2009
DOI: 10.1371/journal.pcbi.1000415
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Exploring the Free Energy Landscape: From Dynamics to Networks and Back

Abstract: Knowledge of the Free Energy Landscape topology is the essential key to understanding many biochemical processes. The determination of the conformers of a protein and their basins of attraction takes a central role for studying molecular isomerization reactions. In this work, we present a novel framework to unveil the features of a Free Energy Landscape answering questions such as how many meta-stable conformers there are, what the hierarchical relationship among them is, or what the structure and kinetics of … Show more

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Cited by 132 publications
(145 citation statements)
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“…A variety of network community detection methods based on meaningful underlying dynamical processes have been proposed in different contexts [27][28][29], and all of them share the feature that detected communities can be very different from those based solely on network connectivity. The identification of relevant dynamical indicators of community structure (as were here time to equilibrium or population of nodes) may be system-dependent, and thus remains at present as an open problem worth pursuing.…”
Section: Discussionmentioning
confidence: 99%
“…A variety of network community detection methods based on meaningful underlying dynamical processes have been proposed in different contexts [27][28][29], and all of them share the feature that detected communities can be very different from those based solely on network connectivity. The identification of relevant dynamical indicators of community structure (as were here time to equilibrium or population of nodes) may be system-dependent, and thus remains at present as an open problem worth pursuing.…”
Section: Discussionmentioning
confidence: 99%
“…Towards a more quantitative analysis of the free-energy surface in terms of kinetically homogeneous regions, or substates, we use a recently introduced procedure to partition the landscape into free-energy basins [31] (which is a simpler version of the Stochastic Steepest Descent algorithm introduced in [32]). To that end, we build a transition-gradient-network from the original one by keeping only one link per node, the one with the highest weight (excluding self links).…”
Section: Resultsmentioning
confidence: 99%
“…For example, visualizing the connectivity of stationary point databases, which constitute kinetic transition networks [14,16,57], enables us to connect the selforganizing properties of a diverse range of systems. In this sense, disconnectivity graphs [17,18] provide a distinct 'phenotype' for a zeroth-order prediction of observable properties, such as heat capacity features, and competing time scales for relaxation.…”
Section: Discussionmentioning
confidence: 99%
“…This software includes tools for global optimization [47] via basin-hopping [48][49][50], location of transition states and characterization of pathways [51], and methods for expanding connected stationary point databases [52] using discrete path sampling [53,54], combined with kinetic analysis [55,56]. Here, we will focus on the insight that can be gained from visualizing the network [14,57] defined by a database of local minima and transition states, and on the emergence of characteristic thermodynamic and kinetic properties as a consequence of particular motifs.…”
Section: Potential Energy Landscapesmentioning
confidence: 99%
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