Characterization of insoluble calcium alginates by solid-state NMR

Three series of 9 insoluble calcium alginate powders with different average calcium contents (1.5, 3.5 and 8%, w/w) are investigated by means of ¹³C solid-state NMR spectroscopy. The effect of the increased calcium content on the determination of the mannuronate (M) to guluronate (G) ratio from spectral deconvolution of the ¹³C CP/MAS spectra is discussed, and the variations observed are commented in function of possible structural modifications related to the interaction with the divalent cations. The possibility of using solid-state NMR spectroscopy for the quantification of the calcium content in unknown alginate samples is explored performing principal component analysis (PCA) of the spectra. The results obtained show that a clear separation of alginates with slightly different calcium content is possible. The proposed method relies on the sole use of the chemical shifts of the signals corresponding to pyranose carbons, suggesting that PCA of solid-state NMR data holds promises as a rapid and undestructive method for screening the calcium content of alginate-based materials with biomedical uses.     

Peak fitting in 2D <Superscript>1</Superscript>H–<Superscript>13</Superscript>C HSQC NMR spectra for metabolomic studies

A modified Lorentzian distribution function is used to model peaks in two-dimensional (2D) ¹H–¹³C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectra. The model fit is used to determine accurate chemical shifts from genuine signals in complex metabolite mixtures such as blood. The algorithm can be used to extract features from a set of spectra from different samples for exploratory metabolomics. First a reference spectrum is created in which the peak intensities are given by the median value over all samples at each point in the 2D spectra so that ¹H–¹³C correlations in any spectra are accounted for. The mathematical model provides a footprint for each peak in the reference spectrum, which can be used to bin the ¹H–¹³C correlations in each HSQC spectrum. The binned intensities are then used as variables in multivariate analyses and those found to be discriminatory are rapidly identified by cross referencing the chemical shifts of the bins with a database of ¹³C and ¹H chemical shift correlations from known metabolites.     

Use of optimized 1D TOCSY NMR for improved quantitation and metabolomic analysis of biofluids

One dimensional selective TOCSY experiments have been shown to be advantageous in providing improved data inputs for principle component analysis (PCA) (Sandusky and Raftery 2005a, b). Better subpopulation cluster resolution in the observed scores plots results from the ability to isolate metabolite signals of interest via the TOCSY based filtering approach. This report reexamines the quantitative aspects of this approach, first by optimizing the 1D TOCSY experiment as it relates to the measurement of biofluid constituent concentrations, and second by comparing the integration of 1D TOCSY read peaks to the bucket integration of 1D proton NMR spectra in terms of precision and accuracy. This comparison indicates that, because of the extensive peak overlap that occurs in the 1D proton NMR spectra of biofluid samples, bucket integrals are often far less accurate as measures of individual constituent concentrations than 1D TOCSY read peaks. Even spectral fitting approaches have proven difficult in the analysis of significantly overlapped spectral regions. Measurements of endogenous taurine made over a sample population of human urine demonstrates that, due to background signals from other constituents, bucket integrals of 1D proton spectra routinely overestimate the taurine concentrations and distort its variation over the sample population. As a result, PCA calculations performed using data matrices incorporating 1D TOCSY determined taurine concentrations produce better scores plot subpopulation cluster resolution.     

Prospects for resonance assignments in multidimensional solid-state NMR spectra of uniformly labeled proteins

Summary The feasibility of assigning the backbone ¹⁵N and ¹³C NMR chemical shifts in multidimensional magic angle spinning NMR spectra of uniformly isotopically labeled proteins and peptides in unoriented solid samples is assessed by means of numerical simulations. The goal of these simulations is to examine how the upper limit on the size of a peptide for which unique assignments can be made depends on the spectral resolution, i.e., the NMR line widths. Sets of simulated three-dimensional chemical shift correlation spectra for artificial peptides of varying length are constructed from published liquid-state NMR chemical shift data for ubiquitin, a well-characterized soluble protein. Resonance assignments consistent with these spectra to within the assumed spectral resolution are found by a numerical search algorithm. The dependence of the number of consistent assignments on the assumed spectral resolution and on the length of the peptide is reported. If only three-dimensional chemical shift correlation data for backbone ¹⁵N and ¹³C nuclei are used, and no residue-specific chemical shift information, information from amino acid side-chain signals, and proton chemical shift information are available, a spectral resolution of 1 ppm or less is generally required for a unique assignment of backbone chemical shifts for a peptide of 30 amino acid residues.     

Characterization of Metabolites in IntactStreptomyces citricolorCulture Supernatants Using High-Resolution Nuclear Magnetic Resonance and Directly Coupled High-Pressure Liquid Chromatography–Nuclear Magnetic Resonance Spectroscopy

A novel NMR spectroscopic approach to the direct biochemical characterization of bacterial culture broths is presented. A variety of one- and two-dimensional 1H NMR spectroscopic methods were used to characterize low-molecular-weight organic components of broth supernatants from cultures of Streptomyces citricolor. By applying 1H NMR spectroscopy to analyze whole, untreated culture supernatants, it was possible to identify and monitor simultaneously a range of media substrates and excreted metabolites. Identified metabolites include 2-phenylethylamine, trehalose, succinate, acetate, uridine, and aristeromycin, a secondary metabolite with antibiotic properties. Directly coupled HPLC-NMR spectroscopy was also applied to the analysis of broth supernatants for the first time, to aid spectral assignments, especially where signals were extensively overlapped in the 1H NMR spectra of the whole broth mixtures. Two-dimensional NMR methods such as 1H-1H correlation spectroscopy, 1H-13C heteronuclear single quantum correlation, and 1H-13C heteronuclear multiple bond correlation aided the structure elucidation and peak assignments of individual components in the mixtures by providing information on 1H-1H coupling networks and 13C chemical shifts. This work shows that high-resolution NMR spectroscopic methods provide a rapid and efficient means of investigating microbial metabolism directly without invasive or destructive sample pretreatment.     

Prediction of peak overlap in NMR spectra

Peak overlap is one of the major factors complicating the analysis of biomolecular NMR spectra. We present a general method for predicting the extent of peak overlap in multidimensional NMR spectra and its validation using both, experimental data sets and Monte Carlo simulation. The method is based on knowledge of the magnetization transfer pathways of the NMR experiments and chemical shift statistics from the Biological Magnetic Resonance Data Bank. Assuming a normal distribution with characteristic mean value and standard deviation for the chemical shift of each observable atom, an analytic expression was derived for the expected overlap probability of the cross peaks. The analytical approach was verified to agree with the average peak overlap in a large number of individual peak lists simulated using the same chemical shift statistics. The method was applied to eight proteins, including an intrinsically disordered one, for which the prediction results could be compared with the actual overlap based on the experimentally measured chemical shifts. The extent of overlap predicted using only statistical chemical shift information was in good agreement with the overlap that was observed when the measured shifts were used in the virtual spectrum, except for the intrinsically disordered protein. Since the spectral complexity of a protein NMR spectrum is a crucial factor for protein structure determination, analytical overlap prediction can be used to identify potentially difficult proteins before conducting NMR experiments. Overlap predictions can be tailored to particular classes of proteins by preparing statistics from corresponding protein databases. The method is also suitable for optimizing recording parameters and labeling schemes for NMR experiments and improving the reliability of automated spectra analysis and protein structure determination.     

Phosphorus-31 nuclear magnetic resonance of ethidium complexes with ribonucleic acid model systems and phenylalanine-accepting transfer ribonucleic acid

The temperature dependence of the 31P NMR spectra of the ethidium complexes with poly(A) X oligo(U) and the 31P spectra of phenylalanine tRNA (yeast) in various molar ratios of ethidium ion (Et) are presented. In the poly(A) X oligo(U) X Et complex, a new peak about 2.0 ppm downfield from the double-helix peak appears. We have assigned this peak to phosphates perturbed by ethidium. The chemical shift of this peak is consistent with the intercalation mode of binding and provides additional support for our hypothesis that 31P shifts are sensitive probes of phosphate ester conformations. The main effect of ethidium on the 31P spectra of tRNAPhe is the broadening of several of the scattered signals. These scattered signals are associated with phosphates involved in tertiary interactions. We propose that these broadened signals arise from phosphates near the Et binding site.     

Classification of commercial Catuaba samples by NMR, HPLC and chemometrics

For over a century, Catuaba has been used in Brazilian folk medicine as an aphrodisiac even though the identity of the plant material employed is often uncertain. The species recommended by the Brazilian Pharmacopeia is Anemopaegma arvense (Bignoniaceae), but many other plants, regionally known as Catuaba, are commercialised. Frequently, the quality control of such a complex system is based on chemical markers that do not supply a general idea of the system. With the advent of the metabolomics approach, a global analysis of samples becomes possible. It appears that (1)H-NMR is the most useful method for such application, since it can be used as a wide-spectrum chemical analysis technique. Unfortunately, the generated spectra is complex so a possible approach is to look at the metabolite profile as a whole using multivariate methods, for example, by application of principal component analysis (PCA). In the present paper, we describe for the first time a proton high-resolution magic angle spinning nuclear magnetic resonance ((1)H-HR-MAS NMR) method coupled with PCA for the metabolomic analysis of some commercial Catuaba samples, which provided a reduction in the time required for such analysis. A comparative study of HPLC, HR-MAS and liquid-NMR techniques is also reported.     

A general Monte Carlo/simulated annealing algorithm for resonance assignment in NMR of uniformly labeled biopolymers

We describe a general computational approach to site-specific resonance assignments in multidimensional NMR studies of uniformly ¹⁵N,¹³C-labeled biopolymers, based on a simple Monte Carlo/simulated annealing (MCSA) algorithm contained in the program MCASSIGN2. Input to MCASSIGN2 includes lists of multidimensional signals in the NMR spectra with their possible residue-type assignments (which need not be unique), the biopolymer sequence, and a table that describes the connections that relate one signal list to another. As output, MCASSIGN2 produces a high-scoring sequential assignment of the multidimensional signals, using a score function that rewards good connections (i.e., agreement between relevant sets of chemical shifts in different signal lists) and penalizes bad connections, unassigned signals, and assignment gaps. Examination of a set of high-scoring assignments from a large number of independent runs allows one to determine whether a unique assignment exists for the entire sequence or parts thereof. We demonstrate the MCSA algorithm using two-dimensional (2D) and three-dimensional (3D) solid state NMR spectra of several model protein samples (α-spectrin SH3 domain and protein G/B1 microcrystals, HET-s_218–289 fibrils), obtained with magic-angle spinning and standard polarization transfer techniques. The MCSA algorithm and MCASSIGN2 program can accommodate arbitrary combinations of NMR spectra with arbitrary dimensionality, and can therefore be applied in many areas of solid state and solution NMR.     

Recommended strategies for spectral processing and post-processing of 1D <Superscript>1</Superscript>H-NMR data of biofluids with a particular focus on urine

¹H NMR spectra from urine can yield information-rich data sets that offer important insights into many biological and biochemical phenomena. However, the quality and utility of these insights can be profoundly affected by how the NMR spectra are processed and interpreted. For instance, if the NMR spectra are incorrectly referenced or inconsistently aligned, the identification of many compounds will be incorrect. If the NMR spectra are mis-phased or if the baseline correction is flawed, the estimated concentrations of many compounds will be systematically biased. Furthermore, because NMR permits the measurement of concentrations spanning up to five orders of magnitude, several problems can arise with data analysis. For instance, signals originating from the most abundant metabolites may prove to be the least biologically relevant while signals arising from the least abundant metabolites may prove to be the most important but hardest to accurately and precisely measure. As a result, a number of data processing techniques such as scaling, transformation and normalization are often required to address these issues. Therefore, proper processing of NMR data is a critical step to correctly extract useful information in any NMR-based metabolomic study. In this review we highlight the significance, advantages and disadvantages of different NMR spectral processing steps that are common to most NMR-based metabolomic studies of urine. These include: chemical shift referencing, phase and baseline correction, spectral alignment, spectral binning, scaling and normalization. We also provide a set of recommendations for best practices regarding spectral and data processing for NMR-based metabolomic studies of biofluids, with a particular focus on urine.     

HIGH‐RESOLUTION MAGIC ANGLE SPINNING NMR ANALYSIS OF WHOLE CELLS OF CHAETOCEROS MUELLERI (BACILLARIOPHYCEAE) AND COMPARISON WITH 13C‐NMR AND DISTORTIONLESS ENHANCEMENT BY POLARIZATION TRANSFER 13C‐NMR ANALYSIS OF LIPOPHILIC EXTRACTS1

Lipid composition in extracted samples of Chaetoceros muelleri Lemmermann was studied with ¹³C‐NMR and distortionless enhancement by polarization transfer (DEPT) ¹³C‐NMR, resulting in well‐resolved ¹³C‐NMR spectra with characteristic resonance signals from carboxylic, olefinic, glyceryl, methylene, and methyl groups. The application of a DEPT pulse sequence aided in the assignment of methylene and methine groups. Resonance signals were compared with literature references, and signal assignment included important unsaturated fatty acids such as eicosapentaenoic and docosahexaenoic and also phospholipids and glycerols. Results from the extracted samples were used to assign resonance signals in a high‐resolution magic angle spinning (HR MAS) DEPT ¹³C spectrum from whole cells of C. muelleri. The NMR analysis on whole cells yielded equally good information on fatty acids and also revealed signals from carbohydrates and amino acids. Broad resonance signals and peak overlapping can be a problem in whole cell analysis, but we found that application of HR MAS gave a well‐resolved spectrum. The chemical shift of metabolites in an NMR spectrum depends on the actual environment of nuclei during analysis, and some differences could therefore be expected between extracted and whole cell samples. The shift differences were small, and assignment from analysis of lipophilic extract could be used to identify peaks in the whole cell spectrum. HR MAS ¹³C‐NMR therefore offers a possibility for broad‐range metabolic profiling directly on whole cells, simultaneously detecting metabolites that are otherwise not detected in the same analytical set up and avoiding tedious extraction procedures.     

High-resolution magic angle spinning 1H NMR analysis of whole cells of Thalassiosira pseudonana (Bacillariophyceae): Broad range analysis of metabolic composition and nutritional value

Chemical composition of the microalga Thalassiosira pseudonana Hasle & Heimdalwas studied with different proton nuclearmagnetic resonance (¹H NMR)techniques, and by comparing NMR spectrafrom extraction samples with a spectrumfrom a sample of whole cells we show thathigh-resolution magic angle spinning (HRMAS) ¹H NMR can be used for broadrange analysis of metabolic composition inmicroalgal whole cells. Signals fromimportant metabolites such aspolyunsaturated fatty acids (PUFAs)eicosapentaenoic (EPA) and docosahexaenoic(DHA) acids were seen in a ¹H NMRspectrum of lipophilic extract, andpossibly also signals from the carotenoidfucoxanthin. In a spectrum of hydrophilicextract we assigned signals to amino acidssuch as glutamine (Gln) and glutamic acid(Glu), carbohydrate and ATP. These findingswere compared to a spectrum of HR MAS¹H NMR analysis of whole cells, whereit was possible to find signals coincidentwith the different metabolites seen inspectra of the extraction samples. Sincethe position of resonance peaks in a NMRspectrum depends on the chemicalsurroundings of each atom at the time ofanalysis some peak shift differencesbetween extract and whole cell samplespectra may occur, but signal shifts werenot significantly different between theanalyses here. In addition, application ofHR MAS highly increased spectral resolutionin the complex whole cell sample. Wetherefore suggest that HR MAS ¹H NMRanalysis is a suitable analysis tool tostudy metabolic composition directly onwhole cells of microalgae, making itpossible to study a broad range ofmetabolites simultaneously without tediousextraction procedures.     

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications

MOTIVATION: The analysis of metabolic processes is becoming increasingly important to our understanding of complex biological systems and disease states. Nuclear magnetic resonance spectroscopy (NMR) is a particularly relevant technology in this respect, since the NMR signals provide a quantitative measure of the metabolite concentrations. However, due to the complexity of the spectra typical of biological samples, the demands of clinical and high-throughput analysis will only be fully met by a system capable of reliable, automatic processing of the spectra. An initial step in this direction has been taken by Targeted Profiling (TP), employing a set of known and predicted metabolite signatures fitted against the signal. However, an accurate fitting procedure for (1)H NMR data is complicated by shift uncertainties in the peak systems caused by measurement imperfections. These uncertainties have a large impact on the accuracy of identification and quantification and currently require compensation by very time consuming manual interactions. Here, we present an approach, termed Extended Targeted Profiling (ETP), that estimates shift uncertainties based on a genetic algorithm (GA) combined with a least squares optimization (LSQO). The estimated shifts are used to correct the known metabolite signatures leading to significantly improved identification and quantification. In this way, use of the automated system significantly reduces the effort normally associated with manual processing and paves the way for reliable, high-throughput analysis of complex NMR spectra. RESULTS: The results indicate that using simultaneous shift uncertainty correction and least squares fitting significantly improves the identification and quantification results for (1)H NMR data in comparison to the standard targeted profiling approach and compares favorably with the results obtained by manual expert analysis. Preservation of the functional structure of the NMR spectra makes this approach more realistic than simple binning strategies.     

CAMRA: Chemical shift based computer aided protein NMR assignments

A suite of programs called CAMRA (Computer Aided Magnetic Resonance Assignment) has been developed for computer assisted residue-specific assignments of proteins. CAMRA consists of three units: ORB, CAPTURE and PROCESS. ORB predicts NMR chemical shifts for unassigned proteins using a chemical shift database of previously assigned homologous proteins supplemented by a statistically derived chemical shift database in which the shifts are categorized according to their residue, atom and secondary structure type. CAPTURE generates a list of valid peaks from NMR spectra by filtering out noise peaks and other artifacts and then separating the derived peak list into distinct spin systems. PROCESS combines the chemical shift predictions from ORB with the spin systems identified by CAPTURE to obtain residue specific assignments. PROCESS ranks the top choices for an assignment along with scores and confidence values. In contrast to other auto-assignment programs, CAMRA does not use any connectivity information but instead is based solely on matching predicted shifts with observed spin systems. As such, CAMRA represents a new and unique approach for the assignment of protein NMR spectra. CAMRA will be particularly useful in conjunction with other assignment methods and under special circumstances, such as the assignment of flexible regions in proteins where sufficient NOE information is generally not available. CAMRA was tested on two medium-sized proteins belonging to the chemokine family. It was found to be effective in predicting the assignment providing a database of previously assigned proteins with at least 30% sequence identity is available. CAMRA is versatile and can be used to include and evaluate heteronuclear and three-dimensional experiments.     

Effect of distortions in the deoxyribose phosphate backbone conformation of duplex oligodeoxyribonucleotide dodecamers containing GT, GG, GA, AC, and GU base-pair mismatches on 31P NMR spectra

We have previously suggested that variations in the 31P chemical shifts of individual phosphates in duplex oligonucleotides are attributable to torsional angle changes in the deoxyribose phosphate backbone. This hypothesis is not directly supported by analysis of the 1H/31P two-dimensional J-resolved spectra of a number of mismatch dodecamer oligonucleotide duplexes including the following sequences: d-(CGTGAATTCGCG), d(CGUGAATTCGCG), d(CGGGAATTCGCG), d(CGAGAATTCGCG), and d(CGCGAATTCACG). The 31P NMR signals of the dodecamer mismatch duplexes were assigned by 2D 1H/31P pure absorption phase constant time (PAC) heteronuclear correlation spectra. From the assigned H3' and H4' signals, the 31P signals of the base-pair mismatch dodecamers were identified. JH3'-P coupling constants for each of the phosphates of the dodecamers were obtained from 1H/31P J-resolved selective proton flip 2D spectra. By use of a modified Karplus relationship, the C4'-C3'-O3'-P torsional angles (epsilon) were obtained. JH3'-P coupling constants were measured for many of the oligonucleotides as a function of temperature. There exists a good linear correlation between 31P chemical shifts and the epsilon torsional angle. This correlation can be further extended to the C3'-O3'-P-O5' torsional angle (zeta) by using a linear relationship between epsilon and zeta obtained from crystal structure studies. The 31P chemical shifts follow the general observation that the more internally the phosphate is located within the oligonucleotide sequence, the more upfield the 31P resonance occurs. In addition, 31P chemical shifts show sequence- and site-specific variations. Analysis of the backbone torsional angle variations from the coupling constant analysis has provided additional information regarding the origin of these variations in 31P chemical shifts.     

Measurement of carbonyl chemical shifts of excited protein states by relaxation dispersion NMR spectroscopy: comparison between uniformly and selectively 13C labeled samples

Carr-Purcell-Meiboom-Gill (CPMG) relaxation dispersion nuclear magnetic resonance (NMR) spectroscopy has emerged as a powerful method for quantifying chemical shifts of excited protein states. For many applications of the technique that involve the measurement of relaxation rates of carbon magnetization it is necessary to prepare samples with isolated (13)C spins so that experiments do not suffer from magnetization transfer between coupled carbon spins that would otherwise occur during the CPMG pulse train. In the case of (13)CO experiments however the large separation between (13)CO and (13)C(alpha) chemical shifts offers hope that robust (13)CO dispersion profiles can be recorded on uniformly (13)C labeled samples, leading to the extraction of accurate (13)CO chemical shifts of the invisible, excited state. Here we compare such chemical shifts recorded on samples that are selectively labeled, prepared using [1-(13)C]-pyruvate and NaH(13)CO(3,) or uniformly labeled, generated from (13)C-glucose. Very similar (13)CO chemical shifts are obtained from analysis of CPMG experiments recorded on both samples, and comparison with chemical shifts measured using a second approach establishes that the shifts measured from relaxation dispersion are very accurate.     

13 C NMR Chemical shifts and carbon-proton coupling constants of some furocoumarins and furochromones

The ¹³C NMR spectra of a variety of furocoumarins, dihydrofurocoumarins and furochromones are reported. The signals were assigned using carbon-proton coupling constants, ring annullation shifts, nuclear Overhauser effect considerations and shift effects caused by monothioester formation. Substituent effects on ¹³C chemical shifts and carbon-proton coupling constants are discussed. Methoxyl induced shifts of 5- and 8-substituted furocoumarins are additive, but their effects cannot be transferred to the furochromone system.     

Probabilistic Identification of Spin Systems and their Assignments including Coil–Helix Inference as Output (PISTACHIO)

We present a novel automated strategy (PISTACHIO) for the probabilistic assignment of backbone and sidechain chemical shifts in proteins. The algorithm uses peak lists derived from various NMR experiments as input and provides as output ranked lists of assignments for all signals recognized in the input data as constituting spin systems. PISTACHIO was evaluate00000000d by comparing its performance with raw peak-picked data from 15 proteins ranging from 54 to 300 residues; the results were compared with those achieved by experts analyzing the same datasets by hand. As scored against the best available independent assignments for these proteins, the first-ranked PISTACHIO assignments were 80–100% correct for backbone signals and 75–95% correct for sidechain signals. The independent assignments benefited, in a number of cases, from structural data (e.g. from NOESY spectra) that were unavailable to PISTACHIO. Any number of datasets in any combination can serve as input. Thus PISTACHIO can be used as datasets are collected to ascertain the current extent of secure assignments, to identify residues with low assignment probability, and to suggest the types of additional data needed to remove ambiguities. The current implementation of PISTACHIO, which is available from a server on the Internet, supports input data from 15 standard double- and triple-resonance experiments. The software can readily accommodate additional types of experiments, including data from selectively labeled samples. The assignment probabilities can be carried forward and refined in subsequent steps leading to a structure. The performance of PISTACHIO showed no direct dependence on protein size, but correlated instead with data quality (completeness and signal-to-noise). PISTACHIO represents one component of a comprehensive probabilistic approach we are developing for the collection and analysis of protein NMR data.Electronic Supplementary Material Electronic Supplementary material is available for this article at and accessible for authorised users.     

Application of ultra-high magnetic field for saccharide molecules: 1H NMR spectra of 6-O-alpha-D-glucopyranosyl-cyclomaltoheptaose and -cyclomaltohexaose

1H NMR spectra of G1-alpha-CD and G1-beta-CD were recorded using a spectrometer equipped with a 21.6 T magnet. An ultra-high magnetic field was effective for detecting 1H NMR signals with a small difference in chemical shifts. Introducing a glucosyl group onto CDs as a branch caused deformation of equilibrated 1H signals of cyclodextrin. Particularly, 1H signals in branched glucose were shifted greatly.     

1H NMR spectra of branched-chain cyclomaltohexaoses (alpha-cyclodextrins)

The 1H NMR spectra of seven branched alpha-cyclodextrins (alpha-CDs) were observed and analyzed in detail. They were compared with spectra of alpha-CD and amylose. Although these branched alpha-CDs consist only of alpha-D-glucose with the same alpha-(1-->4) O-glucosyl binding, aside from one exception, differences in chemical shifts of corresponding signals were significantly large. Especially, differences in the chemical shift in anomeric protons were considerably large. Subtle differences in glucosyl binding directly influences chemical shifts of these protons because anomeric protons are located adjacent to the glucosyl binding sites.