ChemProt: a disease chemical biology database

By O. Taboureau, Sonny Kim Nielsen, Karine Audouze, Nils Weinhold, D. Edsgärd, Francisco S. Roque, I. Kouskoumvekaki, Alina Bora, R. Curpan, T. S. Jensen and 2 more. 2010

121 citations recorded by the source corpus.

Systems pharmacology is an emergent area that studies drug action across multiple scales of complexity, from molecular and cellular to tissue and organism levels. There is a critical need to develop network-based approaches to integrate the growing body of chemical biology knowledge with network biology. Here, we report ChemProt, a disease chemical biology database, which is based on a compilation of multiple chemical–protein annotation resources, as well as disease-associated protein–protein interactions (PPIs). We assembled more than 700 000 unique chemicals with biological annotation for 30 578 proteins. We gathered over 2-million chemical–protein interactions, which were integrated in a quality scored human PPI network of 428 429 interactions. The PPI network layer allows for studying disease and tissue specificity through each protein complex. ChemProt can assist in the in silico evaluation of environmental chemicals, natural products and approved drugs, as well as the selection of new compounds based on their activity profile against most known biological targets, including those related to adverse drug events. Results from the disease chemical biology database associate citalopram, an antidepressant, with osteogenesis imperfect and leukemia and bisphenol A, an endocrine disruptor, with certain types of cancer, respectively. The server can be accessed at http://www.cbs.dtu.dk/services/ChemProt/.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, methodology): “Databases like ChemProt: http://www.cbs.dtu.dk/services/ChemProt including 700,000 chemicals and 2millions of interactions of their target proteins in various species (Taboureau et al., 2011) provide a great help in this process.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “ChemProt [253, 255, 256] was proposed as a disease chemical biology database that integrated data from multiple chemical– protein annotation databases and disease-associated PPI.”