HIT: linking herbal active ingredients to targets

Abstract

The information of protein targets and small molecule has been highly valued by biomedical and pharmaceutical research. Several protein target databases are available online for FDA-approved drugs as well as the promising precursors that have largely facilitated the mechanistic study and subsequent research for drug discovery. However, those related resources regarding to herbal active ingredients, although being unusually valued as a precious resource for new drug development, is rarely found. In this article, a comprehensive and fully curated database for Herb Ingredients’ Targets (HIT, http://lifecenter.sgst.cn/hit/) has been constructed to complement above resources. Those herbal ingredients with protein target information were carefully curated. The molecular target information involves those proteins being directly/indirectly activated/inhibited, protein binders and enzymes whose substrates or products are those compounds. Those up/down regulated genes are also included under the treatment of individual ingredients. In addition, the experimental condition, observed bioactivity and various references are provided as well for user's reference. Derived from more than 3250 literatures, it currently contains 5208 entries about 1301 known protein targets (221 of them are described as direct targets) affected by 586 herbal compounds from more than 1300 reputable Chinese herbs, overlapping with 280 therapeutic targets from Therapeutic Targets Database (TTD), and 445 protein targets from DrugBank corresponding to 1488 drug agents. The database can be queried via keyword search or similarity search. Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.

Identification of Putative Therapeutic Targets in Candida tropicalis: An in silico Approach

“…This enabled us to predict the function of the proteins and genome annotation that resulted in the identification of potential targets. Screening of the potential drug targets was carried out by similarity search using protein sequence of all the potential targets against the Drug Bank ( Knox et al, 2011), TTD (Chen et al, 2002), PDTD (Gao et al, 2008) and HIT (Ye et al, 2011) to reach the novel drug targets. Further the outer membrane proteins were predicted using Trans-Membrane prediction using Hidden Markov Models (TMHMM), that identify surface membrane proteins which could be used as potential drug targets and vaccine candidates (Krogh et al, 2001).…”

In Silico Identification and Characterization of Potent Drug Targets in Fungal Pathogen causing Keratitis

“…Each of the targets was subjected to a homology search against Drug Bank, TTD, PDTD and HIT databases [22] [23] [24] and [ 25].…”

Deciphering the therapeutic mechanisms of Xiao-Ke-An in treatment of type 2 diabetes in mice by a Fangjiomics approach

“…In the second method of network construction, TCM and herb databases, including TCM-ID [16] , HIT [17] , and TCM@Tai-wan [18] , were used to identify the chemicals in XKA by searching the constituent herbs of XKA (Rehmanniae radix, Anemarrhenae rhizoma, Coptidis rhizoma, Lycii cortex, Lycii fructus, Polygonati odorati rhizoma, Ginseng radix et rhizoma and Salviae miltiorrhizae radix et rhizoma). The Traditional Chinese Medicine Integrated Database (TCMID) [19] records TCM-related information collected from different resources and through text-mining methods.…”