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000279438 1001_ $$0P:(DE-2719)9001161$$aBreimann, Stephan$$b0$$eFirst author
000279438 245__ $$aCharting γ-secretase substrates by explainable AI.
000279438 260__ $$a[London]$$bSpringer Nature$$c2025
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000279438 520__ $$aProteases recognize substrates by decoding sequence information-an essential cellular process elusive when recognition motifs are absent. Here, we unravel this problem for γ-secretase, an intramembrane-cleaving protease associated with Alzheimer's disease and cancer, by developing Comparative Physicochemical Profiling (CPP), a sequence-based algorithm for identifying interpretable physicochemical features. We show that CPP deciphers a γ-secretase substrate signature with single-residue resolution, which can explain the conformational transitions observed in substrates upon γ-secretase binding. Using machine learning, we predict the entire human γ-secretase substrate scope, revealing numerous previously unknown substrates. Our approach outperforms state-of-the-art protein language models, improving prediction accuracy from 60% to 90%, and achieves an 88% success rate in experimental validation. Building on these advancements, we identify pathways and diseases not linked before to γ-secretase. Generally, CPP decodes physicochemical signatures-a concept that extends beyond sequence motifs. We anticipate that our approach will be broadly applicable to diverse molecular recognition processes.
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000279438 650_7 $$0EC 3.4.-$$2NLM Chemicals$$aAmyloid Precursor Protein Secretases
000279438 650_2 $$2MeSH$$aAmyloid Precursor Protein Secretases: metabolism
000279438 650_2 $$2MeSH$$aAmyloid Precursor Protein Secretases: chemistry
000279438 650_2 $$2MeSH$$aAmyloid Precursor Protein Secretases: genetics
000279438 650_2 $$2MeSH$$aHumans
000279438 650_2 $$2MeSH$$aMachine Learning
000279438 650_2 $$2MeSH$$aSubstrate Specificity
000279438 650_2 $$2MeSH$$aAlzheimer Disease: metabolism
000279438 650_2 $$2MeSH$$aAlgorithms
000279438 650_2 $$2MeSH$$aProtein Binding
000279438 7001_ $$0P:(DE-2719)2812549$$aKamp, Frits$$b1
000279438 7001_ $$aBasset, Gabriele$$b2
000279438 7001_ $$aAbou-Ajram, Claudia$$b3
000279438 7001_ $$0P:(DE-2719)2812025$$aGüner, Gökhan$$b4$$udzne
000279438 7001_ $$aYanagida, Kanta$$b5
000279438 7001_ $$aOkochi, Masayasu$$b6
000279438 7001_ $$0P:(DE-2719)2810938$$aMüller, Stephan A$$b7
000279438 7001_ $$0P:(DE-2719)2181459$$aLichtenthaler, Stefan F$$b8
000279438 7001_ $$0P:(DE-2719)9001125$$aLangosch, Dieter$$b9
000279438 7001_ $$aFrishman, Dmitrij$$b10
000279438 7001_ $$0P:(DE-2719)2000023$$aSteiner, Harald$$b11$$eLast author
000279438 773__ $$0PERI:(DE-600)2553671-0$$a10.1038/s41467-025-60638-z$$gVol. 16, no. 1, p. 5428$$n1$$p5428$$tNature Communications$$v16$$x2041-1723$$y2025
000279438 7870_ $$0DZNE-2025-00862$$aBreimann, Stephan$$dZenodo, 2025$$iRelatedTo$$r$$tAAanalysis, v1.0.0
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000279438 9201_ $$0I:(DE-2719)1110000-1$$kAG Steiner$$lBiochemistry of γ-Secretase$$x0
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