Molecular modeling simulation studies reveal new potential inhibitors against HPV E6 protein.
Joel Ricci-LópezAbraham Vidal-LimonMatías ZunñigaVerónica A JimènezJoel B AldereteCarlos A BrizuelaSergio A AguilaPublished in: PloS one (2019)
High-risk strains of human papillomavirus (HPV) have been identified as the etiologic agent of some anogenital tract, head, and neck cancers. Although prophylactic HPV vaccines have been approved; it is still necessary a drug-based treatment against the infection and its oncogenic effects. The E6 oncoprotein is one of the most studied therapeutic targets of HPV, it has been identified as a key factor in cell immortalization and tumor progression in HPV-positive cells. E6 can promote the degradation of p53, a tumor suppressor protein, through the interaction with the cellular ubiquitin ligase E6AP. Therefore, preventing the formation of the E6-E6AP complex is one of the main strategies to inhibit the viability and proliferation of infected cells. Herein, we propose an in silico pipeline to identify small-molecule inhibitors of the E6-E6AP interaction. Virtual screening was carried out by predicting the ADME properties of the molecules and performing ensemble-based docking simulations to E6 protein followed by binding free energy estimation through MM/PB(GB)SA methods. Finally, the top-three compounds were selected, and their stability in the E6 docked complex and their effect in the inhibition of the E6-E6AP interaction was corroborated by molecular dynamics simulation. Therefore, this pipeline and the identified molecules represent a new starting point in the development of anti-HPV drugs.
Keyphrases
- high grade
- molecular dynamics simulations
- small molecule
- protein protein
- transcription factor
- induced apoptosis
- molecular docking
- cervical cancer screening
- cell cycle arrest
- molecular dynamics
- binding protein
- signaling pathway
- single cell
- endoplasmic reticulum stress
- emergency department
- gene expression
- cell therapy
- dna methylation
- oxidative stress
- genome wide
- stem cells
- deep learning
- drug induced
- aqueous solution
- electronic health record
- neural network
- virtual reality
- human health