A predictor for toxin-like proteins exposes cell modulator candidates within viral genomes

Guy Naamati, Manor Askenazi, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Motivation: Animal toxins operate by binding to receptors and ion channels. These proteins are short and vary in sequence, structure and function. Sporadic discoveries have also revealed endogenous toxin-like proteins in non-venomous organisms. Viral proteins are the largest group of quickly evolving proteomes. We tested the hypothesis that toxin-like proteins exist in viruses and that they act to modulate functions of their hosts. Results: We updated and improved a classifier for compact proteins resembling short animal toxins that is based on a machinelearning method. We applied it in a large-scale setting to identify toxin-like proteins among short viral proteins. Among the ~ 26 000 representatives of such short proteins, 510 sequences were positively identified. We focused on the 19 highest scoring proteins. Among them, we identified conotoxin-like proteins, growth factors receptor-like proteins and anti-bacterial peptides. Our predictor was shown to enhance annotation inference for many 'uncharacterized' proteins. We conclude that our protocol can expose toxin-like proteins in unexplored niches including metagenomics data and enhance the systematic discovery of novel cell modulators for drug development.

Original languageAmerican English
Pages (from-to)i482-i488
JournalBioinformatics
Volume27
Issue number13
DOIs
StatePublished - 2011
Event19th Annual International Conference on Intelligent Systems for Molecular Biology, Joint with the 10th European Conference on Computational Biology, ISMB/ECCB 2011 - Vienna, Austria
Duration: 17 Jul 201119 Jul 2011

Bibliographical note

Publisher Copyright:
© The Author(s) 2010.

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