Virtual screening of a natural compound library at orthosteric and allosteric binding sites of the neurotensin receptor

Abstract

Molecular dynamics (MD) simulation using the AMBER force field has been performed on the neurotensin (NT) receptor, a class A type G-protein-coupled receptor in its activated conformation co-crystallized with the non-peptide agonists. For structure-based hit molecule identification via natural chemical compound library, orthosteric sites on NT receptor have been mapped by docking using AutoDock4.0 and Vina with the known agonists and antagonists SR48692, SR142948, ML301 and ML314 of the receptor. Furthermore, clustering analysis on the MD trajectories by SIMULAID has been performed to filter receptor conformations for the allosteric binders from the Otava natural compound library. Comparative mappings of contrasting binding region patterns have been done between the crystal structure orthosteric sites as well as the binding regions in the SIMULAID-based cluster center conformations from MD trajectories with the FTmap server using the small organic molecule fragments as the probes. The distinct binding region in the cluster-based conformations in the extracellular region of the receptor has been identified for targeted docking by Otava natural chemical compound library using AutoDock4.0 and Vina docking suites to obtain putative allosteric binders. A group of compounds from the Otava library has been identified as showing high free energy in both AutoDock4.0 and Vina docking suites. Biophysical assessments on the natural compound computational hit molecules will be done to identify lead structures from the hit molecules.

Communicated by Ramaswamy H. Sarma

Keywords:

• G-protein-coupled receptor
• neurotensin receptor
• molecular dynamics
• virtual screening
• allosteric sites
• SIMULAID
• FTmap server
• natural compound library

Acknowledgements

We would like to acknowledge computational resources and staff expertise from the Department of Scientific Computing at Icahn School of Medicine at Mount Sinai as well as NSF-supported XSEDE Super Computer resources in Stampede2 facilities (Allocation # TG-MCB140084). Also, support from St John’s University’s Office of Grants and Sponsored Research is acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.