Knowledge-driven design and optimization of potent symmetric anticancer molecules : A case study on PKM2 activators
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Zusammenfassung
Background
Pyruvate kinase M2 (PKM2) is preferentially expressed as a low-activity dimer over the active tetramer in proliferating tumor cells, resulting in metabolic reprogramming to achieve high energy requirements and nutrient uptake. This leads to a shift from the normal glycolytic pathway causing tumor cells to proliferate uncontrollably. This study utilizes knowledge-based drug discovery to determine the critical features from experimentally known PKM2 activators and design compounds that would significantly confer a stable structural and functional edge over the known compounds which are still at the preclinical stage.
Methods
Conscientious molecular modeling studies were carried out and critical structural features were identified and validated from the knowledge of experimentally known PKM2 activators to confer high-binding affinities. A virtual library of 200 palindromic and non-palindromic activators was designed based on these identified critical features to target a distinct activator binding-site. This binding would favor specific dimer-dimer association and subsequent protein tetramerization. The resultant compounds strongly correlated with identified structural features and binding affinities which further strengthened our findings. The designed activators were then subjected to pharmacokinetic profiling and toxicity prediction, followed by free-binding energy calculations and MD simulations.
Results
All the virtually designed activators comprising the identified critical features were observed to confer high-binding affinities ranging from −9.1 to −15.0 kcal/mol to the receptor protein. The designed activators also demonstrated optimum pharmacokinetic and toxicity profiles.
Conclusion
The best activators selected for MD simulations studies were conclusively observed to stabilize the required tetrameric conformation suggesting that these activators could potentially target PKM2 tetramerization that might restore the normal glycolytic pathway and suppress tumor progression.
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JAISWAL, Eshika, Christoph GLOBISCH, Alok JAIN, 2022. Knowledge-driven design and optimization of potent symmetric anticancer molecules : A case study on PKM2 activators. In: Computers in Biology and Medicine. Elsevier. 2022, 151(Part B), 106313. ISSN 0010-4825. eISSN 1879-0534. Available under: doi: 10.1016/j.compbiomed.2022.106313BibTex
@article{Jaiswal2022Knowl-59938, year={2022}, doi={10.1016/j.compbiomed.2022.106313}, title={Knowledge-driven design and optimization of potent symmetric anticancer molecules : A case study on PKM2 activators}, number={Part B}, volume={151}, issn={0010-4825}, journal={Computers in Biology and Medicine}, author={Jaiswal, Eshika and Globisch, Christoph and Jain, Alok}, note={Article Number: 106313} }
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