α-Conotoxin Decontamination Protocol Evaluation: What Works and What Doesn't

Ribbon α-Conotoxin KTM Exhibits Potent Inhibition of Nicotinic Acetylcholine Receptors

KTM is a 16 amino acid peptide with the sequence WCCSYPGCYWSSSKWC. Here, we present the nuclear magnetic resonance (NMR) structure and bioactivity of this rationally designed α-conotoxin (α-CTx) that demonstrates potent inhibition of rat α3β2-nicotinic acetylcholine receptors (rα3β2-nAChRs). Two bioassays were used to test the efficacy of KTM. First, a qualitative PC12 cell-based assay confirmed that KTM acts as a nAChR antagonist. Second, bioactivity evaluation by two-electrode voltage clamp electrophysiology was used to measure the inhibition of rα3β2-nAChRs by KTM (IC50 = 0.19 ± 0.02 nM), and inhibition of the same nAChR isoform by α-CTx MII (IC50 = 0.35 ± 0.8 nM). The three-dimensional structure of KTM was determined by NMR spectroscopy, and the final set of 20 structures derived from 32 distance restraints, four dihedral angle constraints, and two disulfide bond constraints overlapped with a mean global backbone root-mean-square deviation (RMSD) of 1.7 ± 0.5 Å. The structure of KTM did not adopt the disulfide fold of α-CTx MII for which it was designed, but instead adopted a flexible ribbon backbone and disulfide connectivity of C2-C16 and C3-C8 with an estimated 12.5% α-helical content. In contrast, α-CTx MII, which has a native fold of C2-C8 and C3-C16, has an estimated 38.1% α-helical secondary structure. KTM is the first reported instance of a Framework I (CC-C-C) α-CTx with ribbon connectivity to display sub-nanomolar inhibitory potency of rα3β2-nAChR subtypes.

Snails In Silico: A Review of Computational Studies on the Conopeptides

Marine cone snails are carnivorous gastropods that use peptide toxins called conopeptides both as a defense mechanism and as a means to immobilize and kill their prey. These peptide toxins exhibit a large chemical diversity that enables exquisite specificity and potency for target receptor proteins. This diversity arises in terms of variations both in amino acid sequence and length, and in posttranslational modifications, particularly the formation of multiple disulfide linkages. Most of the functionally characterized conopeptides target ion channels of animal nervous systems, which has led to research on their therapeutic applications. Many facets of the underlying molecular mechanisms responsible for the specificity and virulence of conopeptides, however, remain poorly understood. In this review, we will explore the chemical diversity of conopeptides from a computational perspective. First, we discuss current approaches used for classifying conopeptides. Next, we review different computational strategies that have been applied to understanding and predicting their structure and function, from machine learning techniques for predictive classification to docking studies and molecular dynamics simulations for molecular-level understanding. We then review recent novel computational approaches for rapid high-throughput screening and chemical design of conopeptides for particular applications. We close with an assessment of the state of the field, emphasizing important questions for future lines of inquiry.