Jaiswal Shubham K, Agarwal Shitij Manojkumar, Thodum Parikshit, Sharma Vineet K
MetaBioSys Group, Department of Biological Sciences, Indian Institute of Science Education and Research, Bhopal, Madhya Pradesh 462066, India.
iScience. 2020 Dec 10;24(1):101925. doi: 10.1016/j.isci.2020.101925. eCollection 2021 Jan 22.
In addition to being pivotal for the host health, the skin microbiome possesses a large reservoir of metabolic enzymes, which can metabolize molecules (cosmetics, medicines, pollutants, etc.) that form a major part of the skin exposome. Therefore, to predict the complete metabolism of any molecule by skin microbiome, a curated database of metabolic enzymes (1,094,153), reactions, and substrates from ∼900 bacterial species from 19 different skin sites were used to develop "SkinBug." It integrates machine learning, neural networks, and chemoinformatics methods, and displays a multiclass multilabel accuracy of up to 82.4% and binary accuracy of up to 90.0%. SkinBug predicts all possible metabolic reactions and associated enzymes, reaction centers, skin microbiome species harboring the enzyme, and the respective skin sites. Thus, SkinBug will be an indispensable tool to predict xenobiotic/biotic metabolism by skin microbiome and will find applications in exposome and microbiome studies, dermatology, and skin cancer research.
除了对宿主健康至关重要外，皮肤微生物群还拥有大量代谢酶，这些酶可以代谢构成皮肤暴露组主要部分的分子（化妆品、药物、污染物等）。因此，为了预测皮肤微生物群对任何分子的完整代谢情况，利用一个经过整理的代谢酶数据库（1,094,153个）、反应以及来自19个不同皮肤部位约900种细菌的底物来开发“SkinBug”。它整合了机器学习、神经网络和化学信息学方法，多类多标签准确率高达82.4%，二元准确率高达90.0%。SkinBug可以预测所有可能的代谢反应以及相关酶、反应中心、含有该酶的皮肤微生物物种和相应的皮肤部位。因此，SkinBug将成为预测皮肤微生物群对外源/生物代谢的不可或缺的工具，并将在暴露组和微生物群研究、皮肤病学以及皮肤癌研究中得到应用。
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