Name
Title | ||
|---|---|---|
A predictor for toxin-like proteins exposes cell modulator candidates within viral genomes. |
Abstract | ||
|---|---|---|
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.We updated and improved a classifier for compact proteins resembling short animal toxins that is based on a machine-learning method. We applied it in a large-scale setting to identify toxin-like proteins among short viral proteins. Among the approximately 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.ClanTox is available at http://www.clantox.cs.huji.ac.il. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1093/bioinformatics/btq375 | Bioinformatics |
Keywords | Field | DocType |
il contact,short protein,toxin-like protein,viral genomes,exposes cell modulator candidate,anti-bacterial peptides,animal toxin,annotation inference,viral protein,short viral protein,endogenous toxin-like protein,short animal toxin,metagenomics,proteome,amino acid sequence,conotoxins,algorithms,artificial intelligence,protein folding | Genome,Protein folding,Biology,Genomics,Metagenomics,Receptor,Proteome,Ion channel,Bioinformatics,Genetics,Peptide sequence | Journal |
Volume | Issue | ISSN |
26 | 18 | 1367-4811 |
Citations | PageRank | References |
1 | 0.36 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guy Naamati | 1 | 17 | 1.78 |
| Manor Askenazi | 2 | 17 | 2.69 |
| Michal Linial | 3 | 1502 | 149.92 |