Determination of order parameters and correlation times in proteins

A comparison between Bayesian, Monte Carlo and simple graphical methods

Michael T Mcmahon, Eric Oldfield

Research output: Contribution to journalArticle

Abstract

We describe a novel approach to deducing order parameters and correlation times in proteins using a Bayesian statistical method, and show how likelihood contours, P(τ,S), and confidence levels can be obtained. These results are then compared with those obtained from a simple graphical method, as well as those from Monte Carlo simulations. The Bayes approach has the advantage that it is simple and accurate. Unlike Monte Carlo methods, it gives useful contour plots of probability (also not provided by the simple graphical method), and provides likelihood/confidence information. In addition, the Bayesian approach gives results in very good agreement with those obtained from Monte Carlo simulations, and as such use of Bayesian statistical methods appears to have a promising future for studies of order and dynamics in macromolecules.

Original languageEnglish (US)
Pages (from-to)133-137
Number of pages5
JournalJournal of Biomolecular NMR
Volume13
Issue number2
DOIs
StatePublished - 1999
Externally publishedYes

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Bayes Theorem
Staphylococcal Protein A
Statistical methods
Macromolecules
Monte Carlo Method
Monte Carlo methods
Proteins
Monte Carlo simulation

Keywords

  • Bayesian
  • NMR relaxation

ASJC Scopus subject areas

  • Spectroscopy
  • Biochemistry, Genetics and Molecular Biology(all)
  • Biochemistry

Cite this

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