Prioritisation of Compounds for 3CLpro Inhibitor Development on SARS-CoV-2 Variants.

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

doi:10.3390/molecules26103003

Abstract

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 3CLpro, the main viral protease, as a valuable therapeutic target that possesses no mutations in the described pandemically relevant variants. 3CLpro 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 3CLpro inhibitors to the prepared focused libraries. All virtual screening hits are classified according to 3CLpro 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 3CLpro inhibitors could be used in activity prediction and observed that the field of 3CLpro inhibitors is drastically under-represented compared to the chemical space of viral cysteine protease inhibitors. We postulate that this methodology of 3CLpro inhibitor library preparation and compound prioritisation far surpass the selection of compounds from available commercial “corona focused libraries”.

Highlights

Coronavirus disease (COVID-19) is an infectious disease caused by a novel severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2
We presented a novel in silico approach towards compound prioritisation in the design of novel 3CLpro inhibitors
We coupled the method to a machine learning classification experiment where each compound is classified into the chemical space of 3CLpro inhibitors, into general viral cysteine protease inhibitors, or into a completely novel unrelated chemical space

Summary

Introduction

Coronavirus disease (COVID-19) is an infectious disease caused by a novel severe acute respiratory syndrome coronavirus 2 or SARS-CoV-2. COVID-19 was initially reported in Wuhan province in China and was declared as a global pandemic [1]. COVID-19 is a severe illness similar to flu, with major symptoms being cough, fever and breathing difficulty. The illness can cause systemic inflammation [2,3]. The pathogen SARS-CoV-2 belongs to the Coronaviridae family, an enveloped positive-sense single-stranded (+ssRNA) RNA virus, and is closely related to the previously described SARS-CoV and MERS-CoV coronaviruses [4]. The SARS-CoV-2 genome shares 82% sequence identity with SARS-CoV and 90% identity with MERS-CoV and shares common pathogenesis mechanisms [5]

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Results

Conclusion