Structural feature extraction protocol for classifying reversible membrane binding protein domains.
Machine learning based classification protocols for automated function annotation of protein structures have in many instances proven superior to simpler sequence based procedures. Here we present an automated method for extracting features from protein structures by construction of surface patches to be used in such protocols. The utility of the developed patch-growing procedure is exemplified by its ability to identify reversible membrane binding domains from the C1, C2, and PH families.
Källberg
Morten
M
Department of Bioengineering-Bioinformatics Program, University of Illinois at Chicago, 851 S. Morgan St. Chicago, IL 60607-7052, USA.
Lu
Hui
H
D000465:Algorithms; D001185:Artificial Intelligence; D001331:Automation; D002462:Cell Membrane / Q000378:metabolism*; D030562:Databases, Protein; D006860:Hydrogen Bonding; D057927:Hydrophobic and Hydrophilic Interactions; D011485:Protein Binding; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*; D012997:Solvents; D055672:Static Electricity
D011506:Proteins; D012997:Solvents