Inference of structure ensembles of flexible biomolecules from sparse, averaged data

Simon Olsson; Jes Frellsen; Wouter Boomsma; Kanti V Mardia; Thomas Hamelryck

doi:10.1371/journal.pone.0079439

Inference of structure ensembles of flexible biomolecules from sparse, averaged data

PLoS One. 2013 Nov 7;8(11):e79439. doi: 10.1371/journal.pone.0079439. eCollection 2013.

Authors

Simon Olsson¹, Jes Frellsen, Wouter Boomsma, Kanti V Mardia, Thomas Hamelryck

Affiliation

¹ Bioinformatics Centre, Department of Biology, Faculty of Science, University of Copenhagen, Copenhagen, Denmark.

Abstract

We present the theoretical foundations of a general principle to infer structure ensembles of flexible biomolecules from spatially and temporally averaged data obtained in biophysical experiments. The central idea is to compute the Kullback-Leibler optimal modification of a given prior distribution τ(x) with respect to the experimental data and its uncertainty. This principle generalizes the successful inferential structure determination method and recently proposed maximum entropy methods. Tractability of the protocol is demonstrated through the analysis of simulated nuclear magnetic resonance spectroscopy data of a small peptide.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Biophysics*
Computer Simulation
Models, Theoretical*

Grants and funding

SO and JF acknowledge funding from the Danish Council for Independent Research. http://fivu.dk/en/research-and-innovation/councils-and-commissions/the-danish-council-for-independent-research/the-council-1/the-danish-council-for-independent-research-technology-and-production-sciences?set_language=en&cl=en (Technology and Production Sciences, FTP, 274-09-0184). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.