nmr-relax is designed for the study of the dynamics of proteins or other macromolecules though the analysis of NMR relaxation data. It is a community driven project created by NMR spectroscopists for NMR spectroscopists.
It supports exponential curve fitting for the calculation of the R1 and R2 relaxation rates, calculation of the NOE, reduced spectral density mapping, and the Lipari and Szabo model-free analysis.
Product's homepage
Here are some key features of "nmr-relax":
· Supported NMR theories
The following relaxation data analysis techniques are currently supported by relax:
· Model-free analysis (Lipari and Szabo, 1982a; Lipari and Szabo, 1982b; Clore et al., 1990)
· Reduced spectral density mapping (Farrow et al., 1995, Lefevre et al., 1996)
· Exponential curve fitting (to find the R1 and R2 relaxation rates)
· Steady-state NOE calculation
· Data analysis tools
The following tools are implemented as modular components to be used by any data analysis technique:
· Numerous high-precision optimisation algorithms
· Model selection (d'Auvergne and Gooley, 2003)
· Akaike's Information Criteria (AIC)
· Small sample size corrected AIC (AICc)
· Bayesian or Schwarz Information Criteria (BIC)
· Bootstrap model selection
· Single-item-out cross-validation (CV)
· Hypothesis testing ANOVA model selection (only the model-free specific technique of Mandel et al., 1995 is supported)
· Monte Carlo simulations (error analysis for all data analysis techniques)
· Model elimination - the removal of failed models prior to model selection (d'Auvergne and Gooley, 2006)
Requirements:
· Python
What's New in This Release: [ read full changelog ]
· In addition to many bugfixes, there are improvements to the frame order theory, N-state model analysis, handling of RDC and PCS values, a number of new user functions for structure creation, displacement, and superimposition and for model-free result visualisation in PyMOL, and a redesign of the auto-analyses to have all input data pre-loaded into a relax data pipe for support of non-protein organic molecules in the dauvergne_protocol auto-analysis.
Find us on Google+
