Privileged Substructures to Modulate Protein–Protein Interactions

Abstract

Given the difficulties to identify chemical probes that can modulate protein-protein interactions (PPIs), actors in the field have started to agree on the necessity to use PPI-tailored screening chemical collections. However, which type of scaffolds may promote the binding of compounds to PPI targets remains unclear. In this big data analysis, we have identified a list of privileged chemical substructures that are most often observed within inhibitors of PPIs. Using molecular frameworks as a way to perceive chemical substructures with the combination of an experimental and a machine-learning based predicted data set of iPPI compounds, we propose a list of privileged substructures in the form of scaffolds and chemical moieties that can be substantially chemically functionalized and do not present any toxicophore nor pan-assay interference (PAINS) alerts. We think that such chemical guidance will be valuable for medicinal chemists in their attempt to identify initial quality chemical probes on PPI targets.

Small molecule stabilization of non-native protein-protein interactions of SARS-CoV-2 N protein as a mechanism of action against COVID-19

“…This is particularly interesting since polyphenolic compounds are phytochemicals widely spread throughout plants and fruits, and are known to have medicinal and chemopreventive activities, for example they are good natural antioxidants. Moreover, the flavonoid skeleton has been reported as a privileged substructure for PPIs modulation (Bosc et al, 2017). Interestingly, a previous study has reported that some polyphenols inhibit nucleocapsid phosphoprotein's function in SARS-CoV (Roh, 2012), which has a 90.52% of identity with SARS-CoV-2 analog ( Figure S2).…”

Fr-PPIChem: An Academic Compound Library Dedicated to Protein–Protein Interactions

“…The latter descriptors require to have the active conformation of each molecule. In the absence of such information for the vast majority of the molecules used in this study, up to 50 conformations per molecule were generated using a stochastic method and the MMFF94x force field as described previously 20 . The 3D descriptors were calculated on all the conformations for a given compound and the mean value for each descriptor was kept as the final value.…”

“…Analysis of the compounds present in these databases revealed that, on average, iPPIs are heavier, more hydrophobic, contain more aromatic rings and have different 3D shapes than conventional drugs [14][15][16][17] . These characteristics have been used to build statistical models capable of filtering chemical databases to search for putative iPPIs 14,15,[18][19][20] or to extract PPI-specific chemical information.…”

Exploring the chemical space of protein–protein interaction inhibitors through machine learning

“…Over recent decades, many PPI inhibitors have been screened for their action against PPI-related targets and have achieved considerable clinical success in the treatment of autoimmune diseases and cancer 20 , 21 . Moreover, the physicochemical and pocket properties of PPI inhibitors have been identified through PPI-specific database analysis; studies have suggested that PPI target classes with matching regions in both chemical and target spaces could facilitate the development of iPPIs to the stage of drug candidates 1 , 22 , 23 .…”