RASP: rapid and robust backbone chemical shift assignments from protein structure

Chemical shift prediction has an unappreciated power to guide backbone resonance assignment in cases where protein structure is known. Here we describe Resonance Assignment by chemical Shift Prediction (RASP), a method that exploits this power to derive protein backbone resonance assignments from chemical shift predictions. Robust assignments can be obtained from a minimal set of only the most sensitive triple-resonance experiments, even for spectroscopically challenging proteins. Over a test set of 154 proteins RASP assigns 88 % of residues with an accuracy of 99.7 %, using only information available from HNCO and HNCA spectra. Applied to experimental data from a challenging 34 kDa protein, RASP assigns 90 % of manually assigned residues using only 40 % of the experimental data required for the manual assignment. RASP has the potential to significantly accelerate the backbone assignment process for a wide range of proteins for which structural information is available, including those for which conventional assignment strategies are not feasible.     

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

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

Sequential assignment of 1H, 13C and 15N resonances of human carbonic anhydrase I by triple-resonance NMR techniques and extensive amino acid-specific 15N-labeling

Summary The backbone NMR resonances of human carbonic anhydase I (HCA I) have been assigned. This protein is one of the largest monomeric proteins assigned so far. The assignment was enabled by a combination of 3D triple-resonance experiments and extensive use of amino acid-specific ¹⁵N-labeling. The obtained resonance assignment has been used to evaluate the secondary structure elements present in solution. The solution structure appears to be very similar to the crystal structure, although some differences can be observed. Proton-deuteron exchange experiments have shown that the assignments provide probes that can be used in future folding studies of HCA I.The chemical shift data have been deposited in the BioMagResBank in Madison, WI, U.S.A.     

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.     

Efficient side-chain and backbone assignment in large proteins: Application to tGCN5

In determining the structure of large proteins by NMR, it would be desirable to obtain complete backbone, side-chain, and NOE assignments efficiently, with a minimum number of experiments and samples. Although new strategies have made backbone assignment highly efficient, side-chain assignment has remained more difficult. Faced with the task of assigning side-chains in a protein with poor relaxation properties, the Tetrahymena histone acetyltransferase tGCN5, we have developed an assignment strategy that would provide complete side-chain assignments in cases where fast ¹³C transverse relaxation causes HCCH-TOCSY experiments to fail. Using the strategy presented here, the majority of aliphatic side-chain proton and carbon resonances can be efficiently obtained using optimized H(CC-CO)NH-TOCSY and (H)C(C-CO)NH-TOCSY experiments on a partially deuterated protein sample. Assignments can be completed readily using additional information from a ¹³ C-dispersed NOESY-HSQC spectrum. Combination of these experiments with H(CC)NH-TOCSY and (H)C(C)NH-TOCSY may provide complete backbone and side-chain assignments for large proteins using only one or two samples.     

PACES: Protein sequential assignment by computer-assisted exhaustive search

A crucial step in determining solution structures of proteins using nuclear magnetic resonance (NMR) spectroscopy is the process of sequential assignment, which correlates backbone resonances to corresponding residues in the primary sequence of a protein, today, typically using data from triple-resonance NMR experiments. Although the development of automated approaches for sequential assignment has greatly facilitated this process, the performance of these programs is usually less satisfactory for large proteins, especially in the cases of missing connectivity or severe chemical shift degeneracy. Here, we report the development of a novel computer-assisted method for sequential assignment, using an algorithm that conducts an exhaustive search of all spin systems both for establishing sequential connectivities and then for assignment. By running the program iteratively with user intervention after each cycle, ambiguities in the assignments can be eliminated efficiently and backbone resonances can be assigned rapidly. The efficiency and robustness of this approach have been tested with 27 proteins of sizes varying from 76 amino acids to 723 amino acids, and with data of varying qualities, using experimental data for three proteins, and published assignments modified with simulated noise for the other 24. The complexity of sequential assignment with regard to the size of the protein, the completeness of NMR data sets, and the uncertainty in resonance positions has been examined.Supplementary material to this paper is available in electronic form at http://dx.doi.org/10.1023/A:1023589029301     

CAMRA: Chemical shift based computer aided protein NMR assignments

A suite of programs called CAMRA (Computer Aided Magnetic Resonance Assignment) has been developed for computer assisted residue-specific assignments of proteins. CAMRA consists of three units: ORB, CAPTURE and PROCESS. ORB predicts NMR chemical shifts for unassigned proteins using a chemical shift database of previously assigned homologous proteins supplemented by a statistically derived chemical shift database in which the shifts are categorized according to their residue, atom and secondary structure type. CAPTURE generates a list of valid peaks from NMR spectra by filtering out noise peaks and other artifacts and then separating the derived peak list into distinct spin systems. PROCESS combines the chemical shift predictions from ORB with the spin systems identified by CAPTURE to obtain residue specific assignments. PROCESS ranks the top choices for an assignment along with scores and confidence values. In contrast to other auto-assignment programs, CAMRA does not use any connectivity information but instead is based solely on matching predicted shifts with observed spin systems. As such, CAMRA represents a new and unique approach for the assignment of protein NMR spectra. CAMRA will be particularly useful in conjunction with other assignment methods and under special circumstances, such as the assignment of flexible regions in proteins where sufficient NOE information is generally not available. CAMRA was tested on two medium-sized proteins belonging to the chemokine family. It was found to be effective in predicting the assignment providing a database of previously assigned proteins with at least 30% sequence identity is available. CAMRA is versatile and can be used to include and evaluate heteronuclear and three-dimensional experiments.     

Domain assignment for protein structures using a consensus approach: characterization and analysis.

A consensus approach for the assignment of structural domains in proteins is presented. The approach combines a number of previously published algorithms, and takes advantage of the elevated accuracy obtained when assignments from the individual algorithms are in agreement. The consensus approach is tested on a data set of 55 protein chains, for which domain assignments from four automated methods were known, and for which crystallographers assignments had been reported in the literature. Accuracy was found to increase in this test from 72% using individual algorithms to 100% when all four methods were in agreement. However a consensus prediction using all four methods was only possible for 52% of the dataset. The consensus approach [using three publicly available domain assignment algorithms (PUU, DETECTIVE, DOMAK)] was then used to make domain assignments for a data set of 787 protein chains from the Protein Data Bank. Analysis of the assignments showed 55.7% of assignments could be made automatically, and of these, 13.5% were multi-domain proteins. Of the remaining 44.3% that could not be assigned by the consensus procedure 90.4% had their domain boundaries assigned correctly by at least one of the algorithms. Once identified, these domains were analyzed for trends in their size and secondary structure class. In addition, the discontinuity of each domain along the protein chain was considered.     

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.     

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.     

Modification site localization scoring integrated into a search engine

Large proteomic data sets identifying hundreds or thousands of modified peptides are becoming increasingly common in the literature. Several methods for assessing the reliability of peptide identifications both at the individual peptide or data set level have become established. However, tools for measuring the confidence of modification site assignments are sparse and are not often employed. A few tools for estimating phosphorylation site assignment reliabilities have been developed, but these are not integral to a search engine, so require a particular search engine output for a second step of processing. They may also require use of a particular fragmentation method and are mostly only applicable for phosphorylation analysis, rather than post-translational modifications analysis in general. In this study, we present the performance of site assignment scoring that is directly integrated into the search engine Protein Prospector, which allows site assignment reliability to be automatically reported for all modifications present in an identified peptide. It clearly indicates when a site assignment is ambiguous (and if so, between which residues), and reports an assignment score that can be translated into a reliability measure for individual site assignments.     

An efficient high-throughput resonance assignment procedure for structural genomics and protein folding research by NMR.

Sequence specific resonance assignment is the primary requirement for all investigations of proteins by NMR methods. In the present postgenomic era where structural genomics and protein folding have occupied the center stage of NMR research, there is a high demand on the speed of resonance assignment, whereas the presently available methods based either on NOESY or on some triple-resonance experiments are rather slow. They also have limited success with unfolded proteins because of the lack of NOEs, and poor dispersion of amide and carbon chemical shifts. This paper describes an efficient approach to rapid resonance assignment that is suitable for both folded and unfolded proteins, making use of the triple-resonance experiments described recently [HNN and HN(C)N]. It has three underlying principles. First, the experiments exploit the (15)N chemical shift dispersions which are generally very good for both folded and unfolded proteins, along two of the three dimensions; second, they directly display sequential amide and (15)N correlations along the polypeptide chain, and third, the sign patterns of the diagonal and the sequential peaks originating from any residue are dependent on the nature of the adjacent residues, especially the glycines and the prolines. These lead to so-called "triplet fixed points" which serve as starting points and/or check points during the course of sequential walks, and explicit side chains assignment becomes less crucial for unambiguous backbone assignment. These features significantly enhance the speed of data analysis, reduce the amount of experimentation required, and thus result in a substantially faster and unambiguous assignment. Following the amide and (15)N assignments, the other proton and carbon assignments can be obtained in a straightforward manner, from the well-established three-dimensional triple-resonance experiments. We have successfully tested the new approach with different proteins in the molecular mass range of 10-22 kDa, and for illustration, we present here the backbone results on the HIV-1 protease-tethered dimer (molecular mass approximately 22 kDa), both in the folded and in the unfolded forms, the two ends of the folding funnel. We believe that the new assignment approach will be of great value for both structural genomics and protein folding research by NMR.     

Methyl group assignment using pseudocontact shifts with PARAssign

A new version of the program PARAssign has been evaluated for assignment of NMR resonances of the 76 methyl groups in leucines, isoleucines and valines in a 25 kDa protein, using only the structure of the protein and pseudocontact shifts (PCS) generated with a lanthanoid tag at up to three attachment sites. The number of reliable assignments depends strongly on two factors. The principle axes of the magnetic susceptibility tensors of the paramagnetic centers should not be parallel so as to avoid correlated PCS. Second, the fraction of resonances in the spectrum of a paramagnetic sample that can be paired with the diamagnetic counterparts is critical for the assignment. With the data from two tag positions a reliable assignment could be obtained for 60% of the methyl groups and for many of the remaining resonances the number of possible assignments is limited to two or three. With a single tag, reliable assignments can be obtained for methyl groups with large PCS near the tag. It is concluded that assignment of methyl group resonances by paramagnetic tagging can be particularly useful in combination with some additional data, such as from mutagenesis or NOE-based experiments. Approaches to yield the best assignment results with PCS generating tags are discussed.     

Protein side-chain resonance assignment and NOE assignment using RDC-defined backbones without TOCSY data

One bottleneck in NMR structure determination lies in the laborious and time-consuming process of side-chain resonance and NOE assignments. Compared to the well-studied backbone resonance assignment problem, automated side-chain resonance and NOE assignments are relatively less explored. Most NOE assignment algorithms require nearly complete side-chain resonance assignments from a series of through-bond experiments such as HCCH-TOCSY or HCCCONH. Unfortunately, these TOCSY experiments perform poorly on large proteins. To overcome this deficiency, we present a novel algorithm, called Nasca (NOE Assignment and Side-Chain Assignment), to automate both side-chain resonance and NOE assignments and to perform high-resolution protein structure determination in the absence of any explicit through-bond experiment to facilitate side-chain resonance assignment, such as HCCH-TOCSY. After casting the assignment problem into a Markov Random Field (MRF), Nasca extends and applies combinatorial protein design algorithms to compute optimal assignments that best interpret the NMR data. The MRF captures the contact map information of the protein derived from NOESY spectra, exploits the backbone structural information determined by RDCs, and considers all possible side-chain rotamers. The complexity of the combinatorial search is reduced by using a dead-end elimination (DEE) algorithm, which prunes side-chain resonance assignments that are provably not part of the optimal solution. Then an A* search algorithm is employed to find a set of optimal side-chain resonance assignments that best fit the NMR data. These side-chain resonance assignments are then used to resolve the NOE assignment ambiguity and compute high-resolution protein structures. Tests on five proteins show that Nasca assigns resonances for more than 90% of side-chain protons, and achieves about 80% correct assignments. The final structures computed using the NOE distance restraints assigned by Nasca have backbone RMSD 0.8–1.5 Å from the reference structures determined by traditional NMR approaches.     

NMR assignments of sparsely labeled proteins using a genetic algorithm

Sparse isotopic labeling of proteins for NMR studies using single types of amino acid (¹⁵N or ¹³C enriched) has several advantages. Resolution is enhanced by reducing numbers of resonances for large proteins, and isotopic labeling becomes economically feasible for glycoproteins that must be expressed in mammalian cells. However, without access to the traditional triple resonance strategies that require uniform isotopic labeling, NMR assignment of crosspeaks in heteronuclear single quantum coherence (HSQC) spectra is challenging. We present an alternative strategy which combines readily accessible NMR data with known protein domain structures. Based on the structures, chemical shifts are predicted, NOE cross-peak lists are generated, and residual dipolar couplings (RDCs) are calculated for each labeled site. Simulated data are then compared to measured values for a trial set of assignments and scored. A genetic algorithm uses the scores to search for an optimal pairing of HSQC crosspeaks with labeled sites. While none of the individual data types can give a definitive assignment for a particular site, their combination can in most cases. Four test proteins previously assigned using triple resonance methods and a sparsely labeled glycosylated protein, Robo1, previously assigned by manual analysis, are used to validate the method and develop a criterion for identifying sites assigned with high confidence.     

MONTE: An automated Monte Carlo based approach to nuclear magnetic resonance assignment of proteins

A general-purpose Monte Carlo assignment program has been developed to aid in the assignment of NMR resonances from proteins. By virtue of its flexible data requirements the program is capable of obtaining assignments of both heavily deuterated and fully protonated proteins. A wide variety of source data, such as inter-residue scalar connectivity, inter-residue dipolar (NOE) connectivity, and residue specific information, can be utilized in the assignment process. The program can also use known assignments from one form of a protein to facilitate the assignment of another form of the protein. This attribute is useful for assigning protein-ligand complexes when the assignments of the unliganded protein are known. The program can be also be used as an interactive research tool to assist in the choice of additional experimental data to facilitate completion of assignments. The assignment of a deuterated 45 kDa homodimeric Glutathione-S-transferase illustrates the principal features of the program.     

Reliability of exclusively NOESY-based automated resonance assignment and structure determination of proteins

Protein structure determination by NMR can in principle be speeded up both by reducing the measurement time on the NMR spectrometer and by a more efficient analysis of the spectra. Here we study the reliability of protein structure determination based on a single type of spectra, namely nuclear Overhauser effect spectroscopy (NOESY), using a fully automated procedure for the sequence-specific resonance assignment with the recently introduced FLYA algorithm, followed by combined automated NOE distance restraint assignment and structure calculation with CYANA. This NOESY-FLYA method was applied to eight proteins with 63–160 residues for which resonance assignments and solution structures had previously been determined by the Northeast Structural Genomics Consortium (NESG), and unrefined and refined NOESY data sets have been made available for the Critical Assessment of Automated Structure Determination of Proteins by NMR project. Using only peak lists from three-dimensional ¹³C- or ¹⁵N-resolved NOESY spectra as input, the FLYA algorithm yielded for the eight proteins 91–98 % correct backbone and side-chain assignments if manually refined peak lists are used, and 64–96 % correct assignments based on raw peak lists. Subsequent structure calculations with CYANA then produced structures with root-mean-square deviation (RMSD) values to the manually determined reference structures of 0.8–2.0 Å if refined peak lists are used. With raw peak lists, calculations for 4 proteins converged resulting in RMSDs to the reference structure of 0.8–2.8 Å, whereas no convergence was obtained for the four other proteins (two of which did already not converge with the correct manual resonance assignments given as input). These results show that, given high-quality experimental NOESY peak lists, the chemical shift assignments can be uncovered, without any recourse to traditional through-bond type assignment experiments, to an extent that is sufficient for calculating accurate three-dimensional structures.     

Automated sequence- and stereo-specific assignment of methyl-labeled proteins by paramagnetic relaxation and methyl–methyl nuclear overhauser enhancement spectroscopy

Methyl-transverse relaxation optimized spectroscopy is rapidly becoming the preferred NMR technique for probing structure and dynamics of very large proteins up to ~1 MDa in molecular size. Data interpretation, however, necessitates assignment of methyl groups which still presents a very challenging and time-consuming process. Here we demonstrate that, in combination with a known 3D structure, paramagnetic relaxation enhancement (PRE), induced by nitroxide spin-labels incorporated at only a few surface-exposed engineered cysteines, provides fast, straightforward and robust access to methyl group resonance assignments, including stereoassignments for the methyl groups of leucine and valine. Neither prior assignments, including backbone assignments, for the protein, nor experiments that transfer magnetization between methyl groups and the protein backbone, are required. PRE-derived assignments are refined by 4D methyl–methyl nuclear Overhauser enhancement data, eliminating ambiguities and errors that may arise due to the high sensitivity of PREs to the potential presence of sparsely-populated transient states.     

Backbone assignment and secondary structure of Rnd1, an unusual Rho family small GTPase

Rho GTPases have attracted considerable interest as signaling molecules due to their variety of functional roles in cells. Rnd1 is a relatively recently discovered Rho GTPase with no enzymatic activity against its bound GTP nucleotide, setting it apart from other family members. Research has revealed a critical role for Rnd1 not only in neurite outgrowth, dendrite development, axon guidance, but also in gastric cancer and in endothelial cells during inflammation. Structural information is crucial for understanding the mechanism that forms the basis for protein–protein interactions and functions, but until recently there were no reports of NMR studies directly on the Rnd1 protein. In this paper we report assignments for the majority of Rnd1 NMR resonances based on 2D and 3D NMR spectra. Rnd1 assignment was a challenging task, however, despite optimization strategies that have facilitated NMR studies of the protein (Cao and Buck in Small GTPase 2:295–304, 2012). Besides common triple-resonance experiments, 3D HNCA, 3D HN(CO)CA, 3D HNCO which are usually employed for sequence assignment, 3D NOESY experiments and specific labeling of 13 kinds of amino acids were also utilized to gain as many ¹H(N), ¹³C, and ¹⁵N resonances assignments as possible. For 170 cross peaks observed out of 183 possible mainchain N–H correlations in the ¹H–¹⁵N TROSY spectrum, backbone assignment was finally completed for 127 resonances. The secondary structure was then defined by chemical shifts and TALOS+ based on the assignments. The overall structure in solution compares well with that of Rnd1 in a crystal, except for two short segments, residues 77–83 and residues 127–131. Given that some features are shared among Rho GTPases, Rnd1 assignments are also compared with two other family members, Cdc42 and Rac1. The overall level of Rnd1 assignment is lower than for Cdc42 and Rac1, consistent with its lower stability and possibly increased internal dynamics. However, while the Rnd1 switch II region remained un-assigned, the switch I region could be more fully assigned compared to Cdc42 and Rac1. The NMR assignment and structure analysis reported here provides a robust basis for future study of the binding between Rnd1 and other proteins, as well as for further studies of the molecular function of this unusual GTPase.     

Automatic methyl assignment in large proteins by the MAGIC algorithm

Selective methyl labeling is an extremely powerful approach to study the structure, dynamics and function of biomolecules by NMR. Despite spectacular progress in the field, such studies remain rather limited in number. One of the main obstacles remains the assignment of the methyl resonances, which is labor intensive and error prone. Typically, NOESY crosspeak patterns are manually correlated to the available crystal structure or an in silico template model of the protein. Here, we propose methyl assignment by graphing inference construct, an exhaustive search algorithm with no peak network definition requirement. In order to overcome the combinatorial problem, the exhaustive search is performed locally, i.e. for a small number of methyls connected through-space according to experimental 3D methyl NOESY data. The local network approach drastically reduces the search space. Only the best local assignments are combined to provide the final output. Assignments that match the data with comparable scores are made available to the user for cross-validation by additional experiments such as methyl-amide NOEs. Several NMR datasets for proteins in the 25–50 kDa range were used during development and for performance evaluation against the manually assigned data. We show that the algorithm is robust, reliable and greatly speeds up the methyl assignment task.