The design of TOPK inhibitors using similarity search, molecular docking, and MD simulations
1Department of Medicinal Chemistry and Pharmacognosy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, Jordan.
Abstract:
Cancer is still a major cause of death worldwide. Unfortunately, the majority of current anticancer treatments suffer many limitations, mainly emergence of resistance and lack of selectivity which necessitate the search for new therapeutics. The TOPK enzyme emerges as a promising target due to its overexpression in many cancer types while being rarely detected in normal tissues. Therefore, targeting TOPK would affect the malignant activity of cancerous cells while sparing normal ones. Further, its vital role in cell division, particularly in cytokinesis, adds to its safety to normal non-multiplying cells. In this study, a combined ligand and structure-based approach was utilized to identify potential TOPK inhibitors. Previously, we identified TOPK inhibitors using a structure-based approach following the construction of a 3D homology model of the TOPK enzyme. Herein, the most active identified inhibitor (lead) was used as a search query to conduct similarity search against PubChem and ChemBridge databases. Retrieved hits were filtered using drug-like filters, docked into the ATP binding site of the enzyme, and finally, the binding free energies of all docked poses were calculated. Based on the computational scores, eight hits were selected as potential TOPK inhibitors. The predicted ADMET descriptors of the eight selected hits were generally favorable. Further, MD simulations of the top scoring hit were conducted to investigate its binding dynamics compared to the lead compound and OTS964 which agreed with the docking results and propose the selected hits as potential TOPK inhibitors. Yet, biochemical testing is still needed to validate these results.
Insights
Researchers identified new potential cancer drug candidates targeting the TOPK enzyme, which is overexpressed in many cancers. This approach aims to develop more selective and effective anticancer therapies with fewer side effects.
Area of Science:
- Biochemistry and Medicinal Chemistry
- Computational Drug Discovery
- Oncology Therapeutics
Background:
- Cancer remains a leading global cause of mortality, with existing treatments facing challenges like drug resistance and poor selectivity.
- The TOPK (T-LAK cell origin killer) enzyme is significantly overexpressed in various cancer types but minimally in normal tissues, presenting a promising therapeutic target.
- TOPK's crucial role in cell division, specifically cytokinesis, suggests that targeting it could selectively impact cancer cells while sparing healthy, non-proliferating cells.
Purpose of the Study:
- To identify novel small molecules as potential inhibitors of the TOPK enzyme using a combined computational approach.
- To leverage structure-based drug design and virtual screening to discover new therapeutic agents for cancer treatment.
Main Methods:
- A structure-based approach was used to develop a 3D homology model of the TOPK enzyme.
- Similarity searches against PubChem and ChemBridge databases were performed using a previously identified TOPK inhibitor as a query.
- Retrieved compounds underwent drug-likeness filtering, molecular docking into the ATP binding site, binding free energy calculations, and molecular dynamics simulations.
Main Results:
- Eight potential TOPK inhibitors were selected based on computational scoring, exhibiting favorable predicted ADMET properties.
- Molecular dynamics simulations of the top-scoring hit corroborated docking results, showing comparable binding dynamics to known inhibitors.
- The computational strategy successfully identified promising lead compounds for further experimental validation.
Conclusions:
- The study proposes eight novel compounds as potential TOPK inhibitors, identified through a robust computational drug discovery pipeline.
- These findings highlight the potential of targeting TOPK for developing selective anticancer therapies.
- Further biochemical validation is necessary to confirm the inhibitory activity and therapeutic potential of the identified compounds.
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