Determination of Signaling Pathways in Proteins through Network Theory: Importance of the Topology

Abstract

Network theory methods are being increasingly applied to proteins to investigate complex biological phenomena. Residues that are important for signaling processes can be identified by their condition as critical nodes in a protein structure network. This analysis involves modeling the protein as a graph in which each residue is represented as a node and edges are drawn between nodes that are deemed connected. In this paper, we show that the results obtained from this type of network analysis (i.e., signaling pathways, key residues for signal transmission, etc.) are profoundly affected by the topology of the network, with normally used determination of network edges by geometrical cutoff schemes giving rise to substantial statistical errors. We propose a method of determining protein structure networks by calculating inter-residue interaction energies and show that it gives an accurate and reliable description of the signal-propagation properties of a known allosteric enzyme. We also show that including covalent interactions in the network topology is essential for accurate results to be obtained.

MDN: A Web Portal for Network Analysis of Molecular Dynamics Simulations

“…This is usually done based on interresidue distances and correlation coefficients extracted from MD simulations. We have recently shown that this approach introduces significant statistical errors in the analysis, and networks constructed in this way were not able to identify residues important for signal propagation in the protein imidazole glycerol phosphate synthase (21) or discriminate allosterically active mutants of the lactose repressor protein (22). On the other hand, we showed that assigning network edges based on interaction energies extracted from MD simulations can give an accurate description of signal propagation in proteins (21).…”

“…These are processed to construct the network topology and extract the interaction energies to assign network edges, following methodology previously introduced (21). Briefly, weights u ij are assigned to different residue pairs as follows:…”

“…The resulting network is analyzed to determine nodes important for signaling and the efficiency of signal propagation. The node betweenness is a measure of network centrality suitable for determining important nodes for signaling (21,31). This quantity measures the number of pathways that go through each node in the network.…”

“…A number of specialized methods have been proposed to address this issue (10)(11)(12)(13)(14)(15). Another approach that has been widely used in recent years is to analyze standard MD simulations with network theory (16)(17)(18)(19)(20)(21)(22). This approach involves modeling the protein as a graph, with residues mapped to network nodes, thus yielding a coarse-grained representation of the system.…”

“…We have recently shown that this approach introduces significant statistical errors in the analysis, and networks constructed in this way were not able to identify residues important for signal propagation in the protein imidazole glycerol phosphate synthase (21) or discriminate allosterically active mutants of the lactose repressor protein (22). On the other hand, we showed that assigning network edges based on interaction energies extracted from MD simulations can give an accurate description of signal propagation in proteins (21). In addition, we introduced a network coupling measure that, when applied to protein energy networks, was able to discriminate allosterically active mutants of the lac repressor protein (22).…”