Enzymes are critical proteins in every organism. They speed up essential chemical reactions, help fight diseases, and have a wide use in the pharmaceutical and manufacturing industries. Wet lab experiments to figure out an enzyme's function are time consuming and expensive. Therefore, the need for computational approaches to address this problem are becoming necessary. Usually, an enzyme is extremely specific in performing its function. However, there exist enzymes that can perform multiple functions. A multi-functional enzyme has vast potential as it reduces the need to discover/use different enzymes for different functions. We propose an approach to predict a multi-functional enzyme's function up to the most specific fourth level of the hierarchy of the Enzyme Commission (EC) number. Previous studies can only predict the function of the enzyme till level 1. Using a dataset of 2,583 multi-functional enzymes, we achieved a hierarchical subset accuracy of 71.4% and a Macro F₁ Score of 96.1% at the fourth level. The robustness of the network was further tested on a multi-functional isoforms dataset. Our method is broadly applicable and may be used to discover better enzymes. The web-server can be freely accessed at http://hecnet.cbrlab.org/.

中文翻译：

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一种基于分层深度学习的多功能酶分类方法

酶是每个生物体中的关键蛋白质。它们加速基本的化学反应，帮助对抗疾病，并在制药和制造业中广泛使用。弄清酶功能的湿实验室实验既费时又昂贵。因此，需要计算方法来解决这个问题变得很有必要。通常，一种酶在执行其功能方面是极其特异的。然而，存在可以执行多种功能的酶。多功能酶具有巨大的潜力，因为它减少了为不同功能发现/使用不同酶的需要。我们提出了一种方法来预测多功能酶的功能，直至酶委员会 (EC) 编号层次结构的最具体的第四级。₁四级成绩96.1%。在多功能异构体数据集上进一步测试了网络的稳健性。我们的方法具有广泛的适用性，可用于发现更好的酶。网络服务器可以在 http://hecnet.cbrlab.org/ 上免费访问。