A comprehensive analysis of structure-function relationships in membrane protein families

尽管它们至关重要，但与球状蛋白相比，完整基因组中编码的膜结合蛋白的注释相对较差。本研究的主要目标是建立一个通用的生物信息学平台，用于大规模研究膜蛋白家族。基于我们以前在系统基因组分析方面的工作，我们打算开发一个全面和分层的膜蛋白家族分类，并提供适当的生物信息学工具<$多重序列比对，隐马尔可夫模型<$用于研究它们的结构和功能。特别是，将设计特别考虑预测的结构特征如疏水跨膜片段的比对算法。此外，我们计划开发先进的方法来预测膜蛋白的精确功能特异性。相关突变分析将用于探索相互关联的残基网络，确定赋予功能特异性的残基，并鉴定每个序列家族内的功能亚型。同时，特异性决定残基将划定从多重比对序列使用一种新的自动方法的基础上的遗传算法，不需要在一个给定的家庭的功能亚组的先验知识。准确的功能预测是构建能够阻断细胞特定通道和转运系统的药物的重要前提。在这项工作中产生的方法和数据的另一个潜在的应用将是膜蛋白的结构基因组学的目标选择。此外，该项目的结果将是有用的构建分子模型的许多家庭的膜蛋白，研究螺旋包装，和寡聚化接口。

In spite of their crucial importance membrane-bound proteins encoded in complete genomes are relatively poorly annotated compared to globular proteins. The main goal of the present proposal is to build a general bioinformatics workbench for investigating membrane protein families at large scale. Based on our previous work in systematic genome analysis we intend to develop a comprehensive and hierarchical membrane protein family classification and provide appropriate bioinformatics instruments ¿ multiple sequence alignments, Hidden Markov Models ¿ for investigating aspects of their structure and function. In particular, alignment algorithms specifically taking into account predicted structural features ¿ such as hydrophobic transmembrane segments ¿ will be designed. Furthermore we plan to develop advanced approaches for predicting the precise functional specificity of membrane proteins. Correlated mutation analysis will be applied to explore the networks of inter-correlated residues, determine the residues that confer functional specificity, and to identify functional subtypes within each sequence family. In parallel, specificity-determining residues will be delineated from multiply aligned sequences using a novel automatic approach based on the genetic algorithm that does not require a priori knowledge on functional sub-groups in a given family. Accurate function prediction is an important pre-requisite for constructing drugs capable of blocking specific channels and transport systems of the cell. Another potential application for the methods and data generated in this work will be target selection for structural genomics of membrane proteins. In addition, the results of this project will be useful for constructing molecular models for numerous families of membrane proteins, investigating helix packing, and oligomerization interfaces.