Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth
Abstract
:1. Introduction
2. Results
2.1. Workflow
2.2. 3D QSAR Pharmacophore Generation and Validation
2.3. Identification of Common Feature Pharmacophores
2.4. Generation of Structure-Based Pharmacophores
2.5. Identification of Candidate Compounds through Multiple-Stage PBVS
2.6. NSC609077 Inhibits the Anchorage-Dependent Growth and Motility of Lung Cancer Cells
2.7. Effects of NSC609077 on an EGFR-Associated Signaling Pathway
3. Discussion
4. Materials and Methods
4.1. 3D QSAR Pharmacophore Generation and Evaluation
4.2. Common Feature Pharmacophore Modeling
4.3. Structure-Based Pharmacophore Modeling
4.4. Pharmacophore-Based Virtual Screening (PBVS)
4.5. Molecular Docking Studies
4.6. Cell Culture and Drug Treatment
4.7. Enzyme-Linked Immunosorbent Assay (ELISA)
4.8. Cell Viability Assay
4.9. Clonogenicity Assay
4.10. Scratch-Wound Assay
4.11. Western Blot Analysis
4.12. Statistical Analysis
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
EGFR-TKI | 3D-QSAR Fit Value | CFP Fit Value | SBP Fit Value | LibDock Score | ||
---|---|---|---|---|---|---|
EGFR p.L858R (PDB: 2ITV) | EGFR p.T790M (PDB: 2JIT) | EGFR p.L858R (PDB: 2ITV) | EGFR p.T790M (PDB: 2JIT) | |||
Erlotinib | 3.17 | 1.89 | 1.50 | 1.15 | 116.60 | 110.71 |
Gefitinib | 3.92 | 3.28 | 2.49 | 2.29 | 113.73 | 106.28 |
Afatinib | 5.89 | 2.90 | 2.32 | 2.49 | 111.59 | 107.47 |
Osimertinib | 4.86 | 6.00 | 2.99 | 3.34 | 115.66 | 115.27 |
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Pharmacophore Hypothesis | Total Cost | Cost Difference | RMS | Correlation | Features |
---|---|---|---|---|---|
Hypo1 | 136.11 | 60.98 | 0.88 | 0.92 | HBA, HBD, HY |
Hypo2 | 136.27 | 60.82 | 0.90 | 0.91 | HBA, HBD, HY |
Hypo3 | 144.36 | 52.73 | 1.14 | 0.87 | HBA, HBD, HY |
Hypo4 | 146.30 | 50.79 | 1.19 | 0.85 | HBA, HBD, HY |
Hypo5 | 147.97 | 49.12 | 1.21 | 0.85 | HBD, HY, RA |
Hypo6 | 150.20 | 46.89 | 1.28 | 0.83 | HBA, HBD, HY |
Hypo7 | 150.36 | 46.73 | 1.25 | 0.84 | HBD, HY, RA |
Hypo8 | 150.65 | 46.44 | 1.30 | 0.82 | HBA, HBD, HY |
Hypo9 | 150.97 | 46.12 | 1.30 | 0.82 | HBD, HY, RA |
Hypo10 | 151.64 | 45.45 | 1.27 | 0.84 | HBD, HY, RA |
BindingDB Code | Experimental IC50 nM | Predicted IC50 nM | Error | Experimental Scale ‡ | Predicted Scale ‡ | 3D-QSAR Fit Value |
---|---|---|---|---|---|---|
BDBM3297 | 0.1 | 0.4 | 4.00 | +++ | +++ | 6.79 |
BDBM3520 | 0.3 | 0.4 | 1.33 | +++ | +++ | 6.77 |
BDBM3522 | 0.4 | 0.4 | 1.00 | +++ | +++ | 6.80 |
BDBM3583 | 0.4 | 0.7 | 1.75 | +++ | +++ | 6.58 |
BDBM3294 | 0.8 | 2.2 | 2.75 | +++ | +++ | 6.07 |
BDBM3519 | 2.0 | 9.3 | 4.65 | +++ | +++ | 5.45 |
BDBM3780 | 2.7 | 3.5 | 1.30 | +++ | +++ | 5.87 |
BDBM3546 | 3.8 | 2.4 | −1.58 | +++ | +++ | 6.04 |
BDBM3535 | 4.0 | 2.0 | −2.00 | +++ | +++ | 6.12 |
BDBM3538 | 4.7 | 0.4 | −11.75 | +++ | +++ | 6.79 |
BDBM3767 | 9.4 | 20.4 | 2.17 | +++ | +++ | 5.11 |
BDBM3781 | 20.0 | 66.0 | 3.30 | +++ | +++ | 4.60 |
BDBM3263 | 23.0 | 75.1 | 3.27 | +++ | +++ | 4.54 |
BDBM3264 | 27.0 | 72.8 | 2.70 | +++ | +++ | 4.55 |
BDBM3753 | 31.0 | 55.5 | 1.79 | +++ | +++ | 4.67 |
BDBM3508 | 55.0 | 73.2 | 1.33 | +++ | +++ | 4.55 |
BDBM3503 | 56.0 | 164.8 | 2.94 | +++ | ++ | 4.20 |
BDBM3283 | 58.0 | 81.6 | 1.41 | +++ | +++ | 4.50 |
BDBM3763 | 58.0 | 209.8 | 3.62 | +++ | ++ | 4.09 |
BDBM3744 | 72.0 | 63.6 | −1.13 | +++ | +++ | 4.61 |
BDBM3265 | 80.0 | 72.0 | −1.11 | +++ | +++ | 4.56 |
BDBM3536 | 84.0 | 70.5 | −1.19 | +++ | +++ | 4.57 |
BDBM3518 | 100.0 | 16.8 | −5.95 | ++ | +++ | 5.19 |
BDBM3516 | 120.0 | 79.8 | −1.50 | ++ | +++ | 4.51 |
BDBM3745 | 132.0 | 85.6 | −1.54 | ++ | +++ | 4.48 |
BDBM3292 | 139.0 | 90.7 | −1.53 | ++ | +++ | 4.46 |
BDBM3757 | 147.0 | 57.4 | −2.56 | ++ | +++ | 4.66 |
BDBM3752 | 264.0 | 188.8 | −1.40 | ++ | ++ | 4.14 |
BDBM3256 | 320.0 | 106.8 | −3.00 | ++ | ++ | 4.39 |
BDBM3259 | 344.0 | 202.1 | −1.70 | ++ | ++ | 4.11 |
BDBM3295 | 348.0 | 69.2 | −5.03 | ++ | +++ | 4.58 |
BDBM3510 | 770.0 | 228.5 | −3.37 | ++ | ++ | 4.06 |
BDBM3269 | 5500.0 | 3769.7 | −1.46 | + | + | 2.84 |
Pharmacophore Hypothesis | Rank | Direct Hit | Partial Hit | Max Hit | Features |
---|---|---|---|---|---|
Pharm1 | 54.25 | 1101 | 0010 | 6 | RRHHAA |
Pharm2 | 53.04 | 1111 | 0000 | 6 | HHHDAA |
Pharm3 | 52.64 | 1101 | 0010 | 6 | RRHHAA |
Pharm4 | 52.48 | 1111 | 0000 | 5 | RRHAA |
Pharm5 | 52.32 | 1101 | 0010 | 6 | RRHHAA |
Pharm6 | 52.32 | 1101 | 0010 | 6 | RRHHAA |
Pharm7 | 52.25 | 1101 | 0010 | 6 | RRHHAA |
Pharm8 | 52.25 | 1101 | 0010 | 6 | RRHHAA |
Pharm9 | 52.20 | 1111 | 0000 | 5 | RRHAA |
Pharm10 | 52.20 | 1111 | 0000 | 5 | RRHAA |
NSC Number | 3D-QSAR Fit Value | CFP Fit Value | SBP Fit Value | LibDock Score | Phosphorylation Level (%) | Inhibitory Rate (%) | ||
---|---|---|---|---|---|---|---|---|
EGFR p.L858R (PDB: 2ITV) | EGFR p.T790M (PDB: 2JIT) | EGFR p.L858R (PDB: 2ITV) | EGFR p.T790M (PDB: 2JIT) | |||||
NSC7521 | 6.00 | 3.18 | 2.89 | 2.57 | 124.05 | 122.70 | 75 ± 15 | 25 ± 15 |
NSC342715 | 6.47 | 3.26 | 2.98 | 2.65 | 123.30 | 119.98 | 37 ± 6 * | 63 ± 6 * |
NSC622394 | 6.05 | 3.37 | 1.97 | 2.54 | 148.73 | 123.90 | 32 ± 8 * | 68 ± 8 * |
NSC609077 | 6.22 | 2.79 | 2.77 | 1.68 | 145.57 | 152.88 | 21 ± 6 * | 79 ± 6 * |
NSC622442 | 6.68 | 3.73 | 3.12 | 2.49 | 129.85 | 124.22 | NA | NA |
NSC623897 | 6.03 | 3.77 | 2.48 | 3.33 | 117.06 | 116.17 | NA | NA |
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Weng, C.-W.; Wei, C.-H.; Tsai, J.-Y.; Lai, Y.-H.; Chang, G.-C.; Chen, J.J.W. Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth. Int. J. Mol. Sci. 2022, 23, 3487. https://doi.org/10.3390/ijms23073487
Weng C-W, Wei C-H, Tsai J-Y, Lai Y-H, Chang G-C, Chen JJW. Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth. International Journal of Molecular Sciences. 2022; 23(7):3487. https://doi.org/10.3390/ijms23073487
Chicago/Turabian StyleWeng, Chia-Wei, Chi-Hsuan Wei, Jeng-Yuan Tsai, Yi-Hua Lai, Gee-Chen Chang, and Jeremy J. W. Chen. 2022. "Hybrid Pharmacophore- and Structure-Based Virtual Screening Pipeline to Identify Novel EGFR Inhibitors That Suppress Non-Small Cell Lung Cancer Cell Growth" International Journal of Molecular Sciences 23, no. 7: 3487. https://doi.org/10.3390/ijms23073487