Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome
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{"title"=>"Identifying unexpected therapeutic targets via chemical-protein interactome", "type"=>"journal", "authors"=>[{"first_name"=>"Lun", "last_name"=>"Yang", "scopus_author_id"=>"35286235800"}, {"first_name"=>"Jian", "last_name"=>"Chen", "scopus_author_id"=>"37001581500"}, {"first_name"=>"Leming", "last_name"=>"Shi", "scopus_author_id"=>"7401640714"}, {"first_name"=>"Michael P.", "last_name"=>"Hudock", "scopus_author_id"=>"57190482703"}, {"first_name"=>"Kejian", "last_name"=>"Wang", "scopus_author_id"=>"57193115287"}, {"first_name"=>"Lin", "last_name"=>"He", "scopus_author_id"=>"55158160500"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"20221449", "doi"=>"10.1371/journal.pone.0009568", "sgr"=>"77949760141", "isbn"=>"1932-6203 (Linking)", "scopus"=>"2-s2.0-77949760141", "issn"=>"19326203", "pui"=>"358498977"}, "id"=>"01781dfd-865b-32c7-a45b-22e8588040d9", "abstract"=>"Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover \"behind-the-scenes\" aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.", "link"=>"http://www.mendeley.com/research/identifying-unexpected-therapeutic-targets-via-chemicalprotein-interactome", "reader_count"=>74, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>24, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>7, "Student > Master"=>8, "Other"=>3, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>24, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>7, "Student > Master"=>8, "Other"=>3, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>3, "Agricultural and Biological Sciences"=>36, "Medicine and Dentistry"=>8, "Neuroscience"=>1, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Chemistry"=>9, "Psychology"=>2, "Social Sciences"=>1, "Computer Science"=>9}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>8}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>9}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>36}, "Computer Science"=>{"Computer Science"=>9}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Unspecified"=>{"Unspecified"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Korea (South)"=>1, "Pakistan"=>1, "Hungary"=>1, "United States"=>6, "Italy"=>2, "United Kingdom"=>2, "France"=>1, "Germany"=>3, "Indonesia"=>1}, "group_count"=>5}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/427318", "https://ndownloader.figshare.com/files/427410", "https://ndownloader.figshare.com/files/427466", "https://ndownloader.figshare.com/files/427496", "https://ndownloader.figshare.com/files/427537", "https://ndownloader.figshare.com/files/427566", "https://ndownloader.figshare.com/files/427593"], "description"=>"<div><p>Drug medications inevitably affect not only their intended protein targets but also other proteins as well. In this study we examined the hypothesis that drugs that share the same therapeutic effect also share a common therapeutic mechanism by targeting not only known drug targets, but also by interacting unexpectedly on the same cryptic targets. By constructing and mining an Alzheimer's disease (AD) drug-oriented chemical-protein interactome (CPI) using a matrix of 10 drug molecules known to treat AD towards 401 human protein pockets, we found that such cryptic targets exist. We recovered from CPI the only validated therapeutic target of AD, acetylcholinesterase (ACHE), and highlighted several other putative targets. For example, we discovered that estrogen receptor (ER) and histone deacetylase (HDAC), which have recently been identified as two new therapeutic targets of AD, might already have been targeted by the marketed AD drugs. We further established that the CPI profile of a drug can reflect its interacting character towards multi-protein sets, and that drugs with the same therapeutic attribute will share a similar interacting profile. These findings indicate that the CPI could represent the landscape of chemical-protein interactions and uncover “behind-the-scenes” aspects of the therapeutic mechanisms of existing drugs, providing testable hypotheses of the key nodes for network pharmacology or brand new drug targets for one-target pharmacology paradigm.</p></div>", "links"=>[], "tags"=>["identifying", "therapeutic", "targets", "chemical-protein", "interactome"], "article_id"=>144343, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.s001", "https://dx.doi.org/10.1371/journal.pone.0009568.s002", "https://dx.doi.org/10.1371/journal.pone.0009568.s003", "https://dx.doi.org/10.1371/journal.pone.0009568.s004", "https://dx.doi.org/10.1371/journal.pone.0009568.s005", "https://dx.doi.org/10.1371/journal.pone.0009568.s006", "https://dx.doi.org/10.1371/journal.pone.0009568.s007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Identifying_Unexpected_Therapeutic_Targets_via_Chemical_Protein_Interactome/144343", "title"=>"Identifying Unexpected Therapeutic Targets via Chemical-Protein Interactome", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-03-08 01:12:23"}
  • {"files"=>["https://ndownloader.figshare.com/files/859608"], "description"=>"<p>The AUC was 0.67, 0.77 and 0.82 for the three scoring systems.</p>", "links"=>[], "tags"=>["curves", "classifying", "unidentified", "bindings", "docking", "z-score"], "article_id"=>530055, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curves_representing_the_power_for_classifying_true_and_unidentified_bindings_using_docking_score_Z_score_and_Z_8242_score_respectively_/530055", "title"=>"ROC curves representing the power for classifying true and unidentified bindings using docking score, Z-score and Z′-score respectively.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-08 00:00:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/859698"], "description"=>"<p>(<b>a</b>) Structures of 10 case drug molecules. Drug names followed by numbers indicate the derivatives. (<b>b</b>) Interactome of these 10 drug molecules towards 15 highlighted proteins in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009568#pone-0009568-t001\" target=\"_blank\"><b>Table 1</b></a>. Proteins are represented by their PDB IDs. Blue, red or white squares represent a Z′-value less than, greater than or equal to zero. Deeper color denotes the greater absolute value. Missing value is presented as a grey box. The accredited drug target of AD (human AChE) is marked with 1F8U. The newly candidate therapeutic target of AD, human HDAC7, is marked with 3C0Z. Binding models of an AD drug, physostigmine, to the pockets of the known (AChE) and unexpected (HDAC7) therapeutic targets of AD are enlarged. Though the shapes and the amino acids sequence of the two pockets are different, the drug accommodates to the two pockets both with steric complementarity and low binding free energy estimated by AutoDock.</p>", "links"=>[], "tags"=>["mining", "drug-oriented", "chemical-protein"], "article_id"=>530149, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Constructing_and_mining_an_AD_drug_oriented_chemical_protein_interactome_/530149", "title"=>"Constructing and mining an AD drug-oriented chemical-protein interactome.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-08 00:02:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/859802"], "description"=>"<p>The assumed normality and equal variances of docking scores within each group could not be rejectedin statistical tests. The PDB ID of the representative structures of AChE, HDAC7 and HLA-B*5703 were 1F8U, 3Z0Y and 2BVP respectively. See <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009568#pone.0009568.s002\" target=\"_blank\">Table S2</a></b> for the detail of their interactomes. (<b>a, b</b>) Correlations of docking scores among AChE, HDAC7 and HLA-B*5703 towards 13 probe molecules. (<b>c, d</b>) Correlations of docking scores among these three proteins toward 50 control probes.</p>", "links"=>[], "tags"=>["docking", "scores", "hdac7", "probe"], "article_id"=>530255, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_of_docking_scores_among_AChE_HDAC7_and_HLA_B_5703_towards_probe_molecules_/530255", "title"=>"Correlations of docking scores among AChE, HDAC7 and HLA-B*5703 towards probe molecules.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-08 00:04:15"}
  • {"files"=>["https://ndownloader.figshare.com/files/859894"], "description"=>"<p>The figure demonstrates that 10 AD drugs (red) could be basically separated from 63 schizophrenia drugs (blue) linearly.</p>", "links"=>[], "tags"=>["components", "schizophrenia", "drugs", "docking"], "article_id"=>530343, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_first_two_principle_components_plot_of_AD_and_schizophrenia_drugs_based_on_their_docking_score_vectors_/530343", "title"=>"The first two principle components plot of AD and schizophrenia drugs based on their docking score vectors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-03-08 00:05:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/859970"], "description"=>"<p>For each protein, <i>a</i>, <i>b</i>, <i>c</i>, <i>d</i> values, represents the number of binding (<i>a</i> or <i>b</i>) and non-binding (<i>c</i> or <i>d</i>) by case drug molecules or control drug molecules respectively. The two-sided p values were estimated from Fisher's exact test.</p>", "links"=>[], "tags"=>["highlighted", "drug-oriented", "cpi", "401"], "article_id"=>530422, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proteins_highlighted_from_AD_drug_oriented_CPI_using_401_protein_set_/530422", "title"=>"Proteins highlighted from AD drug-oriented CPI using 401 protein set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-08 00:07:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/859999"], "description"=>"<p>PDB IDs marked with a number indicate the pocket number. Refer to the note of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0009568#pone-0009568-t001\" target=\"_blank\">Table 1</a> for the explanation of <i>a</i>, <i>b</i>, <i>c</i>, <i>d</i> and Sig.</p>", "links"=>[], "tags"=>["highlighted", "drug-oriented", "cpi", "schizophrenia", "drugs"], "article_id"=>530448, "categories"=>["Medicine", "Molecular Biology", "Mental Health", "Neuroscience", "Biochemistry", "Biophysics", "Pharmacology", "Chemistry"], "users"=>["Lun Yang", "Jian Chen", "Leming Shi", "Michael P. Hudock", "Kejian Wang", "Lin He"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0009568.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Proteins_highlighted_from_AD_drug_oriented_CPI_using_schizophrenia_drugs_as_the_control_set_/530448", "title"=>"Proteins highlighted from AD drug-oriented CPI using schizophrenia drugs as the control set.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2010-03-08 00:07:28"}

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Relative Metric

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