The metabolome refers to the complete set of small-molecule chemicals found within a biological sample. The biological sample can be a cell, a cellular organelle, an organ, a tissue, a tissue extract, a biofluid or an entire organism. The small molecule chemicals found in a given metabolome may include both endogenous metabolites that are naturally produced by an organism as well as exogenous chemicals that are not naturally produced by an organism.
The DrugBank database is a comprehensive, freely accessible, online database containing information on drugs and drug targets created and maintained by the University of Alberta and The Metabolomics Innovation Centre located in Alberta, Canada. As both a bioinformatics and a cheminformatics resource, DrugBank combines detailed drug data with comprehensive drug target information. DrugBank has used content from Wikipedia; Wikipedia also often links to Drugbank, posing potential circular reporting issues.
KEGG is a collection of databases dealing with genomes, biological pathways, diseases, drugs, and chemical substances. KEGG is utilized for bioinformatics research and education, including data analysis in genomics, metagenomics, metabolomics and other omics studies, modeling and simulation in systems biology, and translational research in drug development.
Amos Bairoch is a Swiss bioinformatician and Professor of Bioinformatics at the Department of Human Protein Sciences of the University of Geneva where he leads the CALIPHO group at the Swiss Institute of Bioinformatics (SIB) combining bioinformatics, curation, and experimental efforts to functionally characterize human proteins.
Orciprenaline, also known as metaproterenol, is a bronchodilator used in the treatment of asthma. Orciprenaline is a moderately selective β2 adrenergic receptor agonist that stimulates receptors of the smooth muscle in the lungs, uterus, and vasculature supplying skeletal muscle, with minimal or no effect on α adrenergic receptors. The pharmacologic effects of β adrenergic agonist drugs, such as orciprenaline, are at least in part attributable to stimulation through β adrenergic receptors of intracellular adenylyl cyclase, the enzyme which catalyzes the conversion of ATP to cAMP. Increased cAMP levels are associated with relaxation of bronchial smooth muscle and inhibition of release of mediators of immediate hypersensitivity from many cells, especially from mast cells.
Performance-enhancing substances, also known as performance-enhancing drugs (PEDs), are substances that are used to improve any form of activity performance in humans. A well-known example of cheating in sports involves doping in sport, where banned physical performance-enhancing drugs are used by athletes and bodybuilders. Athletic performance-enhancing substances are sometimes referred as ergogenic aids. Cognitive performance-enhancing drugs, commonly called nootropics, are sometimes used by students to improve academic performance. Performance-enhancing substances are also used by military personnel to enhance combat performance.
The DNA Data Bank of Japan (DDBJ) is a biological database that collects DNA sequences. It is located at the National Institute of Genetics (NIG) in the Shizuoka prefecture of Japan. It is also a member of the International Nucleotide Sequence Database Collaboration or INSDC. It exchanges its data with European Molecular Biology Laboratory at the European Bioinformatics Institute and with GenBank at the National Center for Biotechnology Information on a daily basis. Thus these three databanks contain the same data at any given time.
Glycochenodeoxycholic acid is a bile salt formed in the liver from chenodeoxycholic acid and glycine, usually found as the sodium salt. It acts as a detergent to solubilize fats for absorption.
Therapeutic Target Database (TTD) is a pharmaceutical and medical repository constructed by the Innovative Drug Research and Bioinformatics Group (IDRB) at Zhejiang University, China and the Bioinformatics and Drug Design Group at the National University of Singapore. It provides information about known and explored therapeutic protein and nucleic acid targets, the targeted disease, pathway information and the corresponding drugs directed at each of these targets. Detailed knowledge about target function, sequence, 3D structure, ligand binding properties, enzyme nomenclature and drug structure, therapeutic class, and clinical development status. TTD is freely accessible without any login requirement at https://idrblab.org/ttd/.
PDBsum is a database that provides an overview of the contents of each 3D macromolecular structure deposited in the Protein Data Bank. The original version of the database was developed around 1995 by Roman Laskowski and collaborators at University College London. As of 2014, PDBsum is maintained by Laskowski and collaborators in the laboratory of Janet Thornton at the European Bioinformatics Institute (EBI).
The Human Metabolome Database (HMDB) is a comprehensive, high-quality, freely accessible, online database of small molecule metabolites found in the human body. It bas been created by the Human Metabolome Project funded by Genome Canada and is one of the first dedicated metabolomics databases. The HMDB facilitates human metabolomics research, including the identification and characterization of human metabolites using NMR spectroscopy, GC-MS spectrometry and LC/MS spectrometry. To aid in this discovery process, the HMDB contains three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data (Fig. 1–3). The chemical data includes 41,514 metabolite structures with detailed descriptions along with nearly 10,000 NMR, GC-MS and LC/MS spectra.
The Toxin and Toxin-Target Database (T3DB), also known as the Toxic Exposome Database, is a freely accessible online database of common substances that are toxic to humans, along with their protein, DNA or organ targets. The database currently houses nearly 3,700 toxic compounds or poisons described by nearly 42,000 synonyms. This list includes various groups of toxins, including common pollutants, pesticides, drugs, food toxins, household and industrial/workplace toxins, cigarette toxins, and uremic toxins. These toxic substances are linked to 2,086 corresponding protein/DNA target records. In total there are 42,433 toxic substance-toxin target associations. Each toxic compound record (ToxCard) in T3DB contains nearly 100 data fields and holds information such as chemical properties and descriptors, mechanisms of action, toxicity or lethal dose values, molecular and cellular interactions, medical information, NMR an MS spectra, and up- and down-regulated genes. This information has been extracted from over 18,000 sources, which include other databases, government documents, books, and scientific literature.
The Small Molecule Pathway Database (SMPDB) is a comprehensive, high-quality, freely accessible, online database containing more than 600 small molecule (i.e. metabolic) pathways found in humans. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing colorful, detailed, fully searchable, hyperlinked diagrams of five types of small molecule pathways: 1) general human metabolic pathways; 2) human metabolic disease pathways; 3) human metabolite signaling pathways; 4) drug-action pathways and 5) drug metabolism pathways. SMPDB pathways may be navigated, viewed and zoomed interactively using a Google Maps-like interface. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures (Fig. 1). Each small molecule in SMPDB is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. Additionally, all SMPDB pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram. Users can browse the SMPDB (Fig. 2) or search its contents by text searching (Fig. 3), sequence searching, or chemical structure searching. More powerful queries are also possible including searching with lists of gene or protein names, drug names, metabolite names, GenBank IDs, Swiss-Prot IDs, Agilent or Affymetrix microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB's mapping interface.
MetaboAnalyst is a set of online tools for metabolomic data analysis and interpretation, created by members of the Wishart Research Group at the University of Alberta. It was first released in May 2009 and version 2.0 was released in January 2012. MetaboAnalyst provides a variety of analysis methods that have been tailored for metabolomic data. These methods include metabolomic data processing, normalization, multivariate statistical analysis, and data annotation. The current version is focused on biomarker discovery and classification.
Forasartan, otherwise known as the compound SC-52458, is a nonpeptide angiotensin II receptor antagonist (ARB, AT1 receptor blocker).
Model organism databases (MODs) are biological databases, or knowledgebases, dedicated to the provision of in-depth biological data for intensively studied model organisms. MODs allow researchers to easily find background information on large sets of genes, plan experiments efficiently, combine their data with existing knowledge, and construct novel hypotheses. They allow users to analyse results and interpret datasets, and the data they generate are increasingly used to describe less well studied species. Where possible, MODs share common approaches to collect and represent biological information. For example, all MODs use the Gene Ontology (GO) to describe functions, processes and cellular locations of specific gene products. Projects also exist to enable software sharing for curation, visualization and querying between different MODs. Organismal diversity and varying user requirements however mean that MODs are often required to customize capture, display, and provision of data.
Genome mining describes the exploitation of genomic information for the discovery of biosynthetic pathways of natural products and their possible interactions. It depends on computational technology and bioinformatics tools. The mining process relies on a huge amount of data accessible in genomic databases. By applying data mining algorithms, the data can be used to generate new knowledge in several areas of medicinal chemistry, such as discovering novel natural products.
