Computational Studies of Protein-Protein Interactions

蛋白质-蛋白质相互作用的计算研究

• 批准号: 1517221
• 负责人: Benoit Roux
• 金额: $ 101.46万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-15 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1517221&HistoricalAwards=false
• 关键词: Computational; Studies; Protein; Interactions

One of the most basic questions about the protein molecules in the living cell is the manner by which they interact with one another. How proteins recognize and bind (stick) to the correct partner(s) is one of the ways information is communicated within and between the cells. This is how cells process information and make decisions at the molecular level. Atomic-level information is essential to explain the specific interactions between proteins in terms of structure and dynamics. The research project consists in developing and improving the computer-based approach of "molecular dynamics" to calculate and predict accurately how proteins bind to one another. Molecular dynamics (MD) consists of using Newton's classical equation, force = mass x acceleration, to simulate the motions of all the atoms as a function of time. The MD simulation, although an approximation to the real world, provides detailed information about the time course of the atomic motions that cannot be accessed experimentally. The approach based on atomic models and MD simulations is advantageous because it does not rely on any particular assumption about protein binding. Being able to predict the possible binding partners of a protein from computations will open the door to a much better understanding of living cells. In addition to developing methods that will be of broad utility to the scientific community, the project will provide inter-disciplinary training and mentoring to young scientists at the undergraduate and graduate level (including members of under-represented minorities), as well as outreach to high school students. The research project consists in improving the theoretical and computational methods to calculate and predict accurately how molecules bind to one another. According to the theory of statistical thermodynamics, the quantity that controls the association of molecules is the binding free energy. This mathematically well-defined quantity can be calculated using computer simulations of atomic models of the molecules of interest. Molecular dynamics (MD) simulation can help elucidate the fundamental principles governing the binding of biological molecules at the atomic level. MD consists of constructing detailed atomic models of the macromolecules and using Newton's classical equation, F=MA, to literally simulate the dynamical motions of all the atoms as a function of time. The microscopic forces (the "F" in Newton's equation) are approximated by using a potential function, also called a force field, constructed from simple analytical functions. Validating the accuracy of the force field is also an important use of computations based on MD simulations. The calculated trajectory, though an approximation to the real world, provides detailed information about the time course of the atomic motions, which is impossible to access experimentally. Efforts in this area are likely to have a large impact because biology is entering a quantitative era that requires an ability to predict the binding of molecules. The first specific objective of the research is the design and development of novel computational methodologies addressing the specific challenges presented by protein-protein interactions. Sampling the large number of accessible configurations of the solvated proteins is critical. Simply running longer unbiased MD is not going to lead to success, and special enhanced sampling methods are needed to achieve this. The second objective is to validate and test the computational methodology and assess the accuracy of the atomic force field on the basis of well-known protein-protein complexes. It is important to know if the results are correct for the binding free energy, the entropy and enthalpy decomposition, and the kinetic rate of association. In the last stage, the objectives are focused on testing the computational method with respect to increasingly challenging situations.This project is jointly funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Chemical Theory, Models, and Computational Methods Program in the Division of Chemistry in the Directorate of Mathematical and Physical Sciences.

关于活细胞中蛋白质分子的最基本问题之一是它们相互作用的方式。蛋白质如何识别和结合（棒）与正确的伴侣（S）是在单元内和细胞之间传达信息的一种方式之一。这就是细胞处理信息并在分子水平做出决策的方式。原子级信息对于解释结构和动力学方面的蛋白质之间的特定相互作用至关重要。研究项目包括开发和改进基于计算机的“分子动力学”方法，以准确计算和预测蛋白质如何相互结合。分子动力学（MD）包括使用牛顿的经典方程，力=质量X加速度，以模拟所有原子的运动作为时间的函数。 MD仿真虽然与现实世界的近似值，但提供了有关无法通过实验访问的原子运动时间过程的详细信息。基于原子模型和MD模拟的方法是有利的，因为它不依赖于蛋白质结合的任何特定假设。能够从计算中预测蛋白质的可能结合伴侣将为对活细胞的更好理解打开大门。除了开发对科学界具有广泛实用性的方法外，该项目还将向本科和研究生层面的年轻科学家（包括代表性不足的少数群体的成员）提供跨学科的培训和指导，并向高中生宣传。研究项目包括改进理论和计算方法，以准确计算和预测分子如何相互结合。根据统计热力学理论，控制分子缔合的数量是结合自由能。该数学定义明确的数量可以使用感兴趣分子的原子模型进行计算机模拟来计算。分子动力学（MD）模拟可以帮助阐明控制生物分子在原子水平上结合的基本原理。 MD包括构建大分子的详细原子模型，并使用牛顿的经典方程F = mA来字面上模拟所有原子的动力学动作随时间的函数。微观力（牛顿方程中的“ f”）通过使用潜在函数（也称为力场）近似，该函数由简单的分析函数构建。验证力场的准确性也是基于MD模拟的计算的重要用途。计算出的轨迹虽然与现实世界的近似值，但提供了有关原子运动时间过程的详细信息，这是无法通过实验访问的。该领域的努力可能会产生很大的影响，因为生物学进入了一个定量时代，需要预测分子的结合。该研究的第一个具体目标是设计和开发新颖的计算方法论，以解决蛋白质 - 蛋白质相互作用带来的特定挑战。对溶剂蛋白的大量可访问构型进行采样至关重要。简单地运行更长的无偏MD并不会带来成功，并且需要特殊的增强抽样方法来实现这一目标。第二个目标是验证和测试计算方法，并根据众所周知的蛋白质 - 蛋白质复合物评估原子力场的准确性。重要的是要知道结果对于结合自由能，熵和焓分解以及动力学速率是否正确。 In the last stage, the objectives are focused on testing the computational method with respect to increasingly challenging situations.This project is jointly funded by the Molecular Biophysics Cluster in the Division of Molecular and Cellular Biosciences in the Directorate for Biological Sciences and the Chemical Theory, Models, and Computational Methods Program in the Division of Chemistry in the Directorate of Mathematical and Physical Sciences.