Venoms are comprised of diverse mixtures of proteins, peptides, and small molecules. Identifying individual venom components and their target(s) with mechanism of action is now attainable to understand comprehensively the effectiveness of venom cocktails and how they collectively function in the defense and predation of an organism. Here, structure-based computational methods were used with bioinformatics tools to screen and identify potential biological targets of tertiapin (TPN), a venom peptide from Apis mellifera (European honey bee). The small hive beetle (Aethina tumida (A. tumida)) is a natural predator of the honey bee colony and was found to possess multiple inwardly rectifying K+ (Kir) channel subunit genes from a genomic BLAST search analysis. Structure-based virtual screening of homology modelled A. tumida Kir (atKir) channels found TPN to interact with a docking profile and interface "footprint" equivalent to known TPN-sensitive mammalian Kir channels. The results support the hypothesis that atKir channels, and perhaps other insect Kir channels, are natural biological targets of TPN that help defend the bee colony from infestations by blocking K+ transport via atKir channels. From these in silico findings, this hypothesis can now be subsequently tested in vitro by validating atKir channel block as well as in vivo TPN toxicity towards A. tumida. This study highlights the utility and potential benefits of screening in virtual space for venom peptide interactions and their biological targets, which otherwise would not be feasible.
Keywords: bee venom; bioinformatics; computational docking; homology modelling; ion channel structure; protein–peptide interactions; small hive beetle; tertiapin; venom peptides; virtual screening.