Bayesian methods in integrative structure modeling

Habeck, Michael

doi:10.1515/hsz-2023-0145

Cited by 3 publications

(3 citation statements)

References 124 publications

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“…The integration of experimental data in simulations is a powerful approach to increase the resolution of the former and the accuracy of the latter. − This integration is based on two elements: (i) a forward model for the calculation of an experimental observable, given a conformation and (ii) an integration strategy (e.g., restraints or reweighting based on either the maximum entropy principle or Bayesian inference , ). The forward model should be accurate and computationally efficient when the goal is to apply a restraint in a simulation.…”

Section: Discussionmentioning

confidence: 99%

Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations

Ballabio,

Paissoni,

Bollati

et al. 2023

J. Chem. Theory Comput.

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Small-angle X-ray and neutron scattering (SAXS/ SANS) provide valuable insights into the structure and dynamics of biomolecules in solution, complementing a wide range of structural techniques, including molecular dynamics simulations. As contrastbased methods, they are sensitive not only to structural properties but also to solvent−solute interactions. Their use in molecular dynamics simulations requires a forward model that should be as fast and accurate as possible. In this work, we demonstrate the feasibility of calculating SAXS and SANS intensities using a coarsegrained representation consisting of one bead per amino acid and three beads per nucleic acid, with form factors that can be corrected on the fly to account for solvation effects at no additional computational cost. By coupling this forward model with molecular dynamics simulations restrained with SAS data, it is possible to determine conformational ensembles or refine the structure and dynamics of proteins and nucleic acids in agreement with the experimental results. To assess the robustness of this approach, we applied it to gelsolin, for which we acquired SAXS data on its closed state, and to a UP1-microRNA complex, for which we used previously collected measurements. Our hybrid-resolution small-angle scattering (hySAS) implementation, being distributed in PLUMED, can be used with atomistic and coarse-grained simulations using diverse restraining strategies.

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Section: Discussionmentioning

confidence: 99%

Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations

Ballabio,

Paissoni,

Bollati

et al. 2023

J. Chem. Theory Comput.

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“…Bayesian model selection methods have been previously developed for optimizing the molecular dynamics force-field parameters, coarse-grained representations, and number of states, i.e., conformations, of macromolecules (Bonomi et al 2017;Habeck 2023). (Potrzebowski, Trewhella and Andre 2018) used model evidence to find the optimal set of states that describe SAS and NMR data.…”

Section: Introductionmentioning

confidence: 99%

“…e ., conformations, of macromolecules (Bonomi et al . 2017; Habeck 2023). (Potrzebowski, Trewhella and Andre 2018) used model evidence to find the optimal set of states that describe SAS and NMR data.…”

Section: Introductionmentioning

confidence: 99%

Optimizing representations for integrative structural modeling using Bayesian model selection

Arvindekar,

Pathak,

Majila

et al. 2023

Preprint

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MotivationIntegrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually madead hoc, manually.ResultsHere, we report NestOR (Nested Sampling forOptimizingRepresentation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies.AvailabilityNestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available athttps://github.com/isblab/nestor.Data for the benchmark is athttps://www.doi.org/10.5281/zenodo.10360718.Supplementary Information is available online.

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Optimizing representations for integrative structural modeling using Bayesian model selection

Arvindekar,

Pathak,

Majila

et al. 2024

Bioinformatics

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Motivation Integrative structural modeling combines data from experiments, physical principles, statistics of previous structures, and prior models to obtain structures of macromolecular assemblies that are challenging to characterize experimentally. The choice of model representation is a key decision in integrative modeling, as it dictates the accuracy of scoring, efficiency of sampling, and resolution of analysis. But currently, the choice is usually made ad hoc, manually. Results Here, we report NestOR (Nested Sampling for Optimizing Representation), a fully automated, statistically rigorous method based on Bayesian model selection to identify the optimal coarse-grained representation for a given integrative modeling setup. Given an integrative modeling setup, it determines the optimal representations from given candidate representations based on their model evidence and sampling efficiency. The performance of NestOR was evaluated on a benchmark of four macromolecular assemblies. Availability NestOR is implemented in the Integrative Modeling Platform (https://integrativemodeling.org) and is available at https://github.com/isblab/nestor. Data for the benchmark is at https://www.doi.org/10.5281/zenodo.10360718. Supplementary information Supplementary data are available at Bioinformatics online.

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Bayesian methods in integrative structure modeling

Cited by 3 publications

References 124 publications

Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations

Accurate and Efficient SAXS/SANS Implementation Including Solvation Layer Effects Suitable for Molecular Simulations

Optimizing representations for integrative structural modeling using Bayesian model selection

Optimizing representations for integrative structural modeling using Bayesian model selection

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