Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein – Human ACE2 interface

Faria, Sérgio H. D. M.; Teleschi, J. G.

doi:10.1016/j.molstruc.2021.130076

Cited by 6 publications

(4 citation statements)

References 50 publications

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“…Yadav et al suggested the higher reactivity of favipiravir toward Mpro since the presence of electron donor and receptor groups in favipiravir displayed the capability of forming a complex with the external target molecule. Other similar studies include refs and − . DFT calculations by Aitouna et al , indicated the epoxidation reaction of parthenolide and himachalene derivatives presented high chemoselectivity.…”

Section: Methods and Approachesmentioning

confidence: 83%

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein–protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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Section: Methods and Approachesmentioning

confidence: 83%

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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“…Dipyridamole, candesartan cilexetil, candesartan, oxytetracycline, valganciclovir hydrochloride, roxatidine acetate, omeprazole, sulfacetamide, cimetidine, disulfiram, atazanavir, hydroxychloroquine, chloroquine, indinavir montelukast sodium, and maribavir [83] Membrane (M) protein 3I6G Colchicine, remdesivir, bafilomycin A1, temozolomide, and colchicine derivatives [84] Nucleocapsid (N) protein 6M3M Apamycin, camostat, nafamostat, saracatinib, trametinib, cefuroxime, ceftriaxone, cefotaxime [85] RNA-dependent RNA polymerase (RdRp) 7D4F molnupiravir, grazoprevir, ganciclovir, atazanavir, daclatasvir, acyclovir, etravirine, entecavir, efavirenz, asunaprevir, abacavir dolutegravir, lomibuvir, penciclovir, trifluridine, danoprevir, ritonavir, saquinavir, raltegravir, and lamivudine [86] S-protein 6VXX Pemirolast, isoniazid pyruvate, nitrofurantoin, and eriodictyol [87]…”

Section: Targets Pdb Id Repurposed Drugs Referencesmentioning

confidence: 99%

A Comprehensive Review of Drug Repurposing Strategies against Known Drug Targets of COVID-19

Khataniar

Pathak

Rajkhowa

et al. 2022

COVID

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Drug repurposing is a more inexpensive and shorter approach than the traditional drug discovery and development process. The concept of identifying a potent molecule from a library of pre-existing molecules or an already approved drug has become a go-to tactic to accelerate the identification of drugs that can prevent COVID-19. This seemingly uncontrollable disease is caused by SARS-CoV-2. It is a novel virus of the Betacoronavirus genus, exhibiting similarities to the previously reported SAR-CoV genome structure and viral pathogenesis. The emergence of SARS-CoV-2 and the rapid outbreak of COVID-19 have resulted in a global pandemic. Researchers are hard-pressed to develop new drugs for total containment of the disease, thus making the cost-effective drug repurposing a much more feasible approach. Therefore, the current review attempts to collate both the experimental and computational drug repurposing strategies that have been utilized against significant drug targets of SARS-CoV-2. Along with the strategies, the available druggable targets shall also be discussed. However, the occurrence of frequent recombination of the viral genome and time-bound primary analysis, resulting in insignificant data, are two major challenges that drug repurposing still faces.

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“…According to the pathogenesis of SARS-CoV-2 from the perspective of S protein and ACE2 binding, they found some substances, including peptide P6, griffithsin, EK1 and extracts from traditional chinese medicine, which fought against SARS-CoV-2 through binding ACE2 receptor, S protein, or inhibiting the host and virus. Faria et al [ 132 ] also focused on the molecules that can inhibit the interaction between the S protein and human ACE2. They discovered some molecules at the interaction sites: four molecules in Tyr-491(Spike)-Glu-37(ACE2) and one in Gly-488(Spike)-Lys-353(ACE2).…”

Section: Cadd Against Sars-cov-2: Targeting the Structure Proteinmentioning

confidence: 99%

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

Liu

Wan

Wang

2021

Briefings in Bioinformatics

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The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

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Computational search for drug repurposing to identify potential inhibitors against SARS-COV-2 using Molecular Docking, QTAIM and IQA methods in viral Spike protein – Human ACE2 interface

Cited by 6 publications

References 50 publications

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

A Comprehensive Review of Drug Repurposing Strategies against Known Drug Targets of COVID-19

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

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