Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions
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{"title"=>"Exploiting amino acid composition for predicting protein-protein interactions", "type"=>"journal", "authors"=>[{"first_name"=>"Sushmita", "last_name"=>"Roy", "scopus_author_id"=>"7404587557"}, {"first_name"=>"Diego", "last_name"=>"Martinez", "scopus_author_id"=>"7202958664"}, {"first_name"=>"Harriett", "last_name"=>"Platero", "scopus_author_id"=>"35321528400"}, {"first_name"=>"Terran", "last_name"=>"Lane", "scopus_author_id"=>"7101662175"}, {"first_name"=>"Margaret", "last_name"=>"Werner-Washburne", "scopus_author_id"=>"6603752544"}], "year"=>2009, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"70949106297", "doi"=>"10.1371/journal.pone.0007813", "pui"=>"355732643", "pmid"=>"19936254", "scopus"=>"2-s2.0-70949106297", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"bde4254c-456c-3276-9283-e94e6626b0df", "abstract"=>"BACKGROUND: Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.\\n\\nRESULTS: AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.\\n\\nCONCLUSION: AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.", "link"=>"http://www.mendeley.com/research/exploiting-amino-acid-composition-predicting-proteinprotein-interactions", "reader_count"=>57, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>19, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>2, "Student > Master"=>6, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>4, "Researcher"=>19, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>2, "Student > Master"=>6, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>1, "Biochemistry, Genetics and Molecular Biology"=>8, "Agricultural and Biological Sciences"=>39, "Business, Management and Accounting"=>1, "Chemistry"=>1, "Computer Science"=>6}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>39}, "Computer Science"=>{"Computer Science"=>6}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>8}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Korea (South)"=>1, "United States"=>3, "Japan"=>1, "Mexico"=>2, "Tunisia"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/874624"], "description"=>"<p>Results are for SVM classifier.</p>", "links"=>[], "tags"=>["aac"], "article_id"=>545088, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_comparison_of_AAC_features_in_combination_with_domains_/545088", "title"=>"Performance comparison of AAC features in combination with domains.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:24:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/875041"], "description"=>"<p>Overlap of AAC monomers and dimers from different datasets.</p>", "links"=>[], "tags"=>["aac", "monomers", "dimers"], "article_id"=>545499, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g008", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_of_AAC_monomers_and_dimers_from_different_datasets_/545499", "title"=>"Overlap of AAC monomers and dimers from different datasets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:31:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/874697"], "description"=>"<p>Non-domain features (Tuple, Sigprod) were compared on protein pairs with domains (With domains), pairs without domains (No domains) and on the entire dataset (All protein pairs).</p>", "links"=>[], "tags"=>["aac", "non-domain"], "article_id"=>545158, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g003", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_AAC_features_against_other_non_domain_features_/545158", "title"=>"Performance of AAC features against other non-domain features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:25:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/874974"], "description"=>"<p>Only dimers important for prediction are shown with the rest of the protein structure as <i>backbone</i>. Different colors correspond to different dimers. EE: red, AE: green, AD:blue, DA: yellow, DE: magenta, DV: cyan, EK: white, EQ: violet, KA: orange.</p>", "links"=>[], "tags"=>["dimensional", "structures", "etf2", "rnr1", "proteins"], "article_id"=>545428, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g007", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Three_dimensional_structures_of_ETF2_and_RNR1_proteins_obtained_from_the_protein_data_bank_/545428", "title"=>"Three dimensional structures of ETF2 and RNR1 proteins obtained from the protein data bank.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:30:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/874894"], "description"=>"<p> indicates significant overlap (-value0.05) with the complete set of AAC monomers and dimers found to be over-represented in domains involved in protein interactions.</p>", "links"=>[], "tags"=>["aac", "monomers", "dimers", "enriched", "regions"], "article_id"=>545350, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g006", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_the_top_AAC_monomers_and_dimers_that_were_significantly_enriched_in_domain_regions_involved_in_protein_interactions_/545350", "title"=>"Percentage of the top AAC monomers and dimers that were significantly enriched in domain regions involved in protein interactions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:29:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/875350"], "description"=>"<p>Degree specifies the number of interaction partners of a protein.</p>", "links"=>[], "tags"=>["uncharacterized"], "article_id"=>545812, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicted_function_of_uncharacterized_ORFs_/545812", "title"=>"Predicted function of uncharacterized ORFs.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-11-20 01:36:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/875318"], "description"=>"<p>All datasets other than WORM were obtained from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007813#pone.0007813-Stark1\" target=\"_blank\">[35]</a>. WORM was obtained from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007813#pone.0007813-Li1\" target=\"_blank\">[4]</a>.</p>", "links"=>[], "tags"=>["computational biology/genomics", "computational biology/signaling networks", "computational biology/systems biology"], "article_id"=>545774, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.t004", "stats"=>{"downloads"=>0, "page_views"=>31, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Description_of_datasets_/545774", "title"=>"Description of datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-11-20 01:36:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/874774"], "description"=>"<p>Comparison of AAC features against signature product features (Sigprod) on protein interaction datasets from worm and fly.</p>", "links"=>[], "tags"=>["aac", "datasets", "worm"], "article_id"=>545233, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g004", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_AAC_features_against_signature_product_features_Sigprod_on_protein_interaction_datasets_from_worm_and_fly_/545233", "title"=>"Comparison of AAC features against signature product features (Sigprod) on protein interaction datasets from worm and fly.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:27:13"}
  • {"files"=>["https://ndownloader.figshare.com/files/874523"], "description"=>"<p>Results are for three classifiers (MAXENT, SVM, NAIVE BAYES) over three yeast datasets (TWOHYB, AFFMS, PCA). The error bars are obtained from five-fold cross validation.</p>", "links"=>[], "tags"=>["aac"], "article_id"=>544984, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_comparison_of_AAC_features_AAC_monomer_AAC_dimer_against_domains_/544984", "title"=>"Performance comparison of AAC features (AAC monomer, AAC dimer) against domains.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:23:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/434063", "https://ndownloader.figshare.com/files/434101", "https://ndownloader.figshare.com/files/434136", "https://ndownloader.figshare.com/files/434169", "https://ndownloader.figshare.com/files/434231", "https://ndownloader.figshare.com/files/434290"], "description"=>"<div><h3>Background</h3><p>Computational prediction of protein interactions typically use protein domains as classifier features because they capture conserved information of interaction surfaces. However, approaches relying on domains as features cannot be applied to proteins without any domain information. In this paper, we explore the contribution of pure amino acid composition (AAC) for protein interaction prediction. This simple feature, which is based on normalized counts of single or pairs of amino acids, is applicable to proteins from any sequenced organism and can be used to compensate for the lack of domain information.</p><h3>Results</h3><p>AAC performed at par with protein interaction prediction based on domains on three yeast protein interaction datasets. Similar behavior was obtained using different classifiers, indicating that our results are a function of features and not of classifiers. In addition to yeast datasets, AAC performed comparably on worm and fly datasets. Prediction of interactions for the entire yeast proteome identified a large number of novel interactions, the majority of which co-localized or participated in the same processes. Our high confidence interaction network included both well-studied and uncharacterized proteins. Proteins with known function were involved in actin assembly and cell budding. Uncharacterized proteins interacted with proteins involved in reproduction and cell budding, thus providing putative biological roles for the uncharacterized proteins.</p><h3>Conclusion</h3><p>AAC is a simple, yet powerful feature for predicting protein interactions, and can be used alone or in conjunction with protein domains to predict new and validate existing interactions. More importantly, AAC alone performs at par with existing, but more complex, features indicating the presence of sequence-level information that is predictive of interaction, but which is not necessarily restricted to domains.</p></div>", "links"=>[], "tags"=>["exploiting", "amino", "predicting", "protein-protein", "interactions"], "article_id"=>145646, "categories"=>["Cancer", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0007813.s001", "https://dx.doi.org/10.1371/journal.pone.0007813.s002", "https://dx.doi.org/10.1371/journal.pone.0007813.s003", "https://dx.doi.org/10.1371/journal.pone.0007813.s004", "https://dx.doi.org/10.1371/journal.pone.0007813.s005", "https://dx.doi.org/10.1371/journal.pone.0007813.s006"], "stats"=>{"downloads"=>14, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Exploiting_Amino_Acid_Composition_for_Predicting_Protein_Protein_Interactions/145646", "title"=>"Exploiting Amino Acid Composition for Predicting Protein-Protein Interactions", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2009-11-20 01:34:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/875241"], "description"=>"<p>The uncharacterized ORFs are in magenta and the characterized are in yellow.</p>", "links"=>[], "tags"=>["sub", "uncharacterized"], "article_id"=>545707, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g011", "stats"=>{"downloads"=>3, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Protein_interaction_sub_network_with_the_uncharacterized_ORFs_/545707", "title"=>"Protein interaction sub network with the uncharacterized ORFs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:35:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/875100"], "description"=>"<p>Co-expression is measured by Pearson's correlation coefficient.</p>", "links"=>[], "tags"=>["co-expression", "interactions", "non-interactions"], "article_id"=>545564, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g009", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_co_expression_of_predicted_interactions_and_non_interactions_at_different_confidence_levels_/545564", "title"=>"Distribution of co-expression of predicted interactions and non-interactions at different confidence levels.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:32:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/875422"], "description"=>"<p>AAC features enriched in domains in different combinations of the three datasets. Each row represents the features that were exclusive to the dataset combination in the first column. A: Alanine, C: Cysteine, D: Aspartic acid, E: Glutamic acid, F: Phenylalanine, G: Glycine, H: Histidine, I: Isoleucine, K: Lysine, M: Methionine, Q: Glutamine, R: Arginine, T: Threonine, V: Valine, W: Tryptophan, S: Serine, Y: Tyrosine. Bold indicates polar, and underline indicates charged. Non-bold indicates non-polar.</p>", "links"=>[], "tags"=>["monomers", "dimers", "over-represented"], "article_id"=>545879, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AAC_monomers_and_dimers_over_represented_in_protein_interaction_domains_/545879", "title"=>"AAC monomers and dimers over-represented in protein interaction domains.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-11-20 01:37:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/875388"], "description"=>"<p>Number of predicted (using ACC and domains) and known interactions, where known interactions are those present in either AFFMS, TWOHYB or PCA.</p>", "links"=>[], "tags"=>["interactions"], "article_id"=>545848, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.t002", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_predicted_and_true_interactions_at_different_confidence_levels_/545848", "title"=>"Number of predicted and true interactions at different confidence levels.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2009-11-20 01:37:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/874826"], "description"=>"<p>The top features, equals 100, 200 or 300, were obtained from correctly predicted protein pairs per dataset (AFFMS, TWOHYB, PCA). The number of correctly predicted proteins pairs were obtained from the most confident and predicted interactions.</p>", "links"=>[], "tags"=>["aac"], "article_id"=>545286, "categories"=>["Infectious Diseases", "Medicine"], "users"=>["Sushmita Roy", "Diego Martinez", "Harriett Platero", "Terran Lane", "Margaret Werner-Washburne"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0007813.g005", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Percentage_of_the_AAC_features_among_the_top_features_/545286", "title"=>"Percentage of the AAC features among the top features.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2009-11-20 01:28:06"}

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  • {"unique-ip"=>"18", "full-text"=>"18", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"2"}
  • {"unique-ip"=>"14", "full-text"=>"14", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
  • {"unique-ip"=>"7", "full-text"=>"5", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
  • {"unique-ip"=>"10", "full-text"=>"9", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}

Relative Metric

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