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.