Text Mining Improves Prediction of Protein Functional Sites
Publication Date
February 29, 2012
Journal
PLOS ONE
Authors
Karin M. Verspoor, Judith D. Cohn, Komandur E. Ravikumar & Michael E. Wall
Volume
7
Issue
2
Pages
e32171
DOI
https://dx.plos.org/10.1371/journal.pone.0032171
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0032171
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22393388
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3290545
Europe PMC
http://europepmc.org/abstract/MED/22393388
Web of Science
000303003500041
Scopus
84857664634
Mendeley
http://www.mendeley.com/research/text-mining-improves-prediction-protein-functional-sites
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CiteULike | Further Information

Mendeley | Further Information

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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/673933"], "description"=>"<p>Text mining for residue mentions is performed on the abstracts from the primary references cited in the PDB. A text residue is represented by a residue number and one or more 3-letter amino acid codes. An amino acid for a text residue is marked with an asterisk if a text occurrence suggests it is a mutation from wild type. Physical residues corresponding to a text residue are indicated using a PDB ID, chain identifier, residue number, and 3-letter amino acid code.</p>", "links"=>[], "tags"=>["extracting", "residue", "mentions", "residues"], "article_id"=>344418, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g004", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Illustration_of_the_process_for_extracting_residue_mentions_and_mapping_them_to_physical_residues_in_protein_structures_/344418", "title"=>"Illustration of the process for extracting residue mentions and mapping them to physical residues in protein structures.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:13:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/344647", "https://ndownloader.figshare.com/files/344672"], "description"=>"<div><p>We present an approach that integrates protein structure analysis and text mining for protein functional site prediction, called LEAP-FS (Literature Enhanced Automated Prediction of Functional Sites). The structure analysis was carried out using Dynamics Perturbation Analysis (DPA), which predicts functional sites at control points where interactions greatly perturb protein vibrations. The text mining extracts mentions of residues in the literature, and predicts that residues mentioned are functionally important. We assessed the significance of each of these methods by analyzing their performance in finding known functional sites (specifically, small-molecule binding sites and catalytic sites) in about 100,000 publicly available protein structures. The DPA predictions recapitulated many of the functional site annotations and preferentially recovered binding sites annotated as biologically relevant vs. those annotated as potentially spurious. The text-based predictions were also substantially supported by the functional site annotations: compared to other residues, residues mentioned in text were roughly six times more likely to be found in a functional site. The overlap of predictions with annotations improved when the text-based and structure-based methods agreed. Our analysis also yielded new high-quality predictions of many functional site residues that were not catalogued in the curated data sources we inspected. We conclude that both DPA and text mining independently provide valuable high-throughput protein functional site predictions, and that integrating the two methods using LEAP-FS further improves the quality of these predictions.</p> </div>", "links"=>[], "tags"=>["mining", "improves", "sites"], "article_id"=>128132, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0032171.s001", "https://dx.doi.org/10.1371/journal.pone.0032171.s002"], "stats"=>{"downloads"=>3, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Text_Mining_Improves_Prediction_of_Protein_Functional_Sites/128132", "title"=>"Text Mining Improves Prediction of Protein Functional Sites", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-02-29 02:15:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/674482"], "description"=>"a<p>Family-level NSM annotation available.</p>b<p>Family-level NSM-valid annotation available.</p>", "links"=>[], "tags"=>["linked", "residue", "mentions", "high-quality"], "article_id"=>344970, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.t002", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_text_linked_to_residue_mentions_for_a_random_sample_of_high_quality_predictions_/344970", "title"=>"Summary of text linked to residue mentions for a random sample of high-quality predictions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-29 01:22:50"}
  • {"files"=>["https://ndownloader.figshare.com/files/674516"], "description"=>"<p>Glossary of terms and symbols.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Biochemistry"], "article_id"=>345004, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.t003", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Glossary_of_terms_and_symbols_/345004", "title"=>"Glossary of terms and symbols.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-29 01:23:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/673835"], "description"=>"<p>Three corpora were evaluated. <i>E<sub>1</sub></i> contains only annotations of mutations, and is subdivided into development (<i>E<sub>1_dev</sub></i>) and test (<i>E<sub>1_test</sub></i>) corpora. <i>E<sub>2</sub></i> and <i>E<sub>3</sub></i> include annotations of residues and mutations. <i>E<sub>2</sub></i> consists of abstracts only, while <i>E<sub>3</sub></i> consists of full text articles.</p>", "links"=>[], "tags"=>["mining", "extracting", "residue", "mentions", "mutation"], "article_id"=>344322, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g003", "stats"=>{"downloads"=>0, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Intrinsic_evaluation_of_the_text_mining_system_for_extracting_residue_mentions_and_or_mutation_mentions_/344322", "title"=>"Intrinsic evaluation of the text mining system for extracting residue mentions and/or mutation mentions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:12:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/674010"], "description"=>"<p>The number of abstracts with residue mentions is comparable when further constrained to those residues that can be mapped to physical residues in protein structures.</p>", "links"=>[], "tags"=>["text-extracted", "residue", "mentions", "corpus", "abstracts"], "article_id"=>344494, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g005", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Availability_of_text_extracted_residue_mentions_in_the_corpus_of_abstracts_C_/344494", "title"=>"Availability of text-extracted residue mentions in the corpus of abstracts <i>C</i>.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:14:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/674131"], "description"=>"<p>Annotation types and description of stacked bars for NSM, NSM-valid, CSA, and Curated is as described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032171#pone-0032171-g001\" target=\"_blank\">Figure 1</a>.</p>", "links"=>[], "tags"=>["physically-verified", "residues", "annotation"], "article_id"=>344621, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g006", "stats"=>{"downloads"=>5, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overlap_of_physically_verified_text_residues_with_existing_annotation_sets_/344621", "title"=>"Overlap of physically-verified text residues with existing annotation sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:17:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/673559"], "description"=>"<p>Residues in protein domains were annotated using the following sources: 1) NSM, 2) NSM-valid, 3) CSA, and 4) text residue. A domain is labeled as annotated if one or more residues in the domain have an appropriate annotation. Stacked bars for each of the sources include cumulative numbers for direct annotations, annotations transferred at the protein level, and annotations transferred at the family level. The vertical order of the legend reflects the vertical order of the bars.</p>", "links"=>[], "tags"=>["annotations"], "article_id"=>344049, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g001", "stats"=>{"downloads"=>5, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Availability_of_annotations_for_protein_domains_/344049", "title"=>"Availability of annotations for protein domains.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:07:29"}
  • {"files"=>["https://ndownloader.figshare.com/files/673663"], "description"=>"<p>(A) Coverage of NSM-valid sites by predictions is enriched and coverage of NSM-invalid sites is suppressed with respect to all NSM sites. (B) Sets of NSM sites with a text match are enriched in overlaps with predictions. Comparisons are made for all NSM sites, NSM-valid sites, NSM sites containing a CSA annotation, and NSM-valid sites containing a CSA annotation.</p>", "links"=>[], "tags"=>["sets", "nsm", "sites"], "article_id"=>344150, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Coverage_of_sets_of_NSM_sites_by_functional_site_predictions_/344150", "title"=>"Coverage of sets of NSM sites by functional site predictions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:09:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/674236"], "description"=>"<p>(A) Fraction of NSM sites satisfying various thresholds of recall. (B) Fraction of DPA predictions satisfying various thresholds of precision. The performance is compared using NSM vs. NSM-valid sites, and with or without text matches. The precise definitions of the recall and precision are described in the <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0032171#s3\" target=\"_blank\">Methods</a>.</p>", "links"=>[], "tags"=>["Computational biology", "computer science", "Biochemistry"], "article_id"=>344727, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g007", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Residue_wise_performance_of_functional_site_predictions_/344727", "title"=>"Residue-wise performance of functional site predictions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:18:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/674368"], "description"=>"<p>The protein is displayed in wall-eyed stereo pairs as a light blue ribbon with a semitransparent surface. The predicted functional site from structure-based analysis is colored yellow, and the predicted residues mentioned in the abstract of the primary reference are rendered as sticks colored orange (His130) and magenta (Asp168).</p>", "links"=>[], "tags"=>["pdb", "1yk3"], "article_id"=>344858, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.g008", "stats"=>{"downloads"=>5, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_High_quality_functional_site_prediction_with_text_evidence_for_PDB_entry_1YK3_40_/344858", "title"=>"High-quality functional site prediction with text evidence for PDB entry 1YK3 [<b>40</b>].", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-02-29 01:20:58"}
  • {"files"=>["https://ndownloader.figshare.com/files/674446"], "description"=>"a<p>Each entry in the table contains a number of DPA predictions that have a given type of annotation. Percentages are with respect to the numbers in the four subsets of DPA predictions indicated at the head of each of column. The subsets are increasingly restrictive from left to right: predictions in a domain for which a transfer MSA is available; predictions in a domain for which a conservation MSA is available; predictions for which the conservation P-value is no greater than 10<sup>−2</sup>; and predictions for which the conservation P-value is no greater than 10<sup>−3</sup>. Annotations are organized into three major sections that are increasingly expansive from top to bottom, as described in the text: direct annotations, protein-level annotations, and family-level annotations. The meaning of the NSM, NSM-valid, CSA, and Text annotations is as described in the text.</p>", "links"=>[], "tags"=>["dpa", "predictions"], "article_id"=>344938, "categories"=>["Biochemistry", "Biological Sciences", "Information And Computing Sciences"], "users"=>["Karin M. Verspoor", "Judith D. Cohn", "Komandur E. Ravikumar", "Michael E. Wall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0032171.t001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correspondence_of_DPA_predictions_to_annotations_a_/344938", "title"=>"Correspondence of DPA predictions to annotations.<sup>a</sup>", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-02-29 01:22:18"}

PMC Usage Stats | Further Information

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Relative Metric

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