Describing protein
dynamical networks through amino acid
contacts
is a powerful way to analyze complex biomolecular systems. However,
due to the size of the systems, identifying the relevant features
of protein-weighted graphs can be a difficult task. To address this
issue, we present the connected component analysis (CCA) approach
that allows for fast, robust, and unbiased analysis of dynamical perturbation
contact networks (DPCNs). We first illustrate the CCA method as applied
to a prototypical allosteric enzyme, the imidazoleglycerol phosphate
synthase (IGPS) enzyme from
Thermotoga maritima
bacteria. This approach was shown to outperform the clustering methods
applied to DPCNs, which could not capture the propagation of the allosteric
signal within the protein graph. On the other hand, CCA reduced the
DPCN size, providing connected components that nicely describe the
allosteric propagation of the signal from the effector to the active
sites of the protein. By applying the CCA to the IGPS enzyme in different
conditions, i.e., at high temperature and from another organism (yeast
IGPS), and to a different enzyme, i.e., a protein kinase, we demonstrated
how CCA of DPCNs is an effective and transferable tool that facilitates
the analysis of protein-weighted networks.