Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening
Abstract
:1. Introduction
2. Results
2.1. Data Collection and “in-House” Database Construction
2.2. The Preparation of Protein and the Binding Pocket Determination
2.3. The Hybrid Virtual Screening Workflow
2.4. Inhibitory Activity Assay
2.5. Pro-Inflammatory Factor Release Inhibition Assay for Compound 6
2.6. Elucidating the Initial Binding Conformation of JNK3 and Compound 6 through Binding Pose Metadynamics
2.7. Structural Insight into the Precision Binding Mode between JNK3 and Compound 6 through Molecular Dynamics
3. Discussion
4. Materials and Methods
4.1. Protein Preparation
4.2. Database Preparation
4.3. Receptor Grid Generation
4.4. The Hybrid Virtual Screening Workflow
4.5. IC50 Assay
4.6. Pro-Inflammatory Factor Release Inhibition Assay in LPS-Induced RAW264.7
4.7. Binding Pose Metadynamics
4.8. Molecular Dynamics
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Entry | Docking Score (HTVS) (kcal/mol) | Docking Score (SP) (kcal/mol) | Docking Score (XP) (kcal/mol) | MM-GBSA dG Bind (kcal/mol) | Deepdockscore | pIC50 Predicted | Inhibition Rate (%) |
---|---|---|---|---|---|---|---|
Compound 1 | −11.107 | −11.896 | −13.871 | −86.10 | −185.12 | 6.25 | 17.50 ± 0.55 |
Compound 2 | −9.373 | −10.870 | −11.918 | −80.93 | −169.34 | 5.89 | 43.24 ± 5.36 |
Compound 3 | −9.190 | −8.696 | −10.808 | −85.84 | −154.42 | 6.84 | 5.70 ± 1.02 |
Compound 4 | −9.023 | −10.094 | −8.959 | −76.28 | −90.94 | 5.34 | 7.77 ± 0.43 |
Compound 5 | −8.941 | −9.200 | −8.537 | −83.07 | −140.73 | 5.35 | 16.58 ± 0.34 |
Compound 6 | −8.401 | −11.535 | −13.065 | −82.05 | −173.73 | 5.82 | 99.73 ± 0.44 |
Compound 7 | −8.147 | −10.439 | −10.690 | −78.60 | −172.77 | 6.20 | 26.88 ± 3.29 |
Compound 8 | −7.853 | −10.406 | −8.840 | −80.03 | −116.72 | 5.30 | 16.76 ± 0.56 |
Compound 9 | −6.757 | −11.754 | −13.694 | −87.01 | −183.26 | 6.34 | 34.81 ± 0.23 |
Compound 10 | −8.571 | −9.496 | −9.765 | −75.76 | −97.41 | 5.33 | 43.76 ± 1.92 |
Entry | Molecular Weight | XLogP3-AA | Hydrogen Bond Donor Count | Hydrogen Bond Acceptor Count | Rotatable Bond Count | Heavy Atom Count | Topological Polar Surface Area |
---|---|---|---|---|---|---|---|
Compound 1 | 468.6 | 5.3 | 2 | 6 | 9 | 35 | 59.1 |
Compound 2 | 476.0 | 5.3 | 2 | 7 | 7 | 34 | 83.4 |
Compound 3 | 435.5 | 2.1 | 2 | 7 | 7 | 32 | 94.3 |
Compound 4 | 461.0 | 4.6 | 2 | 6 | 8 | 31 | 108.0 |
Compound 5 | 453.6 | 4.60 | 1 | 5 | 7 | 34 | 69.2 |
Compound 6 | 399.5 | 5.6 | 1 | 4 | 5 | 29 | 83.1 |
Compound 7 | 442.5 | 3.1 | 2 | 6 | 5 | 33 | 82.6 |
Compound 8 | 495.5 | 5.6 | 0 | 6 | 4 | 36 | 132.0 |
Compound 9 | 568.7 | 5.9 | 1 | 9 | 9 | 42 | 92.3 |
Compound 10 | 401.0 | 4.3 | 1 | 4 | 5 | 27 | 52.0 |
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Yao, C.; Shen, Z.; Shen, L.; Kadier, K.; Zhao, J.; Guo, Y.; Xu, L.; Cao, J.; Dong, X.; Yang, B. Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening. Pharmaceuticals 2023, 16, 1459. https://doi.org/10.3390/ph16101459
Yao C, Shen Z, Shen L, Kadier K, Zhao J, Guo Y, Xu L, Cao J, Dong X, Yang B. Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening. Pharmaceuticals. 2023; 16(10):1459. https://doi.org/10.3390/ph16101459
Chicago/Turabian StyleYao, Chenpeng, Zheyuan Shen, Liteng Shen, Kailibinuer Kadier, Jingyi Zhao, Yu Guo, Lei Xu, Ji Cao, Xiaowu Dong, and Bo Yang. 2023. "Identification of Potential JNK3 Inhibitors: A Combined Approach Using Molecular Docking and Deep Learning-Based Virtual Screening" Pharmaceuticals 16, no. 10: 1459. https://doi.org/10.3390/ph16101459