An Integrated Approach for Protein Structure Prediction Using Artificial Neural Network

Protein prediction is a fundamental problem in Bioinformatics. Protein structure prediction has vital importance in drug design and biotechnology. Huge amount of biological importance data is being produced and there is great need to transcribe the DNA sequences into amino acid sequences because peptide functions perform important role in body functions of species. Exponential growth of genomic data and complex structure of protein make it challenging to predict its structure. In this paper, we are proposing an integrated approach for the prediction of tri-nucleotide base patterns in DNA strands leading to transcription of peptide regions in genomic sequences. The approach comprise of preprocessing of data, transcription engine and post processing of output. The task has been carried out using series of filters that purify the raw data and assign weights to bases for further feeding to central engine. JOONE (Java Object Oriented Neural network) takes input in the form of segmented data and assign to processes at sigmoid layers. Each layer contains processes and feed forward and back propagation techniques make it possible to predict the sample pattern from genomic sequences of variant sizes.