Protein structure prediction plays a vital role in drug design and biotechnology. Understanding protein structures is necessary to determine the function of a protein and its interaction with DNA, RNA and Enzymes. Experimental techniques such as NMR Spectroscopy and X-ray Crystallography have been the main source of information about protein structures. But these conventional methods are now replaced by Machine learning methods such as Artificial Neural Network (ANN) and Support Vector Machine (SVM)s. In this paper, ANNs are used as a two level classifier to estimate the tertiary structure of proteins. ANNs are trained to make them capable of recognizing the primary sequences and DSSP codes of protein structures and their association with the secondary structure is derived. Based on majority selection, the final secondary structure is evaluated. These secondary structures can be further used as inputs to classify between the basic tertiary folds and subclasses of tertiary folds.

Protein structure prediction is turning out to be one of the major challenges in the field of bio-informatics. It is highly important in medicine, especially in drug design and biotechnology. Proteins, being the basic building unit of all organisms, require experimental techniques for prediction of related structures. Among available methods, softcomputational tools provide readily available solutions for making predictions with less complexity, higher reliability and less time. The Artificial Neural Network (ANN) is one such tool which is used for structure prediction of proteins. This method is a machine learning approach in which ANNs are trained to make them capable of recognizing the 8-level subclasses of secondary structure. After the subclasses are recognized in a given sequence, their association with 3-level secondary protein structures is derived. The final structure is obtained from a majority selection from the protein structure. The work is also done in the reverse way, by predicting the 3-level secondary structure from the primary structure .This is done to confirm the accuracy of the prediction. In this work, ANNs are used as classifier to predict the secondary structure.