Effect of thermostable mutations on the neurotensin receptor 1 (NTSR₁) activation state

Abstract

Neurotensin (NTS) is a 13-amino acid neuropeptide with neuroendocrine and vasoactive functions that is widely expressed in the central nervous system and gastrointestinal tract. NTS is sensed by a multiple cell surface proteins including two G protein-coupling receptors (GPCRs): NTS receptors 1 and 2 (NTSR₁ and NTSR₂). Crystal structures of NTSR₁ have successfully elucidated agonist binding within the orthosteric pocket of receptor but have not revealed the full activation state of the receptor. Recent studies have attempted to address this challenge by improving NTSR₁ crystal formation via thermostable mutants; unfortunately, these mutations exhibit functional defects in the G protein coupling of NTSR₁. Here, we have used molecular dynamics simulations to gain greater insights into how the amino acid substitutions used in these thermostable mutants (E166A, L310A and F358A) impact receptor activation. Our simulations indicate that wild-type NTSR₁ in complex with NTS_8-13 shows more active-like features including a 17.7 Å shift in TM6, reflecting a network of polar and aromatic interactions orchestrating agonist-induced receptor conformational changes. We also provide evidence indicating that F358 is a precursor to the rotamer change observed in W321, and our collective analysis also suggests that mutations E166A and F358A are less impactful to G protein coupling than L310A. Furthermore, we believe that our findings can be used to design future NTSR₁ mutants that do not interfere with agonist-induced conformational changes and downstream G protein coupling and thus produce structures that will allow visualization of the fully activated receptor conformation.

Communicated by Ramaswamy H. Sarma

Keywords:

• NTSR₁
• GPCR
• molecular dynamics
• receptor activation
• thermostable mutant

Disclosure statement

The authors declare no competing financial interests.

Additional information

Funding

This work was supported in part by the National Science Foundation’s XSEDE Supercomputer resources grant TG-MCB160081 to S.N.