UC San Diego

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.