Next Article in Journal
Sublethal Effects of Solanum nigrum Fruit Extract and Its Pure Glycoalkaloids on the Physiology of Tenebrio molitor (Mealworm)
Previous Article in Journal
Ontogenetic Change in the Venom of Mexican Black-Tailed Rattlesnakes (Crotalus molossus nigrescens)
Previous Article in Special Issue
Interactions between Triterpenes and a P-I Type Snake Venom Metalloproteinase: Molecular Simulations and Experiments
 
 
Article

Discovery of Novel Conotoxin Candidates Using Machine Learning

1
Eccles Institute of Human Genetics, University of Utah, Salt Lake City, UT 84112, USA
2
Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA
3
Department of Biology, University of Utah, Salt Lake City, UT 84112, USA
4
Department of Biochemistry, University of Utah, Salt Lake City, UT 84112, USA
5
USTAR Center for Genetic Discovery, University of Utah, Salt Lake City, UT 84112, USA
*
Authors to whom correspondence should be addressed.
Equal contribution.
Toxins 2018, 10(12), 503; https://doi.org/10.3390/toxins10120503
Received: 29 September 2018 / Revised: 12 November 2018 / Accepted: 22 November 2018 / Published: 1 December 2018
(This article belongs to the Special Issue Toxins and Bioinformatics)
Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, and cysteine-rich peptides. Given the diversity and often high affinity for their molecular targets, consisting of ion channels, receptors or transporters, many conotoxins have become invaluable pharmacological probes, drug leads, and therapeutics. Transcriptome sequencing of Conus venom glands followed by de novo assembly and homology-based toxin identification and annotation is currently the state-of-the-art for discovery of new conotoxins. However, homology-based search techniques, by definition, can only detect novel toxins that are homologous to previously reported conotoxins. To overcome these obstacles for discovery, we have created ConusPipe, a machine learning tool that utilizes prominent chemical characters of conotoxins to predict whether a certain transcript in a Conus transcriptome, which has no otherwise detectable homologs in current reference databases, is a putative conotoxin. By using ConusPipe on RNASeq data of 10 species, we report 5148 new putative conotoxin transcripts that have no homologues in current reference databases. 896 of these were identified by at least three out of four models used. These data significantly expand current publicly available conotoxin datasets and our approach provides a new computational avenue for the discovery of novel toxin families. View Full-Text
Keywords: machine learning; conotoxins; cone snails; venom; drug discovery machine learning; conotoxins; cone snails; venom; drug discovery
Show Figures

Figure 1

MDPI and ACS Style

Li, Q.; Watkins, M.; Robinson, S.D.; Safavi-Hemami, H.; Yandell, M. Discovery of Novel Conotoxin Candidates Using Machine Learning. Toxins 2018, 10, 503. https://doi.org/10.3390/toxins10120503

AMA Style

Li Q, Watkins M, Robinson SD, Safavi-Hemami H, Yandell M. Discovery of Novel Conotoxin Candidates Using Machine Learning. Toxins. 2018; 10(12):503. https://doi.org/10.3390/toxins10120503

Chicago/Turabian Style

Li, Qing, Maren Watkins, Samuel D. Robinson, Helena Safavi-Hemami, and Mark Yandell. 2018. "Discovery of Novel Conotoxin Candidates Using Machine Learning" Toxins 10, no. 12: 503. https://doi.org/10.3390/toxins10120503

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop