B-P.40: Structure based scoring of AI-predicted peptide-GPCR complexes for receptor annotation in a non-model insect genome
Genome annotation is essential for linking DNA sequences to biological functions. Neuropeptides are neuromodulatory molecules regulating diverse physiological processes and behaviors, including development, feeding, and courtship, thereby making accurate annotation of the genes encoding them and their receptors critical. Neuropeptides exert their effects by binding to specific receptors, most often G protein-coupled receptors (GPCRs). In non-model insects like Gryllus bimaculatus, annotation of neuropeptides and their receptors remains limited. Our previous work has improved neuropeptide gene functional annotation in this species through de novo genome assembly and homology-based searches against insect databases. However, receptor annotation remains incomplete as sequence-based homology approaches struggle to assign precise functional annotations despite high sequence similarity among GPCRs and limited availability of reference sequences.
In this study, we explore a structure-based strategy to assign candidate neuropeptide receptors in the G. bimaculatus by assessing predicted peptide-receptor complexes using AlphaFold3 and Boltz2. Focusing on the major classes of insect neuropeptide receptors, rhodopsin-like and secretin-like GPCR families, we systematically assign annotated neuropeptides with candidate receptor structures and predict their complexes. To evaluate these combination ligand and receptor models, we apply interface-focused confidence metrics: interface pLDDT, which measures the local reliability of residues at the binding interface, and ipSAE that summarizes conformational plausibility of the interface geometry and alignment features, to score the structural validity of each peptide-receptor interaction. By ranking structure-aware confidence measures for G. bimaculatus neuropeptide putative receptors, we construct a list of prioritized candidates that can guide subsequent experimental validation and improve genome annotation in non-model organisms.
Co-authors: Mika Sakamoto, Hitomi Seike, Shinji Nagata, Yasukazu Nakamura, Takako Mochizuki
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