Model-based assignment and inference of protein backbone Nuclear Magnetic Resonances

Statistical Applications in Genetics and Molecular Biology
Olga VitekChris Bailey-Kellogg

Abstract

Nuclear Magnetic Resonance (NMR) spectroscopy is a key experimental technique used to study protein structure, dynamics, and interactions. NMR methods face the bottleneck of spectral analysis, in particular determining the resonance assignments, which help define the mapping between atoms in the protein and peaks in the spectra. A substantial amount of noise in spectral data, along with ambiguities in interpretation, make this analysis a daunting task, and there exists no generally accepted measure of uncertainty associated with the resulting solutions. This paper develops a model-based inference approach that addresses the problem of characterizing uncertainty in backbone resonance assignment. We argue that NMR spectra are subject to random variation, and ignoring this stochasticity can lead to false optimism and erroneous conclusions. We propose a Bayesian statistical model that accounts for various sources of uncertainty and provides an automatable framework for inference. While assignment has previously been viewed as a deterministic optimization problem, we demonstrate the importance of considering all solutions consistent with the data, and develop an algorithm to search this space within our statistical framework. Our ap...Continue Reading

Citations

Jul 21, 2006·Journal of Biomolecular NMR·Olga VitekJan Vitek
Mar 22, 2014·British Journal of Sports Medicine·Louis J HoltzhausenMarietjie M Nel

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