Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning

• Author(s): de Oliveira Almeida, Rodrigo [UNESP] ; Valente, Guilherme Targino [UNESP]
• Date: 2020
• Persistent ID: http://hdl.handle.net/11449/199306
• Origin: Oasisbr
• Document Type: Journal article
• Language: English

Description

Made available in DSpace on 2020-12-12T01:36:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01-01

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.

Instituto Federal de Educação Ciência e Tecnologia do Sudeste de Minas Gerais Muriaé

Department of Bioprocess and Biotechnology School of Agriculture São Paulo State University (Unesp)

Department of Developmental Genetics Max Planck Institut für Herz- und Lungenforschung Bad Nauheim

Department of Bioprocess and Biotechnology School of Agriculture São Paulo State University (Unesp)