|Journal of Molecular Biology (2004) 336(1):115-30|
|Northeast Structural Genomics Consortium|
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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/.
|chemistry genetics |
|Algorithms Databases, Protein Proteins Computational Biology Protein Conformation Genomics Sequence Analysis, Protein Protein Sorting Signals Decision Trees |
|104 (Last update: 01/19/2019 4:52:20pm)|
|J Mol Biol. 2004 Feb 6;336(1):115-30.|