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Title

Prediction of cystine connectivity using SVM

 

Authors

Jayavardhana Rama G. L.1*, Alistair P. Shilton1, Michael M. Parker2, Marimuthu Palaniswami1

 

Affiliation

 

1Department of Electrical and Electronics Engineering, The University of Melbourne, Parkville, Victoria 3010; 2St. Vincent's Institute of Medical Research, Fitzroy, Victoria - 3065

 

E-mail*

jrgl@ee.unimelb.edu.au; * Corresponding author

 

Article Type

 

Prediction model

 

Date

 

received November 10, 2005; revised December 6, 2005; accepted December 7, 2005; published online December 7, 2005

 

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

 

Citation

 

Jayavardhana Rama et al., Bioinformation 1(2): 69-74 (2005)

 

Edited by

 

P. Kangueane

 

ISSN

 

0973-2063

 

Publisher

 

Biomedical Informatics

 

License

 

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.