Department of Biosystems Science and Engineering and SIB Swiss Institute of Bioinformatics, ETH Zurich, 4056 Basel, Switzerland.
Bioinformatics. 2024 Nov 1;40(11). doi: 10.1093/bioinformatics/btae652.
Relating metabolite and enzyme abundances to metabolic fluxes requires reaction kinetics, core elements of dynamic and enzyme cost models. However, kinetic parameters have been measured only for a fraction of all known enzymes, and the reliability of the available values is unknown.
The ENzyme KInetics Estimator (ENKIE) uses Bayesian Multilevel Models to predict value and uncertainty of KM and kcat parameters. Our models use five categorical predictors and achieve prediction performances comparable to deep learning approaches that use sequence and structure information. They provide calibrated uncertainty predictions and interpretable insights into the main sources of uncertainty. We expect our tool to simplify the construction of priors for Bayesian kinetic models of metabolism.
Code and Python package are available at https://gitlab.com/csb.ethz/enkie and https://pypi.org/project/enkie/.
将代谢物和酶的丰度与代谢通量联系起来需要反应动力学，这是动态和酶成本模型的核心要素。然而，只有一部分已知的酶的动力学参数已经被测量，并且可用值的可靠性是未知的。
酶动力学估算器（ENKIE）使用贝叶斯多层次模型来预测 KM 和 kcat 参数的值和不确定性。我们的模型使用五个分类预测因子，并且预测性能与使用序列和结构信息的深度学习方法相当。它们提供校准的不确定性预测，并对不确定性的主要来源进行可解释的洞察。我们期望我们的工具能够简化代谢动力学贝叶斯模型的先验构建。
代码和 Python 包可在 https://gitlab.com/csb.ethz/enkie 和 https://pypi.org/project/enkie/ 上获得。
