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.

Large libraries of chemical compounds reflect the exponentially growing data-enrichment in drug discovery that trends towards fully automated informatics solutions to study structure - activity relationships by screening docked ligand candidates to biological target structures. We review otherwise disseminated user descriptions of mainly public databases with free access and also our integrated data mining tool GPDBnet for phyto-pharmacology.

A structure-based in silico virtual drug discovery procedure was assessed with severe acute respiratory syndrome coronavirus main protease serving as a case study. First, potential compounds were extracted from protein-ligand complexes selected from Protein Data Bank database based on structural similarity to the target protein. Later, the set of compounds was ranked by docking scores using a Electronic High-Throughput Screening flexible docking procedure to select the most promising molecules. The set of best performing compounds was then used for similarity search over the 1 million entries in the Ligand.Info Meta-Database. Selected molecules having close structural relationship to a 2-methyl-2,4-pentanediol may provide candidate lead compounds toward the development of novel allosteric severe acute respiratory syndrome protease inhibitors.

Cytokinins are plant hormones involved in the essential processes of plant growth and development. They bind with receptors known as CRE1/WOL/AHK4, AHK2, and AHK3, which possess histidine kinase activity. Recently, the sensor domain cyclases/histidine kinases associated sensory extracellular (CHASE) was identified in those proteins but little is known about its structure and interaction with ligands. Distant homology detection methods developed in our laboratory and molecular phylogeny enabled the prediction of the structure of the CHASE domain as similar to the photoactive yellow protein-like sensor domain. We have identified the active site pocket and amino acids that are involved in receptor-ligand interactions. We also show that fold evolution of cytokinin receptors is very important for a full understanding of the signal transduction mechanism in plants.

ORFeus is a fully automated, sensitive protein sequence similarity search server available to the academic community via the Structure Prediction Meta Server (http://BioInfo.PL/Meta/). The goal of the development of ORFeus was to increase the sensitivity of the detection of distantly related protein families. Predicted secondary structure information was added to the information about sequence conservation and variability, a technique known from hybrid threading approaches. The accuracy of the meta profiles created this way is compared with profiles containing only sequence information and with the standard approach of aligning a single sequence with a profile. Additionally, the alignment of meta profiles is more sensitive in detecting remote homology between protein families than if aligning two sequence-only profiles or if aligning a profile with a sequence. The specificity of the alignment score is improved in the lower specificity range compared with the robust sequence-only profiles.

During the herpesvirus replication cycle, viral transcription, DNA replication, formation of capsids and DNA packaging occur in the nucleus. The subsequent nuclear egress of newly synthesized nucleocapsids is performed by budding of the inner leaflet of the nuclear membrane, which creates the primary envelope. Although products of two genes conserved throughout the Herpesviridae family (HSV-1 UL34 and UL31) have previously been shown to be involved in the execution of this process, the molecular basis of their activity is not clear. Here we present results of protein structure prediction for the conserved domain of UL34. The applied methodology suggests that this protein adopts a pleckstrin homology (PH) fold to perform its function. A detailed inspection of the ligand binding site strongly supports the hypothesis that UL34 orthologs can recognize phosphoinositides. Since previous works suggest that alterations of UL34 gene product result in a drastic impairment of primary envelopment of HSV-1 and trapping of capsids in the nucleus, the presented data may lead to the development of novel anti-herpetic therapeutic strategies where analogs of phosphoinositides are administered.

With the increasing amount of data provided by both high-throughput sequencing and structural genomics studies, there is a growing need for tools to augment functional predictions for protein sequences. Broad descriptions of function can be provided by establishing the presence of protein domains associated with a particular function. To extend the domain-based annotation, LigProf provides predictions of potential ligands that bind to a protein, as well as critical residues that stabilize ligands. A P-value statistic for estimating the significance of motif occurrence is provided for all sites. Although the usefulness of the method will rise with increasing numbers of crystallographically solved molecules deposited in the PDB database, we show that it can already be applied successfully to the highly represented ligand-bound protein kinase domains of viral and human origin. The LigProf webserver is freely available at: http://www.cropnet.pl/ligprof . At present, LigProf descriptors annotate and extend major protein families from the PfamA database.

We present here a simple method for fast and accurate comparison of proteins using their structures. The algorithm is based on structural alignment of segments of Calpha chains (with size of 99 or 199 residues). The method is optimized in terms of speed and accuracy. We test it on 97 representative proteins with the similarity measure based on the SCOP classification. We compare our algorithm with the LGscore2 automatic method. Our method has the same accuracy as the LGscore2 algorithm with much faster processing of the whole test set, which is promising. A second test is done using the ToolShop structure prediction evaluation program and shows that our tool is on average slightly less sensitive than the DALI server. Both algorithms give a similar number of correct models, however, the final alignment quality is better in the case of DALI. Our method was implemented under the name 3D-Hit as a web server at http://3dhit.bioinfo.pl/ free for academic use, with a weekly updated database containing a set of 5000 structures from the Protein Data Bank with non-homologous sequences.

In CASP5, the BioInfo.PL group has used the structure prediction Meta Server and the associated newly developed flexible meta-predictor, called 3D-Jury, as the main structure prediction tools. The most important feature of the meta-predictor is a high (86%) correlation between the reported confidence score and the quality of the selected model. The Gene Relational Database (GRDB) was used to confirm the fold recognition results by selecting distant homologues and subsequent structure prediction with the Meta Server. A fragment-splicing procedure was performed as a final processing step with large fragments extracted from selected models using model quality control provided by Verify3D. The comparison of submitted models with the native structure conducted after the CASP meeting showed that the GRDB-supported structure prediction led to a satisfactory template fold selection, whereas the fragment-splicing procedure must be improved in the future.

The 3D jury system has predicted the methyltransferase fold for the nsp13 protein of the SARS coronavirus. Based on the conservation of a characteristic tetrad of residues, the mRNA cap-1 methyltransferase function has been assigned to this protein, which has potential implications for antiviral therapy.

SUMMARY: High-throughput screening (HTS) is a common technique for both drug discovery and basic research, but researchers often struggle with how best to derive hits from HTS data. While a wide range of hit identification techniques exist, little information is available about their sensitivity and specificity, especially in comparison to each other. To address this, we have developed the open-source NoiseMaker software tool for generation of realistically noisy virtual screens. By applying potential hit identification methods to NoiseMaker-simulated data and determining how many of the pre-defined true hits are recovered (as well as how many known non-hits are misidentified as hits), one can draw conclusions about the likely performance of these techniques on real data containing unknown true hits. Such simulations apply to a range of screens, such as those using small molecules, siRNAs, shRNAs, miRNA mimics or inhibitors, or gene over-expression; we demonstrate this utility by using it to explain apparently-conflicting reports about the performance of the B score hit identification method. Availability and Implementation: NoiseMaker is written in C#, an ECMA and ISO standard language compilers for multiple operating systems. Source code, a Windows installer, and complete unit tests are available at http://sourceforge.net/projects/noisemaker. Full documentation and support are provided via an extensive help file and tool-tips, and the developers welcome user suggestions. CONTACT: amanda.birmingham@thermofisher.com SUPPLEMENTARY INFORMATION:[Link to be provided.].

PlantMetabolomics.org (PM) is a web portal and database for exploring, visualizing and downloading plant metabolomics data. Widespread public access to well-annotated metabolomics datasets is essential for establishing metabolomics as a functional genomics tool. PM integrates metabolomics data generated from different analytical platforms from multiple laboratories along with the key visualization tools such as ratio and error plots. Visualization tools can quickly show how one condition compares to another and which analytical platforms show the largest changes. The database tries to capture a complete annotation of the experiment metadata along with the metabolite abundance data based on the evolving Metabolomics Standards Initiative (MSI). PM can be used as a platform for deriving hypotheses by enabling metabolomic comparisons between genetically unique Arabidopsis thaliana populations subjected to different environmental conditions. Each metabolite is linked to relevant experimental data and information from various annotation databases. The portal also provides detailed protocols and tutorials on conducting plant metabolomics experiments to promote metabolomics in the community. PM currently houses Arabidopsis metabolomics data generated by a consortium of laboratories utilizing metabolomics to help elucidate the functions of uncharacterized genes. PM is publicly available at http://www.plantmetabolomics.org.

The treatment of viral diseases remains an intractable problem facing the medical community. Conventional antivirals focus upon selective targeting of virus-encoded targets. However, the plasticity of viral nucleic acid mutation, coupled with the large number of progeny that can emerge from a single infected cells, often conspire to render conventional antivirals ineffective as resistant variants emerge. Compounding this, new viral pathogens are increasingly recognized and it is highly improbable that conventional approaches could address emerging pathogens in a timely manner. Our laboratories have adopted an orthogonal approach to combat viral disease: Target the host to deny the pathogen the ability to cause disease. The advantages of this novel approach are many-fold, including the potential to identify host pathways that are applicable to a broad-spectrum of pathogens. The acquisition of drug resistance might also be minimized since selective pressure is not directly placed upon the viral pathogen. Herein, we utilized this strategy of host-oriented therapeutics to screen small molecules for their abilities to block infection by multiple, unrelated virus types and identified FGI-104. FGI-104 demonstrates broad-spectrum inhibition of multiple blood-borne pathogens (HCV, HBV, HIV) as well as emerging biothreats (Ebola, VEE, Cowpox, PRRSV infection). We also demonstrate that FGI-104 displays an ability to prevent lethality from Ebola in vivo. Altogether, these findings reinforce the concept of host-oriented therapeutics and present a much-needed opportunity to identify antiviral drugs that are broad-spectrum and durable in their application.

Algorithms were developed for ligand-based virtual screening of molecular databases. FieldChopper (FC) is based on the discretization of the electrostatic and van der Waals field into three classes. A model is built from a set of superimposed active molecules. The similarity of the compounds in the database to the model is then calculated using matrices that define scores for comparing field values of different categories. The method was validated using 12 publicly available data sets by comparing the method to the electrostatic similarity comparison program EON. The results suggest that FC is competitive with more complex descriptors and could be used as a molecular sieve in virtual screening experiments when multiple active ligands are known.

ABSTRACT: BACKGROUND: Recent years have seen an explosion in the amount of publicly available chemical and related biological information. A significant step has been the emergence of PubChem, which contains property information for millions of chemical structures, and acts as a repository of compounds and bioassay screening data for the NIH Roadmap. There is a strong need for tools designed for scientists that permit easy download and use of these data. We present one such tool, PubChemSR. IMPLEMENTATION: PubChemSR (Search and Retrieve) is a freely available desktop application written for Windows using Microsoft .NET that is designed to assist scientists in search, retrieval and organization of chemical and biological data from the PubChem database. It employs SOAP web services made available by NCBI for extraction of information from PubChem. Results and Discussion: The program supports a wide range of searching techniques, including queries based on assay or compound keywords and chemical substructures. Results can be examined individually or downloaded and exported in batch for use in other programs such as Microsoft Excel. We believe that PubChemSR makes it straightforward for researchers to utilize the chemical, biological and screening data available in PubChem. We present several examples of how it can be used.

Background The long-term goals of therapy for chronic hepatitis B are to reduce serum HBV DNA to low or undetectable levels and ultimately reduce or prevent the development of cirrhosis and hepatocellular carcinoma. Aim To review the current treatment of chronic hepatitis B, with a focus on diagnosis and management of resistance and active management of suboptimal responses. Methods A systematic review of the literature, with a focus on recent guidelines, was undertaken Results Among the six drugs licensed for the treatment of chronic hepatitis B in the United States, the preferred agents in 2008 will include entecavir, peginterferon alfa-2a, possibly telbivudine, and tenofovir following licensure. When using an oral agent, a major focus of management is the selection of a drug with high potency and low rate of resistance, and active on-treatment management to optimize therapy. Preventing the sequelae of antiviral drug resistance and appropriate management when resistance is initially detected is also a major focus of current management. The addition of an antiviral agent that is not cross-resistant is critical to restore suppression of viral replication. Conclusions Newer agents and modified treatment strategies, especially using combination therapy, holds promise to optimize the management of patient with chronic hepatitis B by achieving the high potency and the lowest rate of resistance.

()New data, tools and services recently made available on the web server (http://cactus.nci.nih.gov) of the Computer-Aided Drug Design (CADD) Group, NCI, NIH, developed in the context of chemoinformatics and drug development work, are presented. These tools are designed for searching for structures in very large databases of small molecules. One of them is a web service-the Chemical Structure Lookup Service (CSLS)-for very rapid structure lookup in an aggregated collection of more than 80 databases comprising more than 27 million unique structures at the time of this writing. CSLS contains pointers to the entries in toxicology-related databases, catalogues of commercially available samples, drugs, assay results data sets, and databases in several other categories. CSLS allows the user to find out very rapidly in which one(s) of all these databases a given structure occurs independent of the representation of the input structure, by making use of InChIs as well as new CACTVS hashcode-based identifiers. These latter, calculable, identifiers are designed to take into account tautomerism, different resonance structures drawn for charged species, and presence of additional fragments. They make possible fine-tunable yet rapid compound identification and database overlap analyses in very large compound collections.

The Swiss Institute of Bioinformatics (SIB), the European Bioinformatics Institute (EBI), and the Protein Information Resource (PIR) form the Universal Protein Resource (UniProt) consortium. Its main goal is to provide the scientific community with a central resource for protein sequences and functional information. The UniProt consortium maintains the UniProt KnowledgeBase (UniProtKB) and several supplementary databases including the UniProt Reference Clusters (UniRef) and the UniProt Archive (UniParc).(1) UniProtKB is a comprehensive protein sequence knowledgebase that consists of two sections: UniProtKB/Swiss-Prot, which contains manually annotated entries, and UniProtKB/TrEMBL, which contains computer-annotated entries. UniProtKB/Swiss-Prot entries contain information curated by biologists and provide users with cross-links to about 100 external databases and with access to additional information or tools.(2) The UniRef databases (UniRef100, UniRef90, and UniRef50) define clusters of protein sequences that share 100, 90, or 50% identity.(3) The UniParc database stores and maps all publicly available protein sequence data, including obsolete data excluded from UniProtKB. The UniProt databases can be accessed online (<webref type="url">http://www.uniprot.org/</webref>) or downloaded in several formats (<webref type="url">ftp://ftp.uniprot.org/pub</webref>). New releases are published every 2 weeks. The purpose of this chapter is to present a guided tour of a UniProtKB/Swiss-Prot entry, paying particular attention to the specificities of plant protein annotation. We will also present some of the tools and databases that are linked to each entry.

ABSTRACT: BACKGROUND: Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (Target Fishing Docking)(http://www.dddc.ac.cn/tarfisdock), which has been used widely by others. Recently, we have constructed a protein target database, Potential Drug Target Database (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation. Description PDTD is a web-accessible protein database for in silico target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores. CONCLUSIONS: PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

Metabolomic databases are useless without accurate description of the biological study design and accompanying metadata reporting on the laboratory workflow from sample preparation to data processing. Here we report on the implementation of a database system that enables investigators to detail and set up a biological experiment, and that also steers laboratory workflows by direct access to the data acquisition instrument. SetupX utilizes orthogonal biological parameters such as genotype, organ, and treatment(s) for delineating the dimensions of a study which define the number of classes under investigation. Publicly available taxonomic and ontology repositories are utilized to ensure data integrity and logic consistency of class designs. Class descriptions are subsequently employed to schedule and randomize data acquisitions, and to deploy metabolite annotations carried out by the seamlessly integrated mass spectrometry database, BinBase. Annotated result data files are housed by SetupX for downloads and queries. Currently, 39 users have generated 48 studies, some of which are made public.