Literature DB >> 34627764

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data.

Francesco Pesce1, Kresten Lindorff-Larsen2.   

Abstract

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.
Copyright © 2021 Biophysical Society. Published by Elsevier Inc. All rights reserved.

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Year:  2021        PMID: 34627764      PMCID: PMC8633713          DOI: 10.1016/j.bpj.2021.10.003

Source DB:  PubMed          Journal:  Biophys J        ISSN: 0006-3495            Impact factor:   4.033


  73 in total

1.  Temperature Dependence of Intrinsically Disordered Proteins in Simulations: What are We Missing?

Authors:  S Jephthah; L Staby; B B Kragelund; M Skepö
Journal:  J Chem Theory Comput       Date:  2019-03-27       Impact factor: 6.006

Review 2.  Validation of Molecular Simulation: An Overview of Issues.

Authors:  Wilfred F van Gunsteren; Xavier Daura; Niels Hansen; Alan E Mark; Chris Oostenbrink; Sereina Riniker; Lorna J Smith
Journal:  Angew Chem Int Ed Engl       Date:  2017-12-27       Impact factor: 15.336

3.  Integrating Molecular Simulation and Experimental Data: A Bayesian/Maximum Entropy Reweighting Approach.

Authors:  Sandro Bottaro; Tone Bengtsen; Kresten Lindorff-Larsen
Journal:  Methods Mol Biol       Date:  2020

4.  SAXS-Restrained Ensemble Simulations of Intrinsically Disordered Proteins with Commitment to the Principle of Maximum Entropy.

Authors:  Markus R Hermann; Jochen S Hub
Journal:  J Chem Theory Comput       Date:  2019-08-26       Impact factor: 6.006

5.  Highly populated turn conformations in natively unfolded tau protein identified from residual dipolar couplings and molecular simulation.

Authors:  Marco D Mukrasch; Phineus Markwick; Jacek Biernat; Martin von Bergen; Pau Bernadó; Christian Griesinger; Eckhard Mandelkow; Markus Zweckstetter; Martin Blackledge
Journal:  J Am Chem Soc       Date:  2007-03-27       Impact factor: 15.419

6.  FoXS: a web server for rapid computation and fitting of SAXS profiles.

Authors:  Dina Schneidman-Duhovny; Michal Hammel; Andrej Sali
Journal:  Nucleic Acids Res       Date:  2010-05-27       Impact factor: 16.971

7.  Refinement of α-Synuclein Ensembles Against SAXS Data: Comparison of Force Fields and Methods.

Authors:  Mustapha Carab Ahmed; Line K Skaanning; Alexander Jussupow; Estella A Newcombe; Birthe B Kragelund; Carlo Camilloni; Annette E Langkilde; Kresten Lindorff-Larsen
Journal:  Front Mol Biosci       Date:  2021-04-22

Review 8.  Combining experiments and simulations using the maximum entropy principle.

Authors:  Wouter Boomsma; Jesper Ferkinghoff-Borg; Kresten Lindorff-Larsen
Journal:  PLoS Comput Biol       Date:  2014-02-20       Impact factor: 4.475

9.  Metainference: A Bayesian inference method for heterogeneous systems.

Authors:  Massimiliano Bonomi; Carlo Camilloni; Andrea Cavalli; Michele Vendruscolo
Journal:  Sci Adv       Date:  2016-01-22       Impact factor: 14.136

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  2 in total

Review 1.  Recent Developments in Data-Assisted Modeling of Flexible Proteins.

Authors:  Cezary Czaplewski; Zhou Gong; Emilia A Lubecka; Kai Xue; Chun Tang; Adam Liwo
Journal:  Front Mol Biosci       Date:  2021-12-24

2.  Integrative Conformational Ensembles of Sic1 Using Different Initial Pools and Optimization Methods.

Authors:  Gregory-Neal W Gomes; Ashley Namini; Claudiu C Gradinaru
Journal:  Front Mol Biosci       Date:  2022-07-18
  2 in total

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