Structure-based design of macrocyclic peptides to generate functional antibodies against G protein-coupled receptors

Domain: neuroscience
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
G protein-coupled receptors (GPCRs) are critical to a variety of pathophysiological processes, making them attractive targets for the development of drugs or relevant diagnostic tools. Although GPCRs have been successfully targeted with small molecules, the production of reliable anti-GPCR antibodies remains a major challenge. To address this issue, we develop a strategy using macrocyclic peptides designed to mimic the three-dimensional structure of GPCR extracellular loops as immunogens and use the chicken, which is genetically distant from mammals, as an immunization host to produce antigen-specific antibodies. The high-affinity neurotensin receptor type 1 (NTS1), overexpressed in many types of human cancer and associated with poor prognosis, is used as a target. Rational design of macrocyclic epitope mimics and linker selection are achieved using modeling and predictive analysis software tools based on available NTS1 crystal structures. This study particularly highlights the critical role of the linker in peptide macrocyclization, which determines whether antibodies can exert antagonistic activity. Overall, this strategy represents a valuable asset to produce effective spatial anti-GPCR antibodies and holds promise for diagnostic and therapeutic applications.

Introduction
G protein-coupled receptors (GPCRs) constitute the largest family of membrane receptors and play a key role in cell physiology and homeostasis1,2. Upon binding of their endogenous ligands, these receptors activate various signaling pathways and subsequently trigger a variety of cellular responses via G proteins and other intracellular effectors3–5. Dysregulation of GPCRs and changes in their expression levels have been linked to various pathological conditions, notably tumor growth and cancer6,7, thereby making them attractive as viable therapeutic targets. Unsurprisingly, over 35% of all drugs approved on the market specifically target this class of receptors8.

Given the importance of the GPCR family as drug targets, the development of reliable anti-GPCR antibodies would be beneficial as research tools for receptor characterization and, potentially, as valuable therapeutics, with broad applications in oncology, inflammation, metabolic and infectious diseases1,9,10. In addition, their growing significance as diagnostic, prognostic and predictive biomarkers cannot be overlooked9–11. There is therefore an urgent need to develop robust approaches to produce specific, selective and reproducible anti-GPCR antibodies. However, producing antibodies that selectively target GPCRs remains challenging12–15. To date, only three anti-GPCR antibodies, mogamulizumab, erenumab, and most recently, talquetamab targeting CCR4 (class A), CGRPR (class B), GPCR5D (class C), respectively, have received approval from the U.S. Food and Drug Administration (FDA)1,16,17.

The production of anti-GPCR antibodies relies on the selection of a suitable antigen and its presentation to trigger antibody generation. The most common method of antibody production involves the immunization of animals, usually mammals, with appropriate antigens. The choice of the antigen format to be injected remains the greatest difficulty, especially when dealing with complex membrane proteins, such as GPCRs that are embedded in the cell membrane18. To date, most antibodies targeting GPCRs have been generated using the N-terminal domain or extracellular loops (ECL1-3) of these receptors, as immunogens1. However, the primary challenge lies in identifying correctly folded epitopes that mimic the native conformation of these membrane-spanning proteins18,19. The high degrees of GPCR sequence and structural homology among species also contribute to the lack of immunogenicity, particularly when the epitope comes from a species closely related to the animal used for antibody production20. In addition, full expression of GPCR proteins is generally weak and of short duration, and the proteins may exhibit misfolding and limited epitope accessibility due to restricted exposure of the extracellular surface10,21.

To overcome these challenges, we have developed a strategy using macrocyclic peptides designed to mimic the three-dimensional (3D) structure of the extracellular loops of GPCRs as immunogenic agents in chickens. Because chickens are a genetically distant species from mammals, this approach is expected to trigger a strong immunogenic response against mammalian epitopes, resulting in the production of reliable anti-GPCR antibodies22,23. In this proof-of-concept study, we selected the high-affinity neurotensin receptor type 1 (NTS1) as a target. NTS1 is overexpressed in several types of cancer cells24,25 and is associated with a poor prognosis26–28. Its natural ligand, neurotensin (NT), is secreted by tumor cells in an autocrine-dependent manner and acts as a growth factor when bound to NTS1, influencing tumor progression, cell invasion and metastasis29,30. The crystal structure of NTS1 in its agonist-bound conformation has also been solved previously31–33, providing valuable insights for predicting the 3D structure of the epitope34.

Herein, we describe a strategy for producing reliable antibodies that recognize both the sequence and 3D structure of the extracellular loops of GPCRs. In particular, this study highlights the critical role of the linker in the macrocyclization of the peptide used as an immunogenic agent. The peptide, which is predicted to adopt the structural conformation corresponding to the second extracellular loop (ECL2) of NTS1 bound to NT, enables the production of an antibody that selectively recognizes the receptor and exerts potent antagonist activity. This functional activity is demonstrated by its ability to block downstream signaling pathways stimulated by NT. A slight modification of the linker used to cyclize the peptide results in antibodies with specific and selective detection properties, which are interesting features for diagnostic applications, but lack antagonist activity. Overall, this proposed technology represents an advance in the development of antibodies targeting GPCRs.

Results
Rational design and synthesis of peptide mimetics of the second extracellular loop of NTS1

To conduct the study, for each extracellular loop of NTS1 (ECL1, ECL2, and ECL3), the design of the macrocyclic antigen was carried out based on two criteria: 1) the size of the cycle, i.e., the number of amino acids in the cycle and the linker enabling macrocyclization. A total of 123 linkers divided into 7 categories (aromatic, biphenyl, indole, piperazine, linear, amino acids and miscellaneous) were evaluated to encompass a broad chemical space and achieve conformational diversity based on the following criteria (Supplementary Fig. 2a): (1) variability in flexibility and chemical structure, (2) ease of synthesis or commercial availability, (3) presence of two functional groups suitable for cyclization, and (4) inclusion of a free thiol group for KLH conjugation (or possibility to insert a cysteine next to the linker). For each loop, between 9 and 12 amino acids were considered, resulting in a total of almost 1500 macrocycles to evaluate in silico. After energy minimization using a model poly-alanine macrocycle and visual inspection, 24 linkers were selected for further molecular dynamics studies, resulting in a total of 288 macrocycles (ECL1, ECL2 and ECL3, from 9 to 12 residues, 24 linkers x 3 loops x 4 sizes) (Supplementary Fig. 2b). Considering the biological results, the molecular modeling, and the selection criteria outlined below, we decided to focus on ECL2.

The peptide sequence selected in this study as the antigenic determinant consists of 11 amino acids (N-R-S-A-D-G-T-H-P-G-G), with 10 to 20 amino acids being the optimal length for an epitope35. The selection and rational design of structurally defined epitope mimetics were based on several criteria, including the relevance of the extracellular domain36, its involvement in receptor activity (as observed with ECL231,37), the peptide’s immunogenicity, the availability of the crystal structure of the agonist-bound rat NTS1 receptor (rNTS1)31 and the feasibility of peptide synthesis. Multiple algorithms implemented in the Molecular Operating Environment (MOE) software for computational design and simulations were then used to select a suitable linker for peptide cyclization and to predict the flexibility and hydrophilicity of the synthesized macrocyclic peptides38.

The peptide macrocycle design process started with a conformational search to identify initial structural states, followed by molecular dynamics (MD) simulations to explore system dynamics. Next, time-lagged independent component analysis (tICA)39, which identifies slow dynamical processes, was applied, followed by Gaussian Hidden Markov Model (GHMM)40 optimization to cluster metastable states and calculate transitions41. The workflow concludes with full validation and comprehensive analysis of the results, providing insight into the conformational behavior and kinetics of the system (Supplementary Fig. 3). From this process, we identified and synthesized three conformationally constrained peptides mimicking the 3D structure of ECL2 by closing the ring with three different linkers: side-chain S-lysine (3Dpeptide-1), side-chain S-ornithine (3Dpeptide-2) or main-chain S-lysine (3Dpeptide-3). A linear peptide (Lin-peptide) was also synthesized as a control (Supplementary Fig. 1, Supplementary Table 1 and Supplementary Fig. 4b, c). The modeling analysis was conducted on all 3D peptides to predict which of the linkers would generate the best ECL2-mimicking conformation. All the predicted conformational states and their corresponding prevalence (represented by colored spheres as a percentage of occurrence) are shown in Fig. 1. To evaluate the extent to which the spatial peptides mimicked ECL2, root-mean-square deviations (RMSDs) of the atomic positions between each 3D peptide and the crystallized ECL2 of rNTS1 were calculated for each predicted state (Table 1). The RMSD values were calculated from the alpha and beta carbons of the five most exposed amino acids of the loop (D-G-T-H-P) after superimposition on the “Align/Superimpose” panel of the sequence editor module of the MOE software. The conformational states with the lowest RMSD values were considered to represent the most relevant mimetics of ECL2.

For comparison, an arbitrary threshold of 1.31 Å was defined as the highest of the three peptides’ lowest RMSDs (3Dpeptide-1: 1.26 Å; 3Dpeptide-2: 1.31 Å; 3Dpeptide-3: 0.62 Å). Hidden Markov modeling of 3Dpeptide-1 trajectories identified six metastable conformations, two of which (states 1 and 3) exhibited RMSDs ≤1.31 Å. Together, these conformations account for a combined occupancy of 45.54% (Table 1). Notably, state 1 achieved the lowest RMSD (1.26 Å) and the highest stationary probability of all predicted conformers for 3Dpeptide-1. Eight conformational states were predicted for 3Dpeptide-2 and seven states for 3Dpeptide-3 (Fig. 1). Only one state (state 8) met the defined threshold of 1.31 Å, corresponding to only 16.18% probability for 3Dpeptide-2. Regarding 3Dpeptide-3, five states fell below the threshold (states 1, 2, 4, 5, and 7), accounting for 85.9% of the calculated occurrences. Accordingly, this peptide was predicted as the most likely to adopt a 3D structure similar to that of ECL2 in the agonist-bound crystal structure of the rNTS1 receptor. The two states 2 and 4 of 3Dpeptide-3 with the lowest RMSD values (0.62 Å and 0.74 Å) displayed the best backbone superimposition with ECL2 of rNTS1. For each peptide, the superimposition of the two predicted states with the lowest RMSD values onto the ECL2 backbone of agonist-bound rNTS1 are shown in the right-hand panels of Fig. 1. This rank order of native-like occupancy mirrors precisely the assay results, demonstrating that higher residence in the ECL2-mimetic conformations drives stronger binding. The implied-timescale curves plateau at ~6 ns, and the CK tests show tight overlap between predicted and observed transition probabilities, confirming that our kinetic models are fully converged and Markovian. These findings thus provide a coherent mechanistic explanation for the experimental potency ranking.

Affinity and selectivity of anti-NTS1 antibodies

Following immunization with the four peptides (Lin-peptide, 3D peptides-1, -2 and -3) (Supplementary Table 1; Supplementary Fig. 4a), chicken polyclonal antibodies (IgY) were extracted and immunopurified to generate four distinct anti-NTS1 antibodies: IgY-Lin, IgY-3D-1, IgY-3D-2, and IgY-3D-3. IgY-Lin was expected to primarily detect the linear epitope (N-R-S-A-D-G-T-H-P-G-G), whereas the other three antibodies were designed to recognize the same epitope predominantly in its 3D structure. To validate the ability of these antibodies to bind to their respective peptides, all the antibodies were first tested via an enzyme-linked immunosorbent assay (ELISA) on fixed peptides (Fig. 2a). Inhibition curves were generated by preincubating each antibody with increasing amounts (0 to 10 μM) of its matching free peptide (e.g., IgY-Lin with Lin-peptide) to block detection of the fixed peptide in a dose-dependent manner. The concentration calculated to inhibit 50% of IgY binding to the fixed peptide was similar for all four antibodies, ranging from 2.2±0.6 nM for IgY-Lin to 7.8±2.3 nM for IgY-3D-3 (IC50 values are reported in Fig. 2a). These results confirm that each antibody has a similar detection capacity for its corresponding peptide and validate the effectiveness of the immunization.

To analyze the selectivity of these anti-NTS1 antibodies, a competition assay was then carried out via ELISA using the four designed peptides as baits and a fixed concentration of 10 μM of each peptide to challenge IgY binding (Fig. 2b). All the antibodies were efficiently blocked by their matching peptides to a similar extent (IgY-Lin: 95%±3.5; IgY-3D-1: 100%±0.1; IgY-3D-2: 99%±1.2; and IgY-3D-3: 97%±2.3). The binding of IgY-Lin to its linear peptide was partially blocked by the addition of the three 3D peptides (up to 62% blockage was achieved by 3D peptide-1), revealing some cross-reactivity. Interestingly, IgY-3D-1 was effectively blocked by its corresponding peptide (3D peptide-1), but was unaffected by the three other peptides, demonstrating high specificity. The IgY-3D-2 and IgY-3D-3 antibodies demonstrated less specificity, compared to the IgY-3D-1 antibody, as the binding of these two antibodies to fixed peptides could be partially displaced by the other peptides, including the linear congener (Fig. 2b). Taken together, these results suggest that the antibodies directed against the 3D peptides display significant specificity toward 3D epitopes compared with a less structured linear epitope.

The importance of the peptide sequence was also evaluated by replacing each amino acid predicted to be predominantly exposed (D-G-T-H-P) with an alanine (A), also known as alanine scanning, which is widely used in epitope mapping and sequence function analyses. This analysis was conducted on the two macrocyclic peptides that best mimicked the 3D structure: 3Dpeptide-1 and 3Dpeptide-3 (Supplementary Table 2). Compared with binding 3Dpeptide-1 WT (IC50 value: 3.2±0.6 nM), IgY-3D-1 showed reduced affinity for all mutated peptides, especially D261A, G217A and T218A, with IC50 values of 2447±852 nM, 1286±354 nM and 708±194 nM, respectively (Fig. 2c). In contrast, IgY-3D-3 binding was less sensitive to these mutations in 3Dpeptide-3, resulting in no significant loss of antibody binding (Fig. 2d). Compared with 3Dpeptide-3 WT (IC50 value: 7.8±2.2 nM), the T218A and H219A mutations even slightly improved IgY-3D-3 binding to these peptides (IC50 values: 0.72±0.04 nM and 0.58±0.07, respectively).

Antibody validation: detection of the NTS1 target protein in cells and brain tissue

We next determined the ability of these anti-NTS1 antibodies to detect the complete target protein in a whole-cell context (Fig. 3). First, immunofluorescence (IF) microscopy was performed using fixed HEK293A cells stably expressing the rNTS1 receptor tagged with hemagglutinin (HA) at its N-terminal extracellular domain (Fig. 3a). A single concentration (5 μg/ml) was used to test the anti-NTS1 antibodies (IgY-Lin, IgY-3D-1, IgY-2, and IgY-3D-3). Colocalization analysis was performed using mouse anti-HA antibodies to validate the receptor subcellular distribution. Signals were detected with dye-conjugated anti-chicken ATTO488 and anti-mouse Alexa Fluor 647 secondary antibodies, which have distinct and nonoverlapping excitation–emission wavelength spectra. For both IgY-3D-1 and IgY-3D-3, z-stack confocal microscopy images revealed strong overlapping of the two fluorescence signals (Fig. 3a). At an identical antibody concentration, IgY-Lin generated a moderate signal, and IgY-3D-2 barely detected NTS1. Immunodetection was fully inhibited in the presence of each matching peptide (Fig. 3a, right panel). To further confirm the effectiveness of these antibodies in recognizing the intended target, a colocalization analysis was performed on all replicates. For each replicate, Pearson and overlap (488/647) coefficients were calculated, and a scatter plot was generated to visualize the correlation between signals (rNTS1 versus HA) (Fig. 3a, middle panel). Scatter plots converging towards the x-axis correspond predominantly to HA detection over rNTS1, as shown with IgY-3D-2. A good correlation, considered here as colocalization, was observed in the scatter plot of IgY-3D-3. The compiled data of the Pearson and overlap coefficients from all the conditions are presented in Fig. 3b, c, respectively. Based on the Pearson coefficient analysis alone, all anti-NTS1 antibodies generated a signal that colocalized with the HA signal, with values between 0.5 and 1.0. This could indicate that none of the antibodies generated non-specific signals. However, the signal intensity differed significantly, and the overlap coefficient analysis revealed that IgY-3D-2 was not able to correctly detect the rNTS1 receptor (Fig. 3c). Indeed, a value less than 0.5 revealed the absence of colocalization (mean=0.27±0.08)42. In contrast, IgY-3D-1 and IgY-3D-3 generated strong detection signals, almost entirely colocalizing with HA, with overlap coefficients of 0.98±0.19 (3D-1) and 0.97±0.11 (3D-3), respectively. IgY-Lin was less potent at detecting rNTS1 since the overlap coefficient was more variable among replicates (0.89±0.3).

As suggested previously43,44, fixing cells or tissues with aldehydes can alter the conformation of protein epitopes. Therefore, antigenic determinants accessible in native proteins may not be exposed in the same way in living and fixed cells. To assess the ability of the isolated antibodies to detect NTS1 in live cells, we performed IF microscopy by incubating living cells with the different anti-NTS1 antibodies and the anti-HA antibody at a single concentration prior to fixation (Supplementary Fig. 5a). Our results revealed that IgY-3D-3 is the only antibody capable of detecting NTS1 in its native conformation in live cells. Accordingly, flow cytometry analysis performed on live cells demonstrated that only IgY-3D-3 achieved signal detection in 89.02% of cells co-labeled with anti-HA antibodies, as observed in the Q1-upper right (UR) quadrant (Supplementary Fig. 5b, c).

To further validate the ability of these anti-NTS1 antibodies to detect the endogenous rNTS1 receptor, immunohistochemistry (IHC) was performed on fixed tissue sections from adult rat brain. Two brain regions previously described as highly expressing rNTS145, namely the substantia nigra and cerebral cortex, are illustrated in Fig. 3d. Within the substantia nigra, pars compacta, IgY-3D-1 and IgY-3D-3 detected intensely stained NTS1-positive nerve cell bodies (arrows). In comparison, although IgY-Lin was able to detect NTS1, rather diffuse staining was observed, making it difficult to identify individual immunoreactive cells. In the auditory cortex, neuronal perikarya immunoreactive to NTS1 were also evident in layers II-III and V. In layer V, NTS1-expressing cells represent mainly pyramidal cells showing strong immunolabeling of the cell body and apical dendrite (arrows).

Antibody Validation: Detection of NTS1 via Biochemical Analyses

We further characterized these anti-NTS1 antibodies via Western blotting (WB) and immunoprecipitation (IP) coupled with mass spectrometry (MS) (Fig. 4). WB is widely used to determine if an antibody detects the denatured antigen (due to the presence of SDS in the lysis buffer), whereas IP is typically conformation sensitive and requires an antibody that recognizes the target protein in its native form46. These assays were carried out on whole-cell lysates derived from HEK293A cells expressing or not HA-tagged rNTS1. First, WB analysis with anti-HA antibodies revealed the presence of two bands at 45 and 55 kDa, corresponding to the expected molecular size of two differentially glycosylated forms of NTS1, as reported previously47,48 (Fig. 4a, red dotted square). Bands of similar molecular weights were also detected with IgY-3D-1 and -3 antibodies, although IgY-3D-3 clearly appeared to be more effective in detecting the NTS1 protein than did IgY-3D-1 (Fig. 4a, red dotted squares). In sharp contrast, IgY-Lin gave rise to nonspecific signals, as proteins of similar molecular weights were detected in HEK293A cells that do not express NTS1, whereas IgY-3D-2 failed to detect NTS1 in WB.

IP experiments were performed using the HEK293A cell lines described above and anti-NTS1 antibodies conjugated to magnetic beads (Fig. 4b). All 3D-IgY antibodies effectively pulled down the receptor to a level comparable to that of the HA control IP. IgY-Lin also led to receptor pull-down but was less effective than the 3D-antibodies (Fig. 4b). We then validated that the protein pulled down in the IP assay was indeed the NTS1 receptor by combining IP and mass spectrometry49,50 (Fig. 4c, d). First, the rNTS1 receptor was isolated by IP with magnetic beads conjugated to IgY-3D-1 and IgY-3D-3 from HEK293A cells expressing HA-rNTS1. WB was performed with an anti-HA-HRP antibody (Fig. 4c). The eluted samples were lyophilized and sent for MS analysis at two independent mass spectrometry research facilities. A trypsin-digested peptide of 17 amino acids was clearly identified from the Rattus norvegicus database, corresponding to the C-terminal domain of NTS1. The identified peptide sequence and the resulting spectrum are shown in Fig. 4d (the complete spectrum is presented in Supplementary Fig. 6, and the ion data are presented in Supplementary Table 3). The interaction between anti-NTS1 antibodies and the receptor was significantly blocked by preincubation with the corresponding peptide (Fig. 4d, insert). These compelling results confirm that both the IgY-3D-1 and IgY-3D-3 antibodies interact specifically with the intended NTS1 target protein.

Antibody functional properties

Although antibodies are extensively used as powerful research tools for studying membrane proteins, they are also suitable candidates for the pharmacological modulation of protein function. To this end, we evaluated the ability of these antibodies to modulate NTS1 receptor activity, which would be of great interest from a therapeutic perspective. Since the crystal structure of NTS1 bound to the C-terminal portion of the endogenous agonist NT (i.e., NT(8-13)) reveals that the ligand-binding pocket for NT is partially capped by the ECL2 hairpin31, we first determined whether these IgY anti-NTS1 antibodies targeting ECL2 competitively displaced radiolabeled NT from its ligand-binding site. Cell membranes from HEK293A cells expressing HA-rNTS1 were thus incubated with radiolabeled NT (125I-Tyr3-NT) in the presence of increasing concentrations of IgY-Lin, IgY-3D-1, -2, or -3 (Fig. 5a). We first observed a dose-dependent displacement of the 125I-Tyr3-NT agonist from its ligand-binding site by IgY-3D-3, with an IC50 of 3.2±0.7 μg/ml. IgY-3D-1 and IgY-Lin were significantly less able to displace radiolabeled NT, with IC50 values of 36.5±11.8 μg/ml and 187±94.9 μg/ml, respectively. IgY-3D-2 was not effective at competing with bound NT, which is consistent with its inability to recognize NTS1 in its denatured or native state. Importantly, the IgY-3D-1 and IgY-3D-3 antibodies produced via the proposed strategy exhibited consistent recognition properties across different production lots generated from chickens immunized with 3Dpeptide-1 or 3Dpeptide-3, as demonstrated by the high level of displacement of 125I-Tyr3-NT from rNTS1 (Supplementary Fig. 7a).

Depending on the cell type, the stimulation of NTS1 by NT leads to the activation of the Gαs, Gαq, Gαi/o, and Gα13 protein signaling pathways as well as to the recruitment of β-arrestins 1 and 251 (Supplementary Fig. 7b). To determine whether the anti-NTS1 antibodies were able to modulate NTS1-dependent activity, we first measured Gαs-dependent cyclic adenosine monophosphate (cAMP) accumulation via a homogeneous time-resolved fluorescence (HTRF) cell-based assay. To this end, CHO-K1 cells stably expressing rNTS1 were stimulated with NT (EC50) and simultaneously incubated with IgY-Lin, IgY-3D-1, -2, or -3 (Fig. 5b). Our results revealed that IgY-3D-3 was the only antibody able to significantly block NTS1-mediated cAMP production. We further examined, via bioluminescence resonance energy transfer (BRET) biosensors, the ability of these anti-NTS1 antibodies to modulate the Gαq, Gαi2, Gα13, GαoB, Gαz and β-arrestin 2 signaling pathways following their binding to rNTS1 transiently expressed in HEK293 cells. Our findings demonstrated that IgY-Lin, IgY-3D-1, and IgY-3D-3 failed to activate (agonist mode) the canonical Gαq and β-arrestin 2 receptor signaling pathways (Fig. 5c, e) and to induce NTS1 coupling to Gα13, GαoB, Gαi2 and Gαz (Supplementary Fig. 7). In the antagonist mode, we found that IgY-3D-3 effectively blocked all signaling pathways associated with NTS1 activation in a concentration-dependent manner (Fig. 5d, f and Supplementary Fig. 7). A nonpeptide NTS1-selective antagonist52, SR48692, was used as a control in the BRET-based assay and was found to reverse NT signaling activity at the NTS1 receptor site. In the same experimental paradigm, IgY-Lin and IgY-3D-1 did not inhibit the ability of NT to induce the functional coupling of NTS1 to G proteins or β-arrestins.

Antibody cross-reactivity and selectivity

This project exploited the important phylogenetic distance between mammals and chickens to generate two robust anti-NTS1 antibodies, namely, IgY-3D-1 and IgY-3D-3, which were designed against the ECL2 region of rNTS1. The ECL2-based macrocycle designed to immunize chickens was modeled on the rNTS1 X-ray structure, the amino acid sequence of which is highly conserved among species, except for in chickens (Gallus gallus; Fig. 6a). Despite the difference in two amino acids (threonine and proline) between the designed peptide and the human NTS1 protein sequence, superimposition of the designed peptide on the human ECL2 protein resulted in high levels of similarity in their backbone structures (Fig. 6b), thus suggesting a possible conserved 3D conformational structure of NTS1 between rat and human NTS1 species. In this context, we evaluated whether IgY-3D-1 and IgY-3D-3 could detect the human NTS1 receptor (hNTS1) in HEK293 cells expressing HA-tagged hNTS1. IF confocal microscopy (Fig. 6c) and IP experiments using whole-cell lysates (Fig. 6d) revealed that the ability of IgY-3D-1, but not IgY-3D-3, to detect hNTS1 was conserved. Accordingly, IgY-3D-1 was very effective at competing with bound NT, exhibiting comparable IC50 values between hNTS1 and rNTS1 (IC50 values of 44.3±20.6 μg/ml and 36.5±11.8 μg/ml, respectively) (Fig. 6e). Surprisingly, IgY-3D-3 partially retained its ability to displace bound NT from hNTS1, with an IC50 of 181±91 μg/ml (compared with 3.2±0.7 μg/ml for rNTS1). In addition, we measured the Gαq-dependent inositol monophosphate (IP1) accumulation using an HTRF cell-based assay with CHO-K1 cells stably expressing hNTS1. Cells were stimulated with NT at its EC50 concentration and simultaneously incubated with IgY-3D-1 or IgY-3D-3 (Fig. 6f). Both IgY-3D-1 and IgY-3D-3 significantly blocked IP1 production. As in the radiolabeled NT displacement assay, IgY-3D-1 showed more effective inhibition than IgY-3D-3. These findings suggest that IgY-3D-1 has the best ability to recognize the human receptor isoform.

Target selectivity is also a crucial element in the development of therapeutic and diagnostic antibodies53. To address this question, IgY-3D-1 and IgY-3D-3 were tested for their ability to detect class A GPCRs closely related to rNTS1, namely, the rat neurotensin receptor type 2 (rNTS2) and the rat apelin receptor (rAPJ). HEK293A cells overexpressing rNTS2, rAPJ or rNTS1 were then processed for WBs (Fig. 6f), native IPs (Fig. 6g) and IF microscopy (Supplementary Fig. 8a, b). Our results revealed that both IgY-3D-1 and IgY-3D-3 selectively and specifically recognized rNTS1 but failed to detect rAPJ and rNTS2. Notably, the amino acid sequence alignments of rNTS2 and rAPJ with the NTS1-ECL2 epitope region presented 36% and 27% similarity with the corresponding rNTS1 region, respectively (Supplementary Fig. 8c), thus limiting the possibility of adopting a similar spatial conformation.

Methods
Synthesis of linear and macrocyclic peptides
The different steps leading to the synthesis and cyclization of macrocyclic peptides mimicking the second extracellular loop (ECL2) of rNTS1 are described in detail in the Supplementary Methods.

Computational Modeling
All molecular modeling work, comprising conformational sampling, MD simulations, structural superimposition, and downstream kinetic was carried out as follows. Initial conformational searches and model preparation were performed using MOE38. MD simulations were then executed with GROMACS 2024.1 under standard conditions72. Resulting MD trajectories were processed with the Python libraries pyEMMA (version 2.5.12) and MSMBuilder (version 3.8.0) to perform time-lagged independent component analysis, construct and interrogate Markov State Models, and carry out estimation, validation, and analysis of molecular-kinetic processes73,74. MD simulation parameters and protocols are described in the following section38.

Conformational search
The X-ray structure of NT(8-13) bound to rNTS1 (PDB ID 4GRV) was used as the template for modeling the primary sequence of designed peptides. Structures were built via the MOE builder and minimized on all atoms using AMBER10:EHT force field and 0.1 kcal/mol/A2 RMS as gradient and the GB/VI solvation model.

Each of 123 linkers was connected to the sequences of each loop using different, appropriate functional groups compatible with different chemistry (Supplementary Figs. 2a, b). This set of linkers allowed us to create a highly varied initial database of macrocyclic peptides, enabling the identification of conformations that best mimic the exposed and stable regions of the ECL2 domain. This diversity in linker properties played a critical role in conformational variability, as linkers are primarily responsible for inducing large-scale structural changes.

A conformational search was then performed for each structure using the Low Mode MD method, which performs molecular dynamics perturbations along low-frequency vibrational modes75. The rejection limit was set at 100, the iteration limit at 10000, the RMS gradient at 0.005, and the MM iteration limit at 500. Two conformations were considered equal if the optimal heavy atom RMS superposition distance was less than 0.25 Å. The lowest-energy conformation found was used as the starting point for molecular dynamics simulations.

Molecular dynamics simulations
All MD simulations were carried out with GROMACS 2024.1 using the CHARMM36 all-atom additive protein force field76. The simulation system was prepared using CHARMM-GUI77 to generate the initial topology. The system was then solved in a cubic box with explicit TIP3P water and neutralized using counterions. Counter-ions were added to neutralize the system, and an initial steepest-descent minimization was carried out. The Verlet cutoff scheme (rlist=1.2 nm) was used for nonbonded searches, with Particle Mesh Ewald for long-range electrostatics78. A second steepest-descent minimization was performed under identical neighbor-list and cutoff settings. All bonds to hydrogen were constrained via LINCS to remove any residual steric clashes79. Subsequently, two equilibration steps, including an NVT under constant volume and temperature, which was maintained at 300 K using the velocity-rescaling thermostat80, followed by an NPT equilibration under constant pressure, which was controlled via the Parrinello–Rahman barostat. Finally, 500 ns of production dynamics were run with the Parrinello–Rahman barostat81. Trajectories were saved every 10 ps for subsequent analysis.

Time-lagged independent component analysis
MD trajectories were analyzed using a custom time-lagged independent component analysis (tICA) pipeline to extract slow collective motions and characterize the underlying conformational landscape39. The backbone-torsion features (ϕ and ψ dihedral angles) were extracted from the resulting MD trajectories using MDtraj and transformed into sine and cosine components for downstream tICA and MSM analyses82,83.

All backbone-torsion features were assembled into a numerical matrix and subjected to time-lagged independent component analysis (tICA). The optimal number of independent components was determined by a slope-average test on the implied timescale spectrum to isolate the most significant slow modes. A tICA lag time of 500 frames (5 ns; dt=0.01 ns) was selected after evaluating convergence of implied timescales across multiple lags and identifying the plateau region73,84,85. Implied timescales were estimated with Bayesian error bounds to assess statistical uncertainty. As shown in Supplementary Fig. 10, the implied relaxation timescales for 3D-peptide-1, 3D-peptide-2, and 3D-peptide-3 converge between 6–7 ns, confirming the validity of the Markovian assumption and supporting the robustness of the resulting Markov State Models. This analysis highlights the dominant slow collective motions that define each peptide’s conformational landscape and validates the chosen lag times for accurate kinetic modeling.

Clustering and markov state model construction
Structural data projected onto the dominant tICA subspace were discretized into kinetically coherent states using a Gaussian Hidden Markov Model (GHMM) framework40. The tICA projection dimension was chosen by a slope-average test of implied timescales to retain only the slowest, most significant modes41. GHMMs were then trained over tICA dimensions and states, each candidate model was scored by the Bayesian Information Criterion to penalize overfitting while rewarding explanatory power86. The optimal GHMM for each peptide was identified as the one minimizing BIC in concert with maximizing the slowest relaxation timescale, thereby ensuring both parsimony and kinetic accuracy.

For 3D-peptide-1, the best model employed three tICA dimensions and six states; for 3D-peptide-2, six dimensions and eight states; and for 3D-peptide-3, three dimensions and seven states (Supplementary Fig. 11). These configurations capture the principal slow conformational transitions and form the basis of robust Markov State Models for each peptide.

The Chapman-Kolmogorov (CK) test was performed to validate the reliability of Markov state models for the three peptides87–89, which assesses the agreement between predicted and empirically estimated transition probabilities over multiple lag times. For each peptide, Bayesian MSMs constructed at a 6 ns lag time produced self-transition probabilities (solid lines with 95 % credible intervals) that closely matched direct estimates from MD trajectories in Supplementary Fig. 12, thereby confirming the Markovian character and kinetic accuracy of the models.

Superimposition of the macrocycle with the X-ray loop
The X-ray structure of rNTS1 bound to NT(8-13) (pdb ID 4GRV) was loaded to MOE, and the protonated and partial charges were assigned. Prior to alignment, a 500 ns MD simulation of the complex was performed to ensure conformational equilibration and to emulate more physiologically relevant conditions. For each representative conformation of the macrocycles ensemble, structures were loaded into MOE, and amino acid residues were aligned to the corresponding loop region of NTS1 via the sequence alignment module within the sequence editor panel. The five most exposed residues (D-G-T-H-P) were selected and used as reference points for superimposition onto the corresponding residues of the macrocycles. RMSD values were calculated based on the Cα and Cβ atoms of these five amino acids to evaluate the accuracy of alignment and conformational similarity.

Chicken immunization and IgY extraction
Primary immunization of KLH-conjugated peptides (Lin-peptide, 3D-peptide-1, 3D-peptide-2 and 3D-peptide-3) was achieved in complete Freund’s adjuvant, while subsequent boosters were given in incomplete Freund’s adjuvant (Supplementary Fig. 4a). KLH-conjugated peptides were administered subcutaneously to Lohmann Brown (Rhode Island breed) or Lohmann White LSL-LITE (Leghorn) hens (26–28 weeks old), with two chickens assigned per peptide group. After three months of immunization with a monthly antigen boost, all eggs were pooled according to antigen and time of collection. Egg yolk was separated from the egg white, and the proteins extracted, as previously described, with a few modifications90. Egg yolk was diluted with two volumes of distilled water, homogenized, and centrifuged. The precipitate was resuspended in 10% (w/v) NaCl prepared in NaOH (0.05 N) to extract the protein fraction, and the homogenate was adjusted to pH 4.0 before centrifugation to remove precipitates. The resulting supernatant was diluted with three volumes of distilled water, centrifuged, and concentrated by ultrafiltration. Immunoglobulins were then precipitated with 20% saturated ammonium sulfate containing 15% (w/v) NaCl, collected by centrifugation, dissolved in distilled water, and desalted using a size-exclusion chromatography column. All extracted IgYs (IgY-Lin, IgY-3D-1, IgY-3D-2 and IgY-3D-3) underwent a second round of immunoaffinity purification using the corresponding peptide conjugated to agarose beads. Briefly, stringent washes with 500 mM NaCl were performed prior to elution to ensure the removal of any nonspecific polyclonal antibody (pAb) binders, allowing only specific NTS1 binders to be collected. Antibodies were then eluted using 100 mM glycine buffer at pH 2.4. The polyclonal antibodies were produced in limited quantities. For further information or to request access to this biological material, please contact Immune Biosolutions.

Enzyme-linked immunosorbent assay (ELISA) of peptides
All the tested peptides were plate-coated at 3.5 μm per well in a clear, Well-CoatedTM Sulfhydryl Binding 8-well strip plate (G-Biosciences, MO 63132-1429, USA) with binding buffer (0.1 M Na2HPO4, 0.15 M NaCl, 10 mM EDTA pH 7.2) overnight at 4°C. For blocking, L-cysteine (1 μM in binding buffer) was added to each well for 1 h at room temperature (RT). The tested antibodies (IgY-Lin, IgY-3D-1, IgY-3D-2 and IgY-3D-3) were diluted to a final concentration of 0.1 μg/ml (0.25% Tween 20 in PBS or 0.3% milk) and preincubated with increasing amounts (from 0.3 nM to 10 µM) of the corresponding free peptide or a fixed concentration (10 μM) of either free peptide. The IgY-peptide mixtures and the positive control (IgY only) were added to individual peptide-coated wells and incubated for 2 h at RT. Four washes were performed with PBS containing 0.25% Tween 20 (PBS-T), followed by incubation with the secondary antibody (alpaca anti-chicken HRP, Immune Biosolutions, Sherbrooke, QC, CA) at a 1:10,000 dilution in PBS-T for 45 min at RT. After four additional washes with PBS-T, 3,3’,5,5’-tetramethylbenzidine (TMB) (Sigma‒Aldrich, St. Louis, Missouri, USA) was added, and the mixture was incubated for 15 min at RT before the reaction was stopped with 2 N HCl. Absorbance at 450 nm was read with a Tecan GENios plate reader (Tecan Austria GmbH, Austria).

Cells and reagents
Human embryonic kidney (HEK) 293A (Thermofisher, Catalog number: R70507) and 293T cells (American Type Culture Collection (ATCC), CR-3216) were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and antibiotics at 37°C and 5% CO2. Chinese hamster ovary (CHO)-K1 cells were obtained from the ATCC (CCL-61) and were maintained in DMEM/F12 supplemented with 10% FBS, L-glutamine, HEPES and antibiotics at 37°C and 5% CO2. All cell culture reagents were from HyClone (Thermo Fisher Scientific Inc., Logan, UT, USA).

HEK293A and CHO-K1 cells were transformed with a lentivirus-based system to stably express either the rat or human NTS1 receptor. To generate the lentiviral vectors expressing HA-tagged rat or human NTS1, the 2xHA-rNTS1 (NCBI accession number: NM_001108967.2) and 3xHA-hNTS1 (NCBI accession number: NM_002531.3) coding sequences were amplified from custom plasmids (p2xHA-rNTS1) and pcDNA3.1+_3xHA-hNTS1 (UMR cDNA resource center, MO, USA) with the following primers: forward for both sequences: 5’-TGCATCTAGAGCCACCATGTACCCATACGA-3’, reverse for rNTS1: 5’-TCACTGCAGAATTCCTAGTACAGGGTCTCC-3’ and reverse for hNTS1: 5’-TCACTGCAGAATTCCTAGTACAGCGTCTCG-3’. The amplified cassettes were then cloned and inserted into pCDH-MCS-Ef1-puro cDNA cloning and expression vectors (System Biosciences, Mountain View, CA, USA) via XbaI and EcoRI restriction enzymes (New England Biolab, Massachusetts, USA). HEK293T cells were then transiently cotransfected with the pCDH lentivectors (containing either rNTS1, hNTS1 or the empty vector), pMD2. G and psPAX2 (gift from Didier Trono, Addgene plasmids #12259 and #12260, respectively) using polyethylenimine as the transfection reagent (MW 25,000; Polysciences, Warrington, PA). The viral particles were then collected 48 h post-transfection, filtered (0.45 μM PVDF syringe filter, Thermo Fisher Scientific Inc., Massachusetts, USA) and used to generate cells stably expressing 2xHA-ratNTS1 or 3xHA-humanNTS1. HEK and CHO-K1 cells expressing pCDH-CMV-NTS1-Ef1-puro were selected with puromycin at concentrations of 1 μg/ml and 8 μg/ml, respectively.

Immunofluorescence microscopy
A total of 120 000 HEK293A cells stably expressing HA-rNTS1 or HA-hNTS1 or transiently expressing HA-tagged rNTS1, HA-rNTS2, or HA-rAPJ were seeded on glass microscope coverslips in 6-well plates. The next day, the cells were washed with PBS, fixed for 10 min in 3.7% formaldehyde-PBS at 37°C, washed three times in PBS and permeabilized with 0.1% Triton X-100 for 2 min on ice. The cells were then washed twice with PBS and blocked with 10% normal goat serum (NGS) in PBS for 1 h at RT. This saturation step was followed by an overnight incubation with a mouse monoclonal HA-probe antibody (1:200, (F-7) sc7392, Santa Cruz Biotechnology, CA, USA) and 5 μg/ml of each chicken polyclonal anti-NTS1 antibody (Lin, 3D-1, 3D-2 and 3D-3) or a mixture containing the antibody and the corresponding peptide at a ratio of 1:10 (IgY:peptide) in 10% PBS. For live cell incubation, all steps were as described above, except for incubation with chicken anti-NTS1 and mouse anti-HA. Live cells were incubated with antibodies at 5 μg/ml in culture medium for 1 h at 37°C before fixation with 3.7% formaldehyde. Fluorescence staining was performed via incubation with an ATTO488-conjugated goat anti-chicken antibody (Immune Biosolutions, Sherbrooke, QC, CA) and an Alexa Fluor 647-conjugated goat anti-mouse antibody (A-21235, Thermo Fisher Scientific Inc., Massachusetts, USA), each at a 1:200 dilution. The cells were washed 4 times in PBS before being mounted in ProLong Gold Antifade Mountant with DAPI (Thermo Fisher Scientific Inc., Massachusetts, USA). Images were obtained with a confocal Olympus IX81 FV1000 LSM microscope using Fluoview FV10-ASW 3.1 Viewer software. Z-stacks (0.5 μm slices) were acquired from randomly chosen fields with identical imaging parameters across all samples.

Tissue fixation and immunohistochemistry
Adult male rats were anesthetized with 5% isoflurane and transcardially perfused with 4% paraformaldehyde (PFA) in phosphate buffer (0.1 M). The brains were cryoprotected overnight (0.1 M phosphate buffer and 30% sucrose) at 4°C and frozen in isopentane at −40°C for 1 min. Coronal sections were cut on a cryostat and collected in PBS for immunostaining. Free-floating brain sections were washed 4 times with PBS and incubated with PBS-H2O2 3% for 1 h. These sections were subsequently washed 4 additional times with PBS, permeabilized and blocked with a blocking solution (PBS, NGS 3% and Triton-X 0.3%) for 1 h. These sections were incubated for 2 h with the chicken antibodies (IgY-Lin, IgY-3D-1/2 and 3) at a concentration of 5 μg/ml in a dilution solution (PBS, NGS 1% and Triton-X 0.3%). After washing, goat anti-chicken HRP-conjugated antibodies (Immune Biosolutions, Sherbrooke, CA) were added (1:200) to the dilution mixture, which was subsequently incubated for 1 h and washed again. Solutions A (1:100) and B (1:100) were diluted in the dilution solution, added to the slides and incubated for 1 h. After washing, DAB solution (PBS, diaminobenzidine 0.05%, H2O2 0.015%) was added for 3 min, and the reaction was stopped with PBS. All steps were performed at RT under agitation. The brain sections were dried and coverslipped with Permount® Mounting Media (SP15-500, Fisher Thermo Scientific Inc., NY, USA). Images were acquired with a Leica DM4000 microscope with Infinity Capture Software v6.5.6.

Flow cytometry
Live HEK293A cells stably expressing HA-rNTS1 were harvested from a 10 cm petri dish with trypsin. The cells were then washed with PBS and subsequently blocked for 30 min on ice with 8% PBS-NGS. Primary antibodies against both chicken polyclonal anti-NTS1 (Lin, 3D-1, 3D-2 and 3D-3) and mouse anti-HA (SC-7392, clone F-7, Santa Cruz, California, USA) were added to 500,000 cells at a total amount of 1 μg and 0.2 μg, respectively, and incubated for 15 min on ice. The cells were then washed twice and incubated for 15 min on ice with secondary antibodies, Alexa Fluor 488-conjugated goat anti-chicken IgY antibody (A-11039, Thermo Fisher Scientific Inc., Massachusetts, USA) and Alexa Fluor 647-conjugated goat anti-mouse antibody IgG (A-21235, Thermo Fisher Scientific Inc., Massachusetts, USA), at a 1:200 dilution. The cells were washed twice and resuspended in PBS. Fluorescence analysis was performed via a Beckman CytoFLEX flow cytometer and CytExpert software v2.3.0.84.

Western blots
HEK293A cells were transiently transfected with equal amounts (6 μg) of HA-tagged rNTS1, HA-tagged rNTS2, HA-tagged rAPJ or the empty vector as a negative control. Whole cells were lysed in reducing lysis buffer (280 mM NaCl, 50 mM Tris, 0.015 g DTT, 0.5% NP-40, pH 8.0) or native lysis buffer (280 mM NaCl, 50 mM Tris, 0.5% sodium deoxycholate (NaDOC), 1% n-dodecyl β-D-maltoside (DDM)) supplemented with complete protease inhibitor cocktail (Roche, Bale, Switzerland) and sonicated for 30 s 48 h post-transfection. Whole-cell lysates were further processed for immunoprecipitation and/or polyacrylamide gel electrophoresis (PAGE). Finally, the lysates were heated at 37°C in denaturing/reducing protein sample buffer (60 mM Tris-HCl pH 6.8, 10% glycerol, 0.002% bromophenol blue, 2% SDS, 2% 2-mercaptoethanol) or native protein sample buffer (31.25 mM Tris-HCl pH 6.8, 25% glycerol, 0.005% bromophenol blue). The protein samples (20 μg of total protein per well) were loaded and resolved via SDS‒PAGE or native PAGE. After transfer to nitrocellulose membranes, the blots were probed with different chicken antibodies (IgY-Lin, IgY-3D-1, IgY-3D-2 and IgY-3D-3) at a concentration of 1 μg/ml or a chicken anti-HA antibody (1:1000, Immune Biosolutions Inc., Sherbrooke, QC, CA), followed by a secondary antibody, goat anti-chicken HRP (1:5000, Immune Biosolutions Inc., Sherbrooke, QC, CA). A rat monoclonal anti-HA high-affinity HRP-conjugated antibody (1:1000, Roche, 12013819001, Sigma‒Aldrich, St. Louis, Missouri, USA) was used to detect the protein expression levels of the whole-cell lysates in the selectivity assay. All the blots were visualized via a chemiluminescence detection system (Bio-Rad Clarity Western ECL, Bio-Rad, Hercules, CA, USA). Images were recorded on a ChemiDoc MP using the software ImageLab version 5.0.

Immunoprecipitations
Equivalent molar ratios of all chicken antibodies tested (IgY-Lin, IgY-3D-1, IgY-3D-2, IgY-3D-3 and IgY anti-HA) were conjugated to maleimide magnetic beads using Traut’s reagent (2-iminothiolane) to introduce free thiols (Immune Biosolution, Inc.). The antibody-coupled magnetic beads (10 μl) were added to each sample (200 μl of whole-cell lysate at 1 mg/ml total protein) and to negative controls, which consisted of lysates of cells transfected with the empty vector or samples containing the targeted receptor with a mixture of pre-incubated IgY peptide at a 1:10 ratio. All the samples were incubated on a rotator at 4°C overnight. The next day, the protein‒antibody‒magnetic bead complexes were washed with 0.01% PBS-T four times, followed by 4 washes with PBS without Tween in the case of samples subjected to mass spectrometry. Immunoprecipitated proteins were eluted with elution buffer (0.5 M ammonium hydroxide) and processed for Western blotting as described above or snap-frozen and further lyophilized (program: −40°C for 6 h; −21°C for 10 h; 20°C for 4 h) via a stoppering tray dryer (Labconco, Kansas City, MO, USA). The samples were sent to two sequencing platforms for mass spectrometry: PhenoSwitch (Sherbrooke, QC, CA) and the proteomic platform of the research center of CHUL (Laval University, QC, CA).

Mass spectrometry and protein identification
Proteins were reconstituted in 100 µl of 50 mM Tris pH 8 + 0.75 M urea, reduced with 10 mM DTT for 15 min at 65°C and alkylated with 15 mM iodoacetamide for 1 h at RT in the dark. Trypsin/LysC (1 µg) was added to each sample, and the proteins were digested overnight at 37°C with agitation. The digestion was stopped by the addition of 2% formic acid, and the peptides were purified via reversed-phase SPE. Acquisitions were performed with an ABSciex TripleTOF 5600 (ABSciex, Foster City, CA, USA) and a Thermo Orbitrap Fusion Tribrid (Thermo Fisher Scientific Inc., Logan, UT, USA) by Phenoswitch and the Proteomic Platform of the Université Laval, respectively. Protein identification from Phenoswitch was performed with ProteinPilot V4.5 beta (ABSciex) with the instrument preset for Triple Tof 5600, with iodoacetamide as a cysteine alkylation as a special factor. A thorough search with false discovery rate analysis was performed with a biological modification emphasis against the rat (Rattus norvegicus) proteome (with custom protein sequences added). For protein identification and data analysis, the global false discovery rate (FDR) was set at 1%, and the local false discovery rate was set at 5%. Peptides for Neurotensin Receptor 1 were reviewed manually to ensure that they met a basic quality standard. The proteomic platform of the Université Laval analyzed all MS/MS samples via Mascot (Matrix Science, London, UK; version 2.5). Mascot was set up to search the CP_RattusNorvegicus_10116_CO_20161019 database (unknown version, 31602 entries), assuming the digestion enzyme trypsin. Mascot was searched with a fragment ion mass tolerance of 0.60 Da and a parent ion tolerance of 10.0 PPM. The carbamidomethyl of cysteine was specified in Mascot as a fixed modification. Deamidation of asparagine and glutamine and oxidation of methionine were specified in Mascot as variable modifications. A scaffold (version Scaffold_4.7.5, Proteome Software Inc., Portland, OR) was used to validate MS/MS-based peptide and protein identification. Peptide identifications were accepted if they could be established at greater than 99.0% probability to achieve an FDR less than 1.0% by the scaffold local FDR algorithm. Protein identifications were accepted if they could be established at greater than 99.0% probability to achieve an FDR less than 1.0% and contained at least 1 identified peptide.

Cyclic AMP and IP-One production measurement
CHO-K1 cells stably expressing HA-rNTS1 (cAMP) or HA-hNTS1 (IP-One) were used to perform competitive immunoassay via homogeneous time-resolved fluorescence (HTRF) technology (cAMP-Gs dynamic or IP-One-Gq dynamic kits, Cisbio US, Bedford, MA, USA). The cells (2000/well) were either stimulated with NT(8-13) (6 nM for cAMP or 0.3 nM for IP-One (EC50) and 10 μM (ECmax) alone or with a mixture of NT(8-13) (6 nM or 0.3 nM) and each IgY tested (-Lin, -3D-1, 3D-2 and 3D-3) for 30 min at 37°C in an Alpha-Plate 384 SW (PerkinElmer, Waltham, MA, USA). Unstimulated cells were used as a negative control. Reagents were added as described in the manufacturer’s protocol. Fluorescence was measured at 620 nm and 665 nm with a Tecan GENios plate reader and Tecan XFluor4 software (Tecan Austria GmbH, Austria).

Binding assay
HEK293 cells expressing HA-tagged rat NTS1 (HA-rNTS1) were frozen when they reached 80% confluency. The cells were scraped off the dish with 10 mM Tris and 1 mM EDTA, pH 7.5, and centrifuged at 15,000 × g for 5 min at 4°C. The pellet was then resuspended in binding buffer. Competitive radioligand binding experiments were performed by incubating 15 μg of cell membranes expressing the HA-rNTS1 receptor with 45 pM 125I-[Tyr3]-NT (2200 Ci/mmol) in binding buffer (50 mM Tris-HCl, pH 7.5, 0.2% BSA) in the presence of increasing concentrations of chicken antibodies (IgY-Lin, IgY-3D-1, IgY-3D-2 and IgY-3D-3) for 60 min at 25°C. After incubation, the binding reaction mixture was transferred to polyethylenimine-coated 96-well filter plates (glass fiber filters GF/B, Millipore, Billerica, MA). The reaction was terminated by filtration, and the plates were washed three times with 200 μL of ice-cold binding buffer. The glass filters were then counted in a γ-counter (2470 Wizard2 software v1.0.8, PerkinElmer). Nonspecific binding was measured in the presence of 10−5 M unlabeled NT(8-13), which represented less than 5% of the total binding. IC50 values were determined from the competition curves as the unlabeled ligand (antibodies) concentration inhibiting half of the 125I-[Tyr3]-NT-specific binding.

Bioluminescence resonance energy transfer (BRET)-based biosensor assays
All BRET assays were performed at Domain Therapeutics NA, Inc. (Montreal, QC, Canada). All the experiments were performed in HEK293T cells transiently expressing the HA-rNTS1 receptor and processed as previously described with minor modifications91. Briefly, 10,000 cells were seeded in an Alpha-Plate 384 SW (PerkinElmer, Waltham, MA, USA) and transfected with the indicated G protein biosensors (Gβγ and Gα subtypes Gq, G13, GoB, Gi2, Gz) or β-arrestin-2 biosensors. Two days post-transfection, the agonist mode was first tested by treating cells with increasing amounts of NT (1-13) (0.1 pM to 0.1 mM), SR48692 (0.1 pM to 0.1 mM) and chicken antibodies, namely, IgY-Lin, IgY-3D-1 and IgY-3D-3 (0.02 µM to 2 µM), for 1 h. The cells were subsequently washed with Tyrode-HEPES buffer (Sigma, Cat# T2145-H9136) and allowed to equilibrate for 60 min at RT. Then, e-Coelenterazine Prolume Purple (1.8 μM, Methoxy e-CTZ; Nanolight, Cat# 369) was added to each well, and a first read was acquired on a Synergy NEO plate reader with BioTek Gen5 software. For the antagonist mode, an EC50 (1 nM) of NT (1-13) was added to the previous wells, which were treated with SR48692 and antibodies, and a second measurement was performed after a 10 min incubation. The BRET signal was calculated as the ratio of acceptor emission to donor emission.

Statistics
All values are expressed as the means ± S.E.M. unless otherwise indicated. GraphPad Prism was used to perform all the statistical tests. One-way ANOVA was generally used to measure the significance of differences between groups, followed by Tukey’s multiple comparisons test. Curves were evaluated via nonlinear regression with three or four parameters, as described in the figure legends.

Ethics statement
All work involving animals and related protocols were approved by the Animal Care Committee of the University of Sherbrooke (protocol n° 2020-2669).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The generation of specific high-affinity GPCR antibodies for use in research, in vitro diagnostics, in vivo imaging and therapy undoubtedly remains to be a challenge. GPCRs contain seven transmembrane domains, which results in a large portion of the protein hidden in the lipid bilayer and also leading to a high flexibility of the conformation. With predominantly extracellular and intracellular loops and the terminal sequences available as antibody binding sites, many approaches to obtain suitable antibodies have failed or have resulted in non-specific antibody preparations. Many of them are sold commercially and they continue to appear in the scientific literature even though their specificity was not validated or disproven.\nThis manuscript thus addresses an important scientific issue. The authors chose to generate macrocyclic peptides derived from the neurotensin receptor 1 extracellular loop 2 sequence and immunize chicken to obtain four different antibodies. The manuscript describes the process of production and characterization of these antibodies using a wide variety of pharmacological and immunological methods. While the concept of using constrained, e.g. cyclic peptides for immunization is not new, the path chosen here is an interesting one, which resulted in unique polyclonal antiboy responses and warrants further investigation.\nHowever, several issues need to be addressed.\ni. My biggest concern is about the principal approach taken here. While using macrocyclic peptides for mimicking ECL conformation is a great idea, and using chicken as a host for immunization is also favorable, the result of the process are polyclonal antibodies, i.e. cocktails of different antibodies resulting from the immunization of an animal. Consequently, all assays done with such a mixture of antibodies will result in data that represent the sum or rather a mean value of data for the many different molecules within a polyclonal antibody. The conclusions drawn from such a dataset will necessarily refer to a collective set of properties with no knowledge about individual molecular structures and their properties. Some of the features identified with the four antibody preparations may stem from different subpopulations of immunoglobulins. Without molecularly defined, i.e. monoclonal antibodies, the approach is severely limited in its use.\nii. The manuscript reports on an antibody mixture from just one animal per peptide, effectively resulting in an N=1 sample size for each condition or each macrocyclic structure. Individual animal differences in the antibody response pattern are not considered. Still, the authors draw conclusions about the association between (linker) structure of the peptides and the resulting polyclonal antibodies.\niii. While immunopurifying serum immunoglobulins will lead to an enrichment of antibodies directed against the immunogen used, this process may also lead to the depletion of the most high-affinity antibodies, which may not be eluted from the peptide agarose. Thus, an important fraction of the immune response may not have been evaluated here. This is another strong argument for cloning individual antibodies using any of the nowadays available strategies.\niv. While GPCR antibodies are not easily obtained, there is a large body of scientific publications describing very efficient approaches, even with chicken as the host species (see e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384726/).", "reviewer_2": "Nepveu-Traversy et. al. report a proof-of-principle study of using computationally designed cyclic peptides as antigen for producing polyclonal antibodies in chicken. Two aspects of this study are novel: i) using a computational approach to design constrained peptides that mimic native conformation of extracellular epitope of GPCR as antigen; and ii) using phylogenetically distant chicken as a host for producing antibodies against mammalian protein, which circumvents poor immunogenicity of mammalian antigens in mammalian hosts. Major concerns are listed below:\n1) It is not clear if IgY antibodies have any effects on human NTS1 signaling beyond displacing radiolabeled NT.\n2) It is reassuring that the authors confirmed batch-to-batch consistence of IgY. It may still be worthwhile to obtain a monoclonal antibody for even greater consistence that is desirable for research and translational applications.\n3) This reviewer is not an expert in GPCR functional assays, but dose-responsive curves for IgY antibodies are notable less reliable than the control compound in Figure 5.", "reviewer_3": "Nice work. There are a few minor issues listed below.\nLine 383: What do you mean with chicken antibodies are easily humanized using phage display? As far as I know, humanization of phage display hits is done after the display by transfering CDRs.\nLine 439: How would an antibody help GPCR dimerization? Simultaneous binding of the two Fabs to protomers in a GPCR dimer should be hard to achieve given the distance of the binding sites. Can you provide (literature) evidence for this claim?\nLine 469: Check the spelling and capitalization of \"nose\" in the algorithm. It's the name of an author of the algorithm.\nLine 471: \"The cutoff was set to (5,7).\" What do you mean with that?\nLine 478: There seems to be an unformatted reference: (Schwantes CR, Panda VS)\nLine 533: Where did this custom plasmid (p2xHA-rNTS1) come from? What is its sequence?\nLine 701: \"...transiently expressing the HA-rNTS1 receptor and processed as previously described with minor modifications68.\" The description of the experiments is very hard to follow. What are the exact assays you used for Gq and beta-arrestin? Reference 68 only gives a vague idea.\nLine 980: \"...,depicted by the colored spheres, ...\" The objects look more like ellipsoids.\nLine 1010: What do you mean with: \"The photos were taken using a 60x magnifying lens and zoomed 3x.\"\nDidn't you use a confocal setup to quantify the colocalization? Could you give some more details about how you selected the imaging plane or if you used a z-stack?\nLine 1055: You state that \"The data represent the mean +/- S.E.M.s of at least three independent experiments performed in triplicate.\" Where are the error bars in panels a,c,d,e, and f?", "reviewer_4": "In this manuscript, Nepveu-Traversy and colleagues propose a new approach for producing specific antibodies against a GPCR extracellular loop. Their strategy consists of an initial rational design of a macrocycle mimicking the GPCR region of interest, which is injected into chickens as immunogenic agents.\nGiven my expertise, I will mainly focus on the in silico methods used for the macrocycle rational design.\nThe approach described here is interesting and could potentially be applied to other proteins with exposed regions.\nHowever, I have several concerns regarding the reproducibility and clarity of the proposed approach:\n1) In the “Conformational Search” section, the authors describe an initial sampling of the peptides' conformational space using LowModeMD, implemented in the MOE software. It is not clear why they used R-field as solvation model instead of Generalized Born, which seems to be recommended for LowModeMD\n(https://video.chemcomp.com/watch/RamuDvR9u5C9BxiEyYyAvq; https://pubs.acs.org/doi/10.1021/ci900508k).\n2) Then, the lowest-energy conformation was used as starting configuration for molecular dynamics simulations. However, the authors should clarify why they used a time step of 3 fs. Usually, the hydrogen bond length is constrained, and a time step of 2 fs is used.\n3) The authors should improve the description of the methods used in the “Analysis of the molecular dynamics simulation” section.\nThey analyzed the molecular dynamics simulations using the time-lagged independent component analysis (tICA). Still, it is not clear which software was used and whether they used the whole trajectory or just the 20000 extracted structures.\nMoreover, they should clearly describe which feature was used for this analysis (e.g., RMSD, internal distances, cartesian coordinates, etc.) and which lag-time was chosen.\nFurthermore, it is not clear how many components were used.\n4) The authors should clearly describe and show (if possible) how “The optimal combination of tICA components was chosen by comparing the corresponding GHMM timescales, and the optimal number of GHMM states was chosen via the Bayes information criterion”, to convince the reader that the chosen combination was actually optimal.\n5) Finally, the authors should show a validation of the identified Markov states showing the convergence of the relaxation timescale vs lag-time plot and a statistical test for the Markovianity like the Chapman-Kolmogorov test (http://www.emma-project.org/latest/tutorials/notebooks/07-hidden-markov-state-models.html; https://doi.org/10.1063/1.4828816).\nIn my opinion, the current description of the computational methods is not enough to understand it, judge its robustness, and reproduce it. In this regard, I have to point out that no simulation data or scripts of the described analyses have been shared.\nTo improve both the reliability and reproducibility of the present study, I would suggest following the “Reliability and reproducibility checklist for molecular dynamics simulations” https://www.nature.com/articles/s42003-023-04653-0 included in the Nature Communication Computational tools reporting guidelines https://www.nature.com/ncomms/submit/resources.\nA couple of minor comments regarding possible typos:\n1) In the “Molecular dynamics simulations” section, no value is mentioned in the sentence “The cutoff was set to” (line 472) and it should be specified what it has been applied to.\n2) In the next line (473), the standard pressure should be 101.325 kPa and not 101.15 kPa.\nTo conclude, I have concerns regarding the transferability of the proposed approach, in particular regarding the computational rational design. With this short simulation, I would expect that the sampled conformations are very similar to the starting one. This would make the proposed approach successful only when the starting configuration is close to the known one (observed in the crystal or cryoEM structure of reference)."}, "author_response": "Comment #1: My biggest concern is about the principal approach taken here. While using macrocyclic peptides for mimicking ECL conformation is a great idea, and using chicken as a host for immunization is also favorable, the result of the process are polyclonal antibodies, i.e. cocktails of different antibodies resulting from the immunization of an animal. Consequently, all assays done with such a mixture of antibodies will result in data that represent the sum or rather a mean value of data for the many different molecules within a polyclonal antibody. The conclusions drawn from such a dataset will necessarily refer to a collective set of properties with no knowledge about individual molecular structures and their properties. Some of the features identified with the four antibody preparations may stem from different subpopulations of immunoglobulins. Without molecularly defined, i.e. monoclonal antibodies, the approach is severely limited in its use.\nThis concern highlights a valid and important point about the nature of the polyclonal antibodies (pAbs) used in the present study. As noted, polyclonal antibodies offer a broader range of target recognition as they consist of several antibodies which bind to different epitopes on the same antigen. In this context, the use of pAbs was prioritized for this work because it can reveal a more comprehensive picture of antigen-antibody interactions, which might be missed if only monoclonal antibodies (mAbs) were used. Furthermore, the fact that these pAbs represent a collection of antibodies allows for better adaptability in dynamic biological environments, where variations in antigen structure can occur. This flexibility can be especially useful for complex targets, such as GPCRs or other membrane proteins, where multiple epitopes may be involved in the functional state of the receptor. While the molecular specificity of mAbs is crucial for later-stage drugs, the polyclonal approach help to identify the most relevant epitopes or functional domains, which can then inform the design of mAbs or other targeted therapies. In fact, the results obtained with these pAbs serve as a foundation for the selection of high-potential monoclonal candidates through further screening. To this end, key organs such as bone marrow and spleen have been preserved to explore the full potential of the immune repertoire generated. The next steps planned are to assess whether we have promising hits using display antibody discoveries, followed by a refinement phase to optimize candidates with molecular specificity and functional activity.\nComment #2: The manuscript reports on an antibody mixture from just one animal per peptide, effectively resulting in an N=1 sample size for each condition or each macrocyclic structure. Individual animal differences in the antibody response pattern are not considered. Still, the authors draw conclusions about the association between (linker) structure of the peptides and the resulting polyclonal antibodies.\nWe thank the reviewer for its comment, which enabled us to clarify this point in the Materials and Methods section. We indeed used 3 hens per peptide. All the eggs were pooled with a specific titer against the relevant immunized peptide to ensure a consistent immune response. In addition, two different lots resulting from separate immunizations in different animals were tested and validated in terms of functionality. As shown in Supplementary Results (Suppl. Figure 7a), both IgY-3D-1 and IgY-3D-3 retained their functional ability to displace radiolabeled NT, despite having been produced several months apart. This demonstrates that phenotypes were reproducible across lots and that constrained peptides consistently induced IgY production.\nComment #3: While immunopurifying serum immunoglobulins will lead to an enrichment of antibodies directed against the immunogen used, this process may also lead to the depletion of the most high-affinity antibodies, which may not be eluted from the peptide agarose. Thus, an important fraction of the immune response may not have been evaluated here. This is another strong argument for cloning individual antibodies using any of the nowadays available strategies.\nWe fully agree with this statement, and it’s an insightful observation. Immunopurification can indeed lead to a trade-off, as some high-affinity antibodies may remain bound to peptide agarose and fail to elute, even with the low pH buffer used in our protocol (glycine pH 2.4, which normally elutes over 97% of binders in our experiment). This potential loss is particularly relevant when trying to capture the full spectrum of the immune response. However, the data we have collected from immunopurified serum offer several advantages that give us valuable insights and guide us in our next steps. First, the antibodies obtained by this process still represent a substantial and diverse fraction of the immune response, specifically enriched with antibodies having moderate to high affinities. This enriched pool provides a clear signal for assessing the overall target recognition profile and the immune response dynamics against the immunogen, offering a solid basis for functional and binding studies. These results also provide an initial but meaningful picture of the antigen-binding landscape, which can guide the next steps in epitope mapping and antibody selection. Additionally, this approach can reveal potential cross-reactive epitopes or areas of high immunogenicity, which can sometimes be masked in screens of individual high-affinity clones. By characterizing these aspects early on, we can make informed decisions for subsequent cloning steps and narrow down the most promising antibodies for therapeutic or diagnostic applications.\nComment #4: While GPCR antibodies are not easily obtained, there is a large body of scientific publications describing very efficient approaches, even with chicken as the host species (see e.g. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384726/).\nWe agree with the comment that although obtaining GPCR antibodies can be challenging, there are indeed some effective approaches documented in the scientific literature, including the use of chickens as a host species. However, it is important to note that significant additional work is still required to generate highly specific and functional anti-GPCR antibodies for research and eventual therapeutic applications. To our knowledge, only three anti-GPCR antibodies have received FDA approval. Chickens offer a valuable advantage, particularly when targeting conserved mammalian proteins and 2 this is one of the reasons why we have favored this species for the generation of our antibodies. Importantly, we also used constrained cyclic peptides for immunization, which can mimic the 3D structure of GPCR extracellular loops.\nResponse to Reviewer #2\nNepveu-Traversy et. al. report a proof-of-principle study of using computationally designed cyclic peptides as antigen for producing polyclonal antibodies in chicken. Two aspects of this study are novel: i) using a computational approach to design constrained peptides that mimic native conformation of extracellular epitope of GPCR as antigen; and ii) using phylogenetically distant chicken as a host for producing antibodies against mammalian protein, which circumvents poor immunogenicity of mammalian antigens in mammalian hosts. Several concerns are listed below:\nComment #1: It is not clear if IgY antibodies have any effects on human NTS1 signaling beyond displacing radiolabeled NT.\nIndeed, the functional properties of IgY on the human NTS1 receptor were initially demonstrated by the displacement of radiolabeled NT, supporting their potential antagonist activity. To address the reviewer’s comment, we therefore performed additional experiments to demonstrate the functional antagonist activity of IgY-3D-1 and IgY-3D-3 at hNTS1. Both antibodies were able to inhibit NT-induced IP1 production at hNTS1 (new Figure 6f). In accordance with the binding data, IgY-3D-1 was more effective than IgY-3D-3 in blocking functional activity at hNTS1.\nComment 2: It is reassuring that the authors confirmed batch-to-batch consistence of IgY. It may still be worthwhile to obtain a monoclonal antibody for even greater consistence that is desirable for research and translational applications.\nThis work serves as a proof-of-concept study to demonstrate the use of constrained peptides for generating chicken antibodies against the 3D structure of GPCRs. IgY polyclonal antibodies are advantageous due to their ease of production in large quantities and cost-effectiveness, particularly when comparing multiple peptides for immunization, as this requires substantial volumes of antibody. However, as you correctly noted, the identification and isolation of functional clones is essential for both research and translational applications, particularly for therapeutic purposes. To address this and facilitate next steps now that the peptide design process has been validated, PBMCs and spleen cells have been isolated from immunized hens for monoclonal antibody discovery using recombinant antibody screening technology in the near future. For the reviewer's information, a recent review (PMID: 34177963) nevertheless highlights the potential of IgY antibodies as promising candidates for therapeutic and diagnostic applications.\nComment 3: This reviewer is not an expert in GPCR functional assays, but dose-responsive curves for IgY antibodies are notable less reliable than the control compound in Figure 5.\nYes, the reviewer is right. The NTS1 antagonist, SR48692 has a higher affinity for NTS1 than the polyclonal IgY, tested in this study. In addition, the concentration of polyclonal IgY represents a diverse population, which probably contains only a fraction of functionally active antibodies. The purpose of this study, however, was to demonstrate that peptide design and use of macrocyclic peptides mimicking the 3D structure of GPCRs can generate functional antibodies against NTS1. This objective was achieved, as IgY-3D-3 was able to block all NTS1signaling pathways activated by NT.\nResponse to Reviewer #3\nNice work. There are a few minor issues listed below.\nComment #1: Line 383: What do you mean with chicken antibodies are easily humanized using phage display? As far as I know, humanization of phage display hits is done after the display by transferring CDRs.\nThank you for bringing this to our attention. The reviewer is correct. Humanization of antibodies from phage display is typically done by grafting the CDRs (complementarity-determining regions) onto a human antibody framework after their selection. The text has been corrected to clarify that humanization was conducted through CDR grafting to avoid any confusion.\nComment #2: Line 439: How would an antibody help GPCR dimerization? Simultaneous binding of the two Fabs to protomers in a GPCR dimer should be hard to achieve given the distance of the binding sites. Can you provide (literature) evidence for this claim?\nThank you for asking. There is some evidence in the literature regarding the effects of antibodies on GPCR dimerization. For instance, the functional monoclonal antibody MAB1/28 has been described to specifically bind the native N-terminal domains of dimeric mGlu7 receptors (PMID: 22747985). Similarly, anti-β1AR mAbs, when presented in dimeric antibody format, have been shown to exert agonist activity by promoting receptor dimerization (PMID: 24253107). Talquetamab, a bispecific antibody has also been proven to interact with a symmetric GPRC5D homodimer (PMID: 39181182; PMID: 38898050). These publications are now cited in the discussion section.\nComment #3: Line 469: Check the spelling and capitalization of \"nose\" in the algorithm. It's the name of an author of the algorithm.\nThank you for pointing out this mistake. The name has been corrected to \"Nosé\" in line 469.\nComment #4: Line 471: \"The cutoff was set to (5,7).\" What do you mean with that?\nThank you for pointing out this omission. We have updated the \"Molecular Dynamics Simulations\" section to clarify that the cut-off range was set to the default values provided by the MOE MD simulation panel, specifically applied to non-bonded interactions, including van der Waals and electrostatic forces. These default settings ensure a balance between computational efficiency and accuracy in capturing the essential dynamics of the system.\nComment #5: Line 478: There seems to be an unformatted reference: (Schwantes CR, Panda VS)\nYes, this has been modified, and the unformatted reference has been removed, reference 63 being the correct one. Thank you for bringing this to our attention.\nComment #6: Line 533: Where did this custom plasmid (p2xHA-rNTS1) come from? What is its sequence?\nThis custom plasmid was first generated by cloning a 2x HA tag into a plasmid containing the nucleotide sequence of rat NTS1 (NM_001108967.2). Similarly, a 3x HA tag was cloned into a custom plasmid containing the nucleotide sequence of human NTS1 (NM_002531.3). These HA-tagged NTS1 proteins were then integrated into a lentiviral system. To clarity and transparency, NCBI accession numbers for rat and human NTS1 gene sequences have been added to the manuscript.\nComment #7: Line 701: \"...transiently expressing the HA-rNTS1 receptor and processed as previously described with minor modifications 68.\" The description of the experiments is very hard to follow. What are the exact assays you used for Gq and beta-arrestin? Reference 68 only gives a vague idea.\nWe agree that the experiment was not clearly explained, so we have added more information, specifically concerning the biosensors used to assess the BRET assay. Indeed, this step was previously missing and can now be read as follows: Briefly, 10,000 cells were seeded in an Alpha-Plate 384 SW (PerkinElmer, Waltham, MA, USA) and transfected with the indicated G protein biosensors (Gβγ and Gα subtypes: Gq, G13, GoB, Gi2, Gz) or β-arrestin-2 biosensors.\nComment #8: Line 980: \"..., depicted by the colored spheres, ...\" The objects look more like ellipsoids.\nIndeed, the shapes are ellipsoids rather than spheres, and this has been modified as suggested.\nComment #9: Line 1010: What do you mean with: \"The photos were taken using a 60x magnifying lens and zoomed 3x. Didn't you use a confocal setup to quantify the colocalization? Could you give some more details about how you selected the imaging plane or if you used a z-stack?\nThis sentence was somewhat confusing, so it has been changed to better reflect how the images were taken. While this is explained in detail in the “Materials and Methods” section and the “Results” section, the following sentence now provides clearer information about the microscopy setup: \" Z-stack photos were taken using a confocal microscope with a 60x objective lens and digitally zoomed by 3x to better highlight the region of interest.\nComment #10: Line 1055: You state that \"The data represent the mean +/- S.E.M.s of at least three independent experiments performed in triplicate.\" Where are the error bars in panels a,c,d,e, and f?\nError bars are not visible because the variability between replicates is too small to be displayed on the graphs. Some error bars are however visible on panel a and b corresponding to radioligand binding studies and cAMP experiments. To ensure accuracy, the text has been modified as follows: \"The data represent the means ± S.E.M. from at least three independent experiments conducted in triplicate for the radiolabeled NT curves and cAMP experiments, while BRET assays were performed in duplicate.\nResponse to Reviewer #4\nIn this manuscript, Nepveu-Traversy and colleagues propose a new approach for producing specific antibodies against a GPCR extracellular loop. Their strategy consists of an initial rational design of a macrocycle mimicking the GPCR region of interest, which is injected into chickens as immunogenic agents. Given my expertise, I will mainly focus on the in silico methods used for the macrocycle rational design.\nThe approach described here is interesting and could potentially be applied to other proteins with exposed regions. However, I have several concerns regarding the reproducibility and clarity of the proposed approach:\nGeneral comments\nOne of the implicit objectives of this project was to develop a simple, reliable and rapid method for classifying hundreds of semi-peptidic macrocycles (>1500) mimicking the extracellular loops of GPCRs, without the need to synthesize them. This methodology could be implemented by scientists not requiring access to high-performance computational tools while providing accurate and meaningful classifications to guide subsequent experimental efforts. Recognizing the computational intensity of simulating such a large database, we sought a rapid yet effective approach to determine macrocyclic peptide conformations. Given that all simulations and analyses were performed on a PC with limited CPUs, the parameter selected, such as simulation time, was a deliberate choice, balancing computational feasibility with the robustness of conformational exploration. A full section has been added to allow the reader to fully understand the process and scope of the preliminary work that led to this publication.\nTo achieve this, we used LowModeMD conformational search, a method well suited to macrocyclic peptides, as it focuses on low-frequency, large-scale molecular motions that can capture significant conformational changes. This approach ensures that the conformations sampled are diverse and energetic by locating local minima in the energy landscape. Following the LowModeMD search, MD simulations were performed to identify possible stable conformations in a dynamic environment. After the MD simulations, we conducted advanced analyses using time-structure independent component analysis (tICA) and Hidden Markov Models (HMM). TICA was employed to identify slow collective motions and capture dominant conformational states within the system. This dimensionality reduction method facilitated the exploration of transitions between conformational states, ensuring that the most meaningful structural features were emphasized.\nSubsequently, HMM analysis was used to model the system's dynamic behavior, estimate state populations, and determine transitions between key conformational states. These steps provided a deeper insight into the conformational landscape and ensured that the final predicted conformations were robust, well-sampled, and representative of the ECL2 domain's exposed area in its optimal structural configuration. Together, this comprehensive workflow allowed us to efficiently and reliably address the conformational diversity of many macrocycles using a simple PC.\nAs suggested, a schematic representation has been added to help the reader understand the computational workflow for predicting peptide conformations (Supplementary Figure 2). In addition, we have provided visual representations of the key conformational states identified by our workflow. These figures illustrate the significant structural transitions observed during simulations and analyses, clearly highlighting the conformational changes induced in macrocycles. By visualizing these states, the structural variability and dynamic nature of the macrocycles becomes evident, further emphasizing the impact of the linker diversity and the robustness of the conformational sampling approach. These representations serve to support our findings and demonstrate the distinct structural features of the final conformations compared with the initial states (Supplementary Figure 4)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "#1: While the reviewer agrees with most of the points raised by the authors in favor of polyclonal antibodies, interpreting data obtained from the complex mixture of individual antibodies they represent still remains associated with uncertainties. Results will represent mean values of data for many different molecules within a polyclonal antibody. The conclusions drawn from such a dataset will necessarily refer to a collective set of properties with no knowledge about individual molecular structures and their properties. Some of the features identified with the four antibody preparations may stem from different subpopulations of immunoglobulins. This clear limitation of the study should at least me appreciated in the discussion section.\n#2: The reviewer appreciates the binding data provided in Supplementary Results (Suppl. Figure 7A) for two different batches of the immunization approaches. While this confirms similar overall displacement by both sera, it does not demonstrate or even investigate whether different batches or different animals inoculated with the same peptide show the same systematic response patterns claimed to be characteristic for different peptides by the authors to exist. In order to demonstrate reproducibility of the reactivity patterns across multiple animals, sera of multiple animals would have to be analyzed for their reactivity pattern, selectivity and other parameters the authors claim are different for different immunogens. If the differences between three batches of a preparation are to be studied, pooling them is not the way to address the problem. I see this as crucial for the validity of the whole study. Unless the authors can demonstrate that differences between sera are systematic in nature and caused by different immunogens, not different individual reactivities, the main point of the study cannot be seen as proven. If titer-adapted pools had been created, it should also be possible to analyze individual sera at least regarding some of the results associated with the main hypothesis of the study.\n#3: The reviewer agrees with the authors’ responses and suggests to include this limitation in the discussion.\n#4: The reviewer did not intend to argue against using chicken as a host species for immunization, on the contrary. The suggestion was meant for the authors to include other published and widely applied approaches for GPCR antibody generation in their discussion.", "reviewer_2": "The authors have more or less addressed my concerns.", "reviewer_3": "The revised version addressed my concerns.\nHere are a few minor issues:\n- page 27, line 580: please check the spelling of \"linet\"\n- page 27, line 584: please check the spelling of \"te\"\n- page 33, line 731: please provide the volume or rephrase: \"... at concentrations of 1 microg and 0.2 microg, ...\"\n- page 34, line 764: \"... were conjugated to maleimide magnetic beads ...\", please clarify how you thiolate the antibodies\n- page 36, line 811: \"cAMP-Gs or IP-One-Gq dynamic kits\", please note that only the cAMP kit has the dynamic label", "reviewer_4": "I am glad that the authors put lots of effort into addressing my concerns.\nI understand the scope of the computational approach, however, I still have concerns regarding the computational results of the present study.\nThe main issue is relative to the convergence behavior of the implied timescales (Supplementary Figure 12). I disagree with the authors, the implied relaxation timescales show a very bad convergence:\n1) The implied timescales are not converging between 3-5 ns, as the authors stated (line 586 of the revised manuscript). In fact, 3D-peptide-1 and 3D-peptide-2 show a big variation in that range, while there is an initial convergence at 6-7 ns. In the 3D-peptide-3, only the first two slowest components converge at 2-3 ns, while the other three have to reach a lag time of 6 ns to converge. Finally, the authors should show the error bars (that can be obtained by a bootstrapping procedure).\n2) The implied timescales of most components are equal or lower than 1 ns. This means that the chosen lag time (which the authors never stated explicitly, but I suppose is 3-5 ns) is longer than the implied timescale. Therefore, it is impossible to resolve the dynamics of the process of interest (https://doi.org/10.33011/livecoms.1.1.5965). Therefore, contrary to what the authors state in the manuscript, the lag times chosen are not sufficient to describe the system's slow kinetics and do not support the validity of the MSM.\n3) The Chapman-Kolmogorov test (Supplementary Figure 13) should report the 95% confidence intervals computed with a Bayesian sampling procedure (https://doi.org/10.1063/1.4934536). Some states (as the authors also acknowledge in the manuscript) show deviations. It is impossible to determine if the deviations are significant without the error estimation. If the deviations are significant, the CK test has to be considered failed.\nTo conclude, I still have concerns regarding the transferability and reproducibility of the proposed computational approach.", "reviewer_5": "The Markov state model analysis seems rigorous and extensive!"}, "author_response": "Reviewer #1\nComment #1: While the reviewer agrees with most of the points raised by the authors in favor of polyclonal antibodies, interpreting data obtained from the complex mixture of individual antibodies they represent still remains associated with uncertainties. Results will represent mean values of data for many different molecules within a polyclonal antibody. The conclusions drawn from such a dataset will necessarily refer to a collective set of properties with no knowledge about individual molecular structures and their properties. Some of the features identified with the four antibody preparations may stem from different subpopulations of immunoglobulins. This clear limitation of the study should at least me appreciated in the discussion section.\nWe appreciate and fully agree with the reviewer’s insightful comment regarding the inherent limitations of polyclonal antibodies in terms of subpopulation variability and data interpretation. These limitations are now acknowledged in the discussion section, emphasizing that the observed features likely arise from multiple immunoglobulin subpopulations and that the data represent an overall estimate rather than individual molecular characteristics. However, our results demonstrate that functional properties were maintained among different production batches, generated in separate animals at different times using the same peptide. This reproducibility highlights the robustness of our approach in the generation of functional antibodies, thereby mitigating concerns regarding batch-to-batch variability.\nFurthermore, while polyclonal antibodies serve as an effective initial tool for broad antigen recognition, our study also lays the groundwork for the future development of monoclonal antibodies. Once the optimal antigenic peptide has been identified, monoclonal antibody production allows for the selection of specific clones with defined characteristics and functionalities. This study serves as a proof-of-concept, demonstrating how computational modeling and in silico characterization can improve antigen design, facilitating the generation of antibodies against challenging targets such as membrane proteins with potential therapeutic applications.\nComment #2: The reviewer appreciates the binding data provided in Supplementary Results (Suppl. Figure 7A) for two different batches of the immunization approaches. While this confirms similar overall displacement by both sera, it does not demonstrate or even investigate whether different batches or different animals inoculated with the same peptide show the same systematic response patterns claimed to be characteristic for different peptides by the authors to exist. In order to demonstrate reproducibility of the reactivity patterns across multiple animals, sera of multiple animals would have to be analyzed for their reactivity pattern, selectivity and other parameters the authors claim are different for different immunogens. If the differences between three batches of a preparation are to be studied, pooling them is not the way to address the problem. I see this as crucial for the validity of the whole study. Unless the authors can demonstrate that differences between sera are systematic in nature and caused by different immunogens, not different individual reactivities, the main point of the study cannot be seen as proven. If titer-adapted pools had been created, it should also be possible to analyze individual sera at least regarding some of the results associated with the main hypothesis of the study.\nWe appreciate the reviewer’s comment and fully understand the importance of demonstrating reproducibility across different immunization batches. In our study, hens were immunized at two distinct time points with two different peptides (3D-peptide-1 and 3D-peptide-3), both designed to target the NTS1 receptor. For each peptide, three hens were used, and their eggs were pooled for immunopurification to ensure consistency in the antibody response. Despite being derived from separate immunization events, the purified polyclonal antibodies (IgY-3D-1 batches 1 and 2, and IgY-3D-3 batches 1 and 2) consistently displaced radiolabeled neurotensin from NTS1. This functional validation, performed using a robust and widely accepted pharmacological assay, supports the reproducibility of our approach.\nThe primary objective of this study is to demonstrate that in silico design of 3D-peptides is a powerful tool for predicting the optimal selection of antigens to generate antibodies targeting complex proteins, such as GPCRs. While the resulting polyclonal antibodies consist of diverse IgY subpopulations, their functional phenotype remains consistent across all batches. In addition, our computational approach takes into account several 3D conformational states of peptides (Figure 1a), which is likely to promote the generation of antibodies recognizing different conformations, thus creating subpopulations with distinct properties. Notably, the most optimized peptide designs allowed the generation of antibodies with functional activity, highlighting the predictive strength of this methodology. By harnessing the intrinsic diversity of polyclonal immune responses, this in silico approach optimizes antigen selection, offering a strategic framework for the generation of functionally relevant antibodies with increased specificity and efficacy. In summary, the knowledge gained from this reproducible polyclonal antibody approach provide a solid foundation for the next phase of discovery, namely the identification of key monoclonal sequences within the polyclonal repertoire that drive antigen specificity and functional activity.\nComment #3:The reviewer agrees with the authors’ responses and suggests to include this limitation in the discussion.\nWe appreciate the reviewer’s suggestion and have now incorporated this limitation into the discussion Section. Specifically, we acknowledge that immunopurification may result in the loss of some high-affinity binders, which could limit the complete representation of the immune response. However, despite this limitation, polyclonal antibodies remain a valuable tool for identifying key antigenic peptides and functional domains, especially when integrated with computational modeling approaches to optimize antigen selection.\nComment #4: The reviewer did not intend to argue against using chicken as a host species for immunization, on the contrary. The suggestion was meant for the authors to include other published and widely applied approaches for GPCR antibody generation in their discussion\nWe appreciate the suggestion and have included publications as proposed in the discussion Section: \"This advantage has been demonstrated in other studies involving chickens for the production of antibodies against challenging targets, including GPCRs (PMID:36039695; PMID: 28055305)\".\nReviewer #2\nNo additional question from Reviewer #2\nComment #1: Nice work. The revised version addressed my concerns.\nHere are a few minor issues:\n page 27, line 580: please check the spelling of \"linet\"\n page 27, line 584: please check the spelling of \"te\"\n page 33, line 731: please provide the volume or rephrase: \"... at concentrations of 1 microg and 0.2 microg, ...\"\n page 34, line 764: \"... were conjugated to maleimide magnetic beads ...\", please clarify how you thiolate the antibodies\n page 36, line 811: \"cAMP-Gs or IP-One-Gq dynamic kits\", please note that only the cAMP kit has the dynamic label\nWe thank the reviewer for highlighting these minor issues. The modifications have been addressed as follows:\n The spelling errors to page 27 have been corrected.\n Page 33, line 731: Now reads \"at a total amount of 1 μg and 0.2 μg…\" This clarification ensures that the amount of each antibody used is specified for a total of 500,000 cells.\n Page 34, line 764: Now reads \"…were conjugated to maleimide magnetic beads using Traut’s reagent (2-iminothiolane) to introduce free thiols…\" This step is crucial, as IgY does not naturally contain free thiol groups, which are required for -SH-maleimide conjugation.\n Page 36, line 811: Now reads \"cAMP-Gs dynamic or IP-One-Gq dynamic kits,\" which is more accurate, as both kits are dynamic assays.\nResponse to Reviewer #4\nI am glad that the authors put lots of effort into addressing my concerns. I understand the scope of the computational approach, however, I still have concerns regarding the computational results of the present study. The main issue is relative to the convergence behavior of the implied timescales (Supplementary Figure 12). I disagree with the authors, the implied relaxation timescales show a very bad convergence:\n1) The implied timescales are not converging between 3-5 ns, as the authors stated (line 586 of the revised manuscript). In fact, 3D-peptide-1 and 3D-peptide-2 show a big variation in that range, while there is an initial convergence at 6-7 ns. In the 3D-peptide-3, only the first two slowest components converge at 2-3 ns, while the other three have to reach a lag time of 6 ns to converge. Finally, the authors should show the error bars (that can be obtained by a bootstrapping procedure).\n2) The implied timescales of most components are equal or lower than 1 ns. This means that the chosen lag time (which the authors never stated explicitly, but I suppose is 3-5 ns) is longer than the implied timescale. Therefore, it is impossible to resolve the dynamics of the process of interest (https://doi.org/10.33011/livecoms.1.1.5965). Therefore, contrary to what the authors state in the manuscript, the lag times chosen are not sufficient to describe the system's slow kinetics and do not support the validity of the MSM.\n3) The Chapman-Kolmogorov test (Supplementary Figure 13) should report the 95% confidence intervals computed with a Bayesian sampling procedure (https://doi.org/10.1063/1.4934536). Some states (as the authors also acknowledge in the manuscript) show deviations. It is impossible to determine if the deviations are significant without the error estimation. If the deviations are significant, the CK test has to be considered failed. To conclude, I still have concerns regarding the transferability and reproducibility of the proposed computational approach.\nWe thank the reviewer for emphasizing the need for further convergence and validation of our kinetic models. To specifically address this comment, we extended all molecular dynamics simulations of the three designed peptides to 500 ns each and reanalyzed the data, thereby improving phase-space sampling and ensuring more robust statistics for downstream analysis. Each trajectory was processed through the same pipeline described previously, now using these longer datasets to refine both dimensionality reduction and model estimation.\nWe applied time-lagged independent component analysis (tICA) to each 500 ns trajectory, choosing a lag time of 500 frames (5 ns at dt = 0.01 ns). We selected the optimal number of tICA dimensions by identifying the final marked drop in the implied-timescale slope spectrum and then built hidden Markov models (HMMs) across a grid of state counts. Model selection was governed by the Bayesian Information Criterion (BIC), seeking the lowest BIC while retaining the slowest physically meaningful timescales. In our automated scan (Supplementary Figure 10), the optimal combinations were 3 dimensions with 6 states for 3D peptide-1, 6 dimensions with 8 states for 3D peptide-2, and 3 dimensions with 7 states for 3D peptide-3.\nSupplementary Figure 10. Bayesian Information Criterion (BIC) scores for Markov models with varying numbers of HMM states (x-axis) and tICA dimensions (color-coded) for three peptides: (a) 3Dpeptide-1, (b) 3Dpeptide-2, and (c) 3Dpeptide-3. The optimal models (red dots) represent the combination of dimensionality and state number that yielded the lowest BIC score for each system.\nWe then recalculated implied timescales up to 10 ns and estimated 95% Bayesian credible intervals via bootstrapping (pyEMMA’s errors='bayes' flag). As reported in Supplementary Figure 9, the slowest relaxation mode begins to plateau around 2–3 ns, but complete convergence of all kinetically significant processes is achieved in approximately 6–7 ns. Consistent with the tutorial guidance to choose a lag time at or beyond the onset of the plateau, we adopted τ = 600 frames (6 ns) for all subsequent MSM and HMM constructions.\nSupplementary Figure 9. Implied timescales of the six slowest dynamical processes for three peptides. Implied timescales as a function of lag time for (a) 3D-peptide-1, (b) 3D-peptide-2, and (c) 3D-peptide-3. Timescales were computed on discrete cluster trajectories using lag frames and Bayesian error estimation (solid lines correspond to the ITS of maximum likelihood MSMs. Confidence intervals are depicted by the shaded areas; they contain 95% of the samples generated by the Bayesian MSM. Sample means are given by dashed lines). Above 6 ns, the implied timescales of the slowest processes are constant within error indicating the onset of Markovian behavior appropriate for MSM construction.\nFinally, we performed Chapman–Kolmogorov (CK) tests on the Bayesian Markov models, evaluating self-transition probabilities displaying 95% credible bands (Supplementary Figure 11). Across all metastable states in the 6-, 8-, and 7-state models, the predicted and directly estimated curves overlap within their Bayesian intervals, confirming the validity of the Markovian assumption at our chosen lag time. Any minor deviations observed at the highest multiples fall well within statistical error and thus do not compromise kinetic fidelity.\nSupplementary Figure 11: Chapman–Kolmogorov validation of Bayesian Markov State Models.\nThe self-transition probabilities for metastable states of (a) 3D-peptide-1, (b) 3D-peptide-2, and (c) 3D-peptide-3, computed at a lag time of 600 frames (τ = 6 ns; dt = 0.01 ns) from a Bayesian MSM with 6, 8, 7 states, respectively. In each subplot, the solid line represents the model-predicted with 95% credible intervals, and the dashed line shows the direct estimate from the MD trajectories, evaluated at multiples of lag time. Close overlap between predicted and estimated curves across all states confirms the validity of the Markovian assumption and the kinetic fidelity of the constructed models.\nThe close agreement between predicted and observed probabilities across all states strongly supports the Markovian assumption of the model.\nResponse to Reviewer #5\nComment #1: The Markov state model analysis seems rigorous and extensive!\nWe thank the reviewer for confirming the robustness of the Markov State Modeling approach used in this study.\nTwo minor comments:\n1) In the supplementary figure 11, the Chapman–Kolmogorov validation plots representing the self-transition probabilities of the defined states are reported. However, panel a has 7 self-transition plots instead of 6. Moreover, there is one additional plot for the transition between state 2 and state 1 in all three panels. I recommend double-checking panel A for consistency and removing the 2->1 CK plots.\nThank you for pointing this out. Yes, the Chapman–Kolmogorov test should have shown 6 metastable states for 3D-peptide-1 in panel a. We have corrected this figure to show the CK test with 6 states for 3D-peptide-1 and 7 states for 3D-peptide-3, which is consistent with our state decomposition and interpretation.\n2) At line 572, the Bayesian Information Criterion reference (number 76) is incorrect. Please cite the appropriate study.\nThe reference has been modified."}, {"round": 3, "reviewer_comments": {"reviewer_1": "Thank you for addressing my concers and suggestions. I have no further questions or remarks.", "reviewer_4": "I thank the authors for addressing my comments and concerns.\nAfter this last round of revisions, I consider the computational part robust for the aims of the present study.\nTwo minor comments:\n1) In the supplementary figure 11, the Chapman–Kolmogorov validation plots representing the self-transition probabilities of the defined states are reported. However, panel a has 7 self-transition plots instead of 6. Moreover, there is one additional plot for the transition between state 2 and state 1 in all three panels. I recommend double-checking panel A for consistency and removing the 2->1 CK plots.\n2) At line 572, the Bayesian Information Criterion reference (number 76) is incorrect. Please cite the appropriate study."}, "author_response": ""}]

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