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Using tiered computational screening to discover small molecule inhibitors of the SARS-CoV-2 NSP3 protein Mac1 domain
* , *
1  Molecular Design Group, School of Chemical Sciences, Dublin City University, Glasnevin, Dublin, Ireland
2  DCU Life Sciences Institute, Dublin City University, Glasnevin, Dublin, Dublin, Ireland
3  School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, Pearse St, Dublin 2, D02 R59, Ireland
Academic Editor: Maria Emília Sousa

Abstract:

As new medications are used to treat COVID-19, many studies have reported that proteins such as spike, polymerase and proteases are prone to high levels of mutation that can create resistance to therapy over time. Thus, it becomes necessary to, not only target other viral proteins such as the non-structural proteins (NSP’s), but to also target the most conserved residues of these proteins. A synergistic combination of bioinformatics, computer-aided drug-design and in-vitro studies can feed into better understanding of SARS-CoV-2 (SC-2) and therefore help in the development of small molecule inhibitors against the NSP’s. As part of our initial anti-viral work, a pharmacophore study on NSP15 found a hit molecule (INS316) that made interactions with Ser293, Lys344 and Leu345 residues which are highly conserved across SC-2.

Our group was selected to enter an international challenge organized by CACHE to find inhibitors for the Mac1 domain of SC-2 NSP3. Our MSA alignment results of ~1 million NSP3 sequences indicated that the Mac1 domain is a highly conserved pocket that can be targeted for developing promising SC-2 inhibitors. We used a tiered screening workflow which included the use of volume/shape information of the binding pockets (fastROCS), use of in-house pharmacophore generation software (MoPBS/MOE) and performed docking in the binding pocket (FRED) to rank compounds for subsequent clustering and to identify hits that bind to these conserved pockets. The primary experimental validation results provided by CACHE found that two of our predicted hits show activity in HTRF and SPR assays.

Keywords: Small molecule inhibitors; SARS-CoV-2 NSP3 protein Mac1 domain;
Comments on this paper
زائد سلطان احمد عبدالله البريهي البريهي
This is very accurate information, and we have benefited greatly from it. Thank you for providing this important information Thank you both. Wishing you continued success and all the best.

Harveen Kaur
Strong rationale and workflow — highlighting conserved NSP targets and combining large-scale MSA with multi-tier screening nicely strengthens the case. Exciting that two predicted Mac1 hits validated experimentally in both HTRF and SPR.

Abdullah Farhan
This work presents a well-structured and scientifically sound approach to targeting the highly conserved Mac1 domain of SARS-CoV-2 NSP3. The integration of large-scale MSA, pharmacophore modeling, shape-based screening, and docking provides a robust workflow that significantly strengthens the rationale for identifying small-molecule inhibitors.
It is especially promising that two predicted hits demonstrated activity in both HTRF and SPR assays—clear evidence that the computational strategy successfully translated into experimental validation.
Excellent and impactful research. Wishing the team continued success in advancing antiviral discovery.

Hamzah Qaid
Valuable information and wonderful performance thank you .

Malhari Nagtilak
For the active hits identified by CACHE, did you observe consistent potency across orthogonal assays (HTRF vs. SPR), and how did you interpret discrepancies?

Josué Castro Yangali
Dear Dr Fayne, I thoroughly enjoyed your presentation on the discovery of inhibitors for the Mac1 domain of NSP3. As a student of Pharmacy and Biochemistry at the National University of Trujillo (Peru), I was impressed by the scale of your computational screening. In my bioinformatics group, we are working on the design of dual hybrid molecules, but seeing how your team managed to screen 1.4 billion Enamine compounds using fastROCS before refining with FRED has opened my eyes to new methodological possibilities. I found the strategy of using dummy atoms to define the volume of the conserved binding pocket brilliant.

In particular, your discussion of Compound 35 (KD 4.8 µM) caught my attention. I greatly appreciate your scientific transparency in mentioning solubility challenges during the compound selection phase; it is a problem that I also try to anticipate in my current designs using ADMET predictors.

I am a young researcher (23 years old) eager to master these CADD (computer-aided drug design) techniques on a large scale. Would it be possible to contact you to enquire about internship or master's degree opportunities in your DCU laboratory? My dream is to learn from pioneers like you in order to apply this technology to drug development in my country.

I greatly appreciate your time and inspiration.



 
 
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