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.     

KUJIRA,a package of integrated modules for systematic and interactive analysis of NMR data directed to high-throughput NMR structure studies

The recent expansion of structural genomics has increased the demands for quick and accurate protein structure determination by NMR spectroscopy. The conventional strategy without an automated protocol can no longer satisfy the needs of high-throughput application to a large number of proteins, with each data set including many NMR spectra, chemical shifts, NOE assignments, and calculated structures. We have developed the new software KUJIRA, a package of integrated modules for the systematic and interactive analysis of NMR data, which is designed to reduce the tediousness of organizing and manipulating a large number of NMR data sets. In combination with CYANA, the program for automated NOE assignment and structure determination, we have established a robust and highly optimized strategy for comprehensive protein structure analysis. An application of KUJIRA in accordance with our new strategy was carried out by a non-expert in NMR structure analysis, demonstrating that the accurate assignment of the chemical shifts and a high-quality structure of a small protein can be completed in a few weeks. The high completeness of the chemical shift assignment and the NOE assignment achieved by the systematic analysis using KUJIRA and CYANA led, in practice, to increased reliability of the determined structure.     

Automated and assisted RNA resonance assignment using NMR chemical shift statistics

The three-dimensional structure determination of RNAs by NMR spectroscopy relies on chemical shift assignment, which still constitutes a bottleneck. In order to develop more efficient assignment strategies, we analysed relationships between sequence and ¹H and ¹³C chemical shifts. Statistics of resonances from regularly Watson–Crick base-paired RNA revealed highly characteristic chemical shift clusters. We developed two approaches using these statistics for chemical shift assignment of double-stranded RNA (dsRNA): a manual approach that yields starting points for resonance assignment and simplifies decision trees and an automated approach based on the recently introduced automated resonance assignment algorithm FLYA. Both strategies require only unlabeled RNAs and three 2D spectra for assigning the H2/C2, H5/C5, H6/C6, H8/C8 and H1′/C1′ chemical shifts. The manual approach proved to be efficient and robust when applied to the experimental data of RNAs with a size between 20 nt and 42 nt. The more advanced automated assignment approach was successfully applied to four stem-loop RNAs and a 42 nt siRNA, assigning 92–100% of the resonances from dsRNA regions correctly. This is the first automated approach for chemical shift assignment of non-exchangeable protons of RNA and their corresponding ¹³C resonances, which provides an important step toward automated structure determination of RNAs.     

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.     

Influence of the completeness of chemical shift assignments on NMR structures obtained with automated NOE assignment

Reliable automated NOE assignment and structure calculation on the basis of a largely complete, assigned input chemical shift list and a list of unassigned NOESY cross peaks has recently become feasible for routine NMR protein structure calculation and has been shown to yield results that are equivalent to those of the conventional, manual approach. However, these algorithms rely on the availability of a virtually complete list of the chemical shifts. This paper investigates the influence of incomplete chemical shift assignments on the reliability of NMR structures obtained with automated NOESY cross peak assignment. The program CYANA was used for combined automated NOESY assignment with the CANDID algorithm and structure calculations with torsion angle dynamics at various degrees of completeness of the chemical shift assignment which was simulated by random omission of entries in the experimental ¹H chemical shift lists that had been used for the earlier, conventional structure determinations of two proteins. Sets of structure calculations were performed choosing the omitted chemical shifts randomly among all assigned hydrogen atoms, or among aromatic hydrogen atoms. For comparison, automated NOESY assignment and structure calculations were performed with the complete experimental chemical shift but under random omission of NOESY cross peaks. When heteronuclear-resolved three-dimensional NOESY spectra are available the current CANDID algorithm yields in the absence of up to about 10% of the experimental ¹H chemical shifts reliable NOE assignments and three-dimensional structures that deviate by less than 2 Å from the reference structure obtained using all experimental chemical shift assignments. In contrast, the algorithm can accommodate the omission of up to 50% of the cross peaks in heteronuclear- resolved NOESY spectra without producing structures with a RMSD of more than 2 Å to the reference structure. When only homonuclear NOESY spectra are available, the algorithm is slightly more susceptible to missing data and can tolerate the absence of up to about 7% of the experimental ¹H chemical shifts or of up to 30% of the NOESY peaks.Abbreviations: BmPBP^A – Bombyx mori pheromone binding protein form A; CYANA – combined assignment and dynamics algorithm for NMR applications; NMR – nuclear magnetic resonance; NOE – nuclear Overhauser effect; NOESY – NOE spectroscopy; RMSD – root-mean-square deviation; WmKT – Williopsis mrakii killer toxin     

Accurate and automated classification of protein secondary structure with PsiCSI

PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without (13)C shifts and Q3 = 86.8% with only H(alpha) shifts available). Finally, errors made by PsiCSI almost exclusively involve the interchange of helix or strand with coil and not helix with strand (<2.5 occurrences per 10000 residues). The automation, increased accuracy, absence of gross errors, and robustness with regards to sparse data make PsiCSI ideal for high-throughput applications, and should improve the effectiveness of hybrid NMR/de novo structure determination methods. A Web server is available for users to submit data and have the assignment returned.     

Chemical shift assignment and structural plasticity of a HIV fusion peptide derivative in dodecylphosphocholine micelles

A "HFPK3" peptide containing the 23 residues of the human immunodeficiency virus (HIV) fusion peptide (HFP) plus three non-native C-terminal lysines was studied in dodecylphosphocholine (DPC) micelles with 2D 1H NMR spectroscopy. The HFP is at the N-terminus of the gp41 fusion protein and plays an important role in fusing viral and target cell membranes which is a critical step in viral infection. Unlike HFP, HFPK3 is monomeric in detergent-free buffered aqueous solution which may be a useful property for functional and structural studies. H alpha chemical shifts indicated that DPC-associated HFPK3 was predominantly helical from I4 to L12. In addition to the highest-intensity crosspeaks used for the first chemical shift assignment (denoted I), there were additional crosspeaks whose intensities were approximately 10% of those used for assignment I. A second assignment (II) for residues G5 to L12 as well as a few other residues was derived from these lower-intensity crosspeaks. Relative to the I shifts, the II shifts were different by 0.01-0.23 ppm with the largest differences observed for HN. Comparison of the shifts of DPC-associated HFPK3 with those of detergent-associated HFP and HFP derivatives provided information about peptide structures and locations in micelles.     

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.     

The protein fluorescence and structural toolkit: Database and programs for the analysis of protein fluorescence and structural data

Protein fluorescence is a powerful tool for studying protein structure and dynamics if we have a means to interpret the spectral data in terms of protein structural properties. Our previous research successfully provided this support through the development of individual software modules implementing the algorithms for fluorescence and structural analyses. Now we have integrated the developed software modules, introduced a new program for the assignment of tryptophan residues to spectral-structural classes, and created a web-based toolkit PFAST: protein fluorescence and structural toolkit: http://pfast.phys.uri.edu/. PFAST contains three modules: (1) FCAT is a fluorescence-correlation analysis tool, which decomposes protein fluorescence spectra to reveal the spectral components of individual tryptophan residues or groups of tryptophan residues located close to each other, and assigns spectral components to one of five previously established spectral-structural classes. (2) SCAT is a structural-correlation analysis tool for the calculation of the structural parameters of the environment of tryptophan residues from the atomic structures of the proteins from the Protein Data Bank (PDB), and for the assignment of tryptophan residues to one of five spectral-structural classes. (3) The last module is a PFAST database that contains protein fluorescence and structural data obtained from results of the FCAT and SCAT analyses.     

Application of automated NOE assignment to three-dimensional structure refinement of a 28 kDa single-chain T cell receptor

An automated procedure for NOE assignment and three-dimensional structure refinement is presented. The input to the procedure consists of (1) an ensemble of preliminary protein NMR structures, (2) partial sequence-specific assignments for the protein and (3) the positions and volumes of unassigned NOESY cross peaks. Chemical shifts for unassigned side chain protons are predicted from the preliminary structures. The chemical shifts and unassigned NOESY cross peaks are input to an automated procedure for NOE assignment and structure calculation (ARIA) [Nilges et al. (1997) J. Mol. Biol., 269, 408–422]. ARIA is optimized for the task of structure refinement of larger proteins. Errors are filtered to ensure that sequence-specific assignments are reliable. The procedure is applied to the 27.8 kDa single-chain T cell receptor (scTCR). Preliminary NMR structures, nearly complete backbone assignments, partial assignments of side chain protons and more than 1300 unassigned NOESY cross peaks are input. Using the procedure, the resonant frequencies of more than 40 additional side chain protons are assigned. Over 400 new NOE cross peaks are assigned unambiguously. Distances derived from the automatically assigned NOEs improve the precision and quality of calculated scTCR structures. In the refined structures, a hydrophobic cluster of side chains on the scTCR surface that binds major histocompatibility complex (MHC)/antigen is revealed. It is composed of the side chains of residues from three loops and stabilizes the conformation of residues that interact with MHC.     

Automated backbone assignment of labeled proteins using the threshold accepting algorithm

The sequential assignment of backbone resonances is the first step in the structure determination of proteins by heteronuclear NMR. For larger proteins, an assignment strategy based on proton side-chain information is no longer suitable for the use in an automated procedure. Our program PASTA (Protein ASsignment by Threshold Accepting) is therefore designed to partially or fully automate the sequential assignment of proteins, based on the analysis of NMR backbone resonances plus C information. In order to overcome the problems caused by peak overlap and missing signals in an automated assignment process, PASTA uses threshold accepting, a combinatorial optimization strategy, which is superior to simulated annealing due to generally faster convergence and better solutions. The reliability of this algorithm is shown by reproducing the complete sequential backbone assignment of several proteins from published NMR data. The robustness of the algorithm against misassigned signals, noise, spectral overlap and missing peaks is shown by repeating the assignment with reduced sequential information and increased chemical shift tolerances. The performance of the program on real data is finally demonstrated with automatically picked peak lists of human nonpancreatic synovial phospholipase A₂, a protein with 124 residues.     

CSSI-PRO: a method for secondary structure type editing,assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone ¹H^α and ¹³C′ chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to α-helical/β-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Peak picking multidimensional NMR spectra with the contour geometry based algorithm CYPICK

The automated identification of signals in multidimensional NMR spectra is a challenging task, complicated by signal overlap, noise, and spectral artifacts, for which no universally accepted method is available. Here, we present a new peak picking algorithm, CYPICK, that follows, as far as possible, the manual approach taken by a spectroscopist who analyzes peak patterns in contour plots of the spectrum, but is fully automated. Human visual inspection is replaced by the evaluation of geometric criteria applied to contour lines, such as local extremality, approximate circularity (after appropriate scaling of the spectrum axes), and convexity. The performance of CYPICK was evaluated for a variety of spectra from different proteins by systematic comparison with peak lists obtained by other, manual or automated, peak picking methods, as well as by analyzing the results of automated chemical shift assignment and structure calculation based on input peak lists from CYPICK. The results show that CYPICK yielded peak lists that compare in most cases favorably to those obtained by other automated peak pickers with respect to the criteria of finding a maximal number of real signals, a minimal number of artifact peaks, and maximal correctness of the chemical shift assignments and the three-dimensional structure obtained by fully automated assignment and structure calculation.     

Chemical shift assignment and structural plasticity of a HIV fusion peptide derivative in dodecylphosphocholine micelles

A “HFPK3” peptide containing the 23 residues of the human immunodeficiency virus (HIV) fusion peptide (HFP) plus three non-native C-terminal lysines was studied in dodecylphosphocholine (DPC) micelles with 2D ¹H NMR spectroscopy. The HFP is at the N-terminus of the gp41 fusion protein and plays an important role in fusing viral and target cell membranes which is a critical step in viral infection. Unlike HFP, HFPK3 is monomeric in detergent-free buffered aqueous solution which may be a useful property for functional and structural studies. H^α chemical shifts indicated that DPC-associated HFPK3 was predominantly helical from I4 to L12. In addition to the highest-intensity crosspeaks used for the first chemical shift assignment (denoted I), there were additional crosspeaks whose intensities were ∼ 10% of those used for assignment I. A second assignment (II) for residues G5 to L12 as well as a few other residues was derived from these lower-intensity crosspeaks. Relative to the I shifts, the II shifts were different by 0.01-0.23 ppm with the largest differences observed for H^N. Comparison of the shifts of DPC-associated HFPK3 with those of detergent-associated HFP and HFP derivatives provided information about peptide structures and locations in micelles.     

Robust structure-based resonance assignment for functional protein studies by NMR

High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly H ^N –H ^N NOEs networks, as well as ¹ H–¹⁵ N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: .     

Mars - robust automatic backbone assignment of proteins

MARS a program for robust automatic backbone assignment of (13)C/(15)N labeled proteins is presented. MARS does not require tight thresholds for establishing sequential connectivity or detailed adjustment of these thresholds and it can work with a wide variety of NMR experiments. Using only (13)C(alpha)/(13)C(beta) connectivity information, MARS allows automatic, error-free assignment of 96% of the 370-residue maltose-binding protein. MARS can successfully be used when data are missing for a substantial portion of residues or for proteins with very high chemical shift degeneracy such as partially or fully unfolded proteins. Other sources of information, such as residue specific information or known assignments from a homologues protein, can be included into the assignment process. MARS exports its result in SPARKY format. This allows visual validation and integration of automated and manual assignment.     

Backbone assignment of proteins with known structure using residual dipolar couplings

A prerequisite for NMR studies of protein-ligand interactions or protein dynamics is the assignment of backbone resonances. Here we demonstrate that protein assignment can significantly be enhanced when experimental dipolar couplings (RDCs) are matched to values back-calculated from a known three-dimensional structure. In case of small proteins, the program MARS allows assignment of more than 90% of backbone resonances without the need for sequential connectivity information. For bigger proteins, we show that the combination of sequential connectivity information with RDC-matching enables more residues to be assigned reliably and backbone assignment to be more robust against missing data. Structural or dynamic deviations from the employed 3D coordinates do not lead to an increased error rate in RDC-supported assignment. RDC-enhanced assignment is particularly useful when chemical shifts and sequential connectivity only provide a few reliable assignments.     

An expectation/maximization nuclear vector replacement algorithm for automated NMR resonance assignments

We report an automated procedure for high-throughput NMR resonance assignment for a protein of known structure, or of an homologous structure. Our algorithm performs Nuclear Vector Replacement (NVR) by Expectation/Maximization (EM) to compute assignments. NVR correlates experimentally-measured NH residual dipolar couplings (RDCs) and chemical shifts to a given a priori whole-protein 3D structural model. The algorithm requires only uniform (15)N-labelling of the protein, and processes unassigned H(N)-(15)N HSQC spectra, H(N)-(15)N RDCs, and sparse H(N)-H(N) NOE's (d(NN)s). NVR runs in minutes and efficiently assigns the (H(N),(15)N) backbone resonances as well as the sparse d(NN)s from the 3D (15)N-NOESY spectrum, in O (n(3)) time. The algorithm is demonstrated on NMR data from a 76-residue protein, human ubiquitin, matched to four structures, including one mutant (homolog), determined either by X-ray crystallography or by different NMR experiments (without RDCs). NVR achieves an average assignment accuracy of over 99%. We further demonstrate the feasibility of our algorithm for different and larger proteins, using different combinations of real and simulated NMR data for hen lysozyme (129 residues) and streptococcal protein G (56 residues), matched to a variety of 3D structural models.     

Protein NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS

Novel algorithms are presented for automated NOESY peak picking and NOE signal identification in homonuclear 2D and heteronuclear-resolved 3D [¹H,¹H]-NOESY spectra during de novoprotein structure determination by NMR, which have been implemented in the new software ATNOS (automated NOESY peak picking). The input for ATNOS consists of the amino acid sequence of the protein, chemical shift lists from the sequence-specific resonance assignment, and one or several 2D or 3D NOESY spectra. In the present implementation, ATNOS performs multiple cycles of NOE peak identification in concert with automated NOE assignment with the software CANDID and protein structure calculation with the program DYANA. In the second and subsequent cycles, the intermediate protein structures are used as an additional guide for the interpretation of the NOESY spectra. By incorporating the analysis of the raw NMR data into the process of automated de novoprotein NMR structure determination, ATNOS enables direct feedback between the protein structure, the NOE assignments and the experimental NOESY spectra. The main elements of the algorithms for NOESY spectral analysis are techniques for local baseline correction and evaluation of local noise level amplitudes, automated determination of spectrum-specific threshold parameters, the use of symmetry relations, and the inclusion of the chemical shift information and the intermediate protein structures in the process of distinguishing between NOE peaks and artifacts. The ATNOS procedure has been validated with experimental NMR data sets of three proteins, for which high-quality NMR structures had previously been obtained by interactive interpretation of the NOESY spectra. The ATNOS-based structures coincide closely with those obtained with interactive peak picking. Overall, we present the algorithms used in this paper as a further important step towards objective and efficient de novoprotein structure determination by NMR.