oaioai:figshare.com:article/1501929

Deconvolution of Complex 1D NMR Spectra Using Objective Model Selection

Abstract

<div><p>Fluorine (<sup>19</sup>F) NMR has emerged as a useful tool for characterization of slow dynamics in <sup>19</sup>F-labeled proteins. One-dimensional (1D) <sup>19</sup>F 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. We have developed a Python-based deconvolution program, <i>decon1d</i>, which uses Bayesian information criteria (BIC) to objectively determine which model (number of peaks) would most likely produce the experimentally obtained data. 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. 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. In contrast, the BIC method used by <i>decond1d</i> 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. The <i>decon1d</i> program is freely available as a downloadable Python script at the project website (<a href="https://github.com/hughests/decon1d/" target="_blank">https://github.com/hughests/decon1d/</a>).</p></div

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oaioai:figshare.com:article/1501929Last time updated on 2/12/2018

This paper was published in FigShare.

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