title: A structure-based protocol for learning the family-specific mechanisms of membrane-binding domains.
abstract: BACKGROUND
  Peripheral membrane-targeting domain (MTD) families, such as C1-, C2- and PH domains, play a key role in signal transduction and membrane trafficking by dynamically translocating their parent proteins to specific plasma membranes when changes in lipid composition occur. It is, however, difficult to determine the subset of domains within families displaying this property, as sequence motifs signifying the membrane binding properties are not well defined. For this reason, procedures based on sequence similarity alone are often insufficient in computational identification of MTDs within families (yielding less than 65% accuracy even with a sequence identity of 70%).
  
  
  RESULTS
  We present a machine learning protocol for determining membrane-targeting properties achieving 85-90% accuracy in separating binding and non-binding domains within families. Our model is based on features from both sequence and structure, thereby incorporation statistics obtained from the entire domain family and domain-specific physical quantities such as surface electrostatics. In addition, by using the enriched rules in alternating decision tree classifiers, we are able to determine the meaning of the assigned function labels in terms of biological mechanisms.
  
  
  CONCLUSIONS
  The high accuracy of the learned models and good agreement between the rules discovered using the ADtree classifier and mechanisms reported in the literature reflect the value of machine learning protocols in both prediction and biological knowledge discovery. Our protocol can thus potentially be used as a general function annotation and knowledge mining tool for other protein domains.
  
  
  BACKGROUND
  metador.bioengr.uic.edu
  
  
  BACKGROUND
  huilu@uic.edu.
authors.affiliation: Department of Bioengineering, University of Illinois at Chicago, Chicago, IL 60607, USA.
authors.forename: Morten
authors.initials: M
authors.lastname: Källberg
authors.forename: Nitin
authors.initials: N
authors.lastname: Bhardwaj
authors.forename: Robert
authors.initials: R
authors.lastname: Langlois
authors.forename: Hui
authors.initials: H
authors.lastname: Lu
mesh_terms: D001185:Artificial Intelligence; D008565:Membrane Proteins / Q000737:chemistry* / Q000145:classification; D008958:Models, Molecular; D051745:Protein Kinase C-delta / Q000737:chemistry; D021382:Protein Sorting Signals; D017434:Protein Structure, Tertiary; D055672:Static Electricity
chemical_list: D008565:Membrane Proteins; D021382:Protein Sorting Signals; D051745:Protein Kinase C-delta