ABSTRACT
The Michaelis-Menten constant Km is a very important parameter to relate enzyme with its substrate in enzymatic reaction. Although Km can be experimentally determined, the Km values are not easily available in literature. With rapid increase of newly designed enzymes, we face the shortage of parameters related to enzymatic reactions. The beta-glucosidase is a crucial enzyme for cellulose hydrolysis and cellobiose is one of its substrates. In this study, we attempt to develop models to predict Km with cellobiose as substrates using information about primary structure of beta-glucosidase. The results show that the 20-1 feedforward backpropagation neural network using the amino-acid distribution probability as predictor works best for prediction of Km values.
