Emanuelsson O, Nielsen H, von Heijne G
Department of Biochemistry, Stockholm University, Sweden.
Protein Sci. 1999 May;8(5):978-84. doi: 10.1110/ps.8.5.978.
We present a neural network based method (ChloroP) for identifying chloroplast transit peptides and their cleavage sites. Using cross-validation, 88% of the sequences in our homology reduced training set were correctly classified as transit peptides or nontransit peptides. This performance level is well above that of the publicly available chloroplast localization predictor PSORT. Cleavage sites are predicted using a scoring matrix derived by an automatic motif-finding algorithm. Approximately 60% of the known cleavage sites in our sequence collection were predicted to within +/-2 residues from the cleavage sites given in SWISS-PROT. An analysis of 715 Arabidopsis thaliana sequences from SWISS-PROT suggests that the ChloroP method should be useful for the identification of putative transit peptides in genome-wide sequence data. The ChloroP predictor is available as a web-server at http://www.cbs.dtu.dk/services/ChloroP/.
我们提出了一种基于神经网络的方法(ChloroP),用于识别叶绿体转运肽及其切割位点。通过交叉验证,我们同源性降低的训练集中88%的序列被正确分类为转运肽或非转运肽。这个性能水平远高于公开可用的叶绿体定位预测器PSORT。切割位点是使用由自动基序发现算法得出的评分矩阵来预测的。在我们的序列集合中,约60%的已知切割位点被预测在SWISS-PROT中给出的切割位点的正负2个残基范围内。对来自SWISS-PROT的715个拟南芥序列的分析表明,ChloroP方法对于在全基因组序列数据中识别假定的转运肽应该是有用的。ChloroP预测器可作为网络服务器在http://www.cbs.dtu.dk/services/ChloroP/上获取。