PINE-SPARKY: graphical interface for evaluating automated probabilistic peak assignments in protein NMR spectroscopy

Bioinformatics. 2009 Aug 15;25(16):2085-7. doi: 10.1093/bioinformatics/btp345. Epub 2009 Jun 3.


Summary: PINE-SPARKY supports the rapid, user-friendly and efficient visualization of probabilistic assignments of NMR chemical shifts to specific atoms in the covalent structure of a protein in the context of experimental NMR spectra. PINE-SPARKY is based on the very popular SPARKY package for visualizing multidimensional NMR spectra (T. D. Goddard and D. G. Kneller, SPARKY 3, University of California, San Francisco). PINE-SPARKY consists of a converter (PINE2SPARKY), which takes the output from an automated PINE-NMR analysis and transforms it into SPARKY input, plus a number of SPARKY extensions. Assignments and their probabilities obtained in the PINE-NMR step are visualized as labels in SPARKY's spectrum view. Three SPARKY extensions (PINE Assigner, PINE Graph Assigner, and Assign the Best by PINE) serve to manipulate the labels that signify the assignments and their probabilities. PINE Assigner lists all possible assignments for a peak selected in the dialog box and enables the user to choose among these. A window in PINE Graph Assigner shows all atoms in a selected residue along with all atoms in its adjacent residues; in addition, it displays a ranked list of PINE-derived connectivity assignments to any selected atom. Assign the Best-by-PINE allows the user to choose a probability threshold and to automatically accept as "fixed" all assignments above that threshold; following this operation, only the less certain assignments need to be examined visually. Once assignments are fixed, the output files generated by PINE-SPARKY can be used as input to PINE-NMR for further refinements.

Availability: The program, in the form of source code and binary code along with tutorials and reference manuals, is available at

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Algorithms
  • Computational Biology / methods*
  • Computer Graphics*
  • Nuclear Magnetic Resonance, Biomolecular / methods*
  • Pattern Recognition, Automated / methods
  • Probability
  • Proteins / chemistry*
  • Software*
  • User-Computer Interface


  • Proteins