An overview of the PubChem BioAssay resource

The PubChem BioAssay database (http://pubchem.ncbi.nlm.nih.gov) is a public repository for biological activities of small molecules and small interfering RNAs (siRNAs) hosted by the US National Institutes of Health (NIH). It archives experimental descriptions of assays and biological test results and makes the information freely accessible to the public. A PubChem BioAssay data entry includes an assay description, a summary and detailed test results. Each assay record is linked to the molecular target, whenever possible, and is cross-referenced to other National Center for Biotechnology Information (NCBI) database records. ‘Related BioAssays’ are identified by examining the assay target relationship and activity profile of commonly tested compounds. A key goal of PubChem BioAssay is to make the biological activity information easily accessible through the NCBI information retrieval system-Entrez, and various web-based PubChem services. An integrated suite of data analysis tools are available to optimize the utility of the chemical structure and biological activity information within PubChem, enabling researchers to aggregate, compare and analyze biological test results contributed by multiple organizations. In this work, we describe the PubChem BioAssay database, including data model, bioassay deposition and utilities that PubChem provides for searching, downloading and analyzing the biological activity information contained therein

NMD SERVER: NATURAL MEDICINES DATABASE FOR DRUG DISCOVERY

Cancer is the most frequently diagnosed disease globally and the second leading cause of the death. Natural Medicines are the alternative form of treatment that includes use of various plants. It is one of the safe treatment option to treat cancer and safer than allopathic medicines in order to reduce side effects. NMD Server: Natural Medicines Database for Drug Discovery is a unique and significant database of its kind, giving researchers, medical practitioners, pharmaceutical industries and students of Life Sciences an instant access to over 354 records of Natural Medicines which may be developed and used for treatment of Cancer. This database constitutes the specific information related to Natural Medicines and their respective target sites. NMD Server: Natural Medicines Database for Drug Discovery provides all the information (database fields) regarding the physiological parameters of database and is considered to be the linked table with pre-determined values and names that are included to aid in populating the fields of the linked tables. There have been many different types of fields with its respective data types that have been designated on the basis of data provided. NMDdock Tools have been integrated in this database for convenience for users like docking analysis of target and natural medicine, Sensitivity & Specificity analysis of natural medicine, Linear Correlation and Regression tool, Sequence Manipulation of target, Statistical Analysis. For the precise information about any particular drug, connectivity has been made with other databases and applications based highly bioinformatics tools have been embedded for convenience of users.&nbsp

FlyRNAi.org—the database of the Drosophila RNAi screening center: 2012 update

FlyRNAi (http://www.flyrnai.org), the database and website of the Drosophila RNAi Screening Center (DRSC) at Harvard Medical School, serves a dual role, tracking both production of reagents for RNA interference (RNAi) screening in Drosophila cells and RNAi screen results. The database and website is used as a platform for community availability of protocols, tools, and other resources useful to researchers planning, conducting, analyzing or interpreting the results of Drosophila RNAi screens. Based on our own experience and user feedback, we have made several changes. Specifically, we have restructured the database to accommodate new types of reagents; added information about new RNAi libraries and other reagents; updated the user interface and website; and added new tools of use to the Drosophila community and others. Overall, the result is a more useful, flexible and comprehensive website and database

Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks

Cellular senescence is a barrier to tumorigenesis in normal cells and tumour cells undergo senescence responses to genotoxic stimuli, which is a potential target phenotype for cancer therapy. However, in this setting, mixed-mode responses are common with apoptosis the dominant effect. Hence, more selective senescence inducers are required. Here we report a machine learning-based in silico screen to identify potential senescence agonists. We built profiles of differentially affected biological process networks from expression data obtained under induced telomere dysfunction conditions in colorectal cancer cells and matched these to a panel of 17 protein targets with confirmatory screening data in PubChem. We trained a neural network using 3517 compounds identified as active or inactive against these targets. The resulting classification model was used to screen a virtual library of ~2M lead-like compounds. 147 virtual hits were acquired for validation in growth inhibition and senescence-associated β-galactosidase (SA-β-gal) assays. Among the found hits a benzimidazolone compound, CB-20903630, had low micromolar IC50 for growth inhibition of HCT116 cells and selectively induced SA-β-gal activity in the entire treated cell population without cytotoxicity or apoptosis induction. Growth suppression was mediated by G1 blockade involving increased p21 expression and suppressed cyclin B1, CDK1 and CDC25C. Additionally, the compound inhibited growth of multicellular spheroids and caused severe retardation of population kinetics in long term treatments. Preliminary structure-activity and structure clustering analyses are reported and expression analysis of CB-20903630 against other cell cycle suppressor compounds suggested a PI3K/AKT-inhibitor-like profile in normal cells, with different pathways affected in cancer cells

RNAiAtlas: a database for RNAi (siRNA) libraries and their specificity

Large-scale RNA interference (RNAi) experiments, especially the ones based on short-interfering RNA (siRNA) technology became increasingly popular over the past years. For such knock-down/screening purposes, different companies offer sets of oligos/reagents targeting the whole genome or a subset of it for various organisms. Obviously, the sequence (and structure) of the corresponding oligos is a key factor in obtaining reliable results in these large-scale studies and the companies use a variety of (often not fully public) algorithms to design them. Nevertheless, as the genome annotations are still continuously changing, oligos may become obsolete, so siRNA reagents should be periodically re-annotated according to the latest version of the sequence database (which of course has serious consequences also on the interpretation of the screening results). In our article, we would like to introduce a new software/database tool, the RNAiAtlas. It has been created for exploration, analysis and distribution of large scale RNAi libraries (currently limited to the human genome) with their latest annotation (including former history) but in addition it contains also specific on-target analysis results (design quality, side effects, off-targets)

Local alignment of ligand binding sites in proteins for polypharmacology and drug repositioning

© Springer Science+Business Media LLC 2017. The administration of drugs is a key strategy in pharmacotherapy to treat diseases. Drugs are typically developed to modulate the function of specific proteins, which are directly associated with particular disease states. Nonetheless, recent studies suggest that protein-drug interactions are rather promiscuous and the majority of pharmaceuticals exhibit activity against multiple, often unrelated proteins. Certainly, the lack of selectivity often leads to drug side effects; on the other hand, these polypharmacological attributes can be used to develop drugs acting on multiple targets within a unique disease pathway, as well as to identify new targets for existing drugs, which is known as drug repositioning. To support drug development and repurposing, we developed eMatchSite, a new approach to detect those binding sites having the capability to bind similar compounds. eMatchSite is available as a standalone software and a webserver at http://www. brylinski.org/ematchsite

ChEMBL: a large-scale bioactivity database for drug discovery

ChEMBL is an Open Data database containing binding, functional and ADMET information for a large number of drug-like bioactive compounds. These data are manually abstracted from the primary published literature on a regular basis, then further curated and standardized to maximize their quality and utility across a wide range of chemical biology and drug-discovery research problems. Currently, the database contains 5.4 million bioactivity measurements for more than 1 million compounds and 5200 protein targets. Access is available through a web-based interface, data downloads and web services at: https://www.ebi.ac.uk/chembldb

CancerResource: a comprehensive database of cancer-relevant proteins and compound interactions supported by experimental knowledge

During the development of methods for cancer diagnosis and treatment, a vast amount of information is generated. Novel cancer target proteins have been identified and many compounds that activate or inhibit cancer-relevant target genes have been developed. This knowledge is based on an immense number of experimentally validated compound–target interactions in the literature, and excerpts from literature text mining are spread over numerous data sources. Our own analysis shows that the overlap between important existing repositories such as Comparative Toxicogenomics Database (CTD), Therapeutic Target Database (TTD), Pharmacogenomics Knowledge Base (PharmGKB) and DrugBank as well as between our own literature mining for cancer-annotated entries is surprisingly small. In order to provide an easy overview of interaction data, it is essential to integrate this information into a single, comprehensive data repository. Here, we present CancerResource, a database that integrates cancer-relevant relationships of compounds and targets from (i) our own literature mining and (ii) external resources complemented with (iii) essential experimental and supporting information on genes and cellular effects. In order to facilitate an overview of existing and supporting information, a series of novel information connections have been established. CancerResource addresses the spectrum of research on compound–target interactions in natural sciences as well as in individualized medicine; CancerResource is available at: http://bioinformatics.charite.de/cancerresource/

Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration

Using an open-access distribution model, the Crystallography Open Database (COD, http://www.crystallography.net) collects all known ‘small molecule / small to medium sized unit cell’ crystal structures and makes them available freely on the Internet. As of today, the COD has aggregated ∼150 000 structures, offering basic search capabilities and the possibility to download the whole database, or parts thereof using a variety of standard open communication protocols. A newly developed website provides capabilities for all registered users to deposit published and so far unpublished structures as personal communications or pre-publication depositions. Such a setup enables extension of the COD database by many users simultaneously. This increases the possibilities for growth of the COD database, and is the first step towards establishing a world wide Internet-based collaborative platform dedicated to the collection and curation of structural knowledge