Spin System Modeling of Nuclear Magnetic Resonance Spectra for Applications in Metabolomics and Small Molecule Screening

Abstract

The exceptionally rich information content of nuclear magnetic resonance (NMR) spectra is routinely used to identify and characterize molecules and molecular interactions in a wide range of applications, including clinical biomarker discovery, drug discovery, environmental chemistry, and metabolomics. The set of peak positions and intensities from a reference NMR spectrum generally serves as the identifying signature for a compound. Reference spectra normally are collected under specific conditions of pH, temperature, and magnetic field strength, because changes in conditions can distort the identifying signatures of compounds. A spin system matrix that parametrizes chemical shifts and coupling constants among spins provides a much richer feature set for a compound than a spectral signature based on peak positions and intensities. Spin system matrices expand the applicability of NMR spectral libraries beyond the specific conditions under which data were collected. In addition to being able to simulate spectra at any field strength, spin parameters can be adjusted to systematically explore alterations in chemical shift patterns due to variations in other experimental conditions, such as compound concentration, pH, or temperature. We present methodology and software for efficient interactive optimization of spin parameters against experimental 1D-1H NMR spectra of small molecules. We have used the software to generate spin system matrices for a set of key mammalian metabolites and are also using the software to parametrize spectra of small molecules used in NMR-based ligand screening. The software, along with optimized spin system matrix data for a growing number of compounds, is available from http://gissmo.nmrfam.wisc.edu/.

NMReDATA, a standard to report the NMR assignment and parameters of organic compounds

“…Determining the coupling constants, even in the case of strong coupling and magnetic equivalence, should be possible by spectral simulation and fitting procedures found in SPINACH [21,22] (all kinds of spectra), ANATOLIA [23] (currently only 1D 1 H spectra) or GISSMO. [24] The 2D correlations reported in NMReDATA can also be verified by checking the presence of signals at the coordinates of the chemical shifts of the correlated spins. The consistency of the reported data with the structure can also be tested by simply verifying that the number of bonds between the correlated spins is compatible with experiments based on scalar coupling (1-bond for HSQC, 2-, 3-, (4-)bonds for COSY and HMBC data).…”

“…Note that if the software generating the NMReDATA does not allow for the assignment of the coupling partners (Figure h), a program which is able to identify that couplings with (nearly) identical values are found for protons that are known to be two or three bonds away could be used to assign coupling constants (Figure i) and construct the coupling network (Figure ). As mentioned earlier, more sophisticated tools using spectral simulations could further increase the reliability of the assignment and the precision of the coupling constants, and also provide reliable spectral parameters (chemical shifts and scalar coupling constants) in strongly coupled systems.…”

New tools and resources in metabolomics: 2016–2017

“…GISSMO (Guided Ideographic Spin System Model Optimization) is a GUI‐based webserver for guided ideographic spin system model optimization, which enables the efficient calculation and refinement of spin system matrices against experimental 1D‐ 1 H NMR spectra of small molecules (Fig. C).…”

Improving the Accuracy of Model-based Quantitative NMR

“…The reduction of a spectrum to a frequency-intensity table of peaks (Krishnamurthy, 2013) allows for easier automation of post-processing tasks and simplifies the analysis of large arrayed datasets (Kriesten et al, 2008;Alsmeyer et al, 2004). Finally, quantum mechanical formulations minimize the number of free model parameters and are inherently invariant with respect to the spectrometer field strength (Kuprov et al, 2007;Tiainen et al, 2014;Dashti et al, 2017); they enable the analysis of highly complex low-resolution spectra acquired on medium-field benchtop instruments and are found successful in modern practical applications (Matviychuk et al, 2019).…”