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

共 50 条

[1] Temperature Prediction in a Public Building Using Artificial Neural Network
Romazanov, Artur
Zakharov, Alexander
Zakharova, Irina
PROCEEDINGS OF THE 8TH SCIENTIFIC CONFERENCE ON INFORMATION TECHNOLOGIES FOR INTELLIGENT DECISION MAKING SUPPORT (ITIDS 2020), 2020, 174 : 30 - 34
[2] An efficient underground water prediction using optimal deep neural network
Sureshkumar, V.
Rajasomashekar, S.
Sarala, B.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2023, 35 (01):
[3] ONP deinking by complex cellulases with different optimal pH
Natl. Pulp and Paper Res. Inst., Beijing 100020, China
Chung kuo Tsao Chih, 9 (8-11):
[4] Prediction of the Freezing Temperature of Saline Soil Using Neural Network Methods
Duan, Jieyun
Xiao, Zean
Zhu, Linze
Li, Kangliang
ATMOSPHERE, 2023, 14 (03)
[5] Prediction of soil temperature using regression and artificial neural network models
Bilgili, Mehmet
METEOROLOGY AND ATMOSPHERIC PHYSICS, 2010, 110 (1-2) : 59 - 70
[6] Austenite formation temperature prediction in steels using an artificial neural network
Arjomandi, M.
Sadati, S. H.
Khorsand, H.
Abdoos, H.
DIFFUSION IN SOLIDS AND LIQUIDS III, 2008, 273-276 : 335 - 341
[7] Prediction of temperature decreasing on a green roof by using artificial neural network
Erdemir, Dogan
Ayata, Tahir
APPLIED THERMAL ENGINEERING, 2017, 112 : 1317 - 1325
[8] Prediction of accumulated temperature in vegetation period using artificial neural network
Mi, Chunqiao
Yang, Jianyu
Li, Shaoming
Zhang, Xiaodong
Zhu, Dehai
MATHEMATICAL AND COMPUTER MODELLING, 2010, 51 (11-12) : 1453 - 1460
[9] Prediction of soil temperature using regression and artificial neural network models
Mehmet Bilgili
Meteorology and Atmospheric Physics, 2010, 110 : 59 - 70
[10] Prediction of the minimum film boiling temperature using artificial neural network
Bahman, Ammar M.
Ebrahim, Shikha A.
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER, 2020, 155

← 1 2 3 4 5 →