A Step-by-Step Computational Protocol for Functional Annotation and Structural Modelling of Insect Chemosensory...

Insects rely on chemosensory proteins, including gustatory receptors, to detect chemical cues that regulate feeding, mating, and oviposition behaviours. Conventional approaches for studying these proteins are limited by the scarcity of experimentally resolved structures, especially in non-model pest species. Here, we present a reproducible computational protocol for the identification, functional annotation, and structural modelling of insect chemosensory proteins, demonstrated using gustatory receptors from the red palm weevil (Rhynchophorus ferrugineus) as an example. The protocol integrates publicly available sequence data with OmicsBox for functional annotation and ColabFold for high-confidence structure prediction, providing a step-by-step framework that can be applied to genome-derived or transcriptomic datasets. The workflow is designed for broad applicability across insect species and generates structurally reliable protein models suitable for downstream applications such as ligand docking or molecular dynamics simulations. By bridging functional annotation with structural characterisation, this protocol enables reproducible studies of chemosensory proteins in agricultural and ecological contexts and supports the development of novel pest management strategies. Key features • Designed for insect chemosensory research, demonstrated using gustatory receptors from the red palm weevil (Rhynchophorus ferrugineus) as a representative pest species. • Combines OmicsBox for functional annotation with ColabFold for reproducible, high-confidence protein structure prediction in a streamlined workflow. • Accepts input from genome assemblies, transcriptomic datasets, or curated sequence databases, enabling broad application across model and non-model insects. • Produces reliable structural models suitable for downstream studies, including ligand screening, molecular dynamics simulations, and comparative evolutionary analyses.