Chaturvedi Nagendra K, Mir Riyaz A, Band Vimla, Joshi Shantaram S, Guda Chittibabu
Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, 985870 Nebraska Medical Center, Omaha, NE 68198, USA.
BMC Res Notes. 2014 Dec 15;7:912. doi: 10.1186/1756-0500-7-912.
Computational methods have been widely used for the prediction of protein subcellular localization. However, these predictions are rarely validated experimentally and as a result remain questionable. Therefore, experimental validation of the predicted localizations is needed to assess the accuracy of predictions so that such methods can be confidently used to annotate the proteins of unknown localization. Previously, we published a method called ngLOC that predicts the localization of proteins targeted to ten different subcellular organelles. In this short report, we describe the accuracy of these predictions using experimental validations.
We have experimentally validated the predicted subcellular localizations of 114 human proteins corresponding to nine different organelles in normal breast and breast cancer cell lines using live cell imaging/confocal microscopy. Target genes were cloned into expression vectors as GFP fusions and cotransfected with RFP-tagged organelle-specific gene marker into normal breast epithelial and breast cancer cell lines. Subcellular localization of each target protein is confirmed by colocalization with a co-expressed organelle-specific protein marker. Our results showed that about 82.5% of the predicted subcellular localizations coincided with the experimentally validated localizations. The highest agreement was found in the endoplasmic reticulum proteins, while the cytoplasmic location showed the least concordance. With the exclusion of cytoplasmic location, the average prediction accuracy increased to 90.4%. In addition, there was no difference observed in the protein subcellular localization between normal and cancer breast cell lines.
The experimentally validated accuracy of ngLOC method with (82.5%) or without cytoplasmic location (90.4%) nears the prediction accuracy of 89%. These results demonstrate that the ngLOC method can be very useful for large-scale annotation of the unknown subcellular localization of proteins.
计算方法已广泛用于蛋白质亚细胞定位预测。然而,这些预测很少经过实验验证,因此仍存在疑问。因此,需要对预测的定位进行实验验证,以评估预测的准确性,以便能够放心地使用此类方法注释未知定位的蛋白质。此前,我们发表了一种名为ngLOC的方法,可预测靶向十种不同亚细胞细胞器的蛋白质的定位。在本简短报告中,我们使用实验验证来描述这些预测的准确性。
我们使用活细胞成像/共聚焦显微镜,通过实验验证了114种人类蛋白质在正常乳腺和乳腺癌细胞系中对应于九种不同细胞器的预测亚细胞定位。将靶基因克隆到表达载体中作为绿色荧光蛋白(GFP)融合体,并与红色荧光蛋白(RFP)标记的细胞器特异性基因标记物共转染到正常乳腺上皮细胞和乳腺癌细胞系中。通过与共表达的细胞器特异性蛋白质标记物共定位来确认每个靶蛋白的亚细胞定位。我们的结果表明,约82.5%的预测亚细胞定位与实验验证的定位一致。在内质网蛋白中一致性最高,而在细胞质定位中一致性最低。排除细胞质定位后,平均预测准确率提高到90.4%。此外,在正常和乳腺癌细胞系之间未观察到蛋白质亚细胞定位的差异。
ngLOC方法经实验验证的准确率(有细胞质定位时为82.5%,无细胞质定位时为90.4%)接近89%的预测准确率。这些结果表明,ngLOC方法对于大规模注释未知蛋白质的亚细胞定位可能非常有用。