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Optimizing GLP-1R Agonist: A Computational Semaglutide Analogue with 112-fold Enhanced Binding Affinity to GLP-1R

Wei Li  *

Submitted:

14 June 2024

Posted:

17 June 2024

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Abstract
Drug-target binding is a crucial parameter in drug discovery and design, ensuring drug efficacy and specificity. Semaglutide, a potent GLP-1 receptor agonist, is widely used to treat type 2 diabetes mellitus by regulating blood glucose levels and promoting weight loss. This study introduces a novel approach utilizing the concept of a general intermolecular binding affinity calculator (GIBAC) for designing semaglutide analogues with enhanced binding affinity to GLP-1R. For the first time, a Val27-Arg28 exchange was manually introduced to strengthen the semaglutide-GLP-1R binding affinity. A comprehensive structural and biophysical analysis was conducted to explore the semaglutide-GLP-1R sequence space, leading to the identification of promising analogues. Among these, one semaglutide analogue demonstrated a binding affinity to GLP-1R that is more than two orders of magnitude (113.3 times) higher than native semaglutide, achieving a Kd of 3.0 x 10-8 M compared to the Kd of 3.4 x 10-6 M for native semaglutide. This article proposes a promising structural biophysical approach for developing GLP-1 receptor agonists with improved efficacy. The prototype GIBAC, termed semaGIBAC, represents a paradigm shift in precise drug discovery and design, advocating for the construction of a full-scale GIBAC to be prioritized within the drug discovery and design community.
Keywords: 
Semaglutide Analogues
;  
GLP-1 Receptor Agonists
;  
Binding Affinity Optimization
;  
Computational Drug Design
Subject: 
Medicine and Pharmacology  -   Endocrinology and Metabolism

1. Introduction

1.1. Intermolecular Binding Affinity in Drug Discovery and Design

Intermolecular interactions are fundamental to numerous biological processes and are crucial for drug discovery and design. Binding affinity ( K d ) and free binding energy ( Δ G ) are key metrics used to describe the strength of these interactions [1,2,3,4,5,6,7,8]. High binding affinity between a drug and its target is critical for drug efficacy, as it allows for effective modulation of the target’s activity at lower drug concentrations, enhancing therapeutic outcomes and minimizing side effects [9,10,11,12,13,14,15]. A thorough understanding of binding affinity aids in the rational design of drugs with optimized potency and selectivity, reducing off-target interactions and adverse effects [6,11,16,17,18,19,20,21]. Recent advances have led to the conceptualization of a general intermolecular binding affinity calculator (GIBAC), first proposed in August 2022 [22]. This concept was further refined in October 2023, incorporating practical applications, technical challenges, and future directions [23]. This study aims to test the feasibility of a real GIBAC by constructing a prototype, thus validating the hypothesis that a GIBAC can be practically implemented [4,22,23,24].

1.2. Clinical Relevance of Semaglutide in the Management of Blood Glocuse and Weight

Semaglutide is a GLP-1 receptor agonist developed by Novo Nordisk for managing type 2 diabetes mellitus (T2DM) [25,26,27]. Sharing 94% sequence homology with human GLP-1, semaglutide effectively binds to GLP-1 receptors, promoting insulin secretion and inhibiting glucagon release from pancreatic beta and alpha cells, respectively [28,29,30]. Approved for its glucose-lowering effects and benefits in weight loss and cardiovascular risk reduction, semaglutide is available in both injectable and oral formulations, offering flexibility in administration [31,32,33,34]. Semaglutide’s therapeutic efficacy stems from its ability to activate GLP-1 receptors, enhancing insulin secretion in a glucose-dependent manner, slowing gastric emptying, suppressing appetite, and promoting satiety [35,36,37]. These properties make it a valuable drug in the management of metabolic disorders.

1.3. Ligand-Receptor Binding Affinity in Drug Design

Understanding ligand-receptor binding affinity is critical in drug design [16]. The availability of structural data from Protein Data Bank [38,39,40,41,42] enables comprehensive biophysical analysis of specific ligand-receptor complexes, informing modifications to enhance binding affinity and drug efficacy [38,39,40,41,43].
In 2021, a Val27-Arg28 exchange (Table 1) was for the first time introduced into the backbone of semaglutide to strengthen the semaglutide-GLP-1R binding affinity to ∼ one-third of the Kd between native semaglutide and GLP-1R [6,45,46], as shown in Figure 1.
Table 1. Strengthening semaglutide-GLP-1R binding affinity via a Val27-Arg28 exchange in the peptide backbone of semaglutide.
Table 1. Strengthening semaglutide-GLP-1R binding affinity via a Val27-Arg28 exchange in the peptide backbone of semaglutide.
PDB file Protein-Protein Complex Δ G (kcal/mol) Kd (M) at 37 ​ ∘ C Fold
4ZGM [44] semaglutide-GLP-1R [44] -7.8 3.4 × 10-6 1
sema.pdb [6] Val27-Arg28 exchange [6] -8.4 1.1 × 10-6 3.09

2. Motivation

The development of semaglutide analogues with increased GLP-1R binding affinity has significant clinical implications, offering potential improvements in glucose control, weight loss, and cardiovascular benefits for patients with type 2 diabetes and obesity [27,44,47]. The Protein Data Bank (PDB) provides a wealth of biomolecular data suitable for building a GIBAC prototype [38,39,40,41,42]. By leveraging structural biology, computational modeling, and biophysical insights, this study aims to design semaglutide derivatives that exhibit tighter interactions with the GLP-1R binding site, thereby enhancing receptor activation and downstream signaling pathways. These novel analogues may represent a new class of GLP-1R agonists with superior therapeutic efficacy and reduced dosing frequency, addressing current limitations in managing metabolic disorders [48,49].

3. Materials and Methods

As listed in Table 2, there is one structure (determined by Cryo-EM) of Semaglutide-bound Glucagon-Like Peptide-1 (GLP-1) Receptor in Complex with Gs protein (PDB ID: 7KI0 [50]) as of June 14, 2024.
Table 2. Experimentally determined semaglutide-related structures (released newest from oldest) in the Protein Data Bank (PDB [38]) as of 2024/06/21 21:39:16, QUERY code: QUERY: Polymer Entity Description = "Semaglutide".
Table 2. Experimentally determined semaglutide-related structures (released newest from oldest) in the Protein Data Bank (PDB [38]) as of 2024/06/21 21:39:16, QUERY code: QUERY: Polymer Entity Description = "Semaglutide".
PDB ID Structure Title (release date from newest to oldest)
7KI0 Semaglutide-bound Glucagon-Like Peptide-1 (GLP-1) Receptor in Complex with Gs protein
However, with a QUERY code: QUERY: Full Text = "Semaglutide", a total of three experimental structures related to semaglutide were found in the Protein Data Bank (PDB [38]), as listed in Table 3.
Table 3. Experimentally determined semaglutide-related structures (released newest from oldest) in the Protein Data Bank (PDB [38]) as of 2024/06/21 21:39:16, QUERY code: QUERY: Full Text = "Semaglutide".
Table 3. Experimentally determined semaglutide-related structures (released newest from oldest) in the Protein Data Bank (PDB [38]) as of 2024/06/21 21:39:16, QUERY code: QUERY: Full Text = "Semaglutide".
PDB ID Structure Title (release date from newest to oldest)
7KI0 Semaglutide-bound Glucagon-Like Peptide-1 (GLP-1) Receptor in Complex with Gs protein
7KI1 Taspoglutide-bound Glucagon-Like Peptide-1 (GLP-1) Receptor in Complex with Gs Protein
4ZGM Crystal structure of Semaglutide peptide backbone in complex with the GLP-1 receptor extracellular domain
Among the three, there is one structure (determined by X-ray diffraction) of the semaglutide peptide backbone in complex with the extracellular domain of GLP-1R (PDB ID: 4ZGM [44]). Briefly, the amino acid sequences of the two chains of semaglutide and GLP-1R (according to PDB entry 4ZGM [44]) are listed in italics in fasta format as below,
>4ZGM_1|Chain A|Glucagon-like peptide 1 receptor|Homo sapiens (9606)
RPQGATVSLWETVQKWREYRRQCQRSLTEDPPPATDLFCNRTFDEYACWPDGEPGSFVNVSCPWYLPWASSVPQGHVYRFCTAEGLWLQKDNSSLPWRDLSECEESKRGERSSPEEQLLFLY
>4ZGM_2|Chain B|Semaglutide peptide backbone; 8Aib,34R-GLP-1(7-37)-OH|Homo sapiens (9606)
HAEGTFTSDVSSYLEGQAAKEFIAWLVRGRG
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Figure 2. Automated in silico generation of synthetic structural and Kd data.
Figure 2. Automated in silico generation of synthetic structural and Kd data.
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First, with PDB entry 4ZGM [44] (Table 3) in place, Modeller [51] was employed to build 10000 structural models with 100% homology to PDB ID: 4ZGM [44], the binding affinities between semaglutide and GLP-1R were calculated using Prodigy [52,53] 10000 times [54]. Second, with PDB entry 4ZGM [44] (Table 3) in place as an initial input, the process of the construction of a prototype GIBAC (semaGIBAC [24]) subsequently consists of an automated in silico generation of synthetic structural and Kd data, as illustrated in Figure 2 and described previously in detail [55]. Briefly, Modeller [51] was employed to build a total of 11200 ( 28 ! 1 ! ( 27 ) ! × 20 1 × 20 ) homology structural models with 95.42% ( 27 / 28 ) homology to PDB ID: 4ZGM [44]. Afterwards, the binding affinities between semaglutide analogues and GLP-1R were calculated using Prodigy [52,53] for 11200 times [54].

4. Results

With the X-ray structure of the semaglutide peptide backbone in complex with the extracellular domain of GLP-1R (PDB ID: 4ZGM [44]) in place, Modeller [51] was employed to build 10000 structural models with 100% homology to PDB ID: 4ZGM [44], and the binding affinity between semaglutide and GLP-1R was calculated using Prodigy [52,53] for native semaglutide (10000 times). As shown in Figure 2, most of the Kd values are located between 2.5 × 10-6 M and 4.0 × 10-6 M, with an average at 3.278 × 10-6 M, which ia rather close to the one Kd (3.4 × 10-6 M) as reported in [6].
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Figure 3. Distribution of the binding affinities between semaglutide (PDB ID: 4ZGM [44]) and GLP-1R as calculated by Prodigy [52,53].
Figure 3. Distribution of the binding affinities between semaglutide (PDB ID: 4ZGM [44]) and GLP-1R as calculated by Prodigy [52,53].
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Secondly, with the X-ray structure of the semaglutide peptide backbone in complex with the extracellular domain of GLP-1R (PDB ID: 4ZGM [44]) as the structural template, a total of s = g ( 28 , 4 ) = 28 ! 4 ! ( 24 ) ! × 20 4 [56] semaglutide variants’ sequence were generated, and plugged into Modeller [51] to build 20 structural models for each semaglutide analogue, and the binding affinity between semaglutide and GLP-1R was calculated using Prodigy [52,53]. In total, the binding affinities of 20 semaglutide analogues to GLP-1R are included in Table 4, including their minimum, maximum, average and standard deviation of the Kd values calculated using Prodigy [52,53] for the semaglutide analogues, each 20 times of homology structural modeling using Modeller [51]. Thus, a total of 8915 semaglutide analogues were also reported in a supplementary file of [54], including their minimum, maximum, average and standard deviation of the Kd values calculated using Prodigy [52,53] for the semaglutide analogues, each 20 times of homology structural modeling using Modeller [51].
Table 4. Computationally designed semaglutide analogues with elevated binding affinity to GLP-1R than native semaglutide. In this table, the binding affinity of semaglutide analogues to GLP-1R is calculated with Prodigy [52,53] at Kd (37 ​ ∘ C ) values, while Muta1, Muta2, Muta3 and Muta4 represent the four site-specific mutations introduced into the backbone of semaglutide, and Min, Max, Mean and Std represent the minimum, the maximum, the average and the standard deviation of the Kd values calculated using Prodigy [52,53] for the semaglutide analogues, each 20 times of homology structural modeling using Modeller [51].
Table 4. Computationally designed semaglutide analogues with elevated binding affinity to GLP-1R than native semaglutide. In this table, the binding affinity of semaglutide analogues to GLP-1R is calculated with Prodigy [52,53] at Kd (37 ​ ∘ C ) values, while Muta1, Muta2, Muta3 and Muta4 represent the four site-specific mutations introduced into the backbone of semaglutide, and Min, Max, Mean and Std represent the minimum, the maximum, the average and the standard deviation of the Kd values calculated using Prodigy [52,53] for the semaglutide analogues, each 20 times of homology structural modeling using Modeller [51].
No. Muta1 Muta2 Muta3 Muta4 Min Max Mean Std
1 G13B_A I20B_Q L23B_Q V24B_N 5.3E-08 2.2E-07 1.337E-07 4.778E-08
2 G13B_A I20B_N L23B_R V24B_N 6.5E-08 2.4E-07 1.344E-07 4.996E-08
3 G13B_A I20B_N L23B_Q V24B_T 6.6E-08 2.2E-07 1.376E-07 4.199E-08
4 G13B_A I20B_T L23B_Q V24B_N 8.0E-08 3.1E-07 1.404E-07 5.478E-08
5 G13B_A I20B_Q L23B_Q V24B_T 6.8E-08 2.0E-07 1.407E-07 3.779E-08
6 G13B_A I20B_S L23B_R V24B_T 6.1E-08 2.5E-07 1.408E-07 5.527E-08
7 G13B_A I20B_Q L23B_R V24B_N 3.0E-08 3.2E-07 1.461E-07 7.095E-08
8 G13B_A I20B_T L23B_R V24B_N 8.3E-08 2.1E-07 1.467E-07 3.690E-08
9 G13B_A I20B_N L23B_R V24B_Q 6.3E-08 2.9E-07 1.487E-07 5.848E-08
10 G13B_A I20B_Q L23B_R V24B_Q 8.6E-08 2.5E-07 1.489E-07 5.170E-08
11 G13B_A I20B_Q L23B_Q V24B_Q 6.3E-08 2.4E-07 1.505E-07 5.269E-08
12 G13B_A I20B_S L23B_R V24B_N 4.4E-08 3.5E-07 1.520E-07 6.568E-08
13 G13B_A I20B_T L23B_R V24B_T 9.4E-08 2.2E-07 1.545E-07 4.188E-08
14 G13B_A I20B_N L23B_Q V24B_N 7.7E-08 2.2E-07 1.559E-07 4.164E-08
15 G13B_A I20B_S L23B_R V24B_Q 7.7E-08 3.0E-07 1.571E-07 6.401E-08
16 G13B_A I20B_S F19B_Q V24B_N 3.5E-08 2.8E-07 1.583E-07 6.648E-08
17 G13B_A I20B_N L23B_Q V24B_Q 8.2E-08 2.9E-07 1.602E-07 5.879E-08
18 G13B_A I20B_N F19B_Q V24B_N 5.0E-08 2.9E-07 1.634E-07 7.035E-08
19 G13B_A I20B_T F19B_Q V24B_Q 9.7E-08 2.9E-07 1.653E-07 4.839E-08
20 G13B_A I20B_N L23B_R V24B_T 8.0E-08 3.4E-07 1.662E-07 8.233E-08
Among the 20 semaglutide analogues included in Table 4, one particular semaglutide analogue stood out, named here as semaglutideX, where the semaglutideX-GLP-1R structural model is reaching a Kd value of 3.0 × 10-8 M, while the Kd is 3.4 × 10-6 M for the binding of native semaglutide to GLP-1 [6].
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Figure 4. Distribution of the binding affinities between semaglutideX (supplementary file semx.pdb) and GLP-1R as calculated by Prodigy [52,53].
Figure 4. Distribution of the binding affinities between semaglutideX (supplementary file semx.pdb) and GLP-1R as calculated by Prodigy [52,53].
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The amino acid sequence of semaglutideX is listed in italics in fasta format as below,
>semaglutideX (supplementary file semx.pdb)
HAEGTFTSDVSSYLEAQAAKEFQAWRNRGRG
For a close comparison, the amino acid sequence of semaglutide (PDB ID: 4ZGM [44]) is listed in italics in fasta format as below,
>4ZGM_2|Chain B|Semaglutide peptide backbone; 8Aib,34R-GLP-1(7-37)-OH|Homo sapiens (9606)
HAEGTFTSDVSSYLEGQAAKEFIAWLVRGRG
and the amino acid sequence of semaglutide with a Val27-Arg28 exchange [6] is listed in italics in fasta format as below,
HAEGTFTSDVSSYLEGQAAKEFIAWLRVGRG
Table 5. The binding affinities of semaglutide, semaglutide with a Val27-Arg28 exchange [6] and semaglutideX to GLP-1R calculated by Prodigy [52,53]. In this table, 4ZGM represents the experimental structure (determined by X-ray diffraction) of the semaglutide peptide backbone in complex with the extracellular domain of GLP-1R (PDB ID: 4ZGM), mutant semaglutide represents the B27Arg-B28Val mutant of semaglutide, whose structural model is described in the supplementary file semx.pdb, and semaglutideX represents a semaglutide variant with four site-specific missense mutations, i.e., G13B_A I20B_Q L23B_R V24B_N.
Table 5. The binding affinities of semaglutide, semaglutide with a Val27-Arg28 exchange [6] and semaglutideX to GLP-1R calculated by Prodigy [52,53]. In this table, 4ZGM represents the experimental structure (determined by X-ray diffraction) of the semaglutide peptide backbone in complex with the extracellular domain of GLP-1R (PDB ID: 4ZGM), mutant semaglutide represents the B27Arg-B28Val mutant of semaglutide, whose structural model is described in the supplementary file semx.pdb, and semaglutideX represents a semaglutide variant with four site-specific missense mutations, i.e., G13B_A I20B_Q L23B_R V24B_N.
PDB file Protein-Protein Complex Δ G (kcal/mol) Kd (M) at 37 ​ ∘ C Fold
4ZGM [44] semaglutide-GLP-1R [44] -7.8 3.4 × 10-6 1
sema.pdb [6] Val27-Arg28 exchange [6] -8.4 1.1 × 10-6 3.09
semx.pdb [54] G13B_A I20B_Q L23B_R V24B_N [54] -10.7 3.0 × 10-8 113.33

5. Conclusion

To sum up, through computational optimization based on structural biophysics-based calculations, this study puts forward a synthetic GLP-1 receptor agonist with a Kd of 3.0 × 10-8 M at 37 ​ ∘ C for GLP-1R. This enhancement in binding affinity correlates with increased receptor activation and improved therapeutic efficacy, offering promising clinical implications for the management of type 2 diabetes and obesity.

6. Discussion

The past three years saw a big step forward in the use of artificial intelligence (AI) in structural biology for protein structure prediction [57,58,59,60,61,62,63], leading to the generation of computational structural data such as AlphaFold database [58,59,60,61,62]. Nonetheless, to train useful AI models for precise drug discovery and design, a huge number of data is needed with reasonable accuracy, buth experimental and synthetic, both structural and biophysical (Kd and G), where a variety of tools are needed, such as molecular docking tools [64,65,66,67], molecular dynamics simulations tools [68,69], side chain placement and energy minimization algorithms [70] to incorporate structural arrangement information of post-translational modifications (PTMs) [71,72,73], post-expression modifications (PEMs) [6,74] into currently available structural models.
In this regard, a set of in silico steps of structural and biophysical data generation are necessary towards a paradigm shift in precise drug discovery and design [54,55]. Take semaglutide for instance, a five-dimensional semaGIBAC requires a total of 314496000000 (Table 6) homology structural models with 82.14% ( 23 / 28 ) homology to PDB ID: 4ZGM [44] to be built by Modeller [51], and subsequently a total of 314496000000 (Table 6) times of Prodigy-based [52,53] calculations of the binding affinities between semaglutide analogues and GLP-1R. Take MoleculeX (a protein consisting of 100 amino acids) as another example, the number soars from 314496000000 to 240920064000000 (Table 6).
Table 6. The Size ( s = g ( k , n ) = k ! n ! ( k − n ) ! × 20 n ) of the synthetic structural data set based on semaglutide-GLP-1R complex structure. where k represents the length of semaglutide backbone, n represents the number of missense mutations introduced into semaglutide backbone, where the value of n / k is key to ensure the overall reasonable accuracy of the synthetic structural data.
Table 6. The Size ( s = g ( k , n ) = k ! n ! ( k − n ) ! × 20 n ) of the synthetic structural data set based on semaglutide-GLP-1R complex structure. where k represents the length of semaglutide backbone, n represents the number of missense mutations introduced into semaglutide backbone, where the value of n / k is key to ensure the overall reasonable accuracy of the synthetic structural data.
Size (s) of the synthetic structural and biophysical data set
Semaglutide backbone (28 Aa) Molecule X (100 Aa)
g(28,1) 28 ! 1 ! ( 27 ) ! × 20 1 560 g(100,1) 100 ! 1 ! ( 99 ) ! × 20 1 2000
g(28,2) 28 ! 2 ! ( 26 ) ! × 20 2 151200 g(100,2) 100 ! 2 ! ( 98 ) ! × 20 2 1980000
g(28,3) 28 ! 3 ! ( 25 ) ! × 20 3 26208000 g(100,3) 100 ! 3 ! ( 97 ) ! × 20 3 1293600000
g(28,4) 28 ! 4 ! ( 24 ) ! × 20 4 3276000000 g(100,4) 100 ! 4 ! ( 96 ) ! × 20 4 627396000000
g(28,5) 28 ! 5 ! ( 23 ) ! × 20 5 314496000000 g(100,5) 100 ! 5 ! ( 95 ) ! × 20 5 240920064000000
Technically, the structural biophysics-based design of semaglutideX consists of the construction of a semaGIBAC prototype, i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, along with partial constructions of another three semaglutide-based GIBACs, i.e., two, three and four-dimensional semaglutide-GLP-1R-based mini static partial GIBACs, where the partiality comes from the numbers of structural biophysics-based calculations as required to build semaglutide-GLP-1R complex structure-based GIBACs, as included in Table 6. In light of this, future work will focus on continued generalization of semaGIBAC, i.e., a one-dimensional semaglutide-GLP-1R-based mini static GIBAC, towards a real GIBAC [22,23] with adequate accuracy, precision and efficiency towards a paradigm shift [75] of precise drug discovery & design, until a real GIBAC comes into being and pushing forward the continued development of the industry [76,77,78].

Ethical statement

No ethical approval is required.

Statement of Usage of Artificial Intelligence

During the preparation of this work, the author used OpenAI’s ChatGPT in order to improve the readability of the manuscript, and to make it as concise and short as possible. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.

Author Contributions

Conceptualization, W.L.; methodology, W.L.; software, W.L.; validation, W.L.; formal analysis, W.L.; investigation, W.L.; resources, W.L.; data duration, W.L.; writing–original draft preparation, W.L.; writing–review and editing, W.L.; visualization, W.L.; supervision, W.L.; project administration, W.L.; funding acquisition, not applicable.

Funding

This research received no external funding.

Acknowledgments

The author is grateful to the communities of structural biology, biophysics, medicinal and computational chemistry and algorithm design, for the continued accumulation of knowledge and data for drug discovery & design, and for the continued development of tools (hardware, software and algorithm) for drug discovery & design.

Conflicts of Interest

The author declares no conflict of interest.

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Preprints 109284 g001
Figure 1. Crystal structure of semaglutide backbone in complex with the GLP-1 receptor extracellular domain.
Figure 1. Crystal structure of semaglutide backbone in complex with the GLP-1 receptor extracellular domain.
Preprints 109284 g001
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