MD, DFT Investigations and Inhibition of the Novel SARS- CoV-2 Mainprotease in Three Cocrystals of Hydrochloro- thiazide

Mary, Y. Sheena; Yadav, Rohitash; Çeli̇k, İsmail; Rad, Ali Shokuhi; Sarala, S.

doi:10.1080/22297928.2021.1934538

Cited by 3 publications

(2 citation statements)

References 57 publications

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“…More studies used DFT to preoptimize the structures of compounds. 609 , 635 − 640 Additionally, the PM7 semiempirical quantum-chemical method was applied to binding calculations of Mpro, 641 , 642 RdRp, 643 PLpro, and S protein. 642 …”

Section: Methods and Approachesmentioning

confidence: 99%

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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: 99%

“…This can explain how parthenolide and himachalene derivatives form. More studies used DFT to preoptimize the structures of compounds. ,− Additionally, the PM7 semiempirical quantum-chemical method was applied to binding calculations of Mpro, , RdRp, PLpro, and S protein …”

Section: Methods and Approachesmentioning

confidence: 99%

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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DFT investigations on conformational analysis, solvation effects, reactivity studies, chemical descriptors and docking of two anti‐cancerous drugs, Lenvatinib and Regorafenib

Al‐Otaibi,

Ullah,

Mary

et al. 2022

Vietnam Journal of Chemistry

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Two anticancer drugs, lenvatinib (LNTB) and regorafenib (RGFB) are evaluated for their solvent effects, wavefunction reactivity properties and different chemical descriptors by means of theoretical methods. Potential energy surface scan studies about all possible rotable bonds are performed and lowest energy conformations of LNTB and RGFB are for the torsion angles C25‐C24‐C29‐N9 and C25‐N10‐C15‐C21. Reactive sites are O and N, H atoms, giving nucleophilic and electrophilic attacks for both the molecules. Solvation free energies are calculated in different solvents and all solvents are suitble except heptane. Electron densities of LNTB and RGFB show that closed‐shell interactions are in different regions. The intramolecular hydrogen bonds of the compounds LNTB and RGFB are found at O2‐H38, Cl1‐H37 & O5‐H47 and F2‐H38, N11‐H45, O7‐H35 & O7‐H37, respectively. Due to anticancer activities, LNTB and RGFB are docked with different proteins which give binding affinities from ‐8.4 to ‐10.5 for RGFB and ‐7.9 to ‐10.1 kcal/mol for LNTB.

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Genomic variation and point mutations analysis of Indian COVID-19 patient samples submitted in GISAID database

Mudgal

Yadav

Haokip

et al. 2021

Journal of the Indian Chemical Society

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Corona virus disease 2019 (COVID-19) endemic has havoc on the world; the causative virus of the pandemic is SARS CoV-2. Pharmaceutical companies and academic institutes are in continuous efforts to identify anti-viral therapy or vaccines, but the most significant challenge faced is the highly evolving genome of SARS CoV-2, which is imparting evolutionary selective benefits to the virus. To understand the viral mutations, we have retrieved nine hundred and thirty-four samples from different states of India via the GISAID database and analysed the frequency of all types of point mutation in all structural, non-structural proteins, and accessory factors of SARS CoV-2. Spike glycol protein, nsp3, nsp6, nsp12, N and NS3 were the most evolving proteins. High frequency point mutations were Q496P (nsp2), A380V (nsp4), A994D (nsp3), L37F (nsp6), P323L & A97V (nsp12), Q57H (ns3), D614G (S), P13L (N), R203K (N), G204R (N) and S194L (N).

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MD, DFT Investigations and Inhibition of the Novel SARS- CoV-2 Mainprotease in Three Cocrystals of Hydrochloro- thiazide

Cited by 3 publications

References 57 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

DFT investigations on conformational analysis, solvation effects, reactivity studies, chemical descriptors and docking of two anti‐cancerous drugs, Lenvatinib and Regorafenib

Genomic variation and point mutations analysis of Indian COVID-19 patient samples submitted in GISAID database

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