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

Ribeiro, André A. S. T.; Ortiz, Vanessa

doi:10.1021/ct400977r

Cited by 61 publications

(106 citation statements)

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“…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).…”

Section: Introductionmentioning

confidence: 93%

“…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:…”

Section: Available Types Of Analysismentioning

confidence: 99%

“…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.…”

Section: Available Types Of Analysismentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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

Ribeiro

Ortiz

2015

Biophysical Journal

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We introduce a web portal that employs network theory for the analysis of trajectories from molecular dynamics simulations. Users can create protein energy networks following methodology previously introduced by our group, and can identify residues that are important for signal propagation, as well as measure the efficiency of signal propagation by calculating the network coupling. This tool, called MDN, was used to characterize signal propagation in Escherichia coli heat-shock protein 70-kDa. Two variants of this protein experimentally shown to be allosterically active exhibit higher network coupling relative to that of two inactive variants. In addition, calculations of partial coupling suggest that this quantity could be used as part of the criteria to determine pocket druggability in drug discovery studies.

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Section: Introductionmentioning

confidence: 93%

Section: Available Types Of Analysismentioning

confidence: 99%

Section: Available Types Of Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Ribeiro

Ortiz

2015

Biophysical Journal

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Mapping Energy Transport Networks in Proteins

Leitner

Yamato

2018

Reviews in Computational Chemistry

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INTRODUCTIONThe response of proteins to chemical reactions or impulsive excitation that occurs within the molecule has fascinated chemists for decades. [1][2][3] In recent years ultrafast X-ray studies have provided ever more detailed information about the evolution of protein structural change following ligand photolysis, 4-5 and time-resolved IR and Raman techniques, e.g., have provided detailed pictures of the nature and rate of energy transport in peptides and proteins, [6][7][8][9][10][11] including recent advances in identifying transport through individual amino acids of several heme proteins. [12][13][14] Computational tools to locate energy transport pathways in proteins have also been advancing. 15 Energy transport pathways in proteins have since some time been identified by molecular dynamics (MD) simulations, [16][17] and more recent efforts have focused on the development of coarse graining approaches, 18-29 some of which have exploited analogies to thermal transport in other molecular materials. [30][31][32][33][34][35] With the identification of pathways in proteins and protein 2 complexes, network analysis has been applied to locate residues that control protein dynamics and possibly allostery, [36][37][38] where chemical reactions at one binding site mediate reactions at distance sites of the protein. In this chapter we review approaches for locating computationally energy transport networks in proteins. We present background into energy and thermal transport in condensed phase and macromolecules that underlies the approaches we discuss before turning to a description of the approaches themselves. We also illustrate the application of the computational methods for locating energy transport networks and simulating energy dynamics in proteins with several examples.One of the themes that we address in this chapter is the difference between energy transport in condensed phase generally and in a folded polymer such as a protein specifically. While the approaches that we present and detail are based on linearresponse theory for transport, we apply them to a system, a protein, where energy transport occurs highly anisotropically.We are mainly concerned with the characterization of such anisotropic transport, not simply the calculation of transport coefficients for a particular object, though that information can also be calculated starting with the approaches we present. The focus here then is on local energy transport coefficients, such as local energy conductivities and diffusivities, as discussed below, and how these local transport properties connect into a network that mediates energy dynamics in the protein. It is the network of such local energy transport coefficients that, once identified and located, can be used to model the global energy dynamics in a protein, as we describe.That energy transport pathways exist, i.e., energy does not simply flow isotropically through a globular protein, is an inherent property of the geometry of a 3 folded protein, [62][63][64][65][66][67][68][69][70] which i...

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The adaptive nature of protein residue networks

Karain

Qaraeen

2017

Proteins

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Protein residue networks PRNs are used to describe proteins. These networks are usually based on an average structure for the protein. However, proteins are dynamic entities that are affected by their surroundings. In this work, we study the effect of temperatures above and below the protein dynamical transition temperature(≈200 K), on three important network parameters gleaned from weighted PRNs for the solvated β-lactamase inhibitory protein BLIP: the betweenness centrality B, the closeness centrality C, and the clustering coefficient CC. The B and C values will be extracted for each node from PRNs at six different temperatures: 150 K, 180 K, 200 K, 220 K, 250 K, and 310 K respectively. The average value for the CC for each PRN will also be calculated at each temperature, respectively. We find that at temperatures ≤200 K, the network nodes with the most significant B and C values tend to have lower relative solvent accessibility RSA values, and to fall within the protein secondary structure elements (α helices and β sheets). At temperatures >200 K, the significant nodes in terms of B and C tend to have larger RSA values, and to fall on the connecting loops in the protein. The average CC decreases in value for the PRNs up to 200 K, and then remains basically constant above 200 K. This clearly shows that any conclusions based on static PRNs should be handled with care. The dynamic nature of proteins and its coupling to the surrounding environment should be taken into consideration when using the PRN paradigm. Proteins 2017; 85:917-923. © 2016 Wiley Periodicals, Inc.

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

Cited by 61 publications

References 62 publications

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

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

Mapping Energy Transport Networks in Proteins

The adaptive nature of protein residue networks

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