In Silico Analysis of Inhibiting Papain-like Protease from SARS-CoV-2 by Using Plant-Derived Peptides

SARS-COV-2 Coronavirus Papain-like Protease PLpro as an Antiviral Target for Inhibitors of Active Site and Protein-Protein Interactions

The papain-like protease PLpro of the SARS-CoV-2 coronavirus is a multifunctional enzyme that catalyzes the proteolytic processing of two viral polyproteins, pp1a and pp1ab. PLpro also cleaves peptide bonds between host cell proteins and ubiquitin (or ubiquitin-like proteins), which is associated with a violation of immune processes. Nine structures of the most effective inhibitors of the PLpro active center were prioritized according to the parameters of biochemical (IC 50) and cellular tests to assess the suppression of viral replication (EC 50) and cytotoxicity (CC 50). A literature search has shown that PLpro can interact with at least 60 potential protein partners in cells, 23 of which are targets for other viral proteins (human papillomavirus and Epstein-Barr virus). The analysis of protein-protein interactions showed that the proteins USP3, UBE2J1, RCHY1, and FAF2 involved in deubiquitinylation and ubiquitinylation processes contain the largest number of bonds with other proteins; the interaction of viral proteins with them can affect the architecture of the entire network of protein-protein interactions. Using the example of a spatial model of the PLpro/ubiquitin complex and a set of 154 naturally occurring compounds with known antiviral activity, 13 compounds (molecular masses in the range of 454-954 Da) were predicted as potential PLpro inhibitors. These compounds bind to the "hot" amino acid residues of the protease at the positions Gly163, Asp164, Arg166, Glu167, and Tyr264 involved in the interaction with ubiquitin. Thus, pharmacological effects on peripheral PLpro sites, which play important roles in binding protein substrates, may be an additional target-oriented antiviral strategy.

Virtual screening based on the structure of more than 105 compounds against four key proteins of SARS-CoV-2: MPro, SRBD, RdRp, and PLpro

Background

SARS-CoV-2 initially originated in Wuhan (China) around December 2019, and spread all over the world. Currently, WHO (Word Health Organization) has licensed several vaccines for this viral infection. However, not everyone can be vaccinated. People with underlying health conditions that weaken their immune systems or those with severe allergies to some vaccine components, may not be able to be vaccinated. Moreover, no vaccination is 100% safe, and the emergence of new SARS-CoV-2 mutations may reduce the efficacy of immunizations. Therefore, it is urgent to develop effective drugs to protect people against this virus.

Material and method

We performed structure-based virtual screening (SBVS) of a library that was built from ChemDiv and PubChem databases against four SARS-CoV-2 target proteins: S-protein (spike), main protease (M_Pro), RNA-dependent RNA polymerase, and PL_pro. A virtual screening study was performed using PyRx and AutoDock tools.

Results

Our results suggest that twenty-five top-ranked drugs with the highest energy binding as the potential inhibitors against four SARS-CoV-2 targets, relative to the reference molecules. Based on the energy binding, we suggest that these compounds could be used to produce effective anti-viral drugs against SARS-CoV-2.

Conclusion

The discovery of novel compounds for COVID-19 using computer-aided drug discovery tools requires knowledge of the structure of coronavirus and various target proteins of the virus. These compounds should be further assessed in experimental assays and clinical trials to validate their actual activity against the disease. These findings may contribute to the drug design studies against COVID-19.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.