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

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。

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

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.