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).

将随机性纳入生物分子机制：研究的新方向