Prediction and analysis of membrane protein structures and their interactions from genome data

根据基因组数据预测和分析膜蛋白结构及其相互作用

• 批准号: BB/E022642/1
• 负责人: Janet Thornton
• 金额: $ 35.94万
• 依托单位: European Bioinformatics Institute
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FE022642%2F1
• 关键词: Prediction; analysis; membrane; protein; structures

About 30% of genes in the human genome code for proteins which are found in cell membranes. These proteins are responsible for maintaining many important processes in cells. Understanding the structure and function of these proteins and studying their properties and biochemical mechanisms are therefore among the most important goals in biological and pharmaceutical research. To understand membrane proteins, it is important to understand how their sequences and structures, and consequently their interactions, have adapted to the chemical and physical properties of biological membranes. With the advent of the structural genomics era, as membrane protein 3D structures are determined, bioinformatics studies are required to analyse and exploit this knowledge at the genome scale. Within this scenario, the main goals of our research project are to develop a pipeline (a closely linked set of computer programs) for the building of membrane protein 3D models at genome-scale and to develop new methods for the analysis and prediction of membrane protein interactions within the biological membrane. We will test our methods on membrane proteins which have experimental data available (e.g. where the number and orientation of transmembrane elements are known) and where possible where the atomic-level 3D structure is known. Currently there are few available resources fully dedicated to membrane proteins, but these do not provide all the information we need (they either contains only structural data or are limited to only few membrane protein classes). We will develop a structure-centric resource including all classes of known membrane protein structures and linking them to the corresponding available genome data. The next step will consist in analysing and classifying all of the transmembrane proteins for which 3D structures have been determined. Unlike existing classification schemes, we will derive a novel method specific for membrane protein classification that will integrate and exploit the existing schemes but develop a set of specific descriptions which will discriminate diverse membrane protein structures in biologically meaningful ways. We also plan to improve the current transmembrane topology prediction by developing a new suite of tools to predict transmembrane topology with increased accuracy combining multiple topological features, like amino acid topogenic propensities, topogenic motifs, sub-cellular location prediction of domains and prediction of signal peptides. We also plan to build a pipeline for building 3D models of membrane proteins across whole genomes. We will develop two different methods to assign membrane proteins to structural families (and build the corresponding 3D models). For sequences with clear similarity to a structural families, sequence profiles will be used for the assignment, while a 'fold recognition' method will be derived for sequences with weak sequence similarity. The interactions of membrane protein interactions with other molecules, i.e. peptides, proteins, lipids and small molecules will be studied to identify interactions for the functioning of specific families. We will analyse binding sites in transmembrane proteins using methods similar to those previously developed in the Thornton group to characterize binding sites in water-soluble proteins. These methods will need to be adapted, since we believe the membrane constraint strongly influences the way cognate partners interact making the interaction patterns peculiar. We will therefore explore the interactions between membrane protein and lipids and their importance for different biological functions and compartmentalisations exploiting the protein structural knowledge developed in our previous structural studies and all currently available resources.

人类基因组中约30%的基因编码细胞膜中发现的蛋白质。这些蛋白质负责维持细胞中的许多重要过程。因此，了解这些蛋白质的结构和功能并研究其性质和生化机制是生物学和药学研究中最重要的目标之一。为了了解膜蛋白，重要的是要了解它们的序列和结构，以及它们的相互作用如何适应生物膜的化学和物理性质。随着结构基因组学时代的到来，随着膜蛋白3D结构的确定，生物信息学研究需要在基因组尺度上分析和利用这些知识。在这种情况下，我们的研究项目的主要目标是开发一个管道（一组紧密相连的计算机程序），用于在基因组规模上构建膜蛋白3D模型，并开发新的方法来分析和预测生物膜内的膜蛋白相互作用。我们将在具有实验数据的膜蛋白上测试我们的方法（例如，已知跨膜元件的数量和方向），并且在可能的情况下，已知原子级3D结构。目前，几乎没有完全致力于膜蛋白的可用资源，但这些资源并不能提供我们需要的所有信息（它们要么只包含结构数据，要么仅限于少数膜蛋白类）。我们将开发一个以结构为中心的资源，包括所有类型的已知膜蛋白结构，并将它们与相应的可用基因组数据联系起来。下一步将包括分析和分类所有已确定3D结构的跨膜蛋白。与现有的分类方案不同，我们将推导出一种新的膜蛋白分类方法，该方法将整合和利用现有的方案，但开发出一套具体的描述，以生物学上有意义的方式区分不同的膜蛋白结构。我们还计划通过开发一套新的工具来预测跨膜拓扑结构，结合多种拓扑特征，如氨基酸拓扑倾向，拓扑基序，结构域的亚细胞位置预测和信号肽预测，提高跨膜拓扑结构的准确性，以改善目前的跨膜拓扑结构预测。我们还计划建立一个管道，用于在整个基因组中构建膜蛋白的3D模型。我们将开发两种不同的方法来分配膜蛋白的结构家族（并建立相应的3D模型）。对于与结构家族具有明显相似性的序列，将使用序列谱进行分配，而对于具有弱序列相似性的序列，将推导出“折叠识别”方法。将研究膜蛋白与其他分子（即肽、蛋白质、脂质和小分子）的相互作用，以确定特定家族功能的相互作用。我们将分析跨膜蛋白中的结合位点，使用类似于桑顿组先前开发的方法来表征水溶性蛋白中的结合位点。这些方法将需要适应，因为我们相信膜约束强烈影响同源合作伙伴的方式进行互动，使互动模式奇特。因此，我们将探索膜蛋白和脂质之间的相互作用，以及它们对不同生物功能和区室化的重要性，利用我们以前的结构研究和所有现有资源中开发的蛋白质结构知识。

项目成果

Mutations at key pore-lining positions differentiate the water permeability of fish lens aquaporin from other vertebrates.

关键孔衬位置的突变使鱼晶状体水通道蛋白的透水性与其他脊椎动物不同。

• DOI: 10.1016/j.febslet.2010.10.058
• 发表时间: 2010
• 期刊: FEBS letters
• 影响因子: 3.5
• 作者: Calvanese L

PoreWalker: a novel tool for the identification and characterization of channels in transmembrane proteins from their three-dimensional structure.

• DOI: 10.1371/journal.pcbi.1000440
• 发表时间: 2009-07
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Pellegrini-Calace M;Maiwald T;Thornton JM
• 通讯作者: Thornton JM