Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data
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{"title"=>"Genomes2Drugs: Identifies target proteins and lead drugs from proteome data", "type"=>"journal", "authors"=>[{"first_name"=>"David", "last_name"=>"Toomey", "scopus_author_id"=>"27568123100"}, {"first_name"=>"Heinrich C.", "last_name"=>"Hoppe", "scopus_author_id"=>"7103057362"}, {"first_name"=>"Marian P.", "last_name"=>"Brennan", "scopus_author_id"=>"8553059100"}, {"first_name"=>"Kevin B.", "last_name"=>"Nolan", "scopus_author_id"=>"7102798726"}, {"first_name"=>"Anthony J.", "last_name"=>"Chubb", "scopus_author_id"=>"6701856799"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"354919691", "scopus"=>"2-s2.0-67650523946", "pmid"=>"19593435", "doi"=>"10.1371/journal.pone.0006195", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"67650523946"}, "id"=>"329135aa-813d-3fa1-9a37-f76fc1381c59", "abstract"=>"BACKGROUND: Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the Plasmodium falciparum malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.\\n\\nCONCLUSIONS/SIGNIFICANCE: Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under 'change-of-application' patents.", "link"=>"http://www.mendeley.com/research/genomes2drugs-identifies-target-proteins-lead-drugs-proteome-data", "reader_count"=>33, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>3, "Student > Master"=>3, "Other"=>4, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>10, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>3, "Student > Postgraduate"=>3, "Student > Master"=>3, "Other"=>4, "Student > Bachelor"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>3, "Chemistry"=>4, "Social Sciences"=>1, "Computer Science"=>3, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>4}, "Social Sciences"=>{"Social Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>4}}, "reader_count_by_country"=>{"Colombia"=>1, "Netherlands"=>1, "Ireland"=>1, "Brazil"=>2, "United Kingdom"=>1, "India"=>1}, "group_count"=>3}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/891650"], "description"=>"<p>Enrichment curves were plotted as described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006195#pone-0006195-g002\" target=\"_blank\">Figure 2</a>. The 5283 protein malarial proteome was ranked by R<sub>huDB</sub>. <i>P. falciparum</i> and malaria related hits from PDB were identified using keyword searching of the 〈pdb_title〉 field. The enrichment percentage as a function of rank are shown in dark blue, while the red line shows an ideal case, and the light blue line indicates a random distribution. The insert highlights the first 500 entries.</p>", "links"=>[], "tags"=>["proteome", "pdb"], "article_id"=>562109, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.g003", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_of_P_falciparum_proteome_by_R_huDB_8211_PDB_targets_/562109", "title"=>"Enrichment of <i>P. falciparum</i> proteome by R<sub>huDB</sub> – PDB targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-10 00:35:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/891404"], "description"=>"<p>Genomes2Drugs is a free online resource. The web interface was written using open-source Java Enterprise Edition, BioJava 1.6 and NetBeans IDE 6.0. Input sequences are aligned against the human proteome, the PDB dataset and the DrugBank target proteins dataset. Only the best results are preserved. The resulting output files are parsed using BioJava and entered into a MySQL 5.1 database, where the results are sorted and ranked. Output XML files are generated from this data.</p>", "links"=>[], "tags"=>["Infectious diseases", "biochemistry/drug discovery", "biochemistry/structural genomics", "computational biology/comparative sequence analysis", "computational biology/protein homology detection", "molecular biology/bioinformatics", "infectious diseases/antimicrobials and drug resistance", "infectious diseases/neglected tropical diseases"], "article_id"=>561845, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schema_of_data_processing_/561845", "title"=>"Schema of data processing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-10 00:30:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/891730"], "description"=>"<p>Enrichment curves were plotted as described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006195#pone-0006195-g002\" target=\"_blank\">Figure 2</a>. The 5283 protein malarial proteome was ranked by R<sub>huDB</sub>. <i>P. falciparum</i> and malaria related hits from DrugBank were identified using keyword searching of DrugBank website <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006195#pone.0006195-Wishart1\" target=\"_blank\">[4]</a>, as shown in supplementary <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0006195#pone.0006195.s003\" target=\"_blank\">Table S2</a> online. The 〈drugbank_title〉 field entries were matched to this list of <i>P. falciparum</i> or malaria related drug targets. The enrichment percentage as a function of rank are shown in dark blue, while the red line shows an ideal case, and the light blue line indicates a random distribution. The insert highlights the first 500 entries.</p>", "links"=>[], "tags"=>["proteome", "drugbank"], "article_id"=>562184, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.g004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_of_P_falciparum_proteome_by_R_huDB_8211_DrugBank_targets_/562184", "title"=>"Enrichment of <i>P. falciparum</i> proteome by R<sub>huDB</sub> – DrugBank targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-10 00:36:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/891829"], "description"=>"<p>Key for output file column headings.</p>", "links"=>[], "tags"=>["Infectious diseases", "biochemistry/drug discovery", "biochemistry/structural genomics", "computational biology/comparative sequence analysis", "computational biology/protein homology detection", "molecular biology/bioinformatics", "infectious diseases/antimicrobials and drug resistance", "infectious diseases/neglected tropical diseases"], "article_id"=>562281, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.t001", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Key_for_output_file_column_headings_/562281", "title"=>"Key for output file column headings.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-10 00:38:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/891782"], "description"=>"ψ<p>BLASTp expect value of the best query/human genome alignment (null = 1000).</p>ξ<p>BLASTp expect value of the best query/DrugBank alignment or query/protein data bank alignment (not null).</p>φ<p>No alignment found between query and either DrugBank or PDB databases (null).</p>", "links"=>[], "tags"=>["ranges"], "article_id"=>562239, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Definition_of_ratio_ranges_and_error_codes_/562239", "title"=>"Definition of ratio ranges and error codes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-07-10 00:37:19"}
  • {"files"=>["https://ndownloader.figshare.com/files/891572"], "description"=>"<p>Enrichment curves plot the accumulation of user-defined ‘hits’ as a function of rank number. Thus in an ideal case (red line), each consecutive entry in the ascending ranked list will be a hit. Alternatively, if ranking provides no selection the hits will be distributed randomly across the genome (light blue line). The enrichment percentage as a function of rank are shown in dark blue. The 5283 proteins in the <i>P. falciparum</i> 3D7 strain test set were searched using Genomes2Drugs and ranked by R<sub>huPDB</sub>. <i>P. falciparum</i> and malaria related hits from PDB were identified using keyword searching of the 〈pdb_title〉 field, and their position in the ranked list identified. The insert, which highlights the first 500 entries, shows that almost 80% of the entries with close homology to known <i>P. falciparum</i> crystal structures were identified in the first 10% of the genome.</p>", "links"=>[], "tags"=>["proteome", "pdb"], "article_id"=>562029, "categories"=>["Medicine", "Molecular Biology", "Infectious Diseases", "Biochemistry", "Virology"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0006195.g002", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Enrichment_of_P_falciparum_proteome_by_R_huPDB_8211_PDB_targets_/562029", "title"=>"Enrichment of <i>P. falciparum</i> proteome by R<sub>huPDB</sub> – PDB targets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-07-10 00:33:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/441604", "https://ndownloader.figshare.com/files/441653", "https://ndownloader.figshare.com/files/441711"], "description"=>"<div><h3>Background</h3><p>Genome sequencing and bioinformatics have provided the full hypothetical proteome of many pathogenic organisms. Advances in microarray and mass spectrometry have also yielded large output datasets of possible target proteins/genes. However, the challenge remains to identify new targets for drug discovery from this wealth of information. Further analysis includes bioinformatics and/or molecular biology tools to validate the findings. This is time consuming and expensive, and could fail to yield novel drugs if protein purification and crystallography is impossible. To pre-empt this, a researcher may want to rapidly filter the output datasets for proteins that show good homology to proteins that have already been structurally characterised or proteins that are already targets for known drugs. Critically, those researchers developing novel antibiotics need to select out the proteins that show close homology to any human proteins, as future inhibitors are likely to cross-react with the host protein, causing off-target toxicity effects later in clinical trials.</p><h3>Methodology/Principal Findings</h3><p>To solve many of these issues, we have developed a free online resource called Genomes2Drugs which ranks sequences to identify proteins that are (i) homologous to previously crystallized proteins or (ii) targets of known drugs, but are (iii) not homologous to human proteins. When tested using the <em>Plasmodium falciparum</em> malarial genome the program correctly enriched the ranked list of proteins with known drug target proteins.</p><h3>Conclusions/Significance</h3><p>Genomes2Drugs rapidly identifies proteins that are likely to succeed in drug discovery pipelines. This free online resource helps in the identification of potential drug targets. Importantly, the program further highlights proteins that are likely to be inhibited by FDA-approved drugs. These drugs can then be rapidly moved into Phase IV clinical studies under ‘change-of-application’ patents.</p></div>", "links"=>[], "tags"=>["identifies", "proteins", "drugs", "proteome"], "article_id"=>147055, "categories"=>["Medicine", "Cancer", "Molecular Biology", "Biochemistry"], "users"=>["David Toomey", "Heinrich C. Hoppe", "Marian P. Brennan", "Kevin B. Nolan", "Anthony J. Chubb"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0006195.s001", "https://dx.doi.org/10.1371/journal.pone.0006195.s002", "https://dx.doi.org/10.1371/journal.pone.0006195.s003"], "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Genomes2Drugs_Identifies_Target_Proteins_and_Lead_Drugs_from_Proteome_Data/147055", "title"=>"Genomes2Drugs: Identifies Target Proteins and Lead Drugs from Proteome Data", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-07-10 01:57:35"}

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