In-Silico Lead Druggable Compounds Identification against SARS COVID-19 Main Protease Target from In-House, Chembridge and Zinc Databases by Structure-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations.
Mehreen GhufranMehran UllahHaider Ali KhanSabreen GhufranMuhammad AyazMuhammad SiddiqSyed Qamar AbbasSyed Shams Ul HassanSimona Gabriela BungauPublished in: Bioengineering (Basel, Switzerland) (2023)
Pharmacological strategies to lower the viral load among patients suffering from severe diseases were researched in great detail during the SARS-CoV-2 outbreak. The viral protease M pro (3CLpro) is necessary for viral replication and is among the main therapeutic targets proposed, thus far. To stop the pandemic from spreading, researchers are working to find more effective M pro inhibitors against SARS-CoV-2. The 33.8 kDa M pro protease of SARS-CoV-2, being a nonhuman homologue, has the possibility of being utilized as a therapeutic target against coronaviruses. To develop drug-like compounds capable of preventing the replication of SARS-main CoV-2's protease (M pro ), a computer-aided drug design (CADD) approach is extremely viable. Using MOE, structure-based virtual screening (SBVS) of in-house and commercial databases was carried out using SARS-CoV-2 proteins. The most promising hits obtained during virtual screening (VS) were put through molecular docking with the help of MOE. The virtual screening yielded 3/5 hits (in-house database) and 56/66 hits (commercial databases). Finally, 3/5 hits (in-house database), 3/5 hits (ZINC database), and 2/7 hits (ChemBridge database) were chosen as potent lead compounds using various scaffolds due to their considerable binding affinity with M pro protein. The outcomes of SBVS were then validated using an analysis based on molecular dynamics simulation (MDS). The complexes' stability was tested using MDS and post-MDS. The most promising candidates were found to exhibit a high capacity for fitting into the protein-binding pocket and interacting with the catalytic dyad. At least one of the scaffolds selected will possibly prove useful for future research. However, further scientific confirmation in the form of preclinical and clinical research is required before implementation.
Keyphrases
- sars cov
- molecular docking
- molecular dynamics simulations
- respiratory syndrome coronavirus
- adverse drug
- anti inflammatory
- binding protein
- primary care
- healthcare
- big data
- coronavirus disease
- metabolic syndrome
- emergency department
- protein protein
- type diabetes
- artificial intelligence
- tissue engineering
- small molecule
- oxide nanoparticles
- deep learning
- bone marrow
- transcription factor