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Identification of attractive drug targets in neglected-disease pathogens using an in silico approach.

Crowther GJ, Shanmugam D, Carmona SJ, Doyle MA, Hertz-Fowler C, Berriman M, Nwaka S, Ralph SA, Roos DS, Van Voorhis WC, Agüero F - PLoS Negl Trop Dis (2010)

Bottom Line: We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens.While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

View Article: PubMed Central - PubMed

Affiliation: Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, United States of America. crowther@u.washington.edu

ABSTRACT

Background: The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/principal findings: Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/significance: Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

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Related in: MedlinePlus

Highlights of the new, improved display of query results in TDRtargets.org.(A) The “Your scoring strategy” panel displays and allows adjustment of weights associated with each criterion. (B) An additional panel shows the distribution of weights among the proteins in the genome. To generate this histogram, all weights in the prioritization strategy were divided into 10 bins; the mean weight for each bin is shown below the x axis. In this example, most proteins had a weight of 0–100, with a small number exceeding 300. (C) Proteins are displayed in descending order of total weight; a grid shows the criteria that were met by each protein.
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pntd-0000804-g001: Highlights of the new, improved display of query results in TDRtargets.org.(A) The “Your scoring strategy” panel displays and allows adjustment of weights associated with each criterion. (B) An additional panel shows the distribution of weights among the proteins in the genome. To generate this histogram, all weights in the prioritization strategy were divided into 10 bins; the mean weight for each bin is shown below the x axis. In this example, most proteins had a weight of 0–100, with a small number exceeding 300. (C) Proteins are displayed in descending order of total weight; a grid shows the criteria that were met by each protein.

Mentions: We have previously described the construction of the TDRtargets.org database, as well as the formulation of searches (queries) to identify proteins meeting criteria of interest and the viewing, saving, and exporting of search results [14]. Since then, while the overall workflow of the database has remained the same, additional genomes and datasets have been included (see below), and several improvements have been implemented on the user interface side of the database. Although users have always been able to perform “weighted union” queries, with different weights (point values) assigned to different user-specified criteria, formulating these queries and viewing and adjusting their results has recently been made more convenient. To construct a weighted union query from the website's target search page, a user (1) selects a pathogen (e.g., P. falciparum), (2) selects a criterion (e.g., functional category  =  enzyme) with which to query the pathogen genes, (3) enters a name and a weight for the query in the “Run this query” sub-menu at the bottom of the page, (4) clicks the “Next Query” button, and (5) repeats steps 2 to 4 until the last criterion is entered, at which point the user selects “Run this query” rather than “Next Query.” The search results are displayed on a page where users have the option of changing the previously entered weights for each criterion (Figure 1). (These results are archived on the user's history page, where he/she can combine different subsets of previous queries with the Union function to obtain new ranked target lists.) The presentation of ranked lists has also been revised to display the criteria met by each protein (Figure 1). Further flexibility in data analysis is provided by an option to export the results to a dynamic spreadsheet so that proteins' fulfillment of individual criteria can be viewed and the weights of the criteria can be adjusted offline.


Identification of attractive drug targets in neglected-disease pathogens using an in silico approach.

Crowther GJ, Shanmugam D, Carmona SJ, Doyle MA, Hertz-Fowler C, Berriman M, Nwaka S, Ralph SA, Roos DS, Van Voorhis WC, Agüero F - PLoS Negl Trop Dis (2010)

Highlights of the new, improved display of query results in TDRtargets.org.(A) The “Your scoring strategy” panel displays and allows adjustment of weights associated with each criterion. (B) An additional panel shows the distribution of weights among the proteins in the genome. To generate this histogram, all weights in the prioritization strategy were divided into 10 bins; the mean weight for each bin is shown below the x axis. In this example, most proteins had a weight of 0–100, with a small number exceeding 300. (C) Proteins are displayed in descending order of total weight; a grid shows the criteria that were met by each protein.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2927427&req=5

pntd-0000804-g001: Highlights of the new, improved display of query results in TDRtargets.org.(A) The “Your scoring strategy” panel displays and allows adjustment of weights associated with each criterion. (B) An additional panel shows the distribution of weights among the proteins in the genome. To generate this histogram, all weights in the prioritization strategy were divided into 10 bins; the mean weight for each bin is shown below the x axis. In this example, most proteins had a weight of 0–100, with a small number exceeding 300. (C) Proteins are displayed in descending order of total weight; a grid shows the criteria that were met by each protein.
Mentions: We have previously described the construction of the TDRtargets.org database, as well as the formulation of searches (queries) to identify proteins meeting criteria of interest and the viewing, saving, and exporting of search results [14]. Since then, while the overall workflow of the database has remained the same, additional genomes and datasets have been included (see below), and several improvements have been implemented on the user interface side of the database. Although users have always been able to perform “weighted union” queries, with different weights (point values) assigned to different user-specified criteria, formulating these queries and viewing and adjusting their results has recently been made more convenient. To construct a weighted union query from the website's target search page, a user (1) selects a pathogen (e.g., P. falciparum), (2) selects a criterion (e.g., functional category  =  enzyme) with which to query the pathogen genes, (3) enters a name and a weight for the query in the “Run this query” sub-menu at the bottom of the page, (4) clicks the “Next Query” button, and (5) repeats steps 2 to 4 until the last criterion is entered, at which point the user selects “Run this query” rather than “Next Query.” The search results are displayed on a page where users have the option of changing the previously entered weights for each criterion (Figure 1). (These results are archived on the user's history page, where he/she can combine different subsets of previous queries with the Union function to obtain new ranked target lists.) The presentation of ranked lists has also been revised to display the criteria met by each protein (Figure 1). Further flexibility in data analysis is provided by an option to export the results to a dynamic spreadsheet so that proteins' fulfillment of individual criteria can be viewed and the weights of the criteria can be adjusted offline.

Bottom Line: We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens.While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

View Article: PubMed Central - PubMed

Affiliation: Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, Seattle, Washington, United States of America. crowther@u.washington.edu

ABSTRACT

Background: The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni.

Methodology/principal findings: Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass) and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths), and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets.

Conclusions/significance: Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest novel target candidates that may merit further study, the lists can easily be modified in a user-specific manner, either by adjusting the weights for chosen criteria or by changing the criteria that are included.

Show MeSH
Related in: MedlinePlus