Abstract:
Telomeres protect chromosomal ends during cell replication. Cell division
shortens telomeres, causing senescence, differentiation, or necrosis and eventually aging.
Aging caused by telomeres shortening is linked to diabetes, hypertension, Alzheimer's,
and cancer. Therefore, activating hTERT (human telomerase reverse transcriptase),
which lengthens telomeres, or boosting telomerase component expression can repair
degeneration and postpone or reverse aging but the hyper activated hTERT promotes cell
proliferation or cancer. hTERT mediated cell proliferation and p53 mediated cell
apoptosis is critical in determining the cell fate. In order to reverse the aging effects of
telomere shortening and keep cell proliferation under control, activities of hTERT and
p53 must be kept within a normal range. A biological regulatory network (BRN) has been
constructed and describe the dual role of Sp1 that activates both hTERT and p53.
Moreover, the normal, moderately active and hyperactive states of Sp1 was related to the
normal, moderately active and hyperactive concentration gradients of hTERT and p53 for
the maintenance of normal cell proliferation and apoptosis. Furthermore, Protein-Protein
docking helped in elucidating the binding patterns of hTERT activators like c-Myc and
STAT3. Molecular dynamic simulations validated the stability of docked complex
binding patterns by RMSD, RMSF, and Radius of gyration. Additionally, the binding
site residues of hTERT Arg-224, Arg-293 and Arg-535 showed hydrogen bonding in c-
Myc-hTERT complex before and after MD simulation, whereas the interacting residues
Gly-35, Arg-63, Asp-147, Trp-203, Ser-206, Ser-227 and Ala-228 showed the stable
hydrogen bonding before and after MD simulation in STAT3-hTERT complex, Arg-208,
Arg-224 and Glu-533 formed the salt bridges in c-Myc-hTERT complex and Arg-63 and
Glu-209 showed salt bridges in STAT3-hTERT complex. Thus, the interaction profiles of
c-Myc-hTERT complex and STAT3-hTERT complex is identified as important for the
future design of artificial activators of hTERT, or to classify the peptides or monoclonal
antibodies of hTERT as activator or not-activator of hTERT by different machine
learning models on the basis of evaluation by these binding patterns.
