BackgroundB-RAF kinase plays an important role both in tumour induction and maintenance in several cancers and it is an attractive new drug target. However, the structural basis of the B-RAF activation is still not well understood.ResultsIn this study we suggest a novel molecular basis of B-RAF activation based on molecular dynamics (MD) simulations of B-RAFWT and the B-RAFV600E, B-RAFK601E and B-RAFD594V mutants. A strong hydrogen bond network was identified in B-RAFWT in which the interactions between Lys601 and the well known catalytic residues Lys483, Glu501 and Asp594 play an important role. It was found that several mutations, which directly or indirectly destabilized the interactions between these residues within this network, contributed to the changes in B-RAF activity.ConclusionOur results showed that the above mechanisms lead to the disruption of the electrostatic interactions between the A-loop and the αC-helix in the activating mutants, which presumably contribute to the flipping of the activation segment to an active form. Conversely, in the B-RAFD594V mutant that has impaired kinase activity, and in B-RAFWT these interactions were strong and stabilized the kinase inactive form.

BackgroundThe transmission electron microscope is used to acquire structural information of macromolecular complexes. However, as any other imaging device, it introduces optical aberrations that must be corrected if high-resolution structural information is to be obtained. The set of all aberrations are usually modeled in Fourier space by the so-called Contrast Transfer Function (CTF). Before correcting for the CTF, we must first estimate it from the electron micrographs. This is usually done by estimating a number of parameters specifying a theoretical model of the CTF. This estimation is performed by minimizing some error measure between the theoretical Power Spectrum Density (PSD) and the experimentally observed PSD. The high noise present in the micrographs, the possible local minima of the error function for estimating the CTF parameters, and the cross-talking between CTF parameters may cause errors in the estimated CTF parameters.ResultsIn this paper, we explore the effect of these estimation errors on the theoretical CTF. For the CTF model proposed in [1] we show which are the most sensitive CTF parameters as well as the most sensitive background parameters. Moreover, we provide a methodology to reveal the internal structure of the CTF model (which parameters influence in which parameters) and to estimate the accuracy of each model parameter. Finally, we explore the effect of the variability in the detection of the CTF for CTF phase and amplitude correction.ConclusionWe show that the estimation errors for the CTF detection methodology proposed in [1] does not show a significant deterioration of the CTF correction capabilities of subsequent algorithms. All together, the methodology described in this paper constitutes a powerful tool for the quantitative analysis of CTF models that can be applied to other models different from the one analyzed here.

BackgroundMethods that can automatically assess the quality of computationally predicted protein structures are important, as they enable the selection of the most accurate structure from an ensemble of predictions. Assessment methods that determine the quality of a predicted structure by comparing it against the various structures predicted by different servers have been shown to outperform approaches that rely on the intrinsic characteristics of the structure itself.ResultsWe examined techniques to estimate the quality of a predicted protein structure based on prediction consensus. LGA is used to align the structure in question to the structures for the same protein predicted by different servers. We examine both static (e.g. averaging) and dynamic (e.g. support vector machine) methods for aggregating these distances on two datasets.ConclusionWe find that a constrained regression approach shows consistently good performance. Although it is not always the absolute best performing scheme, it is always performs on par with the best schemes across multiple datasets. The work presented here provides the basis for the construction of a regression model trained on data from existing structure prediction servers.

BackgroundSolvent accessibility (ASA) of amino acid residues is often transformed from absolute values of exposed surface area to their normalized relative values. This normalization is typically attained by assuming a highest exposure conformation based on extended state of that residue when it is surrounded by Ala or Gly on both sides i.e. Ala-X-Ala or Gly-X-Gly solvent exposed area. Exact sequence context, the folding state of the residues, and the actual environment of a folded protein, which do impose additional constraints on the highest possible (or highest observed) values of ASA, are currently ignored. Here, we analyze the statistics of these constraints and examine how the normalization of absolute ASA values using context-dependent Highest Observed ASA (HOA) instead of context-free extended state ASA (ESA) of residues can influence the performance of sequence-based prediction of solvent accessibility. Characterization of burial and exposed states of residues based on this normalization has also been shown to provide better enrichment of DNA-binding sites in exposed residues.ResultsWe compiled the statistics of highest observed ASA (HOA) of residues in their different contexts and analyzed their distribution in all 400 possible combinations for each residue type. We observe that many trippetides are more exposed than ESA and that HOA residues are often found in turn, coil and bend conformations. On the other hand several residues are never observed in an exposure state close to ESA values. A neural networks trained with HOA-normalized data outperforms the one trained with ESA-normalized values. However, the improvements are subtle in some residues, while they are more significant in others.ConclusionHOA based normalization of solvent accessibility from native structures is proposed and it shows improvement in sequence-based predictability, as well as enrichment in interface residues on surface. There may still be some difference between the highest possible ASA and highest observed ASA due to an insufficiently covered space of ASA distribution in the PDB, which limit the overall improvement in prediction to a relatively modest degree.

BackgroundIn the research on protein functional sites, researchers often need to identify binding-site residues on a protein. A commonly used strategy is to find a complex structure from the Protein Data Bank (PDB) that consists of the protein of interest and its interacting partner(s) and calculate binding-site residues based on the complex structure. However, since a protein may participate in multiple interactions, the binding-site residues calculated based on one complex structure usually do not reveal all binding sites on a protein. Thus, this requires researchers to find all PDB complexes that contain the protein of interest and combine the binding-site information gleaned from them. This process is very time-consuming. Especially, combing binding-site information obtained from different PDB structures requires tedious work to align protein sequences. The process becomes overwhelmingly difficult when researchers have a large set of proteins to analyze, which is usually the case in practice.ResultsIn this study, we have developed a tool for calculating binding-site residues on proteins, TCBRP . For an input protein, TCBRP can quickly find all binding-site residues on the protein by automatically combining the information obtained from all PDB structures that consist of the protein of interest. Additionally, TCBRP presents the binding-site residues in different categories according to the interaction type. TCBRP also allows researchers to set the definition of binding-site residues.ConclusionThe developed tool is very useful for the research on protein binding site analysis and prediction.

BackgroundDisulphide bridges are well known to play key roles in stability, folding and functions of proteins. Introduction or deletion of disulphides by site-directed mutagenesis have produced varying effects on stability and folding depending upon the protein and location of disulphide in the 3-D structure. Given the lack of complete understanding it is worthwhile to learn from an analysis of extent of conservation of disulphides in homologous proteins. We have also addressed the question of what structural interactions replaces a disulphide in a homologue in another homologue.ResultsUsing a dataset involving 34,752 pairwise comparisons of homologous protein domains corresponding to 300 protein domain families of known 3-D structures, we provide a comprehensive analysis of extent of conservation of disulphide bridges and their structural features. We report that only 54% of all the disulphide bonds compared between the homologous pairs are conserved, even if, a small fraction of the non-conserved disulphides do include cytoplasmic proteins. Also, only about one fourth of the distinct disulphides are conserved in all the members in protein families. We note that while conservation of disulphide is common in many families, disulphide bond mutations are quite prevalent. Interestingly, we note that there is no clear relationship between sequence identity between two homologous proteins and disulphide bond conservation. Our analysis on structural features at the sites where cysteines forming disulphide in one homologue are replaced by non-Cys residues show that the elimination of a disulphide in a homologue need not always result in stabilizing interactions between equivalent residues.ConclusionWe observe that in the homologous proteins, disulphide bonds are conserved only to a modest extent. Very interestingly, we note that extent of conservation of disulphide in homologous proteins is unrelated to the overall sequence identity between homologues. The non-conserved disulphides are often associated with variable structural features that were recruited to be associated with differentiation or specialisation of protein function.

BackgroundDuring the last years, methods for remote homology detection have grown more and more sensitive and reliable. Automatic structure prediction servers relying on these methods can generate useful 3D models even below 20% sequence identity between the protein of interest and the known structure (template). When no homologs can be found in the protein structure database (PDB), the user would need to rerun the same search at regular intervals in order to make timely use of a template once it becomes available.ResultsPDBalert is a web-based automatic system that sends an email alert as soon as a structure with homology to a protein in the user's watch list is released to the PDB database or appears among the sequences on hold. The mail contains links to the search results and to an automatically generated 3D homology model. The sequence search is performed with the same software as used by the very sensitive and reliable remote homology detection server HHpred, which is based on pairwise comparison of Hidden Markov models.ConclusionPDBalert will accelerate the information flow from the PDB database to all those who can profit from the newly released protein structures for predicting the 3D structure or function of their proteins of interest.

BackgroundAllosteric coupling, which can be defined as propagation of a perturbation at one region of the protein molecule (such as ligand binding) to distant sites in the same molecule, constitutes the most general mechanism of regulation of protein function. However, unlike molecular details of ligand binding, structural elements involved in allosteric effects are difficult to diagnose. Here, we identified allosteric linkages in the α-subunits of heterotrimeric G proteins, which were evolved to transmit membrane receptor signals by allosteric mechanisms, by using two different approaches that utilize fundamentally different and independent information.ResultsWe analyzed: 1) correlated mutations in the family of G protein α-subunits, and 2) cooperativity of the native state ensemble of the Gαi1 or transducin. The combination of these approaches not only recovered already-known details such as the switch regions that change conformation upon nucleotide exchange, and those regions that are involved in receptor, effector or Gβγ interactions (indicating that the predictions of the analyses can be viewed with a measure of confidence), but also predicted new sites that are potentially involved in allosteric communication in the Gα protein. A summary of the new sites found in the present analysis, which were not apparent in crystallographic data, is given along with known functional and structural information. Implications of the results are discussed.ConclusionA set of residues and/or structural elements that are potentially involved in allosteric communication in Gα is presented. This information can be used as a guide to structural, spectroscopic, mutational, and theoretical studies on the allosteric network in Gα proteins, which will provide a better understanding of G protein-mediated signal transduction.

BackgroundLegionella pneumphila is the causative agent of Legionnaires' disease. A major virulence factor of the pathogen is the homodimeric surface protein Mip. It shows peptidyl-prolyl cis/trans isomerase activty and is a receptor of FK506 and rapamycin, which both inhibit its enzymatic function. Insight into the binding process may be used for the design of novel Mip inhibitors as potential drugs against Legionnaires' disease.ResultsWe have solved the solution structure of free Mip77–213 and the Mip77–213-rapamycin complex by NMR spectroscopy. Mip77–213 showed the typical FKBP-fold and only minor rearrangements upon binding of rapamycin. Apart from the configuration of a flexible hairpin loop, which is partly stabilized upon binding, the solution structure confirms the crystal structure. Comparisons to the structures of free FKBP12 and the FKBP12-rapamycin complex suggested an identical binding mode for both proteins.ConclusionThe structural similarity of the Mip-rapamycin and FKBP12-rapamycin complexes suggests that FKBP12 ligands may be promising starting points for the design of novel Mip inhibitors. The search for a novel drug against Legionnaires' disease may therefore benefit from the large variety of known FKBP12 inhibitors.

BackgroundPrediction 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.ResultsWe 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.ConclusionWe 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.