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UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria
BACKGROUND: Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availa...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3303792/ https://www.ncbi.nlm.nih.gov/pubmed/22431985 http://dx.doi.org/10.1371/journal.pone.0032833 |
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author | Chanumolu, Sree Krishna Rout, Chittaranjan Chauhan, Rajinder S. |
author_facet | Chanumolu, Sree Krishna Rout, Chittaranjan Chauhan, Rajinder S. |
author_sort | Chanumolu, Sree Krishna |
collection | PubMed |
description | BACKGROUND: Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. METHODS: A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. RESULTS: The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. CONCLUSIONS/SIGNIFICANCE: UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar. |
format | Online Article Text |
id | pubmed-3303792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33037922012-03-19 UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria Chanumolu, Sree Krishna Rout, Chittaranjan Chauhan, Rajinder S. PLoS One Research Article BACKGROUND: Targeting conserved proteins of bacteria through antibacterial medications has resulted in both the development of resistant strains and changes to human health by destroying beneficial microbes which eventually become breeding grounds for the evolution of resistances. Despite the availability of more than 800 genomes sequences, 430 pathways, 4743 enzymes, 9257 metabolic reactions and protein (three-dimensional) 3D structures in bacteria, no pathogen-specific computational drug target identification tool has been developed. METHODS: A web server, UniDrug-Target, which combines bacterial biological information and computational methods to stringently identify pathogen-specific proteins as drug targets, has been designed. Besides predicting pathogen-specific proteins essentiality, chokepoint property, etc., three new algorithms were developed and implemented by using protein sequences, domains, structures, and metabolic reactions for construction of partial metabolic networks (PMNs), determination of conservation in critical residues, and variation analysis of residues forming similar cavities in proteins sequences. First, PMNs are constructed to determine the extent of disturbances in metabolite production by targeting a protein as drug target. Conservation of pathogen-specific protein's critical residues involved in cavity formation and biological function determined at domain-level with low-matching sequences. Last, variation analysis of residues forming similar cavities in proteins sequences from pathogenic versus non-pathogenic bacteria and humans is performed. RESULTS: The server is capable of predicting drug targets for any sequenced pathogenic bacteria having fasta sequences and annotated information. The utility of UniDrug-Target server was demonstrated for Mycobacterium tuberculosis (H37Rv). The UniDrug-Target identified 265 mycobacteria pathogen-specific proteins, including 17 essential proteins which can be potential drug targets. CONCLUSIONS/SIGNIFICANCE: UniDrug-Target is expected to accelerate pathogen-specific drug targets identification which will increase their success and durability as drugs developed against them have less chance to develop resistances and adverse impact on environment. The server is freely available at http://117.211.115.67/UDT/main.html. The standalone application (source codes) is available at http://www.bioinformatics.org/ftp/pub/bioinfojuit/UDT.rar. Public Library of Science 2012-03-14 /pmc/articles/PMC3303792/ /pubmed/22431985 http://dx.doi.org/10.1371/journal.pone.0032833 Text en Chanumolu et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Chanumolu, Sree Krishna Rout, Chittaranjan Chauhan, Rajinder S. UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title | UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title_full | UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title_fullStr | UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title_full_unstemmed | UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title_short | UniDrug-Target: A Computational Tool to Identify Unique Drug Targets in Pathogenic Bacteria |
title_sort | unidrug-target: a computational tool to identify unique drug targets in pathogenic bacteria |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3303792/ https://www.ncbi.nlm.nih.gov/pubmed/22431985 http://dx.doi.org/10.1371/journal.pone.0032833 |
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