Not Quite Folded: Challenges in Predicting the hNPS‐hNPSR‐Ile107 Complex With AlphaFold2 Multimer

The human neuropeptide S (NPS) receptor (NPSR) is a Class A peptide G protein‐coupled receptor expressed in the central nervous system and endogenously activated by NPS, a 20‐mer peptide. NPSR activation promotes cellular excitability via Gq and Gs signalling. Studies suggest that receptor antagonists may reduce drug‐seeking behaviours, whilst agonists represent innovative non‐sedating anxiolytics with memory‐enhancing effects. Despite its therapeutic potential, NPSR remains poorly characterised, with neither experimental receptor structures nor drug‐like clinical candidates available. To fill this gap, we applied a previously validated AlphaFold2 Multimer‐based protocol to model the hNPS–hNPSR complex. The model showing higher stability in molecular dynamics simulations and consistency with known structure–activity relationships served as template to design novel hNPS analogues. However, experimental validation through synthesis and in vitro pharmacological evaluation of 20 novel truncated cyclic peptides revealed the model's inability to capture hNPS bioactive conformation, as most analogues were inactive as agonists. By exploiting the stereochemical switch in hNPS hinge region, we identified four novel cyclic antagonists ( 17 – 20 , pA 2 in the 6.10–6.20 range). Our findings highlight strengths and limitations of current peptide‐GPCR modelling strategies and underscore the need for integrating AI predictions with experimental refinement to advance ligand discovery for challenging targets like NPSR.

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Journal
ChemMedChem
Published
2026-08-25
DOI
https://doi.org/10.1002/cmdc.70449
Primary Topic
Receptor Mechanisms and Signaling
Type
article
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article

Not Quite Folded: Challenges in Predicting the hNPS‐hNPSR‐Ile107 Complex With AlphaFold2 Multimer

Salvatore Pacifico, Alfonso Carotenuto, Chiara Ruzza, Delia Preti et al.
ChemMedChem
Receptor Mechanisms and Signaling
article

Not Quite Folded: Challenges in Predicting the hNPS‐hNPSR‐Ile107 Complex With AlphaFold2 Multimer

Salvatore Pacifico, Alfonso Carotenuto, Chiara Ruzza, Delia Preti, Rainer K. Reinscheid, Valentina Albanese, Michela Argentieri, Remo Guerrini, Antonella Ciancetta, Federica Agosta, Alessandra Rizzo, Girolamo Calò
article en

Abstract

The human neuropeptide S (NPS) receptor (NPSR) is a Class A peptide G protein‐coupled receptor expressed in the central nervous system and endogenously activated by NPS, a 20‐mer peptide. NPSR activation promotes cellular excitability via Gq and Gs signalling. Studies suggest that receptor antagonists may reduce drug‐seeking behaviours, whilst agonists represent innovative non‐sedating anxiolytics with memory‐enhancing effects. Despite its therapeutic potential, NPSR remains poorly characterised, with neither experimental receptor structures nor drug‐like clinical candidates available. To fill this gap, we applied a previously validated AlphaFold2 Multimer‐based protocol to model the hNPS–hNPSR complex. The model showing higher stability in molecular dynamics simulations and consistency with known structure–activity relationships served as template to design novel hNPS analogues. However, experimental validation through synthesis and in vitro pharmacological evaluation of 20 novel truncated cyclic peptides revealed the model's inability to capture hNPS bioactive conformation, as most analogues were inactive as agonists. By exploiting the stereochemical switch in hNPS hinge region, we identified four novel cyclic antagonists ( 17 – 20 , pA 2 in the 6.10–6.20 range). Our findings highlight strengths and limitations of current peptide‐GPCR modelling strategies and underscore the need for integrating AI predictions with experimental refinement to advance ligand discovery for challenging targets like NPSR.

ChemMedChemVol. 21(16)
University of Padua (IT), University of Ferrara (IT), Jena University Hospital (DE), University of Naples Federico II (IT)
Good health and well-being
Openalex Percentile: Top 17%
Receptor Mechanisms and Signaling
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