Abstract:One of the major contributors to protein structures is the formation of disulphide bonds between selected pairs of cysteines at oxidized state. Prediction of such disulphide bridges from sequence is challenging given that the possible combination of cysteine pairs as the number of cysteines increases in a protein.Here, we describe a SVM (support vector machine) model for the prediction of cystine connectivity in a protein sequence with and without a priori knowledge on their bonding state. We make use of a new encoding scheme based on physico-chemical properties and statistical features (probability of occurrence of each amino acid residue in different secondary structure states along with PSI-blast profiles). We evaluate our method in SPX (an extended dataset of SP39 (swiss-prot 39) and SP41 (swiss-prot 41) with known disulphide information from PDB) dataset and compare our results with the recursive neural network model described for the same dataset.Keywords: disulphide bridges; prediction; protein fold; SVM model; SPX dataset Background:The completion of the human genome project shows a significant gap between the protein sequence and known structure space. Determination of protein structures using conventional X-ray crystallography and NMR (nuclear magnetic resonance) techniques is not adequate to cover the sequence space in the context of drug discovery. Hence, protein structure prediction using computational methods is becoming critical. However, prediction of protein tertiary structure from sequence is non-trivial and is generally achieved by dividing the problem into finite levels of secondary structures and super secondary structures.

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.

We report the design and development of Thirukkurul, the first text-to-speech converter in Tamil. Syllables of different lengths have been selected as units since Tamil is a syllabic language. Automatic segmentation algorithm [8] has been devised for segmenting syllables into consonant and vowel. The units are pitch marked using Discrete Co-

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