Computational Simulations Identified Marine-Derived Natural Bioactive Compounds as Replication Inhibitors of SARS-CoV-2

Abstract

The rapid spread of COVID-19, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide health emergency. Unfortunately, to date, a very small number of remedies have been to be found effective against SARS-CoV-2 infection. Therefore, further research is required to achieve a lasting solution against this deadly disease. Repurposing available drugs and evaluating natural product inhibitors against target proteins of SARS-CoV-2 could be an effective approach to accelerate drug discovery and development. With this strategy in mind, we derived Marine Natural Products (MNP)-based drug-like small molecules and evaluated them against three major target proteins of the SARS-CoV-2 virus replication cycle. A drug-like database from MNP library was generated using Lipinski’s rule of five and ADMET descriptors. A total of 2,033 compounds were obtained and were subsequently subjected to molecular docking with 3CLpro, PLpro, and RdRp. The docking analyses revealed that a total of 14 compounds displayed better docking scores than the reference compounds and have significant molecular interactions with the active site residues of SARS-CoV-2 virus targeted proteins. Furthermore, the stability of docking-derived complexes was analyzed using molecular dynamics simulations and binding free energy calculations. The analyses revealed two hit compounds against each targeted protein displaying stable behavior, binding affinity, and molecular interactions. Our investigation identified two hit compounds against each targeted proteins displaying stable behavior, higher binding affinity and key residual molecular interactions, with good in silico pharmacokinetic properties, therefore can be considered for further in vitro studies.

Potential Inhibitors Targeting Papain-Like Protease of SARS-CoV-2: Two Birds With One Stone

“…Compared with drug repurposing, screenings for hit compounds, either in vitro or in silico , incorporate a much larger scale of molecules including microbial metabolites, natural products and marine-derived bio-active compounds ( Cragg and Newman, 2013 ; Kumar et al, 2021 ; Quimque et al, 2021 ). Since the outbreak of COVID-19, the widespread use of combinatorial screening has revealed a large mount of hit compounds that specifically target SARS-CoV-2 PLpro ( Amin et al, 2021 ; Gogoi et al, 2021 ; Goyzueta-Mamani et al, 2021 ; Hajbabaie et al, 2021 ; Jade et al, 2021 ; Jamalan et al, 2021 ; Kumar et al, 2021 ; Li et al, 2021 ; Quimque et al, 2021 ; Rahul and Sarkar, 2021 ; Rudrapal et al, 2021 ; Stasiulewicz et al, 2021 ). Hits generated through the initial screens are further validated with an aim to choose the best ones to serve as leads for drug developments.…”

Identification of CDK7 Inhibitors from Natural Sources Using Pharmacoinformatics and Molecular Dynamics Simulations

“…To reduce the cost and time of the screening process, we first filtered the compound libraries based on their Lipinski’s Ro5 and ADMET properties ( Figure 5 and Table S3 ), as suggested in previous reports [ 43 , 44 ]. Ro5 states that compounds have drug-like characteristics when the AlogP value ≤ 5, while HBD and HBA numbers are ≤5 and 10, respectively.…”

“…To study the real-time dynamics, we employed MD simulation studies. The selected protein–ligand complexes were prepared and subjected to MD simulations [ 43 , 44 , 59 ]. For comparative study, the known inhibitors THZ1 and CT7001 were also simulated.…”

“…Four natural compound libraries (ZINC, SuperNatural2, ExiMed, and InterBioScreen) were considered in the present study. To retrieve the drug-like small-molecule compounds from these libraries, it is recommended to filter the huge number of compounds during early steps to save time and unnecessary effort at a later stage in drug discovery [ 43 , 44 ]. Therefore, the libraries were filtered at first with Lipinski’s rule of five (Ro5) and then for their ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties in DS [ 45 , 46 ].…”

Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

“…Consequently, the compounds acquired were docked with the Hpse model by allowing for generation of 10 conformers per ligand. The obtained conformations were clustered to achieve the largest cluster, from which the compounds were evaluated on the basis of two scoring criterions [ 73 ]—Goldscore (high) and Chemscore (low) [ 74 ]—as well as the molecular interactions with the Hpse catalytic site residues (Asp62, Asn64, Thr97, Glu225, Asn227, Lys231, Gln270, Arg272, Glu343, Gly349, Gly350, Ala388 and Tyr391).…”