Journal
JOURNAL OF CHEMICAL PHYSICS
Volume 152, Issue 4, Pages -Publisher
AMER INST PHYSICS
DOI: 10.1063/1.5140070
Keywords
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Funding
- Deutsche Forschungsgemeinschaft [Sto 247/10-2]
- High Performance and Cloud Computing Group at the Zentrum fur Datenverarbeitung of the University of Tubingen
- Rechenzentrum of the University of Freiburg
- Black Forest Grid Initiative
- DFG [INST 37/935-1 FUGG (RV bw16I016), INST 39/963-1 FUGG (RV bw18A004)]
- state of Baden-Wurttemberg through bwHPC
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Recent time-resolved experiments and accompanying molecular dynamics simulations allow us to monitor the flow of vibrational energy in biomolecules. As a simple means to describe these experimental and simulated data, Buchenberg et al. [J. Phys. Chem. Lett. 7, 25 (2016)] suggested a master equation model that accounts for the energy transport from an initially excited residue to some target residue. The transfer rates of the model were obtained from two scaling rules, which account for the energy transport through the backbone and via tertiary contacts, respectively, and were parameterized using simulation data of a small alpha-helical protein at low temperatures. To extend the applicability of the model to general proteins at room temperature, here a new parameterization is presented, which is based on extensive nonequilibrium molecular dynamics simulations of a number of model systems. With typical transfer times of 0.5-1 ps between adjacent residues, backbone transport represents the fastest channel of energy flow. It is well described by a diffusive-type scaling rule, which requires only an overall backbone diffusion coefficient and interatom distances as input. Contact transport, e.g., via hydrogen bonds, is considerably slower (6-30 ps) at room temperature. A new scaling rule depending on the inverse square contact distance is suggested, which is shown to successfully describe the energy transport in the allosteric protein PDZ3. Since both scaling rules require only the structure of the considered system, the model provides a simple and general means to predict energy transport in proteins. To identify the pathways of energy transport, Monte Carlo Markov chain simulations are performed, which highlight the competition between backbone and contact transport channels.
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