Butane dihedral angle dynamics in water is dominated by internal friction

Daldrop, Jan O.; Kappler, Julian; Brünig, Florian N.; Netz, Roland R.

doi:10.1073/pnas.1722327115

Cited by 63 publications

(80 citation statements)

References 60 publications

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“…Note that the difference of the MSD at short times stems from the fact that the dynamics for the RTP is assumed overdamped, while the AOUP that we consider obeys underdamped dynamics. Given the velocity autocorrelation function ˙ x (t)˙ x (0) , the memory kernel may be extracted numerically, see [26,[44][45][46] and appendix D. The method finds the kernel for which eq. (6) holds and thus maps a given MSD to its corresponding effective equilibrium Both show the same structure of a positive delta peak at t = 0 followed by a negative exponential tail.…”

Section: Free Active Particle Modelsmentioning

confidence: 99%

Negative friction memory induces persistent motion

2020

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Abstract. We investigate the mean-square displacement (MSD) for random motion governed by the generalized Langevin equation for memory functions that contain two different time scales: In the first model, the memory kernel consists of a delta peak and a single-exponential and in the second model of the sum of two exponentials. In particular, we investigate the scenario where the long-time exponential kernel contribution is negative. The competition between positive and negative friction memory contributions produces an enhanced transient persistent regime in the MSD, which is relevant for biological motility and active matter systems. Graphical abstract

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Section: Free Active Particle Modelsmentioning

confidence: 99%

Negative friction memory induces persistent motion

2020

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“…important for fast molecular transition such as the dihedral barrier dynamics of butane in solvents [15,16]. The effect of the different memory times of the multi-scale memory kernel that have been extracted from simulations will have to be examined in future work.…”

Section: Non-markovian Effects Have Been Demonstrated To Bementioning

confidence: 99%

Non-Markovian barrier crossing with two-time-scale memory is dominated by the faster memory component

2019

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We investigate non-Markovian barrier-crossing kinetics of a massive particle in one dimension in the presence of a memory function that is the sum of two exponentials with different memory times τ1 and τ2. Our Langevin simulations for the special case where both exponentials contribute equally to the total friction show that the barrier crossing time becomes independent of the longer memory time if at least one of the two memory times is larger than the intrinsic diffusion time. When we associate memory effects with coupled degrees of freedom that are orthogonal to a one-dimensional reaction coordinate, this counterintuitive result shows that the faster orthogonal degrees of freedom dominate barrier-crossing kinetics in the non-Markovian limit and that the slower orthogonal degrees become negligible, quite contrary to the standard time-scale separation assumption and with important consequences for the proper setup of coarse-graining procedures in the non-Markovian case. By asymptotic matching and symmetry arguments, we construct a crossover formula for the barrier crossing time that is valid for general multi-exponential memory kernels. This formula can be used to estimate barrier-crossing times for general memory functions for high friction, i.e. in the overdamped regime, as well as for low friction, i.e. in the inertial regime. Typical examples where our results are important include protein folding in the high-friction limit and chemical reactions such as proton-transfer reactions in the low-friction limit.

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“…Here, we introduce such a method, which is based on the generalized Langevin equation (GLE) [21][22][23]. The GLE has found ample applications in the modeling of molecular systems [24][25][26], microrheological data [27,28], and of non-Markovian dynamics along generalized reaction coordinates [26,[29][30][31][32][33]. The GLE includes colored noise and accounts for non-Markovian effects by a memory kernel that describes for how long the system remembers its past states.…”

Section: Introductionmentioning

confidence: 99%

“…We extract the memory kernel from cell trajectory data without prior assumptions on the functional form of the memory function, and compare the memory functions on the single-cell level. The Gaussian character of the single-cell velocity distributions allows us to use the linear Langevin equation, but we note that our memory-extraction method in principle also works for nonlinear systems [26].…”

Section: Introductionmentioning

confidence: 99%

Non-Markovian data-driven modeling of single-cell motility

et al. 2020

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Trajectories of human breast cancer cells moving on one-dimensional circular tracks are modeled by the non-Markovian version of the Langevin equation that includes an arbitrary memory function. When averaged over cells, the velocity distribution exhibits spurious non-Gaussian behavior, while single cells are characterized by Gaussian velocity distributions. Accordingly, the data are described by a linear memory model which includes different random walk models that were previously used to account for various aspects of cell motility such as migratory persistence, non-Markovian effects, colored noise, and anomalous diffusion. The memory function is extracted from the trajectory data without restrictions or assumptions, thus making our approach truly data driven, and is used for unbiased single-cell comparison. The cell memory displays time-delayed single-exponential negative friction, which clearly distinguishes cell motion from the simple persistent random walk model and suggests a regulatory feedback mechanism that controls cell migration. Based on the extracted memory function we formulate a generalized exactly solvable cell migration model which indicates that negative friction generates cell persistence over long timescales. The nonequilibrium character of cell motion is investigated by mapping the non-Markovian Langevin equation with memory onto a Markovian model that involves a hidden degree of freedom and is equivalent to the underdamped active Ornstein-Uhlenbeck process.

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Butane dihedral angle dynamics in water is dominated by internal friction

Cited by 63 publications

References 60 publications

Negative friction memory induces persistent motion

Negative friction memory induces persistent motion

Non-Markovian barrier crossing with two-time-scale memory is dominated by the faster memory component

Non-Markovian data-driven modeling of single-cell motility

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