Nithya Varadharaju
Department of Animal Health Management, Alagappa University, Karaikudi-630003, India.
Bioinformation. 2019 Dec 31;15(12):863-868. doi: 10.6026/97320630015863. eCollection 2019.
Mitochondria are important sub-cellular organelles in eukaryotes. Defects in mitochondrial system lead to a variety of disease. Therefore, detailed knowledge of mitochondrial proteome is vital to understand mitochondrial system and their function. Sequence databases contain large number of mitochondrial proteins but they are mostly not annotated. In this study, we developed a support vector machine approach, SubmitoLoc, to predict mitochondrial sub cellular locations of proteins based on various sequence derived properties. We evaluated the predictor using 10-fold cross validation. Our method achieved 88.56 % accuracy using all features. Average sensitivity and specificity for four-subclass prediction is 85.37% and 87.25% respectively. High prediction accuracy suggests that SubmitoLoc will be useful for researchers studying mitochondrial biology and drug discovery.
线粒体是真核生物中重要的亚细胞细胞器。线粒体系统的缺陷会导致多种疾病。因此，详细了解线粒体蛋白质组对于理解线粒体系统及其功能至关重要。序列数据库包含大量线粒体蛋白质，但其中大多数未被注释。在本研究中，我们开发了一种支持向量机方法SubmitoLoc，用于基于各种序列衍生特性预测蛋白质的线粒体亚细胞定位。我们使用10折交叉验证对预测器进行了评估。我们的方法使用所有特征时准确率达到了88.56%。四子类预测的平均灵敏度和特异性分别为85.37%和87.25%。高预测准确率表明SubmitoLoc将对研究线粒体生物学和药物发现的研究人员有用。
