TRANSMEMBRANE TOPOLOGY PREDICTION USING DYNAMIC BAYESIAN NETWORKS

使用动态贝叶斯网络进行跨膜拓扑预测

• 批准号: 8171276
• 负责人: William Noble
• 金额: $ 3.71万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8171276
• 关键词: Cleaved cell; Computer Retrieval of Information on Scientific Projects Database; Computing Methodologies; Databases; Enzymes; Funding; Grant; Institution; Integral Membrane Protein; Membrane; Methods; N-terminal; Organism; Peptide Signal Sequences; Pharmaceutical Preparations; Process; Proteins; Research; Research Personnel; Resources; Source; Structure; United States National Institutes of Health; Work; Yeasts; computer based statistical methods; interest; signal peptidase; therapeutic target; three dimensional structure

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.

该子项目是利用该技术的众多研究子项目之一 资源由 NIH/NCRR 资助的中心拨款提供。子项目及 研究者 (PI) 可能已从 NIH 的另一个来源获得主要资金， 因此可以在其他 CRISP 条目中表示。列出的机构是 对于中心来说，它不一定是研究者的机构。 生物学家对跨膜蛋白特别感兴趣，因为它们涉及广泛的过程和功能，并且通常是治疗药物的靶标。通过实验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很少是跨膜蛋白的，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，用于预测跨膜蛋白基本拓扑结构的计算方法引起了极大的兴趣，并且这些方法必须能够区分成熟的跨膜蛋白和首次合成时含有 N 端跨膜信号肽的蛋白，该信号肽被酶信号肽酶从成熟蛋白上切割下来。在这项工作中，我们提出了 Philius，这是一种新的计算方法，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 还为每个预测提供一组置信度分数。此外，我们还对酵母资源中心数据库中超过 600 万种蛋白质进行了预测，并将这些预测公开。