According to the Medical Subject Heading (MeSH), hormones are defined as chemical substances having a specific regulatory effect on the activity of a certain organ or organs, although the classical definition of hormones limits them to the domain of chemical signaling molecules produced by endocrine glands and secreted directly into the bloodstream. Hormones travel through the blood to distant tissues and organs, where they can bind to specific cell sites called receptors. By binding to receptors, hormones trigger various responses in the tissues/cells containing cognate receptors [1
]. On the basis of their chemical natures, hormones are broadly classified into protein/peptide hormones (genome-encoded) and non-peptide hormones (non-genome-encoded). Hormone-receptor interactions are amongst the most important ligand-receptor type of interactions in biological systems. The living multicellular entity depends on complex communication networks for its survival. Hormones, acting as chemical messengers, are the postmen of endocrine machinery. The endocrine system focuses on ligand-receptor interactions to play a critical role in growth and development of multicellular eukaryotes [3
]. The data flow in this area of biological science is rapid and vast. Therefore, collection and compilation of information about these interactions, and underlying molecules (hormones and receptors), will be useful.
In recent years, efforts have been made to collect and organize receptors (like GPCRDB, ORDB, NuReBase and GRIS) [5
]. These databases deal with different classes of receptors in biological system; for example, GPCRDB/GRIS/ORDB for G-protein coupled receptors (GPCRs) and NuReBase for nuclear hormone receptors. Various type of databases; for example SwePep [9
] for endogenous peptides and PepBank [10
] for peptides collected from literature using text mining tools, came into existence recently. There are a few databases which maintain information about ligands and their receptors like PRRDB [11
], GLIDA [12
], and EndoNet [13
]. PRRDB, an immunological database, provides information regarding Pattern Recognition Receptors and their ligands. GLIDA is developed with possible implications in chemical genomic research and GPCR-related drug discovery, whereas EndoNet is an information resource about intercellular regulatory communication. Though existing databases provide important information, there is lack of a comprehensive resource on hormones and their receptors.
In order to complement existing databases in the field, and to understand hormones and their interaction with receptors, we have developed a database called Hmrbase. This database provides comprehensive information about hormones and receptors. Various data fields like hormone precursor, subcellular localization, post-translational modification, taxonomy, source organism, function, description, tissue specificity, molecular weight, similarity to other proteins, and mapping of hormone peptide on its corresponding precursor etc. have been included for peptide hormones and their receptor. For non-peptide hormones, the data fields consist of their names, molecular weights and molecular formulae, IUPAC names, canonical and isomeric smile formulae, melting points, LogP values, water solubility, and their corresponding receptors etc. Various co-ordinate files such as PDB, SDF, and MOL files are available for download. Structure visualization tools such as Advance Chemistry Development (ACD) structure drawing applet [14
] (for 2-D visualization) and Jmol applet [15
] (for 3-D visualization) have been embedded in Hmrbase. Links to neighbors (external links) like Swiss-Prot [16
], PDB [17
], NCBI Gene Database [18
], Pfam [19
], PubChem [20
], KEGG [21
], HMDB [22
], DrugBank [23
], and DrugPedia [24
] have been incorporated in Hmrbase to make it a complete system. Moreover, hormones and receptors entries are linked to their corresponding receptors and hormones, respectively. Sequence similarity search, peptide mapping and domain search tool, in case of protein hormone and receptor, facilitates the extraction of useful information. In addition to text search, a structural similarity-based search option for non-peptide hormones supports the search algorithm. Thus, Hmrbase provides both comprehensive and easy-to-use information related to hormones and their receptors.