The identification of neurotensin NTS1 receptor partial agonists through a ligand-based virtual screening approach

We identified small molecule NTS1R agonist compounds through virtual screening of the corporate database using a ROCS approach that searches multi-conformer representations efficiently. As a starting point for the ROCS search, we used the known NTS1R selective antagonist, SR-48527, based on the hypothesis that NT agonists and antagonists might share similar binding regions. Conformations were expanded and selected as database search queries based on a cluster analysis. The search provided us with virtual hits that were tested in intracellular calcium mobilization assays of NTS1R agonist and antagonist activities measured in FLIPR format as well as in [(3)H]NT competition binding studies. The results indicated that two initial hits produced partial agonist activity with potency in the moderate micromolar range.     

Identification of plasmepsin inhibitors as selective anti-malarial agents using ligand based drug design

We describe the application of ligand based virtual screening technologies towards the discovery of novel plasmepsin (PM) inhibitors, a family of malarial parasitic aspartyl proteases. Pharmacophore queries were used to screen vendor libraries in search of active and selective compounds. The virtual hits were biologically assessed for activity and selectivity using whole cell Plasmodium falciparum parasites and on target in PM II, PM IV and the closely related human homologue, Cathepsin D assays. Here we report the virtual screening highlights, structures of the hits and their demonstrated biological activity.     

A database of mutants and effects of site-directed mutagenesis experiments on G protein-coupled receptors

A database system and computer programs for storage and retrieval of information about guanine nucleotide-binding protein (G protein) -coupled receptor mutants and associated biological effects have been developed. Mutation data on the receptors were collected from the literature and a database of mutants and effects of mutations was developed. The G protein-coupled receptor, family A, point mutation database (GRAP) provides detailed information on ligand-binding and signal transduction properties of more than 2130 receptor mutants. The amino acid sequences of receptors for which mutation experiments have been reported were aligned, and from this alignment mutation data may be retrieved. Alternatively, a search form allowing detailed specification of which mutants to retrieve may be used, for example, to search for specific amino acid substitutions, substitutions in specific protein domains or reported biological effects. Furthermore, ligand and bibliographic oriented queries may be performed. GRAP is available on the Internet (URL: http://www-grap.fagmed.uit.no/GRAP/homepage.html ) using the World-Wide Web system. © 1996 Wiley-Liss, Inc.     

MedEvi: retrieving textual evidence of relations between biomedical concepts from Medline

Search engines running on MEDLINE abstracts have been widely used by biologists to find publications that are related to their research. The existing search engines such as PubMed, however, have limitations when applied for the task of seeking textual evidence of relations between given concepts. The limitations are mainly due to the problem that the search engines do not effectively deal with multi-term queries which may imply semantic relations between the terms. To address this problem, we present MedEvi, a novel search engine that imposes positional restriction on occurrences matching multi-term queries, based on the observation that terms with semantic relations which are explicitly stated in text are not found too far from each other. MedEvi further identifies additional keywords of biological and statistical significance from local context of matching occurrences in order to help users reformulate their queries for better results. AVAILABILITY: http://www.ebi.ac.uk/tc-test/textmining/medevi/     

Using web search query data to monitor dengue epidemics: a new model for neglected tropical disease surveillance

Background

A variety of obstacles including bureaucracy and lack of resources have interfered with timely detection and reporting of dengue cases in many endemic countries. Surveillance efforts have turned to modern data sources, such as Internet search queries, which have been shown to be effective for monitoring influenza-like illnesses. However, few have evaluated the utility of web search query data for other diseases, especially those of high morbidity and mortality or where a vaccine may not exist. In this study, we aimed to assess whether web search queries are a viable data source for the early detection and monitoring of dengue epidemics.

Methodology/Principal Findings

Bolivia, Brazil, India, Indonesia and Singapore were chosen for analysis based on available data and adequate search volume. For each country, a univariate linear model was then built by fitting a time series of the fraction of Google search query volume for specific dengue-related queries from that country against a time series of official dengue case counts for a time-frame within 2003–2010. The specific combination of queries used was chosen to maximize model fit. Spurious spikes in the data were also removed prior to model fitting. The final models, fit using a training subset of the data, were cross-validated against both the overall dataset and a holdout subset of the data. All models were found to fit the data quite well, with validation correlations ranging from 0.82 to 0.99.

Conclusions/Significance

Web search query data were found to be capable of tracking dengue activity in Bolivia, Brazil, India, Indonesia and Singapore. Whereas traditional dengue data from official sources are often not available until after some substantial delay, web search query data are available in near real-time. These data represent valuable complement to assist with traditional dengue surveillance.     

Design and pharmacology of quinuclidine derivatives as M2-selective muscarinic receptor ligands

In our search for M2-selective muscarinic receptor antagonists, we synthesized 1,3-disubstituted indenes. The effects of different basic moieties with regard to binding and selectivity towards the five distinct muscarinic receptor subtypes were investigated. The results show that the quinuclidine series afforded the most promising compounds in terms of both receptor affinity and M2-subtype selectivity.     

Parallel solid-phase synthesis and characterization of new sulfonamide and carboxamide proline derivatives as potential CNS agents

A solid-phase synthesis of the 64-member library of novel sulfonamide and carboxamide proline derivatives, focused on the 5-HT7 receptor antagonist SB-258741, was described. The final compounds were obtained in good yields and high purity upon cleavage from SynPhase Lanterns, functionalized by a BAL linker. The library representatives were screened for 5-HT7, 5-HT1A and D2 receptors to explore the impact of a tertiary amine moiety, the length of an alkylene spacer and the aryl fragment on the receptor affinity. The preliminary biological results provided data for further investigation aimed at a search for 5-HT7 receptor agents, and permitted the identification of several compounds with significant 5-HT1A receptor affinity.     

Assessing the Impact of the National Smoking Ban in Indoor Public Places in China: Evidence from Quit Smoking Related Online Searches

Background

Despite the tremendous economic and health costs imposed on China by tobacco use, China lacks a proactive and systematic tobacco control surveillance and evaluation system, hampering research progress on tobacco-focused surveillance and evaluation studies.

Methods

This paper uses online search query analyses to investigate changes in online search behavior among Chinese Internet users in response to the adoption of the national indoor public place smoking ban. Baidu Index and Google Trends were used to examine the volume of search queries containing three key search terms “Smoking Ban(s),” “Quit Smoking,” and “Electronic Cigarette(s),” along with the news coverage on the smoking ban, for the period 2009–2011.

Findings

Our results show that the announcement and adoption of the indoor public place smoking ban in China generated significant increases in news coverage on smoking bans. There was a strong positive correlation between the media coverage of smoking bans and the volume of “Smoking Ban(s)” and “Quit Smoking” related search queries. The volume of search queries related to “Electronic Cigarette(s)” was also correlated with the smoking ban news coverage.

Interpretation

To the extent it altered smoking-related online searches, our analyses suggest that the smoking ban had a significant effect, at least in the short run, on Chinese Internet users’ smoking-related behaviors. This research introduces a novel analytic tool, which could serve as an alternative tobacco control evaluation and behavior surveillance tool in the absence of timely or comprehensive population surveillance system. This research also highlights the importance of a comprehensive approach to tobacco control in China.     

Mining of assembled expressed sequence tag (EST) data for protein families: application to the G protein-coupled receptor superfamily

The availability of large expressed sequence tag (EST) databases has led to a revolution in the way new genes are identified. Mining of these databases using known protein sequences as queries is a powerful technique for discovering orthologous and paralogous genes. The scientist is often confronted, however, by an enormous amount of search output owing to the inherent redundancy of EST data. In addition, high search sensitivity often cannot be achieved using only a single member of a protein superfamily as a query. In this paper a technique for addressing both of these issues is described. Assembled EST databases are queried with every member of a protein superfamily, the results are integrated and false positives are pruned from the set. The result is a set of assemblies enriched in members of the protein superfamily under consideration. The technique is applied to the G protein-coupled receptor (GPCR) superfamily in the construction of a GPCR Resource. A novel full-length human GPCR identified from the GPCR Resource is presented, illustrating the utility of the method.     

MineBlast: a literature presentation service supporting protein annotation by data mining of BLAST results

MineBlast is a web service for literature search and presentation based on data-mining results received from UniProt. Users can submit a simple list of protein sequences via a web-based interface. MineBlast performs a BLASTP search in UniProt to identify names and synonyms based on homologous proteins and subsequently queries PubMed, using combined search terms inorder to find and present relevant literature.     

Structural basis of the selectivity of the β2-adrenergic receptor for fluorinated catecholamines

The important and diverse biological functions of adrenergic receptors, a subclass of G protein-coupled receptors (GPCRs), have made the search for compounds that selectively stimulate or inhibit the activity of different adrenergic receptor subtypes an important area of medicinal chemistry. We previously synthesized 2-, 5-, and 6-fluoronorepinehprine (FNE) and 2-, 5-, and 6-fluoroepinephrine (FEPI) and found that 2FNE and 2FEPI were selective β-adrenergic agonists and that 6FNE and 6FEPI were selective α-adrenergic agonists, while 5FNE and 5FEPI were unselective. Agonist potencies correlated well with receptor binding affinities. Here, through a combination of molecular modeling and site-directed mutagenesis, we have identified N293 in the β₂-adrenergic receptor as a crucial residue for the selectivity of the receptor for catecholamines fluorinated at different positions.     

An intuitive graphical webserver for multiple-choice protein sequence search

Every day tens of thousands of sequence searches and sequence alignment queries are submitted to webservers. The capitalized word “BLAST” becomes a verb, describing the act of performing sequence search and alignment. However, if one needs to search for sequences that contain, for example, two hydrophobic and three polar residues at five given positions, the query formation on the most frequently used webservers will be difficult. Some servers support the formation of queries with regular expressions, but most of the users are unfamiliar with their syntax. Here we present an intuitive, easily applicable webserver, the Protein Sequence Analysis server, that allows the formation of multiple choice queries by simply drawing the residues to their positions; if more than one residue are drawn to the same position, then they will be nicely stacked on the user interface, indicating the multiple choice at the given position. This computer-game-like interface is natural and intuitive, and the coloring of the residues makes possible to form queries requiring not just certain amino acids in the given positions, but also small nonpolar, negatively charged, hydrophobic, positively charged, or polar ones. The webserver is available at http://psa.pitgroup.org.     

Web Queries as a Source for Syndromic Surveillance

In the field of syndromic surveillance, various sources are exploited for outbreak detection, monitoring and prediction. This paper describes a study on queries submitted to a medical web site, with influenza as a case study. The hypothesis of the work was that queries on influenza and influenza-like illness would provide a basis for the estimation of the timing of the peak and the intensity of the yearly influenza outbreaks that would be as good as the existing laboratory and sentinel surveillance. We calculated the occurrence of various queries related to influenza from search logs submitted to a Swedish medical web site for two influenza seasons. These figures were subsequently used to generate two models, one to estimate the number of laboratory verified influenza cases and one to estimate the proportion of patients with influenza-like illness reported by selected General Practitioners in Sweden. We applied an approach designed for highly correlated data, partial least squares regression. In our work, we found that certain web queries on influenza follow the same pattern as that obtained by the two other surveillance systems for influenza epidemics, and that they have equal power for the estimation of the influenza burden in society. Web queries give a unique access to ill individuals who are not (yet) seeking care. This paper shows the potential of web queries as an accurate, cheap and labour extensive source for syndromic surveillance.     

Identification and initial structure-activity relationships of a novel non-peptide quinolone GnRH receptor antagonist.

Screening of the Merck sample collection for non-peptide compounds with binding affinity for the rat GnRH receptor led to the identification of the substituted quinolone (1) as a lead compound in the search for a non-peptide GnRH receptor antagonist. Substantial improvements in potency (approximately 300 fold) were achieved by addition of an alkyl amine at the 4-position, a 3,5-dimethylphenyl group at the 3-position and 6-nitro-7-chloro-substitution of the 1 H-quinolone core.     

Hubs of knowledge: using the functional link structure in Biozon to mine for biologically significant entities

Background  

Existing biological databases support a variety of queries such as keyword or definition search. However, they do not provide any measure of relevance for the instances reported, and result sets are usually sorted arbitrarily.     

In silico identification of small molecules as novel LXR agonists for the treatment of cardiovascular disease and cancer

Liver X receptor (LXR), a member of the nuclear receptor superfamily, mainly serves as a reverse cholesterol transporter in lipid metabolism. It has been demonstrated that LXR is a promising target for the treatment of cardiovascular diseases. LXR is also involved in cancer metabolism, glucose homeostasis, immunity, and various physiological processes. The antitumor function of LXR has become of great interest to researchers in recent years. However, while it is believed that activating LXR with small molecules could be a promising approach to cancer treatment, effective drugs that target LXR are yet to be reported. To find compounds that are potentially capable of activating LXR, we utilized a high-throughput screening method to search the MolMall database for suitable compounds. Seven candidates with lower GB/SA Hawkins scores than the reference ligand T0901317 were identified. Based on the results of molecular dynamics (MD) simulations, binding free energy analysis, and an analysis of the agonism mechanism, ZINC90512020 and ZINC3845032 were predicted to have high affinities for LXR and high relative stabilization, and were therefore selected as potential LXR agonists. Both of these compounds will undergo further development with a view to utilizing them for the treatment of LXR-related cardiovascular diseases or cancers.     

Molecular docking of inhibitors into monoamine oxidase B

Monoamine oxidase B (MAO-B) functions in the deamination of monoamines, including dopamine and norepinephrine. The search for MAO-B inhibitors increased following the discovery that the enzyme may be responsible for generating neurotoxins from various endogenous or exogenous compounds. Computational screening methods aid in the search for new inhibitors, but validation studies for specific software packages and receptors are necessary for effective application of these methods. In this study, DOCK 6.0.0 was used to dock a series of inhibitors to MAO-B. Included were studies of re-docking ligands into MAO-B crystal structures, after which a set of 30 compounds with known inhibition constants for MAO-B were docked, including 15 strong inhibitors and 15 weak inhibitors. Good agreement was observed between the top experimental inhibitors and the top ranked docking results, and key interactions between the ligands and receptor were identified.     

A web-based tool for in silico biomarker discovery based on tissue-specific protein profiles in normal and cancer tissues

Here we report the development of a publicly available Web-based analysis tool for exploring proteins expressed in a tissue- or cancer-specific manner. The search queries are based on the human tissue profiles in normal and cancer cells in the Human Protein Atlas portal and rely on the individual annotation performed by pathologists of images representing immunohistochemically stained tissue sections. Approximately 1.8 million images representing more than 3000 antibodies directed toward human proteins were used in the study. The search tool allows for the systematic exploration of the protein atlas to discover potential protein biomarkers. Such biomarkers include tissue-specific markers, cell type-specific markers, tumor type-specific markers, markers of malignancy, and prognostic or predictive markers of cancers. Here we show examples of database queries to generate sets of candidate biomarker proteins for several of these different categories. Expression profiles of candidate proteins can then subsequently be validated by examination of the underlying high resolution images. The present study shows examples of search strategies revealing several potential protein biomarkers, including proteins specifically expressed in normal cells and in cancer cells from specified tumor types. The lists of candidate proteins can be used as a starting point for further validation in larger patient cohorts using both immunological approaches and technologies utilizing more classical proteomics tools.     

Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic

Background

Google Flu Trends (GFT) uses anonymized, aggregated internet search activity to provide near-real time estimates of influenza activity. GFT estimates have shown a strong correlation with official influenza surveillance data. The 2009 influenza virus A (H1N1) pandemic [pH1N1] provided the first opportunity to evaluate GFT during a non-seasonal influenza outbreak. In September 2009, an updated United States GFT model was developed using data from the beginning of pH1N1.

Methodology/Principal Findings

We evaluated the accuracy of each U.S. GFT model by comparing weekly estimates of ILI (influenza-like illness) activity with the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). For each GFT model we calculated the correlation and RMSE (root mean square error) between model estimates and ILINet for four time periods: pre-H1N1, Summer H1N1, Winter H1N1, and H1N1 overall (Mar 2009–Dec 2009). We also compared the number of queries, query volume, and types of queries (e.g., influenza symptoms, influenza complications) in each model. Both models'' estimates were highly correlated with ILINet pre-H1N1 and over the entire surveillance period, although the original model underestimated the magnitude of ILI activity during pH1N1. The updated model was more correlated with ILINet than the original model during Summer H1N1 (r = 0.95 and 0.29, respectively). The updated model included more search query terms than the original model, with more queries directly related to influenza infection, whereas the original model contained more queries related to influenza complications.

Conclusions

Internet search behavior changed during pH1N1, particularly in the categories “influenza complications” and “term for influenza.” The complications associated with pH1N1, the fact that pH1N1 began in the summer rather than winter, and changes in health-seeking behavior each may have played a part. Both GFT models performed well prior to and during pH1N1, although the updated model performed better during pH1N1, especially during the summer months.     

A deterministic finite automaton for faster protein hit detection in BLAST.

BLAST is the most popular bioinformatics tool and is used to run millions of queries each day. However, evaluating such queries is slow, taking typically minutes on modern workstations. Therefore, continuing evolution of BLAST--by improving its algorithms and optimizations--is essential to improve search times in the face of exponentially increasing collection sizes. We present an optimization to the first stage of the BLAST algorithm specifically designed for protein search. It produces the same results as NCBI-BLAST but in around 59% of the time on Intel-based platforms; we also present results for other popular architectures. Overall, this is a saving of around 15% of the total typical BLAST search time. Our approach uses a deterministic finite automaton (DFA), inspired by the original scheme used in the 1990 BLAST algorithm. The techniques are optimized for modern hardware, making careful use of cache-conscious approaches to improve speed. Our optimized DFA approach has been integrated into a new version of BLAST that is freely available for download at http://www.fsa-blast.org/.