Protein secondary structure prediction using local adaptive techniques in training neural networks

One of the most significant problems in computer molecular biology today is how to predict a protein's three-dimensional structure from its one-dimensional amino acid sequence or generally call the protein folding problem and difficult to determine the corresponding protein functions. Thus, this paper involves protein secondary structure prediction using neural network in order to solve the protein folding problem. The neural network used for protein secondary structure prediction is multilayer perceptron (MLP) of the feed-forward variety. The training set are taken from the protein data bank which are 120 proteins while 60 testing set is the proteins which were chosen randomly from the protein data bank. Multiple sequence alignment (MSA) is used to get the protein similar sequence and Position Specific Scoring matrix (PSSM) is used for network input. The training process of the neural network involves local adaptive techniques. Local adaptive techniques used in this paper comprises Learning rate by sign changes, SuperSAB, Quickprop and RPROP. From the simulation, the performance for learning rate by Rprop and Quickprop are superior to all other algorithms with respect to the convergence time. However, the best result was obtained using Rprop algorithm.