title: Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins.
abstract: BACKGROUND
  Inferring structural dependencies among a protein's side chains helps us understand their coupled motions. It is known that coupled fluctuations can reveal pathways of communication used for information propagation in a molecule. Side-chain conformations are commonly represented by multivariate angular variables, but existing partial correlation methods that can be applied to this inference task are not capable of handling multivariate angular data. We propose a novel method to infer direct couplings from this type of data, and show that this method is useful for identifying functional regions and their interactions in allosteric proteins.
  
  
  RESULTS
  We developed a novel extension of canonical correlation analysis (CCA), which we call 'kernelized partial CCA' (or simply KPCCA), and used it to infer direct couplings between side chains, while disentangling these couplings from indirect ones. Using the conformational information and fluctuations of the inactive structure alone for allosteric proteins in the Ras and other Ras-like families, our method identified allosterically important residues not only as strongly coupled ones but also in densely connected regions of the interaction graph formed by the inferred couplings. Our results were in good agreement with other empirical findings. By studying distinct members of the Ras, Rho and Rab sub-families, we show further that KPCCA was capable of inferring common allosteric characteristics in the small G protein super-family.
  
  
  METHODS
  https://github.com/lsgh/ismb15
authors.affiliation: Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors.forename: Laleh
authors.initials: L
authors.lastname: Soltan Ghoraie
authors.affiliation: Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors.forename: Forbes
authors.initials: F
authors.lastname: Burkowski
authors.affiliation: Department of Computer Science and Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
authors.forename: Mu
authors.initials: M
authors.lastname: Zhu
mesh_terms: D000465:Algorithms; D000495:Allosteric Site; D003627:Data Interpretation, Statistical; D020559:Monomeric GTP-Binding Proteins / Q000737:chemistry*; D009038:Motion; D011487:Protein Conformation; D020691:rab GTP-Binding Proteins / Q000737:chemistry; D018631:ras Proteins / Q000737:chemistry; D020741:rho GTP-Binding Proteins / Q000737:chemistry
chemical_list: D020559:Monomeric GTP-Binding Proteins; D020691:rab GTP-Binding Proteins; D018631:ras Proteins; D020741:rho GTP-Binding Proteins