bioRxivBiophysicsPreprint
Structural Biophysics-Guided Computational Design of Semaglutide Analogues to Enhance GLP-1R Activation
Abstract
CagriSema is a fixed-dose combination of cagrilintide (an amylin analogue) and semaglutide (a GLP-1 receptor agonist), and is currently an experimental obesity drug developed by Novo Nordisk. In March, 2025, CagriSema underperformed expectations in a Phase III trial, achieving 15.7% weight loss instead of the anticipated 25%, raising concerns about its efficacy and clinical value. Given its chemical composition, the weight-loss efficacy of CagriSema is inextricably linked to the activations of GLP-1R and amylin receptors (AMYRs). With GLP-1R as an example target here, this study employs a structural biophysics-guided computational approach for the design of semaglutide analogues to enhance the activation of its receptor GLP-1R. To fully harness the therapeutic potential of GLP-1R activation, an experimental structural basis (PDB entry 4ZGM) of the GLP-1-GLP-1R interaction is essential for the design of semaglutide analogues, where site-specific missense mutations are engineered into its peptide backbone to establish additional stabilizing interactions with the extracellular domain (ECD) of GLP-1R. Specifically, this study puts forward an automated systemic natural amino acid scanning of the peptide backbone of semaglutide, where PDB entry 4ZGM was used as the structural template for high-throughput structural modeling by Modeller and ligand-receptor binding affinity (Kd) calculations by Prodigy. To sum up, this article reports a total of 564 computationally designed semaglutide analogues with improved GLP-1R ECD binding affinity. Moreover, this study proposes a concept of interfacial electrostatic scaffold comprising four salt bridges at the binding interface of GLP-1R ECD and semaglutide analogues. Drawing parallels with the continued optimization in the past century history of insulin, this article argues that the interfacial electrostatic scaffold here constitutes a robust framework for continued development of next-generation GLP-1R agonists, enabling more effective therapies for patients with diabetes and/or obesity.
- organism: human
- assay: proteomics
- research focus: drug response / drug discovery
- approach: deep learning
- approach: statistical model
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Structure and dynamics of semaglutide and taspoglutide bound GLP-1R-Gs complexes
X. Zhang, M. J. Belousoff, … D. Wootten·bioRxiv·Jan 12, 2021
- An Integrated Computational-Experimental Strategy For the Prediction of Small Molecules as GLP-1R Agonists
Elena Murcia García, Ning Tian, … Horacio Pérez Sánchez·bioRxiv·Apr 1, 2026
- Structural basis of peptidomimetic agonism revealed by small molecule GLP-1R agonists Boc5 and WB4-24
Zhaotong Cong, Qingtong Zhou, … Ming‐Wei Wang·Proceedings of the National Academy of SciencesPeer-reviewed·May 13, 2022
- Semaglutide drives weight loss through cAMP-dependent mechanisms in GLP1R-expressing hindbrain neurons
Claire Gao, Isabelle C. Geneve, … Michael J. Krashes·Nature MetabolismPeer-reviewed·May 22, 2026
- Rational design by structural biology of industrializable, long-acting antihyperglycemic GLP-1 receptor agonists
L. Sun, Z.-M. Zheng, … P. Wang·bioRxiv·Apr 11, 2022
- Blind De Novo Design of Dual Cyclic Peptide Agonists Targeting GCGR and GLP1R
Qiuzhen Li, Elisée Wiita, … Patrick Bryant·bioRxiv·Jun 6, 2025