Sharing the Energy Landscape for Folding and Function: from Proteins to Biomolecular Machines.
Sharing the Energy Landscape for Folding and Function: from Proteins to Biomolecular Machines.
批准号:
1051438
负责人:
Jose Onuchic
金额:
$0.0万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2011
资助国家:
美国
项目状态:
已结题
起止时间:
2011-02-01 至 2012-03-31
中文摘要
该项目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和数学与物理科学理事会物理部的生命系统物理学项目共同支持,其目标是研究蛋白质如何在折叠和功能之间共享其能量景观。这项研究的双重目标是学习控制蛋白质景观的新物理原理,并利用这一理论机制向复杂且尚未理解的生物应用方向发展。令人惊奇的是,细胞是如何创造出大量的分子机器,专门用于执行需要精确控制和维持细胞功能的任务的。由于生物分子是通过热运动波动的,其动力学是扩散的,因此生物机器与传统的热机或宏观世界的机器有着根本的区别。弱环境扰动下热噪声引起的构象变化是生物机械的一个重要特征。因此,它们的行为可以用蛋白质折叠和聚合物动力学的能量景观理论来解释。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其能量景观。目前基于结构的烟雾模型(SBM)套件将被推广到研究复杂生物功能所需的重要特征。尤其重要的是增加这些模型的能力,以纳入许多功能蛋白所占据的多种构象。为了充分探索能量和几何贡献之间的相互作用,SBM将整合多个层次的粗粒化;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,结构约束将减少,需要更少的结构信息而更多的物理信息。这种转变的关键步骤是SBM和显式溶剂模拟的集成。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的时间和较小的系统。这一套方法与最新的艺术实验相结合,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开发的软件是免费传播的,被理论家和实验家广泛使用。这些进展将继续推动与试验组的成功合作。PI在培养分子生物物理学高度跨学科领域的学生和博士后方面有着出色的记录。PI的许多学生和博士后现在都是主要大学的教授。在他的研究中,PI一直很好地代表了女性和来自代表性不足群体的成员。这一强有力的培训工作将在下一时期继续进行。
英文摘要
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.
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会议论文
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