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. 2024 Dec 24;30(1):12.
doi: 10.3390/molecules30010012.

Design, Structure-Activity Relationships, and Computational Modeling Studies of a Series of α-Helix Biased, Ultra-Short Glucagon-like Peptide-1 Receptor Agonists

Jonathon R Sawyer  1   2   3 , Joseph A Audie  4 , Jon Swanson  4 , David Diller  4 , Solimar Santiago  1 , Valentin K Gribkoff  1 , Allison Ackerman  1 , Victor J Hruby  2 , Gianpaolo Gobbo  5 , Michael A Bellucci  5 , William A Glauser  5 , Brad L Pentelute  1 , Tomi K Sawyer  1   6
Affiliations

Design, Structure-Activity Relationships, and Computational Modeling Studies of a Series of α-Helix Biased, Ultra-Short Glucagon-like Peptide-1 Receptor Agonists

Jonathon R Sawyer et al. Molecules. .

Abstract

A systematic structure-activity and computational modeling analysis of a series of glucagon-like peptide-1 receptor (GLP-1R) agonists based upon an ultra-short GLP-1 peptide, H-His-Aib-Glu-Gly-Thr-Phe-Thr-Ser-Asp-Bip-Bip-NH2, was conducted. This highly potent 11-mer peptide led to a deeper understanding of the α-helical bias of strategic α-methylation within the linear parent template as well as optimization of GLP-1R agonist potency by 1000-fold. These data were correlated with previously reported co-structures of both full-length GLP-1 analogs and progenitor N-terminal GLP-1 fragment analogs related to such ultra-short GLP-1R agonist peptides. Furthermore, the development of a quantitative structure-activity relationship (QSAR) model to analyze these findings is described in this study.

Keywords: 2-amino-isobutyric acid (Aib); Cα-methylation; glucagon-like peptide-1 (GLP-1); p-phenyl-phenylalanine (Bip); quantitative structure–activity relationship (QSAR); structure-based design; structure–activity relationship (SAR); α-methyl-phenylalanine (α-MePhe); α-methyl-phenylalanine [2-F] (α-MePhe[2-F]).

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Conflict of interest statement

Authors Jonathon R. Sawyer, Solimar Santiago, Valentin K. Gribkoff, Allison Ackerman, Brad L. Pentelute and Tomi K. Sawyer were employed by the company Resolute Bio. Author Jonathon R. Sawyer was employed by the company Peptide Scientia. Authors Joseph A. Audie, Jon Swanson and David Diller were employed by the company Eudoxia Life Sciences. Authors Gianpaolo Gobbo, Michael A. Bellucci and William A. Glauser were employed by the company XtalPi US, XtalPi Inc. Author Tomi K. Sawyer was employed by the company Maestro Therapeutics. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
GLP-1 and selected FDA-approved GLP-1 analogs. (A) GLP-1 [4], (B) liraglutide [5], (C) dulaglutide [6], and (D) semaglutide [7]. Liraglutide, dulaglutide, and semaglutide are built off the GLP-1 (7–37) fragment. The lipid moieties of liraglutide and semaglutide are linked via γ-L-glutamic acid (γ-Glu). This figure is an approved reproduction taken from the PhD dissertation of Jonathon Sawyer [8].
Figure 2
Figure 2
GLP-1 structure highlighting (bold text) key residues for its biological activity.
Figure 3
Figure 3
BMS ultra-short GLP-11–11 analogs highlighting (see bold text) key sites of modification.
Figure 4
Figure 4
Sosei Heptares GLP-11–11-NH2 analog (described as truncated peptide agonist or TPA) was used for X-ray studies in complex with GLP-1R (Jazayeri et al. 2017 [24]).
Figure 5
Figure 5
RXL-3000:GLP-1R computational model (RXL-3000 in blue and GLP-1R in grey) based on the Sosei Heptares GLP-11–11-NH2 TPA analog complexed with GLP-1R previously determined using cryo-EM methods (5NX2) [24]. Comparative modeling of RXL-3000 based on a previously published [23] GLP-1:GLP-1R structure (6X18) is shown (magenta) to illustrate differences between predicted interactions of the Phe(2-F)6 side chain with a hydrophobic pocket (L141, L144, L384, and L388) based on the Sosei Heptares cryo-EM structure. In both cases, the Phe6 fluorine atom is colored in yellow. See text for details.
Figure 6
Figure 6
Top-down view of the RXL-3000:GLP-1R computational model based on the 5NX2 TPA-based structure (grey/magenta) versus the RXL-3000:GLP-1R model based on the 6X18 GLP-1-based structure (green/blue). RXL-3000 N-termini are depicted as spheres. In both models, the Bip10 side chains are positioned between TM1 and TM2 but with different conformations. Similarly, in both models, the Bip11 side chains are positioned close to ECL2 but with distinct conformations.
Figure 7
Figure 7
RXL-305 GLP-1R computational model based on the TPA:GLP-1R 5NX2 structure (blue/grey) compared to the RXL-3052:GLP-1R model based on the GLP-1:GLP-1R structure (magenta/green). In both models, Aib fails to make contacts with GLP-1R. This suggests that the increased potency of Aib relative to Ala is mediated by Cα-stabilization of the unbound RXL peptide α-helical binding conformation.
Figure 8
Figure 8
Plot of pEC50 values calculated using the QSAR3 model versus the 31 experimentally measured full training set pEC50 values.
Figure 9
Figure 9
Plot of pEC50 values calculated using the retrained QSAR3 models versus the 15 (black) and 6 (grey) experimentally measured potency-matched test sets 1 and 2’s pEC50 values, respectively. The graph includes best-fit lines with constants (y = mx + b) and best-fit lines forced through the origin (y = mx). The best-fit lines for test set 1 are y = 0.8315x + 1.01 and y = 0.9555x; the best-fit lines for test set 2 are y = 0.5176x + 3.5924 and y = 0.9483x.
Figure 10
Figure 10
Key parent peptides for structure–activity analysis described in this study.
See this image and copyright information in PMC

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