A hierarchical deep learning based approach for multi‐functional enzyme classification. (28th June 2021)

• Record Type: Journal Article
• Authors: Khan, Kinaan Aamir; Memon, Safyan Aman; Naveed, Hammad
• Is Part Of: Protein science. Volume 30:Number 9(2021)
• Page Start: 1935
• Page End: 1945
• Publication Date: 2021-06-28
• Subjects: assisted learning -- enzyme function prediction -- hierarchical classification -- isoforms -- multi‐functional enzyme; Proteins -- Periodicals; 572.6
• DOI: 10.1002/pro.4146
• Languages: English
• ISSNs: 0961-8368

Abstract

Abstract: 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/ .