Average Chemical Shift Research Articles

Complex F,Cl Apatite Solid Solution Investigated Using Multinuclear Solid-State NMR Methods

A series of synthetic F,Cl apatites with low hydroxyl content was investigated using 31P, 19F, and 35Cl solid-state magic angle spinning (MAS) NMR spectroscopic methods. 31P single-pulse (SP) NMR spectra show that for each composition the phosphate 31P chemical shift depends on occupancy of the nearest anion site for each composition. Overall the average chemical shift deviates from linear across the F/Cl series. For intermediate compositions 19F SP NMR spectra reveal complex, broadened spectral profiles that are not correlated to composition. 19F{35Cl} TRAPDOR results indicate that the spectral profiles do not reflect occupancy of F and Cl in adjacent column anion sites. We propose that the complex 19F lineshapes are principally due to broad distributions of 19F–Ca distances arising from displacement of F along the anion channel. Approximate fluorine atomic positions are estimated based on 19F chemical shifts of alkaline earth fluoride salts, and the results are in good accord with those proposed for int...

Detection of intermolecular transferred‐NOE interactions in small and medium size protein complexes: RANTES complexed with a CCR5 N‐terminal peptide

NMR is a powerful tool for studying structural details of protein/peptide complexes exhibiting weak to medium binding (KD > 10 μm). However, it has been assumed that intermolecular nuclear Overhauser effect (NOE) interactions are difficult to observe in such complexes. We demonstrate that intermolecular NOEs can be revealed by combining the 13 C-edited/13 C-filtered experiment with the transferred NOE effect (TRNOE). Due to the TRNOE phenomenon, intermolecular NOE cross peaks are characterized by both the chemical shifts (CSs) of the protein protons and the average CSs of the peptide protons, which are dominated by the CSs of the protons of the free peptide. Previously, the TRNOE phenomenon was used almost exclusively to investigate the conformation of small ligands bound to large biomolecules. Here, we demonstrate that TRNOE can be extended to enable the study of intermolecular interactions in small- and medium-sized protein complexes. We used the 13 C-edited/13 C-filtered TRNOE experiment to study the interactions of the chemokine regulated upon activation, normal T cell, expressed and secreted (RANTES) with a 27-residue peptide, containing two sulfotyrosine residues, representing the N-terminal segment of the CCR5 receptor ((Nt-CCR5(1-27). The TRNOE phenomenon led to more than doubling of the signal-to-noise ratios (SNRs) for the intermolecular NOEs observed in the 13 C-edited/13 C-filtered experiment for the 11.5-kDa monomeric RANTES/Nt-CCR5(1-27) complex. An even better improvement in the SNR was achieved with dimeric Nt-CCR5(1-27)/RANTES (23 kDa), especially in comparison with the spectra measured with a 1 : 1 protein to peptide ratio. In principle, the isotope-edited/isotope-filtered TRNOE spectrum can discern all intermolecular interactions involving nonexchangeable protons in the complex.

Effect of positional isomerism and vanadium substitution on 51V magic angle spinning NMR Spectra Of Wells-Dawson polyoxotungstates

We examined the positional isomerism and vanadium substitution on the 51V magic angle spinning NMR spectra of potassium salts of vanadium-substituted polyoxotungstates of the Wells-Dawson series. NMR parameters of this class of catalytically active polyoxotungstates effect of are reported. Multiple species, indicative of differences in the local environment at the substitution sites, are observed in solid-state NMR spectra of the di- and tri- substituted complexes in contrast to solution NMR spectra, where single average chemical shift was observed. The quadrupolar and chemical shift anisotropy parameters depend strongly on the position and the degree of the vanadium substitution into the oxoanion core establishing 51V SATRAS NMR spectroscopy as a sensitive probe of the local electronic environment in these catalytically active solids.

Identifying anticancer peptides by using improved hybrid compositions.

Cancer is one of the main causes of threats to human life. Identification of anticancer peptides is important for developing effective anticancer drugs. In this paper, we developed an improved predictor to identify the anticancer peptides. The amino acid composition (AAC), the average chemical shifts (acACS) and the reduced amino acid composition (RAAC) were selected to predict the anticancer peptides by using the support vector machine (SVM). The overall prediction accuracy reaches to 93.61% in jackknife test. The results indicated that the combined parameter was helpful to the prediction for anticancer peptides.

High temperature (35)Cl nuclear magnetic resonance study of the LiCl-KCl system and the effect of CeCl3 dissolution.

This paper examines the dynamics of the LiCl-KCl system over a range of temperatures in order to understand the local structure surrounding chlorine, which is the common ion in these systems, during molten salt pyro-processing. Chlorine-35 nuclear magnetic resonance (NMR) is sensitive to the local environments of the resonant nuclei and their motion on a diffusive timescale. Thus, it is a good probe of the atomic scale processes controlling the viscosities, diffusivities and conductivities of these molten salts. The average isotropic chemical shifts (((35)Cl)δ) and spin-lattice relaxation times (T1) of (35)Cl in (Li,K)Cl salt mixtures have been obtained over a compositional range of 0-100 mol% KCl with an interval of 10 mol% using high temperature nuclear magnetic resonance (NMR) spectroscopy from room temperature up to 890 °C. The ((35)Cl)δ in the two end member salts are consistent with the cation-anion radius ratio as previously measured on the solid halides and the average radius ratio of cation to anion, can be used to explain the variation of ((35)Cl)δ with composition. The quadrupolar interaction is found to be responsible for the spin-lattice relaxation of the (35)Cl, and the activation energies for T1 relaxation have been obtained for all compositions. The measured T1 ((35)Cl) activation energies do not vary linearly with composition and peak at 50% KCl, which also coincides with the Chemla point for this system. They also are in good agreement with the values from equivalent conductivity measurements. To investigate the response of the system to solutes, 8 wt% of CeCl3 was added to the pure LiCl as a surrogate actinide. The shift induced was 120 ppm and the activation energy for the T1 ((35)Cl) increased by a factor of four. This is a promising preliminary result for probing the effect of actinide dissolution on the dynamics of these pyro-processing salts.

The recognition of multi-class protein folds by adding average chemical shifts of secondary structure elements

The recognition of protein folds is an important step in the prediction of protein structure and function. Recently, an increasing number of researchers have sought to improve the methods for protein fold recognition. Following the construction of a dataset consisting of 27 protein fold classes by Ding and Dubchak in 2001, prediction algorithms, parameters and the construction of new datasets have improved for the prediction of protein folds. In this study, we reorganized a dataset consisting of 76-fold classes constructed by Liu et al. and used the values of the increment of diversity, average chemical shifts of secondary structure elements and secondary structure motifs as feature parameters in the recognition of multi-class protein folds. With the combined feature vector as the input parameter for the Random Forests algorithm and ensemble classification strategy, we propose a novel method to identify the 76 protein fold classes. The overall accuracy of the test dataset using an independent test was 66.69%; when the training and test sets were combined, with 5-fold cross-validation, the overall accuracy was 73.43%. This method was further used to predict the test dataset and the corresponding structural classification of the first 27-protein fold class dataset, resulting in overall accuracies of 79.66% and 93.40%, respectively. Moreover, when the training set and test sets were combined, the accuracy using 5-fold cross-validation was 81.21%. Additionally, this approach resulted in improved prediction results using the 27-protein fold class dataset constructed by Ding and Dubchak.

Natural abundance 17O nuclear magnetic resonance and computational modeling studies of lithium based liquid electrolytes

Natural abundance 17O NMR measurements were conducted on electrolyte solutions consisting of Li[CF3SO2NSO2CF3] (LiTFSI) dissolved in the solvents of ethylene carbonate (EC), propylene carbonate (PC), ethyl methyl carbonate (EMC), and their mixtures at various concentrations. It was observed that 17O chemical shifts of solvent molecules change with the concentration of LiTFSI. The chemical shift displacements of carbonyl oxygen are evidently greater than those of ethereal oxygen, strongly indicating that Li+ ion is coordinated with carbonyl oxygen rather than ethereal oxygen. To understand the detailed molecular interaction, computational modeling of 17O chemical shifts was carried out on proposed solvation structures. By comparing the predicted chemical shifts with the experimental values, it is found that a Li+ ion is coordinated with four double bond oxygen atoms from EC, PC, EMC and TFSI− anion. In the case of excessive amount of solvents of EC, PC and EMC the Li+ coordinated solvent molecules are undergoing quick exchange with bulk solvent molecules, resulting in average 17O chemical shifts. Several kinds of solvation structures are identified, where the proportion of each structure in the liquid electrolytes investigated depends on the concentration of LiTFSI.

Application of data mining tools for classification of protein structural class from residue based averaged NMR chemical shifts

The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for protein structural information that is directly related to function. Nuclear magnetic resonance (NMR) provides powerful means to determine three-dimensional structures of proteins in the solution state. However, translation of the NMR spectral parameters to even low-resolution structural information such as protein class requires multiple time consuming steps. In this paper, we present an unorthodox method to predict the protein structural class directly by using the residue's averaged chemical shifts (ACS) based on machine learning algorithms. Experimental chemical shift information from 1491 proteins obtained from Biological Magnetic Resonance Bank (BMRB) and their respective protein structural classes derived from structural classification of proteins (SCOP) were used to construct a data set with 119 attributes and 5 different classes. Twenty four different classification schemes were evaluated using several performance measures. Overall the residue based ACS values can predict the protein structural classes with 80% accuracy measured by Matthew correlation coefficient. Specifically protein classes defined by mixed αβ or small proteins are classified with >90% correlation. Our results indicate that this NMR-based method can be utilized as a low-resolution tool for protein structural class identification without any prior chemical shift assignments.

The C-terminal domain of eukaryotic acidic ribosomal P2 proteins is intrinsically disordered with conserved structural propensities.

The P2 protein (equivalent of L7/L12 in prokaryotes), a member of the ribosomal stalk in eukaryotes, is highly conserved, particularly its C-terminal domain. In order to understand the sequence-structure-function relationships in eukaryotic C-terminal stretches, about which nothing is known at the moment, we have investigated here, the structural characteristics of these domains of P2 proteins from three different species, namely, human, Plasmodium falciparum, and Toxoplasma gondii; the sequence homology among these is 70% although sequence identity is only 36%. About 50 amino acids of the C-terminal domains of P2 from the three species were expressed and purified. Gel filtration studies indicated peaks for both monomer and oligomer at milimolar concentrations and also suggested monomer-multimer equilibrium. Circular Dichroism showed that this domain does not have stable secondary structures. (1)H-(15)N HSQC spectra in every case showed one set of requisite number of peaks as per the sequence. This indicated that there is rapid multimer-monomer equilibrium in solution and the observed peaks which originate from the monomer reflect average chemical shifts. The spectral dispersion in all the cases is narrow, although there are noticeable differences in the three proteins. Detailed NMR investigations revealed that this protein domain is intrinsically disordered although there are short segments with preferred secondary structural propensities at similar places along the sequence. This may suggest that the sequence is selected in evolution to impart disorder, and thereby accord conformational adaptability.

Application of Machine Learning Method in Genomics and Proteomics

With the avalanche of genomic and proteomic data generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively analyzing and predicting the structure, function, and other properties of DNA and protein. The machine learning methods have become an important strategy for the discovery of potential knowledge in genomics and proteomics. Researches in recent years have shown tremendous advances in the properties prediction of DNA fragments and protein sequences by various pattern recognition methods. These techniques provide economical and timesaving solutions for identifying the properties of DNA and protein. This special issue was hosted for the recent development of the application of machine learning methods in genomics and proteomics. In this special issue, five works focused on the protein classification. How to extract key features from a protein was a key step in the discrimination of protein class. B. Liu et al. proposed to use Position-Specific Score Matrix (PSSM) and Accessible Surface Area (ASA) to formulate protein samples. The hidden Markov support vector machine (HM-SVM) was employed to predict protein binding site. Simulation in fivefold cross-validation on a benchmark dataset including 1124 protein chains showed that their method is more accurate for protein binding site prediction than some state-of-the art methods. This method can also be applied in DNA binding site, vitamin binding site, and posttranslational modification of proteins. Based on chemical shift (CS) information derived from nuclear magnetic resonance (NMR), F. Yonge proposed a novel feature to predict protein supersecondary structures. The quadratic discriminant (QD) analysis was selected as the prediction algorithm. Overall accuracy in threefold cross-validation is 77.3% for predicting four types of supersecondary structures. According to the concept of pseudo amino acids, G.-L. Fan et al. proposed the average chemical shifts (ACS) composition and established an online webserver called acACS which was calculated from average chemical shift information and protein secondary structure. By using SVM as the classification algorithm, the acACS was used in the discrimination between acidic and alkaline enzymes and between bioluminescent and nonbioluminescent proteins. Encouraging results were achieved. The protein secondary structure, structure class, and disorder region can be predicted using the AC-based method. L. Nanni et al. proposed to combine different features to improve protein prediction. These features include amino acids composition, PSSM, and substitution matrix representation (SMR). Each feature is used to train a separate SVM. Total of 15 benchmark datasets were used to evaluate the performance of their proposed method. Comparative results show that the PSSM always produces good accuracies. However, no single descriptor is superior to all others across all test datasets. The major contribution in this paper is to propose an ensemble of classifiers for sequence-based protein classification. H. Lin et al. briefly reviewed the development of ion channel prediction using machine learning method. They initially introduced how to construct a valid and objective benchmark dataset to train and test the predictor. Subsequently, the mathematical descriptors were presented to formulate the ion channel sequences. Moreover, two feature selection techniques on how to optimize feature set were described. Finally, the support vector machine was suggested performing classification. The methods introduced in that review can be generalized into other protein prediction fields as well. The paper from P. Feng et al. was the unique work focused on DNA prediction using machine learning method. They proposed a novel descriptor called pseudo K-tuple nucleotide composition (PseKNC) to formulate the DNA sequences. The feature is calculated from K-tuple nucleotide composition and the structural correlation of DNA dinucleotides. Subsequently, the SVM was used to predict DNase I hypersensitive sites. The jackknife cross-validated accuracy is 83%, which is competitive with that of the existing method. This new descriptor can also be widely used in DNA regulatory elements prediction. Hao Lin Wei Chen Ramu Anandakrishnan Dariusz Plewczynski

Understanding cage effects in imidazolium ionic liquids by 129Xe NMR: MD simulations and relativistic DFT calculations.

(129)Xe NMR has been recently employed to probe the local structure of ionic liquids (ILs). However, no theoretical investigation has been yet reported addressing the problem of the dependence of the chemical shift of xenon on the cage structure of the IL. Therefore, we present here a study of the chemical shift of (129)Xe in two ionic liquids, [bmim][Cl] and [bmim][PF6], by a combination of classical MD simulations and relativistic DFT calculations of the xenon shielding constant. The bulk structure of the two ILs is investigated by means of the radial distribution functions, paying special attention to the local structure, volume, and charge distribution of the cage surrounding the xenon atom. Relativistic DFT calculations, based on the ZORA formalism, on clusters extracted from the trajectory files of the two systems, yield an average relative chemical shift in good agreement with the experimental data. Our results demonstrate the importance of the cage volume and the average charge surrounding the xenon nucleus in the IL cage as the factors determining the effective shielding.

Discriminating protein structure classes by incorporating Pseudo Average Chemical Shift to Chou's general PseAAC and Support Vector Machine

Proteins control all biological functions in living species. Protein structure is comprised of four major classes including all-α class, all-β class, α+β, and α/β. Each class performs different function according to their nature. Owing to the large exploration of protein sequences in the databanks, the identification of protein structure classes is difficult through conventional methods with respect to cost and time. Looking at the importance of protein structure classes, it is thus highly desirable to develop a computational model for discriminating protein structure classes with high accuracy. For this purpose, we propose a silco method by incorporating Pseudo Average Chemical Shift and Support Vector Machine. Two feature extraction schemes namely Pseudo Amino Acid Composition and Pseudo Average Chemical Shift are used to explore valuable information from protein sequences. The performance of the proposed model is assessed using four benchmark datasets 25PDB, 1189, 640 and 399 employing jackknife test. The success rates of the proposed model are 84.2%, 85.0%, 86.4%, and 89.2%, respectively on the four datasets. The empirical results reveal that the performance of our proposed model compared to existing models is promising in the literature so far and might be useful for future research.

Neighboring Residue Effects in Terminally Blocked Dipeptides: Implications for Residual Secondary Structures in Intrinsically Unfolded/Disordered Proteins

For nuclear magnetic resonance (NMR)-based protein structure determinations, the random coil chemical shifts are very important because the secondary and tertiary protein structure predictions become possible by examining deviations of measured chemical shifts from those reference chemical shift values. In addition, neighboring residue effects on chemical shifts and J-coupling constants are crucial in understanding the nature of conformational propensities exhibited by unfolded or intrinsically disordered proteins. We recently reported the 1D NMR results for a complete set of terminally blocked dipeptides (Oh KI, Jung YS, Hwang GS, Cho M. J Biomol NMR 2012;53:25-41), but the NMR resonance assignments were not possible so that the average chemical shifts and J-coupling constants were only considered. In the present work, to thoroughly investigate the neighboring residue effects and random coil chemical shifts we extend the previous studies with 2D NMR, and measured all the (3) J(HNHα) values and H(α) and H(N) chemical shifts of the same set of terminally blocked dipeptides that are free from structural effects like secondary structure, hydrogen-bond, long-range backbone, and side-chain interactions. In particular, the preceding and following residue effects on amino-acid backbone conformational propensities are revealed and directly compared with previous works on either short peptides or empirical chemical shift database.

AcACS: improving the prediction accuracy of protein subcellular locations and protein classification by incorporating the average chemical shifts composition.

The chemical shift is sensitive to changes in the local environments and can report the structural changes. The structure information of a protein can be represented by the average chemical shifts (ACS) composition, which has been broadly applied for enhancing the prediction accuracy in protein subcellular locations and protein classification. However, different kinds of ACS composition can solve different problems. We established an online web server named acACS, which can convert secondary structure into average chemical shift and then compose the vector for representing a protein by using the algorithm of auto covariance. Our solution is easy to use and can meet the needs of users.

Protein structure validation and refinement using amide proton chemical shifts derived from quantum mechanics.

We present the ProCS method for the rapid and accurate prediction of protein backbone amide proton chemical shifts - sensitive probes of the geometry of key hydrogen bonds that determine protein structure. ProCS is parameterized against quantum mechanical (QM) calculations and reproduces high level QM results obtained for a small protein with an RMSD of 0.25 ppm (r = 0.94). ProCS is interfaced with the PHAISTOS protein simulation program and is used to infer statistical protein ensembles that reflect experimentally measured amide proton chemical shift values. Such chemical shift-based structural refinements, starting from high-resolution X-ray structures of Protein G, ubiquitin, and SMN Tudor Domain, result in average chemical shifts, hydrogen bond geometries, and trans-hydrogen bond (h3 JNC') spin-spin coupling constants that are in excellent agreement with experiment. We show that the structural sensitivity of the QM-based amide proton chemical shift predictions is needed to obtain this agreement. The ProCS method thus offers a powerful new tool for refining the structures of hydrogen bonding networks to high accuracy with many potential applications such as protein flexibility in ligand binding.

MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification

Mitochondrial protein of Plasmodium falciparum is an important target for anti-malarial drugs. Experimental approaches for detecting mitochondrial proteins are costly and time consuming. Therefore, MitProt-Pred is developed that utilizes Bi-profile Bayes, Pseudo Average Chemical Shift, Split Amino Acid Composition, and Pseudo Amino Acid Composition based features of the protein sequences. Hybrid feature space is also developed by combining different individual feature spaces. These feature spaces are learned and exploited through SVM based ensemble. MitProt-Pred achieved significantly improved prediction performance for two standard datasets. We also developed the score level ensemble, which outperforms the feature level ensemble.

Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou's pseudo amino acid composition

Bioluminescent proteins are highly sensitive optical reporters for imaging in live animals; they have been extensively used in analytical applications in intracellular monitoring, genetic regulation and detection, and immune and binding assays. In this work, we systematically analyzed the sequence and structure information of 199 bioluminescent and nonbioluminescent proteins, respectively. Based on the results, we presented a novel method called auto covariance of averaged chemical shift (acACS) for extracting structure features from a sequence. A classifier of support vector machine (SVM) fusing increment of diversity (ID) was used to distinguish bioluminescent proteins from nonbioluminescent proteins by combining dipeptide composition, reduced amino acid composition, evolutionary information, and acACS. The overall prediction accuracy evaluated by jackknife validation reached 82.16%. This result was better than that obtained by other existing methods. Improvement of the overall prediction accuracy reached up to 5.33% higher than those of the SVM and auto covariance of sequential evolution information by 10-fold cross-validation. The acACS algorithm also outperformed other feature extraction methods, indicating that our approach is better than other existing methods in the literature.

Predicting acidic and alkaline enzymes by incorporating the average chemical shift and gene ontology informations into the general form of Chou's PseAAC

Knowledge of the adaptation mechanism of enzymes to extreme pH values and distinguishing them from one another are necessary in the proteomics field, and would help in the drug design of stable enzymes. In this work, we have systematically analyzed the information of 105 acidic and 112 alkaline enzymes, and propose an approach for distinguishing acidic enzymes from alkaline enzymes by combining the amino acid composition, reduced amino acid composition, gene ontology, evolutionary information, and auto covariance of averaged chemical shift (acACS). The overall prediction accuracy is 94.01% by 10-fold cross-validation using the algorithm of support vector machine. This result is better than that obtained by other existing methods. The improvement of the overall prediction accuracy reaches up to 3.3% higher than those of the random forest algorithm and secondary structure amino acid composition. The acACS performance is excellent, indicating that our approach is better than other existing methods in the literature. A user-friendly web-server pred-enzymes for predicting acidic and alkaline enzymes has been established, which is accessible to the public.

Volumetric properties underlying ligand binding in a monomeric hemoglobin: A high-pressure NMR study

The 2/2 hemoglobin of the cyanobacterium Synechococcus sp. PCC 7002, GlbN, coordinates the heme iron with two histidines and exists either with a b heme or with a covalently attached heme. The binding of exogenous ligands displaces the distal histidine and induces a conformational rearrangement involving the reorganization of internal void volumes. The formation of passageways within the resulting conformation is thought to facilitate ligand exchange and play a functional role. Here we monitored the perturbation induced by pressure on the ferric bis-histidine and cyanide-bound states of GlbN using 1H–15N HSQC NMR spectroscopy. We inspected the outcome with a statistical analysis of 170 homologous 2/2 hemoglobin sequences. We found that the compression landscape of GlbN, as represented by the variation of an average chemical shift parameter, was highly sensitive to ligand swapping and heme covalent attachment. Stabilization of rare conformers was observed at high pressures and consistent with cavity redistribution upon ligand binding. In all states, the EF loop was found to be exceptionally labile to pressure, suggesting a functional role as a semi-flexible hinge between the adjacent helices. Finally, coevolved clusters presented a common pattern of compensating pressure responses. The high-pressure dissection combined with protein sequence analysis established locations with volumetric signatures relevant to residual communication of 2/2 hemoglobins. This article is part of a Special Issue entitled: Oxygen Binding and Sensing Proteins.

Determination of the position of the N-O function in substituted pyrazine N-oxides by chemometric analysis of carbon-13 nuclear magnetic resonance data

Investigations were carried out applying NMR spectroscopy for the unambiguous determination of the position of the N-oxide function in a set of 2-substituted pyrazine N-oxides synthesized in our group. Applying chemometric techniques of multivariate analysis to the 13C NMR chemical shifts data set, useful relationships for identifying the position of the N-oxide group relative to the substituent were unraveled. The relationships obtained were rationalized in terms of the molecular structures and refined. As a result, an index of N-oxidation (INOx) was defined, computed simply contrasting the average 13C NMR chemical shifts of each pair of carbon atoms bonded to a nitrogen atom. The effect of the substituent was included through a factor x (subscript of INO) close to unity, multiplying the average containing the substituted carbon atom. The approach was successful in recognizing the position of the N-oxide in all the cases studied, as revealed by the sign of INOx (positive for 1-N-oxides and negative for 4-N-oxides). The scope of the methodology was further tested using the 13C NMR chemical shifts of disubstituted pyrazine N-oxides from the literature data.