Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies
Abstract
:1. Introduction
2. Results and Discussion
3. Methods
3.1. Machine Learning Model Building and Screening Natural Compounds
3.2. CTSL Protein Retrieval and Preparation
3.3. Structure-Based Virtual Screening
3.4. Physiochemical and ADMET Properties Prediction
3.5. Molecular Dynamics (MD) Simulation
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Compound ID | Binding Affinity (kcal/mol) | RF_Prediction Score | MolWt | MolLogP | HA | HD | RB |
---|---|---|---|---|---|---|---|
ZINC4097985 | −7.9 | 0.794152879 | 408.403 | −0.3084 | 9 | 4 | 4 |
ZINC4098355 | −7.6 | 0.788373218 | 422.474 | 2.6621 | 8 | 0 | 8 |
ZINC96023730 | −6.4 | 0.684678299 | 484.721 | 6.6687 | 4 | 1 | 10 |
ZINC38429839 | −7.2 | 0.652458095 | 456.711 | 6.6289 | 3 | 2 | 5 |
ZINC13383393 | −6.5 | 0.651968775 | 358.302 | 2.2031 | 6 | 5 | 4 |
ZINC299817526 | −6.6 | 0.648805457 | 468.678 | 7.4357 | 2 | 2 | 9 |
ZINC4098425 | −7.5 | 0.646199152 | 232.235 | 0.969 | 4 | 1 | 1 |
ZINC5665355 | −6.6 | 0.643019115 | 470.694 | 7.6597 | 2 | 2 | 9 |
ZINC1702729 | −6.9 | 0.640874436 | 388.416 | 2.8323 | 7 | 3 | 6 |
ZINC238760072 | −6.5 | 0.633125885 | 376.493 | 4.5247 | 5 | 0 | 8 |
ZINC238790964 | −6.4 | 0.624866697 | 430.541 | 2.3323 | 6 | 3 | 3 |
ZINC28876559 | −6.4 | 0.621405139 | 440.712 | 7.2275 | 2 | 2 | 5 |
ZINC3982483 | −6.6 | 0.620906903 | 406.545 | −0.857 | 8 | 5 | 7 |
AZ12878478 (control) | −6.3 | - | - | - | - | - | - |
Molecule Property | Value | Unit | ||
---|---|---|---|---|
ZINC4097985 | ZINC4098355 | |||
Absorption | ||||
Caco-2 Permeability | −5.51 | −5.27 | log(cm/s) | |
HIA | 62.85 | 66.63 | % | |
Pgp Inhibition | 36.88 | 30.23 | % | |
log D7.4 | 1.83 | 1.72 | log-ratio | |
Aqueous Solubility | −4.46 | −4.09 | log(mol/L) | |
Oral Bioavailability | 36.66 | 40.58 | % | |
Distribution | ||||
BBB | 18.77 | 29.17 | % | |
PPBR | 35.01 | 45.2 | % | |
VDss | 3.13 | 3.39 | L/kg | |
Metabolism | ||||
CYP2C9 | Inhibition | 50.93 | 53.89 | % |
CYP2D6 | 98.02 | 92.92 | ||
CYP3A4 | 34.92 | 35.24 | ||
CYP2C9 | Substrate | 34.14 | 32.66 | |
CYP2D6 | 51.03 | 56.54 | ||
CYP3A4 | 41.78 | 42.75 | ||
Excretion | ||||
Half-Life | 58.17 | 63.46 | h | |
CL-Hepa | 47.36 | 55.3 | μL min−1 (106 cells)−1 | |
CL-Micro | 40.61 | 48.28 | mL min−1 g−1 | |
Toxicity | ||||
hERG Blockers | 43.62 | 40.04 | % | |
Ames | 48.32 | 39.31 | ||
DILI | 49.79 | 59.98 | ||
LD50 | 2.75 | 2.45 | −log(mol/kg) |
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Almalki, A.A.; Shafie, A.; Hazazi, A.; Banjer, H.J.; Bakhuraysah, M.M.; Almaghrabi, S.A.; Alsaiari, A.A.; Alsaeedi, F.A.; Ashour, A.A.; Alharthi, A.; et al. Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies. Int. J. Mol. Sci. 2023, 24, 17208. https://doi.org/10.3390/ijms242417208
Almalki AA, Shafie A, Hazazi A, Banjer HJ, Bakhuraysah MM, Almaghrabi SA, Alsaiari AA, Alsaeedi FA, Ashour AA, Alharthi A, et al. Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies. International Journal of Molecular Sciences. 2023; 24(24):17208. https://doi.org/10.3390/ijms242417208
Chicago/Turabian StyleAlmalki, Abdulraheem Ali, Alaa Shafie, Ali Hazazi, Hamsa Jameel Banjer, Maha M. Bakhuraysah, Sarah Abdullah Almaghrabi, Ahad Amer Alsaiari, Fouzeyyah Ali Alsaeedi, Amal Adnan Ashour, Afaf Alharthi, and et al. 2023. "Targeting Cathepsin L in Cancer Management: Leveraging Machine Learning, Structure-Based Virtual Screening, and Molecular Dynamics Studies" International Journal of Molecular Sciences 24, no. 24: 17208. https://doi.org/10.3390/ijms242417208