English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

Implementation of a Bayesian secondary structure estimation method for the SESCA circular dichroism analysis package

MPS-Authors
/persons/resource/persons15564

Nagy,  G.
Department of Theoretical and Computational Biophysics, MPI for Biophysical Chemistry, Max Planck Society;

/persons/resource/persons15155

Grubmüller,  H.
Department of Theoretical and Computational Biophysics, MPI for biophysical chemistry, Max Planck Society;

External Resource
No external resources are shared
Fulltext (public)

3311294.pdf
(Preprint), 2MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Nagy, G., & Grubmüller, H. (2020). Implementation of a Bayesian secondary structure estimation method for the SESCA circular dichroism analysis package. bioRxiv, 408302. doi:10.1101/2020.12.02.408302.


Cite as: http://hdl.handle.net/21.11116/0000-0008-5109-3
Abstract
Circular dichroism spectroscopy is a structural biology technique frequently applied to determine the secondary structure composition of soluble proteins. Our recently introduced computational analysis package SESCA aids the interpretation of protein circular dichroism spectra and enables the validation of proposed corresponding structural models. To further these aims, we present the implementation and characterization of a new Bayesian secondary structure estimation method in SESCA, termed SESCA_bayes. SESCA_bayes samples possible secondary structures using a Monte Carlo scheme, driven by the likelihood of estimated scaling errors and non-secondary-structure contributions of the measured spectrum. SESCA_bayes provides an estimated secondary structure composition and separate uncertainties on the fraction of residues in each secondary structure class. It also assists efficient model validation by providing a posterior secondary structure probability distribution based on the measured spectrum. Our presented study indicates that SESCA_bayes estimates the secondary structure composition with a significantly smaller uncertainty than its predecessor, SESCA_deconv, which is based on spectrum deconvolution. Further, the mean accuracy of the two methods in our analysis is comparable, but SESCA_bayes provides more accurate estimates for circular dichroism spectra that contain considerable non-SS contributions.