Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data

Abstract Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies...

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Main Authors: Nelly R. Hajizadeh, Daniel Franke, Cy M. Jeffries, Dmitri I. Svergun
Format: Article
Language:English
Published: Nature Publishing Group 2018-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-018-25355-2
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spelling doaj-64ea8341bdef4605ab3728253d908a362020-12-08T06:11:17ZengNature Publishing GroupScientific Reports2045-23222018-05-018111310.1038/s41598-018-25355-2Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering dataNelly R. Hajizadeh0Daniel Franke1Cy M. Jeffries2Dmitri I. Svergun3European Molecular Biology Laboratory (EMBL) Hamburg Outstation, DESYEuropean Molecular Biology Laboratory (EMBL) Hamburg Outstation, DESYEuropean Molecular Biology Laboratory (EMBL) Hamburg Outstation, DESYEuropean Molecular Biology Laboratory (EMBL) Hamburg Outstation, DESYAbstract Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies on a number of extra quantities (partial specific volume, calibrated intensity, accurate solute concentration) and often yields limited accuracy. Concentration-independent methods forgo these requirements being based on the relationship between structural parameters, scattering invariants and particle volume obtained directly from the data. Using a comparative analysis on 165,982 unique scattering profiles calculated from high-resolution protein structures, the performance of multiple concentration-independent MM determination methods was assessed. A Bayesian inference approach was developed affording an accuracy above that of the individual methods, and reports MM estimates together with a credibility interval. This Bayesian approach can be used in combination with concentration-dependent MM methods to further validate the MM of proteins in solution, or as a reliable stand-alone tool in instances where an accurate concentration estimate is not available.https://doi.org/10.1038/s41598-018-25355-2
collection DOAJ
language English
format Article
sources DOAJ
author Nelly R. Hajizadeh
Daniel Franke
Cy M. Jeffries
Dmitri I. Svergun
spellingShingle Nelly R. Hajizadeh
Daniel Franke
Cy M. Jeffries
Dmitri I. Svergun
Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
Scientific Reports
author_facet Nelly R. Hajizadeh
Daniel Franke
Cy M. Jeffries
Dmitri I. Svergun
author_sort Nelly R. Hajizadeh
title Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
title_short Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
title_full Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
title_fullStr Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
title_full_unstemmed Consensus Bayesian assessment of protein molecular mass from solution X-ray scattering data
title_sort consensus bayesian assessment of protein molecular mass from solution x-ray scattering data
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2018-05-01
description Abstract Molecular mass (MM) is one of the key structural parameters obtained by small-angle X-ray scattering (SAXS) of proteins in solution and is used to assess the sample quality, oligomeric composition and to guide subsequent structural modelling. Concentration-dependent assessment of MM relies on a number of extra quantities (partial specific volume, calibrated intensity, accurate solute concentration) and often yields limited accuracy. Concentration-independent methods forgo these requirements being based on the relationship between structural parameters, scattering invariants and particle volume obtained directly from the data. Using a comparative analysis on 165,982 unique scattering profiles calculated from high-resolution protein structures, the performance of multiple concentration-independent MM determination methods was assessed. A Bayesian inference approach was developed affording an accuracy above that of the individual methods, and reports MM estimates together with a credibility interval. This Bayesian approach can be used in combination with concentration-dependent MM methods to further validate the MM of proteins in solution, or as a reliable stand-alone tool in instances where an accurate concentration estimate is not available.
url https://doi.org/10.1038/s41598-018-25355-2
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