Dataset Information

Blind testing of routine, fully automated determination of protein structures from NMR data.

ABSTRACT: The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experimental data sets with unassigned nuclear Overhauser effect spectroscopy (NOESY) peak lists for various proteins of unknown structure, computed structures for each of them using different, fully automatic programs, and compared the results to each other and to the manually solved reference structures that were not available at the time the data were provided. This constitutes a stringent "blind" assessment similar to the CASP and CAPRI initiatives. This study demonstrates the feasibility of routine, fully automated protein structure determination by NMR.

SUBMITTER: Rosato A

PROVIDER: S-EPMC3609704 | biostudies-literature | 2012 Feb

REPOSITORIES: biostudies-literature

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Blind testing of routine, fully automated determination of protein structures from NMR data.

Rosato Antonio A Aramini James M JM Arrowsmith Cheryl C Bagaria Anurag A Baker David D Cavalli Andrea A Doreleijers Jurgen F JF Eletsky Alexander A Giachetti Andrea A Guerry Paul P Gutmanas Aleksandras A Güntert Peter P He Yunfen Y Herrmann Torsten T Huang Yuanpeng J YJ Jaravine Victor V Jonker Hendrik R A HR Kennedy Michael A MA Lange Oliver F OF Liu Gaohua G Malliavin Thérèse E TE Mani Rajeswari R Mao Binchen B Montelione Gaetano T GT Nilges Michael M Rossi Paolo P van der Schot Gijs G Schwalbe Harald H Szyperski Thomas A TA Vendruscolo Michele M Vernon Robert R Vranken Wim F WF Vries Sjoerd de Sd Vuister Geerten W GW Wu Bin B Yang Yunhuang Y Bonvin Alexandre M J J AM

Structure (London, England : 1993) 20120201 2

The protocols currently used for protein structure determination by nuclear magnetic resonance (NMR) depend on the determination of a large number of upper distance limits for proton-proton pairs. Typically, this task is performed manually by an experienced researcher rather than automatically by using a specific computer program. To assess whether it is indeed possible to generate in a fully automated manner NMR structures adequate for deposition in the Protein Data Bank, we gathered 10 experim ...[more]

PMID: 22325772

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Project description:Many diseases cause significant changes to the concentrations of small molecules (a.k.a. metabolites) that appear in a person's biofluids, which means such diseases can often be readily detected from a person's "metabolic profile"-i.e., the list of concentrations of those metabolites. This information can be extracted from a biofluids Nuclear Magnetic Resonance (NMR) spectrum. However, due to its complexity, NMR spectral profiling has remained manual, resulting in slow, expensive and error-prone procedures that have hindered clinical and industrial adoption of metabolomics via NMR. This paper presents a system, BAYESIL, which can quickly, accurately, and autonomously produce a person's metabolic profile. Given a 1D 1H NMR spectrum of a complex biofluid (specifically serum or cerebrospinal fluid), BAYESIL can automatically determine the metabolic profile. This requires first performing several spectral processing steps, then matching the resulting spectrum against a reference compound library, which contains the "signatures" of each relevant metabolite. BAYESIL views spectral matching as an inference problem within a probabilistic graphical model that rapidly approximates the most probable metabolic profile. Our extensive studies on a diverse set of complex mixtures including real biological samples (serum and CSF), defined mixtures and realistic computer generated spectra; involving > 50 compounds, show that BAYESIL can autonomously find the concentration of NMR-detectable metabolites accurately (~ 90% correct identification and ~ 10% quantification error), in less than 5 minutes on a single CPU. These results demonstrate that BAYESIL is the first fully-automatic publicly-accessible system that provides quantitative NMR spectral profiling effectively-with an accuracy on these biofluids that meets or exceeds the performance of trained experts. We anticipate this tool will usher in high-throughput metabolomics and enable a wealth of new applications of NMR in clinical settings. BAYESIL is accessible at http://www.bayesil.ca.

Dataset Information

Blind testing of routine, fully automated determination of protein structures from NMR data.

Publications

Blind testing of routine, fully automated determination of protein structures from NMR data.

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