An 18-amino acid peptide designed to possibly activate the GLP-1 receptor

Protein Data Bank (PDB) coordinates of the GLP-1−GLP-1R (5VAI) (Zhang et al., 2017), Semaglutide (7KI0), Taspoglutide (7KI1) (Zhang et al., 2021), and liraglutide (4APD) were retrieved from the RCSB PDB server (www.rcsb.org↗). Multiple sequence alignments of the peptides were performed through Clustal Omega (Sievers and Higgins, 2018; Madeira et al., 2024). MODELLER (Webb and Sali, 2016), Discovery Studio (Biovia), and PyMOL (Schrӧdinger, Inc.) software were utilized for modeling, initial processing, visualization, analysis, and presentation of GLP-1R and model peptide structures. The data were plotted in GraphPad Prism (www.graphpad.com↗).

The HDOCK web server (Yan et al., 2017b; Yan et al., 2020) was used to perform peptide-protein docking to predict the putative binding mode between the designer peptides and loop remodeled GLP-1R. HDOCK is based on a hybrid algorithm that combines template-based modeling and ab initio free docking. The PDB files of modeled GLP-1R and peptides were uploaded to the HDOCK server, and global docking was performed using the default parameters, which use a fast Fourier transform (FFT)-based rigid-body search method and then evaluate the interface energetics using an intrinsic scoring function (Huang and Zou, 2008; 2014; Yan et al., 2017a). For each docking run, the server generated the top 100 predicted complex structures, along with their HDOCK energy scores and confidence scores. Docking energy scores are calculated using iterative scoring functions ITScorePP or ITScorePR. This energy score indicates the expected binding affinity between the ligand and the receptor; more negative scores indicate higher binding affinity. The confidence score provides an empirical measure of binding likelihood between docked molecules. Confidence score >0.7 suggests strong binding, whereas a value <0.5 indicates poor binding (Li et al., 2022). The best conformation complex (with the highest HDOCK energy score) of the GLP-1R and the peptides was further screened for MD simulation. Additionally, the binding energies and the ∼K_d values of the top docking poses obtained from HDOCK were also estimated using PRODIGY (Xue et al., 2016).

The sequence of the GLP-1 peptide and a few reported GLP-1RAs, like Exendin-4, Semaglutide, Liraglutide, and Taspoglutide, were first aligned to obtain the mutating residue. Further, one of the earlier reported model helical peptides (Hed3: NH₃⁺-YGKAAAAK-Igl-AAAKAAAAK-CO₂⁻) was utilized to generate the template GLP-1 mimetic peptide (Hed6: NH₃⁺-H-Igl-E-Aib-AAAKAAAAKAAAKA-CONH₂), which was subsequently subjected to iterative computational sequence optimization. The 3D structure of the helical peptides was generated using the Discovery Studio Visualizer tool. Subsequently, one of the rationally designed peptides (SR18) was custom-synthesized using the standard Fmoc (Fluorenylmethoxycarbonyl protecting group) solid-phase peptide synthesis method by availing the commercial services of GenScript Biotech. Post cleavage from the resin, the peptide was purified (≥95%) to homogeneity. The analytical high-performance liquid chromatography (HPLC) profile of the peptide was recorded by using a C18 (4.6 × 250 mm²) column at 220 nm under an acetonitrile-water gradient in the presence of 0.05%–0.065% trifluoroacetic acid (TFA). Electrospray ionization mass spectrometry (ESI-MS) confirmed the integrity of the peptide.

The far-UV circular dichroism (CD) spectra of the SR18 were recorded in a quartz (Helma) cell with a path length of 0.1 cm at 25 °C under different solvent conditions, such as 1X PBS (phosphate buffered saline), 30 mM SDS (Sodium dodecyl sulphate), 20% TFE (2,2,2-trifluroethanol), and 20% HFIP (1,1,1,3,3,3-hexafluro-2-propanol), by using the Chirascan CD spectrometer system (Applied Photophysics). The CD spectra were recorded with a time constant of 1 s and a step size of 1 nm, averaging three scans. The peptide stock solution was diluted in the respective solvents, and the peptide concentration was maintained at 100 μM by monitoring the absorbance at 280 nm. After background subtraction of corresponding solvents, the observed ellipticity in millidegrees was converted to mean residual ellipticity [θ_MRE, degcm²·dmol^-1] using the following equation (millidegrees × mean residual weight)/(path length in millimeters × concentration in mg·mL^-1) (Greenfield, 2006). The ⍺-helix fraction (f_a) of SR18 at θ₂₂₂ was calculated as the ratio of [θ₂₂₂]experimental to [θ₂₂₂]theoretical, as described here (Hamley et al., 2025). All data points were plotted in GraphPad Prism, and the spectra were smoothed up to 2–3 units wherever required.

SR18 dissolved in filtered 1X PBS (pH ∼ 7.4) and 20% TFE to a final concentration of 100 μM was subjected to DLS measurement to gauge its hydrodynamic size and particle size distribution, using bovine serum albumin as the reference. Prior to the DLS measurement, the sample was filtered through a 0.22 μm filter to eliminate the dust particles that could interfere with the measurement. The analysis was performed at 25 °C using a Zetasizer Nano ZS (Malvern Instruments) equipped with a 633 nm He-Ne laser, with scattered light detected at 90°. Each measurement consisted of three independent runs, each averaging five acquisitions.

The cytotoxicity profile of SR18 was examined using the 3-(4,5-dimethylthiazol-2-yl) 2,5-diphenyltetrazolium bromide (MTT) assay utilizing HEK-293 and HeLa cell lines obtained from the School of Biological Sciences, NISER, Bhubaneswar. Approximately 4 × 10⁴ cells per well were inoculated into 96-well plates containing Dulbecco’s modified Eagle’s medium (DMEM) enriched with 10% fetal bovine serum and penicillin-streptomycin (10 mL/L), and incubated for 24 h at 37 °C with 5% CO₂. Thereafter, the cells were treated with serially diluted SR18 in culture media and incubated for an additional 18 h at 37 °C in 5% CO₂. Subsequently, 10 µL of MTT solution (5 mg/mL) was introduced to each well, and the plates were incubated for a further 4 h. After 4 h, the suspensions were removed, and 100 µL of dimethyl sulfoxide (DMSO) was added to dissolve the formazan crystals. The plates were further incubated at 37 °C with 5% CO₂ for 30 min to achieve complete solubilization. The absorbance was measured at 570 nm using a SpectraMax iD3 microplate reader (Molecular Devices). Cells treated with PBS served as the negative control, whereas cells exposed to 10% DMSO served as the positive control. After subtracting blank absorbance (medium only), cell viability (%) was calculated as the ratio of OD₅₇₀ of treated cells to OD₅₇₀ of untreated cells. To ensure reproducibility, all experiments were conducted in triplicate and repeated three times independently (n = 3).

Approximately 2 mL of human blood was collected from an anonymous healthy volunteer at the Sanjeevan Health Center, Indian Institute of Technology Bhubaneswar, following written informed consent. Blood was collected in a K2-EDTA-coated tube to prevent coagulation. Erythrocytes were separated by centrifugation at 2,500 rpm for 10 min at room temperature. The collected erythrocytes were then washed four to five times with sterile 1X PBS, with each wash including 10 min of centrifugation, until the supernatant was clear. The purified erythrocytes were then diluted in 1X PBS to a final ratio of 1:50 (Erythrocytes: 1X PBS). Aliquots of the diluted erythrocyte suspension were introduced into microcentrifuge tubes containing 10 μL of SR18 with concentrations ranging from 0.39 to 100 μM, resulting in a final reaction volume of 100 μL. The mixtures were incubated at 37 °C for 60 min, then centrifuged at 2,500 rpm for 10 min at room temperature. After that, ∼50 μL of supernatant was carefully transferred to microtiter plate wells without disturbing the cell pellet, and the cell lysis of the erythrocytes was quantified by measuring OD at 414 nm using SpectraMax iD3 microplate reader (Molecular Devices). Cells treated with 10 μL of 10% Triton X-100 served as the positive control, while cells treated with 10 μL of sterile PBS served as the negative control. All experiments were carried out in triplicate on two distinct experimental sets (n = 2). The proportion of hemolysis was estimated using the formula (Abs_peptide-Abs_PBS)/(Abs_triton-Abs_PBS) × 100.

SR18 was incubated with trypsin at an enzyme-to-peptide ratio of 1:20 (trypsin: peptide) in 50 mM Tris−HCl (pH ∼ 8) at 37 °C with moderate agitation for ∼4 h to evaluate its susceptibility to proteolytic degradation. Subsequently, the reaction mixture was heated to 100 °C for 30 min to inactivate trypsin, then centrifuged at 13,000 g for 30 min to precipitate the enzyme. The resultant supernatant was collected and analyzed using an Exactive Plus Orbitrap liquid chromatography-high-resolution mass spectrometry (LC-HRMS) instrument from Thermo Scientific.

The GLP-1R, respectively complexed to GLP-1 and SR18, was embedded into a 75% POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) and 25% cholesterol (CHL) bilayer using CHARMM-GUI (Jo et al., 2008) before MD simulation. The GLP-1−GLP-1R and SR18−GLP-1R complexes were subjected to MD simulations of 1 μs at 300 K in TIP3 water using CHARMM36 m forcefield (Huang et al., 2017) parameters in the GROMACS engine (Hess et al., 2008), as described in our previous studies (Gupta et al., 2025). Before the production MD run, both systems were energy-minimized to a tolerance of 1,000 kJ mol^-1nm^-1 using the steepest descent method, first in a vacuum and then in the presence of TIP3 water, with a solvent density set to the value corresponding to 1 atm at 300 K. Furthermore, the net charge of the system was neutralized by randomly placing the required number of counterions in the box to maintain a NaCl concentration of 0.15 M. The final GLP-1−GLP-1R complex contained 72,067 atoms, including 16,320 TIP3 water, 44 sodium, 99 chloride, 102 POPC, and 33 CHL molecules, whereas the SR18−GLP-1R complex contained 79,689 atoms, including 18,915 TIP3P water, 51 sodium, 54 chloride, 102 POPC, and 33 CHL molecules. Both systems were equilibrated twice, first under constant number, volume, and temperature (NVT: 5 ns) conditions, and then under constant number, pressure, and temperature (NPT: 10 ns) conditions before the start of the production MD run. With a coupling time constant of 1 ps, the protein and solvent were coupled independently to a V-rescale bath at 300 K. H-bonds were constrained with LINCS with order 4. The non-bonded pair list cutoff was 1.2 nm with a grid function. Numerical integrations were performed in steps of 2 fs, and the coordinates were updated every 10 ps. Conformational clustering of both the trajectories (SR18−GLP-1R and GLP-1−GLP-1R) was performed every 50 ps with a root mean square deviation (RMSD) cutoff of ≤2.5 Å by recruiting the GROMOS fitting method, as defined in GROMACS. The MD trajectories were thoroughly analyzed by recruiting the utility modules available in GROMACS. A similar process was followed for the MD studies of GLP-1, Semaglutide, and SR18 in the presence of TIP3 water. The MD trajectories of the peptides were subjected to conformational clustering at 50 ps intervals using an RMSD cutoff of ≤1.5 Å.

The following equation, ΔG_binding = G_complex −(G_protein + G_ligand), implemented in the gmx_mmpbsa program (Valdes-Tresanco et al., 2021), was used to calculate the binding free energy of the complexes using the Molecular Mechanics Poisson-Boltzmann surface area (MM-PBSA) method. The following equation estimated the free energy contribution: G = E_MM + G_(solv) – TS_(solute), where E_MM (molecular mechanics energy) represents the summation of van der Waals and electrostatic, G_(solv) represents the solvation energy contributed by both polar and non-polar solvation free energy, and TS_(solute) represents the temperature and entropy of the solute. Finally, 300 structures, including 200 conformers randomly selected from the first major cluster and 100 from the second major cluster, were used from the respective MD trajectories to calculate the total binding free energy and decomposition energy of the complexes.

The initial structure of GLP-1R was retrieved from the reported active structural complex of GLP-1R with the native GLP-1 (PDB: 5VAI). The unresolved loop residues (S¹²⁹RRGEK¹³⁴) connecting the N-terminus to the transmembrane helix 1 (TM1) of GLP-1R were appropriately remodeled using MODELLER. The native GLP-1 was then docked to the loop-remodeled GLP-1R using the HDOCK web server, which generated a GLP-1R complex with GLP-1 with a docking energy score of −653.61 and a confidence score of 1. The structural superposition of the modeled complex with 5VAI indicated a backbone RMSD of 1.079 Å (Figure 3), suggesting that the model complex is suitable for further studies.

The model GLP-1–GLP-1R complex was embedded in a POPC-CHL (75:25) bilayer using CHARMM-GUI and subsequently subjected to MD simulations for 1 μs at 300 K as a positive control. The MD data presented in Figure 4 suggest that the GLP-1–GLP-1R complex remains structurally stable throughout the MD simulations, with an average of 15 intermolecular hydrogen bonds between GLP-1 and GLP-1R. Further analysis of the MD trajectory reveals that the central conformer of the most populated cluster of the GLP-1−GLP-1R complex evolved over 1 μs of MD simulations, shares an RMSD of 3.057 Å (Figure 4) with the starting model complex. Notably, the remodeled section of the loop, which was originally not resolved in the cryo-EM studies, adopted an altogether different conformation in the most populated conformer of the complex.

Further analysis of the trajectory suggests that the several important amino acids, such as H7, E9, T11, F12, D15, and F28 of GLP-1 actively interact with the W³⁹, Y¹⁴⁵, Y¹⁵², R¹⁹⁰, K¹⁹⁷, L²⁰¹, N³⁰⁰, R³¹⁰, E³⁶⁴, L³⁸⁴, and L³⁸⁸ of GLP-1R through several hydrogen, hydrophobic, and electrostatic interactions (Figure 5; Supplementary Figure S1). Interestingly, H7, E9, and D15 of GLP-1 destabilize TM6 and ECL3, thereby facilitating the GLP-1R in attaining its active conformation. Further, the H7 of GLP-1 interacts with TM3 and TM5 through hydrogen and hydrophobic interactions with R³¹⁰, I³¹³, and E³⁶⁴ of GLP-1R. Notably, earlier alanine substitution studies have revealed that Y¹⁵², R¹⁹⁰, K¹⁹⁷, L²⁰¹, R²⁹⁹, N³⁰⁰, W³⁰⁶, R³¹⁰, I³¹³, E³⁶⁴, L³⁸⁴, L³⁸⁸ of GLP-1R are the most important residues for GLP-1 binding affinity and potency. Interestingly, except for Y¹⁴⁸, D¹⁹⁸, M²³³, Q²³⁴, Y²³⁵, W²⁸⁴, D³⁷², E³⁷³, R³⁸⁰, K³⁸³, and E³⁸⁷, all other residues were found to maintain sustained intermolecular interactions with GLP-1 over the duration of the MD simulations (Dods and Donnelly, 2015; Liang et al., 2018; Zhang et al., 2020; Deganutti et al., 2022). Therefore, it is reasonable to assume that GLP-1 analogs that could establish stable interaction with these residues of GLP-1R can produce therapeutic effects similar to the current GLP-1R targeting peptides for the management of T2DM.

Sequence comparison of GLP-1 (30 aa) with the other GLP-1 mimetics, such as Taspoglutide (30 aa), Liraglutide (31 aa), and Semaglutide (31 aa), indicates that strategic incorporation of NCAAs, such as Aib, and chemical modification of specific Lys side chains have been successful in improving the pharmacokinetics of the GLP-1 mimetics without affecting their interactions with GLP-1R compared to the native GLP-1. Further, analysis of the reported cryo-EM data of GLP-1–GLP-1R complex indicates that certain residues on GLP-1 do not make significant contact with the ECD of the GLP-1R (Zhang et al., 2017). Therefore, for rational design of peptide mimetics, the entire sequence of GLP-1 may be divided into the N-terminal (14 aa), connector (5 aa), and C-terminal (11 aa) regions, which are collectively important for binding and signaling of GLP-1R. Since short peptides ranging from nine to eleven amino acids have been discussed in the literature earlier as potent GLP-1RAs, we decided to design an intermediate-sized potent peptide (≤20 aa) agonist of GLP-1R by conserving several critical residues of GLP-1 important for GLP-1R binding and signaling. Notably, in our earlier studies, we demonstrated the impact of a few NCAAs on the conformational stability of model helical peptides composed of ≤18 amino acids (Behera et al., 2022), which we subsequently subjected to sequence optimization, producing a potent antimicrobial peptide (Behera et al., 2024a) and a potential antiviral peptide (Behera et al., 2024b) binder targeting the receptor binding domain of SARS-CoV-2.

Therefore, in the current study, we utilized one of the model helical peptides (Hed3: NH₃⁺-YGKAAAAK-Igl-AAAKAAAAK-CO₂⁻) to produce the template GLP-1 mimetic peptide (Hed6: NH₃⁺-H-Igl-E-Aib-AAAKAAAAKAAAKA-CONH₂) and further subjected it to iterative computational sequence optimization to produce a potent helical peptide agonist (SR18) of GLP-1R (Figure 6). The Lys residue at the 17th position was introduced at the C-terminus with an objective to modify its sidechain with a linker in the future (Supplementary Figure S2), so that the peptide can anchor to serum albumin similar to Semaglutide (Knudsen and Lau, 2019). Additionally, the C-terminus was amidated to protect the peptide from carboxypeptidase degradation.

The first four residues of the N-terminus were also fixed for the following reasons. His1 and Glu3 were fixed as critical residues for GLP-1R (Adelhorst et al., 1994; Xiao et al., 2001) Further, whereas Igl (Indanyl glycine), a glycine derivative, was introduced at the second position to protect the dipeptide cleavage by DPP-4 and to improve metabolic stability, including imparting necessary conformational constraints to maintain an appreciable binding affinity at the active site of GLP-1R, similar to the small-molecule GLP-1RAs. Furthermore, the Aib at the fourth position was fixed to help the peptide induce a helical turn at the N-terminus (Karle and Balaram, 1990). Notably, Aib has been incorporated into GLP-1RAs, such as Taspoglutide, Semaglutide, and Tirzepatide, to improve their therapeutic index. The remaining amino acid positions were iterated to produce ∼200 unique peptides, each composed of ≤18 amino acids, out of which 41 peptides were initially shortlisted and ranked (Supplementary Table S1) through screening against loop-remodeled GLP-1R bound to GLP-1 based on their binding energy score and orientation using the HDOCK webserver.

The binding energy scores of the peptides ranged between −352.98 (top rank, confidence score: 0.98) and −202.07 (bottom rank, confidence score: 0.73), compared to GLP-1 (−653.61, confidence score: 1.00), with higher negative values indicating relatively stronger binding affinity. Furthermore, a confidence score >0.7 indicates a higher likelihood of binding to the receptor. Subsequently, 22 peptides (∼50% of the shortlisted peptides) with a confidence score ≥0.92 were superimposed in the context of GLP-1 bound to GLP-1R (Figure 7). Notably, the docking orientation of the top-ranked peptide was observed to be different compared to that of the GLP-1. Interestingly, exendin-4 (9–39), an antagonist of GLP-1R (Avexitide, Phase III), has been shown earlier to bind to the ECD of GLP-1R with an orientation different than that of GLP-1. Notably, activation of GLP-1R requires specific coordinated interactions with potential ligands, as observed in GLP-1 and Semaglutide. Therefore, all high-affinity binding ligands may not necessarily behave as GLP-1RAs and may potentially exhibit alternative functions, such as biased agonists, inverse agonists, and antagonists. In fact, it is reported (Underwood et al., 2010) that exendin-4 (9–39) binds strongly (EC₅₀ ∼5 pM, IC₅₀ ∼0.76 nM) to wild-type GLP-1R than the native GLP-1 (EC₅₀ ∼11 pM, IC₅₀ ∼1 nM). Moreover, the top-ranked peptide did not contain the amino acids in its sequence considered important for activating the GLP-1R and was thus reserved for future evaluations.

However, another peptide, codenamed SR18 (H-Igl-E-Aib-TFTSDLSDHYESKA-CONH₂), which contained several amino acids similar to those found on the GLP-1 and demonstrated a docking orientation similar to the GLP-1 with a binding energy score of −272.14, was rationally shortlisted for further studies as a first-generation GLP-1RA over the top-ranked peptide (H-Igl-E-Aib-AWMTSIISTMWAKH-CONH₂). Interestingly, SR18 also docked to the most populated conformer of the loop-remodeled GLP-1R bound to GLP-1, evolved over 1 μs of the MD simulations in a POPC-CHL bilayer. Notably, SR18 occupied the same orthosteric binding pocket on both conformers of GLP-1R, a binding site typically occupied by GLP-1 and other similar peptide mimetics, as evidenced in reported structural studies (Zhang et al., 2017; Zhang et al., 2020). A comparative docking energy (confidence score) and binding energy for SR18 in reference to GLP-1, Semaglutide, and Avexitide are provided in Table 1.

Notably, SR18 is an 18-amino acid designer polypeptide, modeled as a helix composed of both coded and NCAAs. On the contrary, both GLP-1, the native agonist of GLP-1R, and Semaglutide, the FDA-approved GLP-1 analog, are 31-amino acid helical polypeptides. However, Semaglutide shares ∼94% sequence homology with GLP-1 and harbors a single NCAA (Aib2) and a fatty acid-modified Lysine (Lys20) in its sequence. Therefore, in reference to GLP-1 and Semaglutide, it was pertinent to evaluate whether SR18 in its unbound state could sustain the helical conformation in solution. Thus, for comparison, both GLP-1 and SR18 were submitted to MD simulations for over 100 ns in explicit water at 300 K, including the deacylated Semaglutide.

The comparative data presented in Figure 8 (Supplementary Figure S3) suggest that SR18 can predominantly sustain a helical conformation in solution, similar to GLP-1 and Semaglutide, with some noticeable end fraying, specifically in the C-terminus region, which is generally observed for short peptides. In contrast, end fraying was observed at the N- and C-termini of GLP-1. On the other hand, the deacylated Semaglutide maintained its helical structure with some minimal end fraying observed in the C-terminus region.

Taking cue from the comparative MD simulation data, SR18 was custom-synthesized and purified to homogeneity, with a purity level of ≥95%, as evident from the analytical HPLC profile. The ESI-MS data further confirmed the integrity of SR18. The monoisotopic mass (O_Mol.Wt: 2,124.0) was found to be consistent with the theoretical molecular weight (T_Mol.Wt:2,124.32) of the peptide (Supplementary Figure S4). Interestingly, the CD spectra of the SR18 also demonstrated a signature signal in the far-UV region around 208 and 222 nm, typically observed for moderately ordered short ⍺-helical peptides. Notably, the ⍺-helical signal of SR18 improved further in the presence of the cosolvents, further confirming its ability to adopt a helical conformation under various solvent/cosolvent conditions, including PBS, 20% HFIP, 20% TFE, and 30 mM SDS (Figure 9a). Further, the ⍺-helix fractions (f_a) of SR18 calculated at θ₂₂₂ were found to be in the range of 0.034 in 1X PBS, 0.12 in 20% TFE, 0.22 in 20% HFIP, and 0.17 in 30 mM SDS (Supplementary Table S2), which is noted to be comparable with the reported value of 0.027 for semaglutide (Hamley et al., 2025).

Further, Dynamic Light Scattering (DLS), a widely used technique for determining the hydrodynamic size and size distribution of molecules in a sample by analyzing fluctuations in scattered light due to Brownian motion, was used to evaluate the hydrodynamic size distribution of 100 μM SR18 under different solvent conditions. DLS measurement was carried out in 1X PBS (pH ∼ 7.4) and 20% TFE at 25 °C (Figure 9b). SR18 exhibited an average hydrodynamic diameter of 4.001 nm with a polydispersity index (PDI) of 0.2289, indicating a relatively uniform and predominantly monomeric population in 1 X PBS, compared to 10 μM BSA (hydrodynamic diameter 7.631 nm and PDI 0.1645) as shown in Supplementary Figure S5. In contrast, in 20% TFE, SR18 displayed a significantly higher hydrodynamic diameter of 230.7 nm with a PDI of 0.4292, suggesting that SR18 may form higher-order aggregates or self-assembled structures. Notably, depending on the formulation, Semaglutide is reported to have a particle size ranging from ∼4 to 3,000 nm in DLS measurements (Li et al., 2024; Pinto et al., 2024; Hamley et al., 2025; Lee et al., 2025).

Cytotoxicity profiling of designer peptides is crucial to ensure that they are generally safe and do not harm healthy cells while maintaining therapeutic efficacy. Thus, the cytotoxicity profile of SR18 was evaluated by recruiting HeLa and HEK293 cell lines. As noted in Figure 9c, the viability of HeLa or HEK cells was not substantially impacted by SR18 at concentrations ranging from 0.39 to 100 µM when compared to cells that were treated with 1X PBS alone. In contrast, under the same experimental conditions, 10% DMSO exhibited pronounced cytotoxicity towards both HeLa and HEK293 cell lines, due to its ability to induce cellular stress by developing reactive oxygen species. Thus, the data primarily suggest that SR18 is non-toxic to eukaryotic cells, even at a high concentration under the cell culture conditions.

Similarly, the hemolytic profile of the designer peptides is also essential for assessing their biocompatibility and selectivity towards human erythrocytes. Thus, the hemolytic profile of SR18 was evaluated using a standard hemolytic assay by incubating freshly isolated human erythrocytes with SR18 at concentrations ranging from 0.39 to 100 µM. As judged from the absorbance of hemoglobin at 414 nm shown in Figure 9d, SR18 does not induce significant hemolysis of human RBCs, even at the highest concentration tested. In contrast, 10% Triton X-100, used as a positive control, caused pronounced erythrocyte hemolysis. Thus, these results demonstrate that SR18 is non-hemolytic and non-toxic to human erythrocytes.

Additionally, proteolytic degradation is a major challenge in peptide-based therapeutics, as it results in short half-life, low bioavailability, rapid clearance, and ultimately reduced therapeutic efficacy. This instability often necessitates frequent dosing and can lead to unfavorable pharmacokinetic profiles and potential immunogenic responses from degraded fragments. Though SR18 was designed to resist proteolysis by DPP4 due to the presence of Igl2, the inclusion of Lys17 for future acylation could make SR18 susceptible to proteolysis by other enzymes. Therefore, SR18 was incubated with trypsin (trypsin:peptide = 1:20) for ∼4 h, using SR4, another peptide reported earlier (Ghosh et al., 2024) from our lab as a positive control (Figure 10). The presence of a peak at ∼1,098 Da in the HRMS data (Supplementary Figure S6) corresponding to the fragment expected from cleavage of the amide bond between Lys17 and Ala18 further confirms trypsin’s enzymatic efficiency, as its activity depends on pH, temperature, and peptide structure.

Notably, SR18 also contains a potential trypsin cleavage site (Figure 10), and the HRMS data (Supplementary Figure S7) with a peak around ∼685 Da confirm that trypsin can cleave SR18 between Lys17 and Ala18, leaving the remaining crucial 17 residues intact. Thus, SR18 is likely to retain its function even if partial cleavage occurs under in vivo conditions. Additionally, Lys17, upon further modification (e.g., K26 in Semaglutide), is expected to enhance the binding affinity of SR18 to serum albumin (Supplementary Figure S2), improving its in vivo pharmacological functions. In summary, SR18 has the desired basic pharmacological profile; however, establishing it as a potential lead peptide agonist of GLP-1R requires further experimental evaluations. Although cell signaling studies are desirable for further evaluation of SR18 for obvious reasons, as an alternative, the agonistic potential of SR18 was preliminarily evaluated using computational structural biology techniques instead. In the first phase of the current study, SR18, docked to GLP-1R, was subjected to 1 μs MD simulations to directly compare its agonist-like properties with the established agonists and antagonists of GLP-1R reported earlier.

The SR18–GLP-1R complex embedded in a POPC-CHL (75:25) bilayer was subjected to MD simulation studies over 1 μs at 300 K, similar to the GLP-1–GLP-1R complex. The trajectory analysis of the SR18−GLP-1R complex (Figure 11) revealed that, similar to GLP-1, SR18 also remains bound to the GLP-1R throughout the MD studies with an average of nine intermolecular hydrogen bonds.

Further, like GLP-1, the C-terminal amino acids of SR18 also interact with the N-terminal region of GLP-1R. Additionally, like GLP-1, SR18 also engages with the residues of TM1, TM2, TM6, and TM7 of GLP-1R. Notably, H1, Igl2, E3, F6, T7, S8, L10, D12, and Y14 of SR18 demonstrate similar interaction patterns with K¹⁹⁷, L²⁰¹, R²⁹⁹, W³⁰⁶, L³⁸⁴, and L³⁸⁸ of GLP-1R. Additionally, SR18 also interacts with other important residues of GLP-1R, including D¹⁹⁸, E³⁷³, and E³⁸⁷. The common GLP-1R residues that interact with both peptides and maintain stable intermolecular interactions throughout MD simulations are presented through the interaction map in Figure 12 (Supplementary Figure S8). Interestingly, these residues of the GLP-1R are crucial for intracellular signaling and are responsible for insulin exocytosis.

To further understand the energetic contribution of the crucial residues of GLP-1R toward SR18 compared to GLP-1, the MD trajectories were subjected to gmx_MMPBSA calculations. For the calculation, 300 structures were randomly extracted from the first two major clusters of both complexes. The data in Figure 13 summarizes the energetic contributions of GLP-1R residues, along with critical, or more precisely, the “hotspot” amino acids that contribute to GLP-1R activation and insulin exocytosis. Given the relatively small size of SR18, the estimated binding free energy for SR18−GLP-1R complex is noted to be −98.79 ± 8.77 kcal/mol compared to −167.22 ± 16.20 kcal/mol observed for GLP-1−GLP-1R complex, which suggests that SR18 strongly binds to GLP-1R. Further decomposition analysis of the data reveals that the energetic contribution of 13 important residue pairs is conserved between the SR18−GLP-1R and GLP-1−GLP-1R complexes. Therefore, it is reasonable to believe that SR18 can potentially act as an agonist of GLP-1R.