Toxins 2015, 7(6), 2159-2187; doi:10.3390/toxins7062159
Bioinformatics-Aided Venomics
Institute for Molecular Bioscience, the University of Queensland, Brisbane, QLD 4072, Australia
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Author to whom correspondence should be addressed.
Academic Editor: Bryan Grieg Fry
Received: 1 May 2015 / Revised: 3 June 2015 / Accepted: 5 June 2015 / Published: 11 June 2015
(This article belongs to the Special Issue Selected Papers from the 5th Venoms to Drugs Meeting)
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. View Full-TextKeywords:
toxins; databases; algorithms; proteomics; transcriptomics; phylogeny; molecular modeling
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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MDPI and ACS Style
Kaas, Q.; Craik, D.J. Bioinformatics-Aided Venomics. Toxins 2015, 7, 2159-2187.