Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in Aspergillus niger
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{"title"=>"Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in Aspergillus niger", "type"=>"journal", "authors"=>[{"first_name"=>"Bastiaan A.", "last_name"=>"van den Berg", "scopus_author_id"=>"36612378300"}, {"first_name"=>"Marcel J.T.", "last_name"=>"Reinders", "scopus_author_id"=>"7006221053"}, {"first_name"=>"Marc", "last_name"=>"Hulsman", "scopus_author_id"=>"32367703100"}, {"first_name"=>"Liang", "last_name"=>"Wu", "scopus_author_id"=>"55714344100"}, {"first_name"=>"Herman J.", "last_name"=>"Pel", "scopus_author_id"=>"6603543104"}, {"first_name"=>"Johannes A.", "last_name"=>"Roubos", "scopus_author_id"=>"56345940600"}, {"first_name"=>"Dick", "last_name"=>"de Ridder", "scopus_author_id"=>"7006928685"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"365767526", "sgr"=>"84866972942", "issn"=>"19326203", "pmid"=>"23049690", "scopus"=>"2-s2.0-84866972942", "doi"=>"10.1371/journal.pone.0045869", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"a5ba6593-22f6-30f9-9334-04dc02bdd9bb", "abstract"=>"Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in Aspergillus niger using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at http://bioinformatics.tudelft.nl/hipsec. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy.", "link"=>"http://www.mendeley.com/research/exploring-sequence-characteristics-related-highlevel-production-secreted-proteins-aspergillus-niger", "reader_count"=>48, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>15, "Student > Ph. D. Student"=>13, "Student > Master"=>7, "Other"=>5, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>1, "Student > Doctoral Student"=>1, "Researcher"=>15, "Student > Ph. D. Student"=>13, "Student > Master"=>7, "Other"=>5, "Student > Bachelor"=>3, "Professor"=>3}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>6, "Agricultural and Biological Sciences"=>30, "Medicine and Dentistry"=>2, "Chemistry"=>3, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>30}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>6}}, "reader_count_by_country"=>{"Colombia"=>2, "Czech Republic"=>1, "Turkey"=>1, "United Kingdom"=>1, "France"=>1, "Germany"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/566572"], "description"=>"<p>Amino acid contributions obtained from and trained classifiers are the - and -values respectively, the correlation is denoted by . Contributions are normalized per classifier (axis): each contribution is divided by the maximum absolute contribution. The plots show the contributions obtained from classifiers trained using <b>A)</b> the protein amino acid composition () and <b>B)</b> the predefined amino acid cluster composition ().</p>", "links"=>[], "tags"=>["Infectious diseases", "molecular biology", "biotechnology", "Computational biology", "cell biology", "Biochemistry"], "article_id"=>237063, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.g003", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_hom_and_het_classifiers_/237063", "title"=>"Comparing <i>hom</i> and <i>het</i> classifiers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-01 01:57:43"}
  • {"files"=>["https://ndownloader.figshare.com/files/566794"], "description"=>"<p>Predefined amino acid clusters.</p>", "links"=>[], "tags"=>["amino"], "article_id"=>237292, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.t001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predefined_amino_acid_clusters_/237292", "title"=>"Predefined amino acid clusters.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-01 02:01:32"}
  • {"files"=>["https://ndownloader.figshare.com/files/566830"], "description"=>"<p>Prediction performance scores (auroc).</p>", "links"=>[], "tags"=>["scores"], "article_id"=>237324, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.t002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_performance_scores_auroc_/237324", "title"=>"Prediction performance scores (auroc).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-10-01 02:02:04"}
  • {"files"=>["https://ndownloader.figshare.com/files/566485"], "description"=>"<p>ROC-curves of composition-based classifiers using the codon sequence (), the signal peptide sequence (), and the protein sequence (). Performances are shown for classifiers <b>A)</b> trained and tested on , <b>B)</b> trained and tested on , and <b>C)</b> trained on and tested on .</p>", "links"=>[], "tags"=>["Infectious diseases", "molecular biology", "biotechnology", "Computational biology", "cell biology", "Biochemistry"], "article_id"=>236974, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.g002", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_performances_/236974", "title"=>"Classification performances.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-01 01:56:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/566327"], "description"=>"<p>The different shades of gray denote predicted buried (B) and exposed (E) regions in case of the the solvent accessibility, and predicted helix (H), strand (E), and random coil (C) region in case of the secondary structure.</p>", "links"=>[], "tags"=>["sequence-based"], "article_id"=>236820, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.g001", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Different_sequence_based_protein_representations_/236820", "title"=>"Different sequence-based protein representations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-01 01:53:40"}
  • {"files"=>["https://ndownloader.figshare.com/files/566643"], "description"=>"<p>The heat maps show the combined result of the best performing clusters obtained in 10 CV-loops for both (<b>A</b>) and (<b>B</b>). The values on the diagonals denote how often an amino acid ended up in a cluster (due to selecting the optimal clusters, amino acids might not be selected at all). The colors on the non-diagonal places denote how often two amino acids ended up in the same cluster. Complete linkage hierarchical clustering was used to cluster the heat map, using the euclidean distance as distance measure. The color of the amino acid letters indicates if the amino acid has a positive (green) or negative (red) contribution in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0045869#pone-0045869-g003\" target=\"_blank\">Figure 3A</a>.</p>", "links"=>[], "tags"=>["performing", "amino"], "article_id"=>237141, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.g004", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Best_performing_amino_acid_clusters_/237141", "title"=>"Best performing amino acid clusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-01 01:59:01"}
  • {"files"=>["https://ndownloader.figshare.com/files/299866", "https://ndownloader.figshare.com/files/299909", "https://ndownloader.figshare.com/files/299951", "https://ndownloader.figshare.com/files/300016", "https://ndownloader.figshare.com/files/300079", "https://ndownloader.figshare.com/files/300136", "https://ndownloader.figshare.com/files/300187", "https://ndownloader.figshare.com/files/300239", "https://ndownloader.figshare.com/files/300296", "https://ndownloader.figshare.com/files/300339", "https://ndownloader.figshare.com/files/300381", "https://ndownloader.figshare.com/files/300492", "https://ndownloader.figshare.com/files/300513", "https://ndownloader.figshare.com/files/300528", "https://ndownloader.figshare.com/files/300548", "https://ndownloader.figshare.com/files/300567", "https://ndownloader.figshare.com/files/300588"], "description"=>"<div><p>Protein sequence features are explored in relation to the production of over-expressed extracellular proteins by fungi. Knowledge on features influencing protein production and secretion could be employed to improve enzyme production levels in industrial bioprocesses via protein engineering. A large set, over 600 homologous and nearly 2,000 heterologous fungal genes, were overexpressed in <em>Aspergillus niger</em> using a standardized expression cassette and scored for high versus no production. Subsequently, sequence-based machine learning techniques were applied for identifying relevant DNA and protein sequence features. The amino-acid composition of the protein sequence was found to be most predictive and interpretation revealed that, for both homologous and heterologous gene expression, the same features are important: tyrosine and asparagine composition was found to have a positive correlation with high-level production, whereas for unsuccessful production, contributions were found for methionine and lysine composition. The predictor is available online at <a href=\"http://bioinformatics.tudelft.nl/hipsec\">http://bioinformatics.tudelft.nl/hipsec</a>. Subsequent work aims at validating these findings by protein engineering as a method for increasing expression levels per gene copy.</p> </div>", "links"=>[], "tags"=>["exploring", "characteristics", "high-level", "secreted", "proteins"], "article_id"=>119126, "categories"=>["Biotechnology", "Cancer", "Molecular Biology", "Biochemistry", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0045869.s001", "https://dx.doi.org/10.1371/journal.pone.0045869.s002", "https://dx.doi.org/10.1371/journal.pone.0045869.s003", "https://dx.doi.org/10.1371/journal.pone.0045869.s004", "https://dx.doi.org/10.1371/journal.pone.0045869.s005", "https://dx.doi.org/10.1371/journal.pone.0045869.s006", "https://dx.doi.org/10.1371/journal.pone.0045869.s007", "https://dx.doi.org/10.1371/journal.pone.0045869.s008", "https://dx.doi.org/10.1371/journal.pone.0045869.s009", "https://dx.doi.org/10.1371/journal.pone.0045869.s010", "https://dx.doi.org/10.1371/journal.pone.0045869.s011", "https://dx.doi.org/10.1371/journal.pone.0045869.s012", "https://dx.doi.org/10.1371/journal.pone.0045869.s013", "https://dx.doi.org/10.1371/journal.pone.0045869.s014", "https://dx.doi.org/10.1371/journal.pone.0045869.s015", "https://dx.doi.org/10.1371/journal.pone.0045869.s016", "https://dx.doi.org/10.1371/journal.pone.0045869.s017"], "stats"=>{"downloads"=>118, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Exploring_Sequence_Characteristics_Related_to_High_Level_Production_of_Secreted_Proteins_in_Aspergillus_niger_/119126", "title"=>"Exploring Sequence Characteristics Related to High-Level Production of Secreted Proteins in <em>Aspergillus niger</em>", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-10-01 02:32:06"}
  • {"files"=>["https://ndownloader.figshare.com/files/566729"], "description"=>"<p>For the first three feature selection iterations (-axis), the bar plot shows how often features were selected in the 10 CV-loops for both (<b>A</b>) and (<b>B</b>). Features with a different shade of the same color are correlated (). The letters between brackets in the legend are amino acids that denote either which amino acids are in the cluster, e.g. the basic cluster contains amino acids R, K, and, H, or for which amino acid a codon encodes, e.g. codon TAC encodes for Y.</p>", "links"=>[], "tags"=>["Infectious diseases", "molecular biology", "biotechnology", "Computational biology", "cell biology", "Biochemistry"], "article_id"=>237214, "categories"=>["Molecular Biology", "Biochemistry", "Infectious Diseases", "Biotechnology", "Biological Sciences", "Cell Biology"], "users"=>["Bastiaan A. van den Berg", "Marcel J. T. Reinders", "Marc Hulsman", "Liang Wu", "Herman J. Pel", "Johannes A. Roubos", "Dick de Ridder"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0045869.g005", "stats"=>{"downloads"=>0, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Feature_selection_/237214", "title"=>"Feature selection.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-10-01 02:00:14"}

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Relative Metric

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