Sharing the Energy Landscape for Folding and Function: from Proteins to Biomolecular Machines.

该项目由生物科学局分子和细胞生物科学部的分子生物学和数学和物理科学局物理部的生命系统物理计划联合支持，目的是研究蛋白质如何在折叠和功能之间共享能量图景。这项研究是在双重目标的指导下进行的，即学习支配蛋白质景观的新物理原理，并利用这一理论机制走向复杂的、尚未被理解的生物学应用。令人惊讶的是，细胞创造了许多分子机器，专门用于以精致的精度控制和维护细胞功能所需的任务。由于生物分子通过热运动而波动，其动力学是扩散的，生物机器从根本上不同于传统的热机或宏观世界中的机器。生物机器的关键特征之一是在弱环境扰动下由热噪声引发的构象变化。因此，可以借用蛋白质折叠和聚合物动力学的能量景观理论来解释它们的行为。在这个框架下，能量景观理论可以被推广到研究蛋白质如何在折叠和功能之间共享他们的能量景观。目前的基于结构的烟雾模型(SBM)将被推广到研究复杂生物功能所必需的重要特征。尤其重要的是提高这些模型结合许多功能蛋白质所占据的多种构象的能力。为了充分探索能量和几何贡献之间的相互作用，SBM将整合多个级别的粗粒化；低分辨率模型处理更大的系统和更长的时间，而高分辨率模型专注于细节。同时，结构性约束将减少，需要更少的结构性信息，但需要更多的物理信息。这一转变的一个关键步骤是将SBM和显式溶剂模拟相结合。SBM模型探测长时间和大尺度的分子运动，而显式溶剂模拟处理较短时间和较小的系统。这套方法与最新的实验相结合，将被用来研究整合折叠和功能的问题。PI的能量景观理论在蛋白质折叠领域产生了巨大的影响。由PI开发的软件是自由传播的，被理论家和实验者广泛使用。这些进展将继续推动与实验小组的成功合作。在培养高度跨学科的分子生物物理学领域的学生和博士后方面，PI有着出色的记录。国际和平研究所的许多学生和博士后研究员现在是主要大学的教授。在他的研究中，PI一直有很好的女性代表和代表不足群体的成员。这一强有力的培训工作将在下一阶段继续进行。

The goal of this project, which is jointly supported by Molecular Biophysics in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Physics of Living Systems Program in the Division of Physics in the Mathematical and Physical Sciences Directorate, is to investigate how proteins share their energy landscape between folding and function. This research is guided by the dual goal of learning new physical principles governing protein landscapes and utilizing this theoretical machinery to move towards complex and not yet understood biological applications. It is amazing how cells have created a number of molecular machines specialized for undertaking tasks needed to control and maintain cellular functions with exquisite precision. Due to the fact that biomolecules fluctuate via thermal motion and their dynamics are diffusive, biological machines are fundamentally different from conventional heat engines or machines in the macroscopic world. One of the key features of biological machines is the conformational changes triggered by the thermal noise under weak environmental perturbation. Therefore their behavior can be explained using ideas borrowed from the energy landscape theory of protein folding and polymer dynamics. Under this framework, energy landscape theory can be generalized to investigate how proteins share their energy landscape between folding and function. The current SMOG suite of structure-based models (SBM) will be generalized to investigate important features necessary for complex biological function. Especially important will be to increase the ability of these models to incorporate the multiple conformations occupied by many functional proteins. To fully explore the interplay between energetic and geometrical contributions, SBM's will integrate multiple levels of coarse-graining; lower resolution models handle larger systems and longer times while higher resolution ones focus on details. At the same time, structural constraints will be reduced, requiring less structural but more physical information. A key step in this transition is the integration of SBM and explicit solvent simulations. SBM models probe long-time and large-length scale molecular motions while explicit solvent simulations deal with shorter times and smaller systems. A combination of this suite of methods with new state of the art experiments will be used to investigate problems that integrate folding and function. The PI's energy landscape theory has had an enormous impact in the protein folding community. Software developed by the PI is freely disseminated, which is widely used by theoreticians and experimentalists. These advances will continue to drive successful collaborations with experimental groups. The PI has an outstanding track record in training students and postdoctoral fellows in the highly interdisciplinary field of molecular biophysics. Many of the PI's students and postdoctoral fellows are now professors at major universities. The PI has always had a good representation of women and members from underrepresented groups in his research. This strong training effort will continue during the next period.