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REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures

Mixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with...

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Detalles Bibliográficos
Autores principales: Meisburger, Steve P., Xu, Da, Ando, Nozomi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924237/
https://www.ncbi.nlm.nih.gov/pubmed/33708400
http://dx.doi.org/10.1107/S2052252521000555
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author Meisburger, Steve P.
Xu, Da
Ando, Nozomi
author_facet Meisburger, Steve P.
Xu, Da
Ando, Nozomi
author_sort Meisburger, Steve P.
collection PubMed
description Mixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, the REGALS method (regularized alternating least squares) is introduced, which incorporates simple expectations about the data as prior knowledge, and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, making it well suited for exploring datasets with unknown species. Here, REGALS is applied to the analysis of experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing and time-resolved temperature jump. Based on its performance with these challenging datasets, it is anticipated that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and Python and is available freely as an open-source software package.
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spelling pubmed-79242372021-03-10 REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures Meisburger, Steve P. Xu, Da Ando, Nozomi IUCrJ Research Papers Mixtures of biological macromolecules are inherently difficult to study using structural methods, as increasing complexity presents new challenges for data analysis. Recently, there has been growing interest in studying evolving mixtures using small-angle X-ray scattering (SAXS) in conjunction with time-resolved, high-throughput or chromatography-coupled setups. Deconvolution and interpretation of the resulting datasets, however, are nontrivial when neither the scattering components nor the way in which they evolve are known a priori. To address this issue, the REGALS method (regularized alternating least squares) is introduced, which incorporates simple expectations about the data as prior knowledge, and utilizes parameterization and regularization to provide robust deconvolution solutions. The restraints used by REGALS are general properties such as smoothness of profiles and maximum dimensions of species, making it well suited for exploring datasets with unknown species. Here, REGALS is applied to the analysis of experimental data from four types of SAXS experiment: anion-exchange (AEX) coupled SAXS, ligand titration, time-resolved mixing and time-resolved temperature jump. Based on its performance with these challenging datasets, it is anticipated that REGALS will be a valuable addition to the SAXS analysis toolkit and enable new experiments. The software is implemented in both MATLAB and Python and is available freely as an open-source software package. International Union of Crystallography 2021-02-06 /pmc/articles/PMC7924237/ /pubmed/33708400 http://dx.doi.org/10.1107/S2052252521000555 Text en © Meisburger, Xu and Ando 2021 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/
spellingShingle Research Papers
Meisburger, Steve P.
Xu, Da
Ando, Nozomi
REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title_full REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title_fullStr REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title_full_unstemmed REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title_short REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures
title_sort regals: a general method to deconvolve x-ray scattering data from evolving mixtures
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7924237/
https://www.ncbi.nlm.nih.gov/pubmed/33708400
http://dx.doi.org/10.1107/S2052252521000555
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