Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies

Rajagopal, Kalirajan; Varakumar, Potlapati; Aparna, Baliwada; Byran, Gowramma; Jupudi, Srikanth

doi:10.1080/07391102.2020.1798285

Cited by 32 publications

(18 citation statements)

References 46 publications

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“…However, in the original publication from which the source data for case study 1 was obtained, pladienolide B, 2-deoxy-d-Glucose, ribavirin and NMS-873 were identified as potential drugs of interest [ 3 ], which target various components of the RNA processing and protein machinery, along with glycolysis. In contrast, our list of highest scoring candidates is the anti-cancer drugs amsacrine, ceritinib and crizotinib targeting cell cycle and cell growth pathways, which is supported by some experimental [ 20 ] and theoretical evidence [ 5 , 34 ]. In the first scenario, inhibiting cell metabolism, RNA splicing and translation would limit many biological processes in the cell, including that of some viruses [ 15 ].…”

Section: Discussionmentioning

confidence: 82%

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets

Chen

Philip

Cao

et al. 2021

Briefings in Bioinformatics

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Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug–target analysis, resulting in a list of 84 drug–target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

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Section: Discussionmentioning

confidence: 82%

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets

Chen

Philip

Cao

et al. 2021

Briefings in Bioinformatics

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“…As a result, the free binding energy (Δ G bind ) of the examined systems can be calculated. The binding energy (Δ G bind ) of the complexes can be calculated according to eq , normalΔ G bind = E complexe ( minimized ) + E ligand ( minimized ) − E receptor ( minimized ) …”

Section: Methodsmentioning

confidence: 99%

“…The time course of MD simulation was fixed in 100 ns using the Desmond package of Schrödinger 2020-3 software . The examined systems were solvated using the orthorhombic single point charge (SPC) explicit water model. , To improve and regenerate the protein–ligand systems, the optimized potentials for liquid simulations extended (OPLSe) force field was used. , By adding sodium and chloride ions to the total complexes explored, the charge was neutralized. In addition, the energies of the systems were minimized by 2000 steps before running the MD simulation along a 100 ns path in the NPT ensemble .…”

Section: Methodsmentioning

confidence: 99%

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

et al. 2023

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The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure–activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch–bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.

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“…Fakhar et al screened 3435 anthocyanin substructure compounds by docking and MM/GBSA calculations, reporting the best compound to be 44256921. Some other compounds, such as α-ketoamide covalent inhibitors, macrolactin compounds, echinocandins, essential oil compounds, glucocorticoids, angucycline compounds, hydroxychloroquine derivatives, aminoglycosides, imidazole derivatives, Se-containing heterocyclic compounds, circadian clock modulating compounds, oxazine substituted 9-anilinoacridines, nitric oxide donor furoxan, nitric oxide donor heterocyclic vasodilators, withanone caffeic acid phenethyl ester, tetracycline, β-glutamyl- S -allylcysteine peptides, and others, were also screened by docking and MM/PB(GB)SA simulations. − …”

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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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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Identification of some novel oxazine substituted 9-anilinoacridines as SARS-CoV-2 inhibitors for COVID-19 by molecular docking, free energy calculation and molecular dynamics studies

Cited by 32 publications

References 46 publications

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets

Cyclohexane-1,3-dione Derivatives as Future Therapeutic Agents for NSCLC: QSAR Modeling, In Silico ADME-Tox Properties, and Structure-Based Drug Designing Approach

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

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