Center for Organelle Research, University of Stavanger Stavanger, Norway.
Front Plant Sci. 2012 Aug 27;3:194. doi: 10.3389/fpls.2012.00194. eCollection 2012.
Prediction of subcellular protein localization is essential to correctly assign unknown proteins to cell organelle-specific protein networks and to ultimately determine protein function. For metazoa, several computational approaches have been developed in the past decade to predict peroxisomal proteins carrying the peroxisome targeting signal type 1 (PTS1). However, plant-specific PTS1 protein prediction methods have been lacking up to now, and pre-existing methods generally were incapable of correctly predicting low-abundance plant proteins possessing non-canonical PTS1 patterns. Recently, we presented a machine learning approach that is able to predict PTS1 proteins for higher plants (spermatophytes) with high accuracy and which can correctly identify unknown targeting patterns, i.e., novel PTS1 tripeptides and tripeptide residues. Here we describe the first plant-specific web server PredPlantPTS1 for the prediction of plant PTS1 proteins using the above-mentioned underlying models. The server allows the submission of protein sequences from diverse spermatophytes and also performs well for mosses and algae. The easy-to-use web interface provides detailed output in terms of (i) the peroxisomal targeting probability of the given sequence, (ii) information whether a particular non-canonical PTS1 tripeptide has already been experimentally verified, and (iii) the prediction scores for the single C-terminal 14 amino acid residues. The latter allows identification of predicted residues that inhibit peroxisome targeting and which can be optimized using site-directed mutagenesis to raise the peroxisome targeting efficiency. The prediction server will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants. PredPlantPTS1 is freely accessible at ppp.gobics.de.
亚细胞蛋白质定位预测对于将未知蛋白质正确分配到细胞器特异性蛋白质网络中，并最终确定蛋白质功能至关重要。在过去的十年中，针对后生动物，已经开发了几种计算方法来预测携带过氧化物酶体靶向信号类型 1（PTS1）的过氧化物酶体蛋白。然而，直到现在还缺乏植物特异性 PTS1 蛋白预测方法，并且现有的方法通常无法正确预测具有非典型 PTS1 模式的低丰度植物蛋白。最近，我们提出了一种机器学习方法，该方法能够高精度地预测高等植物（种子植物）的 PTS1 蛋白，并且能够正确识别未知的靶向模式，即新的 PTS1 三肽和三肽残基。在这里，我们描述了第一个植物特异性网络服务器 PredPlantPTS1，用于使用上述基础模型预测植物 PTS1 蛋白。该服务器允许提交来自各种种子植物的蛋白质序列，并且对苔藓植物和藻类也能很好地执行。易于使用的网络界面提供了详细的输出，包括（i）给定序列的过氧化物酶体靶向概率，（ii）是否已经通过实验验证了特定的非典型 PTS1 三肽，以及（iii）单个 C 末端 14 个氨基酸残基的预测分数。后者允许识别预测的抑制过氧化物酶体靶向的残基，并可以使用定点突变来优化这些残基，以提高过氧化物酶体靶向效率。预测服务器将有助于识别低丰度和应激诱导的过氧化物酶体蛋白，并定义拟南芥和农业上重要的作物植物的整个过氧化物酶体蛋白质组。PredPlantPTS1 可在 ppp.gobics.de 免费访问。
