An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts

Journal Article · Thu Sep 22 00:00:00 EDT 2022 · Journal of Cheminformatics

DOI:https://doi.org/10.1186/s13321-022-00587-7· OSTI ID:1925225

Yesiltepe, Yasemin ^[1]; Govind, Niranjan ^[2]; Metz, Thomas O. ^[3]; Renslow, Ryan S.  ^[1]

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1. Washington State Univ., Pullman, WA (United States); Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
2. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
3. Washington State Univ., Pullman, WA (United States)

The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted ¹³>C and ¹H NMR spectra with respect to experimentally measured values. This leads us to the question–what accuracy is required in predicted ¹³C and ¹H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of ¹H and ¹³C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE Office of Science (SC); USDOE Laboratory Directed Research and Development (LDRD) Program; National Institute of Environmental Health Sciences; National Institutes of Health (NIH)
• Grant/Contract Number: AC05-76RL01830; U2CES030170
• OSTI ID: 1925225
• Report Number(s): PNNL-SA-181010
• Journal Information: Journal of Cheminformatics, Vol. 14, Issue 1; ISSN 1758-2946
• Publisher: Chemistry Central Ltd.
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
metabolomics
small molecules
NMR
DFT
quantum chemistry