Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.
Using graph neural networks for site-of-metabolism prediction and its applications to ranking promiscuous enzymatic products.
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DOI：
10.1093/bioinformatics/btad089
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发表时间：
2023-03-01
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--
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While traditionally utilized for identifying site-specific metabolic activity within a compound to alter its interaction with a metabolizing enzyme, predicting the site-of-metabolism (SOM) is essential in analyzing the promiscuity of enzymes on substrates. The successful prediction of SOMs and the relevant promiscuous products has a wide range of applications that include creating extended metabolic models (EMMs) that account for enzyme promiscuity and the construction of novel heterologous synthesis pathways. There is therefore a need to develop generalized methods that can predict molecular SOMs for a wide range of metabolizing enzymes. This article develops a Graph Neural Network (GNN) model for the classification of an atom (or a bond) being an SOM. Our model, GNN-SOM, is trained on enzymatic interactions, available in the KEGG database, that span all enzyme commission numbers. We demonstrate that GNN-SOM consistently outperforms baseline machine learning models, when trained on all enzymes, on Cytochrome P450 (CYP) enzymes, or on non-CYP enzymes. We showcase the utility of GNN-SOM in prioritizing predicted enzymatic products due to enzyme promiscuity for two biological applications: the construction of EMMs and the construction of synthesis pathways. A python implementation of the trained SOM predictor model can be found at https://github.com/HassounLab/GNN-SOM. Supplementary data are available at Bioinformatics online.
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影响因子：
4.2
作者：
Rydberg, Patrik;Gloriam, David E.;Olsen, Lars
通讯作者：
Olsen, Lars
影响因子：
5.2
作者：
Porokhin V;Amin SA;Nicks TB;Gopinarayanan VE;Nair NU;Hassoun S
通讯作者：
Hassoun S
影响因子：
14.9
作者：
Duigou T;du Lac M;Carbonell P;Faulon JL
通讯作者：
Faulon JL
影响因子：
14.9
作者：
Sajed T;Marcu A;Ramirez M;Pon A;Guo AC;Knox C;Wilson M;Grant JR;Djoumbou Y;Wishart DS
通讯作者：
Wishart DS
影响因子：
11.4
作者：
Cheng, Zhuan;Jiang, Jiaqi;Ye, Qin
通讯作者：
Ye, Qin