Membrane protein structure modeling with experimental restraints

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，对于这些蛋白，可以获得有限数量的实验限制条件，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素片段文库(SMotif)，该文库对所有已知的蛋白质构建块进行了详尽的分类。最近，我们已经证明了这个SMotif库在近10年前就饱和了，而新的折叠似乎是现有Smotif的新组合。因此，我们假设所有的蛋白质折叠都可以从这个文库中构建出来。为了建立膜蛋白的模型，我们可以计算所有基序的假设化学移动值，而感兴趣的蛋白质的化学移动值通常可以快速而容易地从初始核磁共振实验中获得和分配。这项提议涉及开发算法，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。提案的第二部分涉及建立一种优化方法(沿Smotif组合的自由度的采样算法和评分函数)，该方法将使用从核磁共振偶极耦合数据获得的额外实验限制条件，将重叠的Smotif快速组装成紧凑的折叠。在项目的后几年，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上，并随后用自旋标记实验验证我们的计算模型。在这一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中提供的膜蛋白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主要靶点。我们致力于提高膜蛋白结构的发现率，从而为更有效、更合理的药物设计奠定基础。

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.