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Bayesian inference of protein conformational ensembles from limited structural data

Potrzebowski, Wojciech LU ; Trewhella, Jill and Andre, Ingemar LU (2018) In PLoS Computational Biology 14(12).
Abstract

Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first... (More)

Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble. This is followed by a complete Bayesian inference of population weights in the selected ensemble. Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise. Using experimental data, we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance (NMR) and structural energies of conformers extracted from the all-atom energy functions. We show that the data from SAXS, NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble.

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author
organization
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Contribution to journal
publication status
published
subject
in
PLoS Computational Biology
volume
14
issue
12
publisher
Public Library of Science
external identifiers
  • scopus:85059274309
ISSN
1553-734X
DOI
10.1371/journal.pcbi.1006641
language
English
LU publication?
yes
id
ab3f1856-ae86-4f84-a732-4e1f33c07132
date added to LUP
2019-01-16 08:27:24
date last changed
2019-09-17 04:47:02
@article{ab3f1856-ae86-4f84-a732-4e1f33c07132,
  abstract     = {<p>Many proteins consist of folded domains connected by regions with higher flexibility. The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners. Small-Angle Scattering (SAS) is well-suited to study the conformational states adopted by proteins in solution. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data. To address these challenges, we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations. The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble. This is followed by a complete Bayesian inference of population weights in the selected ensemble. Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise. Using experimental data, we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance (NMR) and structural energies of conformers extracted from the all-atom energy functions. We show that the data from SAXS, NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble.</p>},
  articleno    = {e1006641},
  author       = {Potrzebowski, Wojciech and Trewhella, Jill and Andre, Ingemar},
  issn         = {1553-734X},
  language     = {eng},
  number       = {12},
  publisher    = {Public Library of Science},
  series       = {PLoS Computational Biology},
  title        = {Bayesian inference of protein conformational ensembles from limited structural data},
  url          = {http://dx.doi.org/10.1371/journal.pcbi.1006641},
  volume       = {14},
  year         = {2018},
}