Conformational Heterogeneity and Interchain Percolation Revealed in an Amorphous Conjugated Polymer

PUCHIK: A Python Package To Analyze Molecular Dynamics Simulations of Aspherical Nanoparticles

Accurately describing a nanoparticle's interface is crucial for understanding its internal structure, interfacial properties, and ultimately, its functionality. While current computational methods provide reasonable descriptions for spherical and quasi-spherical nanoparticles, there remains a need for effective models for aspherical structures such as capsules and rod-like systems. This work introduces Python Utility for Characterizing Heterogeneous Interfaces and Kinetics (PUCHIK), a novel algorithm developed to describe both spherelike and aspherical nanoparticles. With an accurate description of the location of the interface of the nanoparticle, this algorithm then allows for various other important quantities (e.g., densities of different atom/molecule types relative to the interface, volume of the nanoparticle, amount of solubilized molecules within the nanoparticle) to be calculated. Our software development, we focused on providing good performance to computationally demanding projects, while ensuring that the methodological approach can be adapted as a protocol for other code implementations.

Automated Analysis of Soft Matter Interfaces, Interactions, and Self-Assembly with PySoftK

Molecular dynamics simulations have become essential tools in the study of soft matter and biological macromolecules. The large amount of high-dimensional data associated with such simulations does not straightforwardly elucidate the atomistic mechanisms that underlie complex materials and molecular processes. Analysis of these simulations is complicated: the dynamics intrinsic to soft matter simulations necessitates careful application of specific, and often complex, algorithms to extract meaningful molecular scale understanding. There is an ongoing need for high-quality automated computational workflows to facilitate this analysis in a reproducible manner with minimal user input. In this work, we introduce a series of molecular simulation analysis tools for investigating interfaces, molecular interactions (including ring-ring stacking), and self-assembly. In addition, we include a number of auxiliary tools, including a useful function to unwrap molecular structures that are greater than half the length of their corresponding simulation box. These tools are contained in the PySoftK software package, making the application of these algorithms straightforward for the user. These new simulation analysis tools within PySoftK will support high-quality, reproducible analysis of soft matter and biomolecular simulations to bring about new predictive understanding in nano- and biotechnology.

Neuromorphic Computing Primitives Using Polymer-Networked Nanoparticles

Nanoparticle networks have potential applications in brain-like computing yet their ability to adopt different states remains unexplored. In this work, we reveal the dynamics of the attachment of polyelectrolytes onto gold nanoparticles (AuNPs), using a bottom-up two-bead-monomer dissipative particle dynamics (TBM-DPD) model to show the heterogeneity of polymer coverage. We found that the use of one polyelectrolyte homopolymer limits the complexity of the possible engineered nanoparticle networks (ENPNs) that can be built. In addressing this challenge, we first found the commensurability rules between the numbers of AuNPs and poly(allylamine hydrochloride)s (PAHs). This gives rise to a well-defined valency of a AuNP which is the maximum number of PAHs that it can accommodate. We further use an engineered block copolymer, which has a conductive middle block to mediate the distance between a dimer of AuNP. We argue that by controlling the length of conductive block that is connecting the AuNPs and their respective topology, we can have ENPNs potentially adopt multiple states necessary for primitive neuromorphic computing.

Emergent conformational and aggregation properties of synergistic antimicrobial peptide combinations

Synergy between antimicrobial peptides (AMPs) may be the key to their evolutionary success and could be exploited to develop more potent antibacterial agents. One of the factors thought to be essential for AMP potency is their conformational flexibility, but characterising the diverse conformational states of AMPs experimentally remains challenging. Here we introduce a method for characterising the conformational flexibility of AMPs and provide new insights into how the interplay between conformation and aggregation in synergistic AMP combinations yields emergent properties. We use unsupervised learning and molecular dynamics simulations to show that mixing two AMPs from the Winter Flounder family (pleurocidin (WF2) & WF1a) constrains their conformational space, reducing the number of distinct conformations adopted by the peptides, most notably for WF2. The aggregation behaviour of the peptides is also altered, favouring the formation of higher-order aggregates upon mixing. Critically, the interaction between WF1a and WF2 influences the distribution of WF2 conformations within aggregates, revealing how WF1a can modulate WF2 behaviour. Our work paves the way for deeper understanding of the synergy between AMPs, a fundamental process in nature.

Self-assembly of sustainable plant protein protofilaments into a hydrogel for ultra-low friction across length scales

Designing plant protein-based aqueous lubricants can be of great potential to achieve sustainability objectives by capitalising on inherent functional groups without using synthetic chemicals; however, such a concept remains in its infancy. Here, we engineer a class of self-assembled sustainable materials by using plant-based protofilaments and their assembly within a biopolymeric hydrogel giving rise to a distinct patchy architecture. By leveraging physical interactions, this material offers superlubricity with friction coefficients of 0.004-to-0.00007 achieved under moderate-to-high (102-to-103 kPa) contact pressures. Multiscale experimental measurements combined with molecular dynamics simulations reveal an intriguing synergistic mechanism behind such ultra-low friction - where the uncoated areas of the protofilaments glue to the surface by hydrophobic interactions, whilst the hydrogel offers the hydration lubrication. The current approach establishes a robust platform towards unlocking an untapped potential of using plant protein-based building blocks across diverse applications where achieving superlubricity and environmental sustainability are key performance indicators.

Proline-Tyrosine Ring Interactions in Black Widow Dragline Silk Revealed by Solid-State Nuclear Magnetic Resonance and Molecular Dynamics Simulations

Selective one-dimensional 13C-13C spin-diffusion solid-state nuclear magnetic resonance (SSNMR) provides evidence for CH/π ring packing interactions between Pro and Tyr residues in 13C-enriched Latrodectus hesperus dragline silk. The secondary structure of Pro-containing motifs in dragline spider silks consistently points to an elastin-like type II β-turn conformation based on 13C chemical shift analysis. 13C-13C spin diffusion measurements as a function of mixing times allow for the measurement of spatial proximity between the Pro and Tyr rings to be ∼0.5-1 nm, supporting strong Pro-Tyr ring interactions that likely occur through a CH/π mechanism. These results are supported by molecular dynamics (MD) simulations and analysis and reveals new insights into the secondary structure and Pro-Tyr ring stacking interactions for one of nature's toughest biomaterials.

Topology-controlled self-assembly of amphiphilic block copolymers

Contemporary synthetic chemistry approaches can be used to yield a range of distinct polymer topologies with precise control. The topology of a polymer strongly influences its self-assembly into complex nanostructures however a clear mechanistic understanding of the relationship between polymer topology and self-assembly has not yet been developed. In this work, we use atomistic molecular dynamics simulations to provide a nanoscale picture of the self-assembly of three poly(ethylene oxide)-poly(methyl acrylate) block copolymers with different topologies into micelles. We find that the topology affects the ability of the micelle to form a compact hydrophobic core, which directly affects its stability. Also, we apply unsupervised machine learning techniques to show that the topology of a polymer affects its ability to take a conformation in response to the local environment within the micelles. This work provides foundations for the rational design of polymer nanostructures based on their underlying topology.