Predicting More Accurate Structure Of Human Oxidoreductase Protein Family Using Neural Network Technique

摘要

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 α-helix, β-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 identifies the secondary structure of Human Oxidoreductase family with more than 79% accuracy, which is well above any previously reported method.