Prioritisation of compounds for 3CL$^{pro}$ inhibitor development on SARS-CoV-2 variants

Jukič, Marko (author), Škrlj, Blaž (author), Tomšič, Gašper (author), Pleško, Sebastian (author), Podlipnik, Črtomir (author), Bren, Urban (author)

COVID-19 represents a new potentially life-threatening illness caused by severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2 pathogen. In 2021, new variants of the virus with multiple key mutations have emerged, such as B.1.1.7, B.1.351, P.1 and B.1.617, and are threatening to render available vaccines or potential drugs ineffective. In this regard, we highlight 3CL$^{pro}$, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CL$^{pro}$ could therefore provide trans-variant effectiveness that is supported by structural studies and possesses readily available biological evaluation experiments. With this in mind, we performed a high throughput virtual screening experiment using CmDock and the “In-Stock” chemical library to prepare prioritisation lists of compounds for further studies. We coupled the virtual screening experiment to a machine learning-supported classification and activity regression study to bring maximal enrichment and available structural data on known 3CL$^{pro}$ inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CL$^{pro}$ inhibitor, viral cysteine protease or remaining chemical space based on the calculated set of 208 chemical descriptors. Last but not least, we analysed if the current set of 3CL$^{pro}$ inhibitors could be used in activity prediction and observed that the field of 3CL$^{pro}$ inhibitors is drastically underrepresented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CL$^{pro}$ inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial “corona focused libraries”.

• Language: English
• Keywords: COVID-19, SARS-CoV-2, free-energy calculations, in silico drug design, virtual screening, inhibitors, 3C-like protease, M$^{pro}$, 3CL$^{pro}$, high-throughput, chemical library design, machine learning, compound prioritisation
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FKKT - Faculty of Chemistry and Chemical Technology
• Publication status: Published
• Publication version: Version of Record
• Year: 2021
• Number of pages: 14 str.
• Numbering: Vol. 26, iss. 10, art. 3003
• PID: 20.500.12556/RUL-135301
• UDC: 578.834
• ISSN on article: 1420-3049
• DOI: 10.3390/molecules26103003
• COBISS.SI-ID: 63953411
• Publication date in RUL: 07.03.2022

Record is a part of a journal

• Title: Molecules
• Shortened title: Molecules
• Publisher: MDPI
• ISSN: 1420-3049
• COBISS.SI-ID: 18462981

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 18.05.2021

Secondary language

• Language: Slovenian
• Keywords: izračunavanja, inhibitorji, pokazatelji, proteaze, koronavirusi