About PDID

The PDID database provides access to a comprehensive set of putative and native protein-drug interactions in the structural human proteome. The structural human proteome includes about 10,000 human and human-like (with high sequence similarity to human proteins) proteins with known 3-D structures. The database includes data for popular, FDA-approved drugs. The corresponding protein-drug interactions were generated with three predictors, and were collected from and linked with three related databases of known protein-drug interactions.

Tutorial that explains how to use PDID is available here.

Structural human proteome

Drug molecules

Protein-drug interactions

The protein-drug interactions that are made available in the PDID include the known and putative (predicted) interactions.

The known interactions were collected from the DrugBank [1], BindingDB [2], and Protein Data Bank [3] resources. These interactions are annotated in PDID as known and are linked to the corresponding databases.

The putative interactions were predicted with three methods:
1. Customized version of the eFindSite method [4, 5] that predicts targets based on similarity of binding pockets using threading.
2. Customized version of the SMAP method [6] that predicts targets based on similarity of binding pockets and protein fold using profile-profile alignment.
3. The ILbind method [7] that predicts targets using consensus of 15 support vector machines and combines similarity based on threading and profile-profile alignment.

The proteins are mapped into the UniProt database [8] using UniProt identifiers to facilitate mapping between PDID, Protein Data Bank, DrugBank, and BindingDB.

1. Wishart DS, Knox C, Guo AC, et al. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res 34:D668-72
2. Liu T, Lin Y, Wen X, et al. (2007) BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res 35:D198-201
3. Berman HM, Westbrook J, Feng Z, et al. (2000). The Protein Data Bank. Nucleic Acids Res 28:235-42
4. Brylinski M and Feinstein WP. (2013). eFindSite: Improved prediction of ligand binding sites in protein models using meta-threading, machine learning and auxiliary ligands. J Comput Aided Mol Des. 27(6):551-567
5. Feinstein WP and Brylinski M. (2014). eFindSite: Enhanced fingerprint-based virtual screening against predicted ligand binding sites in protein models. Mol Inform. 33(2):135-50
6. Xie L and Bourne PE (2008) Detecting evolutionary relationships across existing fold space, using sequence order independent profile-profile alignments". Proc Natl Acad Sci USA 105(14):5441-6
7. Hu G, Gao J, Wang K, et al. (2012) Finding protein targets for small biologically relevant ligands across fold space using inverse ligand binding predictions. Structure 20:1815-22
8. The UniProt Consortium. Activities at the Universal Protein Resource (UniProt). Nucleic Acids Res 42:D191-8