Computational Identification of New Inhibitors for 3C-Like Protease: A Potential Drug Target in Coronavirus

SARS CoV-2 is a single-strand RNA, positive sense and enveloped beta coronavirus that causes respiratory, nervous, hepatic, and human gastrointestinal diseases. Due to the presence of spike glycoprotein, it appears crown or corona-like. SARS-CoV-2 is more contagious and has caused more deaths and infections. Old-age people and immune-compromised patients face the greatest risk of death. The main protease (Mpro), also known as the 3CL pro, is responsible for virus replication. Because it differs from human proteases, the main protease of SARS-CoV-2 is considered to be an attractive target for drugs. The process of developing a new drug is difficult, time-consuming, expensive, and usually takes more than ten years and billions of dollars. On the other hand, computer-aided techniques have accelerated the process of drugs. The present study aims to identify new inhibitors for the Main protease. The South African natural compound database containing 1012 compounds was docked against the Main protease. The compound with a good docking binding energy score was simulated for a total of 100 ns, and the result was compared with the control compound. The compound SANC00361 had a binding energy score of -7.30 Kcal/mol. The compound SANC00361 was found to be more stable as compared to the control compound. The identified compound SANC00361 may possess the potential to inhibit the Mpro of SARS CoV-2 and can be helpful in treating SARS CoV-2-associated infections.

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Journal
Inkwell Innovations in Life Sciences
Published
2024-12-31
DOI
https://doi.org/10.63079/iils.01.01.033
Primary Topic
Computational Drug Discovery Methods
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article
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article

Computational Identification of New Inhibitors for 3C-Like Protease: A Potential Drug Target in Coronavirus

Amar Ajmal, Muhammad Numan, Syed Faisal Shah, Arif Ali et al.
1 links
Inkwell Innovations in Life Sciences
Computational Drug Discovery Methods
article

Computational Identification of New Inhibitors for 3C-Like Protease: A Potential Drug Target in Coronavirus

Amar Ajmal, Muhammad Numan, Syed Faisal Shah, Arif Ali, Iqra Akbar Iqra
article en

Abstract

SARS CoV-2 is a single-strand RNA, positive sense and enveloped beta coronavirus that causes respiratory, nervous, hepatic, and human gastrointestinal diseases. Due to the presence of spike glycoprotein, it appears crown or corona-like. SARS-CoV-2 is more contagious and has caused more deaths and infections. Old-age people and immune-compromised patients face the greatest risk of death. The main protease (Mpro), also known as the 3CL pro, is responsible for virus replication. Because it differs from human proteases, the main protease of SARS-CoV-2 is considered to be an attractive target for drugs. The process of developing a new drug is difficult, time-consuming, expensive, and usually takes more than ten years and billions of dollars. On the other hand, computer-aided techniques have accelerated the process of drugs. The present study aims to identify new inhibitors for the Main protease. The South African natural compound database containing 1012 compounds was docked against the Main protease. The compound with a good docking binding energy score was simulated for a total of 100 ns, and the result was compared with the control compound. The compound SANC00361 had a binding energy score of -7.30 Kcal/mol. The compound SANC00361 was found to be more stable as compared to the control compound. The identified compound SANC00361 may possess the potential to inhibit the Mpro of SARS CoV-2 and can be helpful in treating SARS CoV-2-associated infections.

Inkwell Innovations in Life SciencesVol. 1(1)
Master's College (US), Abdul Wali Khan University Mardan (PK), Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 69%
Computational Drug Discovery Methods
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Computational Identification of New Inhibitors for 3C-Like Protease: A Potential Drug Target in Coronavirus — Amar Ajmal, Muhammad Numan, et al. · Inkwell Innovations in Life Sciences (2024) | TGRS Research Map | TGRS