Network analysis provides a powerful set of tools for investigating protein allosteric regulation. Commonly utilized methods based on inter-residue contact and pairwise correlated motion often suffer from instabilities, which could lead to inconclusive or misleading results. Current flow betweenness can be shown equivalent to information centrality, which has been well established in other areas, but with more intuitive formulation. This method includes contributions from all possible paths connecting a given set of nodes when assigning weight values to a given edge. Here we demonstrate the utility of the method by comparing it with traditional methods as applied to analysis of serine/threonine kinase. Specifically, three gain of function mutations are known to disrupt normal function of a native inhibitory binding protein without noticeable reduction of the protein’s binding affinity. By calculating the network shift between a wild-type serine/threonine kinase and three gain-of-function mutants (figure 1), we have shown that current-flow betweenness method allows for identification of statistically relevant changes in edge or node path usage with greater sensitivity than when correlations are used directly as network weights. Finally, analysis of flow betweenness versus correlation based edge ranking stability (figure 2) reveals that flow betweenness provides greater stability as compared with standard contact topology and correlation weight schemes. Therefore, current-flow betweenness can be seen to have great potential utility for future works in which network analysis of biomolecular simulations is employed.

Using current-flow betweenness scheme to capture mutation induced disruption of protein allosteric regulation

256th National Meeting (2018)

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