The Prediction of Disulphide Bonding in HIV and other lenti-viruses by Machine Learning Techniques

Abstract

The introduction of disulphide bonds into proteins is an important mechanism by which they have evolved and are evolving. Most protein disulphide bonds are motifs that stabilize the tertiary and quaternary protein structure. These bonds also thought to assist protein folding by decreasing the entropy of the unfolded form. Amino acid cysteine plays a fundamental role in formation of disulphide bonds. In the present study, proteomics of disulphide bonding in HIV is studied through a machine learning model which has been developed to classify disulphide bonds from different species of lentiviruses like bovine immunodeficiency virus (BIV), simian immunodeficiency virus (SIV), Feline immunodeficiency virus, murine infectious virus (MIV) and equine infectious anaemia virus (EIV) and Human immunodeficiency virus (HIV). Phylogenetic relationship is also studied by the prediction of disulphide bonding among these viruses. Hence by different algorithms of WEKA classifier J48 predicts better classification with an accuracy of 89.6104%.