In silico structure prediction, molecular docking and dynamic simulation studies on G Protein-Coupled Receptor 116: a novel insight into breast cancer therapy

Abstract

G Protein-Coupled Receptor gains more importance in cancer research; because of their key role in several physiologic functions of cells. However, most of the GPCR’s are orphan receptors, this hampers the finding of drugs against GPCR. G Protein-Coupled Receptor 116 is an adhesion orphan receptor that intensifies the invasion of cells in Triple-Negative Breast Cancer. In this study, existing FDA approved anticancer drugs were chosen as ligands and molecular docking was performed using in silico protein model of GPR116. Molecular interaction was analyzed carefully to identify the crucial amino acids present in binding pocket. Molecular dynamics simulations study executed to verify the structural and dynamic properties of Doxorubicin–GPR116 protein complex. The results have shown that Doxorubicin, Neratinib maleate, Epirubicin, and Lapatinib Ditosylate have good interaction with GPR116 binding site. Tyrosine 195 (Y195), Cysteine 196 (C196), Argenine 197 (R197), and Tryptophan 100 (W100) are commonly found in the majority of ligand–target interaction, hence based on the computational studies selective amino acids might be crucial for functional properties. Further to confirm crucial amino acids, computational mutation studies were executed. Molecular docking analysis with mutated GPR116 disclosed that significant variation in G score compared withligand–native protein interaction. Hence, the theoretical confirmatory structural properties changes support to prove selective crucial amino acids play the significant role in ligand binding. Molecular dynamic simulation results reveal that the interaction was stable throughout the MD simulation. To the best of our prognosis, GPR116 could be the best molecular target for breast cancer drug discovery.

Communicated by Ramaswamy H. Sarma

Keywords:

• In silico screening
• GPR116
• molecular docking
• molecular simulation
• breast cancer
• drug discovery

Acknowledgements

The authors thank the management and the authorities of Mepco Schlenk Engineering College, Sivakasi, and Karunya Institute of Technology (Deemed to be University), Coimbatore for their extended support in conducting the wet-lab experiment and computational studies.

Disclosure statement

No potential conflict of interest was reported by the authors.