Bioinformatics-aided venomics

Kaas, Quentin and Craik, David J. (2015) Bioinformatics-aided venomics. Toxins, 7 6: 2159-2187. doi:10.3390/toxins7062159

Author Kaas, Quentin
Craik, David J.
Title Bioinformatics-aided venomics
Journal name Toxins   Check publisher's open access policy
ISSN 2072-6651
Publication date 2015-06
Year available 2015
Sub-type Critical review of research, literature review, critical commentary
DOI 10.3390/toxins7062159
Open Access Status DOI
Volume 7
Issue 6
Start page 2159
End page 2187
Total pages 29
Place of publication Basel, Switzerland
Publisher MDPI AG
Collection year 2016
Language eng
Abstract Venomics is a modern approach that combines transcriptomics and proteomics to explore the toxin content of venoms. This review will give an overview of computational approaches that have been created to classify and consolidate venomics data, as well as algorithms that have helped discovery and analysis of toxin nucleic acid and protein sequences, toxin three-dimensional structures and toxin functions. Bioinformatics is used to tackle specific challenges associated with the identification and annotations of toxins. Recognizing toxin transcript sequences among second generation sequencing data cannot rely only on basic sequence similarity because toxins are highly divergent. Mass spectrometry sequencing of mature toxins is challenging because toxins can display a large number of post-translational modifications. Identifying the mature toxin region in toxin precursor sequences requires the prediction of the cleavage sites of proprotein convertases, most of which are unknown or not well characterized. Tracing the evolutionary relationships between toxins should consider specific mechanisms of rapid evolution as well as interactions between predatory animals and prey. Rapidly determining the activity of toxins is the main bottleneck in venomics discovery, but some recent bioinformatics and molecular modeling approaches give hope that accurate predictions of toxin specificity could be made in the near future.
Keyword Toxins
Molecular modeling
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Official 2016 Collection
Institute for Molecular Bioscience - Publications
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Citation counts: TR Web of Science Citation Count  Cited 2 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 09 Jul 2015, 10:13:45 EST by Susan Allen on behalf of Institute for Molecular Bioscience