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

该项目的目标是由生物科学理事会分子和细胞生物科学部的分子生物物理学以及数学和物理科学理事会物理部的生命系统物理学项目共同支持，旨在研究蛋白质如何在折叠和功能之间共享能量景观。 这项研究的双重目标是学习管理蛋白质景观的新物理原理，并利用这种理论机制走向复杂且尚未理解的生物学应用。 令人惊讶的是，细胞如何创造出许多分子机器，专门用于执行精确控制和维持细胞功能所需的任务。由于生物分子通过热运动波动并且它们的动力学是扩散的，生物机器与宏观世界中的传统热机或机器有着根本的不同。生物机器的一个关键特征是在弱环境扰动下由热噪声触发的构象变化。因此，它们的行为可以用借用蛋白质折叠和聚合物动力学的能量景观理论的思想来解释。 在此框架下，能量景观理论可以推广到研究蛋白质如何在折叠和功能之间共享能量景观。 目前的SMOG套件的基于结构的模型（SBM）将被推广到调查复杂的生物功能所必需的重要功能。特别重要的是，将增加这些模型的能力，以纳入许多功能蛋白质所占据的多种构象。为了充分探索能量和几何贡献之间的相互作用，SBM将集成多个级别的粗粒度;低分辨率模型处理更大的系统和更长的时间，而高分辨率模型则专注于细节。与此同时，将减少结构约束，需要更少的结构信息，但更多的物理信息。这种转变的关键一步是SBM和显式溶剂模拟的集成。SBM模型探测长时间和大长度尺度的分子运动，而显式溶剂模拟则处理较短的时间和较小的系统。将这套方法与最先进的实验相结合，将用于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界产生了巨大的影响。 PI开发的软件免费传播，被理论家和实验家广泛使用。这些进展将继续推动与实验组的成功合作。 PI在分子生物物理学的高度跨学科领域培养学生和博士后研究员方面有着出色的记录。PI的许多学生和博士后研究员现在都是主要大学的教授。在PI的研究中，妇女和代表性不足群体的成员一直占很大比例。下一个期间将继续大力开展这一培训工作。

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.