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BLProt: Prediction of Bioluminescent proteins based on Discrete Wavelet Transform and Support Vector Machine

 

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 emits 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) based on 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.

 

Conclusion:

BLProt achieved 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 predictio 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 software and dataset can be downloaded here .