Calculation of centralities in protein kinase A

Abstract

Topological analysis of amino acid networks is a common method that can help to understand the roles of individual residues. The most popular approach for network construction is to create a connection between residues if they interact. These interactions are usually weighted by absolute values of correlation coefficients or mutual information. Here we argue that connections in such networks have to reflect levels of cohesion within the protein instead of a simple fact of interaction between residues. If this is correct, an indiscriminate combination of correlation and anti-correlation, as well as the all-inclusive nature of the mutual information metrics, should be detrimental for the analysis. To test our hypothesis, we studied amino acid networks of the protein kinase A created by Local Spatial Pattern alignment, a method that can detect conserved patterns formed by C-C{beta} vectors. Our results showed that, in comparison with the traditional methods, this approach is more efficient in detecting functionally important residues. Out of four studied centrality metrics, Closeness centrality was the least efficient measure of residue importance. Eigenvector centrality proved to be ineffective as the spectral gap values of the networks were very low due to the bilobal structure of the kinase. We recommend using joint graphs of Betweenness centrality and Degree centrality to visualize different aspects of amino acid roles. Author Summary Protein structures can be viewed as networks of residues with some of them being a part of highly interconnected hubs and some being connectors between the hubs. Analysis of these networks can be helpful for understanding of possible roles of single amino acids. In this paper, we challenged existing methods for the creation of such networks. A traditional way is to connect residues if they can interact. We propose that residues should be connected only if they retain their mutual positions in space during molecular dynamic simulation, that is they move cohesively. We show that this approach improves the efficiency of the analysis indicating that a significant revision of the existing views on amino acid networks is necessary.