Automated protein fold determination using a minimal NMR constraint strategy

Determination of precise and accurate protein structures by NMR generally requires weeks or even months to acquire and interpret all the necessary NMR data. However, even medium-accuracy fold information can often provide key clues about protein evolution and biochemical function(s). In this article we describe a largely automatic strategy for rapid determination of medium-accuracy protein backbone structures. Our strategy derives from ideas originally introduced by other groups for determining medium-accuracy NMR structures of large proteins using deuterated, (13)C-, (15)N-enriched protein samples with selective protonation of side-chain methyl groups ((13)CH(3)). Data collection includes acquiring NMR spectra for automatically determining assignments of backbone and side-chain (15)N, H(N) resonances, and side-chain (13)CH(3) methyl resonances. These assignments are determined automatically by the program AutoAssign using backbone triple resonance NMR data, together with Spin System Type Assignment Constraints (STACs) derived from side-chain triple-resonance experiments. The program AutoStructure then derives conformational constraints using these chemical shifts, amide (1)H/(2)H exchange, nuclear Overhauser effect spectroscopy (NOESY), and residual dipolar coupling data. The total time required for collecting such NMR data can potentially be as short as a few days. Here we demonstrate an integrated set of NMR software which can process these NMR spectra, carry out resonance assignments, interpret NOESY data, and generate medium-accuracy structures within a few days. The feasibility of this combined data collection and analysis strategy starting from raw NMR time domain data was illustrated by automatic analysis of a medium accuracy structure of the Z domain of Staphylococcal protein A.     

NMR analysis and sequence of toxin II from the sea anemone Radianthus paumotensis

Toxin II from Radianthus paumotensis (RpII) has been investigated by high-resolution NMR and chemical sequencing methods. Resonance assignments have been obtained for this protein by the sequential approach. NMR assignments could not be made consistent with the previously reported primary sequence for this protein, and chemical methods have been used to determine a sequence with which the NMR data are consistent. Analysis of the 2D NOE spectra shows that the protein secondary structure is comprised of two sequences of beta-sheet, probably joined into a distorted continuous sheet, connected by turns and extended loops, without any regular alpha-helical segments. The residues previously implicated in activity in this class of proteins, D8 and R13, occur in a loop region.     

Rapid analysis of protein backbone resonance assignments using cryogenic probes,a distributed Linux-based computing architecture,and an integrated set of spectral analysis tools

Rapid data collection, spectral referencing, processing by time domain deconvolution, peak picking and editing, and assignment of NMR spectra are necessary components of any efficient integrated system for protein NMR structure analysis. We have developed a set of software tools designated AutoProc, AutoPeak, and AutoAssign, which function together with the data processing and peak-picking programs NMRPipe and Sparky, to provide an integrated software system for rapid analysis of protein backbone resonance assignments. In this paper we demonstrate that these tools, together with high-sensitivity triple resonance NMR cryoprobes for data collection and a Linux-based computer cluster architecture, can be combined to provide nearly complete backbone resonance assignments and secondary structures (based on chemical shift data) for a 59-residue protein in less than 30 hours of data collection and processing time. In this optimum case of a small protein providing excellent spectra, extensive backbone resonance assignments could also be obtained using less than 6 hours of data collection and processing time. These results demonstrate the feasibility of high throughput triple resonance NMR for determining resonance assignments and secondary structures of small proteins, and the potential for applying NMR in large scale structural proteomics projects.Abbreviations: BPTI – bovine pancreatic trypsin inhibitor; LP – linear prediction; FT – Fourier transform; S/N – signal-to-noise ratio; FID – free induction decay     

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.     

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.     

Protein NMR structure determination with automated NOE assignment using the new software CANDID and the torsion angle dynamics algorithm DYANA

Combined automated NOE assignment and structure determination module (CANDID) is a new software for efficient NMR structure determination of proteins by automated assignment of the NOESY spectra. CANDID uses an iterative approach with multiple cycles of NOE cross-peak assignment and protein structure calculation using the fast DYANA torsion angle dynamics algorithm, so that the result from each CANDID cycle consists of exhaustive, possibly ambiguous NOE cross-peak assignments in all available spectra and a three-dimensional protein structure represented by a bundle of conformers. The input for the first CANDID cycle consists of the amino acid sequence, the chemical shift list from the sequence-specific resonance assignment, and listings of the cross-peak positions and volumes in one or several two, three or four-dimensional NOESY spectra. The input for the second and subsequent CANDID cycles contains the three-dimensional protein structure from the previous cycle, in addition to the complete input used for the first cycle. CANDID includes two new elements that make it robust with respect to the presence of artifacts in the input data, i.e. network-anchoring and constraint-combination, which have a key role in de novo protein structure determinations for the successful generation of the correct polypeptide fold by the first CANDID cycle. Network-anchoring makes use of the fact that any network of correct NOE cross-peak assignments forms a self-consistent set; the initial, chemical shift-based assignments for each individual NOE cross-peak are therefore weighted by the extent to which they can be embedded into the network formed by all other NOE cross-peak assignments. Constraint-combination reduces the deleterious impact of artifact NOE upper distance constraints in the input for a protein structure calculation by combining the assignments for two or several peaks into a single upper limit distance constraint, which lowers the probability that the presence of an artifact peak will influence the outcome of the structure calculation. CANDID test calculations were performed with NMR data sets of four proteins for which high-quality structures had previously been solved by interactive protocols, and they yielded comparable results to these reference structure determinations with regard to both the residual constraint violations, and the precision and accuracy of the atomic coordinates. The CANDID approach has further been validated by de novo NMR structure determinations of four additional proteins. The experience gained in these calculations shows that once nearly complete sequence-specific resonance assignments are available, the automated CANDID approach results in greatly enhanced efficiency of the NOESY spectral analysis. The fact that the correct fold is obtained in cycle 1 of a de novo structure calculation is the single most important advance achieved with CANDID, when compared with previously proposed automated NOESY assignment methods that do not use network-anchoring and constraint-combination.     

A novel strategy for NMR resonance assignment and protein structure determination

The quality of protein structures determined by nuclear magnetic resonance (NMR) spectroscopy is contingent on the number and quality of experimentally-derived resonance assignments, distance and angular restraints. Two key features of protein NMR data have posed challenges for the routine and automated structure determination of small to medium sized proteins; (1) spectral resolution – especially of crowded nuclear Overhauser effect spectroscopy (NOESY) spectra, and (2) the reliance on a continuous network of weak scalar couplings as part of most common assignment protocols. In order to facilitate NMR structure determination, we developed a semi-automated strategy that utilizes non-uniform sampling (NUS) and multidimensional decomposition (MDD) for optimal data collection and processing of selected, high resolution multidimensional NMR experiments, combined it with an ABACUS protocol for sequential and side chain resonance assignments, and streamlined this procedure to execute structure and refinement calculations in CYANA and CNS, respectively. Two graphical user interfaces (GUIs) were developed to facilitate efficient analysis and compilation of the data and to guide automated structure determination. This integrated method was implemented and refined on over 30 high quality structures of proteins ranging from 5.5 to 16.5 kDa in size.     

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.     

1H, 15N, and 13C NMR signal assignments of IIIGlc, a signal-transducing protein of Escherichia coli, using three-dimensional triple-resonance techniques

IIIGlc is an 18.1-kDa signal-transducing phosphocarrier protein of the phosphoenolpyruvate:glycose phosphotransferase system (PTS) of Escherichia coli. Virtually complete (98%) backbone 1H, 15N, and 13C nuclear magnetic resonance (NMR) signal assignments were determined by using a battery of triple-resonance three-dimensional (3D) NMR pulse sequences. In addition, nearly complete (1H, 95%; 13C, 85%) side-chain 1H and 13C signal assignments were obtained from an analysis of 3D 13C HCCH-COSY and HCCH-TOCSY spectra. These experiments rely almost exclusively upon one- and two-bond J couplings to transfer magnetization and to correlate proton and heteronuclear NMR signals. Hence, essentially complete signal assignments of this 168-residue protein were made without any assumptions regarding secondary structure and without the aid of a crystal structure, which is not yet available. Moreover, only three samples, one uniformly 15N-enriched, one uniformly 15N/13C-enriched, and one containing a few types of amino acids labeled with 15N and/or 13C, were needed to make the assignments. The backbone assignments together with the 3D 15N NOESY-HMQC and 13C NOESY-HMQC data have provided extensive information about the secondary structure of this protein [Pelton, J.G., Torchia, D.A., Meadow, N.D., Wong, C.-Y., & Roseman, S (1991) Proc. Natl. Acad. Sci. U.S.A. 88, 3479-3488]. The nearly complete set of backbone and side-chain atom assignments reported herein provide a basis for studies of the three-dimensional structure and dynamics of IIIGlc as well as its interactions with a variety of membrane and cytoplasmic proteins.     

Automated assignment of NOESY NMR spectra using a knowledge based method (KNOWNOE)

Automated assignment of NOESY spectra is a prerequisite for automated structure determination of biological macromolecules. With the program KNOWNOE we present a novel, knowledge based approach to this problem. KNOWNOE is devised to work directly with the experimental spectra without interference of an expert. Besides making use of routines already implemented in AUREMOL, it contains as a central part a knowledge driven Bayesian algorithm for solving ambiguities in the NOE assignments. These ambiguities mainly arise from chemical shift degeneration which allows multiple assignments of cross peaks. Using a set of 326 protein NMR structures, statistical tables in the form of atom-pairwise volume probability distributions (VPDs) were derived. VPDs for all assignment possibilities relevant to the assignments of interproton NOEs were calculated. With these data for a given cross peak with N possible assignments A _i(i = 1,...,N) the conditional probabilities P(A _i, a|V ₀) can be calculated that the assignment A _idetermines essentially all (a-times) of the cross peak volume V ₀. An assignment A _kwith a probability P(A _k, a|V ₀) higher than 0.8 is transiently considered as unambiguously assigned. With a list of unambiguously assigned peaks a set of structures is calculated. These structures are used as input for a next cycle of iteration where a distance threshold D _maxis dynamically reduced. The program KNOWNOE was tested on NOESY spectra of a medium size protein, the cold shock protein (TmCsp) from Thermotoga maritima. The results show that a high quality structure of this protein can be obtained by automated assignment of NOESY spectra which is at least as good as the structure obtained from manual data evaluation.     

SPINS: a laboratory information management system for organizing and archiving intermediate and final results from NMR protein structure determinations

Recent technological advances and experimental techniques have contributed to an increasing number and size of NMR datasets. In order to scale up productivity, laboratory information management systems for handling these extensive data need to be designed and implemented. The SPINS (Standardized ProteIn Nmr Storage) Laboratory Information Management System (LIMS) addresses these needs by providing an interface for archival of complete protein NMR structure determinations, together with functionality for depositing these data to the public BioMagResBank (BMRB). The software tracks intermediate files during each step of an NMR structure-determination process, including: data collection, data processing, resonance assignments, resonance assignment validation, structure calculation, and structure validation. The underlying SPINS data dictionary allows for the integration of various third party NMR data processing and analysis software, enabling users to launch programs they are accustomed to using for each step of the structure determination process directly out of the SPINS user interface.     

A general Monte Carlo/simulated annealing algorithm for resonance assignment in NMR of uniformly labeled biopolymers

We describe a general computational approach to site-specific resonance assignments in multidimensional NMR studies of uniformly ¹⁵N,¹³C-labeled biopolymers, based on a simple Monte Carlo/simulated annealing (MCSA) algorithm contained in the program MCASSIGN2. Input to MCASSIGN2 includes lists of multidimensional signals in the NMR spectra with their possible residue-type assignments (which need not be unique), the biopolymer sequence, and a table that describes the connections that relate one signal list to another. As output, MCASSIGN2 produces a high-scoring sequential assignment of the multidimensional signals, using a score function that rewards good connections (i.e., agreement between relevant sets of chemical shifts in different signal lists) and penalizes bad connections, unassigned signals, and assignment gaps. Examination of a set of high-scoring assignments from a large number of independent runs allows one to determine whether a unique assignment exists for the entire sequence or parts thereof. We demonstrate the MCSA algorithm using two-dimensional (2D) and three-dimensional (3D) solid state NMR spectra of several model protein samples (α-spectrin SH3 domain and protein G/B1 microcrystals, HET-s_218–289 fibrils), obtained with magic-angle spinning and standard polarization transfer techniques. The MCSA algorithm and MCASSIGN2 program can accommodate arbitrary combinations of NMR spectra with arbitrary dimensionality, and can therefore be applied in many areas of solid state and solution NMR.     

Insulin-like growth factor binding protein-2: NMR analysis and structural characterization of the N-terminal domain

The insulin-like growth factor binding proteins are a family of six proteins (IGFBP-1 to -6) that bind insulin-like growth factors-I and -II (IGF-I/II) with high affinity. In addition to regulating IGF actions, IGFBPs have IGF-independent functions. IGFBP-2, the largest member of this family, is over-expressed in many cancers and has been proposed as a possible target for the development of novel anti-cancer therapeutics. The IGFBPs have a common architecture consisting of conserved N- and C-terminal domains joined by a variable linker domain. The solution structure and dynamics of the C-terminal domain of human IGFBP-2 have been reported (Kuang Z. et al. J. Mol. Biol. 364, 690-704, 2006) but neither the N-domain (N-BP-2) nor the linker domain have been characterised. Here we present NMR resonance assignments for human N-BP-2, achieved by recording spectra at low protein concentration using non-uniform sampling and maximum entropy reconstruction. Analysis of secondary chemical shifts shows that N-BP-2 possesses a secondary structure similar to that of other IGFBPs. Although aggregation hampered determination of the solution structure for N-BP-2, a homology model was generated based on the high degree of sequence and structure homology exhibited by the IGFBPs. This model was consistent with experimental NMR and SAXS data and displayed some unique features such as a Pro/Ala-rich non-polar insert, which formed a flexible solvent-exposed loop on the surface of the protein opposite to the IGF-binding interface. NMR data indicated that this loop could adopt either of two alternate conformations in solution - an entirely flexible conformation and one containing nascent helical structure. This loop and an adjacent poly-proline sequence may comprise a potential SH3 domain interaction site for binding to other proteins.     

A novel approach for sequential assignment of 1H, 13C, and 15N spectra of proteins: heteronuclear triple-resonance three-dimensional NMR spectroscopy. Application to calmodulin

A novel approach is described for obtaining sequential assignment of the backbone 1H, 13C, and 15N resonances of larger proteins. The approach is demonstrated for the protein calmodulin (16.7 kDa), uniformly (approximately 95%) labeled with 15N and 13C. Sequential assignment of the backbone residues by standard methods was not possible because of the very narrow chemical shift distribution range of both NH and C alpha H protons in this largely alpha-helical protein. We demonstrate that the combined use of four new types of heteronuclear 3D NMR spectra together with the previously described HOHAHA-HMQC 3D experiment [Marion, D., et al. (1989) Biochemistry 28, 6150-6156] can provide unambiguous sequential assignment of protein backbone resonances. Sequential connectivity is derived from one-bond J couplings and the procedure is therefore independent of the backbone conformation. All the new 3D NMR experiments use 1H detection and rely on multiple-step magnetization transfers via well-resolved one-bond J couplings, offering high sensitivity and requiring a total of only 9 days for the recording of all five 3D spectra. Because the combination of 3D spectra offers at least two and often three independent pathways for determining sequential connectivity, the new assignment procedure is easily automated. Complete assignments are reported for the proton, carbon, and nitrogen backbone resonances of calmodulin, complexed with calcium.     

Assignment of resonances in the 1H NMR spectrum of human lysozyme

Assignments in the 1H NMR spectrum for more than 120 resonances arising from 38 of the 130 amino acid residues of human lysozyme are presented. Assignments have been achieved using a combination of one and two-dimensional NMR techniques. Two-dimensional double-quantum correlated spectroscopy and relayed coherence transfer spectroscopy were found to be particularly useful for the identification of spin systems in the aromatic and methyl regions of the spectrum. These spin systems were assigned to specific residues in human lysozyme with reference to the X-ray crystal structure using one-dimensional nuclear Overhauser enhancement (NOE) data and a computer-based search procedure. Unique assignments were found for resonances of 27 amino acid residues even when a distance constraint on NOE effects of 0.7 nm was used in the search procedure; for the remaining residues closer constraints or additional information were required. The assignments include all but one of the resonances in the aromatic region of the spectrum and all the methyl group resonances in the region upfield of 0.6 ppm. The assignments presented here provide a basis for a comparison of the NMR spectra of human lysozyme and the more widely studied hen lysozyme.     

APART: automated preprocessing for NMR assignments with reduced tedium

MOTIVATION: High-throughput NMR structure determination is a goal that will require progress on many fronts, one of which is rapid resonance assignment. An important rate-limiting step in the resonance assignment process is accurate identification of resonance peaks in the NMR spectra. Peak-picking schemes range from incomplete (which lose essential assignment connectivities) to noisy (which obscure true connectivities with many false ones). We introduce an automated preassignment process that removes false peaks from noisy peak lists by requiring consensus between multiple NMR experiments and exploiting a priori information about NMR spectra. This process is designed to accept multiple input formats and generate multiple output formats, in an effort to be compatible with a variety of user preferences. RESULTS: Automated preprocessing with APART rapidly identifies and removes false peaks from initial peak lists, reduces the burden of manual data entry, and documents and standardizes the peak filtering process. Successful preprocessing is demonstrated by the increased number of correct assignments obtained when data are submitted to an automated assignment program. AVAILABILITY: APART is available from http://sir.lanl.gov/NMR/APART.htm CONTACT: npawley@lanl.gov; rmichalczyk@lanl.gov SUPPLEMENTARY INFORMATION: Manual pages with installation instructions, procedures and screen shots can also be found at http://sir.lanl.gov/NMR/APART_Manual1.pdf.     

Fully automated sequence-specific resonance assignments of hetero- nuclear protein spectra

Full automation of the analysis of spectra is a prerequisite for high-throughput NMR studies in structural or functional genomics. Sequence-specific assignments often form the major bottleneck. Here, we present a procedure that yields nearly complete backbone and side chain resonance assignments starting from a set of heteronuclear three-dimensional spectra. Neither manual intervention, e.g., to correct lists obtained from peak picking before feeding these to an assignment program, nor protein-specific information, e.g., structures of homologous proteins, were required. By combining two earlier published procedures, AUTOPSY [Koradi et al. (1998) J. Magn. Reson., 135, 288–297] and GARANT [Bartels et al. (1996) J. Biomol. NMR, 7, 207–213], with a new program, PICS, all necessary steps from spectra analyses to sequence-specific assignments were performed fully automatically. Characteristic features of the present approach are a flexible design allowing as input almost any combination of NMR spectra, applicability to side chains, robustness with respect to parameter choices (such as noise levels) and reproducibility. In this study, automated resonance assignments were obtained for the 14 kD blue copper protein azurin from P. aeruginosa using five spectra: HNCACB, HNHA, HCCH-TOCSY, ¹⁵N-NOESY-HSQC and ¹³C-NOESY-HSQC. Peaks from these three-dimensional spectra were filtered and calibrated with the help of two two-dimensional spectra: ¹⁵N-HSQC and ¹³C-HSQC. The rate of incorrect assignments is less than 1.5% for backbone nuclei and about 3.5% when side chain protons are also considered.     

Computer-assisted assignment of 2D1H NMR spectra of proteins: Basic algorithms and application to phoratoxin B

Summary A suite of computer programs (CLAIRE) is described which can be of assistance in the process of assigning 2D¹H NMR spectra of proteins. The programs embody a software implementation of the sequential assignment approach first developed by Wüthrich and co-workers (K. Wüthrich. G. Wider, G. Wagner and W. Braun (1982)J. Mol. Biol. 155, 311). After data-abstraction (peakpicking), the software can be used to detect patterns (spin systems), to find cross peaks between patterns in 2D NOE data sets and to generate assignments that are consistent with all available data and which satisfy a number of constraints imposed by the user. An interactive graphics program calledCONPAT is used to control the entire assignment process as well as to provide the essential feedback from the experimental NMR spectra. The algorithms are described in detail and the approach is demonstrated on a set of spectra from the mistletoe protein phoratoxin B, a homolog of crambin. The results obtained compare well with those reported earlier based entirely on a manual assignment process.     

Automated NMR structure determination of stereo-array isotope labeled ubiquitin from minimal sets of spectra using the SAIL-FLYA system

Stereo-array isotope labeling (SAIL) has been combined with the fully automated NMR structure determination algorithm FLYA to determine the three-dimensional structure of the protein ubiquitin from different sets of input NMR spectra. SAIL provides a complete stereo- and regio-specific pattern of stable isotopes that results in sharper resonance lines and reduced signal overlap, without information loss. Here we show that as a result of the superior quality of the SAIL NMR spectra, reliable, fully automated analyses of the NMR spectra and structure calculations are possible using fewer input spectra than with conventional uniformly ¹³C/¹⁵N-labeled proteins. FLYA calculations with SAIL ubiquitin, using a single three-dimensional “through-bond” spectrum (and 2D HSQC spectra) in addition to the ¹³C-edited and ¹⁵N-edited NOESY spectra for conformational restraints, yielded structures with an accuracy of 0.83–1.15 Å for the backbone RMSD to the conventionally determined solution structure of SAIL ubiquitin. NMR structures can thus be determined almost exclusively from the NOESY spectra that yield the conformational restraints, without the need to record many spectra only for determining intermediate, auxiliary data of the chemical shift assignments. The FLYA calculations for this report resulted in 252 ubiquitin structure bundles, obtained with different input data but identical structure calculation and refinement methods. These structures cover the entire range from highly accurate structures to seriously, but not trivially, wrong structures, and thus constitute a valuable database for the substantiation of structure validation methods.