Automated sequencing of amino acid spin systems in proteins using multidimensional HCC(CO)NH-TOCSY spectroscopy and constraint propagation methods from artificial intelligence

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.

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.

Binding site identification and structure determination of protein-ligand complexes by NMR a semiautomated approach

Over the last 15 years, the role of NMR spectroscopy in the lead identification and optimization stages of pharmaceutical drug discovery has steadily increased. NMR occupies a unique niche in the biophysical analysis of drug-like compounds because of its ability to identify binding sites, affinities, and ligand poses at the level of individual amino acids without necessarily solving the structure of the protein-ligand complex. However, it can also provide structures of flexible proteins and low-affinity (K(d)>10(-6)M) complexes, which often fail to crystallize. This chapter emphasizes a throughput-focused protocol that aims to identify practical aspects of binding site characterization, automated and semiautomated NMR assignment methods, and structure determination of protein-ligand complexes by NMR.

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.

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.

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.

Protein assignments without peak lists using higher-order spectra

Despite advances in automating the generation and manipulation of peak lists for assigning biomolecules, there are well-known advantages to working directly with spectra: the eye is still superior to computer algorithms when it comes to picking out peak relationships from contour plots in the presence of confounding factors such as noise, overlap, and spectral artifacts. Here, we present constructs called higher-order spectra for identifying, through direct visual examination, many of the same relationships typically identified by searching peak lists, making them another addition to the set of tools (alongside peak picking and automated assignment) that can be used to solve the assignment problem. The technique is useful for searching for correlated peaks in any spectrum type. Application of this technique to novel, complete sequential assignment of two proteins (AhpFn and IC74(84-143)) is demonstrated. The program "burrow-owl" for the generation and display of higher-order spectra is available at (http://sourceforge.net/projects/burrow-owl) or from the authors.

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.

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.

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.

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.

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

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

Solution NMR structure and folding dynamics of the N terminus of a rat non-muscle alpha-tropomyosin in an engineered chimeric protein

Tropomyosin is an alpha-helical coiled-coil protein that aligns head-to-tail along the length of the actin filament and regulates its function. The solution structure of the functionally important N terminus of a short 247-residue non-muscle tropomyosin was determined in an engineered chimeric protein, GlyTM1bZip, consisting of the first 19 residues of rat short alpha-tropomyosin and the last 18 residues of the GCN4 leucine zipper. A gene encoding GlyTM1bZip was synthesized, cloned and expressed in Escherichia coli. Triple resonance NMR spectra were analyzed with the program AutoAssign to assign its backbone resonances. Multidimensional nuclear Overhauser effect spectra, X-filtered spectra and (3)J(H(N)-H(alpha)) scalar coupling were analyzed using AutoStructure. This is the first application of this new program to determine the three-dimensional structure of a symmetric homodimer and a structure not previously reported. Residues 7-35 in GlyTM1bZip form a coiled coil, but neither end is helical. Heteronuclear (15)N-(1)H nuclear Overhauser effect data showed that the non-helical N-terminal residues are flexible. The (13)C' chemical shifts of the coiled-coil backbone carbonyl groups in GlyTM1bZip showed a previously unreported periodicity, where resonances arising from residues at the coiled-coil interface in a and d positions of the heptad repeat were displaced relatively upfield and those arising from residues in c positions were displaced relatively downfield. Heteronuclear single quantum coherence spectra, collected as a function of temperature, showed that cross-peaks arising from the alpha-helical backbone and side-chains at the coiled-coil interface broadened or shifted with T(M) values approximately 20 degrees C lower than the loss of alpha-helix measured by circular dichroism, suggesting the presence of a folding intermediate. The side-chain of Ile14, a residue essential for binding interactions, exhibited multiple conformations. The conformational flexibility of the N termini of short tropomyosins may be important for their binding specificity.

Automatic assignment of NOESY cross peaks and determination of the protein structure of a new world scorpion neurotoxin using NOAH/DIAMOD

The 3D NMR structures of the scorpion neurotoxin, CsE-v5, were determined from the same NOESY spectra with NOAH/DIAMOD, an automated assignment and 3D structure calculation software package, and with a conventional manual assignment combined with a distance geometry/simulated annealing (X-PLOR) refinement method. The NOESY assignments and the 3D structures obtained from the two independent methods were compared in detail. The NOAH/DIAMOD program suite uses feedback filtering and self-correcting distance geometry methods to automatically assign NOESY spectra and to calculate the 3D structure of a protein. NOESY cross peaks were automatically picked using a standard software package and combined with 74 manually assigned NOESY peaks to start the NOAH/DIAMOD calculations. After 63 NOAH/DIAMOD cycles, using REDAC procedures in the last 8 cycles, and final FANTOM constrained energy minimization, a bundle of 20 structures with the smallest target functions has a RMSD of 0.81 A for backbone atoms and 1.11 A for all heavy atoms to the mean structure. Despite some missing chemical shifts of side chain protons, 776 (including 74 manually assigned) of 1130 NOE peaks were unambiguously assigned, 150 peaks have more than one possible assignment compatible with the bundle structures, and only 30 peaks could not be assigned within the given chemical shift tolerance ranges in either the D1 or the D2 dimension. The remaining 174, mainly weak NOE peaks were not compatible with the final 20 best bundle structures at the last NOAH/DIAMOD cycle. The automatically determined structures agree well with the structures determined independently using the conventional method and the same NMR spectra, with the mean RMSD in well-defined regions of 0.84 A for bb and 1.48 A for all heavy atoms from residues 2-5, 18-26, 32-36, and 39-45. This study demonstrates the potential of the NOAH/DIAMOD program suite to automatically assign NMR data for proteins and determine their structure.

Automated 2D NOESY assignment and structure calculation of Crambin(S22/I25) with the self-correcting distance geometry based NOAH/DIAMOD programs

The NOAH/DIAMOD program suite was used to automatically assign an experimental 2D NOESY spectrum of the 46 residue protein crambin(S22/I25), using feedback filtering and self-correcting distance geometry (SECODG). Automatically picked NOESY cross peaks were combined with 157 manually assigned peaks to start NOAH/DIAMOD calculations. At each cycle, DIAMOD was used to calculate an ensemble of 40 structures from these NOE distance constraints and random starting structures. The 10 structures with smallest target function values were analyzed by the structure-based filter, NOAH, and a new set of possible assignments was automatically generated based on chemical shifts and distance constraints violations. After 60 iterations and final energy minimization, the 10 structures with smallest target functions converged to 1.48 A for backbone atoms. Despite several missing chemical shifts, 426 of 613 NOE peaks were unambiguously assigned; 59 peaks were ambiguously assigned. The remaining 128 peaks picked automatically by FELIX are probably primarily noise peaks, with a few real peaks that were not assigned by NOAH due to the incomplete proton chemical shifts list.

Solution NMR structure and backbone dynamics of the major cold-shock protein (CspA) from Escherichia coli: evidence for conformational dynamics in the single-stranded RNA-binding site

The major cold-shock protein (CspA) from Escherichia coli is a single-stranded nucleic acid-binding protein that is produced in response to cold stress. We have previously reported its overall chain fold as determined by NMR spectroscopy [Newkirk, K., Feng, W., Jiang, W., Tejero, R., Emerson, S. D., Inouye, M., and Montelione, G. T. (1994) Proc. Natl. Acad. Sci. U.S.A. 91, 5114-5118]. Here we describe the complete analysis of 1H, 13C, and 15N resonance assignments for CspA, together with a refined solution NMR structure based on 699 conformational constraints and an analysis of backbone dynamics based on 15N relaxation rate measurements. An extensive set of triple-resonance NMR experiments for obtaining the backbone and side chain resonance assignments were carried out on uniformly 13C- and 15N-enriched CspA. Using a subset of these triple-resonance experiments, the computer program AUTOASSIGN provided automatic analysis of sequence-specific backbone N, Calpha, C', HN, Halpha, and side chain Cbeta resonance assignments. The remaining 1H, 13C, and 15N resonance assignments for CspA were then obtained by manual analysis of additional NMR spectra. Dihedral angle constraints and stereospecific methylene Hbeta resonance assignments were determined using a new conformational grid search program, HYPER, and used together with longer-range constraints as input for three-dimensional structure calculations. The resulting solution NMR structure of CspA is a well-defined five-stranded beta-barrel with surface-exposed aromatic groups that form a single-stranded nucleic acid-binding site. Backbone dynamics of CspA have also been characterized by 15N T1, T2, and heteronuclear 15N-1H NOE measurements and analyzed using the extended Lipari-Szabo formalism. These dynamic measurements indicate a molecular rotational correlation time taum of 4.88 +/- 0.04 ns and provide evidence for fast time scale (taue < 500 ps) dynamics in surface loops and motions on the microsecond to millisecond time scale within the proposed nucleic acid-binding epitope.

Structural characterization of an analog of the major rate-determining disulfide folding intermediate of bovine pancreatic ribonuclease A

The major rate-determining step in the oxidative regeneration of bovine pancreatic ribonuclease A (RNase A) proceeds through des-[40-95] RNase A, a three-disulfide intermediate lacking the Cys40-Cys95 disulfide bond. An analog of this intermediate, [C40A, C95A] RNase A, has been characterized in terms of regular backbone structure and thermodynamic stability at pH 4.6. Nearly complete backbone 1H, 15N, and 13C resonances, and most 13Cbeta side-chain resonances have been assigned for the mutant RNase A using triple-resonance NMR data and a computer program, AUTOASSIGN, for automated analysis of resonance assignments. Comparisons of chemical shift data, 3J(1HN-1Halpha) coupling constants, and NOE data for the mutant and wild-type proteins reveal that the overall chain folds of the two proteins are very similar, with localized structural perturbations in the regions spatially adjacent to the mutation sites in [C40A, C95A] RNase A. More significantly, 1H/2H amide exchange and thermodynamic data reveal a global destabilization of the mutant protein characterized by a significant difference in the midpoint of the thermal transition curves (DeltaTm of 21.8 degrees C) and a significant increase in the slowest exchanging backbone amide 1H/2H exchange rates (10(2)-10(6)-fold faster in the hydrophobic core of [C40A, C95 A] RNase A). Comparisons of the entropy DeltaS degrees (T) and enthalpy DeltaH degrees (T) of unfolding between wild-type and [C40A, C95A] RNase A reveal that some of the global destabilization of the mutant protein arises from entropic and enthalpic changes in the folded state. Implications of these observations for understanding the role of des-[40-95] in the folding pathway of RNase A are discussed.

Automated analysis of protein NMR assignments using methods from artificial intelligence

An expert system for determining resonance assignments from NMR spectra of proteins is described. Given the amino acid sequence, a two-dimensional 15N-1H heteronuclear correlation spectrum and seven to eight three-dimensional triple-resonance NMR spectra for seven proteins, AUTOASSIGN obtained an average of 98% of sequence-specific spin-system assignments with an error rate of less than 0.5%. Execution times on a Sparc 10 workstation varied from 16 seconds for smaller proteins with simple spectra to one to nine minutes for medium size proteins exhibiting numerous extra spin systems attributed to conformational isomerization. AUTOASSIGN combines symbolic constraint satisfaction methods with a domain-specific knowledge base to exploit the logical structure of the sequential assignment problem, the specific features of the various NMR experiments, and the expected chemical shift frequencies of different amino acids. The current implementation specializes in the analysis of data derived from the most sensitive of the currently available triple-resonance experiments. Potential extensions of the system for analysis of additional types of protein NMR data are also discussed.

Protein heteronuclear NMR assignments using mean-field simulated annealing

A computational method for the assignment of the NMR spectra of larger (21 kDa) proteins using a set of six of the most sensitive heteronuclear multidimensional nuclear magnetic resonance experiments is described. Connectivity data obtained from HNC alpha, HN(CO)C alpha, HN(C alpha)H alpha, and H alpha (C alpha CO)NH and spin-system identification data obtained from CP-(H)CCH-TOCSY and CP-(H)C(C alpha CO)NH-TOCSY were used to perform sequence-specific assignments using a mean-field formalism and simulated annealing. This mean-field method reports the resonance assignments in a probabilistic fashion, displaying the certainty of assignments in an unambiguous and quantitative manner. This technique was applied to the NMR data of the 172-residue peptide-binding domain of the E. coli heat-shock protein, DnaK. The method is demonstrated to be robust to significant amounts of missing, spurious, noisy, extraneous, and erroneous data.

Phase labeling of C-H and C-C spin-system topologies: application in constant-time PFG-CBCA(CO)NH experiments for discriminating amino acid spin-system types

Triple-resonance experiments facilitate the determination of sequence-specific resonance assignments of medium-sized 13C,15N-enriched proteins. Some triple-resonance experiments can also be used to obtain information about amino acid spin-system topologies by proper delay tuning. The constant-time PFG-CBCA(CO)NH experiment allows discrimination between five different groups of amino acids by tuning (phase labeling) independently the delays for proton-carbon refocusing and carbon-carbon constant-time frequency labeling. The proton-carbon refocusing delay allows discrimination of spin-system topologies based on the number of protons attached to C alpha and C beta atoms (i.e. C-H phase labeling). In addition, tuning of the carbon-carbon constant-time frequency-labeling delay discriminates topologies based on the number of carbons directly coupled to C alpha and C beta atoms (i.e. C-C phase labeling). Classifying the spin systems into these five groups facilitates identification of amino acid types, making both manual and automated analysis of assignments easier. The use of this pair of optimally tuned PFG-CBCA(CO)NH experiments for distinguishing five spin-system topologies is demonstrated for the 124-residue bovine pancreatic ribonuclease A protein.

Automated analysis of nuclear magnetic resonance assignments for proteins

Recent developments in protein NMR technology provide spectral data that are highly amendable to analysis by computer software systems. Automated methods of analysis use constraint satisfaction, pseudoenergy minimization, directed search, neural net, simulated annealing, and/or genetic algorithms to establish sequential links and sequence-specific assignments. The most advanced systems provide automated analysis of complete backbone and extensive side-chain resonance assignments for proteins of 50-150 amino acids.

Protein three-dimensional structure determination and sequence-specific assignment of 13C and 15N-separated NOE data. A novel real-space ab initio approach

The sequence-specific assignment of resonances is considered to be a requirement for the determination of the three-dimensional (3D) structure of a protein in solution by nuclear magnetic resonance methods. The main source of structural information is the nuclear Overhauser effect spectroscopy (NOESY) spectrum, which contains information about spatially close pairs of protons. Currently, various J-correlated spectra must be recorded in order to obtain the sequence-specific assignments necessary to interpret the NOESY spectra. In this work, a novel procedure to determine the 3D structure and the sequence-specific assignments of a protein using only data from 13C and 15N-separated multidimensional NOESY spectra is described. No information from J-correlated spectra is required. The algorithm is called ANSRS (Assignment of NOESY Spectra in Real Space) and is based on an inversion of the traditional strategy. A 3D real-space structure of detected, but unassigned, 1H spins is calculated from the nuclear Overhauser effect (NOE) distance restraints using a dynamical simulated annealing procedure. The sequence-specific assignments are then determined by searching among the 1H spins in the 3D real-space structure for plausible residue assignments. The search uses a Monte Carlo simulated annealing algorithm based on assignment probabilities derived from the 1H, 15N and 13C chemical shifts, various spatial constraints, and the known sequence of the protein. The procedure has been tested on semi-synthetic data sets comprising published experimental chemical shifts and NOE distance restraints derived from the known 3D structures of the two proteins GAL4 (residues 9 to 41) and bovine pancreatic trypsin inhibitor. The ANSRS procedure was able to determine the sequence-specific assignments for more than 95% of the spins, and was fairly robust with respect to missing NOE data. The potential of the ANSRS approach with respect to automated assignment, reduction of the number of NMR spectra required for a structure determination, assignment of homologous and mutant proteins, and the possibility of analysing spectra recorded at high pH is discussed.

An automated procedure for the assignment of protein 1HN, 15N, 13C alpha, 1H alpha, 13C beta and 1H beta resonances

A computer algorithm that determines the 1HN, 15N, 13C alpha, 1H alpha, 13C beta and 1H beta chemical-shift assignments of protein residues with minimal human intervention is described. The algorithm is implemented as a suite of macros that run under a modified version of the FELIX 1.0 program (Hare Research, Bothell, WA). The input to the algorithm is obtained from six multidimensional, triple-resonance experiments: 3D HNCACB, 3D CBCA(CO)HN, 4D HNCAHA, 4D HN(CO)CAHA, 3D HBHA(CO)NH and 3D HNHA(Gly). For small proteins, the two 4D spectra can be replaced by either the 3D HN(CA)HA, 3D H(CA)NNH, or the 15N-edited TOCSY-HSQC experiments. The algorithm begins by identifying and collecting the intraresidue and sequential resonances of the backbone and 13C beta atoms into groups. These groups are sequentially linked and then assigned to residues by matching the 13C alpha and 13C beta chemical-shift profiles of the linked groups to that of the protein's primary structure. A major strength of the algorithm is its ability to overcome imperfect data, e.g., missing or overlapping peaks. The viability of the procedure is demonstrated with two test cases. In the first, NMR data from the six experiments listed above were used to reassign the backbone resonances of the 93-residue human hnRNP C RNA-binding domain. In the second, a simulated cross-peak list, generated from the published NMR assignments of calmodulin, was used to test the ability of the algorithm to assign the backbone resonances of proteins containing internally homologous segments. Finally, the automated method was used to assign the backbone resonances of apokedarcidin, a previously unassigned, 114-residue protein.