Exploration of Potential Ewing Sarcoma Drugs from FDA-Approved Pharmaceuticals through Computational Drug Repositioning, Pharmacogenomics, Molecular Docking, and MD Simulation Studies

Hassan, Mubashir; Yasir, Muhammad; Shahzadi, Saba; Kloczkowski, Andrzej

doi:10.1021/acsomega.2c00518

Cited by 10 publications

(15 citation statements)

References 140 publications

(113 reference statements)

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“…In addition to its shape and location inside a protein, a binding pocket’s function is determined by the collection of amino acid residues that surround it [ 21 ]. The binding pocket residues of EWS were retrieved from an already published research article [ 13 ] and selected as Ser361, Met397, Ala362, His399, Tyr401, Thr414, and Ser416 ( Figure 3 ).…”

Section: Resultsmentioning

confidence: 99%

“…The position of a ligand in the protein’s holo-structure most likely determines the binding pocket of targeted protein and channels [ 21 ]. The active binding site residues were selected from previously published data [ 13 ] and identified using Discovery Studio and UCSF Chimera 1.10.1.…”

Section: In Silico Methodologymentioning

confidence: 99%

“…The CDOCKER module of Discovery Studio was employed to perform molecular docking of flavonoids to EWS. The attribute of the binding pocket sphere was modified as (X = 8.6488, Y = 3.7819 and Z = 2.6209) and the radius value was adjusted to 6.6378 [ 13 ] for a better conformational position in the active region of the target protein. The ligands (flavonoids) were docked individually to EWS with the default orientation and conformation 10/10.…”

Section: In Silico Methodologymentioning

confidence: 99%

“…Moreover, the C-terminus of EWS-FLI1 maintains FLI1’s DNA-binding domain and specifically interacts with the ACCGGAAG central sequence. EWS-FLI1 primarily binds to GGAA-repetitive areas, resulting in an association between GGAA microsatellites, EWS-FLI1 binding, and target gene expression [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

et al. 2023

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Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.

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Section: Resultsmentioning

confidence: 99%

Section: In Silico Methodologymentioning

confidence: 99%

Section: In Silico Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

et al. 2023

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“…The position of a ligand in the protein's holo-structure most likely determines the binding pocket of the protein. 39 The complex of COX-2 and inhibitor, Vioxx, was retrieved from PDB (PDB ID: 5KIR). The interacting amino acids were selected using the ligand interaction approach of Discovery Studio for the accuracy of binding site generation.…”

Section: ■ Conclusionmentioning

confidence: 99%

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Yasir,

Park,

Han

et al. 2023

ACS Omega

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Artificial intelligence algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Deep-learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained deep-learning model to screen an FDA-approved drug library for novel COX-2 inhibitors. Reference COX-2 data sets, composed of active and decoy compounds, were obtained from the DUD-E database. To extract molecular features, compounds were subjected to RDKit, a cheminformatic toolkit. GraphConvMol, a graph convolutional network model from DeepChem, was applied to obtain a predictive model from the DUD-E data sets. Then, the COX-2 inhibitory potential of the FDA-approved drugs was predicted using the trained deep-learning model. Vismodegib, an anticancer agent that inhibits the hedgehog signaling pathway by binding to smoothened, was predicted to inhibit COX-2. Noticeably, some compounds that exhibit high potential from the prediction were known to be COX-2 inhibitors, indicating the prediction model’s liability. To confirm the COX-2 inhibition activity of vismodegib, molecular docking was carried out with the reference compounds of the COX-2 inhibitor, celecoxib, and ibuprofen. Furthermore, the experimental examination of COX-2 inhibition was also carried out using a cell culture study. Results showed that vismodegib exhibited a highly comparable COX-2 inhibitory activity compared to celecoxib and ibuprofen. In conclusion, the deep-learning model can efficiently improve the virtual screening of drugs, and vismodegib can be used as a novel COX-2 inhibitor.

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Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

Yasir,

Park,

Han

et al. 2023

J. Chem. Inf. Model.

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Machine learning algorithms have been increasingly applied in drug development due to their efficiency and effectiveness. Machine learning-based drug repurposing can contribute to the identification of novel therapeutic applications for drugs with other indications. The current study used a trained machine learning model to screen a vast chemical library for new JAK2 inhibitors, the biological activities of which were reported. Reference JAK2 inhibitors, comprising 1911 compounds, have experimentally determined IC 50 values. To generate the input to the machine learning model, reference compounds were subjected to RDKit, a cheminformatic toolkit, to extract molecular descriptors. A Random Forest Regression model from the Scikit-learn machine learning library was applied to obtain a predictive regression model and to analyze each molecular descriptor's role in determining IC 50 values in the reference data set. Then, IC 50 values of the library compounds, comprised of 1,576,903 compounds, were predicted using the generated regression model. Interestingly, some compounds that exhibit high IC 50 values from the prediction were reported to possess JAK inhibition activity, which indicates the limitations of the prediction model. To confirm the JAK2 inhibition activity of predicted compounds, molecular docking and molecular dynamics simulation were carried out with the JAK inhibitor reference compound, tofacitinib. The binding affinity of docked compounds in the active region of JAK2 was also analyzed by the gmxMMPBSA approach. Furthermore, experimental validation confirmed the results from the computational analysis. Results showed highly comparable outcomes concerning tofacitinib. Conclusively, the machine learning model can efficiently improve the virtual screening of drugs and drug development.

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Exploration of Potential Ewing Sarcoma Drugs from FDA-Approved Pharmaceuticals through Computational Drug Repositioning, Pharmacogenomics, Molecular Docking, and MD Simulation Studies

Cited by 10 publications

References 140 publications

Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

Exploration of Flavonoids as Lead Compounds against Ewing Sarcoma through Molecular Docking, Pharmacogenomics Analysis, and Molecular Dynamics Simulations

Vismodegib Identified as a Novel COX-2 Inhibitor via Deep-Learning-Based Drug Repositioning and Molecular Docking Analysis

Machine Learning-Based Drug Repositioning of Novel Janus Kinase 2 Inhibitors Utilizing Molecular Docking and Molecular Dynamic Simulation

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