Design of Specific Primer Sets for the Detection of B.1.1.7, B.1.351 and P.1 SARS-CoV-2 Variants using Deep Learning (preprint)

As the COVID-19 pandemic persists, new SARS-CoV-2 variants with potentially dangerous features have been identified by the scientific community. Variant B.1.1.7 lineage clade GR from Global Initiative on Sharing All Influenza Data (GISAID) was first detected in the UK, and it appears to possess an increased transmissibility. At the same time, South African authorities reported variant B.1.351, that shares several mutations with B.1.1.7, and might also present high transmissibility. Even more recently, a variant labeled P.1 with 17 non-synonymous mutations was detected in Brazil. In such a situation, it is paramount to rapidly develop specific molecular tests to uniquely identify, contain, and study new variants. Using a completely automated pipeline built around deep learning techniques, we design primer sets specific to variant B.1.1.7, B.1.351, and P.1, respectively. Starting from sequences openly available in the GISAID repository, our pipeline was able to deliver the primer sets in just under 16 hours for each case study. In-silico tests show that the sequences in the primer sets present high accuracy and do not appear in samples from different viruses, nor in other coronaviruses or SARS-CoV-2 variants. The presented methodology can be exploited to swiftly obtain primer sets for each independent new variant, that can later be a part of a multiplexed approach for the initial diagnosis of COVID-19 patients. Furthermore, since our approach delivers primers able to differentiate between variants, it can be used as a second step of a diagnosis in cases already positive to COVID-19, to identify individuals carrying variants with potentially threatening features.

Personalized Virus Load Curves of SARS-CoV-2 Infection (preprint)

We introduce an explicit function that describes virus-load curves on a patient-specific level. This function is based on simple and intuitive model parameters. It allows virus load analysis without solving a full virus load dynamic model. We validate our model on data from influenza A as well as SARS-CoV-2 infection data for Macaque monkeys and humans. Further, we compare the virus load function to an established target model of virus dynamics, which shows an excellent fit. Our virus-load function offers a new way to analyse patient virus load data, and it can be used as input to higher level models for the physiological effects of a virus infection, for models of tissue damage, and to estimate patient risks.

High-throughput screening of the ReFRAME, Pandemic Box, and COVID Box drug repurposing libraries against SARS-CoV2 nsp15 endoribonuclease to identify small-molecule inhibitors of viral activity. (preprint)

SARS-CoV-2 has caused a global pandemic, and has taken over 1.7 million lives as of mid-December, 2020. Although great progress has been made in the development of effective countermeasures, with several pharmaceutical companies approved or poised to deliver vaccines to market, there is still an unmet need of essential antiviral drugs with therapeutic impact for the treatment of moderate-to-severe COVID-19. Towards this goal, a high-throughput assay was used to screen SARS-CoV-2 nsp15 uracil-dependent endonuclease (endoU) function against 13 thousand compounds from drug and lead repurposing compound libraries. While over 80% of initial hit compounds were pan-assay inhibitory compounds, three hits were confirmed as nsp15 endoU inhibitors in the 1-20 M range in vitro. Furthermore, Exebryl-1, a {beta}-amyloid anti-aggregation molecule for Alzheimers therapy, was shown to have antiviral activity between 10 to 66 M, in VERO, Caco-2, and Calu-3 cells. Although the inhibitory concentrations determined for Exebryl-1 exceed those recommended for therapeutic intervention, our findings show great promise for further optimization of Exebryl-1 as an nsp15 endoU inhibitor and as a SARS-CoV-2 antiviral. Author summaryDrugs to treat COVID-19 are urgently needed. To address this, we searched libraries of drugs and drug-like molecules for inhibitors of an essential enzyme of the virus that causes COVID-19, SARS-CoV-2 nonstructural protein (nsp)15. We found several molecules that inhibited the nsp15 enzyme function and one was shown to be active in inhibiting the SARS-CoV-2 virus. This demonstrates that searching for SARS-CoV-2 nsp15 inhibitors can lead inhibitors of SARS-CoV-2, and thus therapeutics for COVID-19. We are currently working to see if these inhibitors could be turned into a drug to treat COVID-19.