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.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Summary Calorie restriction (CR) extends lifespan in diverse species. Mitochondria play a key role in CR adaptation, however, the molecular details remain elusive. We developed and applied a quantitative mass spectrometry method to probe the liver mitochondrial acetyl-proteome during CR vs. control diet in mice that were wild-type or lacked the protein deacetylase SIRT3. Quantification of 3,285 acetylation sites −2,193 from mitochondrial proteins rendered a comprehensive atlas of the acetyl-proteome and enabled global site-specific, relative acetyl occupancy measurements between all four experimental conditions. Bioinformatic and biochemical analyses provided additional support for the effects of specific acetylation on mitochondrial protein function. Our results (1) reveal widespread reprogramming of mitochondrial protein acetylation in response to CR and SIRT3, (2) identify three biochemically distinct classes of acetylation sites, and (3) provide evidence that SIRT3 is a prominent regulator in CR adaptation by coordinately deacetylating proteins involved in diverse pathways of metabolism and mitochondrial maintenance.

Atomic resolution studies of protein kinases have traditionally been carried out in the inhibitory state, limiting our current knowledge on the mechanisms of substrate recognition and catalysis. Using NMR, x-ray crystallography, and thermodynamic measurements we analyzed the substrate recognition process of cAMP-dependent protein kinase (PKA), finding that entropy and protein dynamics play a prominent role. The nucleotide acts as a dynamic and allosteric activator by coupling the two lobes of apo PKA, enhancing the enzyme dynamics synchronously, and priming it for catalysis. The formation of the ternary complex is entropically driven and NMR spin relaxation data reveal that both substrate and PKA are dynamic in the closed state. Our results show that the enzyme toggles between open and closed states, which indicate that a population shift/conformational selection rather than an induced-fit mechanism governs substrate recognition.

One-dimensional (1D) 1 H nuclear magnetic resonance (NMR) spectroscopy is used extensively for high-throughput analysis of metabolites in biological fluids and tissue extracts. Typically, such spectra are treated as multivariate statistical objects rather than as collections of quantifiable metabolites. We report here a two-dimensional (2D) 1 H-13 C NMR strategy (Fast Metabolite Quantification, FMQ by NMR) for identifying and quantifying the ∼40 most abundant metabolites in biological samples. To validate this technique, we prepared mixtures of synthetic compounds and extracts from Arabidopsis thaliana, Saccharomyces cerevisiae and Medicago sativa. We show that accurate (technical error 2.7%) molar concentrations can be determined in 12 minutes using our quantitative 2D 1 H-13 C NMR strategy. In contrast, traditional 1D 1 H NMR analysis resulted in 16.2% technical error under nearly ideal conditions. We propose FMQ by NMR as a practical alternative to 1D 1 H NMR for metabolomics studies in which 200-400 mg (preextraction dry weight) samples can be obtained.One-dimensional (1D) 1 H NMR spectroscopy has been used for decades as an analytical tool for identifying small molecules and measuring their concentrations. 1, 2 Traditionally, quantitative analysis by NMR has been restricted to relatively simple mixtures with minimal peak overlap. In these applications, 1D 1 H NMR is a natural choice, because its peaks scale linearly with concentration and its analytical precision is usually independent of the chemical properties of target molecules. Recently, interest has surged in using NMR for high-throughput analysis of complex biological processes at the metabolic level. 3, 4 These studies, defined as "metabolomics" or "metabonomics", place an emphasis on biomarker discovery or disease classification and are typically centered on unfractionated biological fluids and tissue extracts. 1D 1 H NMR spectra of these samples typically contain hundreds of overlapping resonances (Figure 1) that make traditional NMR-based analytical practices, such as resonance assignment and accurate peak integration, a challenging prospect. As a result, sophisticated statistical tools have been developed to translate spectral data into biologically meaningful information. 4, 5All statistical tools used for analyzing complex spectra face the same fundamental barrier: overlapped peaks do not scale in the discrete linear fashion that typifies well-isolated peaks. They scale as the sum of the total overlapped resonance. Consequently, multivariate and correlation statistics are reporters of overlapped spectral density, not concentrations of specific compounds. Although peak overlap does not interfere with the reproducibility of traditional analyses, 6 it does prevent accurate quantification. Two approaches can be used to overcome this barrier, one mathematical the other experimental. The mathematical approach is to fit overlapped 1 D NMR spectra with modeled peaks. This approach has been successfully applied by Weljie and co-workers. 7 The e...

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