Preprint Article Version 1 This version is not peer-reviewed

Discovery of Novel Conotoxin Candidates Using Machine Learning

Version 1 : Received: 29 September 2018 / Approved: 29 September 2018 / Online: 29 September 2018 (07:39:42 CEST)

A peer-reviewed article of this Preprint also exists.

Li, Q.; Watkins, M.; Robinson, S.D.; Safavi-Hemami, H.; Yandell, M. Discovery of Novel Conotoxin Candidates Using Machine Learning. Toxins 2018, 10, 503. Li, Q.; Watkins, M.; Robinson, S.D.; Safavi-Hemami, H.; Yandell, M. Discovery of Novel Conotoxin Candidates Using Machine Learning. Toxins 2018, 10, 503.

Journal reference: Toxins 2018, 10, 503
DOI: 10.3390/toxins10120503

Abstract

Cone snails (genus Conus) are venomous marine snails that inject prey with a lethal cocktail of conotoxins, small, secreted, 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 5,230 new putative conotoxin transcripts that have no homologues in current reference databases. 893 of these were identified by at least 3 out of 4 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.

Subject Areas

machine learning; conotoxins; cone snails; venom; drug discovery

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