Journal
CURRENT DRUG TARGETS
Volume 20, Issue 5, Pages 540-550Publisher
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1389450119666181002143355
Keywords
Enzyme; family; classification; machine learning methods
Categories
Funding
- National Nature Scientific Foundation of China [31771471]
- Fundamental Research Funds for the Central Universities of China [ZYGX2015Z006, ZYGX2016J125, ZYGX2016J118]
- Natural Science Foundation for Distinguished Young Scholar of Hebei Province [C2017209244]
- Program for the Top Young Innovative Talents of Higher Learning Institutions of Hebei Province [BJ2014028]
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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.
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