In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer
Abstract
:1. Introduction
2. Materials and Methods
2.1. Identification of Target Genes
2.2. Identification of Control Drugs
2.3. Optimization of the Docking Algorithm
2.4. Identification of Potential Phytochemicals
2.5. Identification of Potential Phytochemicals
2.6. Evaluation of Ligand–Target Complex
2.6.1. Molecular Dynamic Simulation
2.6.2. MM-GBSA and MM-PBSA Analysis
3. Results
3.1. Identification of Targets
3.2. Identification of Control Drugs
3.3. Optimization of the Docking Algorithm
3.4. Identification of Phytochemicals from Hydrastis canadensis
3.5. Molecular Docking
3.5.1. Molecular Dynamic Simulation
3.5.2. MM-GBSA and MM-PBSA Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Genes | Betweenness | Closeness | Degree |
---|---|---|---|
EGFR | 13 | 9 | 9 |
IGF1R | 5 | 8.5 | 8 |
ERBB2 | 8.5 | 8.5 | 8 |
ESR1 | 5 | 8.5 | 8 |
Target | Parameters | D1 | D2 | D3 | D4 | D5 | D6 | D7 | D8 | D9 |
---|---|---|---|---|---|---|---|---|---|---|
Hormone-Independent Breast Cancer Targets | ||||||||||
1xkk (EGFR) | Glide | −6.99 | −7.45 | −7.45 | −8.68 | −7.99 | −8.39 | −6.35 | −7.87 | −7.57 |
Autodock | −8.39 | −9.65 | −7.88 | −8.59 | −9.57 | −8.98 | −8.94 | −9.26 | −7.96 | |
RMSD | 2.36 | 1.54 | 1.51 | 2.37 | 2.06 | 1.98 | 2.37 | 1.70 | 3.09 | |
MMGBSA_ΔG | −57.18 | −60.28 | −51.47 | −61.66 | −57.51 | −67.27 | −47.17 | −50.15 | −40.43 | |
MMGBSA_ΔG(NS) | −63.00 | −66.92 | −59.53 | −70.56 | −63.55 | −73.10 | −52.79 | −59.60 | −51.12 | |
3LVP (IGF1R) | Glide | −6.64 | −7.31 | −6.41 | −4.62 | −7.14 | −6.24 | −6.50 | −7.91 | −7.40 |
Autodock | −7.51 | −6.96 | −7.07 | −7.16 | −7.69 | −8.26 | −7.27 | −7.50 | −6.81 | |
RMSD | 0.949 | 1.59 | 1.27 | 2.30 | 1.18 | 3.47 | 2.86 | 2.58 | 2.03 | |
MMGBSA_ΔG | −31.48 | −44.22 | −48.01 | −34.77 | −45.00 | −42.16 | −52.71 | −61.92 | −53.57 | |
MMGBSA_ΔG(NS) | −39.72 | −50.81 | −58.40 | −39.90 | −56.14 | −55.52 | −55.9 | −64.70 | −64.36 | |
Hormone-Dependent Breast Cancer Targets | ||||||||||
3MZW (ERBB2) | Glide | −5.04 | −5.46 | −2.93 | −3.56 | −5.69 | −4.89 | −3.49 | −3.16 | −3.99 |
Autodock | −5.96 | −6.08 | −6.79 | −6.25 | −6.39 | −7.00 | −5.95 | −6.08 | −5.96 | |
RMSD | 2.02 | 1.83 | 2.39 | 1.38 | 1.65 | 2.69 | 2.75 | 2.39 | 4.62 | |
MMGBSA_ΔG | −41.41 | −41.38 | −36.34 | −27.72 | −49.83 | −44.73 | −29.31 | −24.42 | −35.30 | |
MMGBSA_ΔG(NS) | −48.45 | −48.43 | −38.31 | −31.82 | −52.95 | −47.80 | −34.15 | −30.26 | −39.34 | |
1EER (ESR1) | Glide | −7.16 | −7.25 | −7.84 | −6.48 | −6.94 | −5.15 | −5.28 | −6.05 | −5.25 |
Autodock | −8.38 | −8.03 | −8.39 | −7.35 | −8.57 | −7.78 | −7.40 | −7.63 | −6.46 | |
RMSD | 1.17 | 1.98 | 1.09 | 1.68 | 2.76 | 1.12 | 2.40 | 2.04 | 3.30 | |
MMGBSA_ΔG | −52.38 | −51.17 | −29.77 | −56.45 | −66.48 | −45.21 | −50.13 | −39.92 | −36.70 | |
MMGBSA_ΔG(NS) | −54.39 | −56.67 | −34.57 | −58.99 | −68.72 | −51.86 | −52.21 | −49.03 | −42.32 |
Target | D1 | C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 |
---|---|---|---|---|---|---|---|---|---|---|
Hormone-Independent Breast Cancer Targets | ||||||||||
1XKK | −9.570 | −6.060 | −4.660 | −4.710 | −3.896 | −5.980 | −5.330 | −5.950 | −5.450 | −8.280 |
3lvp | −7.690 | −6.830 | −6.240 | −6.680 | −7.060 | −6.560 | −6.170 | −6.150 | −6.060 | −6.770 |
Hormone-Dependent Breast Cancer Targets | ||||||||||
1ERR | −5.890 | −6.070 | −6.600 | −5.10 | −6.820 | −5.840 | −6.140 | −5.970 | −5.290 | −6.750 |
3MZW | −6.330 | −6.520 | −6.820 | −4.510 | −6.000 | −6.350 | −5.960 | −6.120 | −6.500 | −7.380 |
Target | 1XKK | 3LVP | 3MZW | IERR | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Parameter | ΔG_D | ΔG_G | ΔG_P | ΔG_D | ΔG_G | ΔG_P | ΔG_D | ΔG_G | ΔG_P | ΔG_D | ΔG_G | ΔG_P |
D1 | −9.57 | −23.74 | −0.36 | −7.69 | −32.11 | −1.27 | −5.890 | −24.70 | −1.34 | −6.330 | −24.08 | −0.26 |
C4 | −3.90 | −24.82 | −25.32 | −7.06 | −38.24 | −5.55 | −6.820 | −15.89 | −1.72 | −6.000 | −35.24 | −3.67 |
C9 | −8.28 | −25.86 | −24.95 | −6.77 | −31.94 | −3.67 | −6.750 | −32.90 | −0.49 | −7.380 | −31.94 | −5.55 |
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Vyshnavi AM, H.; Sankaran, S.; Namboori PK, K.; Venkidasamy, B.; Hirad, A.H.; Alarfaj, A.A.; Vinayagam, R. In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer. Medicina 2023, 59, 1412. https://doi.org/10.3390/medicina59081412
Vyshnavi AM H, Sankaran S, Namboori PK K, Venkidasamy B, Hirad AH, Alarfaj AA, Vinayagam R. In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer. Medicina. 2023; 59(8):1412. https://doi.org/10.3390/medicina59081412
Chicago/Turabian StyleVyshnavi AM, Hima, Sathianarayanan Sankaran, Krishnan Namboori PK, Baskar Venkidasamy, Abdurahman Hajinur Hirad, Abdullah A. Alarfaj, and Ramachandran Vinayagam. 2023. "In Silico Analysis of the Effect of Hydrastis canadensis on Controlling Breast Cancer" Medicina 59, no. 8: 1412. https://doi.org/10.3390/medicina59081412