Akdemir, A. | Search bioRxiv

bioRxiv2020

The neutralization effect of Montelukast on SARS-CoV-2 is shown by multiscale in silico simulations and combined in vitro studies

Small molecule inhibitors have previously been investigated in different studies as possible therapeutics in the treatment of SARS-CoV-2. In the current drug repurposing study, we identified the leukotriene (D4) receptor antagonist Montelukast as a novel agent that simultaneously targets two important drug targets of SARS-CoV-2. We initially demonstrated the dual inhibition profile of Montelukast through multiscale molecular modeling studies. Next, we characterized its effect on both targets by different in vitro experiments including the Fluorescent Resonance Energy Transfer (FRET)-based main protease enzyme inhibition assay, surface plasmon resonance (SPR) spectroscopy, pseudovirus neutralization on HEK293T / hACE2, and virus neutralization assay using xCELLigence MP real time cell analyzer. Our integrated in silico and in vitro results confirmed the dual potential effect of the Montelukast both on virus entry into the host cell (Spike/ACE2) and on the main protease enzyme inhibition. The virus neutralization assay results showed that while no cytotoxicity of the Montelukast was observed at 12 M concentration, the cell index time 50 (CIT50) value was delayed for 12 hours. Moreover, it was also shown that Favipiravir, a well-known antiviral used in COVID-19 therapy, should be used by 16-fold higher concentrations than Montelukast in order to have the same effect of Montelukast. The rapid use of new small molecules in the pandemic is very important today. Montelukast, whose pharmacokinetic and pharmacodynamic properties are very well characterized and has been widely used in the treatment of asthma since 1998, should urgently be completed in clinical phase studies and if its effect is proven in clinical phase studies, it should be used against COVID-19.

biophysics↗

bioRxiv2019

Nanopore base-calling from a perspective of instance segmentation

BackgroundNanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real time. Through detecting the change of ion currency signals during a DNA/RNA fragments pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore base-calling has a higher error rate than short-read base-calling. Through utilizing deep neural networks, the-state-of-the art nanopore base-callers achieve base-calling accuracy in a range from 85% to 95%.\n\nResultIn this work, we proposed a novel base-calling approach from a perspective of instance segmentation. Different from the previous sequence labeling approaches, we formulated the base-calling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed base-caller URnano achieves competitive results compared to recently proposed CTC-featured base-caller Chiron, on the same amount of training and test data for in-domain evaluation. Our results show that formulating the base-calling problem as a one-dimensional segmentation task is a promising approach.\n\nAvailabilityThe source code and data are available at https://github.com/yaozhong/URnano\n\nContactyaozhong@ims.u-tokyo.ac.jp\n\nSupplementary informationSupplementary data are available at attachment online.

bioinformatics↗