Larissa Ferguson, Sébastien Ouellet, Elke Vandewyer, Christopher Wang, Zaw Wunna, Tony KY Lim, William R Schafer, Isabel Beets
9 min
Identifying endogenous peptide ligands for orphan G protein-coupled receptors (GPCRs) is a critical challenge in molecular biology. While AlphaFold2 (AF2) and its extension, AF-multimer, have revolutionized protein structure prediction, they exhibit only modest performance in distinguishing true peptide agonists from non-agonists. This study introduces DeorphaNN, a specialized graph neural network (GNN) developed to improve the in silico prioritization of GPCR-peptide agonist interactions.
Using an extensive dataset of C. elegans GPCR-peptide interactions, the authors first evaluated the predictive utility of AF-multimer. They discovered that biasing AF-multimer toward active-state GPCR conformations using templates from AF-Multistate significantly improves the prediction of agonist-bound complexes. Further analysis of the hidden representations within AF-multimer revealed that pair representations—which capture pairwise residue-level relationships—outperform single representations. DeorphaNN integrates these structural predictions, interatomic interactions defined by Arpeggio, and deep learning embeddings into a GNN architecture to produce a more precise agonist-likelihood score.
DeorphaNN demonstrates robust generalization across diverse species, showing improved performance on retrospective human and annelid GPCR benchmarks compared to state-of-the-art general protein-protein interaction models. Beyond retrospective success, the authors used DeorphaNN to successfully identify novel endogenous peptide ligands for two previously orphan C. elegans GPCRs (NPR-44 and NPR-33). The model's ability to prioritize agonists that are otherwise overlooked by standard confidence metrics like peptide pLDDT highlights its utility in accelerating experimental deorphanization workflows.
ABSTRACT Peptide-activated G protein-coupled receptors (GPCRs) regulate critical physiological processes such as metabolism, neural signalling, and endocrine function through their interaction with neuropeptides and peptide hormones. Despite their importance, identifying endogenous peptide agonists for GPCRs remains challenging, particularly for orphan receptors without known ligands. Recent advances in deep learning-based protein structure prediction, exemplified by AlphaFold (AF), have shown application beyond structural modelling, including protein-protein interaction prediction. Given that GPCR-peptide agonist interactions represent a specialized form of protein-protein interaction, we leveraged a dataset of experimentally validated agonist and non-agonist GPCR-peptide interactions from Caenorhabditis elegans to evaluate AF-Multimer’s ability to distinguish agonist-bound complexes. When modelling GPCR-peptide complexes, AF-Multimer confidence metrics partially discriminate agonist from non-agonist interactions, with improved discrimination achieved by utilizing AF-Multistate-derived active-state templates. We further investigated whether embeddings from the hidden layer of AF-Multimer’s neural network could distinguish agonist from non-agonist complexes. Feature performance analysis reveals that AF- Multimer’s pair representations outperform single representations, with distinct subregions of the pair representation providing complementary predictive signals. Building on these insights, we developed DeorphaNN, a graph neural network integrating active-state GPCR-peptide structural predictions, interatomic interactions, and deep learning embeddings to prioritize GPCR-peptide agonist interactions. DeorphaNN generalizes across datasets derived from different species, including annelids and humans, and successfully uncovered peptide agonists for two orphan GPCRs. DeorphaNN offers a novel computational resource to accelerate deorphanization by prioritizing GPCR-peptide agonist candidates for AI-guided experimental validation.
Sam: So every included receptor could respond under the screening conditions. Did the predicted structures then separate its activating peptides from the rest?
Alex: Not simply by location. AlphaFold-multimer placed almost all agonists inside the expected binding pocket, but also placed most non-agonists there. A plausible position wasn't enough. Its confidence in the predicted peptide structure was more useful, although still limited.
Sam: That sounds like a starting point rather than a solution. What's the extra idea that makes DeorphaNN different?
Alex: There are two connected steps. First, guide the receptor toward its active shape, rather than accepting AlphaFold's tendency to model an inactive shape. Then combine the predicted molecular contacts with AlphaFold's internal information about relationships between amino acids.
Sam: How do they guide the shape without fixing the answer to a particular peptide?
Alex: They generate an active-state receptor template using a method called AlphaFold-Multistate. They preserve the membrane-spanning parts that move during activation, but trim away the outside regions involved in ligand binding. That leaves room to predict how each peptide interacts, rather than imposing those contacts.
Sam: Does that biological choice help on its own, before they build the new network?
Alex: It does, modestly. They measure ranking with mean average precision, which rewards putting known agonists near the top of each receptor's list. Peptide confidence scored about point three-two without active-state templates, versus point three-six with them. Higher is better, and receptors contribute equally to the average.
Sam: That's a ranking score, not the fraction of experimental tests that will succeed. What information do they add beyond structural confidence?
Alex: AlphaFold produces internal features called pair representations: learned descriptions of relationships between pairs of amino acid residues. These proved more informative than features describing individual residues. Different regions also contributed complementary signals, including the peptide itself, the receptor pocket, and relationships across their interface.
Sam: So averaging everything together could hide which residue interacts with which partner. How does the new model preserve those relationships?
Alex: It builds a graph: residues become nodes, and interactions become links. A graph neural network learns from that connected structure. The graph includes every peptide residue, but only receptor residues predicted to contact the peptide.
Sam: Nearby residues aren't necessarily making meaningful contacts. Does the model treat every close approach alike?
Alex: No. Software called Arpeggio identifies contacts using spatial and chemical criteria, including hydrogen bonds and hydrophobic interactions. The authors attach AlphaFold's learned pair information to those links. Replacing chemically defined contacts with simple distance cutoffs, or removing the link features, reduced performance.
Sam: That supports the combination, rather than saying either structural chemistry or learned features alone are sufficient. How much does the complete model improve the ranking?
Alex: DeorphaNN reached a mean average precision of point four-five, compared with point three-two for peptide confidence without active-state templates. The active-template confidence score remained an intermediate improvement. Test receptors were held out in related groups, and tuning excluded those test groups.
Sam: Worm receptors might still have distinctive patterns. Does the evidence extend beyond that training species?
Alex: The worm-trained model ranked agonists above random in a marine annelid dataset. It also outperformed a general protein-interaction model on a human retrospective benchmark, including after that comparator was fine-tuned. Those results support transfer across species, but the human comparison has an important qualification.
Sam: If the human benchmark contains published agonists, where did its non-agonists come from?
Alex: They were synthetic decoys, selected for peptide dissimilarity using a protein language model. They weren't experimentally established negatives. Training on that augmented human dataset transferred useful predictions back to worms, but that doesn't establish every decoy as biologically inactive.
Sam: The cleaner test would be choosing peptides for receptors the model hasn't seen, then measuring activation. Did they do that prospectively?
Alex: Yes, for two orphan worm receptors. They tested the top five candidates per receptor from a library of 364 peptides. Each shortlist contained an activator. For the receptor NPR-33, the highest-ranked peptide, NLP-70-2, activated it in the cell assay.
Sam: That demonstrates a practical shortlist, not exhaustive discovery. What limitation most affects which useful peptides the model might miss?
Alex: The training labels come from one engineered calcium-signaling assay. A peptide that fails there might activate the receptor through another pathway. The authors explicitly warn that pathway-biased agonists may be mislabeled as negatives. Predicted structures also omit modifications that can matter for peptide activity.
Sam: So who should read the full paper, and where should they start?
Alex: Researchers identifying peptide-receptor partners should start with the novel agonist discovery section, then read the limitations. Computational readers should examine the graph-structure and edge-feature comparisons. Human discovery teams should treat the retrospective results as encouragement, not a demonstrated success rate for uncharacterized receptors. For everyone else: predicting a fit is not predicting a switch.
Sam: A useful shortlist still has to earn its place in the lab.