nmr-spin-system-simulation
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Simulate realistic 1D and 2D metabolomic ¹H NMR spectra by computing theoretical spin multiplets from metabolite concentration and J-coupling inputs, convolving with lineshape functions, and transforming to frequency domain. This skill enables in silico validation of metabolite identification and spectral interpretation without wet-lab acquisition.
When to use
When you have known metabolite concentrations and their spin-system coupling constants (J-values) and need to generate synthetic ¹H NMR spectra for method validation, metabolite library construction, or as a training dataset. Use this skill if your goal is to predict what a real metabolomic NMR spectrum should look like given known chemical composition, or to validate NMR pulse sequence designs before experimental acquisition.
When NOT to use
- Input spectra are already experimental NMR data (use this skill for forward simulation, not inverse deconvolution of measured spectra).
- Metabolite identities or J-coupling constants are unknown or highly uncertain (simulation requires accurate spin-system topology).
- Spin systems exhibit strong coupling (second-order effects); first-order approximation will not capture fine structure accurately.
Inputs
- metabolite concentration vector (one value per metabolite)
- per-metabolite spin-system definitions (chemical shifts in ppm, J-coupling constants in Hz)
- lineshape parameters (Lorentzian or Gaussian function width, baseline offset)
- acquisition parameters (field strength, spectral width, number of points, experiment type: 1D or 2D)
Outputs
- simulated 1D ¹H NMR spectrum (frequency-domain intensity array, ppm vs intensity)
- simulated 2D NMR spectrum (COSY or HSQC/HMQC; 2D intensity array in HDF5 or netCDF format)
- theoretical multiplet patterns (individual spin-system traces before convolution)
- 1D FID (time-domain complex data before Fourier transformation, for diagnostic purposes)
How to apply
Parse metabolite concentration inputs and per-metabolite spin-system coupling constants (J-values) into structured format. Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling interactions. Convolve the simulated multiplets with a Lorentzian or Gaussian lineshape function to model realistic peak broadening. Combine individual metabolite spectra into a single 1D free-induction decay (FID) and apply Fourier transformation to produce the frequency-domain ¹H NMR spectrum. For 2D experiments, compute ¹H–¹H COSY or ¹H–¹³C HSQC/HMQC correlations using appropriate 2D pulse sequence encoding and indirect-dimension evolution. Export simulated 1D and 2D spectra as HDF5 or netCDF arrays compatible with standard NMR data formats for downstream analysis or comparison with experimental data.
Related tools
- MetAssimulo 2 (Web application that implements ¹H NMR spin-system simulation, multiplet generation, convolution, and 1D/2D Fourier transformation; provides parameter input interface and HDF5/netCDF export.) — https://github.com/yanyan5420/MetAssimulo_2
Examples
python3 apps/index.py -p Input/parameters.txt
Evaluation signals
- Simulated multiplet chemical shift positions match expected literature values for the metabolites within ±0.02 ppm tolerance.
- Peak multiplet splittings (coupling patterns) correspond correctly to input J-values: doublets for ²J ~12–15 Hz, triplets for ³J ~5–8 Hz, etc.
- Simulated spectrum baseline is flat and noise-free (artifact of perfect simulation; contrast with experimental data).
- 2D spectrum contours show expected cross-peak positions in COSY (diagonal + off-diagonal for coupled spins) or HSQC (only directly bound ¹H–¹³C pairs).
- Exported HDF5/netCDF files are readable by standard NMR processing software (e.g., TopSpin, MNova) and contain correct metadata (field strength, spectral width, time/frequency axis labels).
Limitations
- First-order simulation assumes weak coupling; strong-coupling regimes (Δν < J, where Δν is chemical shift difference) produce inaccurate multiplets.
- Lineshape convolution uses simplified Lorentzian or Gaussian models; real NMR peaks may exhibit Voigt or other asymmetric profiles.
- Spin-spin relaxation (T2) and field inhomogeneity are not modeled; linewidth is set by manual lineshape parameter only.
- Metabolite concentration range and spin-system parameterization must be provided externally; no automatic inference from experimental spectra.
- No support for time-varying effects (e.g., chemical exchange, saturation transfer) or spectra from intact biological matrices with macromolecular background.
Evidence
- [other] MetAssimulo 2 is a web application designed to simulate realistic 1D and 2D metabolomic 1H NMR spectra.: "MetAssimulo 2 is a web application designed to simulate realistic 1D and 2D metabolomic 1H NMR spectra."
- [other] Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling.: "Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling."
- [other] Convolve simulated multiplets with a Lorentzian or Gaussian lineshape function to approximate realistic peak broadening.: "Convolve simulated multiplets with a Lorentzian or Gaussian lineshape function to approximate realistic peak broadening."
- [other] Combine individual metabolite spectra into a single 1D FID and apply Fourier transformation to produce frequency-domain 1H NMR spectrum.: "Combine individual metabolite spectra into a single 1D FID and apply Fourier transformation to produce frequency-domain 1H NMR spectrum."
- [other] For 2D spectra, compute 1H–1H correlation (COSY) or 1H–13C heteronuclear (HSQC/HMQC) multiplets using appropriate 2D pulse sequences and indirect-dimension evolution.: "For 2D spectra, compute 1H–1H correlation (COSY) or 1H–13C heteronuclear (HSQC/HMQC) multiplets using appropriate 2D pulse sequences and indirect-dimension evolution."
- [other] Save simulated 1D and 2D spectra as HDF5 or netCDF spectral arrays compatible with standard NMR data formats.: "Save simulated 1D and 2D spectra as HDF5 or netCDF spectral arrays compatible with standard NMR data formats."
- [readme] After successfully running the project using the above provided commands, you should see a message indicating:
Dash is running on http://127.0.0.1:8060/ Just simply copy and open this URL in your browser, then start simulating NMR spectra!: "After successfully running the project using the above provided commands, you should see a message indicating: Dash is running on http://127.0.0.1:8060/ Just simply copy and open this URL in your"
1---2name: nmr-spin-system-simulation-23description: Use when when you have known metabolite concentrations and their spin-system coupling constants (J-values) and need to generate synthetic ¹H NMR spectra for method validation, metabolite library construction, or as a training dataset.4license: CC-BY-4.05---67# nmr-spin-system-simulation89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Simulate realistic 1D and 2D metabolomic ¹H NMR spectra by computing theoretical spin multiplets from metabolite concentration and J-coupling inputs, convolving with lineshape functions, and transforming to frequency domain. This skill enables in silico validation of metabolite identification and spectral interpretation without wet-lab acquisition.1314## When to use1516When you have known metabolite concentrations and their spin-system coupling constants (J-values) and need to generate synthetic ¹H NMR spectra for method validation, metabolite library construction, or as a training dataset. Use this skill if your goal is to predict what a real metabolomic NMR spectrum should look like given known chemical composition, or to validate NMR pulse sequence designs before experimental acquisition.1718## When NOT to use1920- Input spectra are already experimental NMR data (use this skill for forward simulation, not inverse deconvolution of measured spectra).21- Metabolite identities or J-coupling constants are unknown or highly uncertain (simulation requires accurate spin-system topology).22- Spin systems exhibit strong coupling (second-order effects); first-order approximation will not capture fine structure accurately.2324## Inputs2526- metabolite concentration vector (one value per metabolite)27- per-metabolite spin-system definitions (chemical shifts in ppm, J-coupling constants in Hz)28- lineshape parameters (Lorentzian or Gaussian function width, baseline offset)29- acquisition parameters (field strength, spectral width, number of points, experiment type: 1D or 2D)3031## Outputs3233- simulated 1D ¹H NMR spectrum (frequency-domain intensity array, ppm vs intensity)34- simulated 2D NMR spectrum (COSY or HSQC/HMQC; 2D intensity array in HDF5 or netCDF format)35- theoretical multiplet patterns (individual spin-system traces before convolution)36- 1D FID (time-domain complex data before Fourier transformation, for diagnostic purposes)3738## How to apply3940Parse metabolite concentration inputs and per-metabolite spin-system coupling constants (J-values) into structured format. Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling interactions. Convolve the simulated multiplets with a Lorentzian or Gaussian lineshape function to model realistic peak broadening. Combine individual metabolite spectra into a single 1D free-induction decay (FID) and apply Fourier transformation to produce the frequency-domain ¹H NMR spectrum. For 2D experiments, compute ¹H–¹H COSY or ¹H–¹³C HSQC/HMQC correlations using appropriate 2D pulse sequence encoding and indirect-dimension evolution. Export simulated 1D and 2D spectra as HDF5 or netCDF arrays compatible with standard NMR data formats for downstream analysis or comparison with experimental data.4142## Related tools4344- **MetAssimulo 2** (Web application that implements ¹H NMR spin-system simulation, multiplet generation, convolution, and 1D/2D Fourier transformation; provides parameter input interface and HDF5/netCDF export.) — https://github.com/yanyan5420/MetAssimulo_24546## Examples4748```49python3 apps/index.py -p Input/parameters.txt50```5152## Evaluation signals5354- Simulated multiplet chemical shift positions match expected literature values for the metabolites within ±0.02 ppm tolerance.55- Peak multiplet splittings (coupling patterns) correspond correctly to input J-values: doublets for ²J ~12–15 Hz, triplets for ³J ~5–8 Hz, etc.56- Simulated spectrum baseline is flat and noise-free (artifact of perfect simulation; contrast with experimental data).57- 2D spectrum contours show expected cross-peak positions in COSY (diagonal + off-diagonal for coupled spins) or HSQC (only directly bound ¹H–¹³C pairs).58- Exported HDF5/netCDF files are readable by standard NMR processing software (e.g., TopSpin, MNova) and contain correct metadata (field strength, spectral width, time/frequency axis labels).5960## Limitations6162- First-order simulation assumes weak coupling; strong-coupling regimes (Δν < J, where Δν is chemical shift difference) produce inaccurate multiplets.63- Lineshape convolution uses simplified Lorentzian or Gaussian models; real NMR peaks may exhibit Voigt or other asymmetric profiles.64- Spin-spin relaxation (T2) and field inhomogeneity are not modeled; linewidth is set by manual lineshape parameter only.65- Metabolite concentration range and spin-system parameterization must be provided externally; no automatic inference from experimental spectra.66- No support for time-varying effects (e.g., chemical exchange, saturation transfer) or spectra from intact biological matrices with macromolecular background.6768## Evidence6970- [other] MetAssimulo 2 is a web application designed to simulate realistic 1D and 2D metabolomic 1H NMR spectra.: "MetAssimulo 2 is a web application designed to simulate realistic 1D and 2D metabolomic 1H NMR spectra."71- [other] Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling.: "Generate theoretical spin multiplets using first-order or density-matrix NMR simulation for each spin system, accounting for chemical shift and J-coupling."72- [other] Convolve simulated multiplets with a Lorentzian or Gaussian lineshape function to approximate realistic peak broadening.: "Convolve simulated multiplets with a Lorentzian or Gaussian lineshape function to approximate realistic peak broadening."73- [other] Combine individual metabolite spectra into a single 1D FID and apply Fourier transformation to produce frequency-domain 1H NMR spectrum.: "Combine individual metabolite spectra into a single 1D FID and apply Fourier transformation to produce frequency-domain 1H NMR spectrum."74- [other] For 2D spectra, compute 1H–1H correlation (COSY) or 1H–13C heteronuclear (HSQC/HMQC) multiplets using appropriate 2D pulse sequences and indirect-dimension evolution.: "For 2D spectra, compute 1H–1H correlation (COSY) or 1H–13C heteronuclear (HSQC/HMQC) multiplets using appropriate 2D pulse sequences and indirect-dimension evolution."75- [other] Save simulated 1D and 2D spectra as HDF5 or netCDF spectral arrays compatible with standard NMR data formats.: "Save simulated 1D and 2D spectra as HDF5 or netCDF spectral arrays compatible with standard NMR data formats."76- [readme] After successfully running the project using the above provided commands, you should see a message indicating: `Dash is running on http://127.0.0.1:8060/` Just simply ***copy and open this URL*** in your browser, then start simulating NMR spectra!: "After successfully running the project using the above provided commands, you should see a message indicating: `Dash is running on http://127.0.0.1:8060/` Just simply copy and open this URL in your"
HolobiomicsLab