Abstract

Background: Investigating the structural attributes of the murine beta3-adrenergic receptor (β3-AR) is imperative for comprehending metabolic regulation, given its close resemblance to the human β3-AR. This receptor holds promise as a target for novel drug development against obesity and diabetes. Despite its potential, the absence of knowledge regarding the structure of murine β3- AR hampers a comprehensive understanding of its functionality.

Objective: Our study aimed to model the three-dimensional (3D) structure of murine β3-AR through various molecular structure prediction and simulation techniques, thus addressing the existing gap in structural information.

Methods: Employing diverse structure prediction programs, we refined the predicted structure of murine β3-AR. Primary sequence analysis offered insights into charge distribution, stability, and hydrophobic properties. The binding sites were identified in the modeled structure. Molecular Dynamics (MD) simulation provided the structural stability and dynamic behavior of the predicted β3- AR structure.

Results: The β3-AR protein exhibited specific characteristics, including a pI of 9.57, an aliphatic index of 98.35, a GRAVY score of 0.289, and the presence of conserved motifs and disulfide linkages. Utilizing the programs such as Phyre2, Swissmodel, I-Tasser, and AlphaFold2, we generated a 3D model of murine β3-AR. Subsequent refinement using ModRefiner revealed a structure comprising 13 helices, 2 strands, and 21 turns. The Ramachandran plot indicated favorable regions for 93.2% of residues, with minimal deviations. A 50 ns MD simulation demonstrated the consistent stability and integrity of the β3-AR protein. The top three binding pockets were identified based on varying areas and volumes. Dynamic behavior within residues Ser 252 and Arg 253 was observed, indicating flexibility in conformation. This study marks the first-ever exploration, offering initial structural insights into murine β3-AR.

Conclusion: This study underscores the critical role of computational approaches in predicting the 3D structure of β3-AR. We derived a refined model by employing diverse prediction techniques, elucidating key features. The findings emphasize the significance of this methodology in comprehending the structural foundation of β3-AR, providing valuable insights for targeted medication development against conditions such as obesity and diabetes.