Protein Topology Classification Using Two-Stage Support Vector Machines

Jayavardhana Gubbi[1] (jrgl@ee.unimelb.edu.au)
Alistair Shilton[1] (apsh@ee.unimelb.edu.au)
Michael Parker[2] (mparker@svi.edu.au)
Marimuthu Palaniswami[1] (swami@ee.unimelb.edu.au)

[1]Department of Electrical and Electronics Engineering, The University of Melbourne, Parkville, Victoria 3010, Australia
[2]St. Vincent's Institute of Medical Research, 9 Princes Street, Fitzroy, Victoria 3065, Australia


Abstract

The determination of the first 3-D model of a protein from its sequence alone is a non-trivial problem. The first 3-D model is the key to the molecular replacement method of solving phase problem in x-ray crystallography. If the sequence identity is more than 30%, homology modelling can be used to determine the correct topology (as defined by CATH) or fold (as defined by SCOP). If the sequence identity is less than 25%, however, the task is very challenging. In this paper we address the topology classification of proteins with sequence identity of less than 25%. The input information to the system is amino acid sequence, the predicted secondary structure and the predicted real value relative solvent accessibility. A two stage support vector machine (SVM) approach is proposed for classifying the sequences to three different structural classes (α, β, α+β) in the first stage and 39 topologies in the second stage. The method is evaluated using a newly curated dataset from CATH with maximum pairwise sequence identity less than 25%. An impressive overall accuracy of 87.44% and 83.15% is reported for class and topology prediction, respectively. In the class prediction stage, a sensitivity of 0.77 and a specificity of 0.91 is obtained. Data file, SVM implementation (SVMHEAVY) and result files can be downloaded from
http://www.ee.unimelb.edu.au/ISSNIP/downloads/.

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Japanese Society for Bioinformatics