Bigelow Henry, Rost Burkhard
Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.
Methods Mol Biol. 2009;528:3-23. doi: 10.1007/978-1-60327-310-7_1.
We identify and describe a set of tools readily available for integral membrane protein prediction. These tools address two problems: finding potential transmembrane proteins in a pool of new sequences, and identifying their transmembrane regions. All methods involve comparing the query protein against one or more target models. In the simplest of these, the target "model" is another protein sequence, while the more elaborate methods group together the entire set of t ansmembrane helical or transmembrane beta-barrel proteins. In general, prediction accuracy either in identifying new integral membrane proteins or transmembrane regions of known integral membrane proteins depends strongly on how closely the query fits the model. Because of this, the best approach is an opportunistic one: submit the protein of interest to all methods and choose the results with the highest confidence scores.
我们识别并描述了一组可用于整合膜蛋白预测的现成工具。这些工具解决两个问题:在一组新序列中找到潜在的跨膜蛋白,以及识别它们的跨膜区域。所有方法都涉及将查询蛋白与一个或多个目标模型进行比较。其中最简单的方法中,目标“模型”是另一个蛋白质序列,而更复杂的方法则将整个跨膜螺旋或跨膜β桶蛋白集合归为一组。一般来说,在识别新的整合膜蛋白或已知整合膜蛋白的跨膜区域时,预测准确性在很大程度上取决于查询蛋白与模型的匹配程度。因此,最佳方法是一种机会主义方法:将感兴趣的蛋白质提交给所有方法,并选择置信度得分最高的结果。