Chen Wang1, Gang Hu2, Kui Wang2, Michal Brylinski3, Lei Xie4, Lukasz Kurgan5. 1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4. 2. School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, People's Republic of China. 3. Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA. 4. Department of Computer Science, Hunter College, City University of New York (CUNY), New York, NY 10065, USA and. 5. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2V4, Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.
Abstract
MOTIVATION: Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome. RESULTS: PDID covers 9652 structures from 3746 proteins and houses 16 800 putative interactions generated from close to 1.1 million accurate, all-atom structure-based predictions for several dozens of popular drugs. The predictions were generated with three modern methods: ILbind, SMAP and eFindSite. They are accompanied by propensity scores that quantify likelihood of interactions and coordinates of the putative location of the binding drugs in the corresponding protein structures. PDID complements the current databases that focus on the curated interactions and the BioDrugScreen database that relies on docking to find putative interactions. Moreover, we also include experimentally curated interactions which are linked to their sources: DrugBank, BindingDB and Protein Data Bank. Our database can be used to facilitate studies related to polypharmacology of drugs including repurposing and explaining side effects of drugs. AVAILABILITY AND IMPLEMENTATION: PDID database is freely available at http://biomine.ece.ualberta.ca/PDID/.
MOTIVATION: Many drugs interact with numerous proteins besides their intended therapeutic targets and a substantial portion of these interactions is yet to be elucidated. Protein-Drug Interaction Database (PDID) addresses incompleteness of these data by providing access to putative protein-drug interactions that cover the entire structural human proteome. RESULTS: PDID covers 9652 structures from 3746 proteins and houses 16 800 putative interactions generated from close to 1.1 million accurate, all-atom structure-based predictions for several dozens of popular drugs. The predictions were generated with three modern methods: ILbind, SMAP and eFindSite. They are accompanied by propensity scores that quantify likelihood of interactions and coordinates of the putative location of the binding drugs in the corresponding protein structures. PDID complements the current databases that focus on the curated interactions and the BioDrugScreen database that relies on docking to find putative interactions. Moreover, we also include experimentally curated interactions which are linked to their sources: DrugBank, BindingDB and Protein Data Bank. Our database can be used to facilitate studies related to polypharmacology of drugs including repurposing and explaining side effects of drugs. AVAILABILITY AND IMPLEMENTATION: PDID database is freely available at http://biomine.ece.ualberta.ca/PDID/.
Authors: H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne Journal: Nucleic Acids Res Date: 2000-01-01 Impact factor: 16.971
Authors: Marnie L MacDonald; Jane Lamerdin; Stephen Owens; Brigitte H Keon; Graham K Bilter; Zhidi Shang; Zhengping Huang; Helen Yu; Jennifer Dias; Tomoe Minami; Stephen W Michnick; John K Westwick Journal: Nat Chem Biol Date: 2006-05-07 Impact factor: 15.040