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Cited by 10Enzyme Structure and FunctionProtein Structure and DynamicsAdvanced Electron Microscopy Techniques and Applications

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Fitting high-resolution electron density maps from atomic models to solution scattering data

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Chamberlain, Sarah R., Moore, Stephen, Grant, Thomas D.·2023·Biophysical Journal
DOI 10.1016/j.bpj.2023.10.034PMC PMC10719074

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Abstract

Solution scattering techniques, such as small- and wide-angle X-ray scattering (SWAXS), provide valuable insights into the structure and dynamics of biological macromolecules in solution. In this study, we present an approach to accurately predict solution X-ray scattering profiles at wide angles from atomic models by generating high-resolution electron density maps. Our method accounts for the excluded volume of bulk solvent by calculating unique adjusted atomic volumes directly from the atomic coordinates. This approach eliminates the need for one of the free fitting parameters commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated that uses the form factor of water. Two parameters, namely the bulk solvent density and the mean hydration shell contrast, are adjusted to best fit the data. Results using eight publicly available SWAXS profiles show high-quality fits to the data. In each case, the optimized parameter values show small adjustments demonstrating that the default values are close to the true solution. Disabling parameter optimization produces significantly more accurate predicted scattering profiles compared to the leading software. The algorithm is computationally efficient, comparable to the leading software and up to 10 times faster for large molecules. The algorithm is encoded in a command line script called denss.pdb2mrc.py and is available open source as part of the DENSS v1.7.0 software package. In addition to improving the ability to compare atomic models to experimental SWAXS data, these developments pave the way for increasing the accuracy of modeling algorithms using SWAXS data and decreasing the risk of overfitting.

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Metadata

Authors
Chamberlain, S. R., Moore, S., & Grant, T. D.
Affiliations
University at Buffalo, State University of New York (US)
Corresponding author
Grant, Thomas D.
ORCID
Venue
Biophysical Journal
Publisher
Elsevier BV
Published
2023-12
Volume
122
Issue
23
Pages
4567-4581
ISSN
0006-3495 (ISSN-L)
DOI
10.1016/j.bpj.2023.10.034
PMC
PMC10719074
OpenAlex
W4388209650
Cited by
10
References
1
Citations by year
2026: 4 · 2025: 2 · 2024: 3 · 2023: 1
FWCI
0.74
Type
journal article
Language
en
Open access
Licence
Locations
  • Biophysical Journal (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • National Institutes of Health
  • National Institute of General Medical Sciences (R01GM133998)
  • National Science Foundation (BioXFEL STC 1231306, MCB 1817862)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Enzyme Structure and Function
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Structural Biology › Advanced Electron Microscopy Techniques and Applications
Keywords
solution X-ray scattering, hydration shell

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