Automated backbone assignment of labeled proteins using the threshold accepting algorithm

ASAP: An automatic sequential assignment program for congested multidimensional solid state NMR spectra

Accurate signal assignments can be challenging for congested solid-state NMR (ssNMR) spectra. We describe an automatic sequential assignment program (ASAP) to partially overcome this challenge. ASAP takes three input files: the residue type assignments (RTAs) determined from the better-resolved NCACX spectrum, the full peak list of the NCOCX spectrum, and the protein sequence. It integrates our auto-residue type assignment strategy (ARTIST) with the Monte Carlo simulated annealing (MCSA) algorithm to overcome the hurdle for accurate signal assignments caused by incomplete side-chain resonances and spectral congestion. Combined, ASAP demonstrates robust performance and accelerates signal assignments of large proteins (>200 residues) that lack crystalline order.

NMR Assignment through Linear Programming

Nuclear Magnetic Resonance (NMR) Spectroscopy is the second most used technique (after X-ray crystallography) for structural determination of proteins. A computational challenge in this technique involves solving a discrete optimization problem that assigns the resonance frequency to each atom in the protein. This paper introduces LIAN (LInear programming Assignment for NMR), a novel linear programming formulation of the problem which yields state-of-the-art results in simulated and experimental datasets.

Evaluation of Multi-Objective Optimization Algorithms for NMR Chemical Shift Assignment

An automated NMR chemical shift assignment algorithm was developed using multi-objective optimization techniques. The problem is modeled as a combinatorial optimization problem and its objective parameters are defined separately in different score functions. Some of the heuristic approaches of evolutionary optimization are employed in this problem model. Both, a conventional genetic algorithm and multi-objective methods, i.e., the non-dominated sorting genetic algorithms II and III (NSGA2 and NSGA3), are applied to the problem. The multi-objective approaches consider each objective parameter separately, whereas the genetic algorithm followed a conventional way, where all objectives are combined in one score function. Several improvement steps and repetitions on these algorithms are performed and their combinations are also created as a hyper-heuristic approach to the problem. Additionally, a hill-climbing algorithm is also applied after the evolutionary algorithm steps. The algorithms are tested on several different datasets with a set of 11 commonly used spectra. The test results showed that our algorithm could assign both sidechain and backbone atoms fully automatically without any manual interactions. Our approaches could provide around a 65% success rate and could assign some of the atoms that could not be assigned by other methods.

Non-Uniform Sampling and J-UNIO Automation for Efficient Protein NMR Structure Determination

High-resolution structure determination of small proteins in solution is one of the big assets of NMR spectroscopy in structural biology. Improvements in the efficiency of NMR structure determination by advances in NMR experiments and automation of data handling therefore attracts continued interest. Here, non-uniform sampling (NUS) of 3D heteronuclear-resolved [(1)H,(1)H]-NOESY data yielded two- to three-fold savings of instrument time for structure determinations of soluble proteins. With the 152-residue protein NP_372339.1 from Staphylococcus aureus and the 71-residue protein NP_346341.1 from Streptococcus pneumonia we show that high-quality structures can be obtained with NUS NMR data, which are equally well amenable to robust automated analysis as the corresponding uniformly sampled data.

Automated structure determination from NMR spectra

Three-dimensional structures of proteins in solution can be calculated on the basis of conformational restraints derived from NMR measurements. This chapter gives an overview of the computational methods for NMR protein structure analysis highlighting recent automated methods for the assignment of NMR spectra, the collection of conformational restraints, and the structure calculation.

Improving the efficiency of branch-and-bound complete-search NMR assignment using the symmetry of molecules and spectra

Nuclear magnetic resonance (NMR) assignment of small molecules is presented as a typical example of a combinatorial optimization problem in chemical physics. Three strategies that help improve the efficiency of solution search by the branch and bound method are presented: 1. reduction of the size of the solution space by resort to a condensed structure formula, wherein symmetric nuclei are grouped together; 2. partitioning of the solution space based on symmetry, that becomes the basis for an efficient branching procedure; and 3. a criterion of selection of input restrictions that leads to increased gaps between branches and thus faster pruning of non-viable solutions. Although the examples chosen to illustrate this work focus on small-molecule NMR assignment, the results are generic and might help solving other combinatorial optimization problems.

On the problem of resonance assignments in solid state NMR of uniformly ¹⁵N,¹³C-labeled proteins

Determination of accurate resonance assignments from multidimensional chemical shift correlation spectra is one of the major problems in biomolecular solid state NMR, particularly for relative large proteins with less-than-ideal NMR linewidths. This article investigates the difficulty of resonance assignment, using a computational Monte Carlo/simulated annealing (MCSA) algorithm to search for assignments from artificial three-dimensional spectra that are constructed from the reported isotropic (15)N and (13)C chemical shifts of two proteins whose structures have been determined by solution NMR methods. The results demonstrate how assignment simulations can provide new insights into factors that affect the assignment process, which can then help guide the design of experimental strategies. Specifically, simulations are performed for the catalytic domain of SrtC (147 residues, primarily β-sheet secondary structure) and the N-terminal domain of MLKL (166 residues, primarily α-helical secondary structure). Assuming unambiguous residue-type assignments and four ideal three-dimensional data sets (NCACX, NCOCX, CONCA, and CANCA), uncertainties in chemical shifts must be less than 0.4 ppm for assignments for SrtC to be unique, and less than 0.2 ppm for MLKL. Eliminating CANCA data has no significant effect, but additionally eliminating CONCA data leads to more stringent requirements for chemical shift precision. Introducing moderate ambiguities in residue-type assignments does not have a significant effect.

Automated robust and accurate assignment of protein resonances for solid state NMR

The process of resonance assignment represents a time-consuming and potentially error-prone bottleneck in structural studies of proteins by solid-state NMR (ssNMR). Software for the automation of this process is therefore of high interest. Procedures developed through the last decades for solution-state NMR are not directly applicable for ssNMR due to the inherently lower data quality caused by lower sensitivity and broader lines, leading to overlap between peaks. Recently, the first efforts towards procedures specifically aimed for ssNMR have been realized (Schmidt et al. in J Biomol NMR 56(3):243-254, 2013). Here we present a robust automatic method, which can accurately assign protein resonances using peak lists from a small set of simple 2D and 3D ssNMR experiments, applicable in cases with low sensitivity. The method is demonstrated on three uniformly (13)C, (15)N labeled biomolecules with different challenges on the assignments. In particular, for the immunoglobulin binding domain B1 of streptococcal protein G automatic assignment shows 100% accuracy for the backbone resonances and 91.8% when including all side chain carbons. It is demonstrated, by using a procedure for generating artificial spectra with increasing line widths, that our method, GAMES_ASSIGN can handle a significant amount of overlapping peaks in the assignment. The impact of including different ssNMR experiments is evaluated as well.

Resonance assignment of the NMR spectra of disordered proteins using a multi-objective non-dominated sorting genetic algorithm

A multi-objective genetic algorithm is introduced to predict the assignment of protein solid-state NMR (SSNMR) spectra with partial resonance overlap and missing peaks due to broad linewidths, molecular motion, and low sensitivity. This non-dominated sorting genetic algorithm II (NSGA-II) aims to identify all possible assignments that are consistent with the spectra and to compare the relative merit of these assignments. Our approach is modeled after the recently introduced Monte-Carlo simulated-annealing (MC/SA) protocol, with the key difference that NSGA-II simultaneously optimizes multiple assignment objectives instead of searching for possible assignments based on a single composite score. The multiple objectives include maximizing the number of consistently assigned peaks between multiple spectra ("good connections"), maximizing the number of used peaks, minimizing the number of inconsistently assigned peaks between spectra ("bad connections"), and minimizing the number of assigned peaks that have no matching peaks in the other spectra ("edges"). Using six SSNMR protein chemical shift datasets with varying levels of imperfection that was introduced by peak deletion, random chemical shift changes, and manual peak picking of spectra with moderately broad linewidths, we show that the NSGA-II algorithm produces a large number of valid and good assignments rapidly. For high-quality chemical shift peak lists, NSGA-II and MC/SA perform similarly well. However, when the peak lists contain many missing peaks that are uncorrelated between different spectra and have chemical shift deviations between spectra, the modified NSGA-II produces a larger number of valid solutions than MC/SA, and is more effective at distinguishing good from mediocre assignments by avoiding the hazard of suboptimal weighting factors for the various objectives. These two advantages, namely diversity and better evaluation, lead to a higher probability of predicting the correct assignment for a larger number of residues. On the other hand, when there are multiple equally good assignments that are significantly different from each other, the modified NSGA-II is less efficient than MC/SA in finding all the solutions. This problem is solved by a combined NSGA-II/MC algorithm, which appears to have the advantages of both NSGA-II and MC/SA. This combination algorithm is robust for the three most difficult chemical shift datasets examined here and is expected to give the highest-quality de novo assignment of challenging protein NMR spectra.

Fast automated NMR spectroscopy of short-lived biological samples

Faster than death: NMR techniques that make use of nonlinear sampling and hyperdimensional processing enable the recording of complete NMR data sets for the automated assignment of the backbone and side-chain resonances of short-lived protein samples of cell lysates.

The architecture of functional modules in the Hsp90 co-chaperone Sti1/Hop

Sti1/Hop is a modular protein required for the transfer of client proteins from the Hsp70 to the Hsp90 chaperone system in eukaryotes. It binds Hsp70 and Hsp90 simultaneously via TPR (tetratricopeptide repeat) domains. Sti1/Hop contains three TPR domains (TPR1, TPR2A and TPR2B) and two domains of unknown structure (DP1 and DP2). We show that TPR2A is the high affinity Hsp90-binding site and TPR1 and TPR2B bind Hsp70 with moderate affinity. The DP domains exhibit highly homologous α-helical folds as determined by NMR. These, and especially DP2, are important for client activation in vivo. The core module of Sti1 for Hsp90 inhibition is the TPR2A-TPR2B segment. In the crystal structure, the two TPR domains are connected via a rigid linker orienting their peptide-binding sites in opposite directions and allowing the simultaneous binding of TPR2A to the Hsp90 C-terminal domain and of TPR2B to Hsp70. Both domains also interact with the Hsp90 middle domain. The accessory TPR1-DP1 module may serve as an Hsp70-client delivery system for the TPR2A-TPR2B-DP2 segment, which is required for client activation in vivo.

Comprehensive automation for NMR structure determination of proteins

This chapter gives an overview of automated protein structure determination by nuclear magnetic resonance (NMR) with the UNIO protocol that enables high to full automation of all NMR data analysis steps involved. Four established algorithms, namely, the MATCH algorithm for sequence-specific resonance assignment, the ASCAN algorithm for side-chain resonance assignment, the CANDID algorithm for NOE assignment, and the ATNOS algorithm for signal identification in NMR spectra, are assembled into three principal UNIO NMR data analysis components (MATCH, ATNOS/ASCAN, and ATNOS/CANDID) that are accessed thanks to a particularly intuitive and flexible, yet powerful graphical user interface (GUI). UNIO is designed to work independently or in association with other NMR software. The principal data analysis components for sequence-specific backbone, side-chain and NOE assignment may be run separately or out of sequence. User-intervention at individual stages is encouraged and facilitated by graphical tools included for the preparation, analysis, validation, and subsequent presentation of the NMR structure.

Structural analysis of the interaction between Hsp90 and the tumor suppressor protein p53

In eukaryotes, the essential dimeric molecular chaperone Hsp90 is required for the activation and maturation of specific substrates such as steroid hormone receptors, tyrosine kinases and transcription factors. Hsp90 is involved in the establishment of cancer and has become an attractive target for drug design. Here we present a structural characterization of the complex between Hsp90 and the tumor suppressor p53, a key mediator of apoptosis whose structural integrity is crucial for cell-cycle control. Using biophysical methods, we show that the human p53 DNA-binding domain interacts with multiple domains of yeast Hsp90. p53 binds to the Hsp90 C-terminal domain in its native-like state in a charge-dependent manner, but it also associates weakly with binding sites in the middle and the N-terminal domains. The fine-tuned interplay between several Hsp90 domains provides the interactions required for efficient chaperoning of p53.

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 (15)N,(13)C-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.

Overview on the use of NMR to examine protein structure

Any protein structure determination process contains several steps, starting from obtaining a suitable sample, then moving on to acquiring data and spectral assignment, and lastly to the final steps of structure determination and validation. This unit describes all of these steps, starting with the basic physical principles behind NMR and some of the most commonly measured and observed phenomena such as chemical shift, scalar and residual coupling, and the nuclear Overhauser effect. Then, in somewhat more detail, the process of spectral assignment and structure elucidation is explained. Furthermore, the use of NMR to study protein-ligand interaction, protein dynamics, or protein folding is described.

Advances in automated NMR protein structure determination

Around half of all protein structures solved nowadays using solution-state nuclear magnetic resonance (NMR) spectroscopy have been because of automated data analysis. The pervasiveness of computational approaches in general hides, however, a more nuanced view in which the full variety and richness of the field appears. This review is structured around a comparison of methods associated with three NMR observables: classical nuclear Overhauser effect (NOE) constraint gathering in contrast with more recent chemical shift and residual dipole coupling (RDC) based protocols. In each case, the emphasis is placed on the latest research, covering mainly the past 5 years. By describing both general concepts and representative programs, the objective is to map out a field in which--through the very profusion of approaches--it is all too easy to lose one's bearings.

Automated protein resonance assignments of magic angle spinning solid-state NMR spectra of β1 immunoglobulin binding domain of protein G (GB1)

Magic-angle spinning solid-state NMR (MAS SSNMR) represents a fast developing experimental technique with great potential to provide structural and dynamics information for proteins not amenable to other methods. However, few automated analysis tools are currently available for MAS SSNMR. We present a methodology for automating protein resonance assignments of MAS SSNMR spectral data and its application to experimental peak lists of the β1 immunoglobulin binding domain of protein G (GB1) derived from a uniformly ¹³C- and ¹⁵N-labeled sample. This application to the 56 amino acid GB1 produced an overall 84.1% assignment of the N, CO, CA, and CB resonances with no errors using peak lists from NCACX 3D, CANcoCA 3D, and CANCOCX 4D experiments. This proof of concept demonstrates the tractability of this problem.

Root-mean-square-deviation-based rapid backbone resonance assignments in proteins

We have shown that the methodology based on the estimation of root-mean-square deviation (RMSD) between two sets of chemical shifts is very useful to rapidly assign the spectral signatures of (1)H(N), (13)C(α), (13)C(β), (13)C', (1)H(α) and (15)N spins of a given protein in one state from the knowledge of its resonance assignments in a different state, without resorting to routine established procedures (manual and automated). We demonstrate the utility of this methodology to rapidly assign the 3D spectra of a metal-binding protein in its holo-state from the knowledge of its assignments in apo-state, the spectra of a protein in its paramagnetic state from the knowledge of its assignments in diamagnetic state and, finally, the spectra of a mutant protein from the knowledge of the chemical shifts of the corresponding wild-type protein. The underlying assumption of this methodology is that, it is impossible for any two amino acid residues in a given protein to have all the six chemical shifts degenerate and that the protein under consideration does not undergo large conformational changes in going from one conformational state to another. The methodology has been tested using experimental data on three proteins, M-crystallin (8.5 kDa, predominantly β-sheet, for apo- to holo-state), Calbindin (7.5 kDa, predominantly α-helical, for diamagnetic to paramagnetic state and apo to holo) and EhCaBP1 (14.3 kDa, α-helical, the wild-type protein with one of its mutant). In all the cases, the extent of assignment is found to be greater than 85%.

A Monte Carlo/simulated annealing algorithm for sequential resonance assignment in solid state NMR of uniformly labeled proteins with magic-angle spinning

We describe a computational approach to sequential resonance assignment in solid state NMR studies of uniformly (15)N,(13)C-labeled proteins with magic-angle spinning. As input, the algorithm uses only the protein sequence and lists of (15)N/(13)C(alpha) crosspeaks from 2D NCACX and NCOCX spectra that include possible residue-type assignments of each crosspeak. Assignment of crosspeaks to specific residues is carried out by a Monte Carlo/simulated annealing algorithm, implemented in the program MC_ASSIGN1. The algorithm tolerates substantial ambiguity in residue-type assignments and coexistence of visible and invisible segments in the protein sequence. We use MC_ASSIGN1 and our own 2D spectra to replicate and extend the sequential assignments for uniformly-labeled HET-s(218-289) fibrils previously determined manually by Siemer et al. (J. Biomol. NMR, 34 (2006) 75-87) from a more extensive set of 2D and 3D spectra. Accurate assignments by MC_ASSIGN1 do not require data that are of exceptionally high quality. Use of MC_ASSIGN1 (and its extensions to other types of 2D and 3D data) is likely to alleviate many of the difficulties and uncertainties associated with manual resonance assignments in solid state NMR studies of uniformly labeled proteins, where spectral resolution and signal-to-noise are often sub-optimal.

SAGA: rapid automatic mainchain NMR assignment for large proteins

Here we describe a new algorithm for automatically determining the mainchain sequential assignment of NMR spectra for proteins. Using only the customary triple resonance experiments, assignments can be quickly found for not only small proteins having rather complete data, but also for large proteins, even when only half the residues can be assigned. The result of the calculation is not the single best assignment according to some criterion, but rather a large number of satisfactory assignments that are summarized in such a way as to help the user identify portions of the sequence that are assigned with confidence, vs. other portions where the assignment has some correlated alternatives. Thus very imperfect initial data can be used to suggest future experiments.

The HSP90 binding mode of a radicicol-like E-oxime determined by docking, binding free energy estimations, and NMR 15N chemical shifts

We determine the binding mode of a macrocyclic radicicol-like oxime to yeast HSP90 by combining computer simulations and experimental measurements. We sample the macrocyclic scaffold of the unbound ligand by parallel tempering simulations and dock the most populated conformations to yeast HSP90. Docking poses are then evaluated by the use of binding free energy estimations with the linear interaction energy method. Comparison of QM/MM-calculated NMR chemical shifts with experimental shift data for a selective subset of backbone (15)N provides an additional evaluation criteria. As a final test we check the binding modes against available structure-activity-relationships. We find that the most likely binding mode of the oxime to yeast HSP90 is very similar to the known structure of the radicicol-HSP90 complex.

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

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

Probabilistic interaction network of evidence algorithm and its application to complete labeling of peak lists from protein NMR spectroscopy

The process of assigning a finite set of tags or labels to a collection of observations, subject to side conditions, is notable for its computational complexity. This labeling paradigm is of theoretical and practical relevance to a wide range of biological applications, including the analysis of data from DNA microarrays, metabolomics experiments, and biomolecular nuclear magnetic resonance (NMR) spectroscopy. We present a novel algorithm, called Probabilistic Interaction Network of Evidence (PINE), that achieves robust, unsupervised probabilistic labeling of data. The computational core of PINE uses estimates of evidence derived from empirical distributions of previously observed data, along with consistency measures, to drive a fictitious system M with Hamiltonian H to a quasi-stationary state that produces probabilistic label assignments for relevant subsets of the data. We demonstrate the successful application of PINE to a key task in protein NMR spectroscopy: that of converting peak lists extracted from various NMR experiments into assignments associated with probabilities for their correctness. This application, called PINE-NMR, is available from a freely accessible computer server (http://pine.nmrfam.wisc.edu). The PINE-NMR server accepts as input the sequence of the protein plus user-specified combinations of data corresponding to an extensive list of NMR experiments; it provides as output a probabilistic assignment of NMR signals (chemical shifts) to sequence-specific backbone and aliphatic side chain atoms plus a probabilistic determination of the protein secondary structure. PINE-NMR can accommodate prior information about assignments or stable isotope labeling schemes. As part of the analysis, PINE-NMR identifies, verifies, and rectifies problems related to chemical shift referencing or erroneous input data. PINE-NMR achieves robust and consistent results that have been shown to be effective in subsequent steps of NMR structure determination.

What mathematicians call the “labeling problem” underlies difficulties in interpreting many classes of complex biological data. To derive valid inferences from multiple, noisy datasets, one must consider all possible combinations of the data to find the solution that best matches the experimental evidence. Exhaustive searches totally outstrip current computer resources, and, as a result, it has been necessary to resort to approximations such as branch and bound or Monte Carlo simulations, which have the disadvantages of being limited to use in separate steps of the analysis and not providing the final results in a probabilistic fashion that allows the quality of the answers to be evaluated. The Probabilistic Interaction Network of Evidence (PINE) algorithm that we present here offers a general solution to this problem. We have demonstrated the usefulness of the PINE approach by applying it to one of the major bottlenecks in NMR spectroscopy. The PINE-NMR server takes as input the sequence of a protein and the peak lists from one or more multidimensional NMR experiments and provides as output a probabilistic assignment of the NMR signals to specific atoms in the protein's covalent structure and a self-consistent probabilistic analysis of the protein's secondary structure.

Automated assignment of NMR chemical shifts using peak-particle dynamics simulation with the DYNASSIGN algorithm

A new algorithm, DYNASSIGN, for the automated assignment of NMR chemical shift resonances was developed in which expected cross peaks in multidimensional NMR spectra are represented by peak-particles and assignment restraints are translated into a potential energy function. Molecular dynamics simulation techniques are used to calculate a trajectory of the system of peak-particles subjected to the potential function in order to find energetically optimal configurations that correspond to correct assignments. Peak-particle dynamics-based simulated annealing was combined with the Hungarian algorithm for local optimization, and a residue-based score was introduced to distinguish between reliable assignments and "unassigned" resonances for which no reliable assignment can be established. The DYNASSIGN algorithm was implemented in the program CYANA and tested with data sets obtained from the experimental NMR data of nine small proteins. With a set of 10 commonly used NMR spectra, on average 82.5% of all backbone and side-chain (1)H, (13)C and (15)N resonances could be assigned with an average error rate of 3.5%.

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

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

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.

Graphical interpretation of Boolean operators for protein NMR assignments

We have developed a graphics based algorithm for semi-automated protein NMR assignments. Using the basic sequential triple resonance assignment strategy, the method is inspired by the Boolean operators as it applies "AND"-, "OR"- and "NOT"-like operations on planes pulled out of the classical three-dimensional spectra to obtain its functionality. The method's strength lies in the continuous graphical presentation of the spectra, allowing both a semi-automatic peaklist construction and sequential assignment. We demonstrate here its general use for the case of a folded protein with a well-dispersed spectrum, but equally for a natively unfolded protein where spectral resolution is minimal.

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.

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.

Spectral fitting for signal assignment and structural analysis of uniformly 13C-labeled solid proteins by simulated annealing based on chemical shifts and spin dynamics

We describe an approach for the signal assignment and structural analysis with a suite of two-dimensional (13)C-(13)C magic-angle-spinning solid-state NMR spectra of uniformly (13)C-labeled peptides and proteins. We directly fit the calculated spectra to experimental ones by simulated annealing in restrained molecular dynamics program CNS as a function of atomic coordinates. The spectra are calculated from the conformation dependent chemical shift obtained with SHIFTX and the cross-peak intensities computed for recoupled dipolar interactions. This method was applied to a membrane-bound 14-residue peptide, mastoparan-X. The obtained C', C(alpha) and C(beta) chemical shifts agreed with those reported previously at the precisions of 0.2, 0.7 and 0.4 ppm, respectively. This spectral fitting program also provides backbone dihedral angles with a precision of about 50 degrees from the spectra even with resonance overlaps. The restraints on the angles were improved by applying protein database program TALOS to the obtained chemical shifts. The peptide structure provided by these restraints was consistent with the reported structure at the backbone RMSD of about 1 A.

Structural Characterization of Spo0E-like Protein-aspartic Acid Phosphatases That Regulate Sporulation in Bacilli

Spore formation is an extreme response of many bacterial species to starvation. In the case of pathogenic species of Bacillus and Clostridium, it is also a component of disease transmission. Entry into the pathway of sporulation in Bacillus subtilis and its relatives is controlled by an expanded two-component system in which starvation signals lead to the activation of sensor kinases and phosphorylation of the master sporulation response regulator Spo0A. Accumulation of threshold concentrations of Spo0A approximately P heralds the commitment to sporulation. Countering the activities of the sensor kinases are phosphatases such as Spo0E, which dephosphorylate Spo0A approximately P and inhibit sporulation. Spo0E-like protein-aspartic acid-phosphate phosphatases, consisting of 50-90 residues, are conserved in sporeforming bacteria and unrelated in sequence to proteins of known structure. Here we determined the structures of the Spo0A approximately P phosphatases BA1655 and BA5174 from Bacillus anthracis using nuclear magnetic resonance spectroscopy. Each is composed of two anti-parallel alpha-helices flanked by flexible regions at the termini. The signature SQELD motif (SRDLD in BA1655) is situated in the middle of helix alpha2 with its polar residues projecting outward. BA5174 is a monomer, whereas BA1655 is a dimer. The four-helix bundle structure in the dimer is reminiscent of the phosphotransferase Spo0B and the chemotaxis phosphatase CheZ, although in contrast to these systems, the subunits in BA1655 are in head-to-tail rather than head-to-head apposition. The implications of the structures for interactions between the phosphatases and their substrate Spo0A approximately P are discussed.

SideLink: automated side-chain assignment of biopolymers from NMR data by relative-hypothesis-prioritization-based simulated logic

Previously we published the development of AutoLink, a program to assign the backbone resonances of macromolecules. The primary limitation of this program has proven to be its inability to directly recognize spectral data, relying on the user to define peak positions in its input. Here, we introduce a new program for the assignment of side-chain resonances. Like AutoLink, this new program, called SideLink, uses Relative Hypothesis Prioritization to emulate "human" logic. To address the higher complexity of side-chain assignment problems, the RHP algorithm has itself been advanced, making it capable of processing almost any combinatorial logic problem. Additionally, SideLink directly examines spectral data, overcoming the need and limitations of prior data interpretation by users.

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

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

Intrinsic inhibition of the Hsp90 ATPase activity

The molecular chaperone Hsp90 is required for the folding and activation of a large number of substrate proteins. These are involved in essential cellular processes ranging from signal transduction to viral replication. For the activation of its substrates, Hsp90 binds and hydrolyzes ATP, which is the key driving force for conformational conversions within the dimeric chaperone. Dimerization of Hsp90 is mediated by a C-terminal dimerization site. In addition, there is a transient ATP-induced dimerization of the two N-terminal ATP-binding domains. The resulting ring-like structure is thought to be the ATPase-active conformation. Hsp90 is a slow ATPase with a turnover number of 1 ATP/min for the yeast protein. A key question for understanding the molecular mechanism of Hsp90 is how ATP hydrolysis is regulated and linked to conformational changes. In this study, we analyzed the activation process structurally and biochemically with a view to identify the conformational limitations of the ATPase reaction cycle. We showed that the first 24 amino acids stabilize the N-terminal domain in a rigid state. Their removal confers flexibility specifically to the region between amino acids 98 and 120. Most surprisingly, the deletion of this structure results in the complete loss of ATPase activity and in increased N-terminal dimerization. Complementation assays using heterodimeric Hsp90 show that this rigid lid acts as an intrinsic kinetic inhibitor of the Hsp90 ATPase cycle preventing N-terminal dimerization in the ground state. On the other hand, this structure acts, in concert with the 24 N-terminal amino acids of the other N-terminal domain, to form an activated ATPase and thus regulates the turnover number of Hsp90.

Solution Structure of the Human Ubiquitin-specific Protease 15 DUSP Domain

Ubiquitin-specific proteases (USPs) can remove covalently attached ubiquitin moieties from target proteins and regulate both the stability and ubiquitin-signaling state of their substrates. All USPs contain a conserved catalytic domain surrounded by one or more subdomains, some of which contribute to target recognition. One such specific subdomain, the DUSP domain (domain present in ubiquitin-specific proteases), is present in at least seven different human USPs that regulate the stability of or interact with the hypoxia-inducible transcription factor HIF1-alpha, the Von Hippel-Lindau protein (pVHL), cullin E3 ligases, and BRCA2. We describe the NMR solution structure of the DUSP domain of human USP15, recently implicated in COP9 (constitutive photomorphogenic gene 9)-signalosome regulation. Its tripod-like structure consists of a 3-fold alpha-helical bundle supporting a triple-stranded anti-parallel beta-sheet. The DUSP domain displays a novel fold, an alpha/beta tripod (AB3). DUSP domain surface properties and previously described work suggest a potential role in protein/protein interaction or substrate recognition.

PASA--a program for automated protein NMR backbone signal assignment by pattern-filtering approach

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

CASA: an efficient automated assignment of protein mainchain NMR data using an ordered tree search algorithm

Rapid analysis of protein structure, interaction, and dynamics requires fast and automated assignments of 3D protein backbone triple-resonance NMR spectra. We introduce a new depth-first ordered tree search method of automated assignment, CASA, which uses hand-edited peak-pick lists of a flexible number of triple resonance experiments. The computer program was tested on 13 artificially simulated peak lists for proteins up to 723 residues, as well as on the experimental data for four proteins. Under reasonable tolerances, it generated assignments that correspond to the ones reported in the literature within a few minutes of CPU time. The program was also tested on the proteins analyzed by other methods, with both simulated and experimental peaklists, and it could generate good assignments in all relevant cases. The robustness was further tested under various situations.

A random graph approach to NMR sequential assignment

Nuclear magnetic resonance (NMR) spectroscopy allows scientists to study protein structure, dynamics and interactions in solution. A necessary first step for such applications is determining the resonance assignment, mapping spectral data to atoms and residues in the primary sequence. Automated resonance assignment algorithms rely on information regarding connectivity (e.g., through-bond atomic interactions) and amino acid type, typically using the former to determine strings of connected residues and the latter to map those strings to positions in the primary sequence. Significant ambiguity exists in both connectivity and amino acid type information. This paper focuses on the information content available in connectivity alone and develops a novel random-graph theoretic framework and algorithm for connectivity-driven NMR sequential assignment. Our random graph model captures the structure of chemical shift degeneracy, a key source of connectivity ambiguity. We then give a simple and natural randomized algorithm for finding optimal assignments as sets of connected fragments in NMR graphs. The algorithm naturally and efficiently reuses substrings while exploring connectivity choices; it overcomes local ambiguity by enforcing global consistency of all choices. By analyzing our algorithm under our random graph model, we show that it can provably tolerate relatively large ambiguity while still giving expected optimal performance in polynomial time. We present results from practical applications of the algorithm to experimental datasets from a variety of proteins and experimental set-ups. We demonstrate that our approach is able to overcome significant noise and local ambiguity in identifying significant fragments of sequential assignments.

The Structure of Bypass of Forespore C, an Intercompartmental Signaling Factor during Sporulation in Bacillus

Sporulation in Bacillus subtilis begins with an asymmetric cell division giving rise to smaller forespore and larger mother cell compartments. Different programs of gene expression are subsequently directed by compartment-specific RNA polymerase sigma-factors. In the final stages, spore coat proteins are synthesized in the mother cell under the control of RNA polymerase containing sigma(K), (Esigma(K)). sigma(K) is synthesized as an inactive zymogen, pro-sigma(K), which is activated by proteolytic cleavage. Processing of pro-sigma(K) is performed by SpoIVFB, a metalloprotease that resides in a complex with SpoIVFA and bypass of forespore (Bof)A in the outer forespore membrane. Ensuring coordination of events taking place in the two compartments, pro-sigma(K) processing in the mother cell is delayed until appropriate signals are received from the forespore. Cell-cell signaling is mediated by SpoIVB and BofC, which are expressed in the forespore and secreted to the intercompartmental space where they regulate pro-sigma(K) processing by mechanisms that are not yet fully understood. Here we present the three-dimensional structure of BofC determined by solution state NMR. BofC is a monomer made up of two domains. The N-terminal domain, containing a four-stranded beta-sheet onto one face of which an alpha-helix is packed, closely resembles the third immunoglobulin-binding domain of protein G from Streptococcus. The C-terminal domain contains a three-stranded beta-sheet and three alpha-helices in a novel domain topology. The sequence connecting the domains contains a conserved DISP motif to which mutations that affect BofC activity map. Possible roles for BofC in the sigma(K) checkpoint are discussed in the light of sequence and structure comparisons.

PFG-NMR analysis of intercompartment exchange and inner droplet size distribution of W/O/W emulsions

Presented is a novel application of pulsed field gradient (PFG)-NMR to the analysis of intercompartment exchange and the inner compartment droplet size distribution of a W/O/W multiple emulsion. The method involves monitoring the diffusional behavior of different components of the emulsion. Pfeuffer et al. [Pfeuffer, J.; Flogel, U.; Dreher, W.; Leibfritz, D. NMR Biomed. 1998, 11(1), 19-31.](1) and Price et al. [Price, W. S.; Barzykin, A. V.; Hayamizu, K.; Tachiya, M. Biophys. J. 1998, 74(5), 2259-2271.](2) proposed methods to extend Kärger's PFG-NMR model of exchange between two compartments to accommodate spherical inner compartments. Each model enables the prediction of the oil membrane permeability, the inner compartment volume fraction, and a representation of the inner compartment droplet size distribution. The models were fitted to PFG-NMR experimental data of W/O/W emulsions. The Pfeuffer et al. model provided the best description of the observed experimental data. Predicted values of permeability and swelling were consistent with those reported in the literature for W/O/W emulsions. The addition of sorbitol to either the inner or outer water compartment resulted in an increase in the oil membrane permeability. Inner compartment droplet size distribution measurements indicate that swelling, rupture, and coalescence are likely to have occurred during the secondary emulsification and emulsion ripening. In its present form, the method still constitutes a fast, noninvasive (no addition of a tracer), and in situ method for comparative analysis of the permeability, stability, and yield of different formulations of multiple emulsions with a single PFG-NMR experiment.

GANA--a genetic algorithm for NMR backbone resonance assignment

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

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 evaluated 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.

Histogram-based scoring schemes for protein NMR resonance assignment

In NMR protein structure determination, after the resonance peaks have been identified and chemical shifts from peaks across multiple spectra have been grouped into spin systems, associating these spin systems to their host residues is the key toward the success of structural information extraction and thus the key to the success of the structure calculation. To achieve accurate enough structure calculation, a near complete and accurate assignment is a prerequisite. There are two pieces of information that can be used into the assignment, one of which is the adjacency information among the spin systems and the other is the signature information of the spin systems. The signature information reflects the fact that, generally speaking, for one type of amino acid residing in a specific local structural environment, the chemical shifts for the atoms inside the amino acid fall into some very narrow distinct ranges. In most of the existing work, normal distributions are assumed with means and standard deviations statistically collected from the available data. In this paper, we followed a simple yet effective histogram-based way to estimate for every spin system the probability that its host is a certain type of amino acid residing in a certain type of secondary structure. We used two combinations of chemical shifts to demonstrate the effectiveness of this type of histogram-based scoring schemes.

An integrated platform for automated analysis of protein NMR structures

Recent developments provide automated analysis of NMR assignments and three-dimensional (3D) structures of proteins. These approaches are generally applicable to proteins ranging from about 50 to 150 amino acids. In this chapter, we summarize progress by the Northeast Structural Genomics Consortium in standardizing the NMR data collection process for protein structure determination and in building an integrated platform for automated protein NMR structure analysis. Our integrated platform includes the following principal steps: (1) standardized NMR data collection, (2) standardized data processing (including spectral referencing and Fourier transformation), (3) automated peak picking and peak list editing, (4) automated analysis of resonance assignments, (5) automated analysis of NOESY data together with 3D structure determination, and (6) methods for protein structure validation. In particular, the software AutoStructure for automated NOESY data analysis is described in this chapter, together with a discussion of practical considerations for its use in high-throughput structure production efforts. The critical area of data quality assessment has evolved significantly over the past few years and involves evaluation of both intermediate and final peak lists, resonance assignments, and structural information derived from the NMR data. Methods for quality control of each of the major automated analysis steps in our platform are also discussed. Despite significant remaining challenges, when good quality data are available, automated analysis of protein NMR assignments and structures with this platform is both fast and reliable.

An algorithm for an automatic NOE pathways analysis of 2D NMR spectra of RNA duplexes

An algorithm is proposed to provide the tool for an automatic resonance assignment of 2D-NOESY spectra of RNA duplexes. The algorithm, based on a certain subproblem of the Hamiltonian path, reduces a number of possible connections between resonances within aromatic and anomeric region of 2D-NOESY spectra. Appropriate pathways between H6/H8 and H1' resonances were obtained by subsequent implementation of experimental data as limiting factors. Predictive power of the algorithm was tested on both experimental and simulated data for RNA and DNA duplexes.

Solution structure of Escherichia coli Par10: The prototypic member of the Parvulin family of peptidyl-prolyl cis/trans isomerases

E. coli Par10 is a peptidyl-prolyl cis/trans isomerase (PPIase) from Escherichia coli catalyzing the isomerization of Xaa-Pro bonds in oligopeptides with a broad substrate specificity. The structure of E. coli Par10 has been determined by multidimensional solution-state NMR spectroscopy based on 1207 conformational constraints (1067 NOE-derived distances, 42 vicinal coupling-constant restraints, 30 hydrogen-bond restraints, and 68 phi/psi restraints derived from the Chemical Shift Index). Simulated-annealing calculations with the program ARIA and subsequent refinement with XPLOR yielded a set of 18 convergent structures with an average backbone RMSD from mean atomic coordinates of 0.50 A within the well-defined secondary structure elements. E. coli Par10 is the smallest known PPIase so far, with a high catalytic efficiency comparable to that of FKBPs and cyclophilins. The secondary structure of E. coli Par10 consists of four helical regions and a four-stranded antiparallel beta-sheet. The N terminus forms a beta-strand, followed by a large stretch comprising three alpha-helices. A loop region containing a short beta-strand separates these helices from a fourth alpha-helix. The C terminus consists of two more beta-strands completing the four-stranded anti-parallel beta-sheet with strand order 2143. Interestingly, the third beta-strand includes a Gly-Pro cis peptide bond. The curved beta-strand forms a hydrophobic binding pocket together with alpha-helix 4, which also contains a number of highly conserved residues. The three-dimensional structure of Par10 closely resembles that of the human proteins hPin1 and hPar14 and the plant protein Pin1At, belonging to the same family of highly homologous proteins.