Version 1
: Received: 22 April 2022 / Approved: 26 April 2022 / Online: 26 April 2022 (03:39:06 CEST)
How to cite:
Mangione, W.; Falls, Z.; Samudrala, R. Optimal COVID-19 Therapeutic Candidate Discovery Using the CANDO Platform. Preprints2022, 2022040224. https://doi.org/10.20944/preprints202204.0224.v1
Mangione, W.; Falls, Z.; Samudrala, R. Optimal COVID-19 Therapeutic Candidate Discovery Using the CANDO Platform. Preprints 2022, 2022040224. https://doi.org/10.20944/preprints202204.0224.v1
Mangione, W.; Falls, Z.; Samudrala, R. Optimal COVID-19 Therapeutic Candidate Discovery Using the CANDO Platform. Preprints2022, 2022040224. https://doi.org/10.20944/preprints202204.0224.v1
APA Style
Mangione, W., Falls, Z., & Samudrala, R. (2022). Optimal COVID-19 Therapeutic Candidate Discovery Using the CANDO Platform. Preprints. https://doi.org/10.20944/preprints202204.0224.v1
Chicago/Turabian Style
Mangione, W., Zackary Falls and Ram Samudrala. 2022 "Optimal COVID-19 Therapeutic Candidate Discovery Using the CANDO Platform" Preprints. https://doi.org/10.20944/preprints202204.0224.v1
Abstract
The worldwide outbreak of SARS-CoV-2 in early 2020 caused numer- ous deaths and unprecedented measures to control its spread. We employed our Computational Analysis of Novel Drug Opportunities (CANDO) multiscale therapeutic discovery, repurposing, and design platform to identify small molecule inhibitors of the virus to treat its resulting indication, COVID-19. Initially, few experimental studies existed on SARS-CoV-2, so we optimized our drug candidate prediction pipelines using results from two independent high-throughput screens against prevalent human coronaviruses. Ranked lists of candidate drugs were generated using our open source cando.py software based on viral protein inhibition and proteomic interaction similarity. For the former viral protein inhibition pipeline, we computed interaction scores between all compounds in the corresponding candidate library and eighteen SARS-CoV proteins using an interaction scoring protocol with extensive parameter optimization which was then applied to the SARS-CoV-2 proteome for prediction. For the latter similarity based pipeline, we computed interaction scores between all compounds and human protein structures in our libraries then used a consensus scoring approach to identify candidates with highly similar proteomic interaction signatures to multiple known anti-coronavirus actives. We published our ranked candidate lists at the very beginning of the COVID-19 pandemic. Since then, 51 of our 276 predictions have demonstrated anti-SARS-CoV-2 activity in published clinical and experimental studies. These results illustrate the ability our platform to rapidly respond to emergent pathogens and provide greater evidence that treating compounds in a multitarget context more accurately describes their behavior in biological systems.
COVID-19; SARS-CoV-2; drug discovery; multitargeting; computational drug repurposing
Subject
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
