Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity
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{"title"=>"Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity", "type"=>"journal", "authors"=>[{"first_name"=>"Catherine", "last_name"=>"Mooney", "scopus_author_id"=>"56251250500"}, {"first_name"=>"Niall J.", "last_name"=>"Haslam", "scopus_author_id"=>"9741796400"}, {"first_name"=>"Gianluca", "last_name"=>"Pollastri", "scopus_author_id"=>"55902203700"}, {"first_name"=>"Denis C.", "last_name"=>"Shields", "scopus_author_id"=>"7102451013"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84867323770", "pui"=>"365821670", "doi"=>"10.1371/journal.pone.0045012", "isbn"=>"1932-6203", "sgr"=>"84867323770", "pmid"=>"23056189"}, "id"=>"28afce73-84ad-3cb3-a3ae-01ec486eb76b", "abstract"=>"The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides.We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4-20 amino acids) and one focused on long peptides (> 20 amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure.We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive.", "link"=>"http://www.mendeley.com/research/towards-improved-discovery-design-functional-peptides-common-features-diverse-classes-permit-general", "reader_count"=>64, "reader_count_by_academic_status"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>14, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>3, "Professor > Associate Professor"=>2, "Researcher"=>11, "Student > Doctoral Student"=>3, "Student > Ph. D. Student"=>16, "Student > Postgraduate"=>2, "Student > Master"=>14, "Other"=>3, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>4, "Unspecified"=>6, "Biochemistry, Genetics and Molecular Biology"=>9, "Agricultural and Biological Sciences"=>34, "Business, Management and Accounting"=>1, "Chemical Engineering"=>1, "Chemistry"=>3, "Computer Science"=>4, "Linguistics"=>1, "Medicine and Dentistry"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>4}, "Chemistry"=>{"Chemistry"=>3}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>34}, "Computer Science"=>{"Computer Science"=>4}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Linguistics"=>{"Linguistics"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>9}, "Unspecified"=>{"Unspecified"=>6}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "reader_count_by_country"=>{"Ireland"=>1, "Poland"=>1, "Israel"=>1}, "group_count"=>3}

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Figshare

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  • {"files"=>["https://ndownloader.figshare.com/files/561780"], "description"=>"<p>Comparison of PeptideRanker (measured at a threshold of 0.5), CAMP and AntiBP2 tested on the independent test set. (A) long peptides (B) short peptides (C) MCC for long and short peptides. AntiBP2 did not return predictions for 234 (out of a total of 946) of the long and 392 (out of a total of 532) of the short peptides. CAMP did not return predictions for 6 of the long and 5 of the short peptides.</p>", "links"=>[], "tags"=>["biotechnology", "Computational biology", "computer science", "pharmacology", "Biochemistry"], "article_id"=>232273, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Independent_test_set_/232273", "title"=>"Independent test set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:37:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/561831"], "description"=>"<p>(A) long peptides (B) short peptides.</p>", "links"=>[], "tags"=>["bioactive", "non-secreted", "peptides", "secreted"], "article_id"=>232320, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Amino_acid_content_of_the_bioactive_peptides_the_non_secreted_control_peptides_and_the_secreted_control_peptides_in_the_independent_test_dataset_/232320", "title"=>"Amino acid content of the bioactive peptides, the non-secreted control peptides and the secreted control peptides in the independent test dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:38:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/561891"], "description"=>"<p>(A) long peptides (B) short peptides.</p>", "links"=>[], "tags"=>["peptide", "classes"], "article_id"=>232388, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Amino_acid_content_of_the_three_peptide_activity_classes_from_the_PeptideDB_70_dataset_/232388", "title"=>"Amino acid content of the three peptide activity classes from the PeptideDB.70 dataset.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:39:48"}
  • {"files"=>["https://ndownloader.figshare.com/files/561997"], "description"=>"<p>(A) 88 long antimicrobial peptides (B) 243 long peptide hormones (C) 81 long toxin/venom peptides (D) 29 short antimicrobial peptides (E) 50 short peptide hormones (F) 34 short toxin/venom peptides. P-value in all six cases.</p>", "links"=>[], "tags"=>["plots", "peptide", "classes", "subset"], "article_id"=>232490, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_plots_for_long_and_short_peptide_activity_classes_of_the_PeptidesDB_70_subset_of_the_independent_test_set_/232490", "title"=>"ROC plots for long and short peptide activity classes of the PeptidesDB.70 subset of the independent test set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:41:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/562071"], "description"=>"<p>(A) long peptides (B) short peptides.</p>", "links"=>[], "tags"=>["coefficient", "observed", "states", "peptide"], "article_id"=>232566, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Matthews_Correlation_Coefficient_MCC_between_observed_and_predicted_states_for_the_long_and_short_peptide_activity_classes_/232566", "title"=>"Matthews Correlation Coefficient (MCC) between observed and predicted states for the long and short peptide activity classes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-08 00:42:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/562119"], "description"=>"<p>Net charge: sum of positively (K and R) and negatively (D and E) charged residues for each peptide. Absolute net charge: modulus of net charge for each peptide.</p>", "links"=>[], "tags"=>["peptide"], "article_id"=>232618, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variability_in_charge_across_the_three_peptide_activity_classes_/232618", "title"=>"Variability in charge across the three peptide activity classes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:43:38"}
  • {"files"=>["https://ndownloader.figshare.com/files/562156"], "description"=>"<p>Specificity (Spec), Sensitivity (Sens) and False Positive Rate (FPR) measured for the bioactive peptide and control peptide classes. Accuracy () and Matthews Correlation Coefficient (MCC) are measured over all peptides, bioactive peptides and control peptides. See the Materials and Methods section for definitions.</p>", "links"=>[], "tags"=>["peptideranker", "five-fold", "cross-validation", "datasets"], "article_id"=>232650, "categories"=>["Information And Computing Sciences", "Biotechnology", "Biological Sciences", "Biochemistry", "Pharmacology"], "users"=>["Catherine Mooney", "Niall J. Haslam", "Gianluca Pollastri", "Denis C. Shields"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045012.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Performance_of_PeptideRanker_measured_in_five_fold_cross_validation_on_the_training_datasets_at_a_threshold_of_0_5_/232650", "title"=>"Performance of PeptideRanker measured in five-fold cross-validation on the training datasets at a threshold of 0.5.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-08 00:44:10"}

PMC Usage Stats | Further Information

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Relative Metric

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