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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。子弹和 调查员（PI）可能已经从其他NIH来源获得了主要资金， 因此可以在其他清晰的条目中代表。列出的机构是 对于中心，这不一定是调查员的机构。 跨膜蛋白对生物学家特别感兴趣，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标。在实验上确定跨膜蛋白的3D结构是一项艰巨的任务，尽管当前有生物体中多达四分之一的蛋白质是跨膜蛋白，但当前已知的三级结构很少是跨膜蛋白。 因此，用于预测跨膜蛋白基本拓扑的计算方法引起了人们的极大兴趣，这些方法必须能够区分成熟的，跨膜蛋白质和蛋白质，当首次合成时，这些蛋白质和蛋白质首次合成时，含有N-末端膜跨膜信号肽，这些信号肽与enzeme signalseme Signalse signalse syzyseme cleace clea clea clea cleaseme offeptase syzyseme signalseme syzeme signalseme。在这项工作中，我们提出了Philius，这是一种新的计算方法，它在检测信号肽并正确预测跨膜蛋白的拓扑结构方面优于先前的方法。 Philius还为每个预测提供了一套置信度得分。此外，我们在酵母资源中心数据库中已经对超过600万个蛋白质做出了预测，我们已经公开提供了这些预测。