Mining the Structural Genomics Pipeline: Identification of Protein Properties that Affect High-throughput Experimental Analysis
Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able...
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Published in | Journal of molecular biology Vol. 336; no. 1; pp. 115 - 130 |
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Main Authors | , , , , , , , , , |
Format | Journal Article |
Language | English |
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England
Elsevier Ltd
06.02.2004
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Abstract | Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized “pipeline schematics”. We find that the properties of a protein that are most significant are: (i) whether it is conserved across many organisms; (ii) the percentage composition of charged residues; (iii) the occurrence of hydrophobic patches; (iv) the number of binding partners it has; and (v) its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from
http://mining.nesg.org/. |
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AbstractList | Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized "pipeline schematics". We find that the properties of a protein that are most significant are: (i.) whether it is conserved across many organisms; (ii). the percentage composition of charged residues; (iii). the occurrence of hydrophobic patches; (iv). the number of binding partners it has; and (v). its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/. Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized 'pipeline schematics'. We find that the properties of a protein that are most significant are: (i) whether it is conserved across many organisms; (ii) the percentage composition of charged residues; (iii) the occurrence of hydrophobic patches; (iv) the number of binding partners it has; and (v) its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from Structural genomics projects represent major undertakings that will change our understanding of proteins. They generate unique datasets that, for the first time, present a standardized view of proteins in terms of their physical and chemical properties. By analyzing these datasets here, we are able to discover correlations between a protein's characteristics and its progress through each stage of the structural genomics pipeline, from cloning, expression, purification, and ultimately to structural determination. First, we use tree-based analyses (decision trees and random forest algorithms) to discover the most significant protein features that influence a protein's amenability to high-throughput experimentation. Based on this, we identify potential bottlenecks in various stages of the structural genomics process through specialized “pipeline schematics”. We find that the properties of a protein that are most significant are: (i) whether it is conserved across many organisms; (ii) the percentage composition of charged residues; (iii) the occurrence of hydrophobic patches; (iv) the number of binding partners it has; and (v) its length. Conversely, a number of other properties that might have been thought to be important, such as nuclear localization signals, are not significant. Thus, using our tree-based analyses, we are able to identify combinations of features that best differentiate the small group of proteins for which a structure has been determined from all the currently selected targets. This information may prove useful in optimizing high-throughput experimentation. Further information is available from http://mining.nesg.org/. |
Author | Wu, Baolin Goh, Chern-Sing Smith, Andrew Zhao, Hongyu Douglas, Shawn M Montelione, Gaetano T Lan, Ning Echols, Nathaniel Gerstein, Mark Milburn, Duncan |
Author_xml | – sequence: 1 givenname: Chern-Sing surname: Goh fullname: Goh, Chern-Sing organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 2 givenname: Ning surname: Lan fullname: Lan, Ning organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 3 givenname: Shawn M surname: Douglas fullname: Douglas, Shawn M organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 4 givenname: Baolin surname: Wu fullname: Wu, Baolin organization: Department of Epidemiology and Public Health, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 5 givenname: Nathaniel surname: Echols fullname: Echols, Nathaniel organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 6 givenname: Andrew surname: Smith fullname: Smith, Andrew organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 7 givenname: Duncan surname: Milburn fullname: Milburn, Duncan organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 8 givenname: Gaetano T surname: Montelione fullname: Montelione, Gaetano T organization: Northeast Structural Genomics Consortium, Robert Wood Johnson Medical School, UMDNJ, Piscataway, NJ 08854, USA – sequence: 9 givenname: Hongyu surname: Zhao fullname: Zhao, Hongyu organization: Department of Epidemiology and Public Health, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA – sequence: 10 givenname: Mark surname: Gerstein fullname: Gerstein, Mark email: mark.gerstein@yale.edu organization: Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/14741208$$D View this record in MEDLINE/PubMed |
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Keywords | structural genomics hydrophobicity oob, out-of-bag COGs charged residues decision trees NLS, nuclear localization signal COG, clusters of orthologous groups |
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SubjectTerms | Algorithms charged residues COGs Computational Biology Databases, Protein Decision Trees Genomics hydrophobicity Protein Conformation Protein Sorting Signals Proteins - chemistry Proteins - genetics Sequence Analysis, Protein structural genomics |
Title | Mining the Structural Genomics Pipeline: Identification of Protein Properties that Affect High-throughput Experimental Analysis |
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