Although irreversible molecular inhomogeneity must be considered, here we focus on methods for interpreting 1D NMR spectra for systems in exchange between multiple conformational states.Д«iomolecules, such as proteins, switch between different conformations, and in general the atoms that make up the protein switch between different chemical shift environments. posttranslational protein modification or protein degradation) or reversible processes such as slow conformation exchange between multiple conformations or ligand-associated states. if only one fluorine tag is attached, it might also yield complex spectra due to various phenomena, including irreversible molecular inhomogeneity (e.g. Although this type of labeling can theoretically produce a single 1D 19F NMR signal, e.g. fluorotryptophan) or through the use of a cysteine-conjugated fluorine tag. To facilitate 19F protein NMR studies, a fluorine probe is attached to a unique site, or several sites, on the protein either via a biosynthetic route (e.g. Despite the significant advances made using multidimensional NMR studies, there has been a recent resurgence in 1D NMR methods, in particular fluorine ( 19F) NMR, to study complex biomolecular interactions in particular because this method can simplify NMR spectra to one or a few NMR detectable nuclei. However, in the past 30 years, significant advances in isotope labeling, NMR pulse sequence development and software methods paved the path for multidimensional NMR studies of biomacromolecules. The decon1d program is freely available as a downloadable Python script at the project website ( ).Д®arly NMR studies of biomacromolecules were performed on relatively simple low molecular weight model systems using one-dimensional (1D) methods. In contrast, the BIC method used by decond1d provides a quantitative method for model comparison that penalizes for model complexity helping to prevent over-fitting of the data and allows identification of the most parsimonious model. In current methods, determination of the deconvolution model best supported by the data is done manually through comparison of residual error values, which can be time consuming and requires model selection by the user. The method also allows for fitting of intermediate exchange spectra, which is not supported by current software in the absence of a specific kinetic model. We have developed a Python-based deconvolution program, decon1d, which uses Bayesian information criteria (BIC) to objectively determine which model (number of peaks) would most likely produce the experimentally obtained data. One-dimensional (1D) 19F NMR spectra of proteins can be broad, irregular and complex, due to exchange of probe nuclei between distinct electrostatic environments and therefore cannot be deconvoluted and analyzed in an objective way using currently available software. Fluorine ( 19F) NMR has emerged as a useful tool for characterization of slow dynamics in 19F-labeled proteins.

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