Beyond Bioinformatics

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

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 Publishing Group

Copyright

Publisher

Copyright Transfer Agreement

The authors of published articles in Bioinformation automatically transfer the copyright to the publisher upon formal acceptance. However, the authors reserve right to use the information contained in the article for non commercial purposes.

License

This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.