Disulphide Bridge Prediction using Fuzzy Support Vector Machines

One of the major contributors to the native form of protein 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 rises 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

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