J-UNIO protocol used for NMR structure determination of the 206-residue protein NP_346487.1 from <Emphasis Type="Italic">Streptococcus pneumoniae TIGR4</Emphasis>

The NMR structure of the 206-residue protein NP_346487.1 was determined with the J-UNIO protocol, which includes extensive automation of the structure determination. With input from three APSY-NMR experiments, UNIO-MATCH automatically yielded 77 % of the backbone assignments, which were interactively validated and extended to 97 %. With an input of the near-complete backbone assignments and three 3D heteronuclear-resolved [¹H,¹H]-NOESY spectra, automated side chain assignment with UNIO-ATNOS/ASCAN resulted in 77 % of the expected assignments, which was extended interactively to about 90 %. Automated NOE assignment and structure calculation with UNIO-ATNOS/CANDID in combination with CYANA was used for the structure determination of this two-domain protein. The individual domains in the NMR structure coincide closely with the crystal structure, and the NMR studies further imply that the two domains undergo restricted hinge motions relative to each other in solution. NP_346487.1 is so far the largest polypeptide chain to which the J-UNIO structure determination protocol has successfully been applied.     

Automated combined assignment of NOESY spectra and three-dimensional protein structure determination

A procedure for automated protein structure determination is presented that is based on an iterative procedure during which the NOESY peak list assignment and the structure calculation are performed simultaneously. The input consists of a list of NOESY peak positions and a list of chemical shifts as obtained from sequence-specific resonance assignment. For the present applications of this approach the previously introduced NOAH routine was implemented in the distance geometry program DIANA. As an illustration, experimental 2D and 3D NOESY cross-peak lists of six proteins have been analyzed, for which complete sequence-specific ¹H assignments are available for the polypeptide backbone and the amino acid side chains. The automated method assigned 70–90% of all NOESY cross peaks, which is on average 10% less than with the interactive approach, and only between 0.8% and 2.4% of the automatically assigned peaks had a different assignment than in the corresponding manually assigned peak lists. The structures obtained with NOAH/DIANA are in close agreement with those from manually assigned peak lists, and with both approaches the residual constraint violations correspond to high-quality NMR structure determinations. Systematic comparisons of the bundles of conformers that represent corresponding automatically and interactively determined structures document the absence of significant bias in either approach, indicating that an important step has been made towards automation of structure determination from NMR spectra.     

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.     

Automated structure determination from NMR spectra

Automated methods for protein structure determination by NMR have increasingly gained acceptance and are now widely used for the automated assignment of distance restraints and the calculation of three-dimensional structures. This review gives an overview of the techniques for automated protein structure analysis by NMR, including both NOE-based approaches and methods relying on other experimental data such as residual dipolar couplings and chemical shifts, and presents the FLYA algorithm for the fully automated NMR structure determination of proteins that is suitable to substitute all manual spectra analysis and thus overcomes a major efficiency limitation of the NMR method for protein structure determination.     

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 NMR structure determination with automated NOE-identification in the NOESY spectra using the new software ATNOS

Novel algorithms are presented for automated NOESY peak picking and NOE signal identification in homonuclear 2D and heteronuclear-resolved 3D [¹H,¹H]-NOESY spectra during de novoprotein structure determination by NMR, which have been implemented in the new software ATNOS (automated NOESY peak picking). The input for ATNOS consists of the amino acid sequence of the protein, chemical shift lists from the sequence-specific resonance assignment, and one or several 2D or 3D NOESY spectra. In the present implementation, ATNOS performs multiple cycles of NOE peak identification in concert with automated NOE assignment with the software CANDID and protein structure calculation with the program DYANA. In the second and subsequent cycles, the intermediate protein structures are used as an additional guide for the interpretation of the NOESY spectra. By incorporating the analysis of the raw NMR data into the process of automated de novoprotein NMR structure determination, ATNOS enables direct feedback between the protein structure, the NOE assignments and the experimental NOESY spectra. The main elements of the algorithms for NOESY spectral analysis are techniques for local baseline correction and evaluation of local noise level amplitudes, automated determination of spectrum-specific threshold parameters, the use of symmetry relations, and the inclusion of the chemical shift information and the intermediate protein structures in the process of distinguishing between NOE peaks and artifacts. The ATNOS procedure has been validated with experimental NMR data sets of three proteins, for which high-quality NMR structures had previously been obtained by interactive interpretation of the NOESY spectra. The ATNOS-based structures coincide closely with those obtained with interactive peak picking. Overall, we present the algorithms used in this paper as a further important step towards objective and efficient de novoprotein structure determination by NMR.     

Efficient analysis of protein 2D NMR spectra using the software packageEASY

Summary The programEASY supports the spectral analysis of biomacromolecular two-dimensional (2D) nuclear magnetic resonance (NMR) data. It provides a user-friendly, window-based environment in which to view spectra for interactive interpretation. In addition, it includes a number of automated routines for peakpicking, spin-system identification, sequential resonance assignment in polypeptide chains, and cross peak integration. In this uniform environment, all resulting parameter lists can be recorded on disk, so that the paper plots and handwritten notes which normally accompany manual assignment of spectra can be largely eliminated. For example, in a protein structure determination by 2D¹H NMR,EASY accepts the frequency domain datasets as input, and after combined use of the automated and interactive routines it can yield a listing of conformational constraints in the format required as input for the calculation of the 3D structure. The program was extensively tested with current protein structure determinations in our laboratory. In this paper, its main features are illustrated with data on the protein basic pancreatic trypsin inhibitor.     

Ansig for Windows: An interactive computer program for semiautomatic assignment of protein NMR spectra

Assignment of NMR spectra is a prerequisite for structure determination of proteins using NMR. The time spent on the assignment is comparatively long compared to that spent on other parts in the protein structure determination process, but it can be shortened by using either interactive or fully automated computer programs. To benefit from the advantages of both types of program we have developed a version of the interactive assignment program ANSIG to include automatized, yet user-supervised, routines. The new program includes tools for (i) semiautomatic sequential assignment, (ii) plotting of distances from PDB structure files directly in NMR spectra and (iii) statistical analysis of distance restraint violations with the possibility to directly zoom to violated NOEs in NOESY spectra.     

Automated amino acid side-chain NMR assignment of proteins using <Superscript>13</Superscript>C- and <Superscript>15</Superscript>N-resolved 3D [<Superscript>1</Superscript>H,<Superscript>1</Superscript>H]-NOESY

ASCAN is a new algorithm for automatic sequence-specific NMR assignment of amino acid side-chains in proteins, which uses as input the primary structure of the protein, chemical shift lists of (1)H(N), (15)N, (13)C(alpha), (13)C(beta) and possibly (1)H(alpha) from the previous polypeptide backbone assignment, and one or several 3D (13)C- or (15)N-resolved [(1)H,(1)H]-NOESY spectra. ASCAN has also been laid out for the use of TOCSY-type data sets as supplementary input. The program assigns new resonances based on comparison of the NMR signals expected from the chemical structure with the experimentally observed NOESY peak patterns. The core parts of the algorithm are a procedure for generating expected peak positions, which is based on variable combinations of assigned and unassigned resonances that arise for the different amino acid types during the assignment procedure, and a corresponding set of acceptance criteria for assignments based on the NMR experiments used. Expected patterns of NOESY cross peaks involving unassigned resonances are generated using the list of previously assigned resonances, and tentative chemical shift values for the unassigned signals taken from the BMRB statistics for globular proteins. Use of this approach with the 101-amino acid residue protein FimD(25-125) resulted in 84% of the hydrogen atoms and their covalently bound heavy atoms being assigned with a correctness rate of 90%. Use of these side-chain assignments as input for automated NOE assignment and structure calculation with the ATNOS/CANDID/DYANA program suite yielded structure bundles of comparable quality, in terms of precision and accuracy of the atomic coordinates, as those of a reference structure determined with interactive assignment procedures. A rationale for the high quality of the ASCAN-based structure determination results from an analysis of the distribution of the assigned side chains, which revealed near-complete assignments in the core of the protein, with most of the incompletely assigned residues located at or near the protein surface.     

KUJIRA,a package of integrated modules for systematic and interactive analysis of NMR data directed to high-throughput NMR structure studies

The recent expansion of structural genomics has increased the demands for quick and accurate protein structure determination by NMR spectroscopy. The conventional strategy without an automated protocol can no longer satisfy the needs of high-throughput application to a large number of proteins, with each data set including many NMR spectra, chemical shifts, NOE assignments, and calculated structures. We have developed the new software KUJIRA, a package of integrated modules for the systematic and interactive analysis of NMR data, which is designed to reduce the tediousness of organizing and manipulating a large number of NMR data sets. In combination with CYANA, the program for automated NOE assignment and structure determination, we have established a robust and highly optimized strategy for comprehensive protein structure analysis. An application of KUJIRA in accordance with our new strategy was carried out by a non-expert in NMR structure analysis, demonstrating that the accurate assignment of the chemical shifts and a high-quality structure of a small protein can be completed in a few weeks. The high completeness of the chemical shift assignment and the NOE assignment achieved by the systematic analysis using KUJIRA and CYANA led, in practice, to increased reliability of the determined structure.     

A computer-based protocol for semiautomated assignments and 3D structure determination of proteins

Summary A strategy is presented for the semiautomated assignment and 3D structure determination of proteins from heteronuclear multidimensional Nuclear Magnetic Resonance (NMR) data. This approach involves the computer-based assignment of the NMR signals, identification of distance restraints from nuclear Overhauser effects, and generation of 3D structures by using the NMR-derived restraints. The protocol is described in detail and illustrated on a resonance assignment and structure determination of the FK506 binding protein (FKBP, 107 amino acids) complexed to the immunosuppressant, ascomycin. The 3D structures produced from this automated protocol attained backbone and heavy atom rmsd of 1.17 and 1.69 Å, respectively. Although more highly resolved structures of the complex have been obtained by standard interpretation of NMR data (Meadows et al. (1993) Biochemistry, 32, 754–765), the structures generated with this automated protocol required minimal manual intervention during the spectral assignment and 3D structure calculations stages. Thus, the protocol may yield an approximate order of magnitude reduction in the time required for the generation of 3D structures of proteins from NMR data.     

Peak picking multidimensional NMR spectra with the contour geometry based algorithm CYPICK

The automated identification of signals in multidimensional NMR spectra is a challenging task, complicated by signal overlap, noise, and spectral artifacts, for which no universally accepted method is available. Here, we present a new peak picking algorithm, CYPICK, that follows, as far as possible, the manual approach taken by a spectroscopist who analyzes peak patterns in contour plots of the spectrum, but is fully automated. Human visual inspection is replaced by the evaluation of geometric criteria applied to contour lines, such as local extremality, approximate circularity (after appropriate scaling of the spectrum axes), and convexity. The performance of CYPICK was evaluated for a variety of spectra from different proteins by systematic comparison with peak lists obtained by other, manual or automated, peak picking methods, as well as by analyzing the results of automated chemical shift assignment and structure calculation based on input peak lists from CYPICK. The results show that CYPICK yielded peak lists that compare in most cases favorably to those obtained by other automated peak pickers with respect to the criteria of finding a maximal number of real signals, a minimal number of artifact peaks, and maximal correctness of the chemical shift assignments and the three-dimensional structure obtained by fully automated assignment and structure calculation.     

Application of automated NOE assignment to three-dimensional structure refinement of a 28 kDa single-chain T cell receptor

An automated procedure for NOE assignment and three-dimensional structure refinement is presented. The input to the procedure consists of (1) an ensemble of preliminary protein NMR structures, (2) partial sequence-specific assignments for the protein and (3) the positions and volumes of unassigned NOESY cross peaks. Chemical shifts for unassigned side chain protons are predicted from the preliminary structures. The chemical shifts and unassigned NOESY cross peaks are input to an automated procedure for NOE assignment and structure calculation (ARIA) [Nilges et al. (1997) J. Mol. Biol., 269, 408–422]. ARIA is optimized for the task of structure refinement of larger proteins. Errors are filtered to ensure that sequence-specific assignments are reliable. The procedure is applied to the 27.8 kDa single-chain T cell receptor (scTCR). Preliminary NMR structures, nearly complete backbone assignments, partial assignments of side chain protons and more than 1300 unassigned NOESY cross peaks are input. Using the procedure, the resonant frequencies of more than 40 additional side chain protons are assigned. Over 400 new NOE cross peaks are assigned unambiguously. Distances derived from the automatically assigned NOEs improve the precision and quality of calculated scTCR structures. In the refined structures, a hydrophobic cluster of side chains on the scTCR surface that binds major histocompatibility complex (MHC)/antigen is revealed. It is composed of the side chains of residues from three loops and stabilizes the conformation of residues that interact with MHC.     

Automatic NOESY assignment in CS-RASREC-Rosetta

We have developed an approach for simultaneous structure calculation and automatic Nuclear Overhauser Effect (NOE) assignment to solve nuclear magnetic resonance (NMR) structures from unassigned NOESY data. The approach, autoNOE-Rosetta, integrates Resolution Adapted Structural RECombination (RASREC) Rosetta NMR calculations with algorithms for automatic NOE assignment. The method was applied to two proteins in the 15–20 kDa size range for which both, NMR and X-ray data, is available. The autoNOE-Rosetta calculations converge for both proteins and yield accurate structures with an RMSD of 1.9 Å to the X-ray reference structures. The method greatly expands the radius of convergence for automatic NOE assignment, and should be broadly useful for NMR structure determination.     

Influence of the completeness of chemical shift assignments on NMR structures obtained with automated NOE assignment

Reliable automated NOE assignment and structure calculation on the basis of a largely complete, assigned input chemical shift list and a list of unassigned NOESY cross peaks has recently become feasible for routine NMR protein structure calculation and has been shown to yield results that are equivalent to those of the conventional, manual approach. However, these algorithms rely on the availability of a virtually complete list of the chemical shifts. This paper investigates the influence of incomplete chemical shift assignments on the reliability of NMR structures obtained with automated NOESY cross peak assignment. The program CYANA was used for combined automated NOESY assignment with the CANDID algorithm and structure calculations with torsion angle dynamics at various degrees of completeness of the chemical shift assignment which was simulated by random omission of entries in the experimental ¹H chemical shift lists that had been used for the earlier, conventional structure determinations of two proteins. Sets of structure calculations were performed choosing the omitted chemical shifts randomly among all assigned hydrogen atoms, or among aromatic hydrogen atoms. For comparison, automated NOESY assignment and structure calculations were performed with the complete experimental chemical shift but under random omission of NOESY cross peaks. When heteronuclear-resolved three-dimensional NOESY spectra are available the current CANDID algorithm yields in the absence of up to about 10% of the experimental ¹H chemical shifts reliable NOE assignments and three-dimensional structures that deviate by less than 2 Å from the reference structure obtained using all experimental chemical shift assignments. In contrast, the algorithm can accommodate the omission of up to 50% of the cross peaks in heteronuclear- resolved NOESY spectra without producing structures with a RMSD of more than 2 Å to the reference structure. When only homonuclear NOESY spectra are available, the algorithm is slightly more susceptible to missing data and can tolerate the absence of up to about 7% of the experimental ¹H chemical shifts or of up to 30% of the NOESY peaks.Abbreviations: BmPBP^A – Bombyx mori pheromone binding protein form A; CYANA – combined assignment and dynamics algorithm for NMR applications; NMR – nuclear magnetic resonance; NOE – nuclear Overhauser effect; NOESY – NOE spectroscopy; RMSD – root-mean-square deviation; WmKT – Williopsis mrakii killer toxin     

Automated backbone assignment of labeled proteins using the threshold accepting algorithm

The sequential assignment of backbone resonances is the first step in the structure determination of proteins by heteronuclear NMR. For larger proteins, an assignment strategy based on proton side-chain information is no longer suitable for the use in an automated procedure. Our program PASTA (Protein ASsignment by Threshold Accepting) is therefore designed to partially or fully automate the sequential assignment of proteins, based on the analysis of NMR backbone resonances plus C information. In order to overcome the problems caused by peak overlap and missing signals in an automated assignment process, PASTA uses threshold accepting, a combinatorial optimization strategy, which is superior to simulated annealing due to generally faster convergence and better solutions. The reliability of this algorithm is shown by reproducing the complete sequential backbone assignment of several proteins from published NMR data. The robustness of the algorithm against misassigned signals, noise, spectral overlap and missing peaks is shown by repeating the assignment with reduced sequential information and increased chemical shift tolerances. The performance of the program on real data is finally demonstrated with automatically picked peak lists of human nonpancreatic synovial phospholipase A₂, a protein with 124 residues.     

Accurate and automated classification of protein secondary structure with PsiCSI

PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without (13)C shifts and Q3 = 86.8% with only H(alpha) shifts available). Finally, errors made by PsiCSI almost exclusively involve the interchange of helix or strand with coil and not helix with strand (<2.5 occurrences per 10000 residues). The automation, increased accuracy, absence of gross errors, and robustness with regards to sparse data make PsiCSI ideal for high-throughput applications, and should improve the effectiveness of hybrid NMR/de novo structure determination methods. A Web server is available for users to submit data and have the assignment returned.     

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.     

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.     

Automated protein structure calculation from NMR data

Current software is almost at the stage to permit completely automatic structure determination of small proteins of <15 kDa, from NMR spectra to structure validation with minimal user interaction. This goal is welcome, as it makes structure calculation more objective and therefore more easily validated, without any loss in the quality of the structures generated. Moreover, it releases expert spectroscopists to carry out research that cannot be automated. It should not take much further effort to extend automation to ca 20 kDa. However, there are technological barriers to further automation, of which the biggest are identified as: routines for peak picking; adoption and sharing of a common framework for structure calculation, including the assembly of an automated and trusted package for structure validation; and sample preparation, particularly for larger proteins. These barriers should be the main target for development of methodology for protein structure determination, particularly by structural genomics consortia.