A Survey for Predicting Enzyme Family Classes Using Machine Learning Methods.

Abstract:

:Enzymes are proteins that act as biological catalysts to speed up cellular biochemical processes. According to their main Enzyme Commission (EC) numbers, enzymes are divided into six categories: EC-1: oxidoreductase; EC-2: transferase; EC-3: hydrolase; EC-4: lyase; EC-5: isomerase and EC-6: synthetase. Different enzymes have different biological functions and acting objects. Therefore, knowing which family an enzyme belongs to can help infer its catalytic mechanism and provide information about the relevant biological function. With the large amount of protein sequences influxing into databanks in the post-genomics age, the annotation of the family for an enzyme is very important. Since the experimental methods are cost ineffective, bioinformatics tool will be a great help for accurately classifying the family of the enzymes. In this review, we summarized the application of machine learning methods in the prediction of enzyme family from different aspects. We hope that this review will provide insights and inspirations for the researches on enzyme family classification.

journal_name

Curr Drug Targets

journal_title

Current drug targets

authors

Tan JX,Lv H,Wang F,Dao FY,Chen W,Ding H

doi

10.2174/1389450119666181002143355

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

540-550

issue

5

eissn

1389-4501

issn

1873-5592

pii

CDT-EPUB-93374

journal_volume

20

pub_type

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