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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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Review Article

Recent Development of Computational Predicting Bioluminescent Proteins

Author(s): Dan Zhang, Zheng-Xing Guan, Zi-Mei Zhang, Shi-Hao Li, Fu-Ying Dao, Hua Tang* and Hao Lin*

Volume 25, Issue 40, 2019

Page: [4264 - 4273] Pages: 10

DOI: 10.2174/1381612825666191107100758

Price: $65

Abstract

Bioluminescent Proteins (BLPs) are widely distributed in many living organisms that act as a key role of light emission in bioluminescence. Bioluminescence serves various functions in finding food and protecting the organisms from predators. With the routine biotechnological application of bioluminescence, it is recognized to be essential for many medical, commercial and other general technological advances. Therefore, the prediction and characterization of BLPs are significant and can help to explore more secrets about bioluminescence and promote the development of application of bioluminescence. Since the experimental methods are money and time-consuming for BLPs identification, bioinformatics tools have played important role in fast and accurate prediction of BLPs by combining their sequences information with machine learning methods. In this review, we summarized and compared the application of machine learning methods in the prediction of BLPs from different aspects. We wish that this review will provide insights and inspirations for researches on BLPs.

Keywords: Bioluminescent proteins, machine learning methods, sequence-derived features, feature analysis, bioinformatics tools.

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