Coevolution Between Three-Finger Toxins and Target Receptors

Background: Three-finger toxins (3FTxs) are a major axis of functional diversification in advanced snake venoms, with canonical paralytic activity mediated through muscle-type nicotinic acetylcholine receptors (nAChRs) and a broader set of non-nicotinic targets. This review integrates evidence bearing on coevolution between 3FTxs and target receptors, spanning toxin origin, diversification, receptor evolution, and ecological context. Methods: The synthesis draws on comparative genomic and transcriptomic studies of 3FTx gene-family evolution, codon-model analyses of selection, structural characterisation of toxin–receptor interfaces, and functional assays (including receptor-mimicking peptide binding) that link sequence variation to binding and toxicity. Results: Across lineages, 3FTx diversification is repeatedly structured by strong constraint on the disulphide-rich scaffold with accelerated change concentrated in solvent-exposed loops, alongside birth–death dynamics and exon/segment-level innovation that expand binding specificity. On the receptor side, resistance-associated variation is most intensively characterised for the nAChR α1 orthosteric site and includes convergent, mechanistically distinct solutions such as electrostatic repulsion and glycosylation-mediated steric interference. Within the predominantly elapid systems currently examined, integrative datasets indicate that prey-selective binding and geographically variable susceptibility can arise from modest substitutions at toxin–receptor interfaces, but they also reveal substantial taxonomic and target-specific biases. Conclusions: Current evidence supports adaptive diversification in both toxins and receptors, while broader evolutionary interpretations are limited by uneven sampling and the frequent lack of matched toxin and receptor variants analysed within a common evolutionary framework. Development of predictive models will require joint pipelines linking genomics, structure-informed evolutionary inference, scalable functional assays, and explicit ecological network context.