Structure Determination Process Research Articles

Structural determination of nerve agent markers using gas chromatography mass spectrometry after derivatization with 3‐pyridyldiazomethane

Nerve agents are a class of organophosphorous chemicals that are prohibited under the Chemical Weapons Convention. Their degradation products, phosphonic acids, are analyzed as markers of nerve agent contamination and use. Because the phosphonic acids are non-volatile and very polar, their identification by GC-MS requires a derivatization step prior to analysis. Standard derivatization methods for gas-chromatography electron-impact mass-spectrometry analysis give very similar spectra for many alkyl phosphonic acid isomers, which complicates the identification process. We present a new reagent, 3-pyridyldiazomethane, for preparing picolinyl ester derivatives of alkyl methylphosphonic acids facilitating the determination of their structure by enhancing predictable fragmentation of the O-alkyl chain. This fragmentation is directed by the nitrogen nucleus of the pyridyl moiety that abstracts hydrogen from the O-alkyl chain, inducing radical cleavage of the carbon-carbon bonds and thereby causing extensive fragmentation that can be used for detailed structure elucidation of the O-alkyl moiety. The separability of related isomers was tested by comparing the spectra of the picolinyl esters formed from twelve hexyl methylphosphonic acid isomers. Spectral library matches and principal component analysis showed that the picolinyl esters were more effectively separated than the corresponding trimethylsilyl derivatives used in the standard operating procedures. The suggested method will improve the unambiguous structural determination process for phosphonic acids.

Magic Angle Spinning NMR Structure Determination of Proteins from Pseudocontact Shifts

Magic angle spinning solid-state NMR is a unique technique to study atomic-resolution structure of biomacromolecules which resist crystallization or are too large to study by solution NMR techniques. However, difficulties in obtaining sufficient number of long-range distance restraints using dipolar coupling based spectra hamper the process of structure determination of proteins in solid-state NMR. In this study it is shown that high-resolution structure of proteins in solid phase can be determined without the use of traditional dipolar-dipolar coupling based distance restraints by combining the measurements of pseudocontact shifts (PCSs) with Rosetta calculations. The PCSs were generated by chelating exogenous paramagnetic metal ions to a tag 4-mercaptomethyl-dipicolinic acid, which is covalently attached to different residue sites in a 56-residue immunoglobulin-binding domain of protein G (GB1). The long-range structural restraints with metal-nucleus distance of up to ∼20 Å are quantitatively extracted from experimentally observed PCSs, and these are in good agreement with the distances back-calculated using an X-ray structure model. Moreover, we demonstrate that using several paramagnetic ions with varied paramagnetic susceptibilities as well as the introduction of paramagnetic labels at different sites can dramatically increase the number of long-range restraints and cover different regions of the protein. The structure generated from solid-state NMR PCSs restraints combined with Rosetta calculations has 0.7 Å root-mean-square deviation relative to X-ray structure.

Roxbin B is Cuspinin: Structural Revision and Total Synthesis

Prompted by the outcome that the synthesized roxbin B was not identical to the natural roxbin B, the structural determination process and spectral data were re-examined, with the finding that roxbin B was very likely to be 1-O-galloyl-2,3-(R);4,6-(S)-bis-O-hexahydroxydiphenoyl-β-d-glucose (cuspinin). Because the (R)-axial chirality is rare in natural products when the hexahydroxydiphenoyl group bridges the 2- and 3-oxygens, the proposed structure of cuspinin was confirmed by the total synthesis, leading to the conclusion that roxbin B is the same as cuspinin.

Solution NMR refinement of a metal ion bound protein using metal ion inclusive restrained molecular dynamics methods

Correctly calculating the structure of metal coordination sites in a protein during the process of nuclear magnetic resonance (NMR) structure determination and refinement continues to be a challenging task. In this study, we present an accurate and convenient means by which to include metal ions in the NMR structure determination process using molecular dynamics (MD) simulations constrained by NMR-derived data to obtain a realistic and physically viable description of the metal binding site(s). This method provides the framework to accurately portray the metal ions and its binding residues in a pseudo-bond or dummy-cation like approach, and is validated by quantum mechanical/molecular mechanical (QM/MM) MD calculations constrained by NMR-derived data. To illustrate this approach, we refine the zinc coordination complex structure of the zinc sensing transcriptional repressor protein Staphylococcus aureus CzrA, generating over 130 ns of MD and QM/MM MD NMR-data compliant sampling. In addition to refining the first coordination shell structure of the Zn(II) ion, this protocol benefits from being performed in a periodically replicated solvation environment including long-range electrostatics. We determine that unrestrained (not based on NMR data) MD simulations correlated to the NMR data in a time-averaged ensemble. The accurate solution structure ensemble of the metal-bound protein accurately describes the role of conformational sampling in allosteric regulation of DNA binding by zinc and serves to validate our previous unrestrained MD simulations of CzrA. This methodology has potentially broad applicability in the structure determination of metal ion bound proteins, protein folding and metal template protein-design studies.

MLP accompanied beam search for the resonance assignment problem

Resonance signal assignment is a well known problem encountered during the process of biomolecule structure determination based on nuclear magnetic resonance (NMR) spectroscopy. As one of the first steps following the experimental part of the process, it significantly affects the quality of a resulting molecule model. In the paper we present a new approach for automated signal assignment in two-dimensional NMR spectra of RNA molecules. The proposed algorithm combines beam search with a neural network in order to reconstruct and evaluate solutions. Computational tests with a set of experimental and simulated NMR spectra show an excellent performance of the approach in comparison with the existing methods. The algorithm implementation is freely available at http://www.cs.put.poznan.pl/mszachniuk/bsmlp.zip .

Refinement of high pressure single-crystal diffraction data using Jana2006

The refinement of high pressure single-crystal data is difficult as the intensities of the reflections from the sample can be falsified due to effects originating from the components of the diamond anvil cell. A series of procedures were developed and implemented into Jana2006 to allow for an easy identification and handling of the outlying intensities in the structure determination process. The use of these procedures is described on the basis of a working example.

Chemical crosslinking and LC/MS analysis to determine protein domain orientation: Application to AbrB

To fully understand the modes of action of multi-protein complexes, it is essential to determine their overall global architecture and the specific relationships between domains and subunits. The transcription factor AbrB is a functional homotetramer consisting of two domains per monomer. Obtaining the high-resolution structure of tetrameric AbrB has been extremely challenging due to the independent character of these domains. To facilitate the structure determination process, we solved the NMR structures of both domains independently and utilized gas-phase cleavable chemical crosslinking and LC/MSn analysis to correctly position the domains within the full tetrameric AbrB protein structure.

Combinatorial chemistry of oxynitride phosphors and discovery of a novel phosphor for use in light emitting diodes, Ca1.5Ba0.5Si5N6O3:Eu2+

Solid-state combinatorial chemistry (combi-chem) has been proven to be a versatile tool for the discovery of novel phosphors in the multi-compositional (oxy)nitride search space. Since the advent of a unique solid-state combi-chem technique involving heuristic optimization strategies such as a non-dominated sorting genetic algorithm (NSGA), we have discovered several novel phosphors. The present investigation was focused on a phosphor that was previously discovered using this technique, but was incompletely identified, on pinpointing the optimum processing composition of the discovered phosphor exhibiting promising PL properties with the assistance of particle swarm optimization (PSO), and on identifying the exact structure based on synchrotron X-ray diffraction data. The exact stoichiometry of the discovered, unknown phosphor was determined to be Ca1.5Ba0.5Si5N6O3:Eu2+ and was indexed as a monoclinic lattice in the Cm space group, with lattice parameters a = 7.07033(2) Å, b = 23.86709(7) Å, c = 4.825304(15) Å, and β = 109.0647(2)°. A structural determination process that involved a direct method, simulated annealing, and Rietveld refinement revealed that the exact structure of Ca1.5Ba0.5Si5N6O3:Eu2+ was somewhat a composite structure combining the well known CaSi2N2O2:Eu2+ and BaSi6N8O:Eu2+ structures. The discovered phosphor showed a broad-band type emission of yellow-red light under UV and blue light excitations, which makes it a promising candidate for practical use in LED applications.

Utilization of protein intrinsic disorder knowledge in structural proteomics

Intrinsically disordered proteins (IDPs) and proteins with long disordered regions are highly abundant in various proteomes. Despite their lack of well-defined ordered structure, these proteins and regions are frequently involved in crucial biological processes. Although in recent years these proteins have attracted the attention of many researchers, IDPs represent a significant challenge for structural characterization since these proteins can impact many of the processes in the structure determination pipeline. Here we investigate the effects of IDPs on the structure determination process and the utility of disorder prediction in selecting and improving proteins for structural characterization. Examination of the extent of intrinsic disorder in existing crystal structures found that relatively few protein crystal structures contain extensive regions of intrinsic disorder. Although intrinsic disorder is not the only cause of crystallization failures and many structured proteins cannot be crystallized, filtering out highly disordered proteins from structure-determination target lists is still likely to be cost effective. Therefore it is desirable to avoid highly disordered proteins from structure-determination target lists and we show that disorder prediction can be applied effectively to enrich structure determination pipelines with proteins more likely to yield crystal structures. For structural investigation of specific proteins, disorder prediction can be used to improve targets for structure determination. Finally, a framework for considering intrinsic disorder in the structure determination pipeline is proposed.

On the use of theCmap in Patterson deconvolution procedures

The cross-correlation function between the target and a model electron density, denoted as the C map, has been crystallographically characterized. In particular, a study of its interatomic vectors and of their relation with the Patterson vectors has been undertaken. Since the C map is not available during the phasing process, the C' map, its centric modification, is considered. It may be computed at any stage of the phasing process and shows properties that are very useful for the crystal structure determination process. It has been combined with the implication transformation method and with vector-superposition techniques for performing the Patterson deconvolution and obtaining an initial model for dual-space recycling. While Patterson methods are traditionally considered to be more efficient for structures containing heavy atoms, the C map extends their potential to light-atom structures (i.e. containing atoms not heavier than O).

Membrane protein structure determination by electron crystallography

During the past year, electron crystallography of membrane proteins has provided structural insights into the mechanism of several different transporters and into their interactions with lipid molecules within the bilayer. From a technical perspective there have been important advances in high-throughput screening of crystallization trials and in automated imaging of membrane crystals with the electron microscope. There have also been key developments in software, and in molecular replacement and phase extension methods designed to facilitate the process of structure determination.

Identification of helix capping and β-turn motifs from NMR chemical shifts

We present an empirical method for identification of distinct structural motifs in proteins on the basis of experimentally determined backbone and (13)C(β) chemical shifts. Elements identified include the N-terminal and C-terminal helix capping motifs and five types of β-turns: I, II, I', II' and VIII. Using a database of proteins of known structure, the NMR chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and β-turn motifs are used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. The trained neural networks, contained in the MICS (motif identification from chemical shifts) program, also provide a confidence level for each of their predictions, and values ranging from ca 0.7-0.9 for the Matthews correlation coefficient of its predictions far exceed those attainable by sequence analysis. MICS is anticipated to be useful both in the conventional NMR structure determination process and for enhancing on-going efforts to determine protein structures solely on the basis of chemical shift information, where it can aid in identifying protein database fragments suitable for use in building such structures.

A Bayesian Approach for Determining Protein Side-Chain Rotamer Conformations Using Unassigned NOE Data

A major bottleneck in protein structure determination via nuclear magnetic resonance (NMR) is the lengthy and laborious process of assigning resonances and nuclear Overhauser effect (NOE) cross peaks. Recent studies have shown that accurate backbone folds can be determined using sparse NMR data, such as residual dipolar couplings (RDCs) or backbone chemical shifts. This opens a question of whether we can also determine the accurate protein side-chain conformations using sparse or unassigned NMR data. We attack this question by using unassigned nuclear Overhauser effect spectroscopy (NOESY) data, which records the through-space dipolar interactions between protons nearby in three-dimensional (3D) space. We propose a Bayesian approach with a Markov random field (MRF) model to integrate the likelihood function derived from observed experimental data, with prior information (i.e., empirical molecular mechanics energies) about the protein structures. We unify the side-chain structure prediction problem with the side-chain structure determination problem using unassigned NMR data, and apply the deterministic dead-end elimination (DEE) and A* search algorithms to provably find the global optimum solution that maximizes the posterior probability. We employ a Hausdorff-based measure to derive the likelihood of a rotamer or a pairwise rotamer interaction from unassigned NOESY data. In addition, we apply a systematic and rigorous approach to estimate the experimental noise in NMR data, which also determines the weighting factor of the data term in the scoring function derived from the Bayesian framework. We tested our approach on real NMR data of three proteins: the FF Domain 2 of human transcription elongation factor CA150 (FF2), the B1 domain of Protein G (GB1), and human ubiquitin. The promising results indicate that our algorithm can be applied in high-resolution protein structure determination. Since our approach does not require any NOE assignment, it can accelerate the NMR structure determination process.

Nuclear magnetic resonance signal chemical shifts and molecular simulations: a multidisciplinary approach to modeling copper protein structures

Nuclear magnetic resonance (NMR) chemical shifts are experimental observables that are available during the first stage of the protein structure determination process. Recently, some methodologies for building structural models of proteins using only these experimental data have been implemented. To assess the potential of these methods for modeling metalloproteins (generally considered a challenging benchmark), we determined the structures of the yeast copper chaperone Atx1 and the CuA domain of Thermus thermophilus cytochrome c oxidase starting from the available chemical shift data. The metal centers were modeled using molecular dynamics simulations with molecular mechanics potentials. The results obtained are evaluated and discussed.

Application of aerodynamic model structure determination to UAV data

AbstractThis paper concerns aircraft system identification and, in particular, the process of aerodynamic model structure determination. Its application to experimental data from unmanned aerial vehicles (UAVs) is also described. The procedure can be particularly useful for determining an aerodynamic model for aircraft with unconventional airframe configurations, which some unmanned aircraft tend to have. Two model structure determination techniques are outlined. The first is the well-established stepwise regression method, while the second is an adaptation of an existing frequency response approach which instead utilises maximum likelihood estimation. Example applications of the methods are presented for two data sources. The first is a set of UAV flight test data and the second is data recorded from dynamic wind tunnel tests on a UAV configuration. For both examples, the model structures determined using stepwise regression and maximum likelihood analysis matched one another, suggesting that the maximum likelihood approach and the chosen thresholds for its statistical metrics were reliable for the data being analysed.

Structure determination from powder diffraction data using a new intensity extraction algorithm

A method is proposed for extracting the relevant structural information from powder diffraction data. The pattern is fitted and a reduced number of intensity values, called `pseudo-intensities', are extracted and used as a substitute for the full pattern in the structure determination algorithm. Since the number of pseudo-intensities is one order of magnitude smaller than the number of data points in the experimental data, the calculation of the agreement factor (Rwp), and hence the evaluation of a trial structure, is carried out more quickly. The method consists of a procedure for stripping out the influence of the profile fit from the full patternRwpfactor, leaving just the important intensity information needed in the structure determination process.

Protein–protein HADDocking using exclusively pseudocontact shifts

In order to enhance the structure determination process of macromolecular assemblies by NMR, we have implemented long-range pseudocontact shift (PCS) restraints into the data-driven protein docking package HADDOCK. We demonstrate the efficiency of the method on a synthetic, yet realistic case based on the lanthanide-labeled N-terminal ε domain of the E. coli DNA polymerase III (ε186) in complex with the HOT domain. Docking from the bound form of the two partners is swiftly executed (interface RMSDs < 1 Å) even with addition of very large amount of noise, while the conformational changes of the free form still present some challenges (interface RMSDs in a 3.1–3.9 Å range for the ten lowest energy complexes). Finally, using exclusively PCS as experimental information, we determine the structure of ε186 in complex with the HOT-homologue θ subunit of the E. coli DNA polymerase III.Electronic supplementary materialThe online version of this article (doi:10.1007/s10858-011-9514-4) contains supplementary material, which is available to authorized users.

X-CHIP: an integrated platform for high-throughput protein crystallization and on-the-chip X-ray diffraction data collection.

The X-CHIP (X-ray Crystallization High-throughput Integrated Platform) is a novel microchip that has been developed to combine multiple steps of the crystallographic pipeline from crystallization to diffraction data collection on a single device to streamline the entire process. The system has been designed for crystallization condition screening, visual crystal inspection, initial X-ray screening and data collection in a high-throughput fashion. X-ray diffraction data acquisition can be performed directly on-the-chip at room temperature using an in situ approach. The capabilities of the chip eliminate the necessity for manual crystal handling and cryoprotection of crystal samples, while allowing data collection from multiple crystals in the same drop. This technology would be especially beneficial for projects with large volumes of data, such as protein-complex studies and fragment-based screening. The platform employs hydrophilic and hydrophobic concentric ring surfaces on a miniature plate transparent to visible light and X-rays to create a well defined and stable microbatch crystallization environment. The results of crystallization and data-collection experiments demonstrate that high-quality well diffracting crystals can be grown and high-resolution diffraction data sets can be collected using this technology. Furthermore, the quality of a single-wavelength anomalous dispersion data set collected with the X-CHIP at room temperature was sufficient to generate interpretable electron-density maps. This technology is highly resource-efficient owing to the use of nanolitre-scale drop volumes. It does not require any modification for most in-house and synchrotron beamline systems and offers a promising opportunity for full automation of the X-ray structure-determination process.

Overview on the Use of NMR to Examine Protein Structure

Any protein structure determination process contains several steps, starting from obtaining a suitable sample, then moving on to acquiring data and spectral assignment, and lastly to the final steps of structure determination and validation. This unit describes all of these steps, starting with the basic physical principles behind NMR and some of the most commonly measured and observed phenomena such as chemical shift, scalar and residual coupling, and the nuclear Overhauser effect. Then, in somewhat more detail, the process of spectral assignment and structure elucidation is explained. Furthermore, the use of NMR to study protein-ligand interaction, protein dynamics, or protein folding is described.

ERROR TOLERANT NMR BACKBONE RESONANCE ASSIGNMENT AND AUTOMATED STRUCTURE GENERATION

Error tolerant backbone resonance assignment is the cornerstone of the NMR structure determination process. Although a variety of assignment approaches have been developed, none works sufficiently well on noisy fully automatically picked peaks to enable the subsequent automatic structure determination steps. We have designed an integer linear programming (ILP) based assignment system (IPASS) that has enabled fully automatic protein structure determination for four test proteins. IPASS employs probabilistic spin system typing based on chemical shifts and secondary structure predictions. Furthermore, IPASS extracts connectivity information from the inter-residue information and the (automatically picked) (15)N-edited NOESY peaks which are then used to fix reliable fragments. When applied to automatically picked peaks for real proteins, IPASS achieves an average precision and recall of 82% and 63%, respectively. In contrast, the next best method, MARS, achieves an average precision and recall of 77% and 36%, respectively. The assignments generated by IPASS are then fed into our protein structure calculation system, FALCON-NMR, to determine the 3D structures without human intervention. The final models have backbone RMSDs of 1.25Å, 0.88Å, 1.49Å, and 0.67Å to the reference native structures for proteins TM1112, CASKIN, VRAR, and HACS1, respectively. The web server is publicly available at http://monod.uwaterloo.ca/nmr/ipass.