In silico identification of potential protein kinase C alpha inhibitors from phytochemicals from IMPPAT database for anticancer therapeutics: a virtual screening approach

Protein Kinase C alpha (PKCa) is a critical signaling molecule that plays a crucial role in various physiological processes, including cell growth, differentiation, and survival. Over the years, there has been a growing interest in targeting PKCa as a promising drug target for the treatment of various diseases, including cancer. Targeting PKCa can, therefore, serve as a potential strategy to prevent cancer progression and enhance the efficacy of conventional anticancer therapies. We conducted a systematic search for promising compounds for their anticancer potential that target PKCa using natural compounds from the IMPPAT database. The initial compounds were screened through various tests, including analysis of their physical and chemical properties, PAINS filter, ADMET analysis, PASS analysis, and specific interaction analysis. We selected those that showed high binding affinity and specificity to PKCa from the screened compounds, and we further analyzed them using molecular dynamics simulations (MDS) and principal component analysis (PCA). Various systematic parameters from the MDS analyses suggested that the protein-ligand complexes were stabilized throughout the simulation trajectories of 100 nanoseconds (ns). Our findings indicated that compounds Nicandrenone and Withaphysalin D bind to PKCa with high stability and affinity, making them potential candidates for further research in cancer therapeutics innovation in clinical contexts.Communicated by Ramaswamy H. Sarma