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

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

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

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

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

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

Modelling the acid/base <Superscript>1</Superscript>H NMR chemical shift limits of metabolites in human urine

Introduction

Despite the use of buffering agents the ¹H NMR spectra of biofluid samples in metabolic profiling investigations typically suffer from extensive peak frequency shifting between spectra. These chemical shift changes are mainly due to differences in pH and divalent metal ion concentrations between the samples. This frequency shifting results in a correspondence problem: it can be hard to register the same peak as belonging to the same molecule across multiple samples. The problem is especially acute for urine, which can have a wide range of ionic concentrations between different samples.

Objectives

To investigate the acid, base and metal ion dependent ¹H NMR chemical shift variations and limits of the main metabolites in a complex biological mixture.

Methods

Urine samples from five different individuals were collected and pooled, and pre-treated with Chelex-100 ion exchange resin. Urine samples were either treated with either HCl or NaOH, or were supplemented with various concentrations of CaCl₂, MgCl₂, NaCl or KCl, and their ¹H NMR spectra were acquired.

Results

Nonlinear fitting was used to derive acid dissociation constants and acid and base chemical shift limits for peaks from 33 identified metabolites. Peak pH titration curves for a further 65 unidentified peaks were also obtained for future reference. Furthermore, the peak variations induced by the main metal ions present in urine, Na⁺, K⁺, Ca²⁺ and Mg²⁺, were also measured.

Conclusion

These data will be a valuable resource for ¹H NMR metabolite profiling experiments and for the development of automated metabolite alignment and identification algorithms for ¹H NMR spectra.

     

Automated Protein NMR Structure Determination Using Wavelet De-noised NOESY Spectra

A major time-consuming step of protein NMR structure determination is the generation of reliable NOESY cross peak lists which usually requires a significant amount of manual interaction. Here we present a new algorithm for automated peak picking involving wavelet de-noised NOESY spectra in a process where the identification of peaks is coupled to automated structure determination. The core of this method is the generation of incremental peak lists by applying different wavelet de-noising procedures which yield peak lists of a different noise content. In combination with additional filters which probe the consistency of the peak lists, good convergence of the NOESY-based automated structure determination could be achieved. These algorithms were implemented in the context of the ARIA software for automated NOE assignment and structure determination and were validated for a polysulfide-sulfur transferase protein of known structure. The procedures presented here should be commonly applicable for efficient protein NMR structure determination and automated NMR peak picking. Electronic supplementary material Electronic supplementary material is available for this article at and accessible for authorised users.     

Innovations in generation and analysis of 2D [(13)C,(1)H] COSY NMR spectra for metabolic flux analysis purposes.

2D [(13)C,(1)H] COSY NMR is used by the metabolic engineering community for determining (13)C-(13)C connectivities in intracellular compounds that contain information regarding the steady-state fluxes in cellular metabolism. This paper proposes innovations in the generation and analysis of these specific NMR spectra. These include a computer tool that allows accurate determination of the relative peak areas and their complete covariance matrices even in very complex spectra. Additionally, a method is introduced for correcting the results for isotopic non-steady-state conditions. The proposed methods were applied to measured 2D [(13)C,(1)H] COSY NMR spectra. Peak intensities in a one-dimensional section of the spectrum are frequently not representative for relative peak volumes in the two-dimensional spectrum. It is shown that for some spectra a significant amount of additional information can be gained from long-range (13)C-(13)C scalar couplings in 2D [(13)C,(1)H] COSY NMR spectra. Finally, the NMR resolution enhancement by dissolving amino acid derivatives in a nonpolar solvent is demonstrated.     

Detecting and accounting for multiple sources of positional variance in peak list registration analysis and spin system grouping

Peak lists derived from nuclear magnetic resonance (NMR) spectra are commonly used as input data for a variety of computer assisted and automated analyses. These include automated protein resonance assignment and protein structure calculation software tools. Prior to these analyses, peak lists must be aligned to each other and sets of related peaks must be grouped based on common chemical shift dimensions. Even when programs can perform peak grouping, they require the user to provide uniform match tolerances or use default values. However, peak grouping is further complicated by multiple sources of variance in peak position limiting the effectiveness of grouping methods that utilize uniform match tolerances. In addition, no method currently exists for deriving peak positional variances from single peak lists for grouping peaks into spin systems, i.e. spin system grouping within a single peak list. Therefore, we developed a complementary pair of peak list registration analysis and spin system grouping algorithms designed to overcome these limitations. We have implemented these algorithms into an approach that can identify multiple dimension-specific positional variances that exist in a single peak list and group peaks from a single peak list into spin systems. The resulting software tools generate a variety of useful statistics on both a single peak list and pairwise peak list alignment, especially for quality assessment of peak list datasets. We used a range of low and high quality experimental solution NMR and solid-state NMR peak lists to assess performance of our registration analysis and grouping algorithms. Analyses show that an algorithm using a single iteration and uniform match tolerances approach is only able to recover from 50 to 80% of the spin systems due to the presence of multiple sources of variance. Our algorithm recovers additional spin systems by reevaluating match tolerances in multiple iterations. To facilitate evaluation of the algorithms, we developed a peak list simulator within our nmrstarlib package that generates user-defined assigned peak lists from a given BMRB entry or database of entries. In addition, over 100,000 simulated peak lists with one or two sources of variance were generated to evaluate the performance and robustness of these new registration analysis and peak grouping algorithms.     

APART: automated preprocessing for NMR assignments with reduced tedium

MOTIVATION: High-throughput NMR structure determination is a goal that will require progress on many fronts, one of which is rapid resonance assignment. An important rate-limiting step in the resonance assignment process is accurate identification of resonance peaks in the NMR spectra. Peak-picking schemes range from incomplete (which lose essential assignment connectivities) to noisy (which obscure true connectivities with many false ones). We introduce an automated preassignment process that removes false peaks from noisy peak lists by requiring consensus between multiple NMR experiments and exploiting a priori information about NMR spectra. This process is designed to accept multiple input formats and generate multiple output formats, in an effort to be compatible with a variety of user preferences. RESULTS: Automated preprocessing with APART rapidly identifies and removes false peaks from initial peak lists, reduces the burden of manual data entry, and documents and standardizes the peak filtering process. Successful preprocessing is demonstrated by the increased number of correct assignments obtained when data are submitted to an automated assignment program. AVAILABILITY: APART is available from http://sir.lanl.gov/NMR/APART.htm CONTACT: npawley@lanl.gov; rmichalczyk@lanl.gov SUPPLEMENTARY INFORMATION: Manual pages with installation instructions, procedures and screen shots can also be found at http://sir.lanl.gov/NMR/APART_Manual1.pdf.     

High-resolution pyrimidine- and ribose-specific 4D HCCH-COSY spectra of RNA using the filter diagonalization method

The NMR spectra of nucleic acids suffer from severe peak overlap, which complicates resonance assignments. 4D NMR experiments can overcome much of the degeneracy in 2D and 3D spectra; however, the linear increase in acquisition time with each new dimension makes it impractical to acquire high-resolution 4D spectra using standard Fourier transform (FT) techniques. The filter diagonalization method (FDM) is a numerically efficient algorithm that fits the entire multi-dimensional time-domain data to a set of multi-dimensional oscillators. Selective 4D constant-time HCCH-COSY experiments that correlate the H5-C5-C6-H6 base spin systems of pyrimidines or the H1'-C1'-C2'-H2' spin systems of ribose sugars were acquired on the (13)C-labeled iron responsive element (IRE) RNA. FDM-processing of these 4D experiments recorded with only 8 complex points in the indirect dimensions showed superior spectral resolution than FT-processed spectra. Practical aspects of obtaining optimal FDM-processed spectra are discussed. The results here demonstrate that FDM-processing can be used to obtain high-resolution 4D spectra on a medium sized RNA in a fraction of the acquisition time normally required for high-resolution, high-dimensional spectra.     

Automated screening for metabolites in complex mixtures using 2D COSY NMR spectroscopy

One of the greatest challenges in metabolomics is the rapid and unambiguous identification and quantification of metabolites in a biological sample. Although one-dimensional (1D) proton nuclear magnetic resonance (NMR) spectra can be acquired rapidly, they are complicated by severe peak overlap that can significantly hinder the automated identification and quantification of metabolites. Furthermore, it is currently not reasonable to assume that NMR spectra of pure metabolites are available a priori for every metabolite in a biological sample. In this paper we develop and report on tests of methods that assist in the automatic identification of metabolites using proton two-dimensional (2D) correlation spectroscopy (COSY) NMR. Given a database of 2D COSY spectra for the metabolites of interest, our methods provide a list sorted by a heuristic likelihood of the metabolites present in a sample that has been analyzed using 2D COSY NMR. Our models attempt to correct the displacement of the peaks that can occur from one sample to the next, due to pH, temperature and matrix effects, using a statistical and chemical model. The correction of one peak can result in an implied correction of others due to spin–spin coupling. Furthermore, these displacements are not independent: they depend on the relative position of functional groups in the molecule. We report experimental results using defined mixtures of amino acids as well as real complex biological samples that demonstrate that our methods can be very effective at automatically and rapidly identifying metabolites.     

Improving mass spectrometry peak detection using multiple peak alignment results

Mass spectrometry data are often corrupted by noise. It is very difficult to simultaneously detect low-abundance peaks and reduce false-positive peak detection caused by noise. In this paper, we propose to improve peak detection using an additional constraint: the consistent appearance of similar true peaks across multiple spectra. We observe that false -positive peaks in general do not repeat themselves well across multiple spectra. When we align all the identified peaks (including false-positive ones) from multiple spectra together, those false-positive peaks are not as consistent as true peaks. Thus, we propose to use information from other spectra in order to reduce false-positive peaks. The new method improves the detection of peaks over the traditional single spectrum based peak detection methods. Consequently, the discovery of cancer biomarkers also benefits from this improvement. Source code and additional data are available at: http://www.ece.ust.hk/ approximately eeyu/mspeak.htm.     

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.     

Automated evaluation of chemical shift perturbation spectra: New approaches to quantitative analysis of receptor-ligand interaction NMR spectra

This paper presents new methods designed for quantitative analysis of chemical shift perturbation NMR spectra. The methods automatically trace the displacements of cross peaks between a perturbed test spectrum and the reference spectrum (or among a series of titration spectra), and measure the changes of chemical shifts, heights, and widths of the altered peaks. The methods are primary aimed at the (1)H-(15)N HSQC spectra of relatively small proteins (<15 kDa) assuming fast exchange between free and ligand-bound states on the chemical shift time scale, or for comparing spectra of free and fully bound states in the slow exchange situation. Using the (1)H-(15)N HSQC spectra from a titration experiment of the 74-residue Pex13p SH3 domain with a Pex14p peptide ligand (14 residues, K (d)= approximately 40 microM), we demonstrate the scope and limits of our automatic peak tracing (APET) algorithm for efficient scoring of high-throughput SAR by NMR type HSQC spectra, and progressive peak tracing (PROPET) algorithm for detailed analysis of ligand titration spectra. Simulated spectra with low signal-to-noise ratios (S/N ranged from 20 to 1) were used to demonstrate the reliability and reproducibility of the results when dealing with poor quality spectra. These algorithms have been implemented in a new software module, FELIX-Autoscreen, for streamlined processing, analysis and visualization of SAR by NMR and other high-throughput receptor/ligand interaction experiments.     

Automation of peak-tracking analysis of stepwise perturbed NMR spectra

We describe a new algorithmic approach able to automatically pick and track the NMR resonances of a large number of 2D NMR spectra acquired during a stepwise variation of a physical parameter. The method has been named Trace in Track (TinT), referring to the idea that a gaussian decomposition traces peaks within the tracks recognised through 3D mathematical morphology. It is capable of determining the evolution of the chemical shifts, intensity and linewidths of each tracked peak.The performances obtained in term of track reconstruction and correct assignment on realistic synthetic spectra were high above 90% when a noise level similar to that of experimental data were considered. TinT was applied successfully to several protein systems during a temperature ramp in isotope exchange experiments. A comparison with a state-of-the-art algorithm showed promising results for great numbers of spectra and low signal to noise ratios, when the graduality of the perturbation is appropriate. TinT can be applied to different kinds of high throughput chemical shift mapping experiments, with quasi-continuous variations, in which a quantitative automated recognition is crucial.     

A model-free algorithm for the removal of baseline artifacts

Summary A novel algorithm for removing baseline distortions in NMR spectra is presented. The algorithm approximates the baseline as the median of the noise extrema. Consequently, the method does not require that NMR peaks be discriminated from noise peaks. In addition, no assumptions regarding the source or functional form of the distortion are made. The algorithm is shown to remove the baseline artifacts present in a particularly distorted NOESY spectrum and to reveal peaks which had been obscured by the artifacts. The parameters and spectral characteristics (signal-to-noise ratio, NMR peak density, peak linewidths) governing the resolution of the calculated baselines are also explored.     

Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications

MOTIVATION: The analysis of metabolic processes is becoming increasingly important to our understanding of complex biological systems and disease states. Nuclear magnetic resonance spectroscopy (NMR) is a particularly relevant technology in this respect, since the NMR signals provide a quantitative measure of the metabolite concentrations. However, due to the complexity of the spectra typical of biological samples, the demands of clinical and high-throughput analysis will only be fully met by a system capable of reliable, automatic processing of the spectra. An initial step in this direction has been taken by Targeted Profiling (TP), employing a set of known and predicted metabolite signatures fitted against the signal. However, an accurate fitting procedure for (1)H NMR data is complicated by shift uncertainties in the peak systems caused by measurement imperfections. These uncertainties have a large impact on the accuracy of identification and quantification and currently require compensation by very time consuming manual interactions. Here, we present an approach, termed Extended Targeted Profiling (ETP), that estimates shift uncertainties based on a genetic algorithm (GA) combined with a least squares optimization (LSQO). The estimated shifts are used to correct the known metabolite signatures leading to significantly improved identification and quantification. In this way, use of the automated system significantly reduces the effort normally associated with manual processing and paves the way for reliable, high-throughput analysis of complex NMR spectra. RESULTS: The results indicate that using simultaneous shift uncertainty correction and least squares fitting significantly improves the identification and quantification results for (1)H NMR data in comparison to the standard targeted profiling approach and compares favorably with the results obtained by manual expert analysis. Preservation of the functional structure of the NMR spectra makes this approach more realistic than simple binning strategies.     

[2-3H]ATP synthesis and 3H NMR spectroscopy of enzyme-nucleotide complexes: ADP and ADP.Vi bound to myosin subfragment 1

Summary The synthesis of [2-³H]ATP with specific activity high enough to use for ³H NMR spectroscopy at micromolar concentrations was accomplished by tritiodehalogenation of 2-Br-ATP. ATP with greater than 80% substitution at the 2-position and negligible tritium levels at other positions had a single ³H NMR peak at 8.20 ppm in 1D spectra obtained at 533 MHz. This result enables the application of tritium NMR spectroscopy to ATP utilizing enzymes.The proteolytic fragment of skeletal muscle myosin, called S1, consists of a heavy chain (95 kDa) and one alkali light chain (16 or 21 kDa) complex that retains myosin ATPase activity. In the presence of Mg²⁺, S1 converts [2-³H]ATP to [2-³H]ADP and the complex S1.Mg[2-³H]ADP has ADP bound in the active site. At 0°C, 1D ³H NMR spectra of S1.Mg[2-³H]ADP have two broadened peaks shifted 0.55 and 0.90 ppm upfield from the peak due to free [2-³H]ADP. Spectra with good signal-to-noise for 0.10 mM S1.Mg[2-³H]ADP were obtained in 180 min. The magnitude of the chemical shift caused by binding is consistent with the presence of an aromatic side chain being in the active site. Spectra were the same for S1 with either of the alkali light chains present, suggesting that the alkali light chains do not interact differently with the active site. The two broad peaks appear to be due to the two conformations of S1 that have been observed previously by other techniques. Raising the temperature to 20 °C causes small changes in the chemical shifts, narrows the peak widths from 150 to 80 Hz, and increases the relative area under the more upfield peak. Addition of orthovanadate (V_i) to produce S1.Mg[2-³H]ADP.V_i shifts both peaks slightly more upfield without chaning their widths or relative areas.     

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.     

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

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