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Open Access Research article

BLProt: prediction of bioluminescent proteins based on support vector machine and relieff feature selection

Krishna Kumar Kandaswamy12*, Ganesan Pugalenthi3, Mehrnaz Khodam Hazrati24, Kai-Uwe Kalies5 and Thomas Martinetz1

Author Affiliations

1 Institute for Neuro- and Bioinformatics, University of Lübeck, 23538 Lübeck, Germany

2 Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, 23538 Lübeck, Germany

3 Bioinformatics Group, Bioscience Core Lab, King Abdullah University of Science and Technology (KAUST), Kingdom of Saudi Arabia

4 Institute for Signal Processing, University of Lübeck, 23538 Lübeck, Germany

5 Centre for Structural and Cell Biology in Medicine, Institute of Biology, University of Lübeck, Germany

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BMC Bioinformatics 2011, 12:345  doi:10.1186/1471-2105-12-345

Published: 17 August 2011

Abstract

Background

Bioluminescence is a process in which light is emitted by a living organism. Most creatures that emit light are sea creatures, but some insects, plants, fungi etc, also emit light. The biotechnological application of bioluminescence has become routine and is considered essential for many medical and general technological advances. Identification of bioluminescent proteins is more challenging due to their poor similarity in sequence. So far, no specific method has been reported to identify bioluminescent proteins from primary sequence.

Results

In this paper, we propose a novel predictive method that uses a Support Vector Machine (SVM) and physicochemical properties to predict bioluminescent proteins. BLProt was trained using a dataset consisting of 300 bioluminescent proteins and 300 non-bioluminescent proteins, and evaluated by an independent set of 141 bioluminescent proteins and 18202 non-bioluminescent proteins. To identify the most prominent features, we carried out feature selection with three different filter approaches, ReliefF, infogain, and mRMR. We selected five different feature subsets by decreasing the number of features, and the performance of each feature subset was evaluated.

Conclusion

BLProt achieves 80% accuracy from training (5 fold cross-validations) and 80.06% accuracy from testing. The performance of BLProt was compared with BLAST and HMM. High prediction accuracy and successful prediction of hypothetical proteins suggests that BLProt can be a useful approach to identify bioluminescent proteins from sequence information, irrespective of their sequence similarity. The BLProt software is available at http://www.inb.uni-luebeck.de/tools-demos/bioluminescent%20protein/BLProt webcite