NMR assignment of the hypothetical ENTH-VHS domain At3g16270 from Arabidopsis thaliana

NMR-based automated protein structure determination

NMR spectra analysis for protein structure determination can now in many cases be performed by automated computational methods. This overview of the computational methods for NMR protein structure analysis presents recent automated methods for signal identification in multidimensional NMR spectra, sequence-specific resonance assignment, collection of conformational restraints, and structure calculation, as implemented in the CYANA software package. These algorithms are sufficiently reliable and integrated into one software package to enable the fully automated structure determination of proteins starting from NMR spectra without manual interventions or corrections at intermediate steps, with an accuracy of 1-2 Å backbone RMSD in comparison with manually solved reference structures.

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

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

Systematic evaluation of combined automated NOE assignment and structure calculation with CYANA

The automated assignment of NOESY cross peaks has become a fundamental technique for NMR protein structure analysis. A widely used algorithm for this purpose is implemented in the program CYANA. It has been used for a large number of structure determinations of proteins in solution but a systematic evaluation of its performance has not yet been reported. In this paper we systematically analyze the reliability of combined automated NOESY assignment and structure calculation with CYANA under a variety of conditions on the basis of the experimental NMR data sets of ten proteins. To evaluate the robustness of the algorithm, the original high-quality experimental data sets were modified in different ways to simulate the effect of data imperfections, i.e. incomplete or erroneous chemical shift assignments, missing NOESY cross peaks, inaccurate peak positions, inaccurate peak intensities, lower dimensionality NOESY spectra, and higher tolerances for the matching of chemical shifts and peak positions. The results show that the algorithm is remarkably robust with regard to imperfections of the NOESY peak lists and the chemical shift tolerances but susceptible to lacking or erroneous resonance assignments, in particular for nuclei that are involved in many NOESY cross peaks.

Increased reliability of nuclear magnetic resonance protein structures by consensus structure bundles

Nuclear magnetic resonance (NMR) structures are represented by bundles of conformers calculated from different randomized initial structures using identical experimental input data. The spread among these conformers indicates the precision of the atomic coordinates. However, there is as yet no reliable measure of structural accuracy, i.e., how close NMR conformers are to the "true" structure. Instead, the precision of structure bundles is widely (mis)interpreted as a measure of structural quality. Attempts to increase precision often overestimate accuracy by tight bundles of high precision but much lower accuracy. To overcome this problem, we introduce a protocol for NMR structure determination with the software package CYANA, which produces, like the traditional method, bundles of conformers in agreement with a common set of conformational restraints but with a realistic precision that is, throughout a variety of proteins and NMR data sets, a much better estimate of structural accuracy than the precision of conventional structure bundles.

Helping hands for budding prospects: ENTH/ANTH/VHS accessory proteins in endocytosis, vacuolar transport, and secretion

Coated vesicles provide a major mechanism for the transport of proteins through the endomembrane system of plants. Transport between the endoplasmic reticulum and the Golgi involves vesicles with COPI and COPII coats, whereas clathrin is the predominant coat in endocytosis and post-Golgi trafficking. Sorting of cargo, coat assembly, budding, and fission are all complex and tightly regulated processes that involve many proteins. The mechanisms and responsible factors are largely conserved in eukaryotes, and increasing organismal complexity tends to be associated with a greater numbers of individual family members. Among the key factors is the class of ENTH/ANTH/VHS domain-containing proteins, which link membrane subdomains, clathrin, and other adapter proteins involved in early steps of clathrin coated vesicle formation. More than 30 Arabidopsis thaliana proteins contain this domain, but their generally low sequence conservation has made functional classification difficult. Reports from the last two years have greatly expanded our knowledge of these proteins and suggest that ENTH/ANTH/VHS domain proteins are involved in various instances of clathrin-related endomembrane trafficking in plants. This review aims to summarize these new findings and discuss the broader context of clathrin-dependent plant vesicular transport.

Prediction of peak overlap in NMR spectra

Peak overlap is one of the major factors complicating the analysis of biomolecular NMR spectra. We present a general method for predicting the extent of peak overlap in multidimensional NMR spectra and its validation using both, experimental data sets and Monte Carlo simulation. The method is based on knowledge of the magnetization transfer pathways of the NMR experiments and chemical shift statistics from the Biological Magnetic Resonance Data Bank. Assuming a normal distribution with characteristic mean value and standard deviation for the chemical shift of each observable atom, an analytic expression was derived for the expected overlap probability of the cross peaks. The analytical approach was verified to agree with the average peak overlap in a large number of individual peak lists simulated using the same chemical shift statistics. The method was applied to eight proteins, including an intrinsically disordered one, for which the prediction results could be compared with the actual overlap based on the experimentally measured chemical shifts. The extent of overlap predicted using only statistical chemical shift information was in good agreement with the overlap that was observed when the measured shifts were used in the virtual spectrum, except for the intrinsically disordered protein. Since the spectral complexity of a protein NMR spectrum is a crucial factor for protein structure determination, analytical overlap prediction can be used to identify potentially difficult proteins before conducting NMR experiments. Overlap predictions can be tailored to particular classes of proteins by preparing statistics from corresponding protein databases. The method is also suitable for optimizing recording parameters and labeling schemes for NMR experiments and improving the reliability of automated spectra analysis and protein structure determination.

Peakmatch: a simple and robust method for peak list matching

Peak lists are commonly used in NMR as input data for various software tools such as automatic assignment and structure calculation programs. Inconsistencies of chemical shift referencing among different peak lists or between peak and chemical shift lists can cause severe problems during peak assignment. Here we present a simple and robust tool to achieve self-consistency of the chemical shift referencing among a set of peak lists. The Peakmatch algorithm matches a set of peak lists to a specified reference peak list, neither of which have to be assigned. The chemical shift referencing offset between two peak lists is determined by optimizing an assignment-free match score function using either a complete grid search or downhill simplex optimization. It is shown that peak lists from many different types of spectra can be matched reliably as long as they contain at least two corresponding dimensions. Using a simulated peak list, the Peakmatch algorithm can also be used to obtain the optimal agreement between a chemical shift list and experimental peak lists. Combining these features makes Peakmatch a useful tool that can be applied routinely before automatic assignment or structure calculation in order to obtain an optimized input data set.

A new algorithm for reliable and general NMR resonance assignment

The new FLYA automated resonance assignment algorithm determines NMR chemical shift assignments on the basis of peak lists from any combination of multidimensional through-bond or through-space NMR experiments for proteins. Backbone and side-chain assignments can be determined. All experimental data are used simultaneously, thereby exploiting optimally the redundancy present in the input peak lists and circumventing potential pitfalls of assignment strategies in which results obtained in a given step remain fixed input data for subsequent steps. Instead of prescribing a specific assignment strategy, the FLYA resonance assignment algorithm requires only experimental peak lists and the primary structure of the protein, from which the peaks expected in a given spectrum can be generated by applying a set of rules, defined in a straightforward way by specifying through-bond or through-space magnetization transfer pathways. The algorithm determines the resonance assignment by finding an optimal mapping between the set of expected peaks that are assigned by definition but have unknown positions and the set of measured peaks in the input peak lists that are initially unassigned but have a known position in the spectrum. Using peak lists obtained by purely automated peak picking from the experimental spectra of three proteins, FLYA assigned correctly 96-99% of the backbone and 90-91% of all resonances that could be assigned manually. Systematic studies quantified the impact of various factors on the assignment accuracy, namely the extent of missing real peaks and the amount of additional artifact peaks in the input peak lists, as well as the accuracy of the peak positions. Comparing the resonance assignments from FLYA with those obtained from two other existing algorithms showed that using identical experimental input data these other algorithms yielded significantly (40-142%) more erroneous assignments than FLYA. The FLYA resonance assignment algorithm thus has the reliability and flexibility to replace most manual and semi-automatic assignment procedures for NMR studies of proteins.

Simultaneous single-structure and bundle representation of protein NMR structures in torsion angle space

A method is introduced to represent an ensemble of conformers of a protein by a single structure in torsion angle space that lies closest to the averaged Cartesian coordinates while maintaining perfect covalent geometry and on average equal steric quality and an equally good fit to the experimental (e.g. NMR) data as the individual conformers of the ensemble. The single representative 'regmean structure' is obtained by simulated annealing in torsion angle space with the program CYANA using as input data the experimental restraints, restraints for the atom positions relative to the average Cartesian coordinates, and restraints for the torsion angles relative to the corresponding principal cluster average values of the ensemble. The method was applied to 11 proteins for which NMR structure ensembles are available, and compared to alternative, commonly used simple approaches for selecting a single representative structure, e.g. the structure from the ensemble that best fulfills the experimental and steric restraints, or the structure from the ensemble that has the lowest RMSD value to the average Cartesian coordinates. In all cases our method found a structure in torsion angle space that is significantly closer to the mean coordinates than the alternatives while maintaining the same quality as individual conformers. The method is thus suitable to generate representative single structure representations of protein structure ensembles in torsion angle space. Since in the case of NMR structure calculations with CYANA the single structure is calculated in the same way as the individual conformers except that weak positional and torsion angle restraints are added, we propose to represent new NMR structures by a 'regmean bundle' consisting of the single representative structure as the first conformer and all but one original individual conformers (the original conformer with the highest target function value is discarded in order to keep the number of conformers in the bundle constant). In this way, analyses that require a single structure can be carried out in the most meaningful way using the first model, while at the same time the additional information contained in the ensemble remains available.

Objective identification of residue ranges for the superposition of protein structures

Background

The automation of objectively selecting amino acid residue ranges for structure superpositions is important for meaningful and consistent protein structure analyses. So far there is no widely-used standard for choosing these residue ranges for experimentally determined protein structures, where the manual selection of residue ranges or the use of suboptimal criteria remain commonplace.

Results

We present an automated and objective method for finding amino acid residue ranges for the superposition and analysis of protein structures, in particular for structure bundles resulting from NMR structure calculations. The method is implemented in an algorithm, CYRANGE, that yields, without protein-specific parameter adjustment, appropriate residue ranges in most commonly occurring situations, including low-precision structure bundles, multi-domain proteins, symmetric multimers, and protein complexes. Residue ranges are chosen to comprise as many residues of a protein domain that increasing their number would lead to a steep rise in the RMSD value. Residue ranges are determined by first clustering residues into domains based on the distance variance matrix, and then refining for each domain the initial choice of residues by excluding residues one by one until the relative decrease of the RMSD value becomes insignificant. A penalty for the opening of gaps favours contiguous residue ranges in order to obtain a result that is as simple as possible, but not simpler. Results are given for a set of 37 proteins and compared with those of commonly used protein structure validation packages. We also provide residue ranges for 6351 NMR structures in the Protein Data Bank.

Conclusions

The CYRANGE method is capable of automatically determining residue ranges for the superposition of protein structure bundles for a large variety of protein structures. The method correctly identifies ordered regions. Global structure superpositions based on the CYRANGE residue ranges allow a clear presentation of the structure, and unnecessary small gaps within the selected ranges are absent. In the majority of cases, the residue ranges from CYRANGE contain fewer gaps and cover considerably larger parts of the sequence than those from other methods without significantly increasing the RMSD values. CYRANGE thus provides an objective and automatic method for standardizing the choice of residue ranges for the superposition of protein structures.

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.

Automated protein structure determination from NMR spectra

Fully automated structure determination of proteins in solution (FLYA) yields, without human intervention, three-dimensional protein structures starting from a set of multidimensional NMR spectra. Integrating existing and new software, automated peak picking over all spectra is followed by peak list filtering, the generation of an ensemble of initial chemical shift assignments, the determination of consensus chemical shift assignments for all (1)H, (13)C, and (15)N nuclei, the assignment of NOESY cross-peaks, the generation of distance restraints, and the calculation of the three-dimensional structure by torsion angle dynamics. The resulting, preliminary structure serves as additional input to the second stage of the procedure, in which a new ensemble of chemical shift assignments and a refined structure are calculated. The three-dimensional structures of three 12-16 kDa proteins computed with the FLYA algorithm coincided closely with the conventionally determined structures. Deviations were below 0.95 A for the backbone atom positions, excluding the flexible chain termini. 96-97% of all backbone and side-chain chemical shifts in the structured regions were assigned to the correct residues. The purely computational FLYA method is suitable for substituting all manual spectra analysis and thus overcomes a main efficiency limitation of the NMR method for protein structure determination.

Structural determinants of human FANCF protein that function in the assembly of a DNA damage signaling complex

Fanconi anemia (FA) is a rare autosomal recessive and X-linked chromosomal instability disorder. At least eight FA proteins (FANCA, B, C, E, F, G, L, and M) form a nuclear core complex required for monoubiquitination of a downstream protein, FANCD2. The human FANCF protein reportedly functions as a molecular adaptor within the FA nuclear complex, bridging between the subcomplexes A:G and C:E. Our x-ray crystallographic studies of the C-terminal domain of FANCF reveal a helical repeat structure similar to the Cand1 regulator of the Cul1-Rbx1-Skp1-Fbox(Skp2) ubiquitin ligase complex. Two C-terminal loops of FANCF are essential for monoubiquitination of FANCD2 and normal cellular resistance to the DNA cross-linking agent mitomycin C. FANCF mutants bearing amino acid substitutions in this C-terminal surface fail to interact with other components of the FA complex, indicating that this surface is critical for the proper assembly of the FA core complex.