Medicines

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

Since the coronavirus Disease (COVID-19) was first detected in Wuhan, China, over 238 million people have been infected with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), of which over 4.86 million have succumbed to. The impact of this global pandemic has not only devastated the worldwide health systems but has also caused significant economic impacts.

It is therefore essential to control the COVID-19 pandemic by accelerating the production of effective vaccinations and treatments against SARS-CoV-2.

To date, several effective vaccines have been produced against SARS-CoV-2, some of which include the Pfizer-BioNTech, Moderna, AstraZeneca, and Sinopharm vaccines. Some of these vaccines are based on the novel messenger ribonucleic acid (mRNA) technology, which functions by encoding the SARS-CoV-2 spike protein to induce an immune response.

As compared to traditional methods of drug/inhibitor design, which are costly and very time-consuming, newer computational drug/inhibitor design methods are highly efficient in predicting or identifying potential molecules for the treatment of diseases. Thus, computer-aided design methods could be utilized for rapidly designing vaccines or drug treatments against mutated SARS-CoV-2.

Recently, a team of researchers conducted a review in the International Journal of Antimicrobial Agents on diverse computational methods for drug/inhibitor design and enzymes as potential targets for inhibitors to treat coronaviruses diseases.

Study: A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Image Credit: bluesroad / Shutterstock.com

Computer-aided drug design (CADD) targeting SARS-CoV-2 Mpro

Mpro is the main protease of SARS-CoV-2 and functions as a key enzyme that mediates viral transcription and replication. Among all coronaviruses, the Mpro is highly conserved; therefore, previous antivirals that target this enzyme could potentially be effective against SARS-CoV-2. There have been several recent studies that utilized CADD to identify anti-SARS-CoV-2 agents against Mpro.

One example is the use of structure-based docking approaches to predict the inhibitory activity and help drug design against SARS-CoV-2 Mpro. Here, the authors analyzed potential drugs by molecular docking to determine the effects of certain antiviral drugs including chloroquine, ribavirin, remdesivir, and honeysuckle.

Deep learning-based approaches have also been used to predict potential inhibitors of SARS-CoV-2 Mpro. Utilizing a pre-trained deep learning drug-target interaction model called Molecule Transformer-Drug Target Interaction, some potential drugs against SARS-CoV-2 have been recognized.

To this end, dolutegravir, efavirenz, atazanavir, ritonavir, and remdesivir were found to exhibit inhibitory potency against the SARS-CoV-2 Mpro. Previous studies also found that drugs designed to target viral proteases including ritonavir, darunavir, and lopinavir, bound to the SARS-CoV-2 replication complex components.

CADD against targeting the structural proteins of SARS-CoV-2

SARS-CoV-2 consists of four primary structural proteins including the spike (S), membrane (M), nucleocapsid (N), and envelope (E) proteins. Due to the S protein mediating the process of viral entry into host cells, it is an attractive target for anti-viral COVID-19 treatment. The development of computational approaches has identified potential SARS-CoV-2 inhibitors that target the S protein.

The angiotensin-converting enzyme 2 (ACE2) receptor has been identified in previous studies as playing a key role in SARS-CoV-2 entry into host cells, which has highlighted this as another potential target for therapeutic intervention against COVID-19.

Another previous study investigated the abilities of certain drugs to block the binding of S protein to ACES2. It was found that certain compounds including griffithsin, peptide P6, EK1, and extracts from traditional Chinese medicine could fight against SARS-CoV-2 through their interactions with the ACE2 receptor, S protein.

The main aim of most computational approaches has been to focus on potential SARS-CoV-2 inhibitors which target the SARS-CoV-2 E, N, and M proteins, which could be utilized for further structure-based virtual screening, as well as other CADD vaccine and drug designs. Through the use of molecular dynamics simulations, several common compounds binding to both E and M proteins were identified as potential inhibitors of their functions.

Implications

Since the emergence of SARS-CoV-2, there has been a great effort into developing drugs and vaccines to combat this virus. The use of technologies such as CADD could help accelerate the process of drug and vaccine development. The current study analyzed the theory and application of these technologies from various research papers, as well as provided new findings of potential inhibitors as treatments for COVID-19.

However, the emergence of SARS-CoV-2 variants is a potential challenge for vaccine developers, as mutations in certain regions could limit the efficacy of previously developed vaccines. This study highlights potential targets for vaccines, inhibitors, or treatments for COVID-19 that may assist in the issue of mutated variants.

Journal references:
  • Liu, Q., Wan, J., & Wang, G. (2021). A survey on computational methods in discovering protein inhibitors of SARS-CoV-2. Briefings in Bioinformatics. doi:10.1093/bib/bbab416.
  • Yu, R., Chen, L., Lan, R., et al. (2020).  Computational screening of antagonists against the SARS-CoV-2 (COVID-19) coronavirus by molecular docking. International Journal of Antimicrobial Agents 56(2). doi:10.1016/j.ijantimicag.2020.106012.  

Content Source: https://www.news-medical.net/news/20211012/A-survey-on-computational-methods-in-discovering-protein-inhibitors-of-SARS-CoV-2.aspx

Gemma Wilson

Gemma is a journalism graduate with keen interest in covering business news – specifically startups. She has as a keen eye for technologies and has predicted quite a few successful startups over the last couple of years.

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