TALOS+: a hybrid method for predicting protein backbone torsion angles from NMR chemical shifts

NMR chemical shifts in proteins depend strongly on local structure. The program TALOS establishes an empirical relation between ¹³C, ¹⁵N and ¹H chemical shifts and backbone torsion angles ϕ and ψ (Cornilescu et al. J Biomol NMR 13 289–302, 1999). Extension of the original 20-protein database to 200 proteins increased the fraction of residues for which backbone angles could be predicted from 65 to 74%, while reducing the error rate from 3 to 2.5%. Addition of a two-layer neural network filter to the database fragment selection process forms the basis for a new program, TALOS+, which further enhances the prediction rate to 88.5%, without increasing the error rate. Excluding the 2.5% of residues for which TALOS+ makes predictions that strongly differ from those observed in the crystalline state, the accuracy of predicted ϕ and ψ angles, equals ±13°. Large discrepancies between predictions and crystal structures are primarily limited to loop regions, and for the few cases where multiple X-ray structures are available such residues are often found in different states in the different structures. The TALOS+ output includes predictions for individual residues with missing chemical shifts, and the neural network component of the program also predicts secondary structure with good accuracy. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

SPARTA+: a modest improvement in empirical NMR chemical shift prediction by means of an artificial neural network

NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and ¹³C^β chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and ¹³C^β atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for δ¹⁵N, δ¹³C’, δ¹³C^α, δ¹³C^β, δ¹H^α and δ¹H^N, respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2–10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.     

Automated prediction of 15N, 13Cα, 13Cβ and 13C′ chemical shifts in proteins using a density functional database

A database of peptide chemical shifts, computed at the density functional level, has been used to develop an algorithm for prediction of ¹⁵N and ¹³C shifts in proteins from their structure; the method is incorporated into a program called SHIFTS (version 4.0). The database was built from the calculated chemical shift patterns of 1335 peptides whose backbone torsion angles are limited to areas of the Ramachandran map around helical and sheet configurations. For each tripeptide in these regions of regular secondary structure (which constitute about 40% of residues in globular proteins) SHIFTS also consults the database for information about sidechain torsion angle effects for the residue of interest and for the preceding residue, and estimates hydrogen bonding effects through an empirical formula that is also based on density functional calculations on peptides. The program optionally searches for alternate side-chain torsion angles that could significantly improve agreement between calculated and observed shifts. The application of the program on 20 proteins shows good consistency with experimental data, with correlation coefficients of 0.92, 0.98, 0.99 and 0.90 and r.m.s. deviations of 1.94, 0.97, 1.05, and 1.08 ppm for ¹⁵N, ¹³C, ¹³C and ¹³C, respectively. Reference shifts fit to protein data are in good agreement with `random-coil' values derived from experimental measurements on peptides. This prediction algorithm should be helpful in NMR assignment, crystal and solution structure comparison, and structure refinement.     

SHIFTX2: significantly improved protein chemical shift prediction

A new computer program, called SHIFTX2, is described which is capable of rapidly and accurately calculating diamagnetic ¹H, ¹³C and ¹⁵N chemical shifts from protein coordinate data. Compared to its predecessor (SHIFTX) and to other existing protein chemical shift prediction programs, SHIFTX2 is substantially more accurate (up to 26% better by correlation coefficient with an RMS error that is up to 3.3× smaller) than the next best performing program. It also provides significantly more coverage (up to 10% more), is significantly faster (up to 8.5×) and capable of calculating a wider variety of backbone and side chain chemical shifts (up to 6×) than many other shift predictors. In particular, SHIFTX2 is able to attain correlation coefficients between experimentally observed and predicted backbone chemical shifts of 0.9800 (¹⁵N), 0.9959 (¹³Cα), 0.9992 (¹³Cβ), 0.9676 (¹³C′), 0.9714 (¹HN), 0.9744 (¹Hα) and RMS errors of 1.1169, 0.4412, 0.5163, 0.5330, 0.1711, and 0.1231 ppm, respectively. The correlation between SHIFTX2’s predicted and observed side chain chemical shifts is 0.9787 (¹³C) and 0.9482 (¹H) with RMS errors of 0.9754 and 0.1723 ppm, respectively. SHIFTX2 is able to achieve such a high level of accuracy by using a large, high quality database of training proteins (>190), by utilizing advanced machine learning techniques, by incorporating many more features (χ₂ and χ₃ angles, solvent accessibility, H-bond geometry, pH, temperature), and by combining sequence-based with structure-based chemical shift prediction techniques. With this substantial improvement in accuracy we believe that SHIFTX2 will open the door to many long-anticipated applications of chemical shift prediction to protein structure determination, refinement and validation. SHIFTX2 is available both as a standalone program and as a web server ().     

Backbone and side-chain assignments of an effector membrane localization domain from Vibrio vulnificus MARTX toxin

¹H, ¹³C, and ¹⁵N chemical shift assignments are presented for the isolated four-helical bundle membrane localization domain from the domain of unknown function 5 (DUF5) effector (MLD_VvDUF5) of the MARTX toxin from Vibrio vulnificus in its solution state. We have assigned 97 % of all backbone and side-chain carbon atoms, including 96 % of all backbone residues. Secondary chemical shift analysis using TALOS+ demonstrates four helices that align with those predicted by structure homology modeling using the MLDs of Pasteurella multocida toxin (PMT) and the clostridial TcdB and TcsL toxins as templates. Future studies will be towards solving the structure and determining the dynamics in the solution state.     

<Superscript>1</Superscript>H, <Superscript>15</Superscript>N, <Superscript>13</Superscript>C backbone resonance assignment of the C-terminal domain of enzyme I from <Emphasis Type="Italic">Thermoanaerobacter tengcongensis</Emphasis>

Phosphoenolpyruvate binding to the C-terminal domain (EIC) of enzyme I of the bacterial phosphotransferase system (PTS) initiates a phosphorylation cascade that results in sugar translocation across the cell membrane and controls a large number of essential pathways in bacterial metabolism. EIC undergoes an expanded to compact conformational equilibrium that is regulated by ligand binding and determines the phosphorylation state of the overall PTS. Here, we report the backbone ¹H, ¹⁵N and ¹³C chemical shift assignments of the 70 kDa EIC dimer from the thermophilic bacterium Thermoanaerobacter tengcongensis. Assignments were obtained at 70 °C by heteronuclear multidimensional NMR spectroscopy. In total, 90% of all backbone resonances were assigned, with 264 out of a possible 299 residues assigned in the ¹H–¹⁵N TROSY spectrum. The secondary structure predicted from the assigned backbone resonance using the program TALOS+ is in good agreement with the X-ray crystal structure of T. tengcongensis EIC. The reported assignments will allow detailed structural and thermodynamic investigations on the coupling between ligand binding and conformational dynamics in EIC.     

Sequential nearest-neighbor effects on computed <Superscript>13</Superscript>C<Superscript>α</Superscript> chemical shifts

To evaluate sequential nearest-neighbor effects on quantum-chemical calculations of ¹³C^α chemical shifts, we selected the structure of the nucleic acid binding (NAB) protein from the SARS coronavirus determined by NMR in solution (PDB id 2K87). NAB is a 116-residue α/β protein, which contains 9 prolines and has 50% of its residues located in loops and turns. Overall, the results presented here show that sizeable nearest-neighbor effects are seen only for residues preceding proline, where Pro introduces an overestimation, on average, of 1.73 ppm in the computed ¹³C^α chemical shifts. A new ensemble of 20 conformers representing the NMR structure of the NAB, which was calculated with an input containing backbone torsion angle constraints derived from the theoretical ¹³C^α chemical shifts as supplementary data to the NOE distance constraints, exhibits very similar topology and comparable agreement with the NOE constraints as the published NMR structure. However, the two structures differ in the patterns of differences between observed and computed ¹³C^α chemical shifts, Δ _ca,i , for the individual residues along the sequence. This indicates that the Δ _ca,i -values for the NAB protein are primarily a consequence of the limited sampling by the bundles of 20 conformers used, as in common practice, to represent the two NMR structures, rather than of local flaws in the structures.     

Identification of helix capping and β-turn motifs from NMR chemical shifts

We present an empirical method for identification of distinct structural motifs in proteins on the basis of experimentally determined backbone and ¹³C^β chemical shifts. Elements identified include the N-terminal and C-terminal helix capping motifs and five types of β-turns: I, II, I′, II′ and VIII. Using a database of proteins of known structure, the NMR chemical shifts, together with the PDB-extracted amino acid preference of the helix capping and β-turn motifs are used as input data for training an artificial neural network algorithm, which outputs the statistical probability of finding each motif at any given position in the protein. The trained neural networks, contained in the MICS (motif identification from chemical shifts) program, also provide a confidence level for each of their predictions, and values ranging from ca 0.7–0.9 for the Matthews correlation coefficient of its predictions far exceed those attainable by sequence analysis. MICS is anticipated to be useful both in the conventional NMR structure determination process and for enhancing on-going efforts to determine protein structures solely on the basis of chemical shift information, where it can aid in identifying protein database fragments suitable for use in building such structures.     

Reduced dimensionality (4,3)D-hnCOCANH experiment: an efficient backbone assignment tool for NMR studies of proteins

Sequence specific resonance assignment of proteins forms the basis for variety of structural and functional proteomics studies by NMR. In this context, an efficient standalone method for rapid assignment of backbone (¹H, ¹⁵N, ¹³C^α and ¹³C′) resonances of proteins has been presented here. Compared to currently available strategies used for the purpose, the method employs only a single reduced dimensionality experiment—(4,3)D-hnCOCANH and exploits the linear combinations of backbone (¹³C^α and ¹³C′) chemical shifts to achieve a dispersion relatively better compared to those of individual chemical shifts (see the text). The resulted increased dispersion of peaks—which is different in sum (CA + CO) and difference (CA ? CO) frequency regions—greatly facilitates the analysis of the spectrum by resolving the problems (associated with routine assignment strategies) arising because of degenerate amide ¹⁵N and backbone ¹³C chemical shifts. Further, the spectrum provides direct distinction between intra- and inter-residue correlations because of their opposite peak signs. The other beneficial feature of the spectrum is that it provides: (a) multiple unidirectional sequential (i→i + 1) ¹⁵N and ¹³C correlations and (b) facile identification of certain specific triplet sequences which serve as check points for mapping the stretches of sequentially connected HSQC cross peaks on to the primary sequence for assigning the resonances sequence specifically. On top of all this, the F ₂–F ₃ planes of the spectrum corresponding to sum (CA + CO) and difference (CA ? CO) chemical shifts enable rapid and unambiguous identification of sequential HSQC peaks through matching their coordinates in these two planes (see the text). Overall, the experiment presented here will serve as an important backbone assignment tool for variety of structural and functional proteomics and drug discovery research programs by NMR involving well behaved small folded proteins (MW < 15 kDa) or a range of intrinsically disordered proteins.      

A maximum entropy approach to the study of residue-specific backbone angle distributions in α-synuclein,an intrinsically disordered protein

α-Synuclein is an intrinsically disordered protein of 140 residues that switches to an α-helical conformation upon binding phospholipid membranes. We characterize its residue-specific backbone structure in free solution with a novel maximum entropy procedure that integrates an extensive set of NMR data. These data include intraresidue and sequential H^N–H^α and H^N–H^N NOEs, values for ³J_HNHα, ¹J_HαCα, ²J_CαN, and ¹J_CαN, as well as chemical shifts of ¹⁵N, ¹³C^α, and ¹³C′ nuclei, which are sensitive to backbone torsion angles. Distributions of these torsion angles were identified that yield best agreement to the experimental data, while using an entropy term to minimize the deviation from statistical distributions seen in a large protein coil library. Results indicate that although at the individual residue level considerable deviations from the coil library distribution are seen, on average the fitted distributions agree fairly well with this library, yielding a moderate population (20–30%) of the PP_II region and a somewhat higher population of the potentially aggregation-prone β region (20–40%) than seen in the database. A generally lower population of the α_R region (10–20%) is found. Analysis of ¹H–¹H NOE data required consideration of the considerable backbone diffusion anisotropy of a disordered protein.     

13C, 15N and 1H backbone and side chain chemical shift assignment of acid-stress bacterial chaperone HdeA at pH 6

HdeA is a small chaperone found in the periplasm of several common pathogenic bacteria (Escherichia coli, Shigella flexneri and Brucella abortus) which are the leading causes of dysentery worldwide, especially in developing countries. Its job is to protect other periplasmic proteins from aggregating as the bacteria pass through the low pH environment of the human stomach on their way to infect the intestines. HdeA is an inactive folded dimer at neutral pH, but becomes a disordered active monomer at pH < 3. To initiate NMR characterization of HdeA at pH 6, 94 % of the backbone and 86 % of the side chain chemical shifts have been assigned. The loop linking helices B and C remains largely unassigned due to missing peaks in the ¹H–¹⁵N HSQC and other spectra, most likely due to intermediate timescale chemical exchange. Many of the weakest intensity backbone peaks correspond to residues that surround this loop within the tertiary structure. Assignment experiments have therefore helped to provide preliminary clues about the region of the protein that may be most responsible for initiating unfolding as the pH drops, and constitute an important first step in improving our understanding of, and ultimately combatting, HdeA activity.     

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.     

Structural dynamics of protein backbone φ angles: extended molecular dynamics simulations versus experimental 3 J scalar couplings

³ J scalar couplings report on the conformational averaging of backbone φ angles in peptides and proteins, and therefore represent a potentially powerful tool for studying the details of both structure and dynamics in solution. We have compared an extensive experimental dataset with J-couplings predicted from unrestrained molecular dynamics simulation using enhanced sampling available from accelerated molecular dynamics or using long timescale trajectories (200 ns). The dynamic fluctuations predicted to be present along the backbone, in agreement with residual dipolar coupling analysis, are compatible with the experimental ³ J scalar couplings providing a slightly better reproduction of these experimental parameters than a high-resolution static structure.     

Backbone and side-chain resonance assignments of the membrane localization domain from Pasteurella multocida toxin

¹H, ¹³C, and ¹⁵N chemical shift assignments are presented for the isolated four-helical bundle membrane localization domain (MLD) from Pasteurella multocida toxin (PMT) in its solution state. We have assigned 99 % of all backbone and side-chain carbon atoms, including 99 % of all backbone residues excluding proline amide nitrogens. Secondary chemical shift analysis using TALOS+ demonstrates four helices, which align with those observed within the MLD in the crystal structure of the C-terminus of PMT (PDB 2EBF) and confirm the use of the available crystal structures as templates for the isolated MLDs.     

A NMR experiment for simultaneous correlations of valine and leucine/isoleucine methyls with carbonyl chemical shifts in proteins

A methyl-detected ‘out-and-back’ NMR experiment for obtaining simultaneous correlations of methyl resonances of valine and isoleucine/leucine residues with backbone carbonyl chemical shifts, SIM-HMCM(CGCBCA)CO, is described. The developed pulse-scheme serves the purpose of convenience in recording a single data set for all Ile^δ1, Leu^δ and Val^γ (ILV) methyl positions instead of acquiring two separate spectra selective for valine or leucine/isoleucine residues. The SIM-HMCM(CGCBCA)CO experiment can be used for ILV methyl assignments in moderately sized protein systems (up to ~100 kDa) where the backbone chemical shifts of ¹³C^α, ¹³C_β and ¹³CO are known from prior NMR studies and where some losses in sensitivity can be tolerated for the sake of an overall reduction in NMR acquisition time.     

Prediction of Xaa-Pro peptide bond conformation from sequence and chemical shifts

We present a program, named Promega, to predict the Xaa-Pro peptide bond conformation on the basis of backbone chemical shifts and the amino acid sequence. Using a chemical shift database of proteins of known structure together with the PDB-extracted amino acid preference of cis Xaa-Pro peptide bonds, a cis/trans probability score is calculated from the backbone and ¹³C^β chemical shifts of the proline and its neighboring residues. For an arbitrary number of input chemical shifts, which may include Pro-¹³C^γ, Promega calculates the statistical probability that a Xaa-Pro peptide bond is cis. Besides its potential as a validation tool, Promega is particularly useful for studies of larger proteins where Pro-¹³C^γ assignments can be challenging, and for on-going efforts to determine protein structures exclusively on the basis of backbone and ¹³C^β chemical shifts.     

A reduced dimensionality NMR pulse sequence and an efficient protocol for unambiguous assignment in intrinsically disordered proteins

Resonance assignment in intrinsically disordered proteins poses a great challenge because of poor chemical shift dispersion in most of the nuclei that are commonly monitored. Reduced dimensionality (RD) experiments where more than one nuclei are co-evolved simultaneously along one of the time axes of a multi-dimensional NMR experiment help to resolve this problem partially, and one can conceive of different combinations of nuclei for co-evolution depending upon the magnetization transfer pathways and the desired information content in the spectrum. Here, we present a RD experiment, (4,3)D-hNCOCAnH, which uses a combination of CO and CA chemical shifts along one of the axes of the 3-dimensional spectrum, to improve spectral dispersion on one hand, and provide information on four backbone atoms of every residue—HN, N, CA and CO chemical shifts—from a single experiment, on the other. The experiment provides multiple unidirectional sequential (i → i ? 1) amide ¹H correlations along different planes of the spectrum enabling easy assignment of most nuclei along the protein backbone. Occasional ambiguities that may arise due to degeneracy of amide proton chemical shifts are proposed to be resolved using the HNN experiment described previously (Panchal et al. in J Biomol NMR 20:135–147, 2001). Applications of the experiment and the assignment protocol have been demonstrated using intrinsically disordered α-synuclein (140 aa) protein.     

Type I and II β-turns prediction using NMR chemical shifts

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of C_ο, C_α, H_N, and N_H chemical shifts were generally (i + 1) > (i + 2). The mean values of C_β and H_α chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2 % with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.     

Cα and Cβ Carbon-13 Chemical Shifts in Proteins From an Empirical Database

We have constructed an extensive database of 13C Cα and Cβ chemical shifts in proteins of solution, for proteins of which a high-resolution crystal structure exists, and for which the crystal structure has been shown to be essentially identical to the solution structure. There is no systematic effect of temperature, reference compound, or pH on reported shifts, but there appear to be differences in reported shifts arising from referencing differences of up to 4.2 ppm. The major factor affecting chemical shifts is the backbone geometry, which causes differences of ca. 4 ppm between typical α- helix and β-sheet geometries for Cα, and of ca. 2 ppm for Cβ. The side-chain dihedral angle χ1 has an effect of up to 0.5 ppm on the Cα shift, particularly for amino acids with branched side-chains at Cβ. Hydrogen bonding to main-chain atoms has an effect of up to 0.9 ppm, which depends on the main- chain conformation. The sequence of the protein and ring-current shifts from aromatic rings have an insignificant effect (except for residues following proline). There are significant differences between different amino acid types in the backbone geometry dependence; the amino acids can be grouped together into five different groups with different φ,ψ shielding surfaces. The overall fit of individual residues to a single non-residue-specific surface, incorporating the effects of hydrogen bonding and χ1 angle, is 0.96 ppm for both Cα and Cβ. The results from this study are broadly similar to those from ab initio studies, but there are some differences which could merit further attention.     

CSI 2.0: a significantly improved version of the Chemical Shift Index

Protein chemical shifts have long been used by NMR spectroscopists to assist with secondary structure assignment and to provide useful distance and torsion angle constraint data for structure determination. One of the most widely used methods for secondary structure identification is called the Chemical Shift Index (CSI). The CSI method uses a simple digital chemical shift filter to locate secondary structures along the protein chain using backbone ¹³C and ¹H chemical shifts. While the CSI method is simple to use and easy to implement, it is only about 75–80 % accurate. Here we describe a significantly improved version of the CSI (2.0) that uses machine-learning techniques to combine all six backbone chemical shifts (¹³C_α, ¹³C_β, ¹³C, ¹⁵N, ¹HN, ¹H_α) with sequence-derived features to perform far more accurate secondary structure identification. Our tests indicate that CSI 2.0 achieved an average identification accuracy (Q3) of 90.56 % for a training set of 181 proteins in a repeated tenfold cross-validation and 89.35 % for a test set of 59 proteins. This represents a significant improvement over other state-of-the-art chemical shift-based methods. In particular, the level of performance of CSI 2.0 is equal to that of standard methods, such as DSSP and STRIDE, used to identify secondary structures via 3D coordinate data. This suggests that CSI 2.0 could be used both in providing accurate NMR constraint data in the early stages of protein structure determination as well as in defining secondary structure locations in the final protein model(s). A CSI 2.0 web server (http://csi.wishartlab.com) is available for submitting the input queries for secondary structure identification.