基于茶蛋白质组学数据分析植物亚细胞定位预测软件的应用

Application analysis of predictors for plant protein subcellular localization based on proteome data of Camellia sinensis(L.)O.Ktze.

以500个茶(Camellia sinensis(L.)O.Ktze.)叶片的蛋白质作为数据集,比较TargetP、WoLF PSORT、LocTree和Plant-mPLoc 4种软件预测亚细胞定位的可信度和灵敏度。结果显示,4种软件预测可信度均高于80%,依次排序为TargetP>LocTree>WoLF PSORT>Plant-mPLoc。其中,LocTree对细胞质蛋白和分泌蛋白检测灵敏度最高,但对叶绿体蛋白灵敏度最低;Plant-mPLoc检测核蛋白最灵敏,但对细胞质蛋白最不敏感;TargetP检测叶绿体蛋白最灵敏,但仅能区分3个亚细胞器官;WoLF PSORT对分泌蛋白检测灵敏度最低,但对其他蛋白均较灵敏。基于上述结果,该研究针对4种软件提出了合理的使用建议。

Several predictors of subcellular localization have been developed,with high-throughput and rapid prediction of protein subcellular localization successfully achieved.However,these predictors also have some disadvantages.Here,using 500 proteins identified from the proteome of Camellia sinensis(L.)O.Ktze.as a dataset,the reliability and sensitivity of subcellular localization predictors were compared,including with TargetP,WoLF PSORT,LocTree,and Plant-mPLoc.Results demonstrated that the prediction reliability of the four predictors exceeded 80%,in the order TargetP>LocTree>WoLF PSORT>Plant-mPLoc.Moreover,among the four predictors,LocTree showed the highest sensitivity for cytoplasmic and secretory proteins,but lowest for chloroplast proteins;Plant-mPLoc was most sensitive to nucleoproteins and most insensitive to cytoplasmic proteins;TargetP was most sensitive to chloroplast proteins,but could only distinguish three subcellular organelles;and WoLF PSORT showed high insensitivity to secretory proteins but high recognition of non-secretory proteins.Based on the aforementioned results,we discuss potential uses of the four predictors,which will provide a reference for high-efficiency prediction of protein subcellular localization.