Brownian dynamics of a neutral protein moving through a nanopore in an electrically biased membrane

Abstract

The ability to separate proteins is desirable for many fields of study, and nanoporous membranes may offer a method for rapid protein filtration at high throughput volume, provided there is an understanding of the protein dynamics involved. In this work, we use Brownian dynamics simulations to study the motion of coarse-grained proteins insulin and ubiquitin in an electrically biased membrane. In our model, the protein is subjected to various biases applied to the silicon membrane equipped with a nanopore of different radii. The time each protein takes to find a cylindrical nanopore embedded in a thin silicon membrane, attempt to translocate it (waiting time), and successfully translocate it in a single attempt (translocation time) is calculated. We observe insulin finding the nanopore and translocating it faster than the electrically neutral ubiquitin due to insulin’s slightly smaller size and net negative charge. While ubiquitin’s dynamics is also affected by the size of the pore, surprisingly, its translocation process is also noticeably changed by the membrane bias. By investigating the protein’s multipole moments, we demonstrate that this behavior is largely due to the protein’s dipole and quadrupole interactions with the membrane potential.

Brownian dynamics of cylindrical capsule-like particles in a nanopore in an electrically biased solid-state membrane

“…An electric bias of V m = ±1 V is applied to Si, resulting in a positive or negative overall effective surface charge, respectively. 37,38 The system's electric potential, ϕ ( r ), is numerically calculated using the Poisson–Nernst–Planck (PNP) approach, as described in our previous works. 35,39 The dependence of the electric potential and electric field in and around the nanopore on the applied membrane bias and membrane surface charge are discussed in greater detail elsewhere.…”

“…35,39 The dependence of the electric potential and electric field in and around the nanopore on the applied membrane bias and membrane surface charge are discussed in greater detail elsewhere. 37,38…”

“…In this work, we approximate a capsule of various length and charge through a coarse-grained method developed earlier 36,38 and illustrated in Fig. 2.…”

“…41. The translational motion of the particle's center of mass position r cm ( t ) 36 in the directions parallel and perpendicular to the capsule's major axis can be determined aswhere r cm = ( r cm‖ , r cmp ), n T = ( n ‖ , n p ) is a random three-dimensional vector with components uniformly distributed between interval [−1, 1], 38,43,44 δ t = 1.0 ps is the time step, and the position of each bead r i , i = 1,…, N is then updated in the membrane reference frame. In eqn (4a) and (4b), the second term is due to the net external forces F i = ( F i ‖ , F i ⊥ ) computed at the previous time step t − δ t and applied to each bead.…”

Multi-resolution simulation of DNA transport through large synthetic nanostructures

“…As introduced above, CG simulations have already provided invaluable information about nanopore systems that would have been difficult to obtain otherwise. 17–21 Specific examples include CG MD simulation of DNA translocation through both the biological nanopore alpha-hemolysin 22 and solid-state nanopores, 23 Brownian dynamics simulations of polymer and nanoparticle translocation through passive and gated solid-state nanopores, 24–26 and protein translocation through a nanopore containing a receptor protein. 27 However, the removal of explicit ions means that ion currents can no longer be calculated directly.…”