ArticleThe plant genome2020
Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning.
Article in The plant genome, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Synthetic Biology of Plants and Microbes for Agriculture, Environment, and Future Applications.Chemical reviews · 2026Review
- Data Science and Plant Metabolomics.Metabolites · 2023Review
- Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.Metabolites · 2021Review
- New Trends in Bioremediation Technologies Toward Environment-Friendly Society: A Mini-Review.Frontiers in bioengineering and biotechnology · 2021Review
- Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning.The plant genome · 2020Article
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Authors and funding
2 authors.
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Abstract
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.
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