DLiP-PPI library: An integrated chemical database of small-to-medium-sized molecules targeting protein-protein interactions

Abstract

Protein-protein interactions (PPIs) are recognized as important targets in drug discovery. The characteristics of molecules that inhibit PPIs differ from those of small-molecule compounds. We developed a novel chemical library database system (DLiP) to design PPI inhibitors. A total of 32,647 PPI-related compounds are registered in the DLiP. It contains 15,214 newly synthesized compounds, with molecular weight ranging from 450 to 650, and 17,433 active and inactive compounds registered by extracting and integrating known compound data related to 105 PPI targets from public databases and published literature. Our analysis revealed that the compounds in this database contain unique chemical structures and have physicochemical properties suitable for binding to the protein-protein interface. In addition, advanced functions have been integrated with the web interface, which allows users to search for potential PPI inhibitor compounds based on types of protein-protein interfaces, filter results by drug-likeness indicators important for PPI targeting such as rule-of-4, and display known active and inactive compounds for each PPI target. The DLiP aids the search for new candidate molecules for PPI drug discovery and is available online (https://skb-insilico.com/dlip).

Keywords: PMI; chemical library; database; drug design; physicochemical property; protein–protein interaction (PPI); rule-of-4.

FIGURE 1

Architecture of the DLiP database system. PPI: protein–protein interaction; cmpds: compounds.

FIGURE 2

Comparison of histograms of molecular properties (molecular weight (A), ALogP (B), hydrogen-bond acceptors (C), number of violations of rule-of-four (D)) of DLiP-PPI library (red), small-molecule approved drugs (blue), and known PPI modulators (green).

FIGURE 3

(A) Histogram of structural similarities (Tanimoto similarity by extended connectivity fingerprint 6 (ECFP6)) between compounds in the DLiP-PPI library and the closest known PPI modulators in the database. (B) Analysis of the steric shape space of non-flat subset of DLiP-PPI library (2,280) by PMI plot. To assess the shape-based distribution of compounds in the PPI library, normalized principal moments of inertia (NPR1 and NPR2) were calculated. (C) Examples of compound structures in the DLiP-PPI library. Compounds with npr1+npr2 values greater than 1.5 and maximum similarity to the closet known PPI modulators less than 0.3 were selected. The PP interface type (type), maximum similarity score (max sim), npr1and npr2 are also shown.

FIGURE 4

Web interface of the DLiP database system showing (A) Top page of the DLiP web interface. Here, users can choose from two main search functions (PPI Library Search and PPI Curation Search) and obtain other information about the DLiP database (ABOUT) from the left menu bar. (B) Search result page of PPI Library Search. (C) Compound information page. Here, users can see the 2D/3D chemical structures of each compound registered in the DLiP database.

FIGURE 5

PPI library search page at which users can search for compounds in the PPI library by drawing a chemical structure, selecting or entering a keyword such as PP interface type, motif sequence or ID, or a SMILES/SMARTS string of a chemical structure.

FIGURE 6

PPI curation search page at which users can select a target of interest from a list of 105 PPI target names, and search for PPI-related compounds by pre-defined drug-likeness rules such as Lipinski’s rule-of-5, rule-of-4, and beyond-rule-of-5.