Systematic search for putative new domain families in Mycoplasma gallisepticum genome
1 Université de La Réunion, Equipe de Bioinformatique, Laboratoire de Biochimie et Génétique Moléculaire, 15 ave René Cassin, 97715 Saint Denis Messag Cedex 09, La Réunion, France
2 National Centre for Biological Sciences, GKVK Campus, Bellary Road, 560065 Bangalore, India
BMC Research Notes 2010, 3:98 doi:10.1186/1756-0500-3-98Published: 12 April 2010
Protein domains are the fundamental units of protein structure, function and evolution. The delineation of different domains in proteins is important for classification, understanding of structure, function and evolution. The delineation of protein domains within a polypeptide chain, namely at the genome scale, can be achieved in several ways but may remain problematic in many instances. Difficulties in identifying the domain content of a given sequence arise when the query sequence has no homologues with experimentally determined structure and searching against sequence domain databases also results in insignificant matches. Identification of domains under low sequence identity conditions and lack of structural homologues acquire a crucial importance especially at the genomic scale.
We have developed a new method for the identification of domains in unassigned regions through indirect connections and scaled up its application to the analysis of 434 unassigned regions in 726 protein sequences of Mycoplasma gallisepticum genome. We could establish 71 new domain relationships and probable 63 putative new domain families through intermediate sequences in the unassigned regions, which importantly represent an overall 10% increase in PfamA domain annotation over the direct assignment in this genome.
The systematic analysis of the unassigned regions in the Mycoplasma gallisepticum genome has provided some insight into the possible new domain relationships and putative new domain families. Further investigation of these predicted new domains may prove beneficial in improving the existing domain prediction algorithms.