Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition.

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.