In-silico discovery of inhibitors against human papillomavirus E1 protein

Abstract

High-risk (HR) Human papillomavirus (e.g. HPV16 and HPV18) causes approximately two-thirds of all cervical cancers in women. Although the first and second-generation vaccines confer some protection against individuals, there are no approved drugs to treat HR-HPV infections to-date. The HPV E1 protein is an attractive drug target because the protein is highly conserved across all HPV types and is crucial for the regulation of viral DNA replication. Hence, we used the Random Forest algorithm to construct a Quantitative-Structure Activity Relationship (QSAR) model to predict the potential inhibitors against the HPV E1 protein. Our QSAR classification model achieved an accuracy of 87.5%, area under the receiver operating characteristic curve of 1.00, and F-measure of 0.87 when evaluated using an external test set. We conducted a drug repurposing campaign by deploying the model to screen the Drugbank database. The top three compounds, namely Cinalukast, Lobeglitazone, and Efatutazone were analyzed for their cell membrane permeability, toxicity, and carcinogenicity. Finally, these three compounds were subjected to molecular docking and 200 ns-long Molecular Dynamics (MD) simulations. The predicted binding free energies for the candidates were calculated using the MM-GBSA method. The binding free energies for Cinalukast, Lobeglitazone, and Efatutazone were −37.84 kcal/mol, −25.30 kcal/mol, and −29.89 kcal/mol respectively. Therefore, we propose their chemical scaffolds for future rational design of E1 inhibitors.

Communicated by Ramaswamy H. Sarma

Keywords:

• Drug repurposing
• HPV18 E1 protein
• virtual screening
• molecular dynamics
• machine learning

Disclosure statement

No potential conflict of interest was reported by the authors.

Data and software availability

Protein and chemicals structures were hydrogenated and energy minimized using the UCSF Chimera package (version 1.15) and PyMol 2.0, respectively. Focused docking was conducted using the Genetic Optimization for Ligand Docking (GOLD; version 5.3.0) using the free academic license courtesy of the Cambridge Crystallographic Data Centre (CCDC). All Molecular Dynamics simulation were conducted using the Assisted Model Building with Energy Refinement (AMBER; version 14.0) using a purchased academic license.

Author contributions

X.C.W. and T.R. conceived the study and guided the experimental design. K.K.J.G. conducted the work. H.S.S. and W.K.Y. provided input on data analysis. All authors were involved in the conception, preparation, and review of the manuscript.

Additional information

Funding

This work is kindly supported by the Swinburne Strategic Research Grant (SSRG-5624) by Swinburne University of Technology Sarawak awarded to X.C.W.