Protein-Ligand Screening

Receptor based techniques

  • Automated HSQC comparison to detect spectral  changes
  • PCA analysis to differentiate origin of changes
  • Transfer of assignments between spectra
  • Peak tracing in titration series
  • Tabulated reports, e.g. with peak distances, volumes and line width

Ligand based techniques

  • Automated 1D matching techniques
  • SeeDs: combined 1D, STD, LOGSY and CPMG  analysis with and without competitor ligand
  • Setup of SeeDs
  • Automated execution of SeeDs
  • Combined data viewing
  • Tabulated reports

Analytical Profiling of complex Mixtures

AMIX offers a profiling module for identification and quantification of complex mixtures. Spectral and molecular information are provided in a spectra base and knowledge base. It applies to 1D and 2D NMR as well as LC-MS spectra.

  • Safer identification via sub-spectra matching using a spectra base with reference data
  • Combined identification in 1D, COSY, TOCSY, HSQC and LC-MS
  • Details provided in the knowledge base include multiplicity and couplings of signals
  • Quantification with 1D or 2D HSQC spectra
  • Relative and absolute quantification possible
  • Different quantification techniques include normal integration, line shape analysis, maximum entropy based methods
  • Full interactive  control of results
  • Report available in short and detailed versions, formats include text, html and xml
  • Result tables may be used as input for PCA analysis (targeted approach)


AMIX fully integrates spectroscopic and statistical analysis of NMR, HPLC and LS-MS data to better find valid metabolomic results.

  • Different bucketing methods including import of external tables
  • Cross-validation and testset validation
  • Model building techniques
  • Full spectra access from scores, influence and Hotellings plots
  • Direct linkage between loadings and spectral regions
  • Flexible display set-up
  • Linkage between loadings and spectra bases for identification of compounds
  • Direct sum formula calculation from mass loadings
  • Analysis of variable distributions to detect up/down regulation
  • Covariance analysis
  • Combined covariance analysis
  • Linkage to external attribute table
  • Combination with profiling results