Summary: SPARKY (Goddard and Kneller, SPARKY 3) remains the most popular software program for NMR data analysis, despite the fact that development of the package by its originators ceased in 2001. We have taken over the development of this package and describe NMRFAM-SPARKY, which implements new functions reflecting advances in the biomolecular NMR field. NMRFAM-SPARKY has been repackaged with current versions of Python and Tcl/Tk, which support new tools for NMR peak simulation and graphical assignment determination. These tools, along with chemical shift predictions from the PACSY database, greatly accelerate protein side chain assignments. NMRFAM-SPARKY supports automated data format interconversion for interfacing with a variety of web servers including, PECAN , PINE, TALOS-N, CS-Rosetta, SHIFTX2 and PONDEROSA-C/S. Availability and implementation: The software package, along with binary and source codes, if desired, can be downloaded freely from http://pine.nmrfam.wisc.edu/download_packages.html. Instruction manuals and video tutorials can be found at http://www.nmrfam.wisc.edu/nmrfam-sparky-distribution.htm. Contact:whlee@nmrfam.wisc.edu or markley@nmrfam.wisc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.

Despite the extensive use of nuclear magnetic resonance (NMR) for metabolomics, no publicly available tools have been designed for identifying and quantifying metabolites across multiple spectra. We introduce here a new open source software tool, rNMR, which provides a simple graphics‐based method for visualizing, identifying, and quantifying metabolites across multiple one‐ or two‐dimensional NMR spectra. rNMR differs from existing software tools for NMR spectroscopy in that analyses are based on regions of interest (ROIs) rather than peak lists. ROIs contain all of the underlying NMR data within user‐defined chemical shift ranges. ROIs can be inspected visually, and they support robust quantification of NMR signals. ROI‐based analyses support simultaneous views of metabolite signals from up to hundreds of spectra, and ROI boundaries can be adjusted dynamically to ensure that signals corresponding to assigned atoms are analyzed consistently throughout the dataset. We describe how rNMR greatly reduces the time required for robust bioanalytical analysis of complex NMR data. An rNMR analysis yields a compact and transparent way of archiving the results from a metabolomics study so that it can be examined and evaluated by others. The rNMR website at http://rnmr.nmrfam.wisc.edu offers downloadable versions of rNMR for Windows, Macintosh, and Linux platforms along with extensive help documentation, instructional videos, and sample data. Copyright © 2009 John Wiley & Sons, Ltd.

Quantitative one-dimensional (1D) ¹H NMR spectroscopy is a useful tool for determining metabolite concentrations because of the direct proportionality of signal intensity to the quantity of analyte. However, severe signal overlap in 1D ¹H NMR spectra of complex metabolite mixtures hinders accurate quantification. Extension of 1D ¹H to 2D ¹H−¹³C HSQC leads to the dispersion of peaks along the ¹³C dimension and greatly alleviates peak overlapping. Although peaks are better resolved in 2D ¹H−¹³C HSQC than in 1D ¹H NMR spectra, the simple proportionality of cross peaks to the quantity of individual metabolites is lost by resonance-specific signal attenuation during the coherence transfer periods. As a result, peaks for individual metabolites usually are quantified by reference to calibration data collected from samples of known concentration. We show here that data from a series of HSQC spectra acquired with incremented repetition times (the time between the end of the first ¹H excitation pulse to the beginning of data acquisition) can be extrapolated back to zero time to yield a time-zero 2D ¹H−¹³C HSQC spectrum (HSQC₀) in which signal intensities are proportional to concentrations of individual metabolites. Relative concentrations determined from cross peak intensities can be converted to absolute concentrations by reference to an internal standard of known concentration. Clustering of the HSQC₀ cross peaks by their normalized intensities identifies those corresponding to metabolites present at a given concentration, and this information can assist in assigning these peaks to specific compounds. The concentration measurement for an individual metabolite can be improved by averaging the intensities of multiple, nonoverlapping cross peaks assigned to that metabolite.

Ferredoxins play an important role as an electron donor in iron–sulfur (Fe–S) cluster biosynthesis. Two ferredoxins, human mitochondrial ferredoxin 1 (FDX1) and human mitochondrial ferredoxin 2 (FDX2), are present in the matrix of human mitochondria. Conflicting results have been reported regarding their respective function in mitochondrial iron–sulfur cluster biogenesis. We report here biophysical studies of the interaction of these two ferredoxins with other proteins involved in mitochondrial iron–sulfur cluster assembly. Results from nuclear magnetic resonance spectroscopy show that both FDX1 and FDX2 (in both their reduced and oxidized states) interact with the protein complex responsible for cluster assembly, which contains cysteine desulfurase (NFS1), ISD11 (also known as LYRM4), and acyl carrier protein (Acp). In all cases, ferredoxin residues close to the Fe–S cluster are involved in the interaction with this complex. Isothermal titration calorimetry results showed that FDX2 binds more tightly to the cysteine desulfurase complex than FDX1 does. The reduced form of each ferredoxin became oxidized in the presence of the cysteine desulfurase complex when l-cysteine was added, leading to its conversion to l-alanine and the generation of sulfide. In an in vitro reaction, the reduced form of each ferredoxin was found to support Fe–S cluster assembly on ISCU; the rate of cluster assembly was faster with FDX2 than with FDX1. Taken together, these results show that both FDX1 and FDX2 can function in Fe–S cluster assembly in vitro.