Protein secondary structure prediction is a problem related to structural bioinformatics which deals with the prediction and analysis of macromolecules i.e. DNA, RNA and protein. It is an important step towards elucidating its three dimensional structure, as well as its function. Secondary structure of a protein can be predicted from its primary structures i.e. from the amino acid sequences or from the residues though challenges exists. For these four methods are used. These are Statistical Approach, Nearest Neighbor method, Neural Network Approach and Hidden Markov Model Approach. The Artificial Neural Network (ANN) approach for prediction of protein secondary structure is the most successful one among all the methods used. In this method, ANNs are trained to make them capable of performing recognition of amino acid patterns in known secondary structure units and these patterns are used to distinguish between the different types of secondary structures. This work is related to the prediction of secondary structure of proteins employing artificial neural network though it is restricted initially to three structures only.

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Artificial Neural network Amino acids Protein structure prediction