Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 334, Issue -, Pages 45-51Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2013.06.003
Keywords
Auto covariance; Jackknife validation; Reduced amino acid alphabet; Increment of diversity
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Funding
- National Natural Science Foundation of China [61063016, 31160188]
- Research Fund for the Doctoral Program of Higher Education of China [20101501110004]
- Science and Technology Foundation of Ministry of Education of China [211030]
- Scientific Research Program at Universities of Inner Mongolia Autonomous Region of China [NJZY13014]
- Natural Science Foundation of Inner Mongolia Autonomous Region of China [2013MS0504]
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Bioluminescent proteins are highly sensitive optical reporters for imaging in live animals; they have been extensively used in analytical applications in intracellular monitoring, genetic regulation and detection, and immune and binding assays. In this work, we systematically analyzed the sequence and structure information of 199 bioluminescent and nonbioluminescent proteins, respectively. Based on the results, we presented a novel method called auto covariance of averaged chemical shift (acACS) for extracting structure features from a sequence. A classifier of support vector machine (SVM) fusing increment of diversity (ID) was used to distinguish bioluminescent proteins from nonbioluminescent proteins by combining dipeptide composition, reduced amino acid composition, evolutionary information, and acACS. The overall prediction accuracy evaluated by jackknife validation reached 82.16%. This result was better than that obtained by other existing methods. Improvement of the overall prediction accuracy reached up to 5.33% higher than those of the SVM and auto covariance of sequential evolution information by 10-fold cross-validation. The acACS algorithm also outperformed other feature extraction methods, indicating that our approach is better than other existing methods in the literature. (C) 2013 Elsevier Ltd. All rights reserved.
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