Structural Bioinformatics Protocol to Modulate the Activity of Human Telomerase (hTERT) For the Age Reversal Outcomes

Abstract

Cell division causes telomere shortening, which contributes to the aging process. The telomeres are regulated by telomerase enzyme however its activity is limited naturally. Therefore, activation of telomere through telomerase (hTERT) may reduce the shortening of telomere which reverse or slow down aging. Various studies have highlighted that the hyper activation of hTERT can cause cell proliferation or cancer. P53 is a gene responsible for cell apoptosis but mutations in it also cause cancer or uncontrolled cell proliferation. Therefore, a normal range of activity is required for both hTERT and p53 to keep cell proliferation and apoptosis in control and to limit or reverse the effects of aging. A Biological Regulatory Network was constructed that proves the role of hTERT mediated cell proliferation and p53 mediated cell apoptosis in cell fate determination. Also the docking and MD simulations of hTERT and its regulators were performed and interaction profiles were identified as important for future design of artificial activators. In this study of p53 and its activators, the protein docking is done with its regulators which determined the binding patterns of p53 activators like Phf20 and p300. Molecular Dynamic Simulation validated the stability of docked complexes through RMSD, RMSF and Radius of Gyration. Additionally, the binding site residues of p53 Arg 158, Arg 267 showed hydrogen bonding in Phf20-p53 complex before and after simulation whereas the interacting residue Glu-224 showed the stable hydrogen bonding before and after simulation in p300-p53 complex. Asp-208, Arg-158 formed the salt bridges in Phf20-p53 complex and Glu-224 and Asp-186 showed salt bridges in p300-p53 complex. Thus the interaction profiles of Phf20-p53 complex and p300-p53 complex is identified as important for the future design of artificial activators of p53 or to classify the peptides or monoclonal antibodies of p53 as activator or nonactivator of p53 by different machine learning models on the basis of evaluation by these binding patterns.