Background

Rhynchophorus ferrugineus, commonly known as the red palm weevil (RPW), is a highly invasive pest that poses a serious threat to palm species globally, particularly date, coconut, and oil palms. Native to the Middle East, RPW has rapidly expanded across tropical and subtropical regions, causing substantial agricultural and economic damage [1,2]. Its concealed infestation behaviour and the difficulty of early detection contribute to its ecological impact [3]. Despite extensive management efforts, control remains challenging due to its complex life cycle, adaptability, and increasing resistance to conventional methods. Current strategies, including chemical insecticides and pheromone traps [4], have shown limited success, hindered by the development of resistance and inefficient monitoring systems [5]. These limitations underscore the need for molecular-level approaches to disrupt pest behaviour. Targeting insect chemosensory systems offers a promising alternative, as these systems are essential for survival and reproduction. Chemosensory proteins, particularly gustatory receptors (GRs), which mediate responses to non-volatile compounds involved in feeding and mating, are particularly relevant. However, GRs in RPW remain poorly characterised, and no experimentally resolved structures are currently available [6,7], which complicates molecular intervention efforts.

To address this gap, we present an integrated computational pipeline that combines OmicsBox-based sequence annotation with high-accuracy structural prediction. Functional annotation performed with OmicsBox [8] enables the reliable identification of candidate GRs, while structure prediction with LocalColabFold [9] generates three-dimensional models enriched with confidence metrics such as pLDDT (predicted local distance difference test) and pTM (predicted template modelling) scores and PAE (predicted alignment error). This dual-layered approach provides not only accurate identification of GRs but also structural insights, including the prediction of potential ligand-binding sites. Such integrative information is critical for advancing molecular studies of insect chemosensation and supports downstream applications such as protein-ligand docking and virtual screening. Previous bioinformatics efforts have generally applied these tools independently, depending on the scope of analysis. OmicsBox has been extensively used for transcriptome and genome annotation in insects, such as the cabbage webworm (Hellula undalis) [10] and the boll weevil (Anthonomus grandis grandis) [11], but these studies were limited to sequence-level analyses. Conversely, ColabFold has gained attention for protein modelling [12], though its applications often depend on pre-annotated datasets without integrated annotation workflows. By combining both strategies into a single reproducible pipeline, our protocol overcomes these limitations and enables a more comprehensive characterisation of GR proteins, particularly in non-model pest species.

This workflow was validated through comparison of RPW GRs with proteins from the well-characterised fruit fly (Drosophila melanogaster), confirming its accuracy and reproducibility. This benchmarking demonstrates the pipeline’s ability to generate consistent, biologically meaningful results and highlights its applicability across insect species. Unlike previous studies that addressed annotation or structural modelling separately, our protocol integrates both, providing a holistic characterisation of GR proteins. Its reproducibility, adaptability, and biological relevance make it a reusable framework for advancing insect chemosensory research and supporting innovative pest management strategies.