Disulphide Bridge Prediction using Fuzzy Support Vector Machines

Abstract

One of the major contributors to the native form of protien is cystines forming covalent bonds in oxidized state. The Prediction of such bridges from the sequence is a very challenging task given that the number of bridges will rise exponentially as the number of cystines increases. We propose a novel technique for disulphide bridge prediction based on Fuzzy Support Vector Machines. We call the system DIzzy. In our investigation, we look at disulphide bond connectivity given two Cystines with and without a priori knowledge of the bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features such as the probability of occurrence of each amino acid in different secondary structure states along with psiblast profiles. The performance is compared with normal support vector machines. We evaluate our method and compare it with the existing method using SPX dataset.

Protein Structure Prediction Using Support Vector Machine

“…Proteins are the basis for the major structural components of animal and human tissue. Extensive biochemical experiments [5], [6], [9], [10] have shown that a protein's function is determined by its structure. Experimental approaches such as X-ray crystallography [11], [12] and nuclear magnetic resonance (NMR) spectroscopy [13], [14] are the main techniques for determining protein structures.…”

“…Experimental approaches such as X-ray crystallography [11], [12] and nuclear magnetic resonance (NMR) spectroscopy [13], [14] are the main techniques for determining protein structures. Since the determination of the first two protein structures (myoglobin and hemoglobin) using X-ray crystallography [5], [6], the number of proteins with solved structures has increased rapidly. Currently, there are about 40 000 proteins with empirically known structures deposited in the Protein Data Bank (PDB) [15].…”