Popowicz, G. M. | Search bioRxiv

bioRxiv2021

Acriflavine, a clinically aproved drug, inhibits SARS-CoV-2 and other betacoronaviruses

SO_SCPLOWUMMARYC_SCPLOWThe COVID-19 pandemic caused by SARS-CoV-2 has been socially and economically devastating. Despite an unprecedented research effort, effective therapeutics are still missing to limit severe disease and mortality. Using high-throughput screening, we identified acriflavine as a potent papain-like protease (PLpro) inhibitor. NMR titrations and a co-crystal structure confirm that acriflavine blocks the PLpro catalytic pocket in an unexpected binding mode. We show that the drug inhibits viral replication at nanomolar concentration in cellular models, in vivo in mice and ex vivo in human airway epithelia, with broad range activity against SARS-CoV-2 and other betacoronaviruses. Considering that acriflavine is an inexpensive drug approved in some countries, it may be immediately tested in clinical trials and play an important role during the current pandemic and future outbreaks.

microbiology↗

bioRxiv2020

Deep learning model can predict water binding sites on the surface of proteins using limited-resolution data

The surfaces of proteins are generally hydrophilic but there have been reports of sites that exhibit an exceptionally high affinity for individual water molecules. Not only do such molecules often fulfil critical biological functions, but also, they may alter the binding of newly designed drugs. In crystal structures, sites consistently occupied in each unit cell yield electron density clouds that represent water molecule presence. These are recorded in virtually all high-resolution structures obtained through X-ray diffraction. In this work, we utilized the wealth of data from the RCSB Protein Data Bank to train a residual deep learning model named hotWater to identify sites on the surface of proteins that are most likely to bind water, the so-called water hot spots. The model can be used to score existing water molecules from a PDB file to provide their ranking according to the predicted binding strength or to scan the surface of a protein to determine the most likely water hot-spots de novo. This is computationally much more efficient than currently used molecular dynamics simulations. Based on testing the model on three example proteins, which have been resolved using both high-resolution X-ray crystallography (providing accurate positions of trapped waters) as well as low-resolution X-ray diffraction, NMR or CryoEM (where structure refinement does not yield water positions), we were able to show that the hotWater method is able to recover in the "water-free" structures many water binding sites known from the high-resolution structures. A blind test on a newly solved protein structure with waters removed from the PDB also showed good prediction of the crystal water positions. This was compared to two known algorithms that use electron density and was shown to have higher recall at resolutions >2.6 [A]. We also show that the algorithm can be applied to novel proteins such as the RNA polymerase complex from SARS-CoV-2, which could be of use in drug discovery. The hotWater model is freely available at (https://pypi.org/project/hotWater/).

bioinformatics↗