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Mathematical Biology

Speaker: Michal Linial, Hebrew University of Jerusalem
Location: 2112 MSB
Start time: Mon, Sep 30 2013, 3:10PM

Most animal toxins are short proteins that appear in venom and vary in sequence, structure and function. Sporadic instances of endogenous toxin-like proteins that function in the non-venom context have been reported. Herein we show that many families of toxin-like proteins remain undiscovered [1]. For the goal of discovering overlooked short functional proteins, we turned to developing a computational method that can characterize and thus detect such proteins. We have successfully utilized machine learning methodology, based on sequence-derived features and guided by the notion of structural stability, a common characteristic of toxins, in order to create a robust characterization of toxin and toxin-like proteins [2]. We screen and applied an extensive search for such proteins in insect, mammalian, marine organisms and recently sequenced genomes. Our method detected dozens of putative novel toxin-like proteins [3]. We suggest that a systematically detection of toxin-like proteins leads to novel pharmaceutical targets and to a deeper understanding of the evolutionary relationship between toxins, viral proteins, immune recognition and cell modulators [4]. Several relevant publications: 1: Naamati G, Askenazi M, Linial M. (2010) A predictor for toxin-like proteins exposes cell modulator candidates within viral genomes. Bioinformatics 26:i482-i488. 2: Naamati G, Askenazi M, Linial M (2009) ClanTox: a classifier of short animal toxins. Nucleic Acids Res. 37:W363-368. 3: Kaplan N, Morpurgo N, Linial M. (2007) Novel families of toxin-like peptides in insects and mammals: a computational approach. J Mol Biol. 369:553-566. 4. Tirosh Y, Ofer D, Eliyahu T, Linial M. (2013) Short toxin-like proteins attack the defense line of innate immunity. Toxins (Basel) 5:1314-1331