Coarse-Grained Models of Proteins

描述(申请人提供)：粗粒度模型可以理解蛋白质运动的许多方面。我们的假设是，解释蛋白质行为的许多方面不需要原子细节，这种简化可以促进更深层次的理解。总体目标是了解蛋白质运动和功能是如何由结构控制的，为什么蛋白质序列折叠成一组有限的结构，并建立紧密堆积和蛋白质形状对其运动的作用。在这个项目中，我们将研究运动、形状、结构、相互作用和协作性水平之间的关系。目标一：用弹性网络模拟蛋白质动力学。我们将使用弹性网络模型来研究蛋白质如何将它们的运动限制在对功能最重要的运动上。简正波分析将被用来识别这些重要的功能运动，并以高计算效率来发展分子机制。我们将研究酶活性部位的原子运动，以了解大域运动如何控制原子运动。我们将使用弹性网络来解释单分子拉动实验，并预测蛋白质解体的顺序。初步结果表明，弹性网络模型不仅适用于天然构象周围的涨落，而且适用于外力使蛋白质变形并破坏其天然接触时产生的瞬变状态。这些结果表明，结构控制着蛋白质的整体运动，即使是瞬时运动。为了进一步验证这一假设，我们将进行更多的单分子拉动模拟，并沿着折叠路径对瞬时蛋白质构象进行结构分析。弹性模型取得的主要成功依赖于对堆积密度和蛋白质形状的良好表示，我们将在目标II中进行研究。目标II：模拟蛋白质堆积和相互作用的协作性。蛋白质中残基的密集堆积是其最重要的特征之一。我们计划继续研究内包装。新潜力的重点将放在蛋白质中氨基酸的相对取向上。我们将开发多体接触势来识别穿线中的诱饵之间的天然结构，并使用正则多面体(如二十面体)或加泰罗尼亚固体(如四面体)研究蛋白质中邻近残基簇的取向分布。我们的基本原理是使用各种多面体模型来理解蛋白质堆积和氨基酸相互作用，以开发改进的多体势能。更好地理解蛋白质内部相互作用的协作性是极其重要的，因为这直接影响蛋白质运动和对力做出反应的方式。这两个目标高度相关，将极大地促进我们对蛋白质结构、动力学和功能的了解。 公共卫生相关性：该项目的成功将影响分子科学的许多领域--从为药物设计选择蛋白质靶标，到对细胞功能的总体理解。该项目的进展对于开发有意义地模拟细胞成分和利用快速增长的细胞成像数据的方法至关重要。通过增强我们对蛋白质行为的基本理解，以及促进更好、更有效地选择药物设计的蛋白质靶标，提高对蛋白质运动建模的能力可以在重要方面影响公众健康。

DESCRIPTION (provided by applicant): Many aspects of protein motion can be comprehended with coarse-grained models. Our hypothesis is that atomic detail is not required to explain many aspects of protein behavior, and this simplification can facilitate a deeper understanding. The overall goal is to develop an understanding of how protein motions and function are controlled by structure, why protein sequences fold to a limited set of structures, and to establish the roles of tight packing and the shapes of proteins on their motions. In this project we will investigate the relationships among motions, shapes, structures, interactions and levels of cooperativity. Aim I: Modeling protein dynamics with Elastic Networks. We will use elastic network models to study how proteins restrict their motions to the motions most essential for function. Normal mode analyses will be performed to discern these important functional motions with high computational efficiency to develop molecular mechanisms. We will investigate the atomic motions in active sites of enzymes to see how the large domain motions control the atom movements. We will use elastic networks to interpret single molecule pulling experiments and predict the order in which proteins unravel. Preliminary results show that elastic network models are applicable not only to fluctuations around native conformations, but also to transient states arising when an external force is applied to deform a protein and break its native contacts. These results suggest that structure controls the global motions of proteins, even for transient states. To further verify this hypothesis we will perform more single molecule pulling simulations, and structural analyses of transient protein conformations along folding pathways. The major successes achieved with the elastic models rely upon having good representations of the packing density and protein shape, which we will investigate in Aim II. Aim II: Modeling Protein Packing and Cooperativity of Interactions. Dense packing of residues in proteins is one of their most important characteristic features. We plan to continue our studies of internal packing. The emphasis for new potentials will be on the relative orientations of amino acids in proteins. We will develop many-body contact potentials for identifying native structures among decoys in threading, and also study orientational distributions within clusters of nearby residues in proteins, using regular polyhedra such as icosahedra, or Catalan solids such as tetrakis hexahedra. Our rationale is to use various polyhedral models to comprehend protein packing and amino acid interactions for developing improved many-body potentials. A better understanding of the cooperativity of interactions within proteins is extremely important because this directly influences the ways in which proteins move and respond to forces. Both Aims are highly interconnected and will significantly advance our knowledge of protein structure, dynamics and function. PUBLIC HEALTH RELEVANCE: Success in this project will affect many fields of molecular science - from the selection of protein targets for drug design to a general comprehension of how cells function. Progress on this project is critical for developing ways to meaningfully simulate cellular components and to utilize the rapidly growing cell imaging data. Improving the abilities to model protein motions can impact public health in important ways by enhancing our basic understanding of protein behavior and by facilitating better, more effective selection of protein targets for drug design.