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Updated: Jun 21, 2025

Combining X-Ray Crystallography with Small Angle X-Ray Scattering to Model Unstructured Regions of Nsa1 from S. Cerevisiae

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Benchmarking predictive methods for small-angle X-ray scattering from atomic coordinates of proteins using maximum

Jill Trewhella¹, Patrice Vachette², Andreas Haahr Larsen³

¹School of Life and Environmental Sciences, University of Sydney, NSW 2006, Australia.

|July 11, 2024

Summary

This summary is machine-generated.

This study compared protein hydration models using small-angle scattering (SAS) data. All-atom molecular dynamics (MD) simulations, though slower, provide more accurate hydration insights than models with adjustable parameters.

Keywords:

SAXS SAXS profile modelling benchmarking biomolecular small-angle scattering consensus data hydration layer maximum likelihood molecular dynamics simulation waters of hydration

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Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Contrast-Matching Detergent in Small-Angle Neutron Scattering Experiments for Membrane Protein Structural Analysis and Ab Initio Modeling

Published on: October 21, 2018

Area of Science:

Biophysics
Structural Biology
Computational Biology

Background:

Small-angle scattering (SAS) is crucial for determining protein structure in solution.
Accurate modeling of protein hydration is essential for interpreting SAS data.
Previous consensus profiles were generated from limited datasets.

Purpose of the Study:

To generate improved consensus SAS profiles using a larger dataset and a maximum likelihood protocol.
To compare the accuracy of different hydration models in fitting experimental SAS data.
To evaluate the reliability of implicit hydration models versus all-atom molecular dynamics (MD) simulations.

Main Methods:

A round-robin exercise generated extensive SAS data for five proteins.
Maximum likelihood analysis was applied to a larger dataset for improved consensus profile generation.
Fits of consensus profiles were investigated using atomic coordinates with implicit hydration models and all-atom MD simulations.

Main Results:

Implicit hydration models, with adjustable parameters, showed good fits but could mask structural details.
All-atom MD simulations, without adjustable parameters, yielded comparable or better fits.
MD simulations accurately accounted for solvent composition (ions) and thermal fluctuations, improving radius of gyration predictions.

Conclusions:

Adjustable parameters in implicit hydration models can obscure real structural differences.
All-atom MD simulations are more robust against false positives and provide a more accurate representation of hydration.
While slower, MD simulations offer superior accuracy for SAS profile interpretation, especially when accounting for solvent composition.