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The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the ‘gold standard’ for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Residual feed intake (RFI) is a feed efficiency measure commonly used in the livestock industry to identify animals that efficiently/inefficiently convert feed into meat or body mass. Selection for low-residual feed intake (LRFI), or feed efficient animals, is gaining popularity among beef producers due to the fact that LRFI cattle eat less and produce less methane per unit weight gain. RFI is a difficult and time-consuming measure to perform, and therefore a simple blood test that could distinguish high-RFI (HRFI) from LRFI animals (early on) would potentially benefit beef farmers in terms of optimizing production or selecting which animals to cull or breed. Using three different metabolomics platforms (nuclear magnetic resonance (NMR) spectrometry, liquid chromatography-tandem mass spectrometry (LC-MS/MS), and inductively coupled plasma mass spectrometry (ICP-MS)) we successfully identified serum biomarkers for RFI that could potentially be translated to an RFI blood test. One set of predictive RFI biomarkers included formate and leucine (best for NMR), and another set included C4 (butyrylcarnitine) and LysoPC(28:0) (best for LC-MS/MS). These serum biomarkers have high sensitivity and specificity (AUROC &gt, 0.85), for distinguishing HRFI from LRFI animals. These results suggest that serum metabolites could be used to inexpensively predict and categorize bovine RFI values. Further validation using a larger, more diverse cohort of cattle is required to confirm these findings.

Mutton and lamb sales continue to grow globally at a rate of 5% per year. However, sheep farming struggles with low profit margins due to high feed costs and modest carcass yields. Selecting those sheep expected to convert feed efficiently and have high carcass merit, as early as possible in their life cycle, could significantly improve the profitability of sheep farming. Unfortunately, direct measurement of feed conversion efficiency (via residual feed intake [RFI]) and carcass merit is a labor-intensive and expensive procedure. Thus, indirect, marker-assisted evaluation of these traits has been explored as a means of reducing the cost of its direct measurement. One promising and potentially inexpensive route to discover biomarkers of RFI and/or carcass merit is metabolomics. Using quantitative metabolomics, we profiled the blood serum metabolome (i.e., the sum of all measurable metabolites) associated with sheep RFI and carcass merit and identified candidate biomarkers of these traits. The study included 165 crossbred ram-lambs that underwent direct measurement of feed consumption to determine their RFI classification (i.e., low vs. high) using the GrowSafe System over a period 40 d. Carcass merit was evaluated after slaughter using standardized methods. Prior to being sent to slaughter, one blood sample was drawn from each animal, and serum prepared and frozen at −80 °C to limit metabolite degradation. A subset of the serum samples was selected based on divergent RFI and carcass quality for further metabolomic analyses. The analyses were conducted using three analytical methods (nuclear magnetic resonance spectroscopy, liquid chromatography mass spectrometry, and inductively coupled mass spectrometry), which permitted the identification and quantification of 161 unique metabolites. Biomarker analyses identified three significant (P < 0.05) candidate biomarkers of sheep RFI (AUC = 0.80), seven candidate biomarkers of carcass yield grade (AUC = 0.77), and one candidate biomarker of carcass muscle-to-bone ratio (AUC = 0.74). The identified biomarkers appear to have roles in regulating energy metabolism and protein synthesis. These results suggest that serum metabolites could be used to categorize and predict sheep for their RFI and carcass merit. Further validation using a larger (3×) and more diverse cohort of sheep is required to confirm these findings.

Protein structure determination using nuclear magnetic resonance (NMR) spectroscopy can be both time-consuming and labor intensive. Here we demonstrate how chemical shift threading can permit rapid, robust, and accurate protein structure determination using only chemical shift data. Threading is a relatively old bioinformatics technique that uses a combination of sequence information and predicted (or experimentally acquired) low-resolution structural data to generate high-resolution 3D protein structures. The key motivations behind using NMR chemical shifts for protein threading lie in the fact that they are easy to measure, they are available prior to 3D structure determination, and they contain vital structural information. The method we have developed uses not only sequence and chemical shift similarity but also chemical shift-derived secondary structure, shift-derived super-secondary structure, and shift-derived accessible surface area to generate a high quality protein structure regardless of the sequence similarity (or lack thereof) to a known structure already in the PDB. The method (called E-Thrifty) was found to be very fast (often

Nuclear magnetic resonance (NMR) spectroscopy is becoming increasingly automated. Most modern NMR spectrometers are now equipped with auto-tune/auto-match probes along with automated locking and shimming systems. Likewise, more and more instruments, especially for NMR-based metabolomics applications, are equipped with automated sample changers. All this instrumental automation allows NMR data to be collected at a rate of >100 samples/day. However, a continuing bottleneck in NMR-based metabolomics has been the time required to manually analyze and annotate the collected NMR spectra. In many cases, manual spectral annotation and analysis can take one or more hours per spectrum. Fortunately, over the past few years, several software tools have been developed that largely automate the spectral deconvolution or spectral annotation process. Using these tools requires that the samples must be prepared and the NMR spectra must be acquired in a very specific manner. In this chapter, we will describe the step-by-step preparation of biofluid samples along with the required protocols for acquiring optimal spectra for automated NMR metabolomics analysis. We will also discuss the use of three common tools (Chenomx NMR Suite, Bayesil, and COLMARm) for (semi-) automated profiling, and annotation of 1D- and 2D-NMR spectra of biofluids.

The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB’s chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC–MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

Chemical shifts are among the most informative parameters in protein NMR. They provide wealth of information about protein secondary and tertiary structure, protein flexibility, and protein-ligand binding. In this report, we review the progress in interpreting and utilizing protein chemical shifts that has occurred over the past 25years, with a particular focus on the large body of work arising from our group and other Canadian NMR laboratories. More specifically, this review focuses on describing, assessing, and providing some historical context for various chemical shift-based methods to: (1) determine protein secondary and super-secondary structure; (2) derive protein torsion angles; (3) assess protein flexibility; (4) predict residue accessible surface area; (5) refine 3D protein structures; (6) determine 3D protein structures and (7) characterize intrinsically disordered proteins. This review also briefly covers some of the methods that we previously developed to predict chemical shifts from 3D protein structures and/or protein sequence data. It is hoped that this review will help to increase awareness of the considerable utility of NMR chemical shifts in structural biology and facilitate more widespread adoption of chemical-shift based methods by the NMR spectroscopists, structural biologists, protein biophysicists, and biochemists worldwide. This article is part of a Special Issue entitled: Biophysics in Canada, edited by Lewis Kay, John Baenziger, Albert Berghuis and Peter Tieleman.

Protein side-chain motions are involved in many important biological processes including enzymatic catalysis, allosteric regulation, and the mediation of protein-protein, protein-DNA, protein-RNA, and protein-cofactor interactions. NMR spectroscopy has long been used to provide insights into the motions of side-chain groups. Currently, the method of choice for studying side-chain dynamics by NMR is the measurement of methyl (2)H autorelaxation. Methyl (2)H autorelaxation exhibits simple relaxation mechanisms and can be straightforwardly converted to meaningful dynamic parameters. However, methyl groups can only be found in 6 of 19 side-chain bearing amino acids. Consequently, only a sparse assessment of protein side-chain dynamics is possible. Therefore, there is a significant interest in developing novel methods of studying side-chain motions that can be applied to all types of side-chains. Here, we show how side-chain chemical shifts can be used to determine the magnitude of fast side-chain motions in proteins. The chemical shift method is applicable to all side-chain bearing residues and does not require any additional measurements beyond standard NMR experiments for backbone and side-chain assignments.

In protein X-ray crystallography, resolution is often used as a good indicator of structural quality. Diffraction resolution of protein crystals correlates well with the number of X-ray observables that are used in structure generation and, therefore, with protein coordinate errors. In protein NMR, there is no parameter identical to X-ray resolution. Instead, resolution is often used as a synonym of NMR model quality. Resolution of NMR structures is often deduced from ensemble precision, torsion angle normality and number of distance restraints per residue. The lack of common techniques to assess the resolution of X-ray and NMR structures complicates the comparison of structures solved by these two methods. This problem is sometimes approached by calculating "equivalent resolution" from structure quality metrics. However, existing protocols do not offer a comprehensive assessment of protein structure as they calculate equivalent resolution from a relatively small number (

Although highly conserved throughout evolution, the exact biological function of the prion protein is still unclear. In an effort to identify the potential biological functions of the prion protein we conducted a small-molecule screening assay using the Syrian hamster prion protein [shPrP(90-232)]. The screen was performed using a library of 149 water-soluble metabolites that are known to pass through the blood-brain barrier. Using a combination of 1D NMR, fluorescence quenching and surface plasmon resonance we identified thiamine (vitamin B1) as a specific prion ligand with a binding constant of ~60 μM. Subsequent studies showed that this interaction is evolutionarily conserved, with similar binding constants being seen for mouse, hamster and human prions. Various protein construct lengths, both with and without the unstructured N-terminal region in the presence and absence of copper, were examined. This indicates that the N-terminus has no influence on the protein's ability to interact with thiamine. In addition to thiamine, the more biologically abundant forms of vitamin B1 (thiamine monophosphate and thiamine diphosphate) were also found to bind the prion protein with similar affinity. Heteronuclear NMR experiments were used to determine thiamine's interaction site, which is located between helix 1 and the preceding loop. These data, in conjunction with computer-aided docking and molecular dynamics, were used to model the thiamine-binding pharmacophore and a comparison with other thiamine binding proteins was performed to reveal the common features of interaction.

PROSESS (PROtein Structure Evaluation Suite and Server) is a web server designed to evaluate and validate protein structures generated by X-ray crystallography, NMR spectroscopy or computational modeling. While many structure evaluation packages have been developed over the past 20 years, PROSESS is unique in its comprehensiveness, its capacity to evaluate X-ray, NMR and predicted structures as well as its ability to evaluate a variety of experimental NMR data. PROSESS integrates a variety of previously developed, well-known and thoroughly tested methods to evaluate both global and residue specific: (i) covalent and geometric quality; (ii) non-bonded/packing quality; (iii) torsion angle quality; (iv) chemical shift quality and (v) NOE quality. In particular, PROSESS uses VADAR for coordinate, packing, H-bond, secondary structure and geometric analysis, GeNMR for calculating folding, threading and solvent energetics, ShiftX for calculating chemical shift correlations, RCI for correlating structure mobility to chemical shift and PREDITOR for calculating torsion angle-chemical shifts agreement. PROSESS also incorporates several other programs including MolProbity to assess atomic clashes, Xplor-NIH to identify and quantify NOE restraint violations and NAMD to assess structure energetics. PROSESS produces detailed tables, explanations, structural images and graphs that summarize the results and compare them to values observed in high-quality or high-resolution protein structures. Using a simplified red–amber–green coloring scheme PROSESS also alerts users about both general and residue-specific structural problems. PROSESS is intended to serve as a tool that can be used by structure biologists as well as database curators to assess and validate newly determined protein structures. PROSESS is freely available at http:// www.prosess.ca.

The protein property prediction and testing database (PPT-DB) is a database housing nearly 30 carefully curated databases, each of which contains commonly predicted protein property information. These properties include both structural (i.e. secondary structure, contact order, disulfide pairing) and dynamic (i.e. order parameters, B-factors, folding rates) features that have been measured, derived or tabulated from a variety of sources. PPT-DB is designed to serve two purposes. First it is intended to serve as a centralized, up-to-date, freely downloadable and easily queried repository of predictable or ‘derived’ protein property data. In this role, PPT-DB can serve as a one-stop, fully standardized repository for developers to obtain the required training, testing and validation data needed for almost any kind of protein property prediction program they may wish to create. The second role that PPT-DB can play is as a tool for homology-based protein property prediction. Users may query PPT-DB with a sequence of interest and have a specific property predicted using a sequence similarity search against PPT-DB's extensive collection of proteins with known properties. PPT-DB exploits the well-known fact that protein structure and dynamic properties are highly conserved between homologous proteins. Predictions derived from PPT-DB's similarity searches are typically 85–95% correct (for categorical predictions, such as secondary structure) or exhibit correlations of >0.80 (for numeric predictions, such as accessible surface area). This performance is 10–20% better than what is typically obtained from standard ‘ab initio’ predictions. PPT-DB, its prediction utilities and all of its contents are available at http://www.pptdb.ca

Protein flexibility lies at the heart of many protein–ligand binding events and enzymatic activities. However, the experimental measurement of protein motions is often difficult, tedious and error-prone. As a result, there is a considerable interest in developing simpler and faster ways of quantifying protein flexibility. Recently, we described a method, called Random Coil Index (RCI), which appears to be able to quantitatively estimate model-free order parameters and flexibility in protein structural ensembles using only backbone chemical shifts. Because of its potential utility, we have undertaken a more detailed investigation of the RCI method in an attempt to ascertain its underlying principles, its general utility, its sensitivity to chemical shift errors, its sensitivity to data completeness, its applicability to other proteins, and its general strengths and weaknesses. Overall, we find that the RCI method is very robust and that it represents a useful addition to traditional methods of studying protein flexibility. We have implemented many of the findings and refinements reported here into a web server that allows facile, automated predictions of model-free order parameters, MD RMSF and NMR RMSD values directly from backbone 1H, 13C and 15N chemical shift assignments. The server is available at http://wishart.biology.ualberta.ca/rci .

Over the past decade, a number of methods have been developed to determine the approximate structure of proteins using minimal NMR experimental information such as chemical shifts alone, sparse NOEs alone or a combination of comparative modeling data and chemical shifts. However, there have been relatively few methods that allow these approximate models to be substantively refined or improved using the available NMR chemical shift data. Here, we present a novel method, called Chemical Shift driven Genetic Algorithm for biased Molecular Dynamics (CS-GAMDy), for the robust optimization of protein structures using experimental NMR chemical shifts. The method incorporates knowledge-based scoring functions and structural information derived from NMR chemical shifts via a unique combination of multi-objective MD biasing, a genetic algorithm, and the widely used XPLOR molecular modelling language. Using this approach, we demonstrate that CS-GAMDy is able to refine and/or fold models that are as much as 10 Å (RMSD) away from the correct structure using only NMR chemical shift data. CS-GAMDy is also able to refine of a wide range of approximate or mildly erroneous protein structures to more closely match the known/correct structure and the known/correct chemical shifts. We believe CS-GAMDy will allow protein models generated by sparse restraint or chemical-shift-only methods to achieve sufficiently high quality to be considered fully refined and “PDB worthy”. The CS-GAMDy algorithm is explained in detail and its performance is compared over a range of refinement scenarios with several commonly used protein structure refinement protocols. The program has been designed to be easily installed and easily used and is available at http://www.gamdy.ca.

Torsion angle restraints are frequently used in the determination and refinement of protein structures by NMR. These restraints may be obtained by J coupling, cross-correlation measurements, nuclear Overhauser effects (NOEs) or secondary chemical shifts. Currently most backbone (phi/psi) torsion angles are determined using a combination of J(HNHalpha) couplings and chemical shift measurements while most side-chain (chi1) angles and cis/trans peptide bond angles (omega) are determined via NOEs. The dependency on multiple experimental (and computational) methods to obtain different torsion angle restraints is both time-consuming and error prone. The situation could be greatly improved if the determination of all torsion angles (phi, psi, chi and omega) could be made via a single type of measurement (i.e. chemical shifts). Here we describe a program, called SHIFTOR, that is able to accurately predict a large number of protein torsion angles (phi, psi, omega, chi1) using only 1H, 13C and 15N chemical shift assignments as input. Overall, the program is 100x faster and its predictions are approximately 20% better than existing methods. The program is also capable of predicting chi1 angles with 81% accuracy and omega angles with 100% accuracy. SHIFTOR exploits many of the recent developments and observations regarding chemical shift dependencies as well as using information in the Protein Databank to improve the quality of its shift-derived torsion angle predictions. SHIFTOR is available as a freely accessible web server at http://wishart.biology.ualberta.ca/shiftor.

The backbone dynamics of the J domain from polyomavirus T antigens have been investigated using 15N NMR relaxation and molecular dynamics simulation. Model-free relaxation analysis revealed picosecond to nanosecond motions in the N terminus, the I-II loop, the C-terminal end of helix II through the HPD loop to the beginning of helix III, and the C-terminal end of helix III to the C terminus. The backbone dynamics of the HPD loop and termini are dominated by motions with moderately large amplitudes and correlation times of the order of a nanosecond or longer. Conformational exchange on the microsecond to millisecond timescale was identified in the HPD loop, the N and C termini, and the I-II loop. A 9.7ns MD trajectory manifested concerted swings of the HPD loop. Transitions between major and minor conformations of the HPD loop featured distinct patterns of change in backbone dihedral angles and hydrogen bonds. Fraying of the C-terminal end of helix II and the N-terminal end of helix III correlated with displacements of the HPD loop. Correlation of crankshaft motions of Gly46 and Gly47 with the collective motions of the HPD loop suggested an important role of the two glycine residues in the mobility of the loop. Fluctuations of the HPD loop correlated with relative reorientation of side-chains of Lys35 and Asp44 that interact with Hsc70.

The NMR structure of the N-terminal, DnaJ-like domain of murine polyomavirus tumor antigens (PyJ) has been determined to high precision, with root mean square deviations to the mean structure of 0.38 A for backbone atoms and 0.94 A for all heavy atoms of ordered residues 5–41 and 50–69. PyJ possesses a three-helix fold, in which anti-parallel helices II and III are bridged by helix I, similar to the four-helix fold of the J domains of DnaJ and human DnaJ-1. PyJ differs significantly in the lengths of N terminus, helix I, and helix III. The universally conserved HPD motif appears to form a His-Pro C-cap of helix II. Helix I features a stabilizing Schellman C-cap that is probably conserved universally among J domains. On the helix II surface where positive charges of other J domains have been implicated in binding of hsp70s, PyJ contains glutamine residues. Nonetheless, chimeras that replace the J domain of DnaJ with PyJ function like wild-type DnaJ in promoting growth of Escherichia coli. This activity can be modulated by mutations of at least one of these glutamines. T antigen mutations reported to impair cellular transformation by the virus, presumably via interactions with PP2A, cluster in the hydrophobic folding core and at the extreme N terminus, remote from the HPD loop.

Chemical cross-linking combined with mass spectrometry is a rapidly developing technique for structural proteomics. Cross-linked proteins are usually digested with trypsin to generate cross-linked peptides, which are then analyzed by mass spectrometry. The most informative cross-links, the interpeptide cross-links, are often large in size, because they consist of two peptides that are connected by a cross-linker. In addition, trypsin targets the same residues as amino-reactive cross-linkers, and cleavage will not occur at these cross-linker-modified residues. This produces high molecular weight cross-linked peptides, which complicates their mass spectrometric analysis and identification. In this paper, we examine a nonspecific protease, proteinase K, as an alternative to trypsin for cross-linking studies. Initial tests on a model peptide that was digested by proteinase K resulted in a "family" of related cross-linked peptides, all of which contained the same cross-linking sites, thus providing additional verification of the cross-linking results, as was previously noted for other post-translational modification studies. The procedure was next applied to the native (PrP(C)) and oligomeric form of prion protein (PrPβ). Using proteinase K, the affinity-purifiable CID-cleavable and isotopically coded cross-linker cyanurbiotindipropionylsuccinimide and MALDI-MS cross-links were found for all of the possible cross-linking sites. After digestion with proteinase K, we obtained a mass distribution of the cross-linked peptides that is very suitable for MALDI-MS analysis. Using this new method, we were able to detect over 60 interpeptide cross-links in the native PrP(C) and PrPβ prion protein. The set of cross-links for the native form was used as distance constraints in developing a model of the native prion protein structure, which includes the 90-124-amino acid N-terminal portion of the protein. Several cross-links were unique to each form of the prion protein, including a Lys(185)-Lys(220) cross-link, which is unique to the PrPβ and thus may be indicative of the conformational change involved in the formation of prion protein oligomers.

Phosphomannomutase/phosphoglucomutase contributes to the infectivity of Pseudomonas aeruginosa, retains and reorients its intermediate by 180°, and rotates domain 4 to close the deep catalytic cleft. Nuclear magnetic resonance (NMR) spectra of the backbone of wild-type and S108C-inactivated enzymes were assigned to at least 90%. (13)C secondary chemical shifts report excellent agreement of solution and crystallographic structure over the 14 α-helices, C-capping motifs, and 20 of the 22 β-strands. Major and minor NMR peaks implicate substates affecting 28% of assigned residues. These can be attributed to the phosphorylation state and possibly to conformational interconversions. The S108C substitution of the phosphoryl donor and acceptor slowed transformation of the glucose 1-phosphate substrate by impairing k(cat). Addition of the glucose 1,6-bisphosphate intermediate accelerated this reaction by 2-3 orders of magnitude, somewhat bypassing the defect and apparently relieving substrate inhibition. The S108C mutation perturbs the NMR spectra and electron density map around the catalytic cleft while preserving the secondary structure in solution. Diminished peak heights and faster (15)N relaxation suggest line broadening and millisecond fluctuations within four loops that can contact phosphosugars. (15)N NMR relaxation and peak heights suggest that domain 4 reorients slightly faster in solution than domains 1-3, and with a different principal axis of diffusion. This adds to the crystallographic evidence of domain 4 rotations in the enzyme, which were previously suggested to couple to reorientation of the intermediate, substrate binding, and product release.

Collective motions on ns-μs time scales are known to have a major impact on protein folding, stability, binding and enzymatic efficiency. It is also believed that these motions may have an important role in the early stages of prion protein misfolding and prion disease. In an effort to accurately characterize these motions and their potential influence on the misfolding and prion disease transmissibility we have conducted a combined analysis of molecular dynamic simulations and NMR-derived flexibility measurements over a diverse range of prion proteins. Using a recently developed numerical formalism, we have analyzed the essential collective dynamics (ECD) for prion proteins from 8 different species including human, cow, elk, cat, hamster, chicken, turtle and frog. We also compared the numerical results with flexibility profiles generated by the random coil index (RCI) from NMR chemical shifts. Prion protein backbone flexibility derived from experimental NMR data and from theoretical computations show strong agreement with each other, demonstrating that it is possible to predict the observed RCI profiles employing the numerical ECD formalism. Interestingly, flexibility differences in the loop between second beta strand (S2) and the second alpha helix (HB) appear to distinguish prion proteins from species that are susceptible to prion disease and those that are resistant. Our results show that the different levels of flexibility in the S2-HB loop in various species are predictable via the ECD method, indicating that ECD may be used to identify disease resistant variants of prion proteins, as well as the influence of prion proteins mutations on disease susceptibility or misfolding propensity.

Although highly conserved throughout evolution, the exact biological function of the prion protein is still unclear. In an effort to identify the potential biological functions of the prion protein we conducted a small-molecule screening assay using the Syrian hamster prion protein [shPrP(90-232)]. The screen was performed using a library of 149 water-soluble metabolites that are known to pass through the blood-brain barrier. Using a combination of 1D NMR, fluorescence quenching and surface plasmon resonance we identified thiamine (vitamin B1) as a specific prion ligand with a binding constant of ~60 μM. Subsequent studies showed that this interaction is evolutionarily conserved, with similar binding constants being seen for mouse, hamster and human prions. Various protein construct lengths, both with and without the unstructured N-terminal region in the presence and absence of copper, were examined. This indicates that the N-terminus has no influence on the protein's ability to interact with thiamine. In addition to thiamine, the more biologically abundant forms of vitamin B1 (thiamine monophosphate and thiamine diphosphate) were also found to bind the prion protein with similar affinity. Heteronuclear NMR experiments were used to determine thiamine's interaction site, which is located between helix 1 and the preceding loop. These data, in conjunction with computer-aided docking and molecular dynamics, were used to model the thiamine-binding pharmacophore and a comparison with other thiamine binding proteins was performed to reveal the common features of interaction.

GeNMR (GEnerate NMR structures) is a web server for rapidly generating accurate 3D protein structures using sequence data, NOE-based distance restraints and/or NMR chemical shifts as input. GeNMR accepts distance restraints in XPLOR or CYANA format as well as chemical shift files in either SHIFTY or BMRB formats. The web server produces an ensemble of PDB coordinates for the protein within 15-25 min, depending on model complexity and completeness of experimental restraints. GeNMR uses a pipeline of several pre-existing programs and servers to calculate the actual protein structure. In particular, GeNMR combines genetic algorithms for structure optimization along with homology modeling, chemical shift threading, torsion angle and distance predictions from chemical shifts/NOEs as well as ROSETTA-based structure generation and simulated annealing with XPLOR-NIH to generate and/or refine protein coordinates. GeNMR greatly simplifies the task of protein structure determination as users do not have to install or become familiar with complex stand-alone programs or obscure format conversion utilities. Tests conducted on a sample of 90 proteins from the BioMagResBank indicate that GeNMR produces high-quality models for all protein queries, regardless of the type of NMR input data. GeNMR was developed to facilitate rapid, user-friendly structure determination of protein structures via NMR spectroscopy. GeNMR is accessible at http://www.genmr.ca.

In this study we describe a novel approach to define structural domains and to characterize the local flexibility in both human and chicken prion proteins. The approach we use is based on a comprehensive theory of collective dynamics in proteins that was recently developed. This method determines the essential collective coordinates, which can be found from molecular dynamics trajectories via principal component analysis. Under this particular framework, we are able to identify the domains where atoms move coherently while at the same time to determine the local main-chain flexibility for each residue. We have verified this approach by comparing our results for the predicted dynamic domain systems with the computed main-chain flexibility profiles and the NMR-derived random coil indexes for human and chicken prion proteins. The three sets of data show excellent agreement. Additionally, we demonstrate that the dynamic domains calculated in this fashion provide a highly sensitive measure of protein collective structure and dynamics. Furthermore, such an analysis is capable of revealing structural and dynamic properties of proteins that are inaccessible to the conventional assessment of secondary structure. Using the collective dynamic simulation approach described here along with a high-temperature simulations of unfolding of human prion protein, we have explored whether locations of relatively low stability could be identified where the unfolding process could potentially be facilitated. According to our analysis, the locations of relatively low stability may be associated with the beta-sheet formed by strands S1 and S2 and the adjacent loops, whereas helix HC appears to be a relatively stable part of the protein. We suggest that this kind of structural analysis may provide a useful background for a more quantitative assessment of potential routes of spontaneous misfolding in prion proteins.

Protein motions play a critical role in many biological processes, such as enzyme catalysis, allosteric regulation, antigen-antibody interactions, and protein-DNA binding. NMR spectroscopy occupies a unique place among methods for investigating protein dynamics due to its ability to provide site-specific information about protein motions over a large range of time scales. However, most NMR methods require a detailed knowledge of the 3D structure and/or the collection of additional experimental data (NOEs, T1, T2, etc.) to accurately measure protein dynamics. Here we present a simple method based on chemical shift data that allows accurate, quantitative, site-specific mapping of protein backbone mobility without the need of a three-dimensional structure or the collection and analysis of NMR relaxation data. Further, we show that this chemical shift method is able to quantitatively predict per-residue RMSD values (from both MD simulations and NMR structural ensembles) as well as model-free backbone order parameters.

Essential collective dynamics is a promising and robust approach for analysing the slow motions of macromolecules from short molecular dynamics trajectories. In this study, an extension of the method to treat a collection of interacting protein molecules is presented. The extension is applied to investigate the effects of dimerization on the collective dynamics of ovine prion protein molecules in two different arrangements. Examination of the structural plasticity shows that aggregation has a restricting effect on the local mobility of the prion protein molecules in the interfacial regions. Domain motions of the two dimeric ovine prion protein conformations are distinctly different and can be related to interatomic correlations at the interface. Correlated motions are among the slow collective modes extensively analysed by considering both main-chain and side-chain atoms. Correlation maps reveal the existence of a vast network of dynamically correlated side groups, which extends beyond individual subunits via interfacial interconnections. The network is formed by a core of hydrophobic side chains surrounded by hydrophilic groups at the periphery. The relevance of these findings are discussed in the context of mutations associated with prion diseases. The binding free energy of the dimeric conformations is evaluated to probe their thermodynamic stability. The descriptions afforded by the analysis of the essential collective dynamics of the prion dimers are consistent with their binding free energies. The agreement validates the extension of the methodology and provides a means of interpreting the collective dynamics in terms of the thermodynamic stability of ovine prion proteins., Erratum published in Volume 80, Issue 9, page 2330, August 2012. Erratum DOI: 10.1002/prot.24139