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Showing 1981 to 1984 of 5186 entries
Published on January 8, 2021
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine.
Abstract: Pharmacotranscriptomics has become a powerful approach for evaluating the therapeutic efficacy of drugs and discovering new drug targets. Recently, studies of traditional Chinese medicine (TCM) have increasingly turned to high-throughput transcriptomic screens for molecular effects of herbs/ingredients. And numerous studies have examined gene targets for herbs/ingredients, and link herbs/ingredients to various modern diseases. However, there is currently no systematic database organizing these data for TCM. Therefore, we built HERB, a high-throughput experiment- and reference-guided database of TCM, with its Chinese name as BenCaoZuJian. We re-analyzed 6164 gene expression profiles from 1037 high-throughput experiments evaluating TCM herbs/ingredients, and generated connections between TCM herbs/ingredients and 2837 modern drugs by mapping the comprehensive pharmacotranscriptomics dataset in HERB to CMap, the largest such dataset for modern drugs. Moreover, we manually curated 1241 gene targets and 494 modern diseases for 473 herbs/ingredients from 1966 references published recently, and cross-referenced this novel information to databases containing such data for drugs. Together with database mining and statistical inference, we linked 12 933 targets and 28 212 diseases to 7263 herbs and 49 258 ingredients and provided six pairwise relationships among them in HERB. In summary, HERB will intensively support the modernization of TCM and guide rational modern drug discovery efforts. And it is accessible through http://herb.ac.cn/.
Published on January 8, 2021
INTEDE: interactome of drug-metabolizing enzymes.
Abstract: Drug-metabolizing enzymes (DMEs) are critical determinant of drug safety and efficacy, and the interactome of DMEs has attracted extensive attention. There are 3 major interaction types in an interactome: microbiome-DME interaction (MICBIO), xenobiotics-DME interaction (XEOTIC) and host protein-DME interaction (HOSPPI). The interaction data of each type are essential for drug metabolism, and the collective consideration of multiple types has implication for the future practice of precision medicine. However, no database was designed to systematically provide the data of all types of DME interactions. Here, a database of the Interactome of Drug-Metabolizing Enzymes (INTEDE) was therefore constructed to offer these interaction data. First, 1047 unique DMEs (448 host and 599 microbial) were confirmed, for the first time, using their metabolizing drugs. Second, for these newly confirmed DMEs, all types of their interactions (3359 MICBIOs between 225 microbial species and 185 DMEs; 47 778 XEOTICs between 4150 xenobiotics and 501 DMEs; 7849 HOSPPIs between 565 human proteins and 566 DMEs) were comprehensively collected and then provided, which enabled the crosstalk analysis among multiple types. Because of the huge amount of accumulated data, the INTEDE made it possible to generalize key features for revealing disease etiology and optimizing clinical treatment. INTEDE is freely accessible at: https://idrblab.org/intede/.
Published on January 8, 2021
MarkerDB: an online database of molecular biomarkers.
Abstract: MarkerDB is a freely available electronic database that attempts to consolidate information on all known clinical and a selected set of pre-clinical molecular biomarkers into a single resource. The database includes four major types of molecular biomarkers (chemical, protein, DNA [genetic] and karyotypic) and four biomarker categories (diagnostic, predictive, prognostic and exposure). MarkerDB provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function. Currently, the database contains 142 protein biomarkers, 1089 chemical biomarkers, 154 karyotype biomarkers and 26 374 genetic markers. These are categorized into 25 560 diagnostic biomarkers, 102 prognostic biomarkers, 265 exposure biomarkers and 6746 predictive biomarkers or biomarker panels. Collectively, these markers can be used to detect, monitor or predict 670 specific human conditions which are grouped into 27 broad condition categories. MarkerDB is available at https://markerdb.ca.
Published on January 8, 2021
The MemMoRF database for recognizing disordered protein regions interacting with cellular membranes.
Abstract: Protein and lipid membrane interactions play fundamental roles in a large number of cellular processes (e.g. signalling, vesicle trafficking, or viral invasion). A growing number of examples indicate that such interactions can also rely on intrinsically disordered protein regions (IDRs), which can form specific reversible interactions not only with proteins but also with lipids. We named IDRs involved in such membrane lipid-induced disorder-to-order transition as MemMoRFs, in an analogy to IDRs exhibiting disorder-to-order transition upon interaction with protein partners termed Molecular Recognition Features (MoRFs). Currently, both the experimental detection and computational characterization of MemMoRFs are challenging, and information about these regions are scattered in the literature. To facilitate the related investigations we generated a comprehensive database of experimentally validated MemMoRFs based on manual curation of literature and structural data. To characterize the dynamics of MemMoRFs, secondary structure propensity and flexibility calculated from nuclear magnetic resonance chemical shifts were incorporated into the database. These data were supplemented by inclusion of sentences from papers, functional data and disease-related information. The MemMoRF database can be accessed via a user-friendly interface at https://memmorf.hegelab.org, potentially providing a central resource for the characterization of disordered regions in transmembrane and membrane-associated proteins.
Published on January 8, 2021
COVID19 Drug Repository: text-mining the literature in search of putative COVID19 therapeutics.
Abstract: The recent outbreak of COVID-19 has generated an enormous amount of Big Data. To date, the COVID-19 Open Research Dataset (CORD-19), lists approximately 130,000 articles from the WHO COVID-19 database, PubMed Central, medRxiv, and bioRxiv, as collected by Semantic Scholar. According to LitCovid (11 August 2020), approximately 40,300 COVID19-related articles are currently listed in PubMed. It has been shown in clinical settings that the analysis of past research results and the mining of available data can provide novel opportunities for the successful application of currently approved therapeutics and their combinations for the treatment of conditions caused by a novel SARS-CoV-2 infection. As such, effective responses to the pandemic require the development of efficient applications, methods and algorithms for data navigation, text-mining, clustering, classification, analysis, and reasoning. Thus, our COVID19 Drug Repository represents a modular platform for drug data navigation and analysis, with an emphasis on COVID-19-related information currently being reported. The COVID19 Drug Repository enables users to focus on different levels of complexity, starting from general information about (FDA-) approved drugs, PubMed references, clinical trials, recipes as well as the descriptions of molecular mechanisms of drugs' action. Our COVID19 drug repository provide a most updated world-wide collection of drugs that has been repurposed for COVID19 treatments around the world.
Published on January 8, 2021
DrugSpaceX: a large screenable and synthetically tractable database extending drug space.
Abstract: One of the most prominent topics in drug discovery is efficient exploration of the vast drug-like chemical space to find synthesizable and novel chemical structures with desired biological properties. To address this challenge, we created the DrugSpaceX (https://drugspacex.simm.ac.cn/) database based on expert-defined transformations of approved drug molecules. The current version of DrugSpaceX contains >100 million transformed chemical products for virtual screening, with outstanding characteristics in terms of structural novelty, diversity and large three-dimensional chemical space coverage. To illustrate its practical application in drug discovery, we used a case study of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, to show DrugSpaceX performing a quick search of initial hit compounds. Additionally, for ligand identification and optimization purposes, DrugSpaceX also provides several subsets for download, including a 10% diversity subset, an extended drug-like subset, a drug-like subset, a lead-like subset, and a fragment-like subset. In addition to chemical properties and transformation instructions, DrugSpaceX can locate the position of transformation, which will enable medicinal chemists to easily integrate strategy planning and protection design.
Published on January 8, 2021
PROMISCUOUS 2.0: a resource for drug-repositioning.
Abstract: The development of new drugs for diseases is a time-consuming, costly and risky process. In recent years, many drugs could be approved for other indications. This repurposing process allows to effectively reduce development costs, time and, ultimately, save patients' lives. During the ongoing COVID-19 pandemic, drug repositioning has gained widespread attention as a fast opportunity to find potential treatments against the newly emerging disease. In order to expand this field to researchers with varying levels of experience, we made an effort to open it to all users (meaning novices as well as experts in cheminformatics) by significantly improving the entry-level user experience. The browsing functionality can be used as a global entry point to collect further information with regards to small molecules ( approximately 1 million), side-effects ( approximately 110 000) or drug-target interactions ( approximately 3 million). The drug-repositioning tab for small molecules will also suggest possible drug-repositioning opportunities to the user by using structural similarity measurements for small molecules using two different approaches. Additionally, using information from the Promiscuous 2.0 Database, lists of candidate drugs for given indications were precomputed, including a section dedicated to potential treatments for COVID-19. All the information is interconnected by a dynamic network-based visualization to identify new indications for available compounds. Promiscuous 2.0 is unique in its functionality and is publicly available at http://bioinformatics.charite.de/promiscuous2.
Published on January 8, 2021
PubChem in 2021: new data content and improved web interfaces.
Abstract: PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves the scientific community as well as the general public, with millions of unique users per month. In the past two years, PubChem made substantial improvements. Data from more than 100 new data sources were added to PubChem, including chemical-literature links from Thieme Chemistry, chemical and physical property links from SpringerMaterials, and patent links from the World Intellectual Properties Organization (WIPO). PubChem's homepage and individual record pages were updated to help users find desired information faster. This update involved a data model change for the data objects used by these pages as well as by programmatic users. Several new services were introduced, including the PubChem Periodic Table and Element pages, Pathway pages, and Knowledge panels. Additionally, in response to the coronavirus disease 2019 (COVID-19) outbreak, PubChem created a special data collection that contains PubChem data related to COVID-19 and the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
Published on January 8, 2021
MASI: microbiota-active substance interactions database.
Abstract: Xenobiotic and host active substances interact with gut microbiota to influence human health and therapeutics. Dietary, pharmaceutical, herbal and environmental substances are modified by microbiota with altered bioavailabilities, bioactivities and toxic effects. Xenobiotics also affect microbiota with health implications. Knowledge of these microbiota and active substance interactions is important for understanding microbiota-regulated functions and therapeutics. Established microbiota databases provide useful information about the microbiota-disease associations, diet and drug interventions, and microbiota modulation of drugs. However, there is insufficient information on the active substances modified by microbiota and the abundance of gut bacteria in humans. Only approximately 7% drugs are covered by the established databases. To complement these databases, we developed MASI, Microbiota-Active Substance Interactions database, for providing the information about the microbiota alteration of various substances, substance alteration of microbiota, and the abundance of gut bacteria in humans. These include 1,051 pharmaceutical, 103 dietary, 119 herbal, 46 probiotic, 142 environmental substances interacting with 806 microbiota species linked to 56 diseases and 784 microbiota-disease associations. MASI covers 11 215 bacteria-pharmaceutical, 914 bacteria-herbal, 309 bacteria-dietary, 753 bacteria-environmental substance interactions and the abundance profiles of 259 bacteria species in 3465 patients and 5334 healthy individuals. MASI is freely accessible at http://www.aiddlab.com/MASI.
Published on January 8, 2021
StreptomeDB 3.0: an updated compendium of streptomycetes natural products.
Abstract: Antimicrobial resistance is an emerging global health threat necessitating the rapid development of novel antimicrobials. Remarkably, the vast majority of currently available antibiotics are natural products (NPs) isolated from streptomycetes, soil-dwelling bacteria of the genus Streptomyces. However, there is still a huge reservoir of streptomycetes NPs which remains pharmaceutically untapped and a compendium thereof could serve as a source of inspiration for the rational design of novel antibiotics. Initially released in 2012, StreptomeDB (http://www.pharmbioinf.uni-freiburg.de/streptomedb) is the first and only public online database that enables the interactive phylogenetic exploration of streptomycetes and their isolated or mutasynthesized NPs. In this third release, there are substantial improvements over its forerunners, especially in terms of data content. For instance, about 2500 unique NPs were newly annotated through manual curation of about 1300 PubMed-indexed articles, published in the last five years since the second release. To increase interoperability, StreptomeDB entries were hyperlinked to several spectral, (bio)chemical and chemical vendor databases, and also to a genome-based NP prediction server. Moreover, predicted pharmacokinetic and toxicity profiles were added. Lastly, some recent real-world use cases of StreptomeDB are highlighted, to illustrate its applicability in life sciences.
Showing 1981 to 1984 of 5186 entries