Coarse-Grained Models of Proteins

描述（由申请人提供）：蛋白质运动的许多方面可以用粗粒度模型来理解。我们的假设是，原子细节不需要解释蛋白质行为的许多方面，这种简化可以促进更深入的理解。总体目标是了解蛋白质的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有限的一组结构，并建立紧密包装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，相互作用和合作水平之间的关系。目的一：用弹性网络建模蛋白质动力学。我们将使用弹性网络模型来研究蛋白质如何将其运动限制在功能最重要的运动上。正常模式分析将进行辨别这些重要的功能运动与高计算效率，开发分子机制。我们将研究酶活性中心的原子运动，以了解大畴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伸实验，并预测蛋白质分解的顺序。初步结果表明，弹性网络模型不仅适用于本地构象周围的波动，但也产生的瞬态时，施加外力变形的蛋白质和打破其天然接触。这些结果表明，结构控制蛋白质的整体运动，即使是瞬态。为了进一步验证这一假设，我们将进行更多的单分子拉伸模拟，和结构分析的瞬时蛋白质构象沿着折叠途径。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。目的二：蛋白质包装和相互作用的协同性建模。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部包装。新潜力的重点将是蛋白质中氨基酸的相对取向。我们将开发多体接触电位识别天然结构之间的诱饵在线程，也研究蛋白质中附近的残基簇内的取向分布，使用正多面体，如二十面体，或加泰罗尼亚固体，如四面体。我们的基本原理是使用各种多面体模型来理解蛋白质包装和氨基酸的相互作用，以开发改进的多体势。更好地理解蛋白质内部相互作用的协同性是非常重要的，因为这直接影响蛋白质移动和响应力的方式。这两个目标是高度相互关联的，并将显着推进我们的蛋白质结构，动力学和功能的知识。 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-从药物设计的蛋白质靶点选择到对细胞功能的一般理解。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。提高蛋白质运动建模的能力可以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点来影响公共健康。

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.