CDI-Type II: Mapping Complex Biomolecular Reactions with Large Scale Replica Exchange Simulations on National Production Cyberinfrastructure

CDI-Type II：通过国家生产网络基础设施上的大规模复制交换模拟来绘制复杂的生物分子反应

• 批准号: 1125332
• 负责人: Darrin York
• 金额: $ 162.5万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-10-01 至 2016-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1125332&HistoricalAwards=false
• 关键词: CDI; Type; II; Mapping; Complex

Large scale, realistic simulations of complex biological and chemical phenomena at the atomic level of resolution level present a grand challenge for molecular simulation. Effective sampling of conformational space may require large numbers of computationally intensive simulations which are coupled to one another. Enhanced conformational sampling algorithms based on the application of biasing forces and replica exchange generalized ensembles, whereby a large number of replicas of the system are simulated in parallel, among the most powerful methods to study a wide variety of physicochemical processes. Uncoupled methods currently in use are very slowly convergent and often of dubious reliability as the independent simulations are not in equilibrium with one another. The key aspect of replica exchange (RE) algorithms is that replicas of the system periodically exchange their state parameters allowing them to rapidly traverse conformational space and to enhance equilibration. Current synchronous formulations of the RE method in wide use, however, are highly limited in terms of scalability and control when many exchanging replicas are involved. This limitation precludes the use of RE simulations to new application areas that require the calculation of high-dimensional free energy surfaces, and necessitate the dynamic control of 103-104 replicas as the landscape evolves. This project involves the development of a robust adaptive force biasing procedure coupled with an asynchronous replica exchange method. The research team is developing a novel infrastructure, the Replica Exchange Frame work (REFW) to enable the execution of very large scale RE simulations on a broad range of production computational resources, including but not limited to NSF TeraGrid (and its successor XD), cloud and campus-level cluster environments, as well as the forthcoming Blue Waters supercomputer. The REFW is being applied to applications that present multiple levels of complexity, such as coupled ligand binding, conformational change and catalysis in the glmS ribozyme/riboswitch that were hitherto not possible.The cyberinfrastructure created by this research team enables realistic simulations of important biological processes that have relevance in many areas of biology, biophysics, medicinal chemistry, and biophysics with the potential to impact human health. Additionally, the REWF may be applied in many other scientific areas that increasingly rely on realistic simulation including catalysis, earthquake prediction and petroleum engineering. The project is also training the next generation of computational scientists to apply these methods to solve high-impact interdisciplinary research problems. The resulting technology and training enables the study of a host of new reactive chemical problems of unprecedented complexity, and greatly facilitates innovation and discovery through advanced computation.This is a Cyber-Enabled Discovery and Innovation Program award and is co-funded by the Division of Chemistry and the Division of Physics in the Directorate for Mathematical and Physical Sciences.

在原子分辨率水平上对复杂的生物和化学现象进行大规模、逼真的模拟对分子模拟提出了巨大的挑战。对构象空间的有效采样可能需要大量相互耦合的计算密集型模拟。基于偏向力和副本交换广义集成的增强构象采样算法，从而并行模拟系统的大量副本，这是研究各种物理化学过程的最强大的方法之一。目前使用的解耦方法收敛速度很慢，而且由于独立的模拟彼此之间不平衡，可靠性往往令人怀疑。副本交换(RE)算法的关键是系统的副本周期性地交换它们的状态参数，从而使它们能够快速地遍历构象空间并增强平衡。然而，当涉及许多交换复制品时，目前广泛使用的RE方法的同步配方在可扩展性和可控性方面受到高度限制。这一限制排除了在需要计算高维自由能表面的新应用领域使用RE模拟的可能性，并需要随着地形的演变动态控制103-104个复制品。该项目涉及开发一种健壮的自适应力偏置程序，与异步副本交换方法相结合。研究团队正在开发一种新的基础设施，即副本交换框架(REFW)，以支持在广泛的生产计算资源上执行超大规模RE模拟，包括但不限于NSF TeraGrid(及其后续XD)、云和园区级集群环境，以及即将推出的Blue Waters超级计算机。REFW正被应用于呈现多层次复杂性的应用，例如GLMS核酶/核糖开关中的偶联配体结合、构象变化和催化，这是迄今为止无法实现的。该研究团队创建的网络基础设施使重要生物过程的现实模拟成为可能，这些过程与生物学、生物物理学、药物化学和生物物理学的许多领域相关，并有可能影响人类健康。此外，REWF还可以应用于许多其他越来越依赖真实模拟的科学领域，包括催化、地震预报和石油工程。该项目还在培训下一代计算科学家，让他们应用这些方法来解决高影响力的跨学科研究问题。由此产生的技术和培训使人们能够研究一系列前所未有的复杂的新的活性化学问题，并通过先进的计算极大地促进创新和发现。这是一个网络驱动的发现和创新计划奖，由数学和物理科学局的化学部和物理部共同资助。