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

Yasir, Muhammad; Park, Jin‐Young; Han, Eun‐Taek; Park, Won Sun; Han, Jin‐Hee; Kwon, Yong-Soo; Lee, Hee-Jae; Hassan, Mubashir; Kloczkowski, Andrzej; Wang, Chun

doi:10.3390/molecules28010414

Cited by 14 publications

(23 citation statements)

References 56 publications

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“…A binding pocket's function is determined by the collection of amino acid residues that surround it, in addition to its shape and location inside a protein. 30 The binding pocket residues of COX-2 were retrieved by employing the Discovery Studio ligand interaction approach and are mentioned as Val344, Tyr385, Trp387, Ser530, Phe518, Arg513, Glu524, Leu531, His90, Arg120, and Tyr355. Moreover, the binding pocket residues were verified from already published data.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

“…Molecular docking is the most widely used method for evaluating the interactions and conformations of ligands with target proteins. 40 It anticipates the association strength or binding affinity between two molecules based on preferred orientation by using scoring algorithms. 30 The water molecules and cocrystallized ligand molecules were removed from the protein, and the hydrogens were added to the protein by Discovery Studio's protein preparation module.…”

Section: ■ Conclusionmentioning

confidence: 99%

“…40 It anticipates the association strength or binding affinity between two molecules based on preferred orientation by using scoring algorithms. 30 The water molecules and cocrystallized ligand molecules were removed from the protein, and the hydrogens were added to the protein by Discovery Studio's protein preparation module. The ligand preparations were also carried out for reference and candidate compounds in which tautomers were generated, ionization was subjected to change, and bad valences were fixed by Discovery Studio's ligand preparation module.…”

Section: ■ Conclusionmentioning

confidence: 99%

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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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Section: ■ Results and Discussionmentioning

confidence: 99%

Section: ■ Conclusionmentioning

confidence: 99%

Section: ■ Conclusionmentioning

confidence: 99%

See 1 more Smart Citation

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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show abstract

“…Molecular docking is the most widely used method for evaluating the interactions and conformations of ligands with target proteins . For instance, it is feasible to anticipate the association strength or binding affinity between two molecules based on preferred orientation by using scoring algorithms . The protein was prepared before molecular docking.…”

Section: Methodsmentioning

confidence: 99%

“…32 For instance, it is feasible to anticipate the association strength or binding affinity between two molecules based on preferred orientation by using scoring algorithms. 33 The protein was prepared before molecular docking. The water molecules and cocrystallized ligand molecule were removed from the protein by UCSF chimera.…”

Section: ■ Introductionmentioning

confidence: 99%

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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Accelerated Degradation of DiXyl-α,ε-Lys-ARP via Interaction between Extra-Added Xylose and Monosubstituted Lys-ARPs during Maillard Reaction

Zhang,

Cui,

Xia

et al. 2024

J. Agric. Food Chem.

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Lysine (Lys) is capable of forming a di-substituted Amadori rearrangement product (ARP) with xylose (Xyl), designated as diXyl-α,ε-Lys-ARP. DiXyl-α,ε-Lys-ARP degradation was characterized by two steps: Initially, Xyl-α-and Xyl-ε-Lys-ARP were formed through elimination or hydrolysis at specific Nα/Nε positions of the corresponding enol and imine intermediates, which were then further degraded to dicarbonyl compounds and regenerated Lys. Xyl-α-or Xyl-ε-Lys-ARP had a reactive free amino group (ε-NH 2 or α-NH 2 ), both of which were still highly reactive and able to undergo further reactions with Xyl. Therefore, the diXyl-α,ε-Lys-ARP/Xyl model system was established to explore the impact of extra-added Xyl on diXyl-α,ε-Lys-ARP degradation behavior. Extra-added Xyl remarkably affected the degradation pathway of diXyl-α,ε-Lys-ARP by capturing the Xyl-α-and Xyl-ε-Lys-ARP to regenerate diXyl-α,ε-Lys-ARP. This interaction between Xyl and mono-substituted Lys-ARPs promoted the shift of chemical equilibrium toward the degradation of diXyl-α,ε-Lys-ARP, thereby accelerating its degradation rate. This degradation was markedly facilitated by the elevated temperature and pH values. Interestingly, the yield of Xyl-α-and Xyl-ε-Lys-ARP was particularly dependent on the pH during diXyl-α,ε-Lys-ARP degradation. Xyl-ε-Lys-ARP was the dominant product at pH 5.5−7.5 while Xyl-α-Lys-ARP possessed a relatively higher content under weak alkaline conditions, which was related to the reactivities of the Nα/Nε positions under various reaction conditions.

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

Cited by 14 publications

References 56 publications

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

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

Accelerated Degradation of DiXyl-α,ε-Lys-ARP via Interaction between Extra-Added Xylose and Monosubstituted Lys-ARPs during Maillard Reaction

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