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Using Langevin dynamics simulations, we investigate the influence of polymer-pore interactions on the dynamics of biopolymer translocation through nanopores. We find that an attractive interaction can significantly change the translocation dynamics. This can be understood by examining the three components of the total translocation time $tau approx tau_1+tau_2+tau_3$ corresponding to the initial filling of the pore, transfer of polymer from the textit{cis} side to the textit{trans} side, and emptying of the pore, respectively. We find that the dynamics for the last process of emptying of the pore changes from non-activated to activated in nature as the strength of the attractive interaction increases, and $tau_3$ becomes the dominant contribution to the total translocation time for strong attraction. This leads to a new dependence of $tau$ as a function of driving force and chain length. Our results are in good agreement with recent experimental findings, and provide a possible explanation for the different scaling behavior observed in solid state nanopores {it vs.} that for the natural $alpha$-hemolysin channel.
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We investigate the dynamics of DNA translocation through a nanopore driven by an external force using Langevin dynamics simulations in two dimensions (2D) to study how the translocation dynamics depend on the details of the DNA sequences. We consider a coarse-grained model of DNA built from two bases $A$ and $C$, having different base-pore interactions, {textit e.g.}, a strong (weak) attractive force between the pore and the base $A$ ($C$) inside the pore. From a series of studies on hetero-DNAs with repeat units $A_mC_n$, we find that the translocation time decreases exponentially as a function of the volume fraction $f_C$ of the base $C$. %($epsilon_{pC} < epsilon_{pA}$). For longer $A$ sequences with $f_C le 0.5$, the translocation time strongly depends on the orientation of DNA, namely which base enters the pore first. Our studies clearly demonstrate that for a DNA of certain length $N$ with repeat units $A_mC_n$, the pattern exhibited by the waiting times of the individual bases and their periodicity can unambiguously determine the values of $m$, $n$ and $N$ respectively. Therefore, a prospective experimental realization of this phenomenon may lead to fast and efficient sequence detection technic.
Using Langevin dynamics simulations, we investigate the dynamics of chaperone-assisted translocation of a flexible polymer through a nanopore. We find that increasing the binding energy $epsilon$ between the chaperone and the chain and the chaperone concentration $N_c$ can greatly improve the translocation probability. Particularly, with increasing the chaperone concentration a maximum translocation probability is observed for weak binding. For a fixed chaperone concentration, the histogram of translocation time $tau$ has a transition from long-tailed distribution to Gaussian distribution with increasing $epsilon$. $tau$ rapidly decreases and then almost saturates with increasing binding energy for short chain, however, it has a minimum for longer chains at lower chaperone concentration. We also show that $tau$ has a minimum as a function of the chaperone concentration. For different $epsilon$, a nonuniversal dependence of $tau$ on the chain length $N$ is also observed. These results can be interpreted by characteristic entropic effects for flexible polymers induced by either crowding effect from high chaperone concentration or the intersegmental binding for the high binding energy.
We determine the scaling exponents of polymer translocation (PT) through a nanopore by extensive computer simulations of various microscopic models for chain lengths extending up to N=800 in some cases. We focus on the scaling of the average PT time $tau sim N^{alpha}$ and the mean-square change of the PT coordinate $<s^2(t)> sim t^beta$. We find $alpha=1+2 u$ and $beta=2/alpha$ for unbiased PT in 2D and 3D. The relation $alpha beta=2$ holds for driven PT in 2D, with crossover from $alpha approx 2 u$ for short chains to $alpha approx 1+ u$ for long chains. This crossover is, however, absent in 3D where $alpha = 1.42 pm 0.01$ and $alpha beta approx 2.2$ for $N approx 40-800$.
The translocation dynamics of a polymer chain through a nanopore in the absence of an external driving force is analyzed by means of scaling arguments, fractional calculus, and computer simulations. The problem at hand is mapped on a one dimensional {em anomalous} diffusion process in terms of reaction coordinate $s$ (i.e. the translocated number of segments at time $t$) and shown to be governed by an universal exponent $alpha = 2/(2 u+2-gamma_1)$ whose value is nearly the same in two- and three-dimensions. The process is described by a {em fractional} diffusion equation which is solved exactly in the interval $0 <s < N$ with appropriate boundary and initial conditions. The solution gives the probability distribution of translocation times as well as the variation with time of the statistical moments: $<s(t)>$, and $<s^2(t)> - < s(t)>^2$ which provide full description of the diffusion process. The comparison of the analytic results with data derived from extensive Monte Carlo (MC) simulations reveals very good agreement and proves that the diffusion dynamics of unbiased translocation through a nanopore is anomalous in its nature.
We investigate the influence of polymer-pore interactions on the translocation dynamics using Langevin dynamics simulations. An attractive interaction can greatly improve translocation probability. At the same time, it also increases translocation time slowly for weak attraction while exponential dependence is observed for strong attraction. For fixed driving force and chain length the histogram of translocation time has a transition from Gaussian distribution to long-tailed distribution with increasing attraction. Under a weak driving force and a strong attractive force, both the translocation time and the residence time in the pore show a non-monotonic behavior as a function of the chain length. Our simulations results are in good agreement with recent experimental data.
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