Marine derivatives prevent wMUS81 in silico studies

Identification of small-molecule inhibitors of human MUS81-EME1/2 by FRET-based high-throughput screening

The MUS81-EME1/2 structure-specific endonucleases play a crucial role in the processing of stalled replication forks and recombination intermediates, and have been recognized as an attractive drug target to potentiate the anti-cancer efficacy of DNA-damaging agents. Currently, no bioactive small-molecule inhibitors of MUS81 are available. Here, we performed a high-throughput small-molecule inhibitors screening, using the FRET-based DNA cleavage assay. From 7920 compounds, we identified dyngo-4a as a potent inhibitor of MUS81 complexes. Dyngo-4a effectively inhibits the endonuclease activities of both MUS81-EME1 and MUS81-EME2 complexes, with IC₅₀ values of 0.57 μM and 2.90 μM, respectively. Surface plasmon resonance (SPR) and electrophoretic mobility shift assay (EMSA) assays reveal that dyngo-4a directly binds to MUS81 complexes (K_D ∼ 0.61 μM) and prevents them from binding to DNA substrates. In HeLa cells, dyngo-4a significantly suppresses bleomycin-triggered H2AX serine 139 phosphorylation (γH2AX). Together, our results demonstrate that dyngo-4a is a potent MUS81 inhibitor, which could be further developed as a potentially valuable chemical tool to explore more physiological roles of MUS81 in the cells.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.