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Automatic Bayesian Weighting for SAXS Data

Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model...

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Autores principales: Spill, Yannick G., Karami, Yasaman, Maisonneuve, Pierre, Wolff, Nicolas, Nilges, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212126/
https://www.ncbi.nlm.nih.gov/pubmed/34150847
http://dx.doi.org/10.3389/fmolb.2021.671011
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author Spill, Yannick G.
Karami, Yasaman
Maisonneuve, Pierre
Wolff, Nicolas
Nilges, Michael
author_facet Spill, Yannick G.
Karami, Yasaman
Maisonneuve, Pierre
Wolff, Nicolas
Nilges, Michael
author_sort Spill, Yannick G.
collection PubMed
description Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state.
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spelling pubmed-82121262021-06-19 Automatic Bayesian Weighting for SAXS Data Spill, Yannick G. Karami, Yasaman Maisonneuve, Pierre Wolff, Nicolas Nilges, Michael Front Mol Biosci Molecular Biosciences Small-angle X-ray scattering (SAXS) experiments are important in structural biology because they are solution methods, and do not require crystallization of protein complexes. Structure determination from SAXS data, however, poses some difficulties. Computation of a SAXS profile from a protein model is expensive in CPU time. Hence, rather than directly refining against the data, most computational methods generate a large number of conformers and then filter the structures based on how well they satisfy the SAXS data. To address this issue in an efficient manner, we propose here a Bayesian model for SAXS data and use it to directly drive a Monte Carlo simulation. We show that the automatic weighting of SAXS data is the key to finding optimal structures efficiently. Another key problem with obtaining structures from SAXS data is that proteins are often flexible and the data represents an average over a structural ensemble. To address this issue, we first characterize the stability of the best model with extensive molecular dynamics simulations. We analyse the resulting trajectories further to characterize a dynamic structural ensemble satisfying the SAXS data. The combination of methods is applied to a tandem of domains from the protein PTPN4, which are connected by an unstructured linker. We show that the SAXS data contain information that supports and extends other experimental findings. We also show that the conformation obtained by the Bayesian analysis is stable, but that a minor conformation is present. We propose a mechanism in which the linker may maintain PTPN4 in an inhibited enzymatic state. Frontiers Media S.A. 2021-06-04 /pmc/articles/PMC8212126/ /pubmed/34150847 http://dx.doi.org/10.3389/fmolb.2021.671011 Text en Copyright © 2021 Spill, Karami, Maisonneuve, Wolff and Nilges. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Molecular Biosciences
Spill, Yannick G.
Karami, Yasaman
Maisonneuve, Pierre
Wolff, Nicolas
Nilges, Michael
Automatic Bayesian Weighting for SAXS Data
title Automatic Bayesian Weighting for SAXS Data
title_full Automatic Bayesian Weighting for SAXS Data
title_fullStr Automatic Bayesian Weighting for SAXS Data
title_full_unstemmed Automatic Bayesian Weighting for SAXS Data
title_short Automatic Bayesian Weighting for SAXS Data
title_sort automatic bayesian weighting for saxs data
topic Molecular Biosciences
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8212126/
https://www.ncbi.nlm.nih.gov/pubmed/34150847
http://dx.doi.org/10.3389/fmolb.2021.671011
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