  -