Abstract
The rapid spread of the coronavirus disease 2019 (COVID-19) has led to the development of therapeutic inhibitor drug of SARS-CoV-2, which can inhibit the viral enzyme RNA-dependent-RNA-polymerase (RdRp), thereby preventing the replication, transcription, and synthesis of RNA virus in the host cells. Previous in-vitro studies revealed that Andrographis paniculata has the potential to inhibit the virus. Therefore, this study aims to isolate the specific compounds of Andrographis paniculata, which play a role in inhibiting SARS-CoV-2 RdRp using molecular docking. A total of 19 compounds were identified in previous literature studies, while remdesivir and favipiravir were used as the positive control. All compounds and proteins were applied to minimize and optimize energy. Furthermore, the docking method was carried out using Autodock 4.2.6 software with a specific grid box containing the active site of RdRp (ID: 6M71), and the Lamarckian Genetic Algorithm was used to determine the conformation. The best docking was screened on ADMET prediction and the binding energy was evaluated. There are 18 compounds of Andrographis paniculata including the top three, namely andrographolactone (∆G = -8.86 kcal/mol), andrographolide (∆G = -7.74 kcal/mol), and andrographidine-A (∆G = -7.68 kcal/mol), which showed the strongest binding affinity to the SARS-CoV-2 RdRp protein compared to other compounds and the positive control remdesivir (∆G = -5.73 kcal/mol) and favipiravir (∆G = -5.20 kcal/mol). Furthermore, active amino acids play a role in this interaction by forming strong hydrogen bonds, such as TYR 619, LYS 621, ASP 760, and ASP 623. Andrographolactone has the highest potential as SARS-CoV-2 RdRp inhibitor, hence, it can be used as a novel therapeutic candidate.
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Recommended Citation
Ahsana, Dina; Pratama, Rizki Rahmadi; Meily, Alfisyahriatunnida; and Andika, Andika
(2022)
"Discovery of SARS-CoV-2 RNA-dependent-RNA-polymerase (RdRp) Inhibitor from Sambiloto (Andrographis paniculata) Based on Molecular Docking and ADMET Prediction Approach,"
Pharmaceutical Sciences and Research: Vol. 9:
No.
2, Article 3.
DOI: 10.7454/psr.v9i2.1236
Available at:
https://scholarhub.ui.ac.id/psr/vol9/iss2/3
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