Determination of order parameters and correlation times in proteins: A comparison between Bayesian, Monte Carlo and simple graphical methods

We describe a novel approach to deducing order parameters and correlation t imes in proteins using a Bayesian statistical method, and show how likeliho od contours, P(tau,S), and confidence levels can be obtained. These results are then compared with those obtained from a simple graphical method, as w ell as those from Monte Carlo simulations. The Bayes approach has thp advan tage that it is simple and accurate. Unlike Monte Carlo methods, it gives u seful plots of probability (also not provided by the simple graphical metho d), and provides likelihood/confidence information. In addition, the Bayesi an approach gives results in very good agreement with those obtained from M onte Carlo simulations, and as such use of Bayesian statistical methods app ears to have a promising future for studies of order and dynamics in macrom olecules.