ncIDP-assign: a SPARKY extension for the effective NMR assignment of intrinsically disordered proteins

We describe here the ncIDP-assign extension for the popular NMR assignment program SPARKY, which aids in the sequence-specific resonance assignment of intrinsically disordered proteins (IDPs). The assignment plugin greatly facilitates the effective matching of a set of connected resonances to the correct position in the sequence by making use of IDP random coil chemical shifts. AVAILABILITY: The ncIDP-assign extension is available at http://www.protein-nmr.org/.     

Resonance assignment strategies for the analysis of nmr spectra of proteins

Determination of the high resolution solution structure of a protein using nuclear magnetic resonance (NMR) spectroscopy requires that resonances observed in the NMR spectra be unequivocally assigned to individual nuclei of the protein. With the advent of modern, two-dimensional NMR techniques arose methodologies for assigning the¹H resonances based on 2D, homonuclear¹H NMR experiments. These include the sequential assignment strategy and the main chain directed strategy. These basic strategies have been extended to include newer 3D homonuclear experiments and 2D and 3D heteronuclear resolved and edited methods. Most recently a novel, conceptually new approach to the problem has been introduced that relies on heteronuclear, multidimensional so-called triple resonance experiments for both backbone and sidechain resonance assignments in proteins. This article reviews the evolution of strategies for the assignment of resonances of proteins.     

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.     

An assignment of intrinsically disordered regions of proteins based on NMR structures

Intrinsically disordered proteins (IDPs) do not adopt stable three-dimensional structures in physiological conditions, yet these proteins play crucial roles in biological phenomena. In most cases, intrinsic disorder manifests itself in segments or domains of an IDP, called intrinsically disordered regions (IDRs), but fully disordered IDPs also exist. Although IDRs can be detected as missing residues in protein structures determined by X-ray crystallography, no protocol has been developed to identify IDRs from structures obtained by Nuclear Magnetic Resonance (NMR). Here, we propose a computational method to assign IDRs based on NMR structures. We compared missing residues of X-ray structures with residue-wise deviations of NMR structures for identical proteins, and derived a threshold deviation that gives the best correlation of ordered and disordered regions of both structures. The obtained threshold of 3.2 Å was applied to proteins whose structures were only determined by NMR, and the resulting IDRs were analyzed and compared to those of X-ray structures with no NMR counterpart in terms of sequence length, IDR fraction, protein function, cellular location, and amino acid composition, all of which suggest distinct characteristics. The structural knowledge of IDPs is still inadequate compared with that of structured proteins. Our method can collect and utilize IDRs from structures determined by NMR, potentially enhancing the understanding of IDPs.     

Main-chain-directed strategy for the assignment of 1H NMR spectra of proteins

A strategy for assigning the resonances in two-dimensional (2D) NMR spectra of proteins is described. The method emphasizes the analysis of through-space relationships between protons by use of the two-dimensional nuclear Overhauser effect (NOE) experiment. NOE patterns used in the algorithm were derived from a statistical analysis of the combinations of short proton-proton distances observed in the high-resolution crystal structures of 21 proteins. One starts with a search for authentic main-chain NH-C alpha H-C beta H J-coupled units, which can be found with high reliability. The many main-chain units of a protein are then placed in their proper juxtaposition by recognition of predefined NOE connectivity patterns. To discover these connectivities, the 2D NOE spectrum is examined, in a prescribed order, for the distinct NOE patterns characteristic of helices, sheets, turns, and extended chain. Finally, the recognition of a few amino acid side-chain types places the discovered secondary structure elements within the polypeptide sequence. Unlike the sequential assignment approach, the main-chain-directed strategy does not rely on the difficult task of recognizing many side-chain spin systems in J-correlated spectra, the assignment process is not in general sequential with the polypeptide chain, and the prescribed connectivity patterns are cyclic rather than linear. The latter characteristic avoids ambiguous branch points in the analysis and imposes an internally confirmatory property on each forward step.     

High-dimensionality 13C direct-detected NMR experiments for the automatic assignment of intrinsically disordered proteins

We present three novel exclusively heteronuclear 5D ¹³C direct-detected NMR experiments, namely (H^N-flipN)CONCACON, (HCA)CONCACON and (H)CACON(CA)CON, designed for easy sequence-specific resonance assignment of intrinsically disordered proteins (IDPs). The experiments proposed have been optimized to overcome the drawbacks which may dramatically complicate the characterization of IDPs by NMR, namely the small dispersion of chemical shifts and the fast exchange of the amide protons with the solvent. A fast and reliable automatic assignment of α-synuclein chemical shifts was obtained with the Tool for SMFT-based Assignment of Resonances (TSAR) program based on the information provided by these experiments.     

Improving the chemical shift dispersion of multidimensional NMR spectra of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) have recently attracted the attention of the scientific community challenging the well accepted structure–function paradigm. In the characterization of the dynamic features of proteins nuclear magnetic resonance spectroscopy (NMR) is a strategic tool of investigation. However the peculiar properties of IDPs, with the lack of a unique 3D structure and their high flexibility, have a strong impact on NMR observables (low chemical shift dispersion, efficient solvent exchange broadening) and thus on the quality of NMR spectra. Key aspects to be considered in the design of new NMR experiments optimized for the study of IDPs are discussed. A new experiment, based on direct detection of ¹³C^α, is proposed.     

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.     

Application of neural networks to automated assignment of NMR spectra of proteins

Summary Simulated neural networks are described which aid the assignment of protein NMR spectra. A network trained to recognize amino acid type from TOCSY data was trained on 148 assigned spin systems from E. coli acyl carrier proteins (ACPs) and tested on spin systems from spinach ACP, which has a 37% sequence homology with E. coli ACP and a similar secondary structure. The output unit corresponding to the correct amino acid is one of the four most activated units in 83% of the spin systems tested. The utility of this information is illustrated by a second network which uses a constraint satisfaction algorithm to find the best fit of the spin systems to the amino acid sequence. Application to a stretch of 20 amino acids in spinach ACP results in 75% correct sequential assignment. Since the output of the amino acid type identification network can be coupled with a variety of sequential assignment strategies, the approach offers substantial potential for expediting assignment of protein NMR spectra.     

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

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

Automated backbone assignment of labeled proteins using the threshold accepting algorithm

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

Computer-assisted assignment of multidimensional NMR spectra of proteins: Application to 3D NOESY-HMQC and TOCSY-HMQC spectra

Summary An algorithm based on the technique of combinatorial minimization is used for the semi-automated assignment of multidimensional heteronuclear spectra. The program (ALFA) produces the best assignment compatible with the available input data. Even partially misleading or missing data do not seriously corrupt the final assignment. Ambiguous sequences of the possible assignment and all alternatives are indicated. The program can also use additional non-spectroscopic data to assist in the assignment procedure. For example, information from the X-ray structure of the protein and/or information about the secondary structure can be used. The assignment procedure was tested on spectra of mucous trypsin inhibitor, a protein of 107 residues.     

Rapid assignment of solution 31P NMR spectra of large proteins by solid-state spectroscopy

The application of the (31)P NMR spectroscopy to large proteins or protein complexes in solution is hampered by a relatively low intrinsic sensitivity coupled with large line widths. Therefore, the assignment of the phosphorus signals by two-dimensional NMR methods in solution is often extremely time consuming. In contrast, the quality of solid-state NMR spectra is not dependent on the molecular mass and the solubility of the protein. For the complex of Ras with the GTP-analogue GppCH(2)p we show solid-state (31)P NMR methods to be more sensitive by almost one order of magnitude than liquid-state NMR. Thus, solid-state NMR seems to be the method of choice for obtaining the resonance assignment of the phosphorus signals of protein complexes in solution. Experiments on Ras.GDP complexes show that the microcrystalline sample can be substituted by a precipitate of the sample and that unexpectedly the two structural states observed earlier in solution are present in crystals as well.     

HA-detected experiments for the backbone assignment of intrinsically disordered proteins

We propose a new alpha proton detection based approach for the sequential assignment of natively unfolded proteins. The proposed protocol superimposes on following features: HA-detection (1) enables assignment of natively unfolded proteins at any pH, i.e., it is not sensitive to rapid chemical exchange undergoing in natively unfolded proteins even at moderately high pH. (2) It allows straightforward assignment of proline-rich polypeptides without additional proline-customized experiments. (3) It offers more streamlined and less ambiguous assignment based on solely intraresidual ¹⁵N(i)-¹³C′(i)-H^α(i) (or ¹⁵N(i)-¹³C^α(i)-H^α(i)) and sequential ¹⁵N(i + 1)-¹³C′(i)-H^α(i) (or ¹⁵N(i + 1)-¹³C^α(i)-H^α(i)) correlation experiments together with efficient use of chemical shifts of ¹⁵N and ¹³C′ nuclei, which show smaller dependence on residue type. We have tested the proposed protocol on two proteins, small globular 56-residue GB1, and highly disordered, proline-rich 47-residue fifth repeat of EspF_U. Using the proposed approach, we were able to assign 90% of ¹H^α, ¹³C^α, ¹³C′, ¹⁵N chemical shifts in EspF_U. We reckon that the HA-detection based strategy will be very useful in the assignment of natively unfolded proline-rich proteins or polypeptide chains.     

Assignment of NMR spectra of proteins using triple-resonance two-dimensional experiments

Summary Two-dimensional versions of HNCA and HNCO experiments are described, which provide essentially the same information as the 3D sequence. A multiple-quantum coherence involving either ¹⁵N and ¹³C or ¹⁵N and ¹³CO is created. One of the two frequencies is given by the middle point between the two cross peaks (zero-and double-quantum) and the other by their separation. Quadrature detection can be performed on either nucleus, modifying only the appearence of the 2D spectrum, but not the information content. These experiments, named MQ-HNCA and MQ-HNCO, are illustrated on a (¹⁵N, ¹³C) doubly labelled cytochrome c₂ from Rhodobacter capsulatus (116 amino acids).Abbreviations HMQC heteronuclear multiple-quantum coherence spectroscopy - HSQC heteronuclear single-quantum coherence spectroscopy - ZQ zero quantum - 2Q double quantum - MQ multiple quantum - TPPI time-proportional phase increment     

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 ¹H, ¹³C and ¹⁵N resonances could be assigned with an average error rate of 3.5%.     

New 13C-detected experiments for the assignment of intrinsically disordered proteins

NMR assignment of intrinsically disordered proteins (IDPs) by conventional HN-detected methods is hampered by the small dispersion of the amide protons chemical shifts and exchange broadening of amide proton signals. Therefore several alternative assignment strategies have been proposed in the last years. Attempting to seize that dispersion of ¹³C′ and ¹⁵N chemical shifts holds even in IDPs, we recently proposed two ¹³C-detected experiments to directly correlate the chemical shifts of two consecutive ¹³C′–¹⁵N groups in proteins, i.e. without mediation of other nuclei. Main drawback of these experiments is the interruption of the connection at prolines. Here we present new ¹³C-detected experiments to correlate consecutive ¹³C′–¹⁵N groups in IDPs, hacacoNcaNCO and hacaCOncaNCO, that overcome this limitation. Moreover, the experiments provide recognition of glycine residues, thereby facilitating the assignment process.