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