BSH-CP based 3D solid-state NMR experiments for protein resonance assignment

We have recently presented band-selective homonuclear cross-polarization (BSH-CP) as an efficient method for CO–CA transfer in deuterated as well as protonated solid proteins. Here we show how the BSH-CP CO–CA transfer block can be incorporated in a set of three-dimensional (3D) solid-state NMR (ssNMR) pulse schemes tailored for resonance assignment of proteins at high static magnetic fields and moderate magic-angle spinning rates. Due to the achieved excellent transfer efficiency of 33 % for BSH-CP, a complete set of 3D spectra needed for unambiguous resonance assignment could be rapidly recorded within 1 week for the model protein ubiquitin. Thus we expect that BSH-CP could replace the typically used CO–CA transfer schemes in well-established 3D ssNMR approaches for resonance assignment of solid biomolecules.     

Automated protein backbone assignment using the projection-decomposition approach

Production of sufficient amounts of human proteins is a frequent bottleneck in structural biology. Here we describe an Escherichia coli-based cell-free system which yields mg-quantities of human proteins in N-terminal fusion constructs with the GB1 domain, which show significantly increased translation efficiency. A newly generated E. coli BL21 (DE3) RIPL-Star strain was used, which contains a variant RNase E with reduced activity and an excess of rare-codon tRNAs, and is devoid of lon and ompT protease activity. In the implementation of the expression system we used freshly in-house prepared cell extract. Batch-mode cell-free expression with this setup was up to twofold more economical than continuous-exchange expression, with yields of 0.2-0.9 mg of purified protein per mL of reaction mixture. Native folding of the proteins thus obtained is documented with 2D [(15)N,(1)H]-HSQC NMR.     

Automatic assignment of protein backbone resonances by direct spectrum inspection in targeted acquisition of NMR data

The necessity to acquire large multidimensional datasets, a basis for assignment of NMR resonances, results in long data acquisition times during which substantial degradation of a protein sample might occur. Here we propose a method applicable for such a protein for automatic assignment of backbone resonances by direct inspection of multidimensional NMR spectra. In order to establish an optimal balance between completeness of resonance assignment and losses of cross-peaks due to dynamic processes/degradation of protein, assignment of backbone resonances is set as a stirring criterion for dynamically controlled targeted nonlinear NMR data acquisition. The result is demonstrated with the 12 kDa ¹³C,¹⁵ N-labeled apo-form of heme chaperone protein CcmE, where hydrolytic cleavage of 29 C-terminal amino acids is detected. For this protein, 90 and 98% of manually assignable resonances are automatically assigned within 10 and 40 h of nonlinear sampling of five 3D NMR spectra, respectively, instead of 600 h needed to complete the full time domain grid. In addition, resonances stemming from degradation products are identified. This study indicates that automatic resonance assignment might serve as a guiding criterion for optimal run-time allocation of NMR resources in applications to proteins prone to degradation. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Automated solid-state NMR resonance assignment of protein microcrystals and amyloids

Solid-state NMR is an emerging structure determination technique for crystalline and non-crystalline protein assemblies, e.g., amyloids. Resonance assignment constitutes the first and often very time-consuming step to a structure. We present ssFLYA, a generally applicable algorithm for automatic assignment of protein solid-state NMR spectra. Application to microcrystals of ubiquitin and the Ure2 prion C-terminal domain, as well as amyloids of HET-s(218–289) and α-synuclein yielded 88–97 % correctness for the backbone and side-chain assignments that are classified as self-consistent by the algorithm, and 77–90 % correctness if also assignments classified as tentative by the algorithm are included.     

RDC derived protein backbone resonance assignment using fragment assembly

Experimental residual dipolar couplings (RDCs) in combination with structural models have the potential for accelerating the protein backbone resonance assignment process because RDCs can be measured accurately and interpreted quantitatively. However, this application has been limited due to the need for very high-resolution structural templates. Here, we introduce a new approach to resonance assignment based on optimal agreement between the experimental and calculated RDCs from a structural template that contains all assignable residues. To overcome the inherent computational complexity of such a global search, we have adopted an efficient two-stage search algorithm and included connectivity data from conventional assignment experiments. In the first stage, a list of strings of resonances (CA-links) is generated via exhaustive searches for short segments of sequentially connected residues in a protein (local templates), and then ranked by the agreement of the experimental ¹³C_α chemical shifts and ¹⁵N-¹H RDCs to the predicted values for each local template. In the second stage, the top CA-links for different local templates in stage I are combinatorially connected to produce CA-links for all assignable residues. The resulting CA-links are ranked for resonance assignment according to their measured RDCs and predicted values from a tertiary structure. Since the final RDC ranking of CA-links includes all assignable residues and the assignment is derived from a “global minimum”, our approach is far less reliant on the quality of experimental data and structural templates. The present approach is validated with the assignments of several proteins, including a 42 kDa maltose binding protein (MBP) using RDCs and structural templates of varying quality. Since backbone resonance assignment is an essential first step for most of biomolecular NMR applications and is often a bottleneck for large systems, we expect that this new approach will improve the efficiency of the assignment process for small and medium size proteins and will extend the size limits assignable by current methods for proteins with structural models.     

Quantitative analysis of backbone motion in proteins using MAS solid-state NMR spectroscopy

We present a comprehensive analysis of protein dynamics for a micro-crystallin protein in the solid-state. Experimental data include ¹⁵N T ₁ relaxation times measured at two different magnetic fields as well as ¹H–¹⁵N dipole, ¹⁵N CSA cross correlated relaxation rates which are sensitive to the spectral density function J(0) and are thus a measure of T ₂ in the solid-state. In addition, global order parameters are included from a ¹H,¹⁵N dipolar recoupling experiment. The data are analyzed within the framework of the extended model-free Clore–Lipari–Szabo theory. We find slow motional correlation times in the range of 5 and 150 ns. Assuming a wobbling in a cone motion, the amplitude of motion of the respective amide moiety is on the order of 10° for the half-opening angle of the cone in most of the cases. The experiments are demonstrated using a perdeuterated sample of the chicken α-spectrin SH3 domain.     

Computational assignment of protein backbone NMR peaks by efficient bounding and filtering

NMR resonance assignment is one of the key steps in solving an NMR protein structure. The assignment process links resonance peaks to individual residues of the target protein sequence, providing the prerequisite for establishing intra- and inter-residue spatial relationships between atoms. The assignment process is tedious and time-consuming, which could take many weeks. Though there exist a number of computer programs to assist the assignment process, many NMR labs are still doing the assignments manually to ensure quality. This paper presents a new computational method based on the combination of a suite of algorithms for automating the assignment process, particularly the process of backbone resonance peak assignment. We formulate the assignment problem as a constrained weighted bipartite matching problem. While the problem, in the most general situation, is NP-hard, we present an efficient solution based on a branch-and-bound algorithm with effective bounding techniques using two recently introduced approximation algorithms. We also devise a greedy filtering algorithm for reducing the search space. Our experimental results on 70 instances of (pseudo) real NMR data derived from 14 proteins demonstrate that the new solution runs much faster than a recently introduced (exhaustive) two-layer algorithm and recovers more correct peak assignments than the two-layer algorithm. Our result demonstrates that integrating different algorithms can achieve a good tradeoff between backbone assignment accuracy and computation time.     

Inferential backbone assignment for sparse data

This paper develops an approach to protein backbone NMR assignment that effectively assigns large proteins while using limited sets of triple-resonance experiments. Our approach handles proteins with large fractions of missing data and many ambiguous pairs of pseudoresidues, and provides a statistical assessment of confidence in global and position-specific assignments. The approach is tested on an extensive set of experimental and synthetic data of up to 723 residues, with match tolerances of up to 0.5 ppm for and resonance types. The tests show that the approach is particularly helpful when data contain experimental noise and require large match tolerances. The keys to the approach are an empirical Bayesian probability model that rigorously accounts for uncertainty in the data at all stages in the analysis, and a hybrid stochastic tree-based search algorithm that effectively explores the large space of possible assignments.     

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.     

GANA--a genetic algorithm for NMR backbone resonance assignment

NMR data from different experiments often contain errors; thus, automated backbone resonance assignment is a very challenging issue. In this paper, we present a method called GANA that uses a genetic algorithm to automatically perform backbone resonance assignment with a high degree of precision and recall. Precision is the number of correctly assigned residues divided by the number of assigned residues, and recall is the number of correctly assigned residues divided by the number of residues with known human curated answers. GANA takes spin systems as input data and uses two data structures, candidate lists and adjacency lists, to assign the spin systems to each amino acid of a target protein. Using GANA, almost all spin systems can be mapped correctly onto a target protein, even if the data are noisy. We use the BioMagResBank (BMRB) dataset (901 proteins) to test the performance of GANA. To evaluate the robustness of GANA, we generate four additional datasets from the BMRB dataset to simulate data errors of false positives, false negatives and linking errors. We also use a combination of these three error types to examine the fault tolerance of our method. The average precision rates of GANA on BMRB and the four simulated test cases are 99.61, 99.55, 99.34, 99.35 and 98.60%, respectively. The average recall rates of GANA on BMRB and the four simulated test cases are 99.26, 99.19, 98.85, 98.87 and 97.78%, respectively. We also test GANA on two real wet-lab datasets, hbSBD and hbLBD. The precision and recall rates of GANA on hbSBD are 95.12 and 92.86%, respectively, and those of hbLBD are 100 and 97.40%, respectively.     

Improved 4D NMR experiments for the assignment of backbone nuclei in13C/15N labelled proteins

Summary We recently proposed a novel 4D NMR strategy for the assignment of backbone nuclei in¹³C/¹⁵N-labelled proteins (Boucher et al., 1992). Intra-residue (and many sequential) assignments are obtained from a HCANNH experiment, whereas sequential assignments are based on a complementary HCA(CO)NNH experiment. We present here new constant time 4D HCANNH, HCA(CO)NNH and HNCAHA experiments that are more sensitive. Some of the data were presented at the 33rd ENC held at Asilomar, California, U.S.A., in April 1992.     

Novel projected 4D triple resonance experiments for polypeptide backbone chemical shift assignment

Here we present a novel suite of projected 4D triple-resonance NMR experiments for efficient sequential assignment of polypeptide backbone chemical shifts in ¹³C/¹⁵N doubly labeled proteins. In the 3D HNN[CAHA] and 3D HNN(CO)[CAHA] experiments, the ¹³C and ¹H chemical shifts evolve in a common dimension and are simultaneously detected in quadrature. These experiments are particularly useful for the assignment of glycine-rich polypeptide segments. Appropriate setting of the ¹H radiofrequency carrier allows one to place cross peaks correlating either backbone ¹⁵N/¹H^N/¹³C or ¹⁵N/¹H^N/¹H chemical shifts in separate spectral regions. Hence, peak overlap is not increased when compared with the conventional 3D HNNCA and HNN(CA)HA. 3D HNN[CAHA] and 3D HNN(CO)[CAHA] are complemented by 3D reduced-dimensionality (RD) HNN COCA and HNN CACO, where ¹³C and ¹³C chemical shifts evolve in a common dimension. The ¹³C shift is detected in quadrature, which yields peak pairs encoding the ¹³C chemical shift in an in-phase splitting. This suite of four experiments promises to be of value for automated high-throughput NMR structure determination in structural genomics, where the requirement to independently sample many indirect dimensions in a large number of NMR experiments may prevent one from accurately adjusting NMR measurement times to spectrometer sensitivity.     

An efficient randomized algorithm for contact-based NMR backbone resonance assignment

MOTIVATION: Backbone resonance assignment is a critical bottleneck in studies of protein structure, dynamics and interactions by nuclear magnetic resonance (NMR) spectroscopy. A minimalist approach to assignment, which we call 'contact-based', seeks to dramatically reduce experimental time and expense by replacing the standard suite of through-bond experiments with the through-space (nuclear Overhauser enhancement spectroscopy, NOESY) experiment. In the contact-based approach, spectral data are represented in a graph with vertices for putative residues (of unknown relation to the primary sequence) and edges for hypothesized NOESY interactions, such that observed spectral peaks could be explained if the residues were 'close enough'. Due to experimental ambiguity, several incorrect edges can be hypothesized for each spectral peak. An assignment is derived by identifying consistent patterns of edges (e.g. for alpha-helices and beta-sheets) within a graph and by mapping the vertices to the primary sequence. The key algorithmic challenge is to be able to uncover these patterns even when they are obscured by significant noise. RESULTS: This paper develops, analyzes and applies a novel algorithm for the identification of polytopes representing consistent patterns of edges in a corrupted NOESY graph. Our randomized algorithm aggregates simplices into polytopes and fixes inconsistencies with simple local modifications, called rotations, that maintain most of the structure already uncovered. In characterizing the effects of experimental noise, we employ an NMR-specific random graph model in proving that our algorithm gives optimal performance in expected polynomial time, even when the input graph is significantly corrupted. We confirm this analysis in simulation studies with graphs corrupted by up to 500% noise. Finally, we demonstrate the practical application of the algorithm on several experimental beta-sheet datasets. Our approach is able to eliminate a large majority of noise edges and to uncover large consistent sets of interactions. AVAILABILITY: Our algorithm has been implemented in the platform-independent Python code. The software can be freely obtained for academic use by request from the authors.     

RIBRA--an error-tolerant algorithm for the NMR backbone assignment problem.

We develop an iterative relaxation algorithm called RIBRA for NMR protein backbone assignment. RIBRA applies nearest neighbor and weighted maximum independent set algorithms to solve the problem. To deal with noisy NMR spectral data, RIBRA is executed in an iterative fashion based on the quality of spectral peaks. We first produce spin system pairs using the spectral data without missing peaks, then the data group with one missing peak, and finally, the data group with two missing peaks. We test RIBRA on two real NMR datasets, hbSBD and hbLBD, and perfect BMRB data (with 902 proteins) and four synthetic BMRB data which simulate four kinds of errors. The accuracy of RIBRA on hbSBD and hbLBD are 91.4% and 83.6%, respectively. The average accuracy of RIBRA on perfect BMRB datasets is 98.28%, and 98.28%, 95.61%, 98.16%, and 96.28% on four kinds of synthetic datasets, respectively.     

Perspectives for sensitivity enhancement in proton-detected solid-state NMR of highly deuterated proteins by preserving water magnetization

In this work, we show how the water flip-back approach that is widely employed in solution-state NMR can be adapted to proton-detected MAS solid-state NMR of highly deuterated proteins. The scheme allows to enhance the sensitivity of the experiment by decreasing the recovery time of the proton longitudinal magnetization. The method relies on polarization transfer from non-saturated water to the protein during the inter-scan delay.     

Automated sequence-specific protein NMR assignment using the memetic algorithm MATCH

MATCH (Memetic Algorithm and Combinatorial Optimization Heuristics) is a new memetic algorithm for automated sequence-specific polypeptide backbone NMR assignment of proteins. MATCH employs local optimization for tracing partial sequence-specific assignments within a global, population-based search environment, where the simultaneous application of local and global optimization heuristics guarantees high efficiency and robustness. MATCH thus makes combined use of the two predominant concepts in use for automated NMR assignment of proteins. Dynamic transition and inherent mutation are new techniques that enable automatic adaptation to variable quality of the experimental input data. The concept of dynamic transition is incorporated in all major building blocks of the algorithm, where it enables switching between local and global optimization heuristics at any time during the assignment process. Inherent mutation restricts the intrinsically required randomness of the evolutionary algorithm to those regions of the conformation space that are compatible with the experimental input data. Using intact and artificially deteriorated APSY-NMR input data of proteins, MATCH performed sequence-specific resonance assignment with high efficiency and robustness.     

Spectral editing of two-dimensional magic-angle-spinning solid-state NMR spectra for protein resonance assignment and structure determination

Several techniques for spectral editing of 2D ¹³C?C¹³C correlation NMR of proteins are introduced. They greatly reduce the spectral overlap for five common amino acid types, thus simplifying spectral assignment and conformational analysis. The carboxyl (COO) signals of glutamate and aspartate are selected by suppressing the overlapping amide N?CCO peaks through ¹³C?C¹⁵N dipolar dephasing. The sidechain methine (CH) signals of valine, lecuine, and isoleucine are separated from the overlapping methylene (CH₂) signals of long-chain amino acids using a multiple-quantum dipolar transfer technique. Both the COO and CH selection methods take advantage of improved dipolar dephasing by asymmetric rotational-echo double resonance (REDOR), where every other ??-pulse is shifted from the center of a rotor period t_r by about 0.15 t_r. This asymmetry produces a deeper minimum in the REDOR dephasing curve and enables complete suppression of the undesired signals of immobile segments. Residual signals of mobile sidechains are positively identified by dynamics editing using recoupled ¹³C?C¹H dipolar dephasing. In all three experiments, the signals of carbons within a three-bond distance from the selected carbons are detected in the second spectral dimension via ¹³C spin exchange. The efficiencies of these spectral editing techniques range from 60?% for the COO and dynamic selection experiments to 25?% for the CH selection experiment, and are demonstrated on well-characterized model proteins GB1 and ubiquitin.