RUI: A variational enhanced sampling approach to enzyme kinetics and protein dynamics in condensed phases

化学系的化学理论、模型和计算方法项目支持西华盛顿大学的James McCarty博士开发分子模拟算法，用于计算溶液中生物分子的动态特性。生物分子的计算机模拟可以刺激工程蛋白和仿生纳米材料的设计和优化。然而，以合理的计算成本获得动态信息受到克服生物系统中罕见事件特征的动力学瓶颈所需的时间的限制。麦卡蒂博士和他的研究小组将利用一种基于罕见事件统计力学的变分形式，来计算溶液中蛋白质和生化反应的长期动态观察结果。这项工作的目标是提供一个计算框架，以弥合计算机模拟和探索长期动态的实验之间的差距。McCarty博士的小组开发的方法将提供给实验和计算研究小组，并将为实验人员提供解释动态测量的新工具。麦卡蒂博士的研究将为本科生提供更多参与计算生物化学研究的机会。计划活动的一个结果预计是将计算的进步整合到本科生物化学实验室，将计算机模拟与实验突变筛选数据相结合。分子模拟中的增强采样方法利用外部偏置电位更有效地采样状态空间。McCarty博士将利用变分方法来增强采样，其中外部偏置电位沿着低维反应坐标加速罕见事件转变。在迭代过程中，通过优化一组变分参数来实时构建偏置势。通过算法的进步、基准测试和这种变分形式的应用，McCarty博士的工作将把变分方法的范围扩展到两个关键领域，这对理解大分子动力学和酶反应速率很重要。(i) McCarty博士将开发从从头算分子动力学（MD）模拟中预测酶促反应平均首次通过时间的方法。（ii） McCarty博士将开发一种方法，从从偏经典（MD）轨迹获得的时间序列数据中提取时间相关函数，该方法有望有效地计算核磁共振弛豫率。在这两个领域中，根据罕见事件的统计力学，通过重新缩放有偏差的模拟时间来计算动态观测值。McCarty博士的工作可能会带来效率的数量级提高，这有可能加速量子力学/分子力学（QM/MM）混合模拟在药物开发和蛋白质工程中的能力，并最终为研究人员提供一种解释动态核磁共振测量的新工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With this award, the Chemical Theory, Models, and Computational Methods program in the Division of Chemistry is supporting Dr. James McCarty of Western Washington University to develop molecular simulation algorithms for computing dynamical properties of biomolecules in solution. Computer simulations of biological molecules can spur the design and optimization of engineered proteins and biomimetic nanomaterials. However, obtaining dynamical information at a reasonable computational cost is limited by the time required to overcome kinetic bottlenecks that characterize rare events in biological systems. Dr. McCarty and his research group will leverage a variational formalism, based on the statistical mechanics of rare events, to compute long-time dynamical observables for proteins in solution and biochemical reactions. A goal of this work is to provide a computational framework to bridge the gap between computer simulations and experiments that probe long-time dynamics. The methods developed by Dr. McCarty’s group will be made accessible to experimental and computational research groups and will provide new tools for experimentalists to interpret dynamical measurements. Dr. McCarty’s research will offer increased access and opportunities for undergraduates to engage in computational biochemistry research. An outcome of the planned activities is expected to be the integration of computational advances into the undergraduate biochemistry lab, pairing computer simulation with experimental mutational screening data.Enhanced sampling methods in molecular simulations use an external bias potential to more efficiently sample state space. Dr. McCarty will make use of a variational approach to enhanced sampling in which an external bias potential accelerates rare-event transitions along a low-dimensional reaction coordinate. The bias potential is constructed on-the-fly by optimizing a set of variational parameters in an iterative procedure. Through algorithmic advances, benchmarking, and applications of this variational formalism, Dr. McCarty’s work will extend the scope of the variational approach into two key areas important for understanding macromolecular dynamics and enzyme reaction rates. (i) Dr. McCarty will be developing methods to predict the mean-first-passage times of enzymatic reactions from ab initio molecular dynamics (MD) simulations. (ii) Dr. McCarty will be developing an approach to extract time correlation functions from time-series data obtained from biased classical (MD) trajectories that is expected to efficiently compute NMR relaxation rates. In both of these areas, dynamic observables are computed by rescaling the biased simulation time according to the statistical mechanics of rare events. The orders-of-magnitude improvement in efficiency that will likely be afforded by Dr. McCarty’s work has the potential to accelerate the capabilities of quantum mechanics/molecular mechanics (QM/MM) hybrid simulations for drug development and protein engineering, and to ultimately provide a new tool for researchers to interpret dynamical NMR measurements.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.