Using kernelized partial canonical correlation analysis to study directly coupled side chains and allostery in small G proteins.

ABSTRACT:

Motivation

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 sim

SUBMITTER: Soltan Ghoraie L 

PROVIDER: S-EPMC4765857 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature