Hunter N. B. Moseley

Reduced-dimensionality NMR spectroscopy for high-throughput protein resonance assignment

A suite of reduced-dimensionality 13C,15N,1H-triple-resonance NMR experiments is presented for rapid and complete protein resonance assignment. Even when using short measurement times, these experiments allow one to retain the high spectral resolution required for efficient automated analysis. “Sampling limited” and “sensitivity limited” data collection regimes are defined, respectively, depending on whether the sampling of the indirect dimensions or the sensitivity of a multidimensional NMR experiments per se determines the minimally required measurement time. We show that reduced-dimensionality NMR spectroscopy is a powerful approach to avoid the “sampling limited regime”—i.e., a standard set of ten experiments proposed here allows one to effectively adapt minimal measurement times to sensitivity requirements. This is of particular interest in view of the greatly increased sensitivity of NMR spectrometers equipped with cryogenic probes. As a step toward fully automated analysis, the program autoassign has been extended to provide sequential backbone and 13Cβ resonance assignments from these reduced-dimensionality NMR data.

Automated analysis of NMR assignments and structures for proteins

Recent developments in protein NMR technology have provided spectral data that are highly amenable to analysis by advanced computer software systems. Specific data collection strategies, coupled with these computer programs, allow automated analysis of extensive backbone and sidechain resonance assignments and three-dimensional structures for proteins of 50 to 200 amino acids.

Assignment validation software suite for the evaluation and presentation of protein resonance assignment data

We present a set of utilities and graphical user interface (GUI) tools for evaluating the quality of protein resonance assignments. The Assignment Validation Software (AVS) suite, together with new GUI features in the AutoAssign software package, provides a set of reports and graphs for validating protein resonance assignment data before its use in structure analysis and/or submission to the BioMagResBank (BMRB). Input includes a listing of resonance assignments and a summary of sequential connectivity data (i.e. triple resonance, NOE, or other data) used in deriving the assignments. These tools are useful for evaluating the accuracy of protein resonance assignments determined by either automated or manual methods.

Stable isotope-resolved metabolomics and applications for drug development

Advances in analytical methodologies, principally nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS), during the last decade have made large-scale analysis of the human metabolome a reality. This is leading to the reawakening of the importance of metabolism in human diseases, particularly cancer. The metabolome is the functional readout of the genome, functional genome, and proteome; it is also an integral partner in molecular regulations for homeostasis. The interrogation of the metabolome, or metabolomics, is now being applied to numerous diseases, largely by metabolite profiling for biomarker discovery, but also in pharmacology and therapeutics. Recent advances in stable isotope tracer-based metabolomic approaches enable unambiguous tracking of individual atoms through compartmentalized metabolic networks directly in human subjects, which promises to decipher the complexity of the human metabolome at an unprecedented pace. This knowledge will revolutionize our understanding of complex human diseases, clinical diagnostics, as well as individualized therapeutics and drug response. In this review, we focus on the use of stable isotope tracers with metabolomics technologies for understanding metabolic network dynamics in both model systems and in clinical applications. Atom-resolved isotope tracing via the two major analytical platforms, NMR and MS, has the power to determine novel metabolic reprogramming in diseases, discover new drug targets, and facilitates ADME studies. We also illustrate new metabolic tracer-based imaging technologies, which enable direct visualization of metabolic processes in vivo. We further outline current practices and future requirements for biochemoinformatics development, which is an integral part of translating stable isotope-resolved metabolomics into clinical reality.

An Integrated Platform for Automated Analysis of Protein NMR Structures

Recent developments provide automated analysis of NMR assignments and three-dimensional (3D) structures of proteins. These approaches are generally applicable to proteins ranging from about 50 to 150 amino acids. In this chapter, we summarize progress by the Northeast Structural Genomics Consortium in standardizing the NMR data collection process for protein structure determination and in building an integrated platform for automated protein NMR structure analysis. Our integrated platform includes the following principal steps: (1) standardized NMR data collection, (2) standardized data processing (including spectral referencing and Fourier transformation), (3) automated peak picking and peak list editing, (4) automated analysis of resonance assignments, (5) automated analysis of NOESY data together with 3D structure determination, and (6) methods for protein structure validation. In particular, the software AutoStructure for automated NOESY data analysis is described in this chapter, together with a discussion of practical considerations for its use in high-throughput structure production efforts. The critical area of data quality assessment has evolved significantly over the past few years and involves evaluation of both intermediate and final peak lists, resonance assignments, and structural information derived from the NMR data. Methods for quality control of each of the major automated analysis steps in our platform are also discussed. Despite significant remaining challenges, when good quality data are available, automated analysis of protein NMR assignments and structures with this platform is both fast and reliable.

Automated protein fold determination using a minimal NMR constraint strategy

Determination of precise and accurate protein structures by NMR generally requires weeks or even months to acquire and interpret all the necessary NMR data. However, even medium‐accuracy fold information can often provide key clues about protein evolution and biochemical function(s). In this article we describe a largely automatic strategy for rapid determination of medium‐accuracy protein backbone structures. Our strategy derives from ideas originally introduced by other groups for determining medium‐accuracy NMR structures of large proteins using deuterated, 13C‐, 15N‐enriched protein samples with selective protonation of side‐chain methyl groups (13CH3). Data collection includes acquiring NMR spectra for automatically determining assignments of backbone and side‐chain 15N, HN resonances, and side‐chain 13CH3 methyl resonances. These assignments are determined automatically by the program AutoAssign using backbone triple resonance NMR data, together with Spin System Type Assignment Constraints (STACs) derived from side‐chain triple‐resonance experiments. The program AutoStructure then derives conformational constraints using these chemical shifts, amide 1H/2H exchange, nuclear Overhauser effect spectroscopy (NOESY), and residual dipolar coupling data. The total time required for collecting such NMR data can potentially be as short as a few days. Here we demonstrate an integrated set of NMR software which can process these NMR spectra, carry out resonance assignments, interpret NOESY data, and generate medium‐accuracy structures within a few days. The feasibility of this combined data collection and analysis strategy starting from raw NMR time domain data was illustrated by automatic analysis of a medium accuracy structure of the Z domain of Staphylococcal protein A.

Isotopomer analysis of lipid biosynthesis by high resolution mass spectrometry and NMR

We have coupled 2D-NMR and infusion FT-ICR-MS with computer-assisted assignment to profile 13C-isotopologues of glycerophospholipids (GPL) directly in crude cell extracts, resulting in very high information throughput of >3000 isobaric molecules in a few minutes. A mass accuracy of better than 1 ppm combined with a resolution of 100,000 at the measured m/z was required to distinguish isotopomers from other GPL structures. Isotopologue analysis of GPLs extracted from LCC2 breast cancer cells grown on [U-13C]-glucose provided a rich trove of information about the biosynthesis and turnover of the GPLs. The isotopologue intensity ratios from the FT-ICR-MS were accurate to approximately 1% or better based on natural abundance background, and depended on the signal-to-nose ratio. The time course of incorporation of 13C from [U-13C]-glucose into a particular phosphatidylcholine was analyzed in detail, to provide a quantitative measure of the sizes of glycerol, acetyl CoA and total GPL pools in growing LCC2 cells. Independent and complementary analysis of the positional 13C enrichment in the glycerol and fatty acyl chains obtained from high resolution 2D NMR was used to verify key aspects of the model. This technology enables simple and rapid sample preparation, has rapid analysis, and is generally applicable to unfractionated GPLs of almost any head group, and to mixtures of other classes of metabolites.

Correcting for the effects of natural abundance in stable isotope resolved metabolomics experiments involving ultra-high resolution mass spectrometry

BackgroundStable isotope tracing with ultra-high resolution Fourier transform-ion cyclotron resonance-mass spectrometry (FT-ICR-MS) can provide simultaneous determination of hundreds to thousands of metabolite isotopologue species without the need for chromatographic separation. Therefore, this experimental metabolomics methodology may allow the tracing of metabolic pathways starting from stable-isotope-enriched precursors, which can improve our mechanistic understanding of cellular metabolism. However, contributions to the observed intensities arising from the stable isotopes natural abundance must be subtracted (deisotoped) from the raw isotopologue peaks before interpretation. Previously posed deisotoping problems are sidestepped due to the isotopic resolution and identification of individual isotopologue peaks. This peak resolution and identification come from the very high mass resolution and accuracy of FT-ICR-MS and present an analytically solvable deisotoping problem, even in the context of stable-isotope enrichment.ResultsWe present both a computationally feasible analytical solution and an algorithm to this newly posed deisotoping problem, which both work with any amount of 13C or 15N stable-isotope enrichment. We demonstrate this algorithm and correct for the effects of 13C natural abundance on a set of raw isotopologue intensities for a specific phosphatidylcholine lipid metabolite derived from a 13C-tracing experiment.ConclusionsCorrection for the effects of 13C natural abundance on a set of raw isotopologue intensities is computationally feasible when the raw isotopologues are isotopically resolved and identified. Such correction makes qualitative interpretation of stable isotope tracing easier and is required before attempting a more rigorous quantitative interpretation of the isotopologue data. The presented implementation is very robust with increasing metabolite size. Error analysis of the algorithm will be straightforward due to low relative error from the implementation itself. Furthermore, the algorithm may serve as an independent quality control measure for a set of observed isotopologue intensities.

Rapid analysis of protein backbone resonance assignments using cryogenic probes, a distributed Linux-based computing architecture, and an integrated set of spectral analysis tools

Rapid data collection, spectral referencing, processing by time domain deconvolution, peak picking and editing, and assignment of NMR spectra are necessary components of any efficient integrated system for protein NMR structure analysis. We have developed a set of software tools designated AutoProc, AutoPeak, and AutoAssign, which function together with the data processing and peak-picking programs NMRPipe and Sparky, to provide an integrated software system for rapid analysis of protein backbone resonance assignments. In this paper we demonstrate that these tools, together with high-sensitivity triple resonance NMR cryoprobes for data collection and a Linux-based computer cluster architecture, can be combined to provide nearly complete backbone resonance assignments and secondary structures (based on chemical shift data) for a 59-residue protein in less than 30 hours of data collection and processing time. In this optimum case of a small protein providing excellent spectra, extensive backbone resonance assignments could also be obtained using less than 6 hours of data collection and processing time. These results demonstrate the feasibility of high throughput triple resonance NMR for determining resonance assignments and secondary structures of small proteins, and the potential for applying NMR in large scale structural proteomics projects.Abbreviations: BPTI – bovine pancreatic trypsin inhibitor; LP – linear prediction; FT – Fourier transform; S/N – signal-to-noise ratio; FID – free induction decay

A novel deconvolution method for modeling UDP-N-acetyl-D-glucosamine biosynthetic pathways based on (13)C mass isotopologue profiles under non-steady-state conditions.

Background Stable isotope tracing is a powerful technique for following the fate of individual atoms through metabolic pathways. Measuring isotopic enrichment in metabolites provides quantitative insights into the biosynthetic network and enables flux analysis as a function of external perturbations. NMR and mass spectrometry are the techniques of choice for global profiling of stable isotope labeling patterns in cellular metabolites. However, meaningful biochemical interpretation of the labeling data requires both quantitative analysis and complex modeling. Here, we demonstrate a novel approach that involved acquiring and modeling the timecourses of 13C isotopologue data for UDP-N-acetyl-D-glucosamine (UDP-GlcNAc) synthesized from [U-13C]-glucose in human prostate cancer LnCaP-LN3 cells. UDP-GlcNAc is an activated building block for protein glycosylation, which is an important regulatory mechanism in the development of many prominent human diseases including cancer and diabetes.