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Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline.

ABSTRACT: A rapid response is necessary to contain emergent biological outbreaks before they can become pandemics. The novel coronavirus (SARS-CoV-2) that causes COVID-19 was first reported in December of 2019 in Wuhan, China and reached most corners of the globe in less than two months. In just over a year since the initial infections, COVID-19 infected almost 100 million people worldwide. Although similar to SARS-CoV and MERS-CoV, SARS-CoV-2 has resisted treatments that are effective against other coronaviruses. Crystal structures of two SARS-CoV-2 proteins, spike protein and main protease, have been reported and can serve as targets for studies in neutralizing this threat. We have employed molecular docking, molecular dynamics simulations, and machine learning to identify from a library of 26 million molecules possible candidate compounds that may attenuate or neutralize the effects of this virus. The viability of selected candidate compounds against SARS-CoV-2 was determined experimentally by biolayer interferometry and FRET-based activity protein assays along with virus-based assays. In the pseudovirus assay, imatinib and lapatinib had IC₅₀ values below 10 μM, while candesartan cilexetil had an IC₅₀ value of approximately 67 µM against M^pro in a FRET-based activity assay. Comparatively, candesartan cilexetil had the highest selectivity index of all compounds tested as its half-maximal cytotoxicity concentration 50 (CC₅₀) value was the only one greater than the limit of the assay (>100 μM).

SUBMITTER: Lau EY

PROVIDER: S-EPMC8315004 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250,000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven promising candidates after experimentally validating only 40 compounds. Notably, we discovered NSC620333, a compound that exhibits a strong binding affinity to nsp14 with a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. We identified new conformational states of the protein and determined that residues Phe367, Tyr368, and Gln354 within the binding pocket serve as stabilizing residues for novel ligand interactions. We also found that metal coordination complexes are crucial for the overall function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC50 value of 70 ± 6 nM). Our computational pipeline accurately predicted the binding pose of SS148, demonstrating its effectiveness and potential in accelerating drug discovery efforts against SARS-CoV-2 and other emerging viruses.

Project description:Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic, and steric features that characterize small-molecule inhibitors binding stably to 3CLpro and by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics (MD) simulation, machine learning, and in vitro experimental validation analyses, which have led to the identification of small-molecule inhibitors of 3CLpro with micromolar activity and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with the available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 μM) and synthetic compounds previously not characterized (e.g., compound CID 46897844, IC50 = 31 μM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, MD simulations, and machine learning can facilitate 3CLpro-targeted small-molecule screening investigations. Different receptor-, ligand-, and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of the identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small-molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.

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Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline.

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