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: 7723731
• 负责人: William Noble
• 金额: $ 2.61万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2009-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7723731
• 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; Range; Research; Research Personnel; Resources; Score; 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.

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