Coarse-Grained Models of Proteins

描述（由申请人提供）：拟议项目的目标是开发几个新的粗粒度计算模型来描述蛋白质结构和蛋白质动力学。这些创新将使我们接近更大的结构，以及更容易地理解重要的功能行为。该项目的实现对于计算细胞生物学的发展和药物设计的实际应用可能是极其重要的。具体目标1：利用高斯网络模型和各向异性网络模型研究蛋白质动力学。我们将开发一种混合粗粒度方法，其中蛋白质的“有趣”或功能部分以比结构其余部分更高的分辨率建模。通过使用这种方法，可以进行正常模式分析，以识别重要的功能运动，具有很高的计算效率的大型生物重要分子。具体目标二：开发一种非常有效的转移矩阵方法，用于在二维方形晶格和三维立方晶格上无磨损地生成晶格蛋白质。所提出的方法是传递矩阵方法的扩展，用于在先前由项目研究人员开发的网格上生成和枚举紧凑自避免行走。具体目标二：进行蛋白质形状及其构象之间的依赖关系的非晶格研究。目标是使用最小的假设集生成可能的三维蛋白质结构库。我们在三维旋转椭球内压缩蛋白质的形状，并在该形状内生成所有可能的紧凑蛋白质构象。我们还将以系统的方式研究蛋白质的结构和形状之间的相互依存关系，以及它们在特定目标1中开发的高斯网络模型的动力学。这些发现为更抽象和数学的生物结构表示开辟了道路，可以直接导致对结构和功能的更好和更完整的理解。

DESCRIPTION (provided by applicant): The goal of the proposed project is to develop several new coarse-grained computational models to describe protein structures and protein dynamics. These innovations will let us approach much larger structures, as well as to comprehend important functional behaviors more readily. The realization of the proposed project might be extremely important for the development of computational cell biology and for practical applications in drug design. The project focuses on three specific aims: Specific Aim I: Study protein dynamics using the Gaussian Network Model and Anisotropic Network Model. We will develop a mixed coarse-grained method where the 'interesting' or functional parts of proteins are modeled at a higher resolution than the remainder of the structure. By using this approach, normal mode analysis can be performed to discern the important functional motions with high computational efficiency for large biologically important molecules. Specific Aim II: Develop an extremely efficient transfer matrix method for attrition-free generation of lattice proteins on the square lattice in 2-dimensions and for the cubic lattice in 3-dimensions. The proposed method is an extension of the transfer matrix method for generating and the enumerating compact self-avoiding walks on lattices previously developed by the project's investigators. Specific Aim Ill: Conduct an off-lattice study of the dependencies between protein shapes and their conformations. The goal is to generate libraries of possible three-dimensional protein structures using a minimal set of assumptions. We constrict the shape of the protein within a three-dimensional ellipsoid of revolution and generate all possible compact protein conformations within the shape. We will also study in a systematic way the interdependence between the structures and the shapes of the proteins, as well as their dynamics with the Gaussian Network Models developed in the Specific Aim 1. These findings open the way for more abstract and mathematical representations of biological structure that can lead directly to a better and more complete comprehension of structure and function.