Department of Physics, School of Physical Science and Technology, Inner Mongolia University, Hohhot 010021, China.
J Theor Biol. 2013 Oct 7;334:45-51. doi: 10.1016/j.jtbi.2013.06.003. Epub 2013 Jun 13.
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.
生物发光蛋白是活体动物成像的高度敏感光学报告器；它们已被广泛应用于细胞内监测、基因调控和检测、免疫和结合分析等分析应用中。在这项工作中，我们分别系统地分析了 199 种生物发光和非生物发光蛋白的序列和结构信息。基于这些结果，我们提出了一种称为平均化学位移自协方差（acACS）的新方法，用于从序列中提取结构特征。支持向量机（SVM）分类器融合增量多样性（ID），通过结合二肽组成、简化氨基酸组成、进化信息和 acACS，将生物发光蛋白与非生物发光蛋白区分开来。通过 Jackknife 验证评估的整体预测准确率达到 82.16%。这一结果优于其他现有方法的结果。通过 10 倍交叉验证，整体预测准确率的提高最高可达 5.33%，优于 SVM 和序列进化信息自协方差的结果。acACS 算法也优于其他特征提取方法，表明我们的方法优于文献中的其他现有方法。
