Prediction of Optimal pH and Temperature of Cellulases Using Neural Network

被引：12

作者：

Yan, Shao-Min ^{[1
]}

Wu, Guang ^{[1
,2
]}

机构：

[1] Guangxi Acad Sci, State Key Lab Nonfood Biomass Enzyme Technol, Natl Engn Res Ctr Nonfood Biorefinery, Guangxi Key Lab Biorefinery, Nanning 530007, Guangxi, Peoples R China

[2] DreamSciTech Consulting, Shenzhen 518054, Guangdong, Peoples R China

来源：

PROTEIN AND PEPTIDE LETTERS | 2012年 / 19卷 / 01期

关键词：

Cellulase; enzyme; optimal pH; optimal temperature; prediction; AMINO-ACID-COMPOSITION; INFLUENZA-A VIRUS; SUPPORT VECTOR MACHINES; PROTEIN STRUCTURAL CLASSES; MUTATED PRIMARY-STRUCTURE; QUANTITATIVE RELATIONSHIP; SUBCELLULAR LOCATION; FUNCTIONAL DOMAIN; LIGNOCELLULOSIC BIOMASS; H5N1; HEMAGGLUTININS;

D O I：

10.2174/092986612798472794

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

Cellulase is an important enzyme widely used in various industries, and now in fermentation of biomass into biofuels. Enzymatic function of cellulase is closely related to pH, temperature, substrate concentration, etc. For newly found cellulase, it would be more cost-effective to predict its optimal pH and temperature before conducting the costly experiments. In this study, we used a 20-2 feedforward backpropagation neural network to build the relationship between information obtained from primary structure of cellulase with optimal pH and temperature to predict the optimal pH and temperature in cellulases. The results show that the amino-acid distribution probability representing the primary structure of cellulase can predict both optimal pH and temperature, whereas various properties of amino acids related to the primary structure cannot do so.

引用

页码：29 / 39

页数：11

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