In Silico Approach for Predicting Toxicity of Peptides and Proteins
Publication Date
September 13, 2013
Journal
PLOS ONE
Authors
Sudheer Gupta, Pallavi Kapoor, Kumardeep Chaudhary, Ankur Gautam, et al
Volume
8
Issue
9
Pages
e73957
DOI
https://dx.plos.org/10.1371/journal.pone.0073957
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0073957
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24058508
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3772798
Europe PMC
http://europepmc.org/abstract/MED/24058508
Web of Science
000324408400047
Scopus
84884141339
Mendeley
http://www.mendeley.com/research/silico-approach-predicting-toxicity-peptides-proteins
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Mendeley | Further Information

{"title"=>"In Silico Approach for Predicting Toxicity of Peptides and Proteins", "type"=>"journal", "authors"=>[{"first_name"=>"Sudheer", "last_name"=>"Gupta", "scopus_author_id"=>"55581149100"}, {"first_name"=>"Pallavi", "last_name"=>"Kapoor", "scopus_author_id"=>"55189775400"}, {"first_name"=>"Kumardeep", "last_name"=>"Chaudhary", "scopus_author_id"=>"55189567100"}, {"first_name"=>"Ankur", "last_name"=>"Gautam", "scopus_author_id"=>"50461268500"}, {"first_name"=>"Rahul", "last_name"=>"Kumar", "scopus_author_id"=>"57200450925"}, {"first_name"=>"Gajendra P.S.", "last_name"=>"Raghava", "scopus_author_id"=>"7003507401"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"369813431", "issn"=>"19326203", "isbn"=>"1932-6203", "doi"=>"10.1371/journal.pone.0073957", "scopus"=>"2-s2.0-84884141339", "pmid"=>"24058508", "sgr"=>"84884141339"}, "id"=>"2e513386-981a-356c-8643-dfb77e60c98d", "abstract"=>"BACKGROUND: Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed in silico models for predicting toxicity of peptides and proteins.\\n\\nDESCRIPTION: We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.\\n\\nCONCLUSION: ToxinPred is a unique in silico method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (http://crdd.osdd.net/raghava/toxinpred/).", "link"=>"http://www.mendeley.com/research/silico-approach-predicting-toxicity-peptides-proteins", "reader_count"=>86, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>4, "Other"=>6, "Student > Master"=>13, "Student > Bachelor"=>7, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>2, "Researcher"=>23, "Student > Doctoral Student"=>7, "Student > Ph. D. Student"=>20, "Student > Postgraduate"=>4, "Other"=>6, "Student > Master"=>13, "Student > Bachelor"=>7, "Professor"=>2}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Environmental Science"=>2, "Biochemistry, Genetics and Molecular Biology"=>12, "Agricultural and Biological Sciences"=>44, "Medicine and Dentistry"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>2, "Chemistry"=>6, "Computer Science"=>8, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Chemistry"=>{"Chemistry"=>6}, "Physics and Astronomy"=>{"Physics and Astronomy"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>44}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>12}, "Unspecified"=>{"Unspecified"=>4}, "Environmental Science"=>{"Environmental Science"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"Colombia"=>1, "United States"=>1, "France"=>1, "India"=>2}, "group_count"=>9}

CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1204094"], "description"=>"<p>Maximum and minimum scoring residues at every position as observed in quantitative matrix (main dataset).</p>", "links"=>[], "tags"=>["scoring", "residues", "observed", "quantitative", "matrix"], "article_id"=>799131, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Sudheer Gupta", "Pallavi Kapoor", "Kumardeep Chaudhary", "Ankur Gautam", "Rahul Kumar", "Gajendra P. S. Raghava"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073957.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maximum_and_minimum_scoring_residues_at_every_position_as_observed_in_quantitative_matrix_main_dataset_/799131", "title"=>"Maximum and minimum scoring residues at every position as observed in quantitative matrix (main dataset).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-13 01:51:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1204111"], "description"=>"<div><p>Background</p><p>Over the past few decades, scientific research has been focused on developing peptide/protein-based therapies to treat various diseases. With the several advantages over small molecules, including high specificity, high penetration, ease of manufacturing, peptides have emerged as promising therapeutic molecules against many diseases. However, one of the bottlenecks in peptide/protein-based therapy is their toxicity. Therefore, in the present study, we developed <i>in silico</i> models for predicting toxicity of peptides and proteins.</p><p>Description</p><p>We obtained toxic peptides having 35 or fewer residues from various databases for developing prediction models. Non-toxic or random peptides were obtained from SwissProt and TrEMBL. It was observed that certain residues like Cys, His, Asn, and Pro are abundant as well as preferred at various positions in toxic peptides. We developed models based on machine learning technique and quantitative matrix using various properties of peptides for predicting toxicity of peptides. The performance of dipeptide-based model in terms of accuracy was 94.50% with MCC 0.88. In addition, various motifs were extracted from the toxic peptides and this information was combined with dipeptide-based model for developing a hybrid model. In order to evaluate the over-optimization of the best model based on dipeptide composition, we evaluated its performance on independent datasets and achieved accuracy around 90%. Based on above study, a web server, ToxinPred has been developed, which would be helpful in predicting (i) toxicity or non-toxicity of peptides, (ii) minimum mutations in peptides for increasing or decreasing their toxicity, and (iii) toxic regions in proteins.</p><p>Conclusion</p><p>ToxinPred is a unique <i>in silico</i> method of its kind, which will be useful in predicting toxicity of peptides/proteins. In addition, it will be useful in designing least toxic peptides and discovering toxic regions in proteins. We hope that the development of ToxinPred will provide momentum to peptide/protein-based drug discovery (<a href=\"http://crdd.osdd.net/raghava/toxinpred/\" target=\"_blank\">http://crdd.osdd.net/raghava/toxinpred/</a>).</p></div>", "links"=>[], "tags"=>["predicting", "toxicity", "peptides", "proteins"], "article_id"=>799148, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Sudheer Gupta", "Pallavi Kapoor", "Kumardeep Chaudhary", "Ankur Gautam", "Rahul Kumar", "Gajendra P. S. Raghava"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073957", "stats"=>{"downloads"=>3, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_In_Silico_Approach_for_Predicting_Toxicity_of_Peptides_and_Proteins/799148", "title"=>"<i>In Silico</i> Approach for Predicting Toxicity of Peptides and Proteins", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-13 01:51:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1204106"], "description"=>"<p>MCC, Matthew’s correlation coefficient; AUC, area under the curve.</p>", "links"=>[], "tags"=>["developed", "motifs", "dipeptide"], "article_id"=>799143, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Sudheer Gupta", "Pallavi Kapoor", "Kumardeep Chaudhary", "Ankur Gautam", "Rahul Kumar", "Gajendra P. S. Raghava"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073957.t004", "stats"=>{"downloads"=>0, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_performance_of_model_developed_using_motifs_and_dipeptide_composition_on_main_dataset_/799143", "title"=>"The performance of model developed using motifs and dipeptide composition on main dataset.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-13 01:51:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/1204105"], "description"=>"<p>MCC, Matthew’s correlation coefficient; AUC, area under the curve.</p>", "links"=>[], "tags"=>["quantitative", "matix"], "article_id"=>799142, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Sudheer Gupta", "Pallavi Kapoor", "Kumardeep Chaudhary", "Ankur Gautam", "Rahul Kumar", "Gajendra P. S. Raghava"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073957.t005", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_performance_of_quantitative_matix_based_method_on_various_datasets_/799142", "title"=>"The performance of quantitative matix based method on various datasets.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-09-13 01:51:46"}
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  • {"files"=>["https://ndownloader.figshare.com/files/1204089"], "description"=>"<p>Comparison of average amino acid composition between various classes of therapeutic peptides.</p>", "links"=>[], "tags"=>["amino", "classes", "therapeutic"], "article_id"=>799126, "categories"=>["Biological Sciences", "Information And Computing Sciences"], "users"=>["Sudheer Gupta", "Pallavi Kapoor", "Kumardeep Chaudhary", "Ankur Gautam", "Rahul Kumar", "Gajendra P. S. Raghava"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0073957.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_average_amino_acid_composition_between_various_classes_of_therapeutic_peptides_/799126", "title"=>"Comparison of average amino acid composition between various classes of therapeutic peptides.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-09-13 01:51:46"}
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PMC Usage Stats | Further Information

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Relative Metric

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