Prediction of Optimal pH and Temperature of Cellulases Using Neural Network

Title: Prediction of Optimal pH and Temperature of Cellulases Using Neural Network

Volume: 19 Issue: 1

Author(s): Shao-Min Yan and Guang Wu

Affiliation:

Keywords: Cellulase, backpropagation, haemoglobins, HIV protease, Prediction Model, Amino-Acid Distribution, Statistics, hydrophilicity, hydrophobicity, cross-validation, jackknife test, neural network, optimal pH, tan-sigmoid, fastest algorithm

Abstract: 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.

Cite this article as:

Yan Shao-Min and Wu Guang, Prediction of Optimal pH and Temperature of Cellulases Using Neural Network, Protein & Peptide Letters 2012; 19 (1) . https://dx.doi.org/10.2174/092986612798472794