Discovery of Novel Conotoxin Candidates Using Machine Learning
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Discovery of Novel Conotoxin Candidates Using Machine Learning. / Li, Qing; Watkins, Maren; Robinson, Samuel D; Safavi-Hemami, Helena; Yandell, Mark.
I: Toxins, Bind 10, Nr. 12, 503, 2018.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Discovery of Novel Conotoxin Candidates Using Machine Learning
AU - Li, Qing
AU - Watkins, Maren
AU - Robinson, Samuel D
AU - Safavi-Hemami, Helena
AU - Yandell, Mark
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Animals
KW - Conotoxins/genetics
KW - Conus Snail/genetics
KW - Machine Learning
KW - Sequence Analysis, RNA
KW - Transcriptome
U2 - 10.3390/toxins10120503
DO - 10.3390/toxins10120503
M3 - Journal article
C2 - 30513724
VL - 10
JO - Toxins
JF - Toxins
SN - 2072-6651
IS - 12
M1 - 503
ER -
ID: 232823063