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Mar. Drugs 2019, 17(3), 145; https://doi.org/10.3390/md17030145

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

1
Theoretical Biology and Biophysics Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2
Center for Nonlinear Studies, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
3
Biosecurity and Public Health Group, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Current address: New Mexico Consortium and Pebble Labs Inc., Los Alamos, NM 87544, USA.
Received: 18 January 2019 / Revised: 21 February 2019 / Accepted: 22 February 2019 / Published: 1 March 2019
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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. View Full-Text
Keywords: conotoxins; conopeptides; computational studies; molecular dynamics; machine learning; docking; review; drug design; ion channels conotoxins; conopeptides; computational studies; molecular dynamics; machine learning; docking; review; drug design; ion channels
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Mansbach, R.A.; Travers, T.; McMahon, B.H.; Fair, J.M.; Gnanakaran, S. Snails In Silico: A Review of Computational Studies on the Conopeptides. Mar. Drugs 2019, 17, 145.

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