Cargando…
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
The turnover number k(cat), a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental k(cat) estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly d...
Autores principales: | Kroll, Alexander, Rousset, Yvan, Hu, Xiao-Pan, Liebrand, Nina A., Lercher, Martin J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10338564/ https://www.ncbi.nlm.nih.gov/pubmed/37438349 http://dx.doi.org/10.1038/s41467-023-39840-4 |
Ejemplares similares
-
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
por: Heckmann, David, et al.
Publicado: (2018) -
A general model to predict small molecule substrates of enzymes based on machine and deep learning
por: Kroll, Alexander, et al.
Publicado: (2023) -
Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers
por: Heckmann, David, et al.
Publicado: (2020) -
Substrate-Free High-Throughput Screening Identifies Selective Inhibitors for Uncharacterized Enzymes
por: Bachovchin, Daniel A., et al.
Publicado: (2009) -
Deep learning allows genome-scale prediction of Michaelis constants from structural features
por: Kroll, Alexander, et al.
Publicado: (2021)