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

Abstract

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.

ConoMode, a database for conopeptide binding modes

“…Recent advancements in cryo-electron microscopy (cryo-EM) technology have made it possible to resolve the relatively high-resolution structure of ion channels, such as nAChR, eukaryotic Nav and voltage-gated calcium channels ( 23–27 ). The appearances of these structures significantly increased the accuracy of computationally modeling the interactions between these ion channels and their targeting conotoxins ( 29–32 ). To date, only a few conotoxin-bound ion channel structures are present in the Protein Data Bank (PDB) ( 23 , 28 ).…”

“…To date, only a few conotoxin-bound ion channel structures are present in the Protein Data Bank (PDB) ( 23 , 28 ). Thus, computational modeling of the interactions between conotoxins and their targeting receptors will continue to be the most efficient approach for understanding the structure-activity relationship of conotoxins at the molecular level ( 29 , 30 ).…”

A Graph-Directed Approach for Creation of a Homology Modeling Library: Application to Venom Structure Prediction

“…Structural characterization stands as a rate-limiting step for high-throughput screening for both therapeutic design and toxin threat characterization, as identified sequences often far outnumber determined structures. For example, only about 3% of sequences isolated from cone snail venom have corresponding experimentally-determined structures [Mansbach et al, 2019].…”

“…In our approach, we employ the number and placement of cysteines within a sequence as a rough initial estimate of functional and structural relatedness. We apply our approach to the so-called conotoxins, which are small, cysteine-rich peptide toxins produced by the cone snails [Uribe et al, 2018, Mansbach et al, 2019.…”

Editorial: Chemical Design and Biomedical Applications of Disulfide-rich Peptides: Challenges and Opportunities

“…Indeed, the structural analysis of disulfide-containing peptides and proteins is not only important for structureactivity-relationship studies in the drug development process of an existing lead compound, but also for theoretical approaches using computational tools which aim at a better understanding and improved prediction of e.g., folding pathways of multiple disulfide-bonded peptides in the context of isomer formation. Exploiting computers to study and predict the problems of protein/peptide folding, binding to receptors, protein engineering, and drug discovery has gained significant momentum in recent years with the incurrence of efficient data mining algorithms and advanced dynamical force fields which can model even the most complex of the systems, evolving structurally and dynamically with unabated accuracy (Huang et al, 2016;Rosenfeld et al, 2016;van Gunsteren et al, 2018;Mansbach et al, 2019). The accessibility to such highly refined and efficient computational resources, many of which are publicly accessible or open source, has paved the way for studying the structure-function relationships of biological systems over wide temporal and spatial scales.…”