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NOBEL PRIZE 2026

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NIHNATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESR35

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

Completed
Institution
University of Utah, UT
Principal investigator
Jessica Swanson
Funding
$376K
Duration
2021–2026
Open portfolio

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Abstract

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).

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R35NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCESSpecial Emphasis Panel[ZRG1-CB-V(55)R]hydrolysismultiscaletransitionskineticbiomolecularatpaaanew direction

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