Membrane protein structure modeling with experimental restraints

具有实验限制的膜蛋白结构建模

• 批准号: 8025220
• 负责人: Andras Fiser
• 金额: $ 31.54万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 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
• 来源: https://reporter.nih.gov/project-details/8025220
• 关键词: Accounting; Algorithms; Base Sequence; Benchmarking; Chemicals; Classification; Computer Simulation; Computing Methodologies; Coupling; Data; Drug Delivery Systems; Drug Design; Elements; Exhibits; Fingerprint; Foundations; Freedom; Homologous Gene; Hybrids; Integral Membrane Protein; Libraries; Measurement; Membrane Proteins; Methods; Modeling; Molecular Conformation; Noise; Pattern; Pattern Recognition; Peptide Signal Sequences; Pharmaceutical Preparations; Proteins; Residual state; Sampling; Spin Labels; Structure; Techniques; Technology; base; interest; method development; novel; protein folding; protein structure; research study; restraint

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，其中有限数量的实验限制是可用的，但实验结构是难以获得的。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分类。最近，我们已经证明，这个Smotifs库在大约10年前就饱和了，新的褶皱似乎是现有Smotifs的新组合。因此，我们假设所有的蛋白质折叠都应该可以从这个文库中构建。为了模拟膜蛋白，我们可以计算所有Smotifs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的化学位移模式，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个优化方法（采样算法沿着的Smotif组合和评分函数的自由度），将快速组装重叠Smotif到紧凑的褶皱使用额外的实验限制从NMR偶极耦合数据。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，并随后用自旋标记实验验证我们的计算模型。 本申请中开发的技术将为膜蛋白的有效建模提供所需的基础，其中PDB中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点。我们的努力集中在提高膜蛋白结构的发现率，因此将为更有效的合理药物设计奠定基础。 公共卫生相关性：大多数目前已知的药物靶向膜蛋白，其中只有约0.5%的结构特征。在这个建议中，我们将开发一个片段组装建模方法，利用NMR化学位移数据和我们最近开发的超二级结构库。我们的努力是关于增加发现膜蛋白结构的速率，并将为这类重要蛋白质的有效合理药物设计奠定基础。