The virtual screening application for searching potential antiviral agents to treat COVID-19 disease

Evseeva, Larysa V.; Ivanov, V. V.; Karpina, Veronika R.; Коваленко, С. Н.; Kuznetsov, Ihor E.; Langer, Thomas; Maes, Louis; Kovalenko, S. M.

doi:10.24959/ophcj.20.200019

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…We also generated a tetrad mutant F147L/F149L/F229L/F231L by replacing residues F147, F149, F229, and F231 with Leu. These mutants were made using PyMol software package 16,17 …”

Section: Methodsmentioning

confidence: 99%

Exploring the molecular basis of UG‐rich RNA recognition by the human splicing factor TDP‐43 using molecular dynamics simulation and free energy calculation

Sun

Chen

et al. 2021

J Comput Chem

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Transactivation response element RNA/DNA-binding protein 43 (TDP-43) is involved in the regulation of alternative splicing of human neurodegenerative disease-related genes through binding to long UG-rich RNA sequences. Mutations in TDP-43, most in the homeodomain, cause neurological disorders such as amyotrophic lateral sclerosis and fronto temporal lobar degeneration. Several mutants destabilize the structure and disrupt RNA-binding activity. The biological functions of these mutants have been characterized, but the structural basis behind the loss of RNA-binding activity is unclear. Focused on the specific TDP-43-ssRNA complex (PDB code 4BS2), we applied molecular dynamics simulations and the molecular mechanics Poisson-Boltzmann surface area free energy calculation to characterize and explore the structural and dynamic effects between ssRNA and TDP-43. The energetic analysis indicated that the intermolecular van der Waals interaction and nonpolar solvation energy play an important role in the binding process of TDP-43 and ssRNA.Compared with the wild-type TDP-43, the reduction of the polar or non-polar interaction between all the mutants F149A, D105A/S254A, R171A/D174A, F147L/F149L/ F229L/F231L and ssRNA is the main reason for the reduction of its binding free energy.Decomposing energies suggested that the extensive interactions between TDP-43 and the nitrogenous bases of ssRNA are responsible for the specific ssRNA recognition by TDP-43. These results elucidated the TDP-43-ssRNA interaction comprehensively and further extended our understanding of the previous experimental data. The uncovering of TDP-43-ssRNA recognition mechanism will provide us useful insights and new chances for the development of anti-neurodegenerative drugs.

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“…We also generated a tetrad mutant F147L/F149L/F229L/F231L by replacing residues F147, F149, F229, and F231 with Leu. These mutants were made using PyMol software package 16,17 …”

Section: Methodsmentioning

confidence: 99%

Exploring the molecular basis of UG‐rich RNA recognition by the human splicing factor TDP‐43 using molecular dynamics simulation and free energy calculation

Sun

Chen

et al. 2021

J Comput Chem

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“…ORFs encode these proteins at the 3 ′ -end of genomic RNA. 9,12,28 6. Structural and non-structural proteins of SARS-CoV-2…”

Section: Molecular Architecture Of the Virus Sars-cov-2mentioning

confidence: 99%

Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2

Yevsieieva,

Lohachova,

Kyrychenko

et al. 2023

RSC Adv.

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“…A critical assessment of docking programs and scoring functions in terms of reproducing crystallographically determined protein/ligand complex structures is available in [93]. Numerous molecular docking studies have been reported in the context of repurposing and discovering potent non-covalent and covalent inhibitors for critical proteases of the virus SARS-CoV-2 [111][112][113][114][115][116][117][118]. Some excellent reviews of this topic are given elsewhere [1,2,7,119].…”

Section: Pharmacophore-based Virtual Screeningmentioning

confidence: 99%

Recent advances in computational drug discovery for therapy against coronavirus SARS-CoV-2

Ivanov,

Lohachova,

Kolesnik

et al. 2023

SR: PS

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Despite essential experimental efforts focused on studying severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), computational chemistry methods are promising complementary tools in combating coronavirus disease 2019 (COVID-19). The present review aims to provide readers with the recent progress and advances in computational approaches currently used to streamline drug discovery and development in the context of COVID-19. Our review is dual purpose. It is intended (a) to familiarize the readership with the general concept of QSAR, in silico screening, molecular docking and molecular dynamics (MD) simulations and (b) to provide key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. We outline how structure- and ligand-based drug design can accelerate the structural elucidation of pharmacological drug targeting and the discovery of preclinical drug candidate molecules. Several examples of MD computational studies demonstrate how atomistic MD simulations can facilitate our understanding of the molecular basis of drug actions and biological mechanisms of virus inhibition in atomic detail. Finally, the short- and long-term perspectives in computational drug discovery are discussed. The aim of this study is to summarize the last three years' progress and advances in computational approaches currently used to streamline the drug discovery and development process in the context of COVID-19. Materials and methods. The literature overview of QSAR, in silico screening, machine learning, molecular docking and molecular dynamics (MD) simulations is given in the context of COVID-19. The literature search was performed using online databases, such as Scopus, Web of Science, PDB-protein databank, and PubMed, focusing on the following keywords - human coronavirus, QSAR, molecular docking, virtual screening, machine learning, molecular dynamics, Mpro and PLpro proteases, SARS-CоV-2, respectively. Results. The review familiarizes the readership with the general concept of QSAR, in silico screening, machine learning, molecular docking and MD simulations and provides key examples of the recent applications of these computational tools in discovering novel therapeutic agents against COVID-19. Conclusions. New insight into the recent progress and achievements in computer-guided drug discovery for therapeutic agents against SARS-CoV-2 is provided

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The virtual screening application for searching potential antiviral agents to treat COVID-19 disease

Cited by 3 publications

References 24 publications

Exploring the molecular basis of UG‐rich RNA recognition by the human splicing factor TDP‐43 using molecular dynamics simulation and free energy calculation

Exploring the molecular basis of UG‐rich RNA recognition by the human splicing factor TDP‐43 using molecular dynamics simulation and free energy calculation

Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2

Recent advances in computational drug discovery for therapy against coronavirus SARS-CoV-2

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