Dataset Information

PhIN: A Protein Pharmacology Interaction Network Database.

ABSTRACT: Network pharmacology is a new and hot concept in drug discovery for its ability to investigate the complexity of polypharmacology, and becomes more and more important in drug development. Here we report a protein pharmacology interaction network database (PhIN), aiming to assist multitarget drug discovery by providing comprehensive and flexible network pharmacology analysis. Overall, PhIN contains 1,126,060 target-target interaction pairs in terms of shared compounds and 3,428,020 pairs in terms of shared scaffolds, which involve 12,419,700 activity data, 9,414 targets, 314 viral targets, 652 pathways, 1,359,400 compounds, and 309,556 scaffolds. Using PhIN, users can obtain interacting target networks within or across human pathways, between human and virus, by defining the number of shared compounds or scaffolds under an activity cutoff. We expect PhIN to be a useful tool for multitarget drug development. PhIN is freely available at http://cadd.pharmacy.nankai.edu.cn/phin/.

SUBMITTER: Wang Z

PROVIDER: S-EPMC4394615 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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PhIN: A Protein Pharmacology Interaction Network Database.

Wang Z Z Li J J Dang R R Liang L L Lin J J

CPT: pharmacometrics & systems pharmacology 20150318 3

Network pharmacology is a new and hot concept in drug discovery for its ability to investigate the complexity of polypharmacology, and becomes more and more important in drug development. Here we report a protein pharmacology interaction network database (PhIN), aiming to assist multitarget drug discovery by providing comprehensive and flexible network pharmacology analysis. Overall, PhIN contains 1,126,060 target-target interaction pairs in terms of shared compounds and 3,428,020 pairs in terms ...[more]

PMID: 26225242

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Project description:BackgroundMost proteins function by interacting with other molecules. Their interaction interfaces are highly conserved throughout evolution to avoid undesirable interactions that lead to fatal disorders in cells. Rational drug discovery includes computational methods to identify the interaction sites of lead compounds to the target molecules. Identifying and classifying protein interaction interfaces on a large scale can help researchers discover drug targets more efficiently.DescriptionWe introduce a large-scale protein domain interaction interface database called InterPare http://interpare.net. It contains both inter-chain (between chains) interfaces and intra-chain (within chain) interfaces. InterPare uses three methods to detect interfaces: 1) the geometric distance method for checking the distance between atoms that belong to different domains, 2) Accessible Surface Area (ASA), a method for detecting the buried region of a protein that is detached from a solvent when forming multimers or complexes, and 3) the Voronoi diagram, a computational geometry method that uses a mathematical definition of interface regions. InterPare includes visualization tools to display protein interior, surface, and interaction interfaces. It also provides statistics such as the amino acid propensities of queried protein according to its interior, surface, and interface region. The atom coordinates that belong to interface, surface, and interior regions can be downloaded from the website.ConclusionInterPare is an open and public database server for protein interaction interface information. It contains the large-scale interface data for proteins whose 3D-structures are known. As of November 2004, there were 10,583 (Geometric distance), 10,431 (ASA), and 11,010 (Voronoi diagram) entries in the Protein Data Bank (PDB) containing interfaces, according to the above three methods. In the case of the geometric distance method, there are 31,620 inter-chain domain-domain interaction interfaces and 12,758 intra-chain domain-domain interfaces.

Dataset Information

PhIN: A Protein Pharmacology Interaction Network Database.

Publications

PhIN: A Protein Pharmacology Interaction Network Database.

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