RESCUE: An artificial neural network tool for the NMR spectral assignment of proteins

The assignment of the ¹H spectrum of a protein or a polypeptide is the prerequisite for advanced NMR studies. We present here an assignment tool based on the artificial neural network technology, which determines the type of the amino acid from the chemical shift values observed in the ¹ H spectrum. Two artificial neural networks have been trained and extensively tested against a non-redundant subset of the BMRB chemical shift data bank [Seavey, B.R. et al. (1991) J. Biomol. NMR, 1, 217–236]. The most promising of the two accomplishes the analysis in two steps, grouping related amino acids together. It presents a mean rate of success above 80% on the test set. The second network tested separates down to the single amino acid; it presents a mean rate of success of 63%. This tool has been used to assist the manual assignment of peptides and proteins and can also be used as a block in an automated approach to assignment. The program has been called RESCUE and is made publicly available at the following URL: http://www.infobiosud.univ-montp1.fr/rescue.     

Predicting 15N chemical shifts in proteins using the preceding residue-specific individual shielding surfaces from φ, ψi−1, and χ1torsion angles

Empirical shielding surfaces are most commonly used to predict chemical shifts in proteins from known backbone torsion angles, phi and psi. However, the prediction of (15)N chemical shifts using this technique is significantly poorer, compared to that for the other nuclei such as (1)H(alpha), (13)C(alpha), and (13)C(beta). In this study, we investigated the effects from the preceding residue and the side-chain geometry, chi(1), on (15)N chemical shifts by statistical methods. For an amino acid sequence XY, the (15)N chemical shift of Y is expressed as a function of the amino acid types of X and Y, as well as the backbone torsion angles, phi and psi(i-1). Accordingly, 380 empirical 'Preceding Residue Specific Individual (PRSI)' (15)N chemical shift shielding surfaces, representing all the combinations of X and Y (except for Y=Pro), were built and used to predict (15)N chemical shift from phi and psi(i-1). We further investigated the chi(1) effects, which were found to account for differences in (15)N chemical shifts by approximately 5 ppm for amino acids Val, Ile, Thr, Phe, His, Tyr, and Trp. Taking the chi(1) effects into account, the chi(1)-calibrated PRSI shielding surfaces (XPRSI) were built and used to predict (15)N chemical shifts for these amino acids. We demonstrated that (15)N chemical shift predictions are significantly improved by incorporating the preceding residue and chi(1) effects. The present PRSI and XPRSI shielding surfaces were extensively compared with three recently published programs, SHIFTX (Neal et al., 2003), SHIFTS (Xu and Case, 2001 and 2002), and PROSHIFT (Meiler, 2003) on a set of ten randomly selected proteins. A set of Java programs using XPRSI shielding surfaces to predict (15)N chemical shifts in proteins were developed and are freely available for academic users at http://www.pronmr.com or by sending email to one of the authors Yunjun Wang (yunjunwang@yahoo.com).     

Stereospecific assignment of the asparagine and glutamine sidechain amide protons in proteins from chemical shift analysis

Side chain amide protons of asparagine and glutamine residues in random-coil peptides are characterized by large chemical shift differences and can be stereospecifically assigned on the basis of their chemical shift values only. The bimodal chemical shift distributions stored in the biological magnetic resonance data bank (BMRB) do not allow such an assignment. However, an analysis of the BMRB shows, that a substantial part of all stored stereospecific assignments is not correct. We show here that in most cases stereospecific assignment can also be done for folded proteins using an unbiased artificial chemical shift data base (UACSB). For a separation of the chemical shifts of the two amide resonance lines with differences ≥0.40 ppm for asparagine and differences ≥0.42 ppm for glutamine, the downfield shifted resonance lines can be assigned to H^δ21 and H^ε21, respectively, at a confidence level >95%. A classifier derived from UASCB can also be used to correct the BMRB data. The program tool AssignmentChecker implemented in AUREMOL calculates the Bayesian probability for a given stereospecific assignment and automatically corrects the assignments for a given list of chemical shifts.     

Practical use of chemical shift databases for protein solid-state NMR: 2D chemical shift maps and amino-acid assignment with secondary-structure information

We introduce a Python-based program that utilizes the large database of ¹³C and ¹⁵N chemical shifts in the Biological Magnetic Resonance Bank to rapidly predict the amino acid type and secondary structure from correlated chemical shifts. The program, called PACSYlite Unified Query (PLUQ), is designed to help assign peaks obtained from 2D ¹³C–¹³C, ¹⁵N–¹³C, or 3D ¹⁵N–¹³C–¹³C magic-angle-spinning correlation spectra. We show secondary-structure specific 2D ¹³C–¹³C correlation maps of all twenty amino acids, constructed from a chemical shift database of 262,209 residues. The maps reveal interesting conformation-dependent chemical shift distributions and facilitate searching of correlation peaks during amino-acid type assignment. Based on these correlations, PLUQ outputs the most likely amino acid types and the associated secondary structures from inputs of experimental chemical shifts. We test the assignment accuracy using four high-quality protein structures. Based on only the Cα and Cβ chemical shifts, the highest-ranked PLUQ assignments were 40–60 % correct in both the amino-acid type and the secondary structure. For three input chemical shifts (CO–Cα–Cβ or N–Cα–Cβ), the first-ranked assignments were correct for 60 % of the residues, while within the top three predictions, the correct assignments were found for 80 % of the residues. PLUQ and the chemical shift maps are expected to be useful at the first stage of sequential assignment, for combination with automated sequential assignment programs, and for highly disordered proteins for which secondary structure analysis is the main goal of structure determination.     

RefDB: a database of uniformly referenced protein chemical shifts

RefDB is a secondary database of reference-corrected protein chemical shifts derived from the BioMagResBank (BMRB). The database was assembled by using a recently developed program (SHIFTX) to predict protein ¹H, ¹³C and ¹⁵N chemical shifts from X-ray or NMR coordinate data of previously assigned proteins. The predicted shifts were then compared with the corresponding observed shifts and a variety of statistical evaluations performed. In this way, potential mis-assignments, typographical errors and chemical referencing errors could be identified and, in many cases, corrected. This approach allows for an unbiased, instrument-independent solution to the problem of retrospectively re-referencing published protein chemical shifts. Results from this study indicate that nearly 25% of BMRB entries with ¹³C protein assignments and 27% of BMRB entries with ¹⁵N protein assignments required significant chemical shift reference readjustments. Additionally, nearly 40% of protein entries deposited in the BioMagResBank appear to have at least one assignment error. From this study it evident that protein NMR spectroscopists are increasingly adhering to recommended IUPAC ¹³C and ¹⁵N chemical shift referencing conventions, however, approximately 20% of newly deposited protein entries in the BMRB are still being incorrectly referenced. This is cause for some concern. However, the utilization of RefDB and its companion programs may help mitigate this ongoing problem. RefDB is updated weekly and the database, along with its associated software, is freely available at http://redpoll.pharmacy.ualberta.ca and the BMRB website.     

Validation of archived chemical shifts through atomic coordinates

The public archives containing protein information in the form of NMR chemical shift data at the BioMagResBank (BMRB) and of 3D structure coordinates at the Protein Data Bank are continuously expanding. The quality of the data contained in these archives, however, varies. The main issue for chemical shift values is that they are determined relative to a reference frequency. When this reference frequency is set incorrectly, all related chemical shift values are systematically offset. Such wrongly referenced chemical shift values, as well as other problems such as chemical shift values that are assigned to the wrong atom, are not easily distinguished from correct values and effectively reduce the usefulness of the archive. We describe a new method to correct and validate protein chemical shift values in relation to their 3D structure coordinates. This method classifies atoms using two parameters: the per‐atom solvent accessible surface area (as calculated from the coordinates) and the secondary structure of the parent amino acid. Through the use of Gaussian statistics based on a large database of 3220 BMRB entries, we obtain per‐entry chemical shift corrections as well as Z scores for the individual chemical shift values. In addition, information on the error of the correction value itself is available, and the method can retain only dependable correction values. We provide an online resource with chemical shift, atom exposure, and secondary structure information for all relevant BMRB entries ( http://www.ebi.ac.uk/pdbe/nmr/vasco ) and hope this data will aid the development of new chemical shift‐based methods in NMR. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Prediction algorithm for amino acid types with their secondary structure in proteins (PLATON) using chemical shifts

The algorithm PLATON is able to assign sets of chemical shifts derived from a single residue to amino acid types with its secondary structure (amino acid species). A subsequent ranking procedure using optionally two different penalty functions yields predictions for possible amino acid species for the given set of chemical shifts. This was demonstrated in the case of the -spectrin SH3 domain and applied to 9 further protein data sets taken from the BioMagRes database. A database consisting of reference chemical shift patterns (reference CSPs) was generated from assigned chemical shifts of proteins with known 3D-structure. This reference CSP database is used in our approach for extracting distributions of amino acid types with their most likely secondary structure elements (namely -helix, -sheet, and coil) for single amino acids by comparison with query CSPs. Results obtained for the 10 investigated proteins indicates that the percentage of correct amino acid species in the first three positions in the ranking list, ranges from 71.4% to 93.2% for the more favorable penalty function. Where only the top result of the ranking list for these 10 proteins is considered, 36.5% to 83.1% of the amino acid species are correctly predicted. The main advantage of our approach, over other methods that rely on average chemical shift values is the ability to increase database content by incorporating newly derived CSPs, and therefore to improve PLATON's performance over time.     

An automated system designed for large scale NMR data deposition and annotation: application to over 600 assigned chemical shift data entries to the BioMagResBank from the Riken Structural Genomics/Proteomics Initiative internal database

Biomolecular NMR chemical shift data are key information for the functional analysis of biomolecules and the development of new techniques for NMR studies utilizing chemical shift statistical information. Structural genomics projects are major contributors to the accumulation of protein chemical shift information. The management of the large quantities of NMR data generated by each project in a local database and the transfer of the data to the public databases are still formidable tasks because of the complicated nature of NMR data. Here we report an automated and efficient system developed for the deposition and annotation of a large number of data sets including (1)H, (13)C and (15)N resonance assignments used for the structure determination of proteins. We have demonstrated the feasibility of our system by applying it to over 600 entries from the internal database generated by the RIKEN Structural Genomics/Proteomics Initiative (RSGI) to the public database, BioMagResBank (BMRB). We have assessed the quality of the deposited chemical shifts by comparing them with those predicted from the PDB coordinate entry for the corresponding protein. The same comparison for other matched BMRB/PDB entries deposited from 2001-2011 has been carried out and the results suggest that the RSGI entries greatly improved the quality of the BMRB database. Since the entries include chemical shifts acquired under strikingly similar experimental conditions, these NMR data can be expected to be a promising resource to improve current technologies as well as to develop new NMR methods for protein studies.     

Empirical correlation between protein backbone <Superscript>15</Superscript>N and <Superscript>13</Superscript>C secondary chemical shifts and its application to nitrogen chemical shift re-referencing

The linear analysis of chemical shifts (LACS) has provided a robust method for identifying and correcting ¹³C chemical shift referencing problems in data from protein NMR spectroscopy. Unlike other approaches, LACS does not require prior knowledge of the three-dimensional structure or inference of the secondary structure of the protein. It also does not require extensive assignment of the NMR data. We report here a way of extending the LACS approach to ¹⁵N NMR data from proteins, so as to enable the detection and correction of inconsistencies in chemical shift referencing for this nucleus. The approach is based on our finding that the secondary ¹⁵N chemical shift of the backbone nitrogen atom of residue i is strongly correlated with the secondary chemical shift difference (experimental minus random coil) between the alpha and beta carbons of residue i − 1. Thus once alpha and beta ¹³C chemical shifts are available (their difference is referencing error-free), the ¹⁵N referencing can be validated, and an appropriate offset correction can be derived. This approach can be implemented prior to a structure determination and can be used to analyze potential referencing problems in database data not associated with three-dimensional structure. Application of the LACS algorithm to the current BMRB protein chemical shift database, revealed that nearly 35% of the BMRB entries have δ ¹⁵N values mis-referenced by over 0.7 ppm and over 25% of them have δ ¹H^N values mis-referenced by over 0.12 ppm. One implication of the findings reported here is that a backbone ¹⁵N chemical shift provides a better indicator of the conformation of the preceding residue than of the residue itself. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Identify Beta-Hairpin Motifs with Quadratic Discriminant Algorithm Based on the Chemical Shifts

Successful prediction of the beta-hairpin motif will be helpful for understanding the of the fold recognition. Some algorithms have been proposed for the prediction of beta-hairpin motifs. However, the parameters used by these methods were primarily based on the amino acid sequences. Here, we proposed a novel model for predicting beta-hairpin structure based on the chemical shift. Firstly, we analyzed the statistical distribution of chemical shifts of six nuclei in not beta-hairpin and beta-hairpin motifs. Secondly, we used these chemical shifts as features combined with three algorithms to predict beta-hairpin structure. Finally, we achieved the best prediction, namely sensitivity of 92%, the specificity of 94% with 0.85 of Mathew’s correlation coefficient using quadratic discriminant analysis algorithm, which is clearly superior to the same method for the prediction of beta-hairpin structure from 20 amino acid compositions in the three-fold cross-validation. Our finding showed that the chemical shift is an effective parameter for beta-hairpin prediction, suggesting the quadratic discriminant analysis is a powerful algorithm for the prediction of beta-hairpin.     

Complete (1)H and (13)C NMR chemical shift assignments of mono-, di-, and trisaccharides as basis for NMR chemical shift predictions of polysaccharides using the computer program casper

The computer program casper uses (1)H and (13)C NMR chemical shift data of mono- to trisaccharides for the prediction of chemical shifts of oligo- and polysaccharides. In order to improve the quality of these predictions the (1)H and (13)C, as well as (31)P when applicable, NMR chemical shifts of 30 mono-, di-, and trisaccharides were assigned. The reducing sugars gave two distinct sets of NMR resonances due to the α- and β-anomeric forms. In total 35 (1)H and (13)C NMR chemical shift data sets were obtained from the oligosaccharides. One- and two-dimensional NMR experiments were used for the chemical shift assignments and special techniques were employed in some cases such as 2D (1)H,(13)C-HSQC Hadamard Transform methodology which was acquired approximately 45 times faster than a regular t(1) incremented (1)H,(13)C-HSQC experiment and a 1D (1)H,(1)H-CSSF-TOCSY experiment which was able to distinguish spin-systems in which the target protons were only 3.3Hz apart. The (1)H NMR chemical shifts were subsequently refined using total line-shape analysis with the PERCH NMR software. The acquired NMR data were then utilized in the casper program (http://www.casper.organ.su.se/casper/) for NMR chemical shift predictions of the O-antigen polysaccharides from Klebsiella O5, Shigella flexneri serotype X, and Salmonella arizonae O62. The data were compared to experimental data of the polysaccharides from the two former strains and the lipopolysaccharide of the latter strain showing excellent agreement between predicted and experimental (1)H and (13)C NMR chemical shifts.     

Probing multiple effects on 15N, 13C alpha, 13C beta,and 13C' chemical shifts in peptides using density functional theory

We have used density functional calculations on model peptides to study conformational effects on (15)N, (13)C alpha, (13)C beta, and (13)C' chemical shifts, associated with hydrogen bonding, backbone conformation, and side-chain orientation. The results show a significant dependence on the backbone torsion angles of the nearest three residues. Contributions to (15)N chemical shifts from hydrogen bonding (up to 8 ppm), backbone conformation (up to 13 ppm), side-chain orientation and neighborhood residue effects (up to 22 ppm) are significant, and a unified theory will be required to account for their behavior in proteins. In contrast to this, the dependence on sequence and hydrogen bonding is much less for (13)C alpha and (13)C beta chemical shifts (<0.5 ppm), and moderate for carbonyl carbon shifts (<2 ppm). The effects of side-chain orientation are mainly limited to the residue itself for both nitrogen and carbon, but the chi(1) effect is also significant for the nitrogen shift of the following residue and for the (13)C' shift of the preceding residue. The calculated results are used, in conjunction with an additive model of chemical shift contributions, to create an algorithm for prediction of (15)N and (13)C shifts in proteins from their structure; this includes a model to extrapolate results to regions of torsion angle space that have not been explicitly studied by density functional theory (DFT) calculations. Crystal structures of 20 proteins with measured shifts have been used to test the prediction scheme. Root mean square deviations between calculated and experimental shifts 2.71, 1.22, 1.31, and 1.28 ppm for N, C alpha, C beta, and C', respectively. This prediction algorithm should be helpful in NMR assignment, crystal and solution structure comparison, and structure refinement.     

A probabilistic approach for validating protein NMR chemical shift assignments

It has been estimated that more than 20% of the proteins in the BMRB are improperly referenced and that about 1% of all chemical shift assignments are mis-assigned. These statistics also reflect the likelihood that any newly assigned protein will have shift assignment or shift referencing errors. The relatively high frequency of these errors continues to be a concern for the biomolecular NMR community. While several programs do exist to detect and/or correct chemical shift mis-referencing or chemical shift mis-assignments, most can only do one, or the other. The one program (SHIFTCOR) that is capable of handling both chemical shift mis-referencing and mis-assignments, requires the 3D structure coordinates of the target protein. Given that chemical shift mis-assignments and chemical shift re-referencing issues should ideally be addressed prior to 3D structure determination, there is a clear need to develop a structure-independent approach. Here, we present a new structure-independent protocol, which is based on using residue-specific and secondary structure-specific chemical shift distributions calculated over small (3–6 residue) fragments to identify mis-assigned resonances. The method is also able to identify and re-reference mis-referenced chemical shift assignments. Comparisons against existing re-referencing or mis-assignment detection programs show that the method is as good or superior to existing approaches. The protocol described here has been implemented into a freely available Java program called “Probabilistic Approach for protein Nmr Assignment Validation (PANAV)” and as a web server () which can be used to validate and/or correct as well as re-reference assigned protein chemical shifts.     

A probabilistic model for secondary structure prediction from protein chemical shifts

Protein chemical shifts encode detailed structural information that is difficult and computationally costly to describe at a fundamental level. Statistical and machine learning approaches have been used to infer correlations between chemical shifts and secondary structure from experimental chemical shifts. These methods range from simple statistics such as the chemical shift index to complex methods using neural networks. Notwithstanding their higher accuracy, more complex approaches tend to obscure the relationship between secondary structure and chemical shift and often involve many parameters that need to be trained. We present hidden Markov models (HMMs) with Gaussian emission probabilities to model the dependence between protein chemical shifts and secondary structure. The continuous emission probabilities are modeled as conditional probabilities for a given amino acid and secondary structure type. Using these distributions as outputs of first‐ and second‐order HMMs, we achieve a prediction accuracy of 82.3%, which is competitive with existing methods for predicting secondary structure from protein chemical shifts. Incorporation of sequence‐based secondary structure prediction into our HMM improves the prediction accuracy to 84.0%. Our findings suggest that an HMM with correlated Gaussian distributions conditioned on the secondary structure provides an adequate generative model of chemical shifts. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Quantitative comparison of the hydrogen bond network of A-state and native ubiquitin by hydrogen bond scalar couplings

The backbone hydrogen bond (H-bond) network of the partially folded A-state of ubiquitin (60% methanol, 40% water, pH 2) has been characterized quantitatively by (h3)J(NC)(') H-bond scalar couplings between the (15)N nuclei of amino acid H-bond donors and the (13)C carbonyl nuclei of the acceptors. Results on (h3)J(NC)(') couplings and the amide proton ((1)H(N)) chemical shifts for the A-state are compared quantitatively to the native state. The (h3)J(NC)(') correlations of the A-state show intact, nativelike H-bonds of the first beta-hairpin beta1/beta2 and the alpha-helix, albeit at lower strength, whereas the H-bonds in the C-terminal part change from a pure beta-structure to an all alpha-helical H(N)(i)-->O(i-4) connectivity pattern. A residue-specific analysis reveals that the conformations within the conserved secondary structure segments are much more homogeneous in the A-state than in the native state. Thus, the strong asymmetry of (h3)J(NC)(') couplings and (1)H(N) chemical shifts between the interior and exterior sides of the native state alpha-helix vanishes in the A-state. This indicates that the bend of this helix around the native state hydrophobic core is released in the homogeneous solvent environment of the A-state. Similarly, an irregularity in the behavior of H-bond I3-->L15 in hairpin beta1/beta2, which results from strong contacts to strand beta5 in the native state, is absent in the A-state. These findings rationalize the behavior of the (1)H(N) chemical shifts in both states and indicate that the A-state is in many aspects similar to the onset of thermal denaturation of the native state.     

Nearest-neighbor effects on backbone alpha and beta carbon chemical shifts in proteins

We present a method for analyzing the chemical shift database to yield information on nearest-neighbor effects on carbon-13 chemical shift values for alpha and beta carbons of amino acids in proteins. For each amino acid sequence XYZ, we define two correction factors, Delta(XY) s and Delta(YZ) s , representing the effects on (delta13 Calpha-delta13 Cbeta) for residue Y from the preceding residue (X) and the following residue (Z), where X, Y, and Z represent one of the 20 naturally occurring amino acids, Delta designates the change in value or the correction factor (in ppm), and s is an index standing for one of three "pseudo secondary structure states" derived from chemical shift dispersions, which we show represent residues in primarily alpha-helix, beta-strand, and non-alphabeta(coil). The correction factors were obtained from maximum likelihood fitting of (delta13 Calpha-delta13 Cbeta) values from the chemical shifts of 651 proteins to a mixture of three Gaussians. These correction factors were derived strictly from the analysis of assigned chemical shifts, without regard to the three-dimensional structures of these proteins. The corrections factors were found to differ according to the secondary structural environment of the central residue (deduced from the chemical shift distribution) as well as by different identities of the nearest neighboring residues in the sequence. The areas subsumed by the sequence-dependent chemical shift distributions report on the relative energies of the sequences in different pseudo secondary structural environments, and the positions of the peaks indicate the chemical shifts of lowest energy conformations. As such, these results have potential applications to the determination of dihedral angle restraints from chemical shifts for structure determination and to more accurate predictions of chemical shifts in proteins of known structure. From a database of chemical shifts associated well-defined three-dimensional structures, comparisons were made between DSSP designations derived from three-dimensional structure and pseudo secondary structure designations derived from nearest-neighbor corrected chemical shift analysis. The high level of agreement between the two approaches to classifying secondary structure provides a measure of confidence in this chemical shift-based approach to the analysis of protein structure.     

Relationship between nuclear magnetic resonance chemical shift and protein secondary structure

An analysis of the 1H nuclear magnetic resonance chemical shift assignments and secondary structure designations for over 70 proteins has revealed some very strong and unexpected relationships. Similar studies, performed on smaller databases, for 13C and 15N chemical shifts reveal equally strong correlations to protein secondary structure. Among the more interesting results to emerge from this work is the finding that all 20 naturally occurring amino acids experience a mean alpha-1H upfield shift of 0.39 parts per million (from the random coil value) when placed in a helical configuration. In a like manner, the alpha-1H chemical shift is found to move downfield by an average of 0.37 parts per million when the residue is placed in a beta-strand or extended configuration. Similar changes are also found for amide 1H, carbonyl 13C, alpha-13C and amide 15N chemical shifts. Other relationships between chemical shift and protein conformation are also uncovered; in particular, a correlation between helix dipole effects and amide proton chemical shifts as well as a relationship between alpha-proton chemical shifts and main-chain flexibility. Additionally, useful relationships between alpha-proton chemical shifts and backbone dihedral angles as well as correlations between amide proton chemical shifts and hydrogen bond effects are demonstrated.     

1H, 13C and 15N random coil NMR chemical shifts of the common amino acids. I. Investigations of nearest-neighbor effects

Summary In this study we report on the ¹H, ¹³C and ¹⁵N NMR chemical shifts for the random coil state and nearest-neighbor sequence effects measured from the protected linear hexapeptide Gly-Gly-X-Y-Gly-Gly (where X and Y are any of the 20 common amino acids). We present data for a set of 40 peptides (of the possible 400) including Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly, measured under identical aqueous conditions. Because all spectra were collected under identical experimental conditions, the data from the Gly-Gly-X-Ala-Gly-Gly series provide a complete and internally consistent set of ¹H, ¹³C and ¹⁵N random coil chemical shifts for all 20 common amino acids. In addition, studies were also conducted into nearest-neighbor effects on the random coil shift arising from a variety of X and Y positional substitutions. Comparisons between the chemical shift measurements obtained from Gly-Gly-X-Ala-Gly-Gly and Gly-Gly-X-Pro-Gly-Gly reveal significant systematic shift differences arising from the presence of proline in the peptide sequence. Similarly, measurements of the chemical shift changes occurring for both alanine and proline (i.e., the residues in the Y position) are found to depend strougly on the type of amino acid substituted into the X position. These data lend support to the hypothesis that sequence effects play a significant role in determining peptide and protein chemical shifts.     

4D prediction of protein <Superscript>1</Superscript>H chemical shifts

A 4D approach for protein ¹H chemical shift prediction was explored. The 4th dimension is the molecular flexibility, mapped using molecular dynamics simulations. The chemical shifts were predicted with a principal component model based on atom coordinates from a database of 40 protein structures. When compared to the corresponding non-dynamic (3D) model, the 4th dimension improved prediction by 6–7%. The prediction method achieved RMS errors of 0.29 and 0.50 ppm for Hα and HN shifts, respectively. However, for individual proteins the RMS errors were 0.17–0.34 and 0.34–0.65 ppm for the Hα and HN shifts, respectively. X-ray structures gave better predictions than the corresponding NMR structures, indicating that chemical shifts contain invaluable information about local structures. The ¹H chemical shift prediction tool 4DSPOT is available from .     

1H, 13C, and 15N NMR assignments of StnII-R29Q, a defective lipid binding mutant of the sea anemone actinoporin Sticholysin II

Several studies have been made to characterize the molecular properties and activity of Sticholysin II (StnII), a 175 amino acid protein secreted by the sea anemone Stichodactyla helianthus. In particular, the biochemical characterization of different mutants of this protein have been shown to be essential for the rational understanding of its activity. Here we report the nearly complete NMR ¹⁵N, ¹³C and ¹H chemical shift assignments, at pH 4.0 and 25°C, of a less hemolytic and defective lipid binding mutant of StnII, the R29Q variant (BMRB no 16362).