Linarin as an Anti-Cancer Therapeutic: In-Silico and Machine Learning Approach for HPV

Human Papillomavirus (HPV) is one of the principal causes of death among females in emerging nations. It is a sexually transmitted virus that shatters the cell cycle by two of its oncoproteins i.e., E6 and E7. HPV E7 oncoprotein binds to the pRb (Retinoblastoma tumor suppressor gene). This binding hinders the association of pRb with E2F causing the release of transcription factor E2F, vital for the cell cycle to proceed for the S phase thus causing abnormal unregulated cell division and growth. Precautionary HPV Vaccines are available, however infected populations are deprived of therapeutic treatments based on drugs. To rectify this issue, we try to recognize bioactive molecule inhibitors of the HPV E7 oncoprotein using bioinformatics softwares and in-silico molecular docking. In this in-silico study, flavonoid compounds were virtually screened and observed for their ability to inhibit E7 oncoprotein. The virtual screening was conducted in several steps starting with molecular docking to find their potential binding energy with the target followed by validating results with machine learning using Python. According to the literature review Baicalein is considered as a most potent inhibitor of E7 (taken as control). In this study, we worked on approximately 50 flavonoids out of which 15 have shown significant binding energies. Usually, the bioactive compounds present in flavonoids will not significantly cause a side effect. Linarin has shown maximum binding energy of ΔG -10.6 kcal/mol comparable to that of the standard inhibitor Baicalein with ΔG -7.36 kcal/mol. Docking results were further validated by machine learning. Linarin can be easily extracted from the Angiosperms and used as an herbal product to inhibit HPV E7 oncoprotein. The compounds identified here contribute to fascinating beginnings for further medical research and the development of therapeutic treatment.