NK-RCPP: a receptor-conditioned computational workflow for prioritizing neurokinin-receptor peptide candidates

Prioritizing peptides for closely related G protein-coupled receptors requires comparing target-directed scores with evidence from other structural contexts. We applied the Neurokinin Receptor-Conditioned Peptide Prioritization workflow (NK-RCPP) to 96 generated candidates for neurokinin receptors NK1R, NK2R and NK3R. The retrospective analysis combined receptor-matched on-target REF15 scores with 4,518 paired structural-neighbour comparisons covering 82 candidates. Lower on-target scores than the matched controls were observed for 14/32 NK1R, 23/32 NK2R and 6/32 NK3R candidates. Adding the neighbour-score criterion retained 16 of the 33 evaluable on-target-favoured candidates, reducing that pool by 51.5%. At matched receptor-specific shortlist sizes, half of the candidate identities differed from on-target-only selection. NK2R-04 and NK2R-19 remained selected at the higher neighbour-fraction threshold of 0.75. Fourteen NK2R candidates lacked neighbour summaries, leaving their joint-rule status unresolved. Sequence-frequency and embedding analyses described the candidates within a 1,048-peptide comparison space, and an exploratory 10-peptide NK2R comparison showed differences in standardized scores with uncertain score-potency agreement. These findings show how paired structural-neighbour scoring and threshold choice shape candidate selection within a receptor family. The resulting shortlist provides a defined set for testing whether joint prioritization enriches for active, receptor-preferred peptides.