Energy Propagation and Network Energetic Coupling in Proteins

Abstract

Understanding how allosteric proteins respond to changes in their environment is a major goal of current biological research. We show that these responses can be quantified by analyzing protein energy networks using a method recently developed in our group. On the basis of this method, we introduce here a quantity named energetic coupling, which we show is able to discriminate allosterically active mutants of the lactose repressor (LacI) protein, and of the catabolite activator protein (CAP), a dynamically driven allosteric protein. Our method assumes that allostery and signal transmission can be more accurately described as efficient energy propagation, and not as the more widely used atomic motion correlations. We demonstrate the validity of this assumption by performing energy-propagation simulations. Finally, we present results from energy-propagation simulations performed on folded and fully extended conformations of the postsynaptic density protein 95 (PSD-95). They show that the protein backbone provides a more efficient route for energy transfer, when compared to secondary or tertiary contacts. On the basis of this, we propose energy propagation through the backbone as a possible explanation for the observation that intrinsically disordered proteins can efficiently transmit signals while lacking a well-defined tertiary structure.

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).…”

“…The network coupling, recently introduced by our group (22), is a measure of the efficiency of signal propagation through the network. It uses a network efficiency measure previously introduced by Latora and Marchiori (33):…”

“…The ideal network uses the pathways calculated for the analyzed network but gives maximum weight to all edges along these pathways (22). We note that Eq.…”

“…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.…”

“…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). These analyses were done with a customized version of the GROMACS software (5), along with several programs written entirely by our group.…”