Käll Lukas, Krogh Anders, Sonnhammer Erik L L
Center for Genomics and Bioinformatics, Karolinska Institutet, S-17177 Stockholm, Sweden.
Nucleic Acids Res. 2007 Jul;35(Web Server issue):W429-32. doi: 10.1093/nar/gkm256. Epub 2007 May 5.
When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hence this is an important issue in protein annotation. To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.
在使用传统的跨膜拓扑结构和信号肽预测工具（如TMHMM和SignalP）时，这两种类型的预测之间存在大量重叠。将这些方法应用于五个完整的蛋白质组，我们发现所有预测的信号肽中有30% - 65%以及所有预测的跨膜拓扑结构中有25% - 35%存在重叠。这会影响对5% - 10%的蛋白质组的预测，因此这是蛋白质注释中的一个重要问题。为了解决这个问题，我们之前设计了一种隐马尔可夫模型Phobius，它结合了跨膜拓扑结构和信号肽预测。该方法在跨膜片段和信号肽之间做出最优选择，并且还允许进行受限和富含同源性的预测。我们在此提供一个网络界面（http://phobius.cgb.ki.se和http://phobius.binf.ku.dk）来访问Phobius。
