Application of 2D and 3D NMR experiments to the conformational study of a diantennary oligosaccharide

Summary By the application of homonuclear 3D NOE-HOHAHA and heteronuclear 3D HMQC-NOE experiments in studies of complex oligosaccharides. NOEs can be investigated which are hidden in conventional 2D NOE spectra. In the 3D NOE-HOHAHA spectrum ₃ cross sections were considered to be the most suitable for assignment of NOEs. Alternatively, these cross sections could be measured separately in selective 2D HOHAHA-NOE spectroscopy. The advantages and limitations of the 2D alternative are compared with those of the 3D NOE-HOHAHA approach. In 3D HMQC-NOE spectroscopy the larger chemical shift displacement of the carbon spectrum with respect to the proton spectrum can be used to unmask NOEs hidden in the bulk region. If the extra proton dimension is not needed, 2D HMQC-NOE is a good alternative.The suitability of 2D and 3D NOE-HOHAHA and HMQC-NOE experiments for the estimation of proton-proton distances is demonstrated by comparing the results of these experiments on a diantennary asparagine-linked oligosaccharide with those of a conventional 2D NOE experiment. NOEs identified in the 2D and 3D NOE-HOHAHA as well as HMQC-NOE experiments, so far not identified or not quantified in 2D NOE experiments, are discussed in relation to each glycosidic linkage. The flexibility of the Man(1-3)Man linkage is demonstrated, confirming the existence of an ensemble of conformations for this linkage.     

Evaluating conformational changes in protein structures binding RNA

Many protein-RNA recognition events are known to exhibit conformational changes from qualitative observations of individual complexes. However, a quantitative estimation of conformational changes is required if protein-RNA docking and template-based methods for RNA binding site prediction are to be developed. This study presents the first quantitative evaluation of conformational changes that occur when proteins bind RNA. The analysis of twelve RNA-binding proteins in the bound and unbound states using error-scaled difference distance matrices is presented. The binding site residues are mapped to each structure, and the conformational changes that affect these residues are evaluated. Of the twelve proteins four exhibit greater movements in nonbinding site residues, and a further four show the greatest movements in binding site residues. The remaining four proteins display no significant conformational change. When interface residues are found to be in conformationally variable regions of the protein they are typically seen to move less than 2 A between the bound and unbound conformations. The current data indicate that conformational changes in the binding site residues of RNA binding proteins may not be as significant as previously suggested, but a larger data set is required before wider conclusions may be drawn. The implications of the observed conformational changes for protein function prediction are discussed.     

Refinement of protein termini in template-based modeling using conformational space annealing

The rapid increase in the number of experimentally determined protein structures in recent years enables us to obtain more reliable protein tertiary structure models than ever by template-based modeling. However, refinement of template-based models beyond the limit available from the best templates is still needed for understanding protein function in atomic detail. In this work, we develop a new method for protein terminus modeling that can be applied to refinement of models with unreliable terminus structures. The energy function for terminus modeling consists of both physics-based and knowledge-based potential terms with carefully optimized relative weights. Effective sampling of both the framework and terminus is performed using the conformational space annealing technique. This method has been tested on a set of termini derived from a nonredundant structure database and two sets of termini from the CASP8 targets. The performance of the terminus modeling method is significantly improved over our previous method that does not employ terminus refinement. It is also comparable or superior to the best server methods tested in CASP8. The success of the current approach suggests that similar strategy may be applied to other types of refinement problems such as loop modeling or secondary structure rearrangement.     

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.     

Evolution and similarity evaluation of protein structures in contact map space

Prediction of fold from amino acid sequence of a protein has been an active area of research in the past few years, but the limited accuracy of existing techniques emphasizes the need to develop newer approaches to tackle this task. In this study, we use contact map prediction as an intermediate step in fold prediction from sequence. Contact map is a reduced graph-theoretic representation of proteins that models the local and global inter-residue contacts in the structure. We start with a population of random contact maps for the protein sequence and "evolve" the population to a "high-feasibility" configuration using a genetic algorithm. A neural network is employed to assess the feasibility of contact maps based on their 4 physically relevant properties. We also introduce 5 parameters, based on algebraic graph theory and physical considerations, that can be used to judge the structural similarity between proteins through contact maps. To predict the fold of a given amino acid sequence, we predict a contact map that will sufficiently approximate the structure of the corresponding protein. Then we assess the similarity of this contact map with the representative contact map of each fold; the fold that corresponds to the closest match is our predicted fold for the input sequence. We have found that our feasibility measure is able to differentiate between feasible and infeasible contact maps. Further, this novel approach is able to predict the folds from sequences significantly better than a random predictor.     

Evolution of the multidomain protein wheat germ agglutinin

Summary We compared the homologous amino acid sequences of hevein and each of the four domains (A, B, C, and D) of wheat germ agglutinin and used them to construct a pseudophylogenetic tree relating these sequences to a hypothetical common ancestor sequence. In the crystal structure of the wheat germ agglutinin dimer, six pseudo-twofold rotational symmetry axes have previously been located in addition to the true twofold axis. Four of these relate two nonidentical domains to each other in each of the four possible pairs constituting the sugar-binding sites (A₁D₂, A₂D₁, B₁C₂, and B₂C₁). The remaining two relate contiguous unique pairs of sugar-binding sites to each other (A₁D₂ to B₁C₂, and A₂D₁ to B₂C₁). These latter two sets of pairs are related to each other by the true twofold axis. Side chains that mediate sugar binding in the interfaces of each of the four pairs were found to be largely conserved. The sequence homology, taken together with these pseudo-symmetry elements in the dimer structure, suggests a pathway for the evolution of the four-domain molecule from a single-domain dimer that can be correlated with simultaneous development of the saccharide-binding sites.     

Prediction of residues in discontinuous B-cell epitopes using protein 3D structures

Discovery of discontinuous B-cell epitopes is a major challenge in vaccine design. Previous epitope prediction methods have mostly been based on protein sequences and are not very effective. Here, we present DiscoTope, a novel method for discontinuous epitope prediction that uses protein three-dimensional structural data. The method is based on amino acid statistics, spatial information, and surface accessibility in a compiled data set of discontinuous epitopes determined by X-ray crystallography of antibody/antigen protein complexes. DiscoTope is the first method to focus explicitly on discontinuous epitopes. We show that the new structure-based method has a better performance for predicting residues of discontinuous epitopes than methods based solely on sequence information, and that it can successfully predict epitope residues that have been identified by different techniques. DiscoTope detects 15.5% of residues located in discontinuous epitopes with a specificity of 95%. At this level of specificity, the conventional Parker hydrophilicity scale for predicting linear B-cell epitopes identifies only 11.0% of residues located in discontinuous epitopes. Predictions by the DiscoTope method can guide experimental epitope mapping in both rational vaccine design and development of diagnostic tools, and may lead to more efficient epitope identification.     

CLEMAPS: multiple alignment of protein structures based on conformational letters

CLEMAPS is a tool for multiple alignment of protein structures. It distinguishes itself from other existing algorithms for multiple structure alignment by the use of conformational letters, which are discretized states of 3D segmental structural states. A letter corresponds to a cluster of combinations of three angles formed by C(alpha) pseudobonds of four contiguous residues. A substitution matrix called CLESUM is available to measure the similarity between any two such letters. The input 3D structures are first converted to sequences of conformational letters. Each string of a fixed length is then taken as the center seed to search other sequences for neighbors of the seed, which are strings similar to the seed. A seed and its neighbors form a center-star, which corresponds to a fragment set of local structural similarity shared by many proteins. The detection of center-stars using CLESUM is extremely efficient. Local similarity is a necessary, but insufficient, condition for structural alignment. Once center-stars are found, the spatial consistency between any two stars are examined to find consistent star duads using atomic coordinates. Consistent duads are later joined to create a core for multiple alignment, which is further polished to produce the final alignment. The utility of CLEMAPS is tested on various protein structure ensembles.     

Assessing a novel approach for predicting local 3D protein structures from sequence

We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly.     

Prediction of protein secondary structures from conformational biases

We use LINUS (the "Local Independently Nucleated Units of Structure"), a procedure developed by Srinivasan and Rose, to provide a physical interpretation of and predict the secondary structures of proteins. The secondary structure type at a given site is identified by the largest conformational bias during short simulations. We examine the rate of successful prediction as a function of temperature and the interaction window. At high temperatures, there is a large propensity for the establishment of beta-strands whereas alpha-helices appear only when the temperature is lower than a certain threshold value. It is found that there exists an optimal temperature at which the correct secondary structures are predicted most accurately. We find that this temperature is close to the peak temperature of the specific heat. Changing the interaction window or carrying out longer simulations approaching equilibrium lead to little change in the optimal success rate. Our findings are in accord with the observation by Srinivasan and Rose that the secondary structures are mainly determined by local interactions and appear in the early stage of folding.     

Phylogeny of a Growth Hormone-Like Cytokine Superfamily Based upon 3D Structure

Abstract Cytokines are of central importance in the regulation of hematopoiesis, immunity, inflammation, tissue remodeling, and embryonic development. Cytokine research is expected to provide the key to pharmacological manipulation of the immune response and commands the attention of a massive and highly focused biotechnology industry. Based upon the hypothetical secondary and tertiary structures, a superfamily of growth hormone (GH)-like cytokine was identified previously. Here, we report the phylogeny of this superfamily based upon 3D structural data from the Protein Data Bank. First, a retrieving program is designed to abstract their secondary structures and associated atomic coordinates. Helices, digitized as vectors in the Cartesian coordinate system, are collected from the retrieved atomic coordinates at the α carbons of the protein backbone. Then the scalar value and vector angle against the reference vector, usually the first vector, are calculated. Furthermore, cluster analysis among various cytokines is performed on their helical scales and helical angles. As a result, GH is close to the cluster formed by ciliary neurotrophic factor and granulocyte colony-stimulating factor (CSF); leptin and erythropoietin are in descending order close to the cluster formed by interleukin (IL)-6 and IL-10; the former seven members in the two subgroups above join together and form one group with leukemia inhibitory factor; granulocyte–macrophage CSF, IL-2, IL-4, and IL-5 are in descending order close to the cluster formed by IL-3 and macrophage CSF; and the latter six members form another group. Finally, it is demonstrated that the phylogeny of GH-like cytokines above is consistent with the evolutionary relationship of their gene organization, gene localization, receptor module composition, and receptor module compatibility.     

Library of disordered patterns in 3D protein structures

Intrinsically disordered regions serve as molecular recognition elements, which play an important role in the control of many cellular processes and signaling pathways. It is useful to be able to predict positions of disordered regions in protein chains. The statistical analysis of disordered residues was done considering 34,464 unique protein chains taken from the PDB database. In this database, 4.95% of residues are disordered (i.e. invisible in X-ray structures). The statistics were obtained separately for the N- and C-termini as well as for the central part of the protein chain. It has been shown that frequencies of occurrence of disordered residues of 20 types at the termini of protein chains differ from the ones in the middle part of the protein chain. Our systematic analysis of disordered regions in PDB revealed 109 disordered patterns of different lengths. Each of them has disordered occurrences in at least five protein chains with identity less than 20%. The vast majority of all occurrences of each disordered pattern are disordered. This allows one to use the library of disordered patterns for predicting the status of a residue of a given protein to be ordered or disordered. We analyzed the occurrence of the selected patterns in three eukaryotic and three bacterial proteomes.     

A new bioinformatic approach to detect common 3D sites in protein structures

An innovative bioinformatic method has been designed and implemented to detect similar three-dimensional (3D) sites in proteins. This approach allows the comparison of protein structures or substructures and detects local spatial similarities: this method is completely independent from the amino acid sequence and from the backbone structure. In contrast to already existing tools, the basis for this method is a representation of the protein structure by a set of stereochemical groups that are defined independently from the notion of amino acid. An efficient heuristic for finding similarities that uses graphs of triangles of chemical groups to represent the protein structures has been developed. The implementation of this heuristic constitutes a software named SuMo (Surfing the Molecules), which allows the dynamic definition of chemical groups, the selection of sites in the proteins, and the management and screening of databases. To show the relevance of this approach, we focused on two extreme examples illustrating convergent and divergent evolution. In two unrelated serine proteases, SuMo detects one common site, which corresponds to the catalytic triad. In the legume lectins family composed of >100 structures that share similar sequences and folds but may have lost their ability to bind a carbohydrate molecule, SuMo discriminates between functional and non-functional lectins with a selectivity of 96%. The time needed for searching a given site in a protein structure is typically 0.1 s on a PIII 800MHz/Linux computer; thus, in further studies, SuMo will be used to screen the PDB.     

Application of a directed conformational search for generating 3-D coordinates for protein structures from alpha-carbon coordinates.

A directed conformational search algorithm using the program CONGEN (ref. 3), which samples backbone conformers, is described. The search technique uses information from the partially built structures to direct the search process and is tested on the problem of generating a full set of backbone Cartesian coordinates given only alpha-carbon coordinates. The method has been tested on six proteins of known structure, varying in size and classification, and was able to generate the original backbone coordinates with RMSs ranging from 0.30-0.87A for the alpha-carbons and 0.5-0.99A RMSs for the backbone atoms. Cis peptide linkages were also correctly identified. The procedure was also applied to two proteins available with only alpha-carbon coordinates in the Brookhaven Protein Data Bank; thioredoxin (SRX) and triacylglycerol acylhydrolase (TGL). All-atom models are proposed for the backbone of both these proteins. In addition, the technique was applied to randomized coordinates of flavodoxin to assess the effects of irregularities in the data on the final RMS. This study represents the first time a deterministic conformational search was used on such a large scale.     

Population structure in the Connecticut Valley: II. A comparison of multidimensional scaling solutions of migration matrices and isonymy

In a previous paper (Swedlund et al., 1984) we have described the population structure of the historical Connecticut River Valley of Massachusetts in terms of matrimonial migration matrices. Using procedures described by Morton (1973), Harpending and Jenkins (1974), Jorde (1980), and others the exchanges between subdivisions which make up the matrices are made column stochastic and analyzed to predict genetic kinship. Subsequently the kinship estimates within and between subdivisions can be interpreted as genetic covariance and compared to the actual geographic distances between the respective subdivisions using a principal components analysis. In the present paper we extend these results by applying nonmetric multidimensional scaling to the migration matrices, and to isonymy matrices based on the same communities. We demonstrate that the multidimensional scaling configurations of marital migration represent the actual geographic relationships between the communities quite effectively for this particular case study from historical Massachusetts. Moreover, we argue that while these migration data may provide good estimates of social and genetic exchange between the subdivisions, surname analysis may also be informative of processes not revealed in the migration matrices alone.     

Reconstruction of 3D structures from protein contact maps

The prediction of the protein tertiary structure from solely its residue sequence (the so called Protein Folding Problem) is one of the most challenging problems in Structural Bioinformatics. We focus on the protein residue contact map. When this map is assigned it is possible to reconstruct the 3D structure of the protein backbone. The general problem of recovering a set of 3D coordinates consistent with some given contact map is known as a unit-disk-graph realization problem and it has been recently proven to be NP-Hard. In this paper we describe a heuristic method (COMAR) that is able to reconstruct with an unprecedented rate (3-15 seconds) a 3D model that exactly matches the target contact map of a protein. Working with a non-redundant set of 1760 proteins, we find that the scoring efficiency of finding a 3D model very close to the protein native structure depends on the threshold value adopted to compute the protein residue contact map. Contact maps whose threshold values range from 10 to 18 Ångstroms allow reconstructing 3D models that are very similar to the proteins native structure.     

ProSAT: functional annotation of protein 3D structures

ProSAT (for Protein Structure Annotation Tool) is a tool to facilitate interactive visualization of non-structure-based functional annotations in protein 3D structures. It performs automated mapping of the functional annotations onto the protein structure and allows functional sites to be readily identified upon visualization. The current version of ProSAT can be applied to large datasets of protein structures for fast visual identification of active and other functional sites derived from the SwissProt and Prosite databases.     

Isotope-edited multidimensional NMR of calcineurin B in the presence of the non-deuterated detergent CHAPS

Summary At the concentration needed for NMR, the calcium-saturated form of calcineurin B dissolved in water shows resonance line widths that indicate aggregation of this protein. Although the line width or aggregation state can be influenced to some degree by temperature, pH, and salt concentrations, in the absence of detergent no conditions could be found where the protein behaved as a monomeric unit. In the presence of a 10- to 20-fold molar excess of the zwitterionic detergent 3-[(3-cholamidopropyl)-dimethyl-ammonio]-1-propanesulfonate (CHAPS), resonance line widths were considerably narrower and were compatible with a protein of 25 kDa. The presence of the NMR signals of the non-deuterated CHAPS does not interfere with modern isotope-directed NMR studies as the signals from protons not attached to ¹⁵N or ¹³C are removed by isotope filtering and purge pulses.     

Pre-docking filter for protein and ligand 3D structures

Virtual drug screening using protein-ligand docking techniques is a time-consuming process, which requires high computational power for binding affinity calculation. There are millions of chemical compounds available for docking. Eliminating compounds that are unlikely to exhibit high binding affinity from the screening set should speed-up the virtual drug screening procedure. We performed docking of 6353 ligands against twenty-one protein X-ray crystal structures. The docked ligands were ranked according to their calculated binding affinities, from which the top five hundred and the bottom five hundred were selected. We found that the volume and number of rotatable bonds of the top five hundred docked ligands are similar to those found in the crystal structures and corresponded with the volume of the binding sites. In contrast, the bottom five hundred set contains ligands that are either too large to enter the binding site, or too small to bind with high specificity and affinity to the binding site. A pre-docking filter that takes into account shapes and volumes of the binding sites as well as ligand volumes and flexibilities can filter out low binding affinity ligands from the screening sets. Thus, the virtual drug screening procedure speed is increased.