Prediction and Classification of ABC Transporters in Geobacter sulfurreducens PCA Using Computational Approaches

Title:Prediction and Classification of ABC Transporters in Geobacter sulfurreducens PCA Using Computational Approaches

Volume: 9 Issue: 2

Author(s): Ashok Selvaraj, Venil Sumantran, Nupoor Chowdhary and Gopal Ramesh Kumar

Affiliation:

Keywords: Abc transporters, biofilm, geobacter sulfurreducens pca, phylogenetic analysis, sequence similarity, sub-class.

Abstract: Geobacter sulfurreducens PCA plays an important role in electricity production and bioremediation, as it can reduce uranium to an insoluble form, and uses organic compounds as electron donors. ATP-binding cassette (ABC) transporters are important as they regulate respiration and biofilm formation, which in turn affect the rate of electricity production and bioremediation. Thus we focused on identifying ABC transporters by functional genomic re-annotation, KEGG pathway analysis, and phylogenetic analysis of hypothetical proteins of G. sulfurreducens PCA. Our prediction is based on five 1-dimensional tools including BLAST, family, domain, orthologous groups and signature recognition search. We define a prediction of gene function with high confidence, when 3 or more functional prediction tools indicated the same function for a given hypothetical gene. From our integrated re-annotation approach, we predicted eleven new ABC transporters and phylogenetically sub-classified these genes based on sequence similarity with known sub-classes of ABC transporters. These eleven new ABC transporters were also identified by KEGG pathway analysis. Overall, these 11 newly predicted ABC proteins can be sub-classified as five ABC transporter substrate binding proteins, three ABC-2 type transporters, 2 permeases, and 1 phosphate transporter.

Cite this article as:

Selvaraj Ashok, Sumantran Venil, Chowdhary Nupoor and Kumar Ramesh Gopal, Prediction and Classification of ABC Transporters in Geobacter sulfurreducens PCA Using Computational Approaches, Current Bioinformatics 2014; 9 (2) . https://dx.doi.org/10.2174/1574893608999140109113236