DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates.

Autor: Qiu S; Department of Engineering Science, University of Oxford, OX1 3PJ, United Kingdom., Zhao S; Radcliffe Department of Medicine, University of Oxford, OX3 9DU, United Kingdom., Yang A; Department of Engineering Science, University of Oxford, OX1 3PJ, United Kingdom.
Jazyk: angličtina
Zdroj: Briefings in bioinformatics [Brief Bioinform] 2023 Nov 22; Vol. 25 (1).
DOI: 10.1093/bib/bbad506
Abstrakt: The enzyme turnover rate, ${k}_{cat}$, quantifies enzyme kinetics by indicating the maximum efficiency of enzyme catalysis. Despite its importance, ${k}_{cat}$ values remain scarce in databases for most organisms, primarily because of the cost of experimental measurements. To predict ${k}_{cat}$ and account for its strong temperature dependence, DLTKcat was developed in this study and demonstrated superior performance (log10-scale root mean squared error = 0.88, R-squared = 0.66) than previously published models. Through two case studies, DLTKcat showed its ability to predict the effects of protein sequence mutations and temperature changes on ${k}_{cat}$ values. Although its quantitative accuracy is not high enough yet to model the responses of cellular metabolism to temperature changes, DLTKcat has the potential to eventually become a computational tool to describe the temperature dependence of biological systems.
(© The Author(s) 2024. Published by Oxford University Press.)
Databáze: MEDLINE
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