Computational Approach to the Discovery of Phytochemical Molecules with Therapeutic Potential Targets to the PKCZ protein

Abstract

Background: Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies in humans and the average 5-year survival rate is one of the lowest among aggressive cancers. Protein kinase C zeta (PKCZ) is highly expressed in head and neck tumors, and the inhibition of PKCZ reduces MAPK activation in five of seven head and neck tumors cell lines. Considering the worldwide HNSCC problems, there is an urgent need to develop new drugs to treat this disease, that present low toxicity, effective results and that are relatively inexpensive. Method: A unified approach involving homology modeling, docking and molecular dynamics simulations studies on PKCZ are presented. The in silico study on this enzyme was undertaken using 10 compounds from latex of Euphorbia tirucalli L. (aveloz). Results: The binding free energies highlight that the main contribution in energetic terms for the compounds-PKCZ interactions is based on van der Waals. The per-residue decomposition free energy from the PKCZ revealed that the compounds binding were favorably stabilized by residues Glu300, Ileu383 and Asp394. Based on the docking, Xscore and molecular dynamics results, euphol, ß-sitosterol and taraxasterol were confirmed as the promising lead compounds. Conclusion: The present study should therefore play a guiding role in the experimental design and development of euphol, ß-sitosterol and taraxasterol as anticancer agents in head and neck tumors. They are potential lead compounds, better than other ligands based on the best values of docking and MM-PBSA energy.

Computational Prediction of Binding Affinity for CDK2-ligand Complexes. A Protein Target for Cancer Drug Discovery

“…SAnDReS aims to integrate all necessary steps to create machine learning models in one suite of programs [44]. SAnDReS has been applied to a wide range of different protein systems [125][126][127][128][129][130][131][132][133][134][135][136][137][138][139][140][141][142][143] and has successfully generated machine learning models that outperform classical scoring functions in the prediction of binding affinity [45,[144][145][146][147][148][149][150][151].…”