PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites
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{"title"=>"PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites", "type"=>"journal", "authors"=>[{"first_name"=>"Jiangning", "last_name"=>"Song", "scopus_author_id"=>"56023619300"}, {"first_name"=>"Hao", "last_name"=>"Tan", "scopus_author_id"=>"35346147400"}, {"first_name"=>"Andrew J.", "last_name"=>"Perry", "scopus_author_id"=>"8614519800"}, {"first_name"=>"Tatsuya", "last_name"=>"Akutsu", "scopus_author_id"=>"7102080520"}, {"first_name"=>"Geoffrey I.", "last_name"=>"Webb", "scopus_author_id"=>"7201584558"}, {"first_name"=>"James C.", "last_name"=>"Whisstock", "scopus_author_id"=>"56247908700"}, {"first_name"=>"Robert N.", "last_name"=>"Pike", "scopus_author_id"=>"55566936800"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84870484133", "doi"=>"10.1371/journal.pone.0050300", "pui"=>"366195699", "pmid"=>"23209700", "scopus"=>"2-s2.0-84870484133", "issn"=>"19326203", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"dbe31449-d546-3984-a7d5-f7eedbe7df9b", "abstract"=>"The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by in silico approaches. To address this problem, we present PROSPER, an integrated feature-based server for in silico identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at http://lightning.med.monash.edu.au/PROSPER/.", "link"=>"http://www.mendeley.com/research/prosper-integrated-featurebased-tool-predicting-protease-substrate-cleavage-sites", "reader_count"=>97, "reader_count_by_academic_status"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>10, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>5, "Professor > Associate Professor"=>1, "Researcher"=>21, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>39, "Student > Postgraduate"=>3, "Student > Master"=>9, "Other"=>2, "Student > Bachelor"=>10, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>3, "Unspecified"=>7, "Environmental Science"=>1, "Biochemistry, Genetics and Molecular Biology"=>17, "Agricultural and Biological Sciences"=>49, "Medicine and Dentistry"=>4, "Pharmacology, Toxicology and Pharmaceutical Science"=>2, "Physics and Astronomy"=>2, "Chemistry"=>7, "Computer Science"=>4, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Chemistry"=>{"Chemistry"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>49}, "Computer Science"=>{"Computer Science"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>17}, "Unspecified"=>{"Unspecified"=>7}, "Environmental Science"=>{"Environmental Science"=>1}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>2}}, "reader_count_by_country"=>{"Canada"=>2, "Norway"=>1, "Ireland"=>1, "Denmark"=>2, "Italy"=>1, "Israel"=>1, "Australia"=>1, "Switzerland"=>1}, "group_count"=>3}

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/534912"], "description"=>"<p>The predictive performances of four different encoding schemes were compared, including “BEAA”, “BEAA+BPBSA”, “BEAA+BPBDISO” and “ALL”. See the main text for details of different sequence encoding schemes.</p>", "links"=>[], "tags"=>["contributions", "solvent", "accessibility", "predictive", "prosper", "evaluated", "f-score", "mcc"], "article_id"=>205392, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g006", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Relative_contributions_of_secondary_structure_solvent_accessibility_and_native_disorder_features_to_the_predictive_performance_of_PROSPER_evaluated_using_F_score_A_and_MCC_B_respectively_/205392", "title"=>"Relative contributions of secondary structure, solvent accessibility and native disorder features to the predictive performance of PROSPER evaluated using F-score (A) and MCC (B), respectively.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:29:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/535136"], "description"=>"<p>The different colored circles denote different prediction tools: PROSPER, red; PoPS, blue; SitePrediction, green. The sum of the numbers in each color represents the number of known cleavage sites that were correctly predicted by the tool. The number in each overlapping region represents the number of known cleavage sites that were correctly predicted by two or all tools. For example, for MMP-2, 276 sites were correctly predicted by PROSPER and by no other tool, 78 were correctly predicted by PROSPER and SitePrediction, 24 by PROSPER and PoPS and 193 by all three tools. The number in the inner circle “Overall” represents the total number of correctly predicted known cleavage sites by the corresponding tool.</p>", "links"=>[], "tags"=>["diagrams", "consistency", "pops"], "article_id"=>205627, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g008", "stats"=>{"downloads"=>0, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Venn_diagrams_showing_the_prediction_consistency_between_PROSPER_PoPS_and_SitePrediction_/205627", "title"=>"Venn diagrams showing the prediction consistency between PROSPER, PoPS and SitePrediction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:33:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/535041"], "description"=>"<p>Given a local window of P8-P8′, there are twenty different types of feature descriptors in total, including the BEAA profile for each subsite from P8-P8′, and the BPBAA, BPBSS, BPBSA, and BPBDISO profiles. Here, caspase-3 was used as an example to calculate the MeanDecreaseAccuracy value for each feature type based on the feature set.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Biochemistry"], "article_id"=>205530, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g007", "stats"=>{"downloads"=>4, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_relative_importance_of_various_feature_descriptors_/205530", "title"=>"The relative importance of various feature descriptors.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:32:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/534613"], "description"=>"<p>In each panel, from the left to right, are the assignments at each position for secondary structure (three states: “H”, helix; “E”, strand; “C”, coil), solvent accessibility (two states: “e”, exposed; “b”, buried) and native disorder (two states: “*”, disordered; “.”, ordered), respectively. (A) caspase-1; (B) caspase-3; (C) caspase-7 and (D) caspase-6.</p>", "links"=>[], "tags"=>["determinants", "protease", "substrate", "specificity", "occurrences", "positions", "cleavage"], "article_id"=>205095, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Structural_determinants_of_protease_substrate_specificity_based_on_occurrences_at_the_P6_P6_positions_for_cleavage_sites_/205095", "title"=>"Structural determinants of protease substrate specificity based on occurrences at the P6-P6′ positions for cleavage sites.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:24:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/534309"], "description"=>"<p>Panels A to G correspond to caspase-1, 3, 7, 6, 8, granzyme B (human) and granzyme B (mouse), respectively. Heat map diagrams were rendered using the pro Fit program from QuantumSoft <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Schilling1\" target=\"_blank\">[15]</a>.</p>", "links"=>[], "tags"=>["occurrences", "positions", "cleavage", "sites", "granzyme", "displayed", "two-dimensional"], "article_id"=>204793, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Amino_acid_occurrences_in_P6_P6_positions_for_the_cleavage_sites_of_caspase_1_3_7_6_8_granzyme_B_human_and_granzyme_B_mouse_displayed_in_the_form_of_a_two_dimensional_heat_map_/204793", "title"=>"Amino acid occurrences in P6-P6′ positions for the cleavage sites of caspase-1, 3, 7, 6, 8, granzyme B (human) and granzyme B (mouse), displayed in the form of a two-dimensional heat map.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:19:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/535572"], "description"=>"a<p>Substrate datasets extracted from a recent update of the MEROPS database <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Rawlings1\" target=\"_blank\">[34]</a>, <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Rawlings2\" target=\"_blank\">[35]</a>;</p>b<p>Substrate dataset extracted from experimental data derived from N-terminal positional proteomics <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Kleifeld1\" target=\"_blank\">[20]</a>;</p>c<p>“-” denotes that the prediction result for this protease family is not available for this tool.</p><p>Note that these data are only available for a few proteases, including caspase-3, MMP-2, granzyme B (human) and granzyme B (mouse). The accuracy or sensitivity was calculated as the percentage of the known cleavage sites that were correctly predicted.</p>", "links"=>[], "tags"=>["prosper", "pops", "siteprediction", "datasets", "extracted", "proteomics", "profiling", "studies", "merops"], "article_id"=>206050, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t004", "stats"=>{"downloads"=>1, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Performance_comparison_of_PROSPER_with_PoPS_and_SitePrediction_based_on_independent_test_datasets_extracted_from_recent_proteomics_profiling_studies_and_a_recent_update_of_the_MEROPS_database_/206050", "title"=>"Performance comparison of PROSPER with PoPS and SitePrediction based on independent test datasets extracted from recent proteomics profiling studies and a recent update of the MEROPS database.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:40:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/535706"], "description"=>"a<p>“-” denotes that the prediction result at this specificity level is not available by this tool.</p><p>Specificity (Sp) levels were set as close as possible to one another among the different tools. For PROSPER, 5-fold cross-validation ± feature selection and self-consistency tests were performed to compare to PoPS and SitePrediction. The 3 different types of PROSPER models built are named PROSPER<sup>5CV</sup>, PROSPER<sup>select</sup> and PROSPER<sup>Self</sup>, respectively. Sensitivity (Sn) values at different Sp levels were compared. The best prediction performance at each Specificity level is highlighted in bold.</p>", "links"=>[], "tags"=>["prosper", "pops", "siteprediction", "predicting", "cleavage", "sites"], "article_id"=>206192, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t003", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Performance_comparison_of_PROSPER_with_PoPS_and_SitePrediction_for_predicting_cleavage_sites_for_selected_enzymes_/206192", "title"=>"Performance comparison of PROSPER with PoPS and SitePrediction for predicting cleavage sites for selected enzymes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:43:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/534157"], "description"=>"<p>There are several stages: (A) training datasets and independent test dataset of protease substrates were extracted from multiple resources. These included major comprehensive databases such as MEROPS, CutDB and PMAP, as well as recent proteome-wide profiling studies or the literature. (B) Useful sequence and structure features flanking the cleavage sites were derived and investigated, including local amino acid sequences, predicted secondary structure, solvent accessibility and native disorder. (C) The derived sequence and structural features were entered, following which cleavage probability models were built based on support vector regression (SVR) from the training dataset. In particular, the bi-profile Bayesian feature extraction was applied to extract and integrate the derived features into SVR models, which have been shown to be able to further improve prediction performance. (D) After building the PROSPER models, substrate sequence scanning predictions were made, and (E) PROSPER was further validated using a set of recently identified novel substrates reported in the literature or experimentally verified using positional proteomic approaches.</p>", "links"=>[], "tags"=>["overview", "prosper"], "article_id"=>204647, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g001", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Schematic_overview_of_the_PROSPER_approach_/204647", "title"=>"Schematic overview of the PROSPER approach.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:17:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/534433"], "description"=>"<p>Panels A–G correspond to caspase-1, 3, 7, 6, 8, granzyme B (human) and granzyme B (mouse), respectively. Here, extended window sizes for the P8-P8′ sites were examined in order to cover more specificity determining positions. The sequence logo diagrams were generated using the WebLogo program <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Schneider1\" target=\"_blank\">[96]</a>. To better reflect the occurrence rate of each amino acid type, the sequence logo ordinates have been scaled in bits.</p>", "links"=>[], "tags"=>["logo", "representations", "occurrences", "amino", "residues", "cleavage", "positions", "granzyme"], "article_id"=>204910, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g003", "stats"=>{"downloads"=>1, "page_views"=>68, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Sequence_logo_representations_of_the_occurrences_of_amino_acid_residues_in_the_cleavage_site_P8_P8_positions_of_caspase_1_3_7_6_8_granzyme_B_human_and_granzyme_B_mouse_/204910", "title"=>"Sequence logo representations of the occurrences of amino acid residues in the cleavage site P8-P8′ positions of caspase-1, 3, 7, 6, 8, granzyme B (human) and granzyme B (mouse).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:21:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/535518"], "description"=>"<p>The significantly enriched GO terms of the predicted caspase-3 substrates according to three major categories (Molecular Function, Biological Process and Cellular Component) were obtained using the gene list enrichment analysis tool, ToppFun <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050300#pone.0050300-Chen4\" target=\"_blank\">[86]</a>. The <i>P</i>-value of each GO term in the predicted substrates was calculated by randomly sampling the whole genome.</p>", "links"=>[], "tags"=>["enriched", "ontology", "caspase-3"], "article_id"=>206012, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t006", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_significantly_enriched_Gene_Ontology_GO_terms_of_the_predicted_caspase_3_substrates_/206012", "title"=>"The significantly enriched Gene Ontology (GO) terms of the predicted caspase-3 substrates.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:40:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/535401"], "description"=>"<p>The horizontal axis represents the amino acid position; the vertical axis represents the cleavage probability score generated by PROSPER. The predicted solvent exposed and natively disordered regions on the top of each figure are highlighted by green and red, respectively. A higher threshold value of 0.8 for making the positive cleavage site prediction is denoted by a dashed red line. P4-P4′ sites of the experimentally verified cleavage sites are labeled.</p>", "links"=>[], "tags"=>["scanning", "huntingtin", "prosper", "caspase-3", "cleavage", "zoomed-in", "flanking", "experimentally", "verified", "sites"], "article_id"=>205881, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g010", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Full_length_sequence_scanning_of_huntingtin_protein_by_PROSPER_for_caspase_3_cleavage_sites_with_the_zoomed_in_view_of_the_region_flanking_experimentally_verified_cleavage_sites_of_huntingtin_/205881", "title"=>"Full-length sequence scanning of huntingtin protein by PROSPER for caspase-3 cleavage sites, with the zoomed-in view of the region flanking experimentally verified cleavage sites of huntingtin.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:38:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/535247"], "description"=>"<p>A sample output from the PROSPER server for the substrate, serine/arginine-rich splicing factor 1 (SRSF1) (Uniprot ID: Q07955).</p>", "links"=>[], "tags"=>["prosper", "server", "splicing"], "article_id"=>205738, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g009", "stats"=>{"downloads"=>0, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/A_sample_output_from_the_PROSPER_server_for_the_substrate_serine_arginine_rich_splicing_factor_1_SRSF1_Uniprot_ID_Q07955_/205738", "title"=>"A sample output from the PROSPER server for the substrate, serine/arginine-rich splicing factor 1 (SRSF1) (Uniprot ID: Q07955).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:35:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/534731"], "description"=>"<p>For clarity, the ROC curves with high prediction specificities were displayed. Panels A–H correspond to caspase-3, 7, 6, 8, granzyme B (human), granzyme B (mouse), MMP-2 and MMP-3, respectively. Yellow: ROC curves of the trained PROSPER models based on the sequence encoding scheme “BEAA” which includes the binary encoding amino acid sequence profile surrounding the cleavage site; green: ROC curves based on the sequence encoding scheme “BEAA+BPBSS”, which includes the binary encoding amino acid sequence profile plus the bi-profile Bayesian secondary structure profile; blue: ROC curves based on the sequence encoding scheme “BEAA+BPBSA”, which includes the binary encoding amino acid sequence profile plus the bi-profile Bayesian solvent accessibility profile; cyan: ROC curves based on the sequence encoding scheme “BEAA+BPBDISO”, which includes the binary encoding amino acid sequence profile and bi-profile Bayesian native disorder profile; orange: ROC curves based on the sequence encoding scheme “BEAA+BPBAA+BPBSS+BPBSA+BPBDISO”, which includes all of the relevant features; red: ROC curves based on the most informative features as selected by a random forest algorithm.</p>", "links"=>[], "tags"=>["prosper", "models", "cleavage"], "article_id"=>205221, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.g005", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Assessing_the_performance_of_PROSPER_models_for_cleavage_site_prediction_of_eight_proteases_based_on_gradually_increased_features_to_evaluate_the_relative_contribution_of_each_type_of_feature_/205221", "title"=>"Assessing the performance of PROSPER models for cleavage site prediction of eight proteases, based on gradually increased features to evaluate the relative contribution of each type of feature.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-11-29 01:27:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/535608"], "description"=>"<p>This specificity level was used to generate high-confidence prediction results.</p>", "links"=>[], "tags"=>["substrate", "cleavage", "predictions", "specificity"], "article_id"=>206099, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t005", "stats"=>{"downloads"=>0, "page_views"=>36, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Proteome_wide_substrate_cleavage_site_predictions_at_the_100_Specificity_level_by_PROSPER_/206099", "title"=>"Proteome-wide substrate cleavage site predictions at the 100% Specificity level by PROSPER.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:41:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/535660"], "description"=>"<p>Performance of PROSPER for predicting cleavage sites of 24 protease families under consideration in this study, measured by Accuracy, Sensitivity, Specificity, F-score and MCC, respectively.</p>", "links"=>[], "tags"=>["prosper", "predicting", "cleavage", "sites", "24", "protease", "f-score"], "article_id"=>206148, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t002", "stats"=>{"downloads"=>3, "page_views"=>17, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Performance_of_PROSPER_for_predicting_cleavage_sites_of_24_protease_families_under_consideration_in_this_study_measured_by_Accuracy_Sensitivity_Specificity_F_score_and_MCC_respectively_/206148", "title"=>"Performance of PROSPER for predicting cleavage sites of 24 protease families under consideration in this study, measured by Accuracy, Sensitivity, Specificity, F-score and MCC, respectively.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:42:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/535736"], "description"=>"<p>“†” indicates the substrate cleavage site after the P1 position.</p>", "links"=>[], "tags"=>["cleavage", "protease", "substrates", "merops"], "article_id"=>206226, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0050300.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Summary_of_the_number_type_and_the_P4_P4_cleavage_pattern_of_protease_substrates_as_described_by_the_MEROPS_database_/206226", "title"=>"Summary of the number, type and the P4-P4′ cleavage pattern of protease substrates as described by the MEROPS database.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-11-29 01:43:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/287843", "https://ndownloader.figshare.com/files/288517", "https://ndownloader.figshare.com/files/288473", "https://ndownloader.figshare.com/files/288410", "https://ndownloader.figshare.com/files/288344", "https://ndownloader.figshare.com/files/288286", "https://ndownloader.figshare.com/files/288224", "https://ndownloader.figshare.com/files/288151", "https://ndownloader.figshare.com/files/288079", "https://ndownloader.figshare.com/files/288018", "https://ndownloader.figshare.com/files/287986", "https://ndownloader.figshare.com/files/287956", "https://ndownloader.figshare.com/files/287922", "https://ndownloader.figshare.com/files/288660"], "description"=>"<div><p>The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s). Knowledge of the substrate specificity of a protease can dramatically improve our ability to predict its target protein substrates, but this information must be utilized in an effective manner in order to efficiently identify protein substrates by <em>in silico</em> approaches. To address this problem, we present PROSPER, an integrated feature-based server for <em>in silico</em> identification of protease substrates and their cleavage sites for twenty-four different proteases. PROSPER utilizes established specificity information for these proteases (derived from the MEROPS database) with a machine learning approach to predict protease cleavage sites by using different, but complementary sequence and structure characteristics. Features used by PROSPER include local amino acid sequence profile, predicted secondary structure, solvent accessibility and predicted native disorder. Thus, for proteases with known amino acid specificity, PROSPER provides a convenient, pre-prepared tool for use in identifying protein substrates for the enzymes. Systematic prediction analysis for the twenty-four proteases thus far included in the database revealed that the features we have included in the tool strongly improve performance in terms of cleavage site prediction, as evidenced by their contribution to performance improvement in terms of identifying known cleavage sites in substrates for these enzymes. In comparison with two state-of-the-art prediction tools, PoPS and SitePrediction, PROSPER achieves greater accuracy and coverage. To our knowledge, PROSPER is the first comprehensive server capable of predicting cleavage sites of multiple proteases within a single substrate sequence using machine learning techniques. It is freely available at <a href=\"http://lightning.med.monash.edu.au/PROSPER/\">http://lightning.med.monash.edu.au/PROSPER/</a>.</p> </div>", "links"=>[], "tags"=>["feature-based", "predicting", "protease", "substrate", "cleavage", "sites"], "article_id"=>116703, "categories"=>["Information And Computing Sciences", "Biological Sciences", "Biochemistry"], "users"=>["Jiangning Song", "Hao Tan", "Andrew J. Perry", "Tatsuya Akutsu", "Geoffrey I. Webb", "James C. Whisstock", "Robert N. Pike"], "doi"=>[nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil, nil], "stats"=>{"downloads"=>21, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/PROSPER_An_Integrated_Feature_Based_Tool_for_Predicting_Protease_Substrate_Cleavage_Sites/116703", "title"=>"PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-11-29 01:51:43"}

PMC Usage Stats | Further Information

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