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Langevin Modeling of Rare Biomolecular Processes

稀有生物分子过程的朗之万模型

基本信息

批准号：
323580824
负责人：
Professor Dr. Gerhard Stock
金额：
--
依托单位：
Abteilung Biomolekulare Dynamik
依托单位国家：
德国
项目类别：
Research Grants
财政年份：
2016
资助国家：
德国
起止时间：
2015-12-31 至 2020-12-31
项目状态：
已结题

来源：
https://gepris.dfg.de/gepris/projekt/323580824?language=en
关键词：
Langevin Modeling Rare Biomolecular Processes

项目摘要

The in silico prediction of rare biomolecular events represents a long-standing problem, because slow (say, millisecond timescale) processes are typically out of reach of current all-atom molecular dynamics (MD) simulations. To circumvent this time scale problem, one often invokes massive parallel computing strategies or various enhanced sampling techniques such as replica exchange, steered or targeted MD. While these approaches may provide an efficient sampling of the system's free energy landscape, a dynamical description requires some coarse-grained "post-simulation'' model that is capable of rebuilding the kinetics from the sampled data. To this end, we have recently proposed a data-driven Langevin equation (dLE) approach that constructs a low-dimensional dynamical model from a given MD trajectory. Now, the challenge is to exploit the virtues of the dLE formulation to facilitate its practical application to various biomolecular systems, ranging from polypeptides to multidomain proteins. Here two viable and promising routes will be followed: (1) Using temperature as driving force to overcome energy barriers, we will construct a dLE model from replica exchange MD simulations. Moreover, we will exploit a fluctuation-dissipation theorem in order to make low-temperature predictions from high-temperature simulations. (2) Employing enhanced sampling methods such as metadynamics and steered or targeted MD, we perform an efficient sampling of the overall free energy landscape and choose a number of starting configurations for subsequent short MD trajectories, which generate input data for the dLE model. Adopting protein systems of increasing size and dynamical complexity, we will ultimately consider the systems studied by our experimental collaborators and aim to compare computational and experimental predictions of molecular structures and conformational transition rates.

罕见生物分子事件的计算机预测是一个长期存在的问题，因为目前的全原子分子动力学（MD）模拟通常无法实现缓慢（比如毫秒时间尺度）的过程。为了规避这个时间尺度问题，人们经常调用大规模并行计算策略或各种增强的采样技术，如副本交换、导向或目标MD。虽然这些方法可以提供系统自由能景观的有效采样，但动态描述需要一些粗粒度的“后模拟”模型，能够从采样数据中重建动力学。为此，我们最近提出了一种数据驱动的朗格万方程（dLE）方法，该方法从给定的MD轨迹构建一个低维动态模型。现在，挑战是利用dLE配方的优点，促进其在各种生物分子系统中的实际应用，从多肽到多结构域蛋白。本文将遵循两种可行且有前景的途径：(1)利用温度作为克服能量障碍的驱动力，从副本交换MD模拟中构建dLE模型。此外，我们将利用涨落耗散定理，以便从高温模拟中进行低温预测。(2)采用元动力学和定向MD等增强的采样方法，我们对整体自由能景观进行了有效采样，并为随后的短MD轨迹选择了一些起始配置，这些配置为dLE模型生成输入数据。采用越来越大的尺寸和动态复杂性的蛋白质系统，我们将最终考虑我们的实验合作者所研究的系统，并旨在比较分子结构和构象转变速率的计算和实验预测。