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Prevention and detection of misfolded amyloid proteins and their β-structure-rich aggregates are the two promising but different (pre)clinical strategies to treat and diagnose neurodegenerative diseases including Alzheimer's diseases (AD) and type II diabetes (T2D). Conventional strategies prevent the design of new pharmaceutical molecules with both amyloid inhibition and detection functions. Here, we propose a “like-interacts-like” design principle to de novo design a series of new self-assembling peptides (SAPs), enabling them to specifically and strongly interact with conformationally similar β-sheet motifs of Aβ (association with AD) and hIAPP (association with T2D). Collective in vitro experimental data from thioflavin (ThT), atomic force microscopy (AFM), circular dichroism (CD), and cell assay demonstrate that SAPs possess two integrated functions of (i) amyloid inhibition for preventing both Aβ and hIAPP aggregation by 34–61% and reducing their induced cytotoxicity by 7.6–35.4% and (ii) amyloid sensing for early detection of toxic Aβ and hIAPP aggregates using in-house SAP-based paper sensors and SPR sensors. The presence of both amyloid inhibition and detection in SAPs stems from strong molecular interactions between amyloid aggregates and SAPs, thus providing a new multi-target model for expanding the new therapeutic potentials of SAPs and other designs with built-in amyloid inhibition and detection functions. 

Amyloid cross-seeding, as a result of direct interaction and co-aggregation between different disease-causative peptides, is considered as a main mechanism for the spread of the overlapping pathology across different cells and tissues between different protein-misfolding diseases (PMDs). Despite the biomedical significance of amyloid cross-seeding in amyloidogenesis, it remains a great challenge to discover amyloid cross-seeding systems and reveal their cross-seeding structures and mechanisms. Herein, we are the first to report that GNNQQNY – a short fragment from yeast prion protein Sup35 – can cross-seed with both amyloid-β (Aβ, associated with Alzheimer's disease) and human islet amyloid polypeptide (hIAPP, associated with type II diabetes) to form β-structure-rich assemblies and to accelerate amyloid fibrillization. Dry, steric β-zippers, formed by the two β-sheets of different amyloid peptides, provide generally interactive and structural motifs to facilitate amyloid cross-seeding. The presence of different steric β-zippers in a variety of GNNQQNY-Aβ and GNNQQNY-hIAPP assemblies also explains amyloid polymorphism. In addition, alteration of steric zipper formation by single-point mutations of GNNQQNY and interactions of GNNQQNY with different Aβ and hIAPP seeds leads to different amyloid cross-seeding efficiencies, further confirming the existence of cross-seeding barriers. This work offers a better structural-based understanding of amyloid cross-seeding mechanisms linked to different PMDs. 

Double-network (DN) hydrogels, consisting of two contrasting and interpenetrating polymer networks, are considered as perhaps the toughest soft-wet materials. Current knowledge of DN gels from synthesis methods to toughening mechanisms almost exclusively comes from chemically-linked DN hydrogels by experiments. Molecular modeling and simulations of inhomogeneous DN structure in hydrogels have proved to be extremely challenging. Herein, we developed a new multiscale simulation platform to computationally investigate the early fracture of physically-chemically linked agar/polyacrylamide (agar/PAM) DN hydrogels at a long timescale. A “random walk reactive polymerization” (RWRP) was developed to mimic a radical polymerization process, which enables to construct a physically-chemically linked agar/PAM DN hydrogel from monomers, while conventional and steered MD simulations were conducted to examine the structural-dependent energy dissipation and fracture behaviors at the relax and deformation states. Collective simulation results revealed that energy dissipation of agar/PAM hydrogels was attributed to a combination of the pulling out of agar chains from the DNs, the disruption of massive hydrogen bonds between and within DN structures, and the strong association of water molecules with both networks, thus explaining a different mechanical enhancement of agar/PAM hydrogels. This computational work provided atomic details of network structure, dynamics, solvation, and interactions of a hybrid DN hydrogel, and a different structural-dependent energy dissipation mode and fracture behavior of a hybrid DN hydrogel, which help to design tough hydrogels with new network structures and efficient energy dissipation modes. Additionally, the RWRP algorithm can be generally applied to construct the radical polymerization-produced hydrogels, elastomers, and polymers.

 

Stimuli-responsive hydrogel strain sensors that synergize the advantages of both soft-wet hydrogels and smart functional materials have attracted rapidly increasing interest for exploring the opportunities from material design principles to emerging applications in electronic skins, health monitors, and human–machine interfaces. Stimuli-responsive hydrogel strain sensors possess smart and on-demand ability to specifically recognize various external stimuli and convert them into strain-induced mechanical, thermal, optical, and electrical signals. This review presents an up-to-date summary over the past five years on hydrogel strain sensors from different aspects, including material designs, gelation/fabrication methods, stimuli-responsive principles, and sensing performance. Hydrogel strain sensors are classified into five major categories based on the nature of the stimuli, and representative examples from each category are carefully selected and discussed in terms of structures, response mechanisms, and potential medical applications. Finally, current challenges and future perspectives of hydrogel strain sensors are tentatively proposed to stimulate more and better research in this emerging field. 

Zwitterionic materials are an important class of antifouling biomaterials for various applications. Despite such desirable antifouling properties, molecular-level understanding of the structure–property relationship associated with surface chemistry/topology/hydration and antifouling performance still remains to be elucidated. In this work, we computationally studied the packing structure, surface hydration, and antifouling property of three zwitterionic polymer brushes of poly(carboxybetaine methacrylate) (pCBMA), poly(sulfobetaine methacrylate) (pSBMA), and poly((2-(methacryloyloxy)ethyl)phosporylcoline) (pMPC) brushes and a hydrophilic PEG brush using a combination of molecular mechanics (MM), Monte Carlo (MC), molecular dynamics (MD), and steered MD (SMD) simulations. We for the first time determined the optimal packing structures of all polymer brushes from a wide variety of unit cells and chain orientations in a complex energy landscape. Under the optimal packing structures, MD simulations were further conducted to study the structure, dynamics, and orientation of water molecules and protein adsorption on the four polymer brushes, while SMD simulations to study the surface resistance of the polymer brushes to a protein. The collective results consistently revealed that the three zwitterionic brushes exhibited stronger interactions with water molecules and higher surface resistance to a protein than the PEG brush. It was concluded that both the carbon space length between zwitterionic groups and the nature of the anionic groups have a distinct effect on the antifouling performance, leading to the following antifouling ranking of pCBMA > pMPC > pSBMA. This work hopefully provides some structural insights into the design of new antifouling materials beyond traditional PEG-based antifouling materials.