nmr-relax nmr-relax is designed for the study of the dynamics of proteins or other macromolecules.
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nmr-relax Description

nmr-relax is designed for the study of the dynamics of proteins or other macromolecules. 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. 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: · This is a very minor bugfix release that solves issues with the value.write() user function and with error analysis when performing relaxation curve-fitting.

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