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Prediction of Optimal pH and Temperature of Cellulases Using Neural Network

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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.

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

Document Type: Research Article

Publication date: January 1, 2012

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  • Protein & Peptide Letters publishes short papers in all important aspects of protein and peptide research, including structural studies, recombinant expression, function, synthesis, enzymology, immunology, molecular modeling, drug design etc. Manuscripts must have a significant element of novelty, timeliness and urgency that merit rapid publication. Reports of crystallisation, and preliminary structure determinations of biologically important proteins are acceptable. Purely theoretical papers are also acceptable provided they provide new insight into the principles of protein/peptide structure and function.
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