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Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering

Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of ‘unstructured’ and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray s...

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Autores principales: Tria, Giancarlo, Mertens, Haydyn D. T., Kachala, Michael, Svergun, Dmitri I.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Union of Crystallography 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4392415/
https://www.ncbi.nlm.nih.gov/pubmed/25866658
http://dx.doi.org/10.1107/S205225251500202X
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author Tria, Giancarlo
Mertens, Haydyn D. T.
Kachala, Michael
Svergun, Dmitri I.
author_facet Tria, Giancarlo
Mertens, Haydyn D. T.
Kachala, Michael
Svergun, Dmitri I.
author_sort Tria, Giancarlo
collection PubMed
description Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of ‘unstructured’ and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS) is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM) [Bernadó et al. (2007 ▶). J. Am. Chem. Soc. 129, 5656–5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed.
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spelling pubmed-43924152015-04-10 Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering Tria, Giancarlo Mertens, Haydyn D. T. Kachala, Michael Svergun, Dmitri I. IUCrJ Research Papers Dynamic ensembles of macromolecules mediate essential processes in biology. Understanding the mechanisms driving the function and molecular interactions of ‘unstructured’ and flexible molecules requires alternative approaches to those traditionally employed in structural biology. Small-angle X-ray scattering (SAXS) is an established method for structural characterization of biological macromolecules in solution, and is directly applicable to the study of flexible systems such as intrinsically disordered proteins and multi-domain proteins with unstructured regions. The Ensemble Optimization Method (EOM) [Bernadó et al. (2007 ▶). J. Am. Chem. Soc. 129, 5656–5664] was the first approach introducing the concept of ensemble fitting of the SAXS data from flexible systems. In this approach, a large pool of macromolecules covering the available conformational space is generated and a sub-ensemble of conformers coexisting in solution is selected guided by the fit to the experimental SAXS data. This paper presents a series of new developments and advancements to the method, including significantly enhanced functionality and also quantitative metrics for the characterization of the results. Building on the original concept of ensemble optimization, the algorithms for pool generation have been redesigned to allow for the construction of partially or completely symmetric oligomeric models, and the selection procedure was improved to refine the size of the ensemble. Quantitative measures of the flexibility of the system studied, based on the characteristic integral parameters of the selected ensemble, are introduced. These improvements are implemented in the new EOM version 2.0, and the capabilities as well as inherent limitations of the ensemble approach in SAXS, and of EOM 2.0 in particular, are discussed. International Union of Crystallography 2015-02-26 /pmc/articles/PMC4392415/ /pubmed/25866658 http://dx.doi.org/10.1107/S205225251500202X Text en © Giancarlo Tria et al. 2015 http://creativecommons.org/licenses/by/2.0/uk/ This is an open-access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.
spellingShingle Research Papers
Tria, Giancarlo
Mertens, Haydyn D. T.
Kachala, Michael
Svergun, Dmitri I.
Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title_full Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title_fullStr Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title_full_unstemmed Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title_short Advanced ensemble modelling of flexible macromolecules using X-ray solution scattering
title_sort advanced ensemble modelling of flexible macromolecules using x-ray solution scattering
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4392415/
https://www.ncbi.nlm.nih.gov/pubmed/25866658
http://dx.doi.org/10.1107/S205225251500202X
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