Aproaches to Prediction of Protein Structure: a Review

1Er. Amanpreet Kaur, Student, Dept. of Computer Science & Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, INDIA 2Dr. Baljit Singh Khehra, Professor and Head, Dept. of Computer Science & Engineering, Baba Banda Singh Bahadur Engineering College, Fatehgarh Sahib, Punjab, INDIA ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Data mining software is one of the most important analytical tool for analyzing data. It allows users to analyze data from many different dimensions, categorize it, and summarize the relationships identified. Protein structure prediction (PSP) is the most important and challenging problem in bioinformatics today. This is due to the fact that the biological function of the protein is determined by its structure. While there is a gap between the number of known protein structures and the number of known protein sequences, protein structure prediction aims at reducing this structure –sequence gap. Protein structure can be experimentally determined using either X-ray crystallography or Nuclear Magnetic Resonance (NMR). However, these empirical techniques are very time consuming, so various machine learning approaches have been developed for protein structure prediction like HMM, SVM and NN. In this paper we give a general introductory background to the area and a literature survey about the machine learning approaches. These approaches depends on the chemical and physical properties of the constituent amino acids. Not all machine learning algorithms have the same performance, so we represent the general success keys for any such algorithm.