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Open Access Open Badges Research article

Conotoxin protein classification using free scores of words and support vector machines

Nazar Zaki1*, Stefan Wolfsheimer2, Gregory Nuel2 and Sawsan Khuri3

Author Affiliations

1 Faculty of Information Technology, UAE University, 17551 Al-Ain, UAE

2 MAP5, University Paris-Descartes, 45 rue des Saints-Peres, Paris, France

3 Center for Computational Science, University of Miami, USA

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BMC Bioinformatics 2011, 12:217  doi:10.1186/1471-2105-12-217

Published: 29 May 2011



Conotoxin has been proven to be effective in drug design and could be used to treat various disorders such as schizophrenia, neuromuscular disorders and chronic pain. With the rapidly growing interest in conotoxin, accurate conotoxin superfamily classification tools are desirable to systematize the increasing number of newly discovered sequences and structures. However, despite the significance and extensive experimental investigations on conotoxin, those tools have not been intensively explored.


In this paper, we propose to consider suboptimal alignments of words with restricted length. We developed a scoring system based on local alignment partition functions, called free score. The scoring system plays the key role in the feature extraction step of support vector machine classification. In the classification of conotoxin proteins, our method, SVM-Freescore, features an improved sensitivity and specificity by approximately 5.864% and 3.76%, respectively, over previously reported methods. For the generalization purpose, SVM-Freescore was also applied to classify superfamilies from curated and high quality database such as ConoServer. The average computed sensitivity and specificity for the superfamily classification were found to be 0.9742 and 0.9917, respectively.


The SVM-Freescore method is shown to be a useful sequence-based analysis tool for functional and structural characterization of conotoxin proteins. The datasets and the software are available at webcite.