Zeitschrift für Proteomik und Bioinformatik
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Predicting secondary structure of Oxidoreductase protein family using Bayesian Regularization Feed-forward Backpropagation ANN Technique

Brijesh Singh Yadav, Mayank Pokhariyal, Barkha Ratta, Gaurava Rai, Meeta Saxena, Bhaskar Sharma and K.P.Mishra

Arti fi cial Neural Networks (ANNs) are simpli fi ed models of the nervous system, in which neurons are considered as simple processing units linked with weighted connections called synaptic ef fi cacies. These weights are gradually adjusted according to a learning algorithm.Oxidoreductase any of a class of enzymes that catalyse oxidation–reduction reactions, i.e. they are involved in the transfer of hydrogen or electrons between molecules. They include the oxidases and dehydrogenases.

In this paper, an attempt has been made to develop a neural network-based method for predicting the secondary structure of protein ( Human Oxidoreductase family). The neural network has been trained using Bayesian Regularization Feed-forward Backpropagation Neural Network Technique to predict the -helix, -sheet and coil regions of this protein family. Feed-forward neural network have been trained by analyzing windows of 25 parameters for predicting the central residue of protein sequence. PSI-BLAST has been used for multiple-sequence alignment. SCOP and PDB database has been used for searching the primary and secondary structure of proteins and for training the data set. The method correctly identi fi es the secondary structure of Human Oxidoreductase family with more than 79% accuracy, which is well above any previously reported method.