Lipid-anchor display on peptoid nanosheets via co-assembly for multivalent pathogen recognition

Protein-mimetic peptoid nanoarchitectures for pathogen recognition and neutralization

Recent outbreaks of both new and existing infectious pathogens have threatened healthcare systems around the world. Therefore, it is vital to detect and neutralize pathogens to prevent their spread and treat infected patients. This consideration has led to the development of biosensors and antibiotics inspired by the structure and function of antibodies and antimicrobial peptides (AMPs), which constitute adaptive and innate immunity, efficiently protecting the human body against invading pathogens. Herein, we provide an overview of recent advances in the detection and neutralization of pathogens using protein-mimetic peptoid nanoarchitectures. Peptoids are bio-inspired and sequence-defined polymers composed of repeating N-substituted glycine units. They can spontaneously fold into well-defined three-dimensional nanostructures that encode chemical information depending on their sequences. Loop-functionalized peptoid nanosheets have been constructed by mimicking antibodies containing chemically variable loops as binding motifs for their respective target pathogen. Furthermore, by mimicking the cationic amphipathic features of natural AMPs, helical peptoids and their assemblies have been developed to achieve selective anti-infective activity owing to their intrinsic ability to interact with bacterial membranes and viral envelopes. We believe that this mini-review furnishes in-depth insight into how to construct protein-like nanostructures via the self-assembly of peptoids for application in the detection of pathogens and the treatment of infectious diseases for future healthcare applications.

Applications of Material-Binding Peptides: A Review

Material-binding peptides (MBPs) are functionalized adhesive materials consisting of a few to several dozen amino acids. This affinity between MBPs and materials is regulated by multiple interactions, including hydrogen bonding, electrostatic, hydrophobic interactions, and π-π stacking. They show selective binding and high affinity to a diverse range of inorganic and organic materials, such as silicon-based materials, metals, metal compounds, carbon materials, and polymers. They are used to improve the biocompatibility of materials, increase the efficiency of material synthesis, and guide the controlled synthesis of nanomaterials. In addition, these can be used for precise targeting of proteins by conjugating to target biomolecules. In this review, we summarize the main designs and applications of MBPs in recent years. The discussions focus on more efficient and functional peptides, including evolution and overall design of MBPs. We have also highlighted the recent applications of MBPs, such as functionalization of material surfaces, synthesis of nanomaterials, drug delivery, cancer therapy, and plastic degradation. Besides, we also discussed the development trend of MBPs. This interpretation will accelerate future investigations to bottleneck the drawbacks of available MBPs, promoting their commercial applications.

Bottom-up supramolecular assembly in two dimensions

The self-assembly of molecules in two dimensions (2D) is gathering attention from all disciplines across the chemical sciences. Attracted by the interesting properties of two-dimensional inorganic analogues, monomers of different chemical natures are being explored for the assembly of dynamic 2D systems. Although many important discoveries have been already achieved, great challenges are still to be addressed in this field. Hierarchical multicomponent assembly, directional non-covalent growth and internal structural control are a just a few of the examples that will be discussed in this perspective about the exciting present and the bright future of two-dimensional supramolecular assemblies.

The self-assembly of molecules in two dimensions (2D) is gathering attention from all disciplines across the chemical sciences. This perspective discusses the main strategies to direct the supramolecular self-assembly of organic monomers in 2D.

Hierarchical Nanomaterials Assembled from Peptoids and Other Sequence-Defined Synthetic Polymers

In nature, the self-assembly of sequence-specific biopolymers into hierarchical structures plays an essential role in the construction of functional biomaterials. To develop synthetic materials that can mimic and surpass the function of these natural counterparts, various sequence-defined bio- and biomimetic polymers have been developed and exploited as building blocks for hierarchical self-assembly. This review summarizes the recent advances in the molecular self-assembly of hierarchical nanomaterials based on peptoids (or poly-N-substituted glycines) and other sequence-defined synthetic polymers. Modern techniques to monitor the assembly mechanisms and characterize the physicochemical properties of these self-assembly systems are highlighted. In addition, discussions about their potential applications in biomedical sciences and renewable energy are also included. This review aims to highlight essential features of sequence-defined synthetic polymers (e.g., high stability and protein-like high-information content) and how these unique features enable the construction of robust biomimetic functional materials with high programmability and predictability, with an emphasis on peptoids and their self-assembled nanomaterials.

Transfer learning of memory kernels for transferable coarse-graining of polymer dynamics

The present work concerns the transferability of coarse-grained (CG) modeling in reproducing the dynamic properties of the reference atomistic systems across a range of parameters. In particular, we focus on implicit-solvent CG modeling of polymer solutions. The CG model is based on the generalized Langevin equation, where the memory kernel plays the critical role in determining the dynamics in all time scales. Thus, we propose methods for transfer learning of memory kernels. The key ingredient of our methods is Gaussian process regression. By integration with the model order reduction via proper orthogonal decomposition and the active learning technique, the transfer learning can be practically efficient and requires minimum training data. Through two example polymer solution systems, we demonstrate the accuracy and efficiency of the proposed transfer learning methods in the construction of transferable memory kernels. The transferability allows for out-of-sample predictions, even in the extrapolated domain of parameters. Built on the transferable memory kernels, the CG models can reproduce the dynamic properties of polymers in all time scales at different thermodynamic conditions (such as temperature and solvent viscosity) and for different systems with varying concentrations and lengths of polymers.

Diblock copolypeptoids: a review of phase separation, crystallization, self-assembly and biological applications

Diblock copolypeptoids have the capacity to phase separate, crystallize, and self-assemble into a variety of nanostructures, which have shown great potential in a variety of biological applications.