Dokument: A general model to predict small molecule substrates of enzymes based on machine and deep learning
| Titel: | A general model to predict small molecule substrates of enzymes based on machine and deep learning | |||||||
| URL für Lesezeichen: | https://docserv.uni-duesseldorf.de/servlets/DocumentServlet?id=67454 | |||||||
| URN (NBN): | urn:nbn:de:hbz:061-20241111-122438-8 | |||||||
| Kollektion: | Publikationen | |||||||
| Sprache: | Englisch | |||||||
| Dokumententyp: | Wissenschaftliche Texte » Artikel, Aufsatz | |||||||
| Medientyp: | Text | |||||||
| Autoren: | Kroll, Alexander [Autor] Ranjan, Sahasra [Autor] Engqvist, Martin K.M. [Autor] Lercher, Martin [Autor] | |||||||
| Dateien: |
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| Beschreibung: | For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science. | |||||||
| Rechtliche Vermerke: | Originalveröffentlichung:
Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. (2023). A general model to predict small molecule substrates of enzymes based on machine and deep learning [OnlineRessource]. Nature Communications, 14, Article 2787. https://doi.org/10.1038/s41467-023-38347-2 | |||||||
| Lizenz: |  Dieses Werk ist lizenziert unter einer Creative Commons Namensnennung 4.0 International Lizenz | |||||||
| Fachbereich / Einrichtung: | Mathematisch- Naturwissenschaftliche Fakultät | |||||||
| Dokument erstellt am: | 11.11.2024 | |||||||
| Dateien geändert am: | 11.11.2024 |
