Water purification by membranes is widely investigated to address concerns related to the scarcity of clean water. Achieving high flux and rejection simultaneously is a difficult challenge using such membranes because these properties are mutually exclusive in common artificial membranes. Nature has developed a method for this task involving water-channel membrane proteins known as aquaporins. Here, the design and fabrication of graphene oxide (GO)-based membranes with a surface-tethered peptide motif designed to mimic the water-selective filter of natural aquaporins is reported. The short RF8 (RFRFRFRF, where R and F represent arginine and phenylalanine, respectively) octapeptide is a concentrated form of the core component of the Ar/R (aromatic/arginine) water-selective filter in aquaporin. The resulting GO-RF8 shows superior flux and high rejection similar to natural aquaporins. Molecular dynamics simulation reveal the unique configuration of RF8 peptides and the transport of water in GO-RF8 membranes, supporting that RF8 effectively emulates the core function of aquaporins.

The biological function of proteins is closely related to its structural motion. For instance, structurally misfolded proteins do not function properly. Although we are able to experimentally obtain structural information on proteins, it is still challenging to capture their dynamics, such as transition processes. Therefore, we need a simulation method to predict the transition pathways of a protein in order to understand and study large functional deformations. Here, we present a new simulation method called normal mode-guided elastic network interpolation (NGENI) that performs normal modes analysis iteratively to predict transition pathways of proteins. To be more specific, NGENI obtains displacement vectors that determine intermediate structures by interpolating the distance between two end-point conformations, similar to a morphing method called elastic network interpolation. However, the displacement vector is regarded as a linear combination of the normal mode vectors of each intermediate structure, in order to enhance the physical sense of the proposed pathways. As a result, we can generate more reasonable transition pathways geometrically and thermodynamically. By using not only all normal modes, but also in part using only the lowest normal modes, NGENI can still generate reasonable pathways for large deformations in proteins. This study shows that global protein transitions are dominated by collective motion, which means that a few lowest normal modes play an important role in this process. NGENI has considerable merit in terms of computational cost because it is possible to generate transition pathways by partial degrees of freedom, while conventional methods are not capable of this.

Key properties of two-dimensional (2D) layered materials are highly strain tunable, arising from bond modulation and associated reconfiguration of the energy bands around the Fermi level. Approaches to locally controlling and patterning strain have included both active and passive elastic deformation via sustained loading and templating with nanostructures. Here, by float-capturing ultrathin flakes of single-crystal 2H-MoS 2 on amorphous holey silicon nitride substrates, we find that highly symmetric, high-fidelity strain patterns are formed. The hexagonally arranged holes and surface topography combine to generate highly conformal flakesubstrate coverage creating patterns that match optimal centroidal Voronoi tessellation in 2D Euclidean space. Using TEM imaging and diffraction, as well as AFM topographic mapping, we determine that the substrate-driven 3D geometry of the flakes over the holes consists of symmetric, out-of-plane bowl-like deformation of up to 35 nm, with in-plane, isotropic tensile strains of up to 1.8% (measured with both selected-area diffraction and AFM). Atomistic and image simulations accurately predict spontaneous formation of the strain patterns, with van der Waals forces and substrate topography as the input parameters. These results show that predictable patterns and 3D topography can be spontaneously induced in 2D materials captured on bare, holey substrates. The method also enables electron scattering studies of precisely aligned, substrate-free strained regions in transmission mode.

Carbon nanotubes (CNTs) have been considered a prominent nano-channel in cell membranes because of their prominent ion-conductance and ion-selectivity, offering agents for a biomimetic channel platform. Using a coarse-grained molecular dynamics simulation, we clarify a construction mechanism of vertical CNT nano-channels in a lipid membrane for a long period, which has been difficult to observe in previous CNT-lipid interaction simulations. The result shows that both the lipid coating density and length of CNT affect the suitable fabrication condition for a vertical and stable CNT channel. Also, simulation elucidated that a lipid coating on the surface of the CNT prevents the CNT from burrowing into the lipid membrane and the vertical channel is stabilized by the repulsion force between the lipids in the coating and membrane. Our study provides an essential understanding of how CNTs can form stable and vertical channels in the membrane, which is important for designing new types of artificial channels as biosensors for bio-fluidic studies.

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