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| 001 | 279438 | ||
| 005 | 20250720001547.0 | ||
| 024 | 7 | _ | |a 10.1038/s41467-025-60638-z |2 doi |
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| 100 | 1 | _ | |a Breimann, Stephan |0 P:(DE-2719)9001161 |b 0 |e First author |
| 245 | _ | _ | |a Charting γ-secretase substrates by explainable AI. |
| 260 | _ | _ | |a [London] |c 2025 |b Springer Nature |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a Amyloid Precursor Protein Secretases |0 EC 3.4.- |2 NLM Chemicals |
| 650 | _ | 2 | |a Amyloid Precursor Protein Secretases: metabolism |2 MeSH |
| 650 | _ | 2 | |a Amyloid Precursor Protein Secretases: chemistry |2 MeSH |
| 650 | _ | 2 | |a Amyloid Precursor Protein Secretases: genetics |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Machine Learning |2 MeSH |
| 650 | _ | 2 | |a Substrate Specificity |2 MeSH |
| 650 | _ | 2 | |a Alzheimer Disease: metabolism |2 MeSH |
| 650 | _ | 2 | |a Algorithms |2 MeSH |
| 650 | _ | 2 | |a Protein Binding |2 MeSH |
| 700 | 1 | _ | |a Kamp, Frits |0 P:(DE-2719)2812549 |b 1 |
| 700 | 1 | _ | |a Basset, Gabriele |b 2 |
| 700 | 1 | _ | |a Abou-Ajram, Claudia |b 3 |
| 700 | 1 | _ | |a Güner, Gökhan |0 P:(DE-2719)2812025 |b 4 |u dzne |
| 700 | 1 | _ | |a Yanagida, Kanta |b 5 |
| 700 | 1 | _ | |a Okochi, Masayasu |b 6 |
| 700 | 1 | _ | |a Müller, Stephan A |0 P:(DE-2719)2810938 |b 7 |
| 700 | 1 | _ | |a Lichtenthaler, Stefan F |0 P:(DE-2719)2181459 |b 8 |
| 700 | 1 | _ | |a Langosch, Dieter |0 P:(DE-2719)9001125 |b 9 |
| 700 | 1 | _ | |a Frishman, Dmitrij |b 10 |
| 700 | 1 | _ | |a Steiner, Harald |0 P:(DE-2719)2000023 |b 11 |e Last author |
| 773 | _ | _ | |a 10.1038/s41467-025-60638-z |g Vol. 16, no. 1, p. 5428 |0 PERI:(DE-600)2553671-0 |n 1 |p 5428 |t Nature Communications |v 16 |y 2025 |x 2041-1723 |
| 787 | 0 | _ | |a Breimann, Stephan |d Zenodo, 2025 |i RelatedTo |0 DZNE-2025-00862 |r |t AAanalysis, v1.0.0 |
| 856 | 4 | _ | |u https://pub.dzne.de/record/279438/files/DZNE-2025-00769%20SUP.zip |
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