Calculation of centralities in protein kinase A

Abstract

Topological analysis of protein residue networks (PRNs) is a common method that can help to understand the roles of individual residues. Here, we used protein kinase A as a study object and asked what already known functionally important residues can be detected by network analysis. Along several traditional approaches to weight edges in PRNs we used local spatial pattern (LSP) alignment that assigns high weights to edges only if CαCβ vectors for the corresponding residues retain their mutual positions and orientation. Our results show that even short molecular dynamic simulations of 10 to 20 ns can give convergent values for betweenness and degree centralities calculated from the LSP-based PRNs. Using these centralities, we were able to clearly distinguish a group of residues that are highly conserved in protein kinases and play important functional and regulatory roles. In comparison, traditional methods based on cross-correlation and linear mutual information were much less efficient for this particular task. These results call for reevaluation of the current methods to generate PRNs.

Single-residue mutation in protein kinase C toggles between cancer and neurodegeneration

“…For MD and analyses we used the C1A domain of PKCγ whose structure is solved (PDB: 2E73) and has 95% sequence identity to PKCβII; residue numbering as in PKCβII. Following 50 ns MD in triplicate, local spatial pattern (LSP) alignment-based protein residue networks (PRN) were created to detect stable regions and identify functional residues in the domain (61). Degree Centrality (DC) and Betweenness Centrality (BC) values for these networks were calculated as these measures have been shown to be effective in identifying functionally important nodes within a network (62, 63).…”

“…To understand the structural basis of R42P disruption of PKC function, we used molecular dynamics simulations followed by LSP alignment to study changes in stable regions of the C1A domain. This approach has been shown to out-perform traditional interaction-based methods to identify critical regulatory residues in protein kinase A (61). Applying this method, we observed a rewiring of the C1A domain when the R42P mutation was introduced (Figure 5C-D).…”

“…To generate LSP-based networks, three 50 ns MD trajectories were split into 15 intervals 10 ns each. From each interval, 100 structures were extracted with a 0.1 ns step, and LSP-alignment was performed within each group of 100 structures using inhouse software in all-to-all manner as described earlier (61). Briefly, prior to alignment, each protein structure was represented as an undirected weighted graph with residues as nodes and links were formed between residues if the distance between the corresponding Cα atoms was less than 12Å.…”

Protein Kinase Structure and Dynamics: Role of the αC-β4 Loop

“…LSP-alignment is a graph-theory based method that implements a Protein Residue Network (PRN) approach (72). As we demonstrated, two major centralities of such PRNs can contain important information on the local stability (Degree centrality, DC) and global connectivity (Betweenness centrality, BC) of the protein (68) ( Figure 6B ). Our purpose here was to identify changes in LSP-based PRNs associated with the F100A mutation, specifically assessing whether these changes relate to the dynamic features of the αC-β4 loop and correlate with the NMR results.…”

“…Initially, it was utilized to identify conserved hydrophobic ensembles in protein kinases (8, 9). More recently, this technique was applied to Molecular Dynamics (MD) simulations, in an effort to analyze stable regions in Protein Kinase A (68). By comparing spatial patterns formed by Cα-Cβ vectors in differing conformations generated via MD simulation it is possible to analyze thermal vibrations of residues.…”