Conotoxin Prediction: New Features to Increase Prediction Accuracy

Abstract

Conotoxins are toxic, disulfide-bond-rich peptides from cone snail venom that target a wide range of receptors and ion channels with multiple pathophysiological effects. Conotoxins have extraordinary potential for medical therapeutics that include cancer, microbial infections, epilepsy, autoimmune diseases, neurological conditions, and cardiovascular disorders. Despite the potential for these compounds in novel therapeutic treatment development, the process of identifying and characterizing the toxicities of conotoxins is difficult, costly, and time-consuming. This challenge requires a series of diverse, complex, and labor-intensive biological, toxicological, and analytical techniques for effective characterization. While recent attempts, using machine learning based solely on primary amino acid sequences to predict biological toxins (e.g., conotoxins and animal venoms), have improved toxin identification, these methods are limited due to peptide conformational flexibility and the high frequency of cysteines present in toxin sequences. This results in an enumerable set of disulfide-bridged foldamers with different conformations of the same primary amino acid sequence that affect function and toxicity levels. Consequently, a given peptide may be toxic when its cysteine residues form a particular disulfide-bond pattern, while alternative bonding patterns (isoforms) or its reduced form (free cysteines with no disulfide bridges) may have little or no toxicological effects. Similarly, the same disulfide-bond pattern may be possible for other peptide sequences and result in different conformations that all exhibit varying toxicities to the same receptor or to different receptors. We present here new features, when combined with primary sequence features to train machine learning algorithms to predict conotoxins, that significantly increase prediction accuracy.

Keywords: collisional cross section; conotoxins; ion mobility–mass spectrometry; machine learning; post-translational modifications; prediction.

Figure 1

Example of conotoxins adopting different structures while sharing the same primary amino acid sequence (a–d) or conotoxins sharing the same disulfide bond pattern but different primary amino acid sequences (e,f). (a) Alpha conotoxin AuIB in its native conformation with a disulfide bond pattern of Cys2-Cys8 and Cys3-Cys15 (PDB: 1MXN [18]). (b) Alpha conotoxin AuIB in its ribbon (isoform) conformation with a disulfide bond pattern of Cys2-Cys15 and Cys3-Cys8 (PDB: 1MXP [18]). (c) Mu conotoxin KIIIA with a disulfide bond pattern of Cys1-Cys9, Cys2-Cys15, and Cys4-Cys16 (PDB: 7SAV [19]). (d) Mu conotoxin KIIIA with a disulfide bond pattern of Cys1-Cys16, Cys2-Cys9, and Cys4-Cys15 (PDB: 7SAW [19]). (e) Kappa conotoxin PVIIA with a disulfide bond pattern of Cys1-Cys16, Cys8-Cys20, and Cys15-Cys26 (PDB: 1AV3 [20]). (f) Omega conotoxin MVIIA with a disulfide bond pattern of Cys1-Cys16, Cys15-Cys25, and Cys8-Cys20 (PDB: 1DW4 [21]).

Figure 2

Features were divided into four groups (P, P2, SS, and CCS), and the effect of each feature group was evaluated with regard to conotoxin prediction accuracy.

Figure 3

Workflow for feature extraction.

Figure 4

Overall ML pipeline describing the process of using a dataset to train and cross-validate a classifier.