Molecular arms race classifier for decrypting venom peptide and ion channel interactions

Animal venoms comprise an astonishing number of peptides, proteins and small molecules. The diversity of venom compounds arises from evolutionary adaptations resulting in both offensive and defensive traits in predators and prey alike. This concept, referred to as "arms race", underpins the specificity and selectivity of venom compounds for certain molecular targets, like ion channels. Ion channels are essential regulators of cellular processes, and their dysfunction or dysregulation facilitates a wide range of diseases. Venom peptides and their derivatives are powerful modulators of ion channel activity, with several already in clinical use as FDA-approved therapeutics. Despite the remarkable potential of venom compounds, for the majority, their ion channel targets and modes of action remain largely uncharacterized. We hypothesize that venom peptides are constrained by and converge on molecular structures and targets despite their rapid sequence divergence due to arms race evolution. Here, we introduce a machine learning approach termed Molecular Arms Race Classifier (MARC), which predicts the ion channel targets - sodium, potassium, and calcium ion channels - of cysteine-rich venom peptides. MARC leverages evolutionary scale modeling (ESM) for feature extraction along with random forest classification to enable predictive functional annotation of venom compounds by their putative ion channel targets. MARC performs multi-class classification across four categories (sodium, calcium, potassium and non-ion-channels), to predict the ion channel targets of novel cysteine-rich venom compounds. We trained, tested, and cross-validated MARC on 5165 peptide and protein sequences sourced from in-house venom gland transcriptomes and public databases spanning diverse taxa, including sea anemones, snakes, scorpions, spiders, cone snails, and terebrid snails. We identified 28 novel terebrid snail venom peptides (teretoxins) predicted to target potassium ion channels. Orthogonal validation using docking and molecular dynamics simulations suggests the stability of the best docked pose of the K⁺ channels, KcsA and MthK, to the teretoxin, Cje1.9, supporting MARC's robust prediction of Cje1.9 as a K⁺ channel-targeting peptide. Taken together, these results indicate that MARC is a cost-effective method for screening vast peptide and protein libraries to identify potential ion channel targeting compounds, paving the way to design novel peptides selectively targeting distinct ion channel classes.