Most of the bioinformatics tools for enzyme annotation focus on enzymatic function assignments. Sequence similarity to well‐characterized enzymes is often used for functional annotation and to assign metabolic pathways. However, these approaches are not feasible for all sequences leading to inaccurate annotations or lack of metabolic pathway information. Here we present the mApLe (metabolic pathway predictor of plant enzymes), a high‐performance machine learning‐based tool with models to label the metabolic pathway of enzymes rather than specifying enzymes’ reactions. The mApLe uses molecular descriptors of the enzyme sequences to perform predictions without considering sequence similarities with reference sequences. Hence, mApLe can classify a diversity of enzymes, even the ones without any homolog or with incomplete EC numbers. This tool can be used to improve the quality of genomic annotation of plants or to narrow down the number of candidate genes for metabolic engineering researches. The mApLe tool is available online, and the GUI can be locally installed.

中文翻译：

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在不使用序列相似性的情况下预测植物酶的代谢途径：机器学习模型

用于酶注释的大多数生物信息学工具集中于酶功能分配。与特征明确的酶的序列相似性通常用于功能注释和分配代谢途径。然而，这些方法对于导致注释不正确或代谢途径信息缺乏的所有序列都是不可行的。在这里，我们介绍了mApLe（植物酶的代谢途径预测因子），这是一种基于机器学习的高性能工具，其模型可以标记酶的代谢途径，而不是指定酶的反应。mApLe使用酶序列的分子描述符进行预测，而无需考虑与参考序列的序列相似性。因此，mApLe可以对多种酶进行分类，即使是没有任何同源性或EC号不完整的酶。此工具可用于提高植物基因组注释的质量或缩小代谢工程研究的候选基因的数量。mApLe工具可在线获得，并且GUI可以在本地安装。