<Emphasis Type="Italic">Fastbreak</Emphasis>: a tool for analysis and visualization of structural variations in genomic data

Genomic studies are now being undertaken on thousands of samples requiring new computational tools that can rapidly analyze data to identify clinically important features. Inferring structural variations in cancer genomes from mate-paired reads is a combinatorially difficult problem. We introduce Fastbreak, a fast and scalable toolkit that enables the analysis and visualization of large amounts of data from projects such as The Cancer Genome Atlas.     

An approach of encoding for prediction of splice sites using SVM

In splice sites prediction, the accuracy is lower than 90% though the sequences adjacent to the splice sites have a high conservation. In order to improve the prediction accuracy, much attention has been paid to the improvement of the performance of the algorithms used, and few used for solving the fundamental issues, namely, nucleotide encoding. In this paper, a predictor is constructed to predict the true and false splice sites for higher eukaryotes based on support vector machines (SVM). Four types of encoding, which were mono-nucleotide (MN) encoding, MN with frequency difference between the true sites and false sites (FDTF) encoding, Pair-wise nucleotides (PN) encoding and PN with FDTF encoding, were applied to generate the input for the SVM. The results showed that PN with FDTF encoding as input to SVM led to the most reliable recognition of splice sites and the accuracy for the prediction of true donor sites and false sites were 96.3%, 93.7%, respectively, and the accuracy for predicting of true acceptor sites and false sites were 94.0%, 93.2%, respectively.     

Mireval: a web tool for simple microRNA prediction in genome sequences

SUMMARY: We have developed an online tool called mirEval which can search sequences of up to 10 000 nt for novel microRNAs in multiple organisms. It is a comprehensive tool, easy to use and very informative. It will allow users with no prior knowledge of in-silico detection of microRNAs to take advantage of the most successful approaches to investigate sequences of interest. AVAILABILITY: The mirEval web server is available at http://tagc.univ-mrs.fr/mireval     

New fluorescent probes for ligand-binding assays of odorant-binding proteins

Fluorescence-linked binding assays allow determination of dissociation constants at equilibrium and have recently become increasingly popular, thanks to their ease of operation. Currently used probes, such as 1-aminoanthracene and N-phenyl-1-naphthylamine, are excited and emit in the ultraviolet region, but alternative ligands operating in the visible spectrum would be highly desirable for applications in biosensing devices. Based on the two above structures, we have designed and synthesised six new fluorescent probes to be used in ligand-binding assays. The compounds are derivatives of naphatalene, anthracene and fluoranthene and present two aromatic moieties linked by an amine nitrogen. We have measured the emission spectra of the new probes and their binding to three odorant-binding proteins. The probes bind the tested proteins with different affinities, generally with dissociation constants about one order of magnitude lower than the parent compounds. The extended aromatic systems present in the new compounds produced a shift of both excitation and emission peaks at higher wavelength, close or within the visible spectrum, thus facilitating measurements in biosensors for odorants and small organic molecules using optical devices.     

Adjusting for selection on synonymous sites in estimates of evolutionary distance

Evolution at silent sites is often used to estimate the pace of selectively neutral processes or to infer differences in divergence times of genes. However, silent sites are subject to selection in favor of preferred codons, and the strength of such selection varies dramatically across genes. Here, we use the relationship between codon bias and synonymous divergence observed in four species of the genus Saccharomyces to provide a simple correction for selection on silent sites.     

Sequence analysis,structure and binding site prediction of Sigma 1 receptor protein by in silico method

Sigma 1 Receptor is a subtype of opioid receptor that participates in membrane remodeling and cellular differentiation in thenervous system. Sigma1 Receptor protein with amino acid length ranging from 229 is widely distributed in the liver andmoderately in the intestine, kidney, white pulp of the spleen, adrenal gland, brain, placenta and the lung. In this study, the threedimensional structure for sigma 1 receptor protein has been developed by in- silico analysis based on evolutionary trace analysis of37 sigma proteins from different sources. The present work focus on identification of functionally important residues and itsinteraction with antipsychotic drugs reported in literature.     

A normalised scale for structural genomics target ranking: the OB-Score

Target selection and ranking is fundamental to structural genomics. We present a Z-score scale, the "OB-Score", to rank potential targets by their predicted propensity to produce diffraction-quality crystals. The OB-Score is derived from a matrix of predicted isoelectric point and hydrophobicity values for nonredundant PDB entries solved to or=1 member with a high OB-Score, presenting favourable candidates for structural studies.     

GluR2 ligand-binding core complexes: importance of the isoxazolol moiety and 5-substituent for the binding mode of AMPA-type agonists

X-ray structures of the GluR2 ligand-binding core in complex with (S)-Des-Me-AMPA and in the presence and absence of zinc ions have been determined. (S)-Des-Me-AMPA, which is devoid of a substituent in the 5-position of the isoxazolol ring, only has limited interactions with the partly hydrophobic pocket of the ligand-binding site, and adopts an AMPA-like binding mode. The structures, in comparison with other agonist complex structures, disclose the relative importance of the isoxazolol ring and of the substituent in the 5-position for the mode of binding. A relationship appears to exist between the extent of interaction of the ligand with the hydrophobic pocket and the affinity of the ligand.     

Solution structure of the twelfth cysteine-rich ligand-binding repeat in rat megalin

Megalin, an approx. 600 kDa transmembrane glycoprotein that acts as multi-ligand transporter, is a member of the low density lipoprotein receptor gene family. Several cysteine-rich repeats, each consisting of about 40 residues, are responsible for the multispecific binding of ligands. The solution structure of the twelfth cysteine-rich ligand-binding repeat with class A motif found in megalin features two short beta-strands and two helical turns, yielding the typical fold with a I-III, II-V and IV-VI disulfide bridge connectivity pattern and a calcium coordination site at the C-terminal end. The resulting differences in electrostatic surface potential compared to other ligand-binding modules of this gene family, however, may be responsible for the functional divergence.     

A tool for the prediction of functionally important sites in proteins using a library of functional templates

Understanding and characterizing the biochemical and evolutionary information within the wealth of protein sequence and structural data, particularly at functionally important sites, is very important. A comprehensive analysis of physico-chemical properties and evolutionary conservation patterns at the molecular and biological function level is expected to yield important clues for identifying similar sites in as-yet uncharacterized proteins. We present a library of protein functional templates (PFTs) designed to represent the compositional and evolutionary conservation patterns of functional sites at the molecular and biological function level. Subsequently we developed LIMACS (LInear MAtching of Conservation Scores), a software tool that uses the template library for the prediction of functionally important sites in a multiple sequence alignment, transferring the molecular function annotation from the most-similar functional site in the template library to a predicted site.     

Using a neural network and spatial clustering to predict the location of active sites in enzymes

Structural genomics projects aim to provide a sharp increase in the number of structures of functionally unannotated, and largely unstudied, proteins. Algorithms and tools capable of deriving information about the nature, and location, of functional sites within a structure are increasingly useful therefore. Here, a neural network is trained to identify the catalytic residues found in enzymes, based on an analysis of the structure and sequence. The neural network output, and spatial clustering of the highly scoring residues are then used to predict the location of the active site.A comparison of the performance of differently trained neural networks is presented that shows how information from sequence and structure come together to improve the prediction accuracy of the network. Spatial clustering of the network results provides a reliable way of finding likely active sites. In over 69% of the test cases the active site is correctly predicted, and a further 25% are partially correctly predicted. The failures are generally due to the poor quality of the automatically generated sequence alignments.We also present predictions identifying the active site, and potential functional residues in five recently solved enzyme structures, not used in developing the method. The method correctly identifies the putative active site in each case. In most cases the likely functional residues are identified correctly, as well as some potentially novel functional groups.     

A less laborious approach to the high-throughput production of recombinant proteins in Escherichia coli using 2-liter plastic bottles

Contemporary approaches to biology often call for the high-throughput production of large amounts of numerous proteins for structural or functional studies. Even with the highly efficient protein expression systems developed in Escherichia coli, production of these proteins is laborious and time-consuming. We have simplified established protocols by the use of disposable culture vessels: common 2-liter polyethylene terephthalate beverage bottles. The bottles are inexpensive, fit conveniently in commonly available flask holders, and, because they are notched, provide sufficient aeration to support the growth of high-density cultures. The use of antibiotics and freshly prepared media alleviates the need for sterilization of media and significantly reduces the labor involved. Uninoculated controls exhibited no growth during the time required for protein expression in experimental cultures. The yield, solubility, activity, and pattern of crystallization of proteins expressed in bottles were comparable to those obtained under conventional culture conditions. After use, the bottles are discarded, reducing the risk of cross-contamination of subsequent cultures. The approach appears to be suitable for high-throughput production of proteins for structural or functional studies.     

Enzyme/non-enzyme discrimination and prediction of enzyme active site location using charge-based methods

Calculations of charge interactions complement analysis of a characterised active site, rationalising pH-dependence of activity and transition state stabilisation. Prediction of active site location through large DeltapK(a)s or electrostatic strain is relevant for structural genomics. We report a study of ionisable groups in a set of 20 enzymes, finding that false positives obscure predictive potential. In a larger set of 156 enzymes, peaks in solvent-space electrostatic properties are calculated. Both electric field and potential match well to active site location. The best correlation is found with electrostatic potential calculated from uniform charge density over enzyme volume, rather than from assignment of a standard atom-specific charge set. Studying a shell around each molecule, for 77% of enzymes the potential peak is within that 5% of the shell closest to the active site centre, and 86% within 10%. Active site identification by largest cleft, also with projection onto a shell, gives 58% of enzymes for which the centre of the largest cleft lies within 5% of the active site, and 70% within 10%. Dielectric boundary conditions emphasise clefts in the uniform charge density method, which is suited to recognition of binding pockets embedded within larger clefts. The variation of peak potential with distance from active site, and comparison between enzyme and non-enzyme sets, gives an optimal threshold distinguishing enzyme from non-enzyme. We find that 87% of the enzyme set exceeds the threshold as compared to 29% of the non-enzyme set. Enzyme/non-enzyme homologues, structural genomics annotated proteins and catalytic/non-catalytic RNAs are studied in this context.     

A structure-based method for identifying DNA-binding proteins and their sites of DNA-interaction

A classification model of a DNA-binding protein chain was created based on identification of alpha helices within the chain likely to bind to DNA. Using the model, all chains in the Protein Data Bank were classified. For many of the chains classified with high confidence, previous documentation for DNA-binding was found, yet no sequence homology to the structures used to train the model was detected. The result indicates that the chain model can be used to supplement sequence based methods for annotating the function of DNA-binding. Four new candidates for DNA-binding were found, including two structures solved through structural genomics efforts. For each of the candidate structures, possible sites of DNA-binding are indicated by listing the residue ranges of alpha helices likely to interact with DNA.     

Recognizing and defining true Ras binding domains II: in silico prediction based on homology modelling and energy calculations

Considering the large number of putative Ras effector proteins, it is highly desirable to develop computational methods to be able to identify true Ras binding molecules. Based on a limited sequence homology among members of the Ras association (RA) and Ras binding (RB) sub-domain families of the ubiquitin super-family, we have built structural homology models of Ras proteins in complex with different RA and RB domains, using the FOLD-X software. A critical step in our approach is to use different templates of Ras complexes, in order to account for the structural variation among the RA and RB domains. The homology models are validated by predicting the effect of mutating hot spot residues in the interface, and residues important for the specificity of interaction with different Ras proteins. The FOLD-X calculated energies of the best-modelled complexes are in good agreement with previously published experimental data and with new data reported here. Based on these results, we can establish energy thresholds above, or below which, we can predict with 96% confidence that a RA/RB domain will or will not interact with Ras. This study shows the importance of in depth structural analysis, high quality force-fields and modelling for correct prediction. Our work opens the possibility of genome-wide prediction for this protein family and for others, where there is enough structural information.     

Activation tagging in plants: a tool for gene discovery

A significant limitation of classical loss-of-function screens designed to dissect genetic pathways is that they rarely uncover genes that function redundantly, are compensated by alternative metabolic or regulatory circuits, or which have an additional role in early embryo or gametophyte development. Activation T-DNA tagging is one approach that has emerged in plants to help circumvent these potential problems. This technique utilises a T-DNA sequence that contains four tandem copies of the cauliflower mosaic virus (CaMV) 35S enhancer sequence. This element enhances the expression of neighbouring genes either side of the randomly integrated T-DNA tag, resulting in gain-of-function phenotypes. Activation tagging has identified a number of genes fundamental to plant development, metabolism and disease resistance in Arabidopsis. This review provides selected examples of these discoveries to highlight the utility of this technology. The recent development of activation tagging strategies for other model plant systems and the construction of new more sophisticated vectors for the generation of conditional alleles are also discussed. These recent advances have significantly expanded the horizons for gain-of-function genetics in plants.     

Functional sites in F1-ATPases: Location and interactions

This review focuses on the location and interaction of three functional sites in F₁-ATPases. These are catalytic sites which are located in subunits, noncatalytic nucleotide-binding sites which are located at interfaces of and subunits and modulate the hydrolytic activity of the enzyme, and a site that binds inhibitory amphipathic cations which is at an interface of and subunits. The latter site may participate in transmission of conformational signals between catalytic sites in F₁ and the proton-conducting apparatus of F₀ in the intact ATP synthases.