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

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 fro...

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Autores principales: Chamberlain, Sarah R., Moore, Stephen, Grant, Thomas D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312546/
https://www.ncbi.nlm.nih.gov/pubmed/37398274
http://dx.doi.org/10.1101/2023.06.02.543451
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author Chamberlain, Sarah R.
Moore, Stephen
Grant, Thomas D.
author_facet Chamberlain, Sarah R.
Moore, Stephen
Grant, Thomas D.
author_sort Chamberlain, Sarah R.
collection PubMed
description 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 a free fitting parameter commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated which 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 results in a significant improvement of the calculated scattering profiles compared to the leading software. The algorithm is computationally efficient, showing more than tenfold reduction in execution time compared to the leading software. 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 (https://github.com/tdgrant1/denss). 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 utilizing SWAXS data while decreasing the risk of overfitting.
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spelling pubmed-103125462023-07-01 Fitting high-resolution electron density maps from atomic models to solution scattering data Chamberlain, Sarah R. Moore, Stephen Grant, Thomas D. bioRxiv Article 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 a free fitting parameter commonly used in existing algorithms, resulting in improved accuracy of the calculated SWAXS profile. An implicit model of the hydration shell is generated which 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 results in a significant improvement of the calculated scattering profiles compared to the leading software. The algorithm is computationally efficient, showing more than tenfold reduction in execution time compared to the leading software. 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 (https://github.com/tdgrant1/denss). 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 utilizing SWAXS data while decreasing the risk of overfitting. Cold Spring Harbor Laboratory 2023-06-03 /pmc/articles/PMC10312546/ /pubmed/37398274 http://dx.doi.org/10.1101/2023.06.02.543451 Text en https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
spellingShingle Article
Chamberlain, Sarah R.
Moore, Stephen
Grant, Thomas D.
Fitting high-resolution electron density maps from atomic models to solution scattering data
title Fitting high-resolution electron density maps from atomic models to solution scattering data
title_full Fitting high-resolution electron density maps from atomic models to solution scattering data
title_fullStr Fitting high-resolution electron density maps from atomic models to solution scattering data
title_full_unstemmed Fitting high-resolution electron density maps from atomic models to solution scattering data
title_short Fitting high-resolution electron density maps from atomic models to solution scattering data
title_sort fitting high-resolution electron density maps from atomic models to solution scattering data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312546/
https://www.ncbi.nlm.nih.gov/pubmed/37398274
http://dx.doi.org/10.1101/2023.06.02.543451
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