Prediction of cystine connectivity using SVM.
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.
Jayavardhana Rama
G L
GL
Department of Electrical and Electronics Engineering, The University of Melbourne, Parkville, Victoria. jrgl@ee.unimelb.edu.au
Shilton
Alistair P
AP
Parker
Michael M
MM
Palaniswami
Marimuthu
M