Current Challenges and Opportunities in Designing Protein-Protein Interaction Targeted Drugs

Abstract

It has been noticed that the efficiency of drug development has been decreasing in the past few decades. To overcome the situation, protein-protein interactions (PPIs) have been identified as new drug targets as early as 2000. PPIs are more abundant in human cells than single proteins and play numerous important roles in cellular processes including diseases. However, PPIs have very different physicochemical features from the conventional drug targets, which make targeting PPIs challenging. Therefore, as of now, only a small number of PPI inhibitors have been approved or progressed to a stage of clinical trial. In this article, we first overview previous works that analyzed differences between PPIs with PPI targeting ligands and conventional drugs with their binding pockets. Then, we constructed an up-to-date list of PPI targeting drugs that have been approved or are currently under clinical trial and have bound drug-target structures available. Using the dataset, we analyzed the PPIs and their ligands using several scores of druggability. Druggability scores showed that PPI sites and their drugs targeting PPIs are less druggable than conventional binding pockets and drugs, which also indicates that PPI drugs do not follow the conventional rules for drug design, such as Lipinski's rule of five. Our analyses suggest that developing a new rule would be beneficial for guiding PPI-drug discovery.

Keywords: PPI; PPI drugs; drug discovery; protein–protein interaction.

Figure 1

The ligand druggability relative to the protein pocket druggability for PPI drugs/candidates. QED and SiteScore were computed for PPI drugs and candidates in Table 2. Yellow circles, small-molecule drugs; blue squares, antibodies; red diamonds, proteins. Approved drugs are shown in a darker color. Compounds labeled as a and b are shown below in the panel. QED was not computed for antibodies and shown at an extra column marked with *SiteMap (ver. 5.0.011), the software we used for computing SiteScore, offers two modes. As default, we used the option of “Identify top-ranked potential receptor binding sites”, which automatically detects a pocket and computes SiteScore. Scores were properly computed with this option for small compound binding pockets but often failed for PPI surfaces because of their flat shape. In such cases, we used another option, “Evaluate a single binding site region”, which computes SiteScore for a user-specified region. For binding sites for antibodies, we combined it with an additional option of “detect shallow binding site” option. (A) The chemical structures of the three compounds specified by a circle in the graph. (B) The chemical structure of the compound specified at the right bottom in the graph.

Figure 2

A few structures of PPI drugs in complex with target proteins. (A) The crystal structure of BCL2 with Venetoclax (PDB ID: 6O0K). (B) The crystal structure of BCL2 in complex with a BAX BH3 peptide (PDB ID: 2XA0). (C) The 2D structure of Venetoclax; (D) Superposition of Venetoclax and BAX BH3 peptide (magenta). (E) The SiteMap result calculated under the “Evaluate a single binding site region” condition with “detect shallow binding site” option for the BCL2/venetoclax complex (PDB ID: 6O0K). The white dots indicate the size of the pocket that is reflected in SiteScore. The color represents pharmacophoric features: yellow, hydrophobic; blue hydrogen bond donor; red, hydrogen bond acceptor. (F) The SiteMap result calculated with the “Identify top-ranked potential receptor binding sites” condition without the “detect shallow binding site” option for the biological assembly of TTR in complex with two Tafamidis units (green) (PDB ID: 3TCT). (G) The SiteMap result calculated with the “Evaluate a single binding site region” option with the “detect shallow binding site” option for the monomer of TTR and Tafamidis (green) (PDB ID: 3TCT).

Figure 3

Distributions of druggability scores and small-molecule 2D descriptors of PPI drugs/candidates and non-PPI ligands. PPI drug/candidates are only limited to small molecules. Non-PPI ligands include natural ligands and are not limited to drug and drug candidates. (A) The ligand druggability relative to the protein pocket druggability. PPI drugs include drug candidates. (B) Box plots of druggability score distribution. A line through each box shows the median. (C) Distributions of small-molecule 2D descriptors used for QED and the RO4. Black lines on the plots show the criteria of the RO4.

Figure 4

The number of bound fragments computed by FTMap at PPI-drug-binding sites and drug-binding pockets. (A) The total number of binding fragments and the average number of binding fragments per residue for PPI drug binding sites (blue) and drug-binding pockets (red). (B) PPI-drug-binding sites were further split into two classes: Blue, Class 1, protein-peptide interaction complexes; red, Class 2, protein–protein interaction complexes. (C) The average number of binding fragments per drug non-binding residues (x-axis) and drug-binding residues (y-axis). (D) The FTMap result for HIV protease (PDB ID: 1HPX), selected as a typical drug-binding pocket. The docking poses of fragments are colored in blue and binding pocket residues defined by considering the cognate binding ligand are colored red. (E) The FTMap results for integrase from the integrase/LEDGF complex (PDB ID: 2B4J) as an example of PPI interfaces. (F) The fragment docking result for MDM from the MDM/P53 complex (PDB ID: 1YCQ). A 17 amino-acids-long helical peptide from P53 is colored in light blue, and fragments bound to the receptor structure are shown in blue.