Integrating Stochasticity into Biomolecular Mechanisms: A New Direction for Biomolecular Modeling

将随机性整合到生物分子机制中：生物分子建模的新方向

• 批准号: 10277296
• 负责人: Jessica Swanson
• 金额: $ 36.45万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-18 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10277296
• 关键词: ATP Hydrolysis; ATP phosphohydrolase; Active Biological Transport; Address; Binding; Biology; Collaborations; Data; Event; Free Energy; Goals; Heterogeneity; Hydrolysis; Kinetics; Methods; Modeling; Molecular Conformation; Pathway interactions; Play; Process; Reaction; Research; Role; Sampling; Secondary to; System; Testing; Uncertainty; Universities; Utah; antiporter; base; biophysical properties; chemical reaction; experimental study; innovation; kinetic model; machine learning method; molecular dynamics; quantum; simulation; single molecule; stoichiometry; tool

Integrating Stochasticity into Biomolecular Mechanisms: A New Direction for Biomolecular Modeling Abstract It is increasingly apparent that kinetic selection plays an important role in biology. However, we are just beginning to have the tools necessary to quantify, characterize and understand it. For biomolecular processes involving multiple rare-event transitions, the canonical assumption is that mechanisms proceed following a consistent order of transitions (following a single-pathway). However, increasing evidence from single molecule experiments and biophysical measurements suggests that multiple pathways are not only possible, but essential. The goal of the proposed research is to develop an experimentally-directed stochastic simulation framework for mapping out mechanistic heterogeneity. As applications, I will focus, first, on secondary active transport in the ClC Cl-/H+ antiporter and ATP hydrolysis driven translocation in several AAA+ ATPases, two processes involving chemical reactions and thus requiring multiscale methods that bridge the quantum to classical realms. The proposed approach to multiscale kinetic modeling is focused on multistep biomolecular transformations, which makes it unique to many other domains of established kinetic modeling. Thus, new methods will be developed and best practices from other domains will be adapted. It combines a bottom-up calculation of rate coefficients for kinetically relevant transitions from multiscale simulations, with a top-down parameter refinement based on experimental data. Innovation is proposed to refine the kinetic solution space with Bayesian parameter estimation, global sensitivity analysis, uncertainty quantification, reaction path analysis and machine learning methods. These methods will be used to better characterize the Cl-/H+ exchange mechanism in the ClC-ec1 antiporter in collaboration with Merritt Maduke (Stanford). The kinetic landscape for the wildtype system will be studied to address the role of pathway heterogeneity, the origin of the non-integral 2.2:1 Cl-:H+ stoichiometry, and the relevance of the alternating access mechanism. Similar to secondary active transport, ATP-driven processes inherently involve multiple rate-influencing steps (ATP binding, hydrolysis, Pi release, ADP release, and all of the associated conformational changes). A multiscale reactive molecular dynamics method will be developed to describe ATP hydrolysis. Additionally, enhanced free energy sampling will be used to characterize other transitions and multiscale kinetic models will be developed to probe the role of kinetic selectivity and to test the controversial stochastic versus sequential proposed mechanisms in AAA+ ATPases in collaboration with Chris Hill (University of Utah).

将随机性整合到生物分子机制中：一个新方向 生物分子建模 抽象的 越来越明显的是，动力学选择在生物学中起着重要作用。但是，我们才刚刚开始 拥有量化，表征和理解的必要工具。用于涉及的生物分子过程 多个罕见事件过渡，规范的假设是机制在一致之后进行 过渡顺序（遵循单个通道）。但是，越来越多的单分子证据 实验和生物物理测量表明，多种途径不仅可能，而且是必不可少的。 拟议的研究的目的是开发实验指导的随机模拟 绘制机械异质性的框架。作为应用程序，我首先将重点放在次要上 在CLC CL-/H+抗胞菌中的主动转运和ATP水解驱动的易位在几个AAA+中 ATPases，两个涉及化学反应的过程，因此需要桥接的多尺度方法 古典领域的量子。 提出的多尺度动力学建模方法集中于多步生物分子转化， 这使其与已建立的动力学建模的许多其他领域具有独特性。因此，新方法将是 从其他领域的开发和最佳实践将得到调整。它结合了速率的自下而上的计算 从多尺度模拟的动力学相关过渡的系数，并具有自上而下的参数优化 基于实验数据。提出了创新以优化使用贝叶斯参数的动力学溶液空间 估计，全球灵敏度分析，不确定性量化，反应路径分析和机器学习 方法。这些方法将用于更好地表征CLC-EC1中Cl-/H+交换机制 与梅里特·马德克（Merritt Maduke）（斯坦福大学）合作的反毒物。野生型系统的动力学景观将是 研究以解决途径异质性的作用，非整合2.2：1 cl-：h+ stoichiementry的起源， 以及交替访问机制的相关性。 与二级活动传输相似，ATP驱动的过程固有地涉及多个速率影响步骤 （ATP结合，水解，PI释放，ADP释放以及所有相关的构象变化）。一个 将开发多尺度反应性分子动力学方法来描述ATP水解。此外， 增强的自由能抽样将用于表征其他过渡，多尺度动力学模型将 开发以探测动力学选择性的作用，并测试有争议的随机性与顺序 与Chris Hill（犹他大学）合作，AAA+ ATPASE的拟议机制。