Abstract
Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593 small-to-medium-sized newly synthesized molecules. This study validates a promising combined method using PPI-oriented library and ligand-based virtual screening (LBVS) to discover novel PPI inhibitory compounds for Kelch-like ECH-associated protein 1 (Keap1) and nuclear factor erythroid 2-related factor 2 (Nrf2). We performed LBVS with two random forest models against our PPI library and the following time-resolved fluorescence resonance energy transfer (TR-FRET) assays of 620 compounds identified 15 specific hit compounds. The high hit rates for the entire PPI library (estimated 0.56–1.3%) and the LBVS (maximum 5.4%) compared to a conventional screening library showed the utility of the library and the efficiency of LBVS. All the hit compounds possessed novel structures with Tanimoto similarity ≤ 0.26 to known Keap1/Nrf2 inhibitors and aqueous solubility (AlogP < 5). Reasonable binding modes were predicted using 3D alignment of five hit compounds and a Keap1/Nrf2 peptide crystal structure. Our results represent a new, efficient method combining the PPI library and LBVS to identify novel PPI inhibitory ligands with expanded chemical space.
Highlights
Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits
High-throughput screening (HTS) is commonly used to find active compounds in early drug discovery processes, but it has been shown that the rate of obtaining hit compounds is significantly low for HTS targeting PPIs using a chemical library composed of small-molecule compounds[5]
We conceived that a similar approach using ligand-based virtual screening (LBVS) could efficiently streamline the compounds in a PPI library to a limited number, to use in an experimental validation of the library for a specific PPI target, which leads to discovery of hit compounds at a lower cost
Summary
Protein–protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. SBVS involves docking of compounds into the target protein structure, whereas LBVS uses activity information of known ligands to create prediction models without requiring the knowledge of protein crystal structures Such an in silico approach is important in the rational design of PPI-modulating molecules. Melagraki et al.[12] combined SBVS and LBVS to discover novel small-molecule PPI inhibitors of tumor necrosis factor (TNF) and receptor activator of nuclear factor κB ligand (RANKL) They created a ligand-based model from 2,481 known TNF inhibitors using majority vote of outputs from three machine learning algorithms: k-nearest neighbor, nearest neighbor, and random forest (RF), and used it to analyze compounds shortlisted using SBVS of 14,400 commercial compounds. Inhibition of their PPI activates Nrf[2] and is a promising therapeutic target for diseases such as neurodegenerative disease, diabetes, liver disease, and sepsis
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