NMR: prediction of protein flexibility

We present a protocol for predicting protein flexibility from NMR chemical shifts. The protocol consists of (i) ensuring that the chemical shift assignments are correctly referenced or, if not, performing a reference correction using information derived from the chemical shift index, (ii) calculating the random coil index (RCI), and (iii) predicting the expected root mean square fluctuations (RMSFs) and order parameters (S2) of the protein from the RCI. The key advantages of this protocol over existing methods for studying protein dynamics are that (i) it does not require prior knowledge of a protein's tertiary structure, (ii) it is not sensitive to the protein's overall tumbling and (iii) it does not require additional NMR measurements beyond the standard experiments for backbone assignments. When chemical shift assignments are available, protein flexibility parameters, such as S2 and RMSF, can be calculated within 1-2 h using a spreadsheet program.     

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.     

PROSHIFT: protein chemical shift prediction using artificial neural networks

The importance of protein chemical shift values for the determination of three-dimensional protein structure has increased in recent years because of the large databases of protein structures with assigned chemical shift data. These databases have allowed the investigation of the quantitative relationship between chemical shift values obtained by liquid state NMR spectroscopy and the three-dimensional structure of proteins. A neural network was trained to predict the ¹H, ¹³C, and ¹⁵N of proteins using their three-dimensional structure as well as experimental conditions as input parameters. It achieves root mean square deviations of 0.3 ppm for hydrogen, 1.3 ppm for carbon, and 2.6 ppm for nitrogen chemical shifts. The model reflects important influences of the covalent structure as well as of the conformation not only for backbone atoms (as, e.g., the chemical shift index) but also for side-chain nuclei. For protein models with a RMSD smaller than 5 Å a correlation of the RMSD and the r.m.s. deviation between the predicted and the experimental chemical shift is obtained. Thus the method has the potential to not only support the assignment process of proteins but also help with the validation and the refinement of three-dimensional structural proposals. It is freely available for academic users at the PROSHIFT server: www.jens-meiler.de/proshift.html     

Direct methods and residue type specific isotope labeling in NMR structure determination and model-driven sequential assignment

Direct methods in NMR based structure determination start from an unassigned ensemble of unconnected gaseous hydrogen atoms. Under favorable conditions they can produce low resolution structures of proteins. Usually a prohibitively large number of NOEs is required, to solve a protein structure ab-initio, but even with a much smaller set of distance restraints low resolution models can be obtained which resemble a protein fold. One problem is that at such low resolution and in the absence of a force field it is impossible to distinguish the correct protein fold from its mirror image. In a hybrid approach these ambiguous models have the potential to aid in the process of sequential backbone chemical shift assignment when ¹³C^β and ¹³C′ shifts are not available for sensitivity reasons. Regardless of the overall fold they enhance the information content of the NOE spectra. These, combined with residue specific labeling and minimal triple-resonance data using ¹³C^α connectivity can provide almost complete sequential assignment. Strategies for residue type specific labeling with customized isotope labeling patterns are of great advantage in this context. Furthermore, this approach is to some extent error-tolerant with respect to data incompleteness, limited precision of the peak picking, and structural errors caused by misassignment of NOEs. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Solution structure of the first immunoglobulin domain of human myotilin

Myotilin is a 57 kDa actin-binding and -bundling protein that consists of a unique serine-rich amino-terminus, two Ig-domains and a short carboxy-terminus with a PDZ-binding motif. Myotilin localizes in sarcomeric Z-discs, where it interacts with several sarcomeric proteins. Point mutations in myotilin cause muscle disorders morphologically highlighted by sarcomeric disarray and aggregation. The actin-binding and dimerization propensity of myotilin has been mapped to the Ig-domains. Here we present high-resolution structure of the first Ig-domain of myotilin (MyoIg1) determined with solution state NMR spectroscopy. Nearly complete chemical shift assignments of MyoIg1 were achieved despite several missing backbone ¹H-¹⁵N-HSQC signals. The structure derived from distance and dihedral angle restraints using torsion angle dynamics was further refined using molecular dynamics. The structure of MyoIg1 exhibits I-type Ig-fold. The absence of several backbone ¹H-¹⁵N-HSQC signals can be explained by conformational exchange taking place at the hydrophobic core of the protein. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized 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 J_NC'') 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.     

Phosphorylation-induced changes in backbone dynamics of the dematin headpiece C-terminal domain

Dematin is an actin-binding protein abundant in red blood cells and other tissues. It contains a villin-type ‘headpiece’ F-actin-binding domain at its extreme C-terminus. The isolated dematin headpiece domain (DHP) undergoes a significant conformational change upon phosphorylation. The mutation of Ser74 to Glu closely mimics the phosphorylation of DHP. We investigated motions in the backbone of DHP and its mutant DHPS74E using several complementary NMR relaxation techniques: laboratory frame ¹⁵N NMR relaxation, which is sensitive primarily to the ps–ns time scale, cross-correlated chemical shift modulation NMR relaxation detecting correlated μs–ms time scale motions of neighboring ¹³C′ and ¹⁵N nuclei, and cross-correlated relaxation of two ¹⁵N–¹H dipole–dipole interactions detecting slow motions of backbone NH vectors in successive amino acid residues. The results indicate a reduction in mobility upon the mutation in several regions of the protein. The additional salt bridge formed in DHPS74E that links the N- and C-terminal subdomains is likely to be responsible for these changes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

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 ().     

Highly automated protein backbone resonance assignment within a few hours: the «BATCH» strategy and software package

Sequential resonance assignment represents an essential step towards the investigation of protein structure, dynamics, and interaction surfaces. Although the experimental sensitivity has significantly increased in recent years, with the availability of high field magnets and cryogenically cooled probes, resonance assignment, even of small globular proteins, still generally requires several days of data collection and analysis using standard protocols. Here we introduce the BATCH strategy for fast and highly automated backbone resonance assignment of ¹³C, ¹⁵N-labelled proteins. BATCH makes use of the fast data acquisition and analysis tools BEST, ASCOM, COBRA, and HADAMAC, recently developed in our laboratory. An improved Hadamard encoding scheme, presented here, further increases the performance of the HADAMAC experiment. A new software platform, interfaced to the NMRView software package, has been developed that enables highly automated NMR data processing and analysis, sequential resonance assignment, and ¹³C chemical shift extraction. We demonstrate for four small globular proteins that sequential resonance assignment can be routinely obtained within a few hours, or less, in a highly automated and robust way. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

13C and 15N chemical shift assignments and secondary structure of the B3 immunoglobulin-binding domain of streptococcal protein G by magic-angle spinning solid-state NMR spectroscopy

Complete ¹³C and ¹⁵N assignments of the B3 IgG-binding domain of protein G (GB3) in the microcrystalline solid phase, obtained using 2D and 3D MAS NMR, are presented. The chemical shifts are used to predict the protein backbone conformation and compared with solution-state shifts. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

PASA – A Program for Automated Protein NMR Backbone Signal Assignment by Pattern-Filtering Approach

We present a new program, PASA (Program for Automated Sequential Assignment), for assigning protein backbone resonances based on multidimensional heteronuclear NMR data. Distinct from existing programs, PASA emphasizes a per-residue-based pattern-filtering approach during the initial stage of the automated ¹³C^α and/or ¹³C^β chemical shift matching. The pattern filter employs one or multiple constraints such as ¹³C^α/C^β chemical shift ranges for different amino acid types and side-chain spin systems, which helps to rule out, in a stepwise fashion, improbable assignments as resulted from resonance degeneracy or missing signals. Such stepwise filtering approach substantially minimizes early false linkage problems that often propagate, amplify, and ultimately cause complication or combinatorial explosion of the automation process. Our program (http://www.lerner.ccf.org/moleccard/qin/) was tested on four representative small-large sized proteins with various degrees of resonance degeneracy and missing signals, and we show that PASA achieved the assignments efficiently and rapidly that are fully consistent with those obtained by laborious manual protocols. The results demonstrate that PASA may be a valuable tool for NMR-based structural analyses, genomics, and proteomics. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Backbone 1H, 13C, and 15N chemical shift assignment for HIV-1 protease subtypes and multi-drug resistant variant MDR 769

HIV-1 protease (HIV-1PR) is an essential drug target in the treatment of patients infected with HIV-1. Mutations are found to arise in over 38 of 99 amino acid sites in this protein in response to drug therapy or natural selection, where many are found combinations that alter enzyme kinetics or inhibitor susceptibility without a clear structural mechanism. In efforts to understand how these mutations alter the flexibility and dynamics of HIV-1PR, we report the backbone ¹H, ¹³C, and ¹⁵N chemical shift assignments for subtypes C, circulating recombinant form CRF01_AE and a multi-drug resistant variant MDR 769. These assignments are essential for future work aimed at characterizing backbone dynamics, exchange dynamics and dynamics of protein/substrate or protein/inhibitor interactions.     

Comparative analysis of essential collective dynamics and NMR-derived flexibility profiles in evolutionarily diverse prion proteins

Collective motions on ns-µs time scales are known to have a major impact on protein folding, stability, binding and enzymatic efficiency. It is also believed that these motions may have an important role in the early stages of prion protein misfolding and prion disease. In an effort to accurately characterize these motions and their potential influence on the misfolding and prion disease transmissibility we have conducted a combined analysis of molecular dynamic simulations and NMR-derived flexibility measurements over a diverse range of prion proteins. Using a recently developed numerical formalism, we have analyzed the essential collective dynamics (ECD) for prion proteins from eight different species including human, cow, elk, cat, hamster, chicken, turtle and frog. We also compared the numerical results with flexibility profiles generated by the random coil index (RCI) from NMR chemical shifts. Prion protein backbone flexibility derived from experimental NMR data and from theoretical computations show strong agreement with each other, demonstrating that it is possible to predict the observed RCI profiles employing the numerical ECD formalism. Interestingly, flexibility differences in the loop between second b strand (S2) and the second a helix (HB) appear to distinguish prion proteins from species that are susceptible to prion disease and those that are resistant. Our results show that the different levels of flexibility in the S2-HB loop in various species are predictable via the ECD method, indicating that ECD may be used to identify disease resistant variants of prion proteins, as well as the influence of prion proteins mutations on disease susceptibility or misfolding propensity.Key words: prion proteins structural stability, molecular dynamics simulation, essential collective dynamics, protein dynamic domains, biomolecular NMR, rigid loop     

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.     

Hash: a program to accurately predict protein Hα shifts from neighboring backbone shifts

Chemical shifts provide not only peak identities for analyzing nuclear magnetic resonance (NMR) data, but also an important source of conformational information for studying protein structures. Current structural studies requiring H^α chemical shifts suffer from the following limitations. (1) For large proteins, the H^α chemical shifts can be difficult to assign using conventional NMR triple-resonance experiments, mainly due to the fast transverse relaxation rate of C^α that restricts the signal sensitivity. (2) Previous chemical shift prediction approaches either require homologous models with high sequence similarity or rely heavily on accurate backbone and side-chain structural coordinates. When neither sequence homologues nor structural coordinates are available, we must resort to other information to predict H^α chemical shifts. Predicting accurate H^α chemical shifts using other obtainable information, such as the chemical shifts of nearby backbone atoms (i.e., adjacent atoms in the sequence), can remedy the above dilemmas, and hence advance NMR-based structural studies of proteins. By specifically exploiting the dependencies on chemical shifts of nearby backbone atoms, we propose a novel machine learning algorithm, called Hash, to predict H^α chemical shifts. Hash combines a new fragment-based chemical shift search approach with a non-parametric regression model, called the generalized additive model, to effectively solve the prediction problem. We demonstrate that the chemical shifts of nearby backbone atoms provide a reliable source of information for predicting accurate H^α chemical shifts. Our testing results on different possible combinations of input data indicate that Hash has a wide rage of potential NMR applications in structural and biological studies of proteins.     

PCS-based structure determination of protein–protein complexes

A simple and fast nuclear magnetic resonance method for docking proteins using pseudo-contact shift (PCS) and ¹H^N/¹⁵N chemical shift perturbation is presented. PCS is induced by a paramagnetic lanthanide ion that is attached to a target protein using a lanthanide binding peptide tag anchored at two points. PCS provides long-range (~40 Å) distance and angular restraints between the lanthanide ion and the observed nuclei, while the ¹H^N/¹⁵N chemical shift perturbation data provide loose contact-surface information. The usefulness of this method was demonstrated through the structure determination of the p62 PB1-PB1 complex, which forms a front-to-back 20 kDa homo-oligomer. As p62 PB1 does not intrinsically bind metal ions, the lanthanide binding peptide tag was attached to one subunit of the dimer at two anchoring points. Each monomer was treated as a rigid body and was docked based on the backbone PCS and backbone chemical shift perturbation data. Unlike NOE-based structural determination, this method only requires resonance assignments of the backbone ¹H^N/¹⁵N signals and the PCS data obtained from several sets of two-dimensional ¹⁵N-heteronuclear single quantum coherence spectra, thus facilitating rapid structure determination of the protein–protein complex.     

Comparative analysis of essential collective dynamics and NMR-derived flexibility profiles in evolutionarily diverse prion proteins

Collective motions on ns-µs time scales are known to have a major impact on protein folding, stability, binding and enzymatic efficiency. It is also believed that these motions may have an important role in the early stages of prion protein misfolding and prion disease. In an effort to accurately characterize these motions and their potential influence on the misfolding and prion disease transmissibility we have conducted a combined analysis of molecular dynamic simulations and NMR-derived flexibility measurements over a diverse range of prion proteins. Using a recently developed numerical formalism, we have analyzed the essential collective dynamics (ECD) for prion proteins from eight different species including human, cow, elk, cat, hamster, chicken, turtle and frog. We also compared the numerical results with flexibility profiles generated by the random coil index (RCI) from NMR chemical shifts. Prion protein backbone flexibility derived from experimental NMR data and from theoretical computations show strong agreement with each other, demonstrating that it is possible to predict the observed RCI profiles employing the numerical ECD formalism. Interestingly, flexibility differences in the loop between second b strand (S2) and the second a helix (HB) appear to distinguish prion proteins from species that are susceptible to prion disease and those that are resistant. Our results show that the different levels of flexibility in the S2-HB loop in various species are predictable via the ECD method, indicating that ECD may be used to identify disease resistant variants of prion proteins, as well as the influence of prion proteins mutations on disease susceptibility or misfolding propensity.     

Utilization of Methyl Proton Resonances in Cross-Saturation Measurement for Determining the Interfaces of Large Protein–Protein Complexes

Cross-saturation experiments allow the identification of the contact residues of large protein complexes (MW>50 K) more rigorously than conventional NMR approaches which involve chemical shift perturbations and hydrogen-deuterium exchange experiments [Takahashi et al. (2000) Nat. Struct. Biol., 7, 220–223]. In the amide proton-based cross-saturation experiment, the combined use of high deuteration levels for non-exchangeable protons of the ligand protein and a solvent with a low concentration of ¹H₂O greatly enhanced the selectivity of the intermolecular cross-saturation phenomenon. Unfortunately, experimental limitations caused losses in sensitivity. Furthermore, since main chain amide protons are not generally exposed to solvent, the efficiency of the saturation transfer directed to the main chain amide protons is not very high. Here we propose an alternative cross-saturation experiment which utilizes the methyl protons of the side chains of the ligand protein. Owing to the fast internal rotation along the methyl axis, we theoretically and experimentally demonstrated the enhanced efficiency of this approach. The methyl-utilizing cross-saturation experiment has clear advantages in sensitivity and saturation transfer efficiency over the amide proton-based approach. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.