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.
Highlights
The completion of the human genome project shows a significant gap between the protein sequence and known structure space
Prediction of disulphide bonds from sequence has a critical role to play in protein fold identification and folding simulation
The results were compared with the recursive neural network model by Cheng and colleagues [8] in SPX dataset
Summary
The completion of the human genome project shows a significant gap between the protein sequence and known structure space. Fariselli et al, [2] proposed a disulphide prediction model combining a neural network based predictor and evolutionary data with an accuracy of 81%. Ferre and Clote [7] emphasized the importance of secondary structure and solvent accessibility information in the development of a di-residue neural network model for predicting disulphide bridges.
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