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首页> 外文期刊>Journal of Molecular Biology >A combined transmembrane topology and signal Peptide prediction method.
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A combined transmembrane topology and signal Peptide prediction method.

机译：跨膜拓扑和信号肽组合预测方法。

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An inherent problem in transmembrane protein topology prediction and signal peptide prediction is the high similarity between the hydrophobic regions of a transmembrane helix and that of a signal peptide, leading to cross-reaction between the two types of predictions. To improve predictions further, it is therefore important to make a predictor that aims to discriminate between the two classes. In addition, topology information can be gained when successfully predicting a signal peptide leading a transmembrane protein since it dictates that the N terminus of the mature protein must be on the non-cytoplasmic side of the membrane. Here, we present Phobius, a combined transmembrane protein topology and signal peptide predictor. The predictor is based on a hidden Markov model (HMM) that models the different sequence regions of a signal peptide and the different regions of a transmembrane protein in a series of interconnected states. Training was done on a newly assembled and curated dataset. Compared to TMHMM and SignalP, errors coming from cross-prediction between transmembrane segments and signal peptides were reduced substantially by Phobius. False classifications of signal peptides were reduced from 26.1% to 3.9% and false classifications of transmembrane helices were reduced from 19.0% to 7.7%. Phobius was applied to the proteomes of Homo sapiens and Escherichia coli. Here we also noted a drastic reduction of false classifications compared to TMHMM/SignalP, suggesting that Phobius is well suited for whole-genome annotation of signal peptides and transmembrane regions. The method is available at as well as at
机译：跨膜蛋白拓扑预测和信号肽预测中的固有问题是跨膜螺旋的疏水区与信号肽的疏水区之间的高度相似性，导致两种类型的预测之间发生交叉反应。为了进一步改善预测，因此，重要的是要建立一个旨在区分这两个类别的预测器。另外，当成功预测导致跨膜蛋白的信号肽时，可以获得拓扑信息，因为它指示成熟蛋白的N端必须在膜的非细胞质侧。在这里，我们介绍了Phobius，这是一种跨膜蛋白拓扑结构和信号肽预测因子的组合。预测器基于隐马尔可夫模型（HMM），该模型在一系列互连状态下对信号肽的不同序列区域和跨膜蛋白的不同区域进行建模。培训是在新组装的精选数据集上进行的。与TMHMM和SignalP相比，Phobius大大减少了跨膜片段和信号肽之间交叉预测产生的误差。信号肽的错误分类从26.1％减少到3.9％，跨膜螺旋的错误分类从19.0％减少到7.7％。 Phobius被应用于智人和大肠杆菌的蛋白质组中。在这里，我们还注意到与TMHMM / SignalP相比，错误分类的急剧减少，这表明Phobius非常适合信号肽和跨膜区域的全基因组注释。该方法可用于以及

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