The turnover number is an important parameter to distinguish whether an enzyme is practically workable.
Therefore the prediction of turnover number of enzyme will reduce the workload to conduct time-consuming and costly
experiments to determine the turnover number. However, no studies have been so far conducted to predict them with respect
to cellulose 1,4-beta-cellobiosidase, which is an enzyme used in industries, especially in bio-fuel industry. It is important
to develop methods to predict the turnover numbers of cellulose 1,4-beta-cellobiosidases from both wild-type and
mutations. In this study, we used neural network models with different amino acid properties, pH levels, temperatures and
substrates as inputs to predict the turnover number. The results show that 11 out of 25 amino acid properties analyzed can
work as predictor and the amino acid distribution probability is the best one because it can reach smaller mean squared errors
during convergence and higher correlation coefficient in two-layer neural network models. This study demonstrates
the probability that the neural network model can approximately predict the turnover number of cellulose 1,4-betacellobiosidase.
Keywords: Cellulose 1, 4-beta-cellobiosidase, prediction, turnover number, parameter, enzyme, experiment, bio-fuel industry, mutation, neural network model, amino acid
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Published on: 18 January, 2013
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