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 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/.
Goh
Chern-Sing
CS
Molecular Biophysics and Biochemistry, Yale University, 266 Whitney Ave, New Haven, CT 06520, USA.
Lan
Ning
N
Douglas
Shawn M
SM
Wu
Baolin
B
Echols
Nathaniel
N
Smith
Andrew
A
Milburn
Duncan
D
Montelione
Gaetano T
GT
Zhao
Hongyu
H
Gerstein
Mark
M
D000465:Algorithms; D019295:Computational Biology; D030562:Databases, Protein; D003663:Decision Trees; D023281:Genomics; D011487:Protein Conformation; D021382:Protein Sorting Signals; D011506:Proteins / Q000737:chemistry* / Q000235:genetics*; D020539:Sequence Analysis, Protein
D021382:Protein Sorting Signals; D011506:Proteins