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2000
Volume 21, Issue 18
  • ISSN: 1570-1808
  • E-ISSN: 1875-628X

Abstract

Background

Thymidine kinase 2 (TK2) is a crucial enzyme in the mitochondrial pyrimidine salvage pathway, strongly associated with several mitochondrial diseases. Current treatments frequently damage mitochondria, leading to a decrease in cellular energy output.

Objective

This study aimed to use computational approaches to identify inhibitors of TK2 that could prevent these harmful consequences.

Methods

The initial screening process entailed the application of machine learning algorithms, more particularly a Random Forest model, which was trained on 189 FDA-approved drugs and decoy datasets obtained from the DUD-E database. Its purpose was to identify potential inhibitors. The molecular docking technique was employed to evaluate the affinity of the chosen medicines towards TK2. Molecular dynamics (MD) simulations lasting 100 nanoseconds were employed to conduct additional validation by examining the dynamic interactions between the top-found compounds and TK2.

Results

Three hit compounds (3168, 5209502, and 135402009) were identified through the screening process for their high affinity for TK2. Compound 5209502 had the most stable interaction with the lowest root-mean-square deviation (RMSD) in the molecular dynamics (MD) simulations and maintained 12 hydrogen bonds consistently. MM/GBSA computations verified that 5209502 exhibited the strongest binding affinity with a binding free energy of -62.14 kcal/mol, which was notably lower than that of the control ligand.

Conclusion

Compound 5209502 is a promising candidate for additional experimental evaluation because of its notable stability and great affinity for TK2. This chemical may provide a focused and less harmful treatment for mitochondrial illnesses linked to TK2 malfunction.

© 2024 Bentham Science Publishers
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Screening of FDA-approved Drugs against Protein Thymidine Kinase 2 Using Machine Learning Method Validated Applying Molecular Dynamics and Free Energy Landscape Calculation
Letters in Drug Design & Discovery 21, 4278 (2024); https://doi.org/10.2174/0115701808321363241002110548
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  • Article Type:     Research Article
Keyword(s): machine learning; mitochondrial DNA depletion syndrome; molecular docking; molecular dynamics; random forest model; Tyrosine kinase-2

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