Membrane protein structure modeling with experimental restraints

具有实验限制的膜蛋白结构建模

基本信息

批准号：
8247719
负责人：
Andras Fiser
金额：
$ 31.7万
依托单位：
ALBERT EINSTEIN COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2011
资助国家：
美国
起止时间：
2011-05-01 至 2015-04-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): In this project we will develop a computational approach to model membrane proteins for which a limited number of experimental restraints are available but for which the experimental structure is difficult to obtain. We will utilize our recently developed fragment library of supersecondary structure elements (Smotifs) that exhaustively classifies all known building blocks of proteins. Recently we have shown that this library of Smotifs saturated almost 10 years ago, and that new folds seem to be a novel combination of existing Smotifs. Therefore we hypothesize that all protein folds should be possible to build from this library. In order to model membrane proteins we can calculate hypothetical chemical shift values for all our Smotifs, while chemical shift values for a protein of interest can usually be quickly and easily obtained and assigned from initial NMR experiments. This proposal is concerned with developing algorithms that can match experimentally observed and theoretically calculated chemical shift patterns of Smotifs and therefore identify a subset of Smotif conformations that form a protein. The second part of the proposal is concerned of setting up an optimization approach (a sampling algorithm along the degrees of freedom of Smotif combinations and a scoring function) that will rapidly assemble overlapping Smotifs into compact folds using additional experimental restraints obtained from NMR dipolar coupling data. In later years of the project we will apply our technique on specific proteins for which chemical shift and dipolar coupling data were obtained and subsequently verify our computational models with spin labeling experiments. The technologies developed in this application will provide the foundation required for efficient modeling of membrane proteins for which a very limited number of experimental structures are available in the PDB. Meanwhile membrane proteins constitute the majority of targets of currently known drugs. Our effort is focused on increasing the rate of discovering membrane protein structures and therefore will lay a foundation for more effective rational drug design. PUBLIC HEALTH RELEVANCE: The majority of currently known drugs target membrane proteins, of which only about 0.5% have been structurally characterized. In this proposal we will develop a fragment assembly modeling approach that takes advantage of NMR chemical shift data and our recently developed supersecondary structure library. Our effort is concerned with increasing the rate of discovering membrane protein structures and will lay a foundation for effective rational drug design for this important class of proteins.

描述（由申请人提供）：在这个项目中，我们将开发一种计算方法来模拟膜蛋白，对于该方法，可用的实验限制数量有限，但很难获得实验结构。我们将利用我们最近开发的超二级结构元件（Smotifs）片段库，对所有已知的蛋白质构建模块进行详尽的分类。最近，我们发现这个 Smotif 库在大约 10 年前就已经饱和，并且新的折叠似乎是现有 Smotif 的新颖组合。因此，我们假设所有蛋白质折叠都可以从该文库构建。为了对膜蛋白进行建模，我们可以计算所有 Smotif 的假设化学位移值，而感兴趣的蛋白质的化学位移值通常可以快速、轻松地从初始 NMR 实验中获得和分配。该提案涉及开发算法，该算法可以匹配实验观察到的和理论计算的 Smotif 化学位移模式，从而识别形成蛋白质的 Smotif 构象的子集。该提案的第二部分涉及建立一种优化方法（沿着 Smotif 组合自由度的采样算法和评分函数），该方法将使用从 NMR 偶极耦合数据获得的附加实验约束快速将重叠的 Smotif 组装成紧凑的折叠。在该项目的后期，我们将把我们的技术应用于获得化学位移和偶极耦合数据的特定蛋白质，并随后通过自旋标记实验验证我们的计算模型。本应用中开发的技术将为膜蛋白的有效建模提供所需的基础，而 PDB 中可用的实验结构数量非常有限。同时膜蛋白构成了目前已知药物的大部分靶标。我们的努力重点是提高膜蛋白结构的发现率，从而为更有效的合理药物设计奠定基础。公共健康相关性：目前已知的大多数药物都以膜蛋白为靶点，其中只有约 0.5% 的结构得到了表征。在本提案中，我们将开发一种片段组装建模方法，该方法利用 NMR 化学位移数据和我们最近开发的超二级结构库。我们的努力涉及提高膜蛋白结构的发现率，并将为这一类重要蛋白质的有效合理药物设计奠定基础。