DiPPI: A Curated Data Set for Drug-like Molecules in Protein-Protein Interfaces

Abstract

Proteins interact through their interfaces, and dysfunction of protein-protein interactions (PPIs) has been associated with various diseases. Therefore, investigating the properties of the drug-modulated PPIs and interface-targeting drugs is critical. Here, we present a curated large data set for drug-like molecules in protein interfaces. We further introduce DiPPI (Drugs in Protein-Protein Interfaces), a two-module web site to facilitate the search for such molecules and their properties by exploiting our data set in drug repurposing studies. In the interface module of the web site, we present several properties, of interfaces, such as amino acid properties, hotspots, evolutionary conservation of drug-binding amino acids, and post-translational modifications of these residues. On the drug-like molecule side, we list drug-like small molecules and FDA-approved drugs from various databases and highlight those that bind to the interfaces. We further clustered the drugs based on their molecular fingerprints to confine the search for an alternative drug to a smaller space. Drug properties, including Lipinski's rules and various molecular descriptors, are also calculated and made available on the web site to guide the selection of drug molecules. Our data set contains 534,203 interfaces for 98,632 protein structures, of which 55,135 are detected to bind to a drug-like molecule. 2214 drug-like molecules are deposited on our web site, among which 335 are FDA-approved. DiPPI provides users with an easy-to-follow scheme for drug repurposing studies through its well-curated and clustered interface and drug data and is freely available at http://interactome.ku.edu.tr:8501.

Figure 1

Flowchart of the DiPPI process. Interface clusters are created as described in Abali et al. Drug-like small molecule data are curated from five databases. Relevant physicochemical properties are calculated for interface and drug-like molecules.

Figure 2

Distribution of drug-like small molecules with respect to the source database. Single dots show the individual contribution from each source, while connections show the mutual contribution of connected sources. The numbers on top show the total number contributed by each entry. For example, 7147 molecules are solely found in BindingDB, whereas 1548 molecules exist in the Open Targets Platform and BindingDB. Database size represents the total number of drug-like molecules each data source contributes.

Figure 3

Members of a randomly selected cluster for (a) ECFP4 fingerprints and (b) pharmacophore fingerprints.

Figure 4

Example query for the “Query by Interface” page in DiPPI.

Figure 5

Example query for the “Query by Drug” page in DiPPI.

Figure 6

Mifepristone (orange) bound to the PPAR γ (medium purple)-nuclear receptor coactivator 1 (cyan) interface is docked to the RXR α (cornflower blue)-nuclear receptor coactivator 1 (pink) interface in the same protein–protein interface cluster found in DiPPI. As a result, mifepristone used for medical abortion is suggested to be repurposed for cancer treatment.

Figure 7

Similarity of PPAR γ with mifepristone and RXRA with mifepristone. Mifepristone is not directly bound to the interface since it is bound to a nearby residue of the interface. (A) Structural similarity of the mifepristone binding region in PPAR γ (PDB ID: 3QT0) and in RXRA (docked structure) is highlighted. (B) Protein–ligand interaction diagram for mifepristone with PPAR γ and mifepristone (ligand ID: 486) with RXRA. Hydrogen bonds are represented by green dashed lines. Hydrophobic contacts are represented by red spoked arcs. (C) Structural alignment of PPAR γ (medium purple) with mifepristone (yellow) and RXRA (cornflower blue) with mifepristone (orange) is shown. Both protein structures in cyan and pink are nuclear receptor coactivator 1.