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Ligand-induced conformational changes in a thermophilic ribose-binding protein
Background:
Members of the periplasmic binding proteins (PBP) superfamily are involved in transport and signaling processes in both prokaryotes and eukaryotes. Biological responses are typically mediated by ligand-induced conformational changes in which the binding event is coupled to a hinge-bending motion that brings together two domains in a closed form. In all PBP-mediated biological processes, downstream partners recognize the closed form of the protein. This motion has also been exploited in protein engineering experiments to construct biosensors that transduce ligand binding to a variety of physical signals. Understanding the mechanistic details of PBP conformational changes, both global (hinge bending, twisting, shear movements) and local (rotamer changes, backbone motion), therefore is not only important for understanding their biological function but also for protein engineering experiments.
Results:
Here we present biochemical characterization and crystal structure determination of the periplasmic ribose-binding protein (RBP) from the hyperthermophile Thermotoga maritima in its ribose-bound and unliganded state. The T. maritima RBP (tmRBP) has 39 % sequence identity and is considerably more resistant to thermal denaturation (appTm value is 108 degreesC) than the mesophilic Escherichia coli homolog (ecRBP) (appTm value is 56 degreesC). Polar ligand interactions and ligand-induced global conformational changes are conserved among ecRBP and tmRBP; however local structural rearrangements involving side-chain motions in the ligand-binding site are not conserved.
Conclusions:
Although the large-scale ligand-induced changes are mediated through similar regions, and are produced by similar backbone movements in tmRBP and ecRBP, the small-scale ligand-induced structural rearrangements differentiate the mesophile and thermophile. This suggests there are mechanistic differences in the manner by which these two proteins bind their ligands and are an example of how two structurally similar proteins utilize different mechanisms to form a ligand-bound state.
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HOLLOW: Generating Accurate Representations of Channel and Interior Surfaces in Molecular Structures
Background:
An accurate rendering of interior surfaces can facilitate the analysis of mechanisms at atomic-level detail, such as the transport of substrates in the ammonia channel. In molecular viewers, one must remove the exterior surface that obscures the channel surface by clipping the viewing plane or manually selecting the channel residues in order to display a partial surface. Neither method is entirely satisfactory, as unwanted additional pieces of surfaces are always generated.
Results:
To cleanly visualize a channel surface, we present HOLLOW, a program that generates a "casting" of the interior volume of the protein as dummy atoms. We show that the molecular surface of the dummy atoms closely approximates the channel surface, where this complementary surface of the protein channel can be displayed without superfluous surfaces.
Conclusions:
The use of HOLLOW significantly simplifies the generation of channel surfaces, and other interior surfaces of protein structures. HOLLOW is written in PYTHON and is available at http://hollow.sourceforge.net.
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Type II restriction endonuclease R.Hpy188I belongs to the GIY-YIG nuclease superfamily, but exhibits an unusual active site
Background:
Catalytic domains of Type II restriction endonucleases (REases) belong to a few unrelated three-dimensional folds. While the PD-(D/E)XK fold is most common among these enzymes, crystal structures have been also determined for single representatives of two other folds: PLD (R.BfiI) and half-pipe (R.PabI). Bioinformatics analyses supported by mutagenesis experiments suggested that some REases belong to the HNH fold (e.g. R.KpnI), and that a small group represented by R.Eco29kI belongs to the GIY-YIG fold. However, for a large fraction of REases with known sequences, the three-dimensional fold and the architecture of the active site remain unknown, mostly due to extreme sequence divergence that hampers detection of homology to enzymes with known folds.
Results:
R.Hpy188I is a Type II REase with unknown structure. PSI-BLAST searches of the non-redundant protein sequence database reveal only 1 homolog (R.HpyF17I, with nearly identical amino acid sequence and the same DNA sequence specificity). Standard application of state-of-the-art protein fold-recognition methods failed to predict the relationship of R.Hpy188I to proteins with known structure or to other protein families. In order to increase the amount of evolutionary information in the multiple sequence alignment, we have expanded our sequence database searches to include sequences from metagenomics projects. This search resulted in identification of 23 further members of R.Hpy188I family, both from metagenomics and the non-redundant database. Moreover, fold-recognition analysis of the extended R.Hpy188I family revealed its relationship to the GIY-YIG domain and allowed for computational modeling of the R.Hpy188I structure. Analysis of the R.Hpy188I model in the light of sequence conservation among its homologs revealed an unusual variant of the active site, in which the typical Tyr residue of the YIG half-motif had been substituted by a Lys residue. Moreover, some of its homologs have the otherwise invariant Arg residue in a non-homologous position in sequence that nonetheless allows for spatial conservation of the guanidino group potentially involved in phosphate binding.
Conclusions:
The present study eliminates a significant "white spot" on the structural map of REases. It also provides important insight into sequence-structure-function relationships in the GIY-YIG nuclease superfamily. Our results reveal that in the case of proteins with no or few detectable homologs in the standard "non-redundant" database, it is useful to expand this database by adding the metagenomic sequences, which may provide evolutionary linkage to detect more remote homologs.
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LRRML: a conformational database and an XML description of leucine-rich repeats (LRRs)
Background:
Leucine-rich repeats (LRRs) are present in more than 6000 proteins. They are found in organisms ranging from viruses to eukaryotes and play an important role in protein-ligand interactions. To date, more than one hundred crystal structures of LRR containing proteins have been determined. This knowledge has increased our ability to use the crystal structures as templates to model LRR proteins with unknown structures. Since the individual three-dimensional LRR structures are not directly available from the established databases and since there are only a few detailed annotations for them, a conformational LRR database useful for homology modeling of LRR proteins is desirable.
Description: We developed LRRML, a conformational database and an extensible markup language (XML) description of LRRs. The release 0.2 contains 1261 individual LRR structures, which were identified from 112 PDB structures and annotated manually. An XML structure was defined to exchange and store the LRRs. LRRML provides a source for homology modeling and structural analysis of LRR proteins. In order to demonstrate the capabilities of the database we modeled the mouse Toll-like receptor 3 (TLR3) by multiple templates homology modeling and compared the result with the crystal structure.
Conclusion:
LRRML is an information source for investigators involved in both theoretical and applied research on LRR proteins. It is available at http://zeus.krist.geo.uni-muenchen.de/~lrrml.
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A knowledge-based structure-discriminating function that requires only main-chain atom coordinates
Background:
The use of knowledge-based potential function is a powerful method for protein structure evaluation. A variety of formulations that evaluate single or multiple structural features of proteins have been developed and studied. The performance of functions is often evaluated by discrimination ability using decoy structures of target proteins. A function that can evaluate coarse-grained structures is advantageous from many aspects, such as relatively easy generation and manipulation of model structures; however, the reduction of structural representation is often accompanied by degradation of the structure discrimination performance.
Results:
We developed a knowledge-based pseudo-energy calculating function for protein structure discrimination. The function (Discriminating Function using Main-chain Atom Coordinates, DFMAC) consists of six pseudo-energy calculation components that deal with different structural features. Only the main-chain atom coordinates of N, C-alpha, and C atoms for the respective amino acid residues are required as input data for structure evaluation. The 231 target structures in 12 different types of decoy sets were separated into 154 and 77 targets, and function training and the subsequent performance test were performed using the respective target sets. Fifty-nine (76.6%) native and 68 (88.3%) near-native (< 2.0 A C-alpha RMSD) targets in the test set were successfully identified. The average C-alpha RMSD of the test set resulted in 1.174 with the tuned parameters. The major part of the discrimination performance was supported by the orientation-dependent component.
Conclusions:
Despite the reduced representation of input structures, DFMAC showed considerable structure discrimination ability. The function can be applied to the identification of near-native structures in structure prediction experiments.
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Protein Functional Surfaces: Global Shape Matching and Local Spatial Alignments of Ligand Binding Sites
Background:
Protein surfaces comprise only a fraction of the total residues but are the most conserved functional features of proteins. Surfaces performing identical functions are found in proteins absent of any sequence or fold similarity. While biochemical activity can be attributed to a few key residues, the broader surrounding environment plays an equally important role.
Results:
We describe a methodology that attempts to optimize two components, global shape and local physicochemical texture, for evaluating the similarity between a pair of surfaces. Surface shape similarity is assessed using a three-dimensional object recognition algorithm and physicochemical texture similarity is assessed through a spatial alignment of conserved residues between the surfaces. The comparisons are used in tandem to efficiently search the Global Protein Surface Survey (GPSS), a library of annotated surfaces derived from structures in the PDB, for studying evolutionary relationships and uncovering novel similarities between proteins.
Conclusions:
We provide an assessment of our method using library retrieval experiments for identifying functionally homologous surfaces binding different ligands, functionally diverse surfaces binding the same ligand, and binding surfaces of ubiquitous and conformationally flexible ligands. Results using surface similarity to predict function for proteins of unknown function are reported. Additionally, an automated analysis of the ATP binding surface landscape is presented to provide insight into the correlation between surface similarity and function for structures in the PDB and for the subset of protein kinases.
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Flanking p10 contribution and sequence bias in matrix based epitope prediction: revisiting the assumption of independent binding pockets
Background:
Eluted natural peptides from major histocompatibility molecules show patterns of conserved residues. Crystallographic structures show that the bound peptide in class II major histocompatibility complex adopts a near uniform polyproline II-like conformation. This way allele-specific favoured residues are able to anchor into pockets in the binding groove leaving other peptide side chains exposed for recognition by T cells. The anchor residues form a motif. This sequence pattern can be used to screen large sequences for potential epitopes. Quantitative matrices extend the motif idea to include the contribution of non-anchor peptide residues. This report examines two new matrices that extend the binding register to incorporate the polymorphic p10 pocket of human leukocyte antigen DR1. Their performance is quantified against experimental binding measurements and against the canonical nine-residue register matrix.
Results:
One new matrix shows significant improvement over the base matrix; the other does not. The new matrices differ in the sequence of the peptide library.
Conclusions:
One of the extended quantitative matrices showed significant improvement in prediction over the original nine residue matrix and over the other extended matrix. Proline in the sequence of the peptide library of the better performing matrix presumably stabilizes the peptide conformation through neighbour interactions. Such interactions may influence epitope prediction in this test of quantitative matrices. This calls into question the assumption of the independent contribution of individual binding pockets.
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Quantifying information transfer by protein domains: Analysis of the Fyn SH2 domain structure
Background:
Efficient communication between distant sites within a protein is essential for cooperative biological response. Although often associated with large allosteric movements, more subtle changes in protein dynamics can also induce long-range correlations. However, an appropriate formalism that directly relates protein structural dynamics to information exchange between functional sites is still lacking.
Results:
Here we introduce a method to analyze protein dynamics within the framework of information theory and show that signal transduction within proteins can be considered as a particular instance of communication over a noisy channel. In particular, we analyze the conformational correlations between protein residues and apply the concept of mutual information to quantify information exchange. Mapping out changes of mutual information on the protein structure then allows visualizing how distal communication is achieved. We illustrate the approach by analyzing information transfer by the SH2 domain of Fyn tyrosine kinase, obtained from Monte Carlo dynamics simulations. Our analysis reveals that the Fyn SH2 domain forms a noisy communication channel that couples residues located in the phosphopeptide binding site and a number of residues at the other side of the domain near the linkers that connect the SH2 domain to the SH3 and Kinase domains. We find that for this particular domain, communication is affected by a series of contiguous residues that connect distal sites by crossing the core of the SH2 domain.
Conclusions:
As a result, our method provides a means to directly map the exchange of biological information on the structure of protein domains, making it clear how binding triggers conformational changes in the protein structure. As such it provides a structural road, next to the existing attempts at sequence level, to predict long-range interactions within protein structures.
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Identification of new, well-populated amino-acid sidechain rotamers involving hydroxyl-hydrogen atoms and sulfhydryl-hydrogen atoms
Background:
An important element in homology modeling is the use of rotamers to parameterize the sidechain conformation. Despite the many libraries of sidechain rotamers that have been developed, a number of rotamers have been overlooked, due to the fact that they involve hydrogen atoms.
Results:
We identify new, well-populated rotamers that involve the hydroxyl-hydrogen atoms of Ser, Thr and Tyr, and the sulfhydryl-hydrogen atom of Cys, using high-resolution crystal structures (<1.2 Å). Although there were refinement artifacts in these structures, comparison with the electron-density maps allowed the placement of hydrogen atoms involved in hydrogen bonds. The χ2 rotamers in Ser, Thr and Cys are consistent with tetrahedral bonding, while the χ3 rotamers in Tyr are consistent with trigonal-planar bonding. Similar rotamers are found in hydrogen atoms that were computationally placed with the Reduce program from the Richardson lab.
Conclusion:
Knowledge of these new rotamers will improve the evaluation of hydrogen-bonding networks in protein structures.
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The crystal structure of the catalytic domain of a eukaryotic guanylate cyclase
Background:
Soluble guanylate cyclases generate cyclic GMP when bound to nitric oxide, thereby linking nitric oxide levels to the control of processes such as vascular homeostasis and neurotransmission. The guanylate cyclase catalytic module, for which no structure has been determined at present, is a class III nucleotide cyclase domain that is also found in mammalian membrane-bound guanylate and adenylate cyclases.
Results:
We have determined the crystal structure of the catalytic domain of a soluble guanylate cyclase from the green algae Chlamydomonas reinhardtii at 2.55 Å resolution, and show that it is a dimeric molecule.
Conclusion:
Comparison of the structure of the guanylate cyclase domain with the known structures of adenylate cyclases confirms the close similarity in architecture between these two enzymes, as expected from their sequence similarity. The comparison also suggests that the crystallized guanylate cyclase is in an inactive conformation, and the structure provides indications as to how activation might occur. We demonstrate that the two active sites in the dimer exhibit positive cooperativity, with a Hill coefficient of ~1.5. Positive cooperativity has also been observed in the homodimeric mammalian membrane-bound guanylate cyclases. The structure described here provides a reliable model for functional analysis of mammalian guanylate cyclases, which are closely related in sequence.
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A method for probabilistic mapping between protein structure and function taxonomies through cross training
Background:
Prediction of function of proteins on the basis of structure and vice versa is a partially solved problem, largely in the domain of biophysics and biochemistry. This underlies the need of computational and bioinformatics approach to solve the problem. Large and organized latent knowledge on protein classification exists in the form of independently created protein classification databases. By creating probabilistic maps between classes of structural classification databases (e.g. SCOP 1) and classes of functional classification databases (e.g. PROSITE 2), structure and function of proteins could be probabilistically related.
Results:
We demonstrate that PROSITE and SCOP have significant semantic overlap, in spite of independent classification schemes. By training classifiers of SCOP using classes of PROSITE as attributes and vice versa, accuracy of Support Vector Machine classifiers for both SCOP and PROSITE was improved. Novel attributes, 2-D elastic profiles and Blocks were used to improve time complexity and accuracy. Many relationships were extracted between classes of SCOP and PROSITE using decision trees.
Conclusion:
We demonstrate that presented approach can discover new probabilistic relationships between classes of different taxonomies and render a more accurate classification. Extensive mappings between existing protein classification databases can be created to link the large amount of organized data. Probabilistic maps were created between classes of SCOP and PROSITE allowing predictions of structure using function, and vice versa. In our experiments, we also found that functions are indeed more strongly related to structure than are structure to functions.
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