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Prioritisation of Compounds for 3CL^pro Inhibitor Development on SARS-CoV-2 Variants.

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 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 under-represented 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".

SUBMITTER: Jukic M

PROVIDER: S-EPMC8158358 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The main protease, Mpro, of SARS-CoV-2 is required to cleave the viral polyprotein into precise functional units for virus replication and pathogenesis. Here, we report quantitative reporters for Mpro function in living cells in which protease inhibition by genetic or chemical methods results in robust signal readouts by fluorescence (enhanced green fluorescent protein [eGFP]) or bioluminescence (firefly luciferase). These gain-of-signal systems are scalable to high-throughput platforms for quantitative discrimination between Mpro mutants and/or inhibitor potencies as evidenced by validation of several reported inhibitors. Additional utility is shown by single Mpro amino acid variants and structural information combining to demonstrate that both inhibitor conformational dynamics and amino acid differences are able to influence inhibitor potency. We further show that a recent variant of concern (Omicron) has an unchanged response to a clinically approved drug, nirmatrelvir, whereas proteases from divergent coronavirus species show differential susceptibility. Together, we demonstrate that these gain-of-signal systems serve as robust, facile, and scalable assays for live cell quantification of Mpro inhibition, which will help expedite the development of next-generation antivirals and enable the rapid testing of emerging variants. IMPORTANCE The main protease, Mpro, of SARS-CoV-2 is an essential viral protein required for the earliest steps of infection. It is therefore an attractive target for antiviral drug development. Here, we report the development and implementation of two complementary cell-based systems for quantification of Mpro inhibition by genetic or chemical approaches. The first is fluorescence based (eGFP), and the second is luminescence based (firefly luciferase). Importantly, both systems rely upon gain-of-signal readouts such that stronger inhibitors yield higher fluorescent or luminescent signal. The high versatility and utility of these systems are demonstrated by characterizing Mpro mutants and natural variants, including Omicron, as well as a panel of existing inhibitors. These systems rapidly, safely, and sensitively identify Mpro variants with altered susceptibilities to inhibition, triage-nonspecific, or off-target molecules and validate bona fide inhibitors, with the most potent thus far being the first-in-class drug nirmatrelvir.

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Prioritisation of Compounds for 3CL^pro Inhibitor Development on SARS-CoV-2 Variants.

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OmicsDI is part of the ELIXIR infrastructure

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Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Prioritisation of Compounds for 3CL^pro Inhibitor Development on SARS-CoV-2 Variants.