User:Pawel Szczesny/Notebook/bacteriocins

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<!-- sibboleth --><div id="lncal1" style="border:0px;"><div style="display:none;" id="id">lncal1</div><div style="display:none;" id="dtext">08/04/2008</div><div style="display:none;" id="page">User:Pawel Szczesny/Notebook/bacteriocins</div><div style="display:none;" id="fmt">yyyy/MM/dd</div><div style="display:none;" id="css">OWWNB</div><div style="display:none;" id="month"></div><div style="display:none;" id="year"></div><div style="display:none;" id="readonly">Y</div></div>

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Project Description/Abstract

Protein or peptide-based antibiotics have been discovered in all three domains of life (named bacteriocins, eucaryocins and archeocins). There's a considerable amount of research conducted on their structure and mechanisms of activity. That includes various ways of their classification, such as: method of killing (pore forming, dnase, nuclease, murein production inhibition, etc), genetics (large plasmids, small plasmids, chromosomal), molecular weight and chemistry (large protein, polypeptide, with/without sugar moiety, containing atypical amino acids like lanthionine) and method of production (ribosomal, post ribosomal modifications, non-ribosomal).

Because with modern sensitive tools for finding sequence similarity I see homology between seemingly non-related gene-encoded bacteriocins, there's a chance, that using sequence information one would be able to come up with new classification schema. Hopefully, that schema later would allow to build a tool for prediction of bacteriocins from genomes. Given recently increasing interest in novel peptide-based antimicrobial drugs, there's also hope that evolutionary approach to bacteriocin research can lead to some new insights in that area too (although this is only nonsupported hope).

Project plan:

   * data collection (literature, PDB structures, existing classifications, protein annotations)
   * data clustering (sequence based)
   * enriching dataset with HHsenser for each cluster from previous point
   * data clustering
   * construction of manually curated alignments for each cluster and later profiles for genome-wide scans
   * exhaustive search for bacteriocins in available genomes and phylogenetically enriched clustering


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