Prediction of Hemoglobin Structure from DNA Sequence through Neural Network and Hidden Markov Model

One of the greatest challenges today in bioinformatics is to predict the structure of the protein from the DNA sequence. Protein structural domains are often associated with a particular protein function also the structure contains a valuable information to the biologists instead of the meaningless sequence. Because the experimental techniques that used to determine protein structure such as the x-ray crystallography and Nuclear Magnetic Resonance "NMR" spectroscopy are very expensive and can not be applied all the time, so the prediction may be the way to get the protein structure. In this work we will be able to predict the 3D structure of hemoglobin using two techniques; the neural network and hidden Markov model. Also, the prediction of the secondary structure is applied using multiple alignments.