TRANSMEMBRANE TOPOLOGY PREDICTION USING DYNAMIC BAYESIAN NETWORKS

该子项目是利用 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得主要资金， 因此可以在其他CRISP条目中表示。列出的机构是 中心，不一定是研究者的机构。 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，并且通常是治疗药物的靶点。通过实验确定跨膜蛋白的3D结构是一项困难的任务，并且目前已知的三级结构中很少有跨膜蛋白，尽管给定生物体中多达四分之一的蛋白质是跨膜蛋白。 因此，用于预测跨膜蛋白的基本拓扑结构的计算方法具有很大的意义，并且这些方法必须能够区分成熟的跨膜蛋白和在首次合成时含有N-末端跨膜信号肽的蛋白，所述N-末端跨膜信号肽通过酶信号肽酶从成熟蛋白裂解。在这项工作中，我们提出了Philius，一种新的计算方法，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。Philius还为每个预测提供了一组置信度得分。此外，我们还对酵母资源中心数据库中的600多万种蛋白质进行了预测，并将这些预测公开发布。

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.