Coarse-Grained Models of Proteins

蛋白质的粗粒度模型

• 批准号: 7254261
• 负责人: ROBERT L JERNIGAN
• 金额: $ 24.86万
• 依托单位: IOWA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7254261
• 关键词: Behavior; Biological; Cellular biology; Cereals; Classification; Comprehension; Computer Simulation; Dependency; Development; Dimensions; Drug Design; Generations; Goals; Lead; Libraries; Methods; Modeling; Molecular Conformation; Motion; Protein Conformation; Protein Dynamics; Proteins; Research Personnel; Resolution; Shapes; Structure; Walking; abstracting; innovation; interest; network models; practical application; protein structure

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.

描述(由申请人提供)：该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。这些创新将使我们能够更容易地处理更大的结构，以及更容易地理解重要的功能行为。该项目的实现可能对计算细胞生物学的发展和药物设计的实际应用具有极其重要的意义。该项目集中于三个具体目标：具体目标I：使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的其余部分更高的分辨率建模。利用这种方法，简正波分析可以高效率地识别具有重要生物意义的大分子的重要功能运动。具体目标II：发展一种非常有效的转移矩阵方法，用于在二维正方形晶格和三维立方晶格上无磨损地产生晶格蛋白质。该方法是该项目研究人员先前开发的生成和计数格上紧致自回避游动的传递矩阵法的扩展。具体目标一：对蛋白质形状和构象之间的依赖关系进行非晶格研究。目标是使用最小的一组假设来生成可能的三维蛋白质结构的库。我们将蛋白质的形状压缩在一个旋转的三维椭球体内，并在该形状内产生所有可能的致密蛋白质构象。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赖以及它们的动力学。这些发现为更抽象和更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。