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Open Access Methodology article

antibacTR: dynamic antibacterial-drug-target ranking integrating comparative genomics, structural analysis and experimental annotation

Alejandro Panjkovich1, Isidre Gibert12 and Xavier Daura13*

Author Affiliations

1 Institute of Biotechnology and Biomedicine (IBB), Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Spain

2 Departament de Genètica i de Microbiologia, UAB, 08193 Bellaterra, Spain

3 Catalan Institution for Research and Advanced Studies (ICREA), 08010 Barcelona, Spain

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BMC Genomics 2014, 15:36  doi:10.1186/1471-2164-15-36

Published: 17 January 2014

Abstract

Background

Development of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment.

Results

In this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites.

Conclusions

Given its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at ‘http://bioinf.uab.cat/antibactr webcite’.

Keywords:
Antibacterial-drug target; Gram-negative bacteria; Target druggability