TRANSMEMBRANE TOPOLOGY PREDICTION USING DYNAMIC BAYESIAN NETWORKS

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 跨膜蛋白是生物学家特别感兴趣的，因为它们参与了广泛的过程和功能，并且经常是治疗药物的靶标。在实验上确定跨膜蛋白的三维结构是一项艰巨的任务，目前已知的三级结构中很少有跨膜蛋白，尽管在给定的生物体中多达四分之一的蛋白质是跨膜蛋白。因此，预测跨膜蛋白基本拓扑的计算方法是非常有意义的，这些方法必须能够区分成熟的跨膜蛋白和第一次合成时含有N-端跨膜信号肽的蛋白质，该信号肽是由酶信号肽酶从成熟蛋白中切割出来的。在这项工作中，我们提出了一种新的计算方法飞利浦，它在检测信号肽和正确预测跨膜蛋白的拓扑结构方面优于以前的方法。飞利浦还为每个预测提供了一套置信度分数。此外，我们在酵母资源中心数据库中对600多万种蛋白质进行了预测，并将这些预测公之于众。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Transmembrane proteins are of particular interest to biologists because they are involved in a broad range of processes and functions and are often the targets of therapeutic drugs. Experimentally determining the 3D structure of a transmembrane protein is a difficult task, and few of the currently known tertiary structures are of transmembrane proteins, despite the fact that as many as one quarter of the proteins in a given organism are transmembrane proteins. Computational methods for predicting the basic topology of a transmembrane protein are therefore of great interest, and these methods must be able to distinguish between mature, membrane-spanning proteins and proteins which, when first synthesized, contain an N-terminal membrane-spanning signal peptide which is cleaved from the mature protein by the enzyme signal peptidase. In this work, we present Philius, a new computational approach that outperforms previous methods in detecting signal peptides and correctly predicting the topology of transmembrane proteins. Philius also supplies a set of confidence scores with each prediction. In addition, we have made predictions for over six million proteins in the Yeast Resource Center database and we have made these predictions publicly available.