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SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria

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Title SHARP: genome-scale identification of gene-protein-reaction associations in cyanobacteria
 
Creator KRISHNAKUMAR, S
DURAI, DA
WANGIKAR, PP
VISWANATHAN, GA
 
Subject Cyanobacteria
SHARP
Geneprotein-reaction (GPR) association
Genome scale
Metabolic network reconstruction
PSI-BLAST
METABOLIC RECONSTRUCTIONS
FUNCTIONAL ANNOTATION
FUNCTION PREDICTION
FAMILIES DATABASE
ESCHERICHIA-COLI
PATHWAY
ENZYME
NETWORKS
TOOLS
CONSTRUCTION
 
Description Genome scale metabolic model provides an overview of an organism's metabolic capability. These genome-specific metabolic reconstructions are based on identification of gene to protein to reaction (GPR) associations and, in turn, on homology with annotated genes from other organisms. Cyanobacteria are photosynthetic prokaryotes which have diverged appreciably from their nonphotosynthetic counterparts. They also show significant evolutionary divergence from plants, which are well studied for their photosynthetic apparatus. We argue that context-specific sequence and domain similarity can add to the repertoire of the GPR associations and significantly expand our view of the metabolic capability of cyanobacteria. We took an approach that combines the results of context-specific sequence-to-sequence similarity search with those of sequence-to-profile searches. We employ PSI-BLAST for the former, and CDD, Pfam, and COG for the latter. An optimization algorithm was devised to arrive at a weighting scheme to combine the different evidences with KEGG-annotated GPRs as training data. We present the algorithm in the form of software "Systematic, Homology-based Automated Re-annotation for Prokaryotes (SHARP)." We predicted 3,781 new GPR associations for the 10 prokaryotes considered of which eight are cyanobacteria species. These new GPR associations fall in several metabolic pathways and were used to annotate 7,718 gaps in the metabolic network. These new annotations led to discovery of several pathways that may be active and thereby providing new directions for metabolic engineering of these species for production of useful products. Metabolic model developed on such a reconstructed network is likely to give better phenotypic predictions.
 
Publisher SPRINGER
 
Date 2014-10-16T06:01:08Z
2014-10-16T06:01:08Z
2013
 
Type Article
 
Identifier PHOTOSYNTHESIS RESEARCH, 118(1-2)181-190
0166-8595
1573-5079
http://dx.doi.org/10.1007/s11120-013-9910-6
http://dspace.library.iitb.ac.in/jspui/handle/100/15402
 
Language en