Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 µM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 µM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 μM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 μM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors. Graphical Image 1

Psychological reactions to COVID-19: Survey data assessing perceived susceptibility, distress, mindfulness, and preventive health behaviors.

The COVID-19 pandemic created a complex psychological environment for persons in America. A total of 450 USA MTurk workers completed measures of: (a) basic demographic characteristics; (b) health risk factors for COVID-19; (c) perceived susceptibility variables related to COVID-19; (d) COVID-19 preventive health behaviors; and (e) distress, physical symptoms, and quality of life measures. The surveys were completed between April 9, 2020 and April 18, 2020. This recruitment period corresponded to the first 2-3 weeks of lockdown in most of the USA. Follow-up surveys were completed by 151 of the USA participants between June 19, 2020 and July 11, 2020 (approximately 2 months after the first measurement). These data permit evaluation of relationships among demographic variables, COVID-19 stress and coping, COVID-19 preventive health behavior, and the role of mindfulness as a possible moderator of distress as well as a predictor of preventive health behavior. The availability of follow-up data permit longitudinal analyses that provide a stronger basis for causal inference.

A transferable deep learning approach to fast screen potential antiviral drugs against SARS-CoV-2.

The COVID-19 pandemic calls for rapid development of effective treatments. Although various drug repurpose approaches have been used to screen the FDA-approved drugs and drug candidates in clinical phases against SARS-CoV-2, the coronavirus that causes this disease, no magic bullets have been found until now. In this study, we used directed message passing neural network to first build a broad-spectrum anti-beta-coronavirus compound prediction model, which gave satisfactory predictions on newly reported active compounds against SARS-CoV-2. Then, we applied transfer learning to fine-tune the model with the recently reported anti-SARS-CoV-2 compounds and derived a SARS-CoV-2 specific prediction model COVIDVS-3. We used COVIDVS-3 to screen a large compound library with 4.9 million drug-like molecules from ZINC15 database and recommended a list of potential anti-SARS-CoV-2 compounds for further experimental testing. As a proof-of-concept, we experimentally tested seven high-scored compounds that also demonstrated good binding strength in docking studies against the 3C-like protease of SARS-CoV-2 and found one novel compound that can inhibit the enzyme. Our model is highly efficient and can be used to screen large compound databases with millions or more compounds to accelerate the drug discovery process for the treatment of COVID-19.

Reversed-engineered human alveolar lung-on-a-chip model.

Here, we present a physiologically relevant model of the human pulmonary alveoli. This alveolar lung-on-a-chip platform is composed of a three-dimensional porous hydrogel made of gelatin methacryloyl with an inverse opal structure, bonded to a compartmentalized polydimethylsiloxane chip. The inverse opal hydrogel structure features well-defined, interconnected pores with high similarity to human alveolar sacs. By populating the sacs with primary human alveolar epithelial cells, functional epithelial monolayers are readily formed. Cyclic strain is integrated into the device to allow biomimetic breathing events of the alveolar lung, which, in addition, makes it possible to investigate pathological effects such as those incurred by cigarette smoking and severe acute respiratory syndrome coronavirus 2 pseudoviral infection. Our study demonstrates a unique method for reconstitution of the functional human pulmonary alveoli in vitro, which is anticipated to pave the way for investigating relevant physiological and pathological events in the human distal lung.