Vibrational energy flow in the villin headpiece subdomain: Master equation simulations

Leitner, David M.; Buchenberg, Sebastian; Brettel, Paul; Stock, Gerhard

doi:10.1063/1.4907881

Cited by 57 publications

(91 citation statements)

References 73 publications

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“…While such a volume remains somewhat ambiguous, it cancels out in the definition of the local energy diffusivity, Eq. [22] below. The mode diffusivity can then be expressed in terms of the matrix elements of the heat current operator,…”

Section: Energy Diffusion In Terms Of Normal Modesmentioning

confidence: 98%

“…Those matrix elements, in turn, can be used in Eq. [22] to calculate the mode diffusivity for an aperiodic object. The mode diffusivity, together with the heat capacity for that mode, which is given by Eq.…”

Section: Energy Diffusion In Terms Of Normal Modesmentioning

confidence: 99%

“…As with the calculation in Eq. [22], we replace in practice the delta function in Eq. [34] with a rectangular region in the frequency difference so that several modes, at least about 5, are included in the sum.…”

Section: Communication Mapsmentioning

confidence: 99%

“…In a recent study of the 36-amino acid fragment from the villin headpiece subdomain, HP36, the results of a master equation simulation using the rate constants obtained from communication maps were compared with results of all-atom nonequilibrium simulations. 22 The master equation is, dP(t) dt = k P(t), [40] where P is a vector with elements corresponding to the population of each residue and k is the matrix of transition probabilities between residues. The elements of the matrix, k ij { } , are the rate constants for energy transfer between a pair of residues, i and j.…”

Section: Applications Communication Maps: Illustrative Examplesmentioning

confidence: 99%

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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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Section: Energy Diffusion In Terms Of Normal Modesmentioning

confidence: 98%

Section: Energy Diffusion In Terms Of Normal Modesmentioning

confidence: 99%

Section: Communication Mapsmentioning

confidence: 99%

Section: Applications Communication Maps: Illustrative Examplesmentioning

confidence: 99%

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Mapping Energy Transport Networks in Proteins

Leitner

Yamato

2018

Reviews in Computational Chemistry

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“…Future theoretical work will examine other substituted benzenes to explore the effect of different substituents on energy flow through the molecule. It would also be interesting to extend the analysis to larger organic molecules, such as peptides and proteins, to further examine properties of these molecules that control energy flow [61][62][63][64][65].…”

Section: Discussionmentioning

confidence: 99%

Quantum bottlenecks and unidirectional energy flow in molecules

Leitner

Pandey

2015

Annalen der Physik

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Quantum mechanical effects can enable energy to flow more efficiently in one direction along a molecule than in others. Ultrafast spectroscopic experiments on substituted benzenes [J. Phys. Chem. B 117, 10898 (2013)] reveal such an asymmetry in the flow of vibrational energy between the two chemical groups of the molecule, i.e., between the phenyl and the substituent. We examine theoretically energy flow in toluene, one of the substituted benzenes probed in the recent experiments, and show that quantum mechanical bottlenecks give rise to a preferred direction of energy flow in this molecule.

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Temperature Dependence of Thermal Conductivity of Proteins: Contributions of Thermal Expansion and Grüneisen Parameter

Leitner

2024

ChemPhysChem

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The thermal conductivity of many materials depends on temperature due to several factors, including variation of heat capacity with temperature, changes in vibrational dynamics with temperature, and change in volume with temperature. For proteins some, but not all, of these influences on the variation of thermal conductivity with temperature have been investigated in the past. In this study, we examine the influence of change in volume, and corresponding changes in vibrational dynamics, on the temperature dependence of the thermal conductivity. Using a measured value for the coefficient of thermal expansion and recently computed values for the Grüneisen parameter of proteins we find that the thermal conductivity increases with increasing temperature due to change in volume with temperature. We compare the impact of thermal expansion on the variation of the thermal conductivity with temperature found in this study with contributions of heat capacity and anharmonic coupling examined previously. Using values of thermal transport coefficients computed for proteins we also model heating of water in a protein solution following photoexcitation.

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Vibrational energy flow in the villin headpiece subdomain: Master equation simulations

Cited by 57 publications

References 73 publications

Mapping Energy Transport Networks in Proteins

Mapping Energy Transport Networks in Proteins

Quantum bottlenecks and unidirectional energy flow in molecules

Temperature Dependence of Thermal Conductivity of Proteins: Contributions of Thermal Expansion and Grüneisen Parameter

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