Identification of HER2 inhibitors from curcumin derivatives using combination of in silico screening and molecular dynamics simulation

Virtual screening and molecular dynamics simulation of natural compounds as potential inhibitors of serine/threonine kinase 16 for anticancer drug discovery

Serine/threonine kinase 16 (STK 16) is involved in many facets of cellular regulation; activation of STK 16 plays a crucial role in the migration of cancer cells. Therefore, it is a novel target for the discovery of anticancer agents. Herein, virtual screening and dynamics simulation were used to screen a large library of natural compounds against STK 16 using Schrodinger suit 2021-2 and GROMACS 2021.6. The results predicted five molecules with high binding affinity against the target, with NPC132329 (Arcyriaflavin C) and NPC160898 having higher binding affinity and molecular mechanics generalized born surface area (MM/GBSA), suggesting that it is better than the standard inhibitor. The molecular dymanics (MD) simulation studies showed that the STK 16-NPC132329 complex has the lowest root mean square deviation, and STK 16-NPC160898 was the most stable compared with the standard drug and selective STK 16 inhibitor. The minimal fluctuation was observed in the STK 16-NPC132329 and STK 16-NPC160898 complexes based on the root mean square fluctuation trajectory with NPC132329 and NPC160898 forming 2 and 3 hydrogen bonds respectively with the amino acid residue of the target's binding site. Overall, NPC132329 and NPC160898 are better STK 16 inhibitors than the standard drug and selective inhibitor, which can be further studied to discover novel anticancer drugs.

Computational insights into overcoming resistance mechanisms in targeted therapies for advanced breast cancer: focus on EGFR and HER2 co-inhibition

In the present study, the formation of a heterodimer involving both epidermal growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) has been explored as a potential therapeutic mechanism to inhibit the progression of breast cancer. Virtual screening using molecular docking resulted in the three hit compounds (ZINC08382411, ZINC08382438, and ZINC08382292) with minimum binding scores and commonly binding to both receptors. Further, MD simulation analysis of these complexes illustrated the high stability of these compounds with EGFR and HER2. RMSD showed that ZINC08382411 displayed the most stable RMSD of 2 - 3 Å when bound to both receptors, suggesting to have strong compatibility with the active site of the receptor. Hydrogen bond analysis showed that ZINC08382411 forms the maximum number of H-bonds (2 to 3) in both EGFR and HER2 bound complexes, with the highest occupancy of 62% and 79%, respectively. Binding free energy calculation showed that ZINC08382411 possesses maximum affinity towards both the receptors with ΔGbind = -129.628 and -164.063 kJ/mol, respectively. This approach recognizes the significance of EGFR and HER2 in breast cancer development and aims to disrupt their collaborative signaling, which is known to promote the antagonistic behavior of cancer cells. By focusing on this EGFR/HER2 heterodimer, the study offers a promising avenue for identifying a potential candidate (ZINC08382411) that may inhibit breast cancer cell growth and potentially improve patient outcomes. The study's findings may contribute to the ongoing efforts to advance breast cancer treatment strategies.Communicated by Ramaswamy H. Sarma.

Integrating biocomputational techniques for Breast cancer drug discovery via the HER-2, BCRA, VEGF and ER protein targets

Computational modelling remains an indispensable technique in drug discovery. With myriad of high computing resources, and improved modelling algorithms, there has been a high-speed in the drug development cycle with promising success rate compared to the traditional route. For example, lapatinib; a well-known anticancer drug with clinical applications was discovered with computational drug design techniques. Similarly, molecular modelling has been applied to various disease areas ranging from cancer to neurodegenerative diseases. The techniques ranges from high-throughput virtual screening, molecular mechanics with generalized Born and surface area solvation (MM/GBSA) to molecular dynamics simulation. This review focuses on the application of computational modelling tools in the identification of drug candidates for Breast cancer. First, we begin with a succinct overview of molecular modelling in the drug discovery process. Next, we take note of special efforts on the developments and applications of combining these techniques with particular emphasis on possible breast cancer therapeutic targets such as estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), vascular endothelial growth factor (VEGF), breast cancer gene 1 (BRCA1), and breast cancer gene 2 (BRCA2). Finally, we discussed the search for covalent inhibitors against these receptors using computational techniques, advances, pitfalls, possible solutions, and future perspectives.