An automated framework for NMR chemical shift calculations of small organic molecules

Yesiltepe, Yasemin; Nuñez, Jamie; Colby, Sean; Thomas, Dennis; Borkum, Mark; Reardon, Patrick N.; Washton, Nancy M.; Metz, Thomas O.; Teeguarden, Justin G.; Govind, Niranjan; Renslow, Ryan

doi:10.1186/s13321-018-0305-8

Cited by 46 publications

(62 citation statements)

References 144 publications

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“…Efforts are also directed to increase the availability of DFT-based chemical shift calculations and automate them. For instance, Yesiltepe et al [80] developed the in silico Chemical Library Engine (ISiCLE) module to accurately and automatically calculate NMR chemical shifts of small organic molecules. ISiCLE predicts NMR chemical shifts via the open-source, high-performance computational chemistry software NWChem.…”

Section: Nmr Chemical Shift Prediction: Synergy Between Empirical and Dft Approachesmentioning

confidence: 99%

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

Elyashberg¹,

Argyropoulos²

2020

Magnetic Reson in Chemistry

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The first efforts for the development of methods for Computer-Assisted Structure Elucidation (CASE) were published more than 50 years ago. CASE expert systems based on one-dimensional (1D) and two-dimensional (2D) Nuclear Magnetic Resonance (NMR) data have matured considerably by now. The structures of a great number of complex natural products have been elucidated and/or revised using such programs. In this article, we discuss the most likely directions in which CASE will evolve. We act on the premise that a synergistic interaction exists between CASE, new NMR experiments, and methods of computational chemistry, which are continuously being improved. The new developments in NMR experiments (long-range correlation experiments, pure-shift methods, coupling constants measurement and prediction, residual dipolar couplings [RDCs]), and residual chemical shift anisotropies [RCSAs], evolution of density functional theory (DFT), and machine learning algorithms will have an influence on CASE systems and vice versa. This is true also for new techniques for chemical analysis (Atomic Force Microscopy [AFM], "crystalline sponge" X-ray analysis, and micro-Electron Diffraction [micro-ED]), which will be used in combination with expert systems. We foresee that CASE will be utilized widely and become a routine tool for NMR spectroscopists and analysts in academic and industrial laboratories. We believe that the "golden age" of CASE is still in the future.

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Section: Nmr Chemical Shift Prediction: Synergy Between Empirical and Dft Approachesmentioning

confidence: 99%

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

Elyashberg¹,

Argyropoulos²

2020

Magnetic Reson in Chemistry

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“…Community wide sharing and curation of metabolomics data and associated metadata, reference databases, computational tool development, and knowledge dissemination will continue to be crucial for accelerating metabolomics research (Wang et al, 2016 , 2020 ; Picache et al, 2020 ) but the challenge of identifying unknown molecules, especially those for which reference standards do not exist, remains a major roadblock. As a result, there has been growing interest in what has been termed “standards free” approaches, wherein reference values are determined through in silico methods, including quantum chemical simulations (Paglia et al, 2014 ; Yesiltepe et al, 2018 ; Colby et al, 2019 ), machine learning (Allen et al, 2014 ; Hufsky et al, 2014 ; Dührkop et al, 2015 ; Wolfer et al, 2016 ; Zhou et al, 2016 ; Zhou Z. et al, 2017 ; Zhou Z.W. et al, 2017 ; Bach et al, 2018 ), deep learning (Gómez-Bombarelli et al, 2018 ; Kang and Cho, 2018 ; Colby et al, 2020 ), and quantitative structure-activity/property relationship (QSAR/QSPR) models (Wong and Burkowski, 2009 ; Schneider and Schneider, 2016 ; Miyao et al, 2017 ).…”

Section: Standards-free Metabolomics and Computational Library Buildimentioning

confidence: 99%

Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them

Couvillion

Agrawal

Colby

et al. 2020

Front. Cell. Infect. Microbiol.

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Even as the field of microbiome research has made huge strides in mapping microbial community composition in a variety of environments and organisms, explaining the phenotypic influences on the host by microbial taxa—both known and unknown—and their specific functions still remain major challenges. A pressing need is the ability to assign specific functions in terms of enzymes and small molecules to specific taxa or groups of taxa in the community. This knowledge will be crucial for advancing personalized therapies based on the targeted modulation of microbes or metabolites that have predictable outcomes to benefit the human host. This perspective article advocates for the combined use of standards-free metabolomics and activity-based protein profiling strategies to address this gap in functional knowledge in microbiome research via the identification of novel biomolecules and the attribution of their production to specific microbial taxa.

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“…NMR isotropic shieldings were calculated for all optimized molecules at the B3LYP/cc-pVDZ [140,144] level of theory. Based on our previous assessment [132], this method provides reliable chemical shifts [112] and yields isotropic shieldings with a reasonably low computational cost [145]. The gauge-invariant atomic orbital (GIAO) approach [146] was used to compute 13…”

Section: Computational Detailsmentioning

confidence: 99%

“…The NMR chemical shifts for all molecules in this study were calculated using the In Silico Chemical Library Engine (ISiCLE) [132] (see github.com/pnnl/isicle for the latest version of ISiCLE).…”

Section: Computational Detailsmentioning

confidence: 99%

“…Since we do not have experimental NMR data for the 11 thousand molecules in our two sets, in step II we create representative NMR data for comparisons: the calculated NMR spectra (generated in Step I) are considered as surrogate experimental shifts data and new lists of chemical shifts are created synthetically by adding Gaussian distributed noise. Although the error distributions of NMR chemical shifts were reported to also obey a student t-distribution in other studies[132,[147][148][149][150], we assume errors for both carbons and protons follow a Gaussian distribution…”

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confidence: 99%

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An Initial Investigation of Accuracy Required for the Identification of Small Molecules in Complex Samples Using Quantum Chemical Calculated NMR Chemical Shifts

Yesiltepe¹,

Govind²,

Metz³

et al. 2020

Preprint

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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 13C 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 ppm and 7.1 ppm, respectively, and below 0.5 ppm 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%.

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An automated framework for NMR chemical shift calculations of small organic molecules

Cited by 46 publications

References 144 publications

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

Computer Assisted Structure Elucidation (CASE): Current and future perspectives

Who Is Metabolizing What? Discovering Novel Biomolecules in the Microbiome and the Organisms Who Make Them

An Initial Investigation of Accuracy Required for the Identification of Small Molecules in Complex Samples Using Quantum Chemical Calculated NMR Chemical Shifts

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