The PSIPRED protein structure prediction server

The PSIPRED protein structure prediction server allows users to submit a protein sequence, perform a prediction of their choice and receive the results of the prediction both textually via e-mail and graphically via the web. The user may select one of three prediction methods to apply to their sequence: PSIPRED, a highly accurate secondary structure prediction method; MEMSAT 2, a new version of a widely used transmembrane topology prediction method; or GenTHREADER, a sequence profile based fold recognition method.

SVRMHC prediction server for MHC-binding peptides

The binding between antigenic peptides (epitopes) and the MHC molecule is a key step in the cellular immune response. Accurate in silico prediction of epitope-MHC binding affinity can greatly expedite epitope screening by reducing costs and experimental effort. Recently, we demonstrated the appealing performance of SVRMHC, an SVR-based quantitative modeling method for peptide-MHC interactions, when applied to three mouse class I MHC molecules. Subsequently, we have greatly extended the construction of SVRMHC models and have established such models for more than 40 class I and class II MHC molecules. Here we present the SVRMHC web server for predicting peptide-MHC binding affinities using these models. Benchmarked percentile scores are provided for all predictions. The larger number of SVRMHC models available allowed for an updated evaluation of the performance of the SVRMHC method compared to other well- known linear modeling methods. SVRMHC is an accurate and easy-to-use prediction server for epitope-MHC binding with significant coverage of MHC molecules. We believe it will prove to be a valuable resource for T cell epitope researchers.

Cloning and expression of zebra fish ferroportin and investigating its influence on anemia

Iron is required for many microbes and pathogens for their survival and proliferation including Leishmania which cause leishmaniasis. Leishmaniasis is an increasingly serious infectious disease with a wide spectrum of clinical manifestations. These range from localized cutaneous leishmaniasis (CL) lesions to a lethal visceral form. Certain strains such as BALB/c mice fail to control L. major infection and develop progressive lesions and systemic disease. These mice are thought to be a model of non-healing forms of the human disease such as kala-azar or diffuse cutaneous leishmaniasis. Progression of disease in BALB/c mice has been associated with the anemia, in last days of their survival, the progressive anemia is considered to be one of the reasons of their death. Ferroportin (Fpn), a key regulator of iron homeostasis is a conserved membrane protein that exports iron across the duodenal enterocytes as well as macrophages and hepatocytes into the blood circulation. Fpn has also critical influence on survival and proliferation of many microorganisms whose growth is dependent upon iron, thus preparation of Fpn is needed to study the role of iron in immune responses and pathogenesis of micoorganisms. To prepare and characterize a recombinant ferroportin, total RNA was extracted from Indian zebrafish duodenum, and used to synthesize cDNA by RT-PCR. PCR product was first cloned in Topo TA vector and then subcloned into the GFP expression vector pEGFP–N1. The final resulted plasmid (pEGFP-ZFpn) was used for expression of FPN-EGFP protein in Hek 293T cells. The expression was confirmed by fluorescence microscopy and flow cytometery. Recombinant Fpn was further characterized by submission of its predicted amino acid sequences to the TMHMM V2.0 prediction server (hidden Markov model), NetOGlyc 3.1 server and NetNGlyc 3.1 server. Data emphasised that obtained Fpn from indian zebrafish contained eight transmembrane domains with N- and C-termini inside the cytoplasm and harboured 78 mucin-type glycosylated amino acid. The results indicate that the prepared and characterized recombinant Fpn protein has no membrane topology difference compared to other Fpn described by other researcher. Our next aim was to deliver recombinant plasmid (pEGFP-ZFpn) to entrocyte cells. However, naked therapeutic genes are rapidly degraded by nucleases, showing poor cellular uptake, nonspecificity to the target cells, and low transfection efficiency. The development of safe and efficient gene carriers is one of the prerequisites for the success of gene therapy. Chitosan and alginate 139 polymers were used for oral gene carrier because of their biodegradability, biocompatibility and their mucoadhesive and permeability-enhancing properties in the gut. Nanoparticles comprising Alginate/Chitosan polymers were prepared by pregel preparation method. The resulting nanoparticles had a loading efficiency of 95% and average size of 188 nm as confirmed by PCS method and SEM images had showed spherical particles. BALB/c mice were divided to three groups. The first and second group were fed with chitosan/alginate nanoparticles containing the pEGFP-ZFpn and pEGFP plasmid, respectively (30 μgr/mice) and the third group (control) didn’t get any nanoparticles. The result showed BALB/c mice infected by L.major, resulted in higher hematocryte and iron level in pEGFP-ZFpn fed mice than that in other groups. Consentration of cytokines determined by ELISA showed lower levels of IL-4 and IL-10 and higher levels of IFN-γ/IL-4 and IFN-γ/IL-10 ratios in pEGFP-ZFpn fed mice than that in other groups. Morover more limited increase of footpad thickness and significant reduction of viable parasites in lymph node was seen in pEGFP-ZFpn fed mice. The results showed the first group exhibited a highr hematocryte and iron compared to the other groups. These data strongly suggests the in vivo administration of chitosan/alginate nanoparticles containing pEGFP-ZFpn suppress Th2 response and may be used to control the leishmaniasis .

Cloning, expression, purification and characterization of recombinant glutathione-S-transferase from Xylella fastidiosa

Xylella fastidiosa is an important pathogen bacterium transmitted by xylem-feedings leafhoppers that colonizes the xylem of plants and causes diseases on several important crops including citrus variegated chlorosis (CVC) in orange and lime trees. Glutathione-S-transferases (GST) form a group of multifunctional isoenzymes that catalyzes both glutathione (GSH)-dependent conjugation and reduction reactions involved in the cellular detoxification of xenobiotic and endobiotic compounds. GSTs are the major detoxification enzymes found in the intracellular space and mainly in the cytosol from prokaryotes to mammals, and may be involved in the regulation of stress-activated signals by suppressing apoptosis signal-regulating kinase 1. In this study, we describe the cloning of the glutathione-S-transferase from X. fastidiosa into pET-28a(+) vector, its expression in Escherichia coli, purification and initial structural characterization. The purification of recombinant xfGST (rxfGST) to near homogeneity was achieved using affinity chromatography and size-exclusion chromatography (SEC). SEC demonstrated that rxfGST is a homodimer in solution. The secondary and tertiary structures of recombinant protein were analyzed by circular dichroism and fluorescence spectroscopy, respectively. The enzyme was assayed for activity and the results taken together indicated that rxfGST is a stable molecule, correctly folded, and highly active. Several members of the GST family have been extensively studied. However, xfGST is part of a less-studied subfamily which yet has not been structurally and biochemically characterized. In addition, these studies should provide a useful basis for future studies and biotechnological approaches of rxfGST. (C) 2008 Elsevier Inc. All rights reserved.

O uso de rede neural artificial MLP na predição de estruturas secundárias de proteínas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

CSSP (Consensus Secondary Structure Prediction): a web-based server for structural biologists

Sequence-structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

StaRProtein: a web server for prediction of stability of repeat proteins

Repeat proteins have become increasingly important due to their capability to bind to almost any proteins and the potential as alternative therapy to monoclonal antibodies. In the past decade repeat proteins have been designed to mediate specific protein-protein interactions. The tetratricopeptide and ankyrin repeat proteins are two classes of helical repeat proteins that form different binding pockets to accommodate various partners. It is important to understand the factors that define folding and stability of repeat proteins in order to prioritize the most stable designed repeat proteins to further explore their potential binding affinities. Here we developed distance-dependant statistical potentials using two classes of alpha-helical repeat proteins, tetratricopeptide and ankyrin repeat proteins respectively, and evaluated their efficiency in predicting the stability of repeat proteins. We demonstrated that the repeat-specific statistical potentials based on these two classes of repeat proteins showed paramount accuracy compared with non-specific statistical potentials in: 1) discriminate correct vs. incorrect models 2) rank the stability of designed repeat proteins. In particular, the statistical scores correlate closely with the equilibrium unfolding free energies of repeat proteins and therefore would serve as a novel tool in quickly prioritizing the designed repeat proteins with high stability. StaRProtein web server was developed for predicting the stability of repeat proteins.

Prediction of global and local model quality in CASP8 using the ModFOLD server

The development of effective methods for predicting the quality of three-dimensional (3D) models is fundamentally important for the success of tertiary structure (TS) prediction strategies. Since CASP7, the Quality Assessment (QA) category has existed to gauge the ability of various model quality assessment programs (MQAPs) at predicting the relative quality of individual 3D models. For the CASP8 experiment, automated predictions were submitted in the QA category using two methods from the ModFOLD server-ModFOLD version 1.1 and ModFOLDclust. ModFOLD version 1.1 is a single-model machine learning based method, which was used for automated predictions of global model quality (QMODE1). ModFOLDclust is a simple clustering based method, which was used for automated predictions of both global and local quality (QMODE2). In addition, manual predictions of model quality were made using ModFOLD version 2.0-an experimental method that combines the scores from ModFOLDclust and ModFOLD v1.1. Predictions from the ModFOLDclust method were the most successful of the three in terms of the global model quality, whilst the ModFOLD v1.1 method was comparable in performance to other single-model based methods. In addition, the ModFOLDclust method performed well at predicting the per-residue, or local, model quality scores. Predictions of the per-residue errors in our own 3D models, selected using the ModFOLD v2.0 method, were also the most accurate compared with those from other methods. All of the MQAPs described are publicly accessible via the ModFOLD server at: http://www.reading.ac.uk/bioinf/ModFOLD/. The methods are also freely available to download from: http://www.reading.ac.uk/bioinf/downloads/.

The DISOPRED server for the prediction of protein disorder

Dynamically disordered regions appear to be relatively abundant in eukaryotic proteomes. The DISOPRED server allows users to submit a protein sequence, and returns a probability estimate of each residue in the sequence being disordered. The results are sent in both plain text and graphical formats, and the server can also supply predictions of secondary structure to provide further structural information.

The FunFOLD2 server for the prediction of protein-ligand interactions

The FunFOLD2 server is a new independent server that integrates our novel protein–ligand binding site and quality assessment protocols for the prediction of protein function (FN) from sequence via structure. Our guiding principles were, first, to provide a simple unified resource to make our function prediction software easily accessible to all via a simple web interface and, second, to produce integrated output for predictions that can be easily interpreted. The server provides a clean web interface so that results can be viewed on a single page and interpreted by non-experts at a glance. The output for the prediction is an image of the top predicted tertiary structure annotated to indicate putative ligand-binding site residues. The results page also includes a list of the most likely binding site residues and the types of predicted ligands and their frequencies in similar structures. The protein–ligand interactions can also be interactively visualized in 3D using the Jmol plug-in. The raw machine readable data are provided for developers, which comply with the Critical Assessment of Techniques for Protein Structure Prediction data standards for FN predictions. The FunFOLD2 webserver is freely available to all at the following web site: http://www.reading.ac.uk/bioinf/FunFOLD/FunFOLD_form_2_0.html.

VaxiJen:a server for prediction of protective antigens, tumour antigens and subunit vaccines

Background - Vaccine development in the post-genomic era often begins with the in silico screening of genome information, with the most probable protective antigens being predicted rather than requiring causative microorganisms to be grown. Despite the obvious advantages of this approach – such as speed and cost efficiency – its success remains dependent on the accuracy of antigen prediction. Most approaches use sequence alignment to identify antigens. This is problematic for several reasons. Some proteins lack obvious sequence similarity, although they may share similar structures and biological properties. The antigenicity of a sequence may be encoded in a subtle and recondite manner not amendable to direct identification by sequence alignment. The discovery of truly novel antigens will be frustrated by their lack of similarity to antigens of known provenance. To overcome the limitations of alignment-dependent methods, we propose a new alignment-free approach for antigen prediction, which is based on auto cross covariance (ACC) transformation of protein sequences into uniform vectors of principal amino acid properties. Results - Bacterial, viral and tumour protein datasets were used to derive models for prediction of whole protein antigenicity. Every set consisted of 100 known antigens and 100 non-antigens. The derived models were tested by internal leave-one-out cross-validation and external validation using test sets. An additional five training sets for each class of antigens were used to test the stability of the discrimination between antigens and non-antigens. The models performed well in both validations showing prediction accuracy of 70% to 89%. The models were implemented in a server, which we call VaxiJen. Conclusion - VaxiJen is the first server for alignment-independent prediction of protective antigens. It was developed to allow antigen classification solely based on the physicochemical properties of proteins without recourse to sequence alignment. The server can be used on its own or in combination with alignment-based prediction methods.

LIPPRED:a web server for accurate prediction of lipoprotein signal sequences and cleavage sites

Bacterial lipoproteins have many important functions and represent a class of possible vaccine candidates. The prediction of lipoproteins from sequence is thus an important task for computational vaccinology. Naïve-Bayesian networks were trained to identify SpaseII cleavage sites and their preceding signal sequences using a set of 199 distinct lipoprotein sequences. A comprehensive range of sequence models was used to identify the best model for lipoprotein signal sequences. The best performing sequence model was found to be 10-residues in length, including the conserved cysteine lipid attachment site and the nine residues prior to it. The sensitivity of prediction for LipPred was 0.979, while the specificity was 0.742. Here, we describe LipPred, a web server for lipoprotein prediction; available at the URL: http://www.jenner.ac.uk/LipPred/. LipPred is the most accurate method available for the detection of SpaseIIcleaved lipoprotein signal sequences and the prediction of their cleavage sites.