Stockholm Bioinformatics Centre, Albanova, Stockholm University, 10691 Stockholm, Sweden.
BMC Bioinformatics. 2009 Sep 28;10:314. doi: 10.1186/1471-2105-10-314.
Transmembrane (TM) proteins are proteins that span a biological membrane one or more times. As their 3-D structures are hard to determine, experiments focus on identifying their topology (i. e. which parts of the amino acid sequence are buried in the membrane and which are located on either side of the membrane), but only a few topologies are known. Consequently, various computational TM topology predictors have been developed, but their accuracies are far from perfect. The prediction quality can be improved by applying a consensus approach, which combines results of several predictors to yield a more reliable result.
A novel TM consensus method, named MetaTM, is proposed in this work. MetaTM is based on support vector machine models and combines the results of six TM topology predictors and two signal peptide predictors. On a large data set comprising 1460 sequences of TM proteins with known topologies and 2362 globular protein sequences it correctly predicts 86.7% of all topologies.
Combining several TM predictors in a consensus prediction framework improves overall accuracy compared to any of the individual methods. Our proposed SVM-based system also has higher accuracy than a previous consensus predictor. MetaTM is made available both as downloadable source code and as DAS server at http://MetaTM.sbc.su.se.
跨膜（TM）蛋白是一种多次跨越生物膜的蛋白质。由于其三维结构难以确定，实验主要集中在鉴定其拓扑结构（即氨基酸序列的哪些部分埋在膜中，哪些位于膜的两侧）上，但已知的拓扑结构很少。因此，已经开发了各种计算 TM 拓扑结构预测器，但它们的准确性远非完美。通过应用共识方法可以提高预测质量，该方法将几个预测器的结果结合起来，得出更可靠的结果。
本工作提出了一种新的 TM 共识方法，名为 MetaTM。MetaTM 基于支持向量机模型，结合了六种 TM 拓扑预测器和两种信号肽预测器的结果。在一个包含 1460 个已知拓扑结构的 TM 蛋白序列和 2362 个球状蛋白序列的大型数据集上，它正确预测了所有拓扑结构的 86.7%。
在共识预测框架中结合几种 TM 预测器可以提高整体准确性，优于任何一种单独的方法。我们提出的基于 SVM 的系统也比以前的共识预测器具有更高的准确性。MetaTM 可作为可下载的源代码和 DAS 服务器在 http://MetaTM.sbc.su.se 上获得。
