One of the most important macromolecule found in every organism is protein. It performs a wide variety of functions. Improper functioning of certain proteins leads to various abnormalities in the human body. The proper functioning of a protein is determined by its structure. Hence, protein structure prediction is in great demand in the field of bio-informatics. Along with the experimental methods, now-a-days soft computational tools are also used to predict the structure of a protein. The Artificial Neural Network (ANN) is one such tool which is used for structure prediction of proteins. Different 3D images of proteins are collected from Protein Data Bank (PDB) [1]. The dimensions of these images are very large. In this paper, Self-Organizing Map (SOM) is used to reduce the dimensions of the images of proteins which are next used as ANN's inputs for training, enabling it to recognize various structures of protein. The proposed SOM based approach is found to be superior in terms of computational complexity compared to Principal Component Analysis (PCA) based method.
['Sanyukta Chetia', 'Kandarpa Kumar Sarma']
SOM based dimension reduction techniques for quaternary protein structure prediction