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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Review Article

Remarks on Computational Method for Identifying Acid and Alkaline Enzymes

Author(s): Hongfei Li, Haoze Du, Xianfang Wang*, Peng Gao, Yifeng Liu and Weizhong Lin

Volume 26, Issue 26, 2020

Page: [3105 - 3114] Pages: 10

DOI: 10.2174/1381612826666200617170826

Price: $65

Abstract

The catalytic efficiency of the enzyme is thousands of times higher than that of ordinary catalysts. Thus, they are widely used in industrial and medical fields. However, enzymes with protein structure can be destroyed and inactivated in high temperature, over acid or over alkali environment. It is well known that most of enzymes work well in an environment with pH of 6-8, while some special enzymes remain active only in an alkaline environment with pH > 8 or an acidic environment with pH < 6. Therefore, the identification of acidic and alkaline enzymes has become a key task for industrial production. Because of the wide varieties of enzymes, it is hard work to determine the acidity and alkalinity of the enzyme by experimental methods, and even this task cannot be achieved. Converting protein sequences into digital features and building computational models can efficiently and accurately identify the acidity and alkalinity of enzymes. This review summarized the progress of the digital features to express proteins and computational methods to identify acidic and alkaline enzymes. We hope that this paper will provide more convenience, ideas, and guides for computationally classifying acid and alkaline enzymes.

Keywords: Amino acid composition, pseudo amino acid composition, evolutionary information, dipeptide composition, average chemical shift, feature selection techniques.

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