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In silico virtual screening, characterization, docking and molecular dynamics studies of crucial SARS-CoV-2 proteins.

ABSTRACT: The ongoing pandemic COVID-19 (COrona Virus Immuno Deficiency-2019) which is caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome-CoronaVirus-2) has emerged as a pandemic with 400,000 plus deaths till date. We do not have any drug or vaccine available for the inhibition of this deadly virus. The expedition for searching a potential drug or vaccine against COVID-19 will be of massive potential and favor. This study is focused on finding an effective natural origin compound which can put a check on the activity of this virus. We chose important proteins from the SARS-CoV-2 genome such as NSP4, NSP15 and RdRp along-with the human ACE2 receptor which is the first point of contact with the virus. Virtual screening and followed up molecular docking resulted in Baicalin and Limonin as the final lead molecules. 200 ns of MD simulation for each protein-ligand complex provides the insights that Baicalin has a potential to target NSP4, NSP15 and RdRp proteins. Limonin which is largely used in traditional Indian medicine system is found to inhibit the human ACE2 receptor (making it inefficient in binding to the receptor binding domain of SARS-CoV-2). Our studies propose Baicalin and Limonin in combination to be studied in vitro and in vivo against COVID-19. Communicated by Ramaswamy H. Sarma.

SUBMITTER: Alazmi M

PROVIDER: S-EPMC7484580 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background:In experimental therapy of cancer, survivin is considered to be one of the well-established targets. Studies have found that it is overexpression in most of the human tumors, but it is rarely found in normal tissues. It is having varied structural and functional role. It controls cell division and cellular stress response and also regulates metastasis and migration of cancerous cells. It has also been recognized as a biomarker which makes it unconventional drug target. In spite of being one of the centrally active components in metastasis and invasion, their clinical use is minimal. To increase the therapeutic efficiency of cancer and its various stages, it is important to survey novel reagents targeting the pathways and mechanism involving survivin. Objective:The aim of this study was to identify novel survivin inhibitor candidates using in silico screening. Materials and Methods:In this course of work, virtual screening on a dataset of natural compounds retrieved from ZINC and other libraries were performed. Comparative analysis of the protein was done by studying the binding affinity of inhibitors that are already available. The best interacting complex was set for molecular dynamics simulation for 25 ns to validate the stability of system. These molecules were checked for their toxicity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties using OSIRIS and pre-ADMET tools. Results:We discovered ten such candidates with better binding efficiency with survivin in comparison to marketed chemical against the same. Furthermore, these inhibitor candidates did not induce cell toxicity. Binding affinity of reference molecules was varied from -6.8 to -8.5 kcal/mol while that of top scoring compound ZINC00689728 is -9.3 kcal/mol binding energy. Good placement and strong bond formation of selected molecule was observed during course of work. It is also having permissible ADMET property. Conclusion:Considering all the parameters, the screened molecule can be considered as a potential lead compound for designing new drug against survivin. Further investigation and testing will be required to make it to the final stage. SUMMARY:Survivin is one of the important protein of metastasis. Inhibiting survivin might led to the increased therapeutic efficiency of cancer. In this work we are screening library of natural compounds in view of finding some potent inhibitor against survivin. Abbreviations used: MD: Molecular dynamics, LogS: Aqueous solubility, Acceptor HB: Hydrogen bond acceptor, Donor HB: Donor hydrogen bond donor, ADMET: Absorption, distribution, metabolism, excretion, and toxicity, RCSB: Research Collaboratory for Structural Bioinformatics, OPLS: Optimized potentials for liquid simulations, RMSD: Root-mean-square deviation.

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In silico virtual screening, characterization, docking and molecular dynamics studies of crucial SARS-CoV-2 proteins.

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