Self-assembling peptide semiconductors

Accelerating Cellular Uptake with Unnatural Amino Acid for Inhibiting Immunosuppressive Cancer Cells

Targeting immunosuppressive metastatic cancer cells is a key challenge in therapy. We recently have shown that a rigid-rod aromatic, pBP-NBD, that responds to enzymes and kill immunosuppressive metastatic osteosarcoma (mOS) and castration resistant prostate cancer (CRPC) cells in mimetic bone microenvironment. However, pBP-NBD demonstrated moderate efficacy against CRPC cells. To enhance activity, we incorporated the unnatural amino acid L- or D-4,4'-biphenylalanine (L- or D-BiP) into pBP-NBD, drastically increasing cellular uptake and CRPC inhibition. Specifically, we inserted BiP into pBP-NBD to target mOS (Saos2 and SJSA1) and CRPC (VCaP and PC3) cells with overexpressed phosphatases. Our results show that the D-peptide backbone with an aspartate methyl diester at the C-terminal offers the highest activity against these immunosuppressive mOS and CRPC cells. Importantly, imaging shows that the peptide assemblies almost instantly enter the cells and accumulate primarily within the endoplasmic reticulum of Saos2, SJSA1, and PC3 cells and at the lysosomes of VCaP cells. By using BiP to boost cellular uptake and self-assembly within cancer cells, this work illustrates an unnatural hydrophobic amino acid as a versatile and effective residue to boost endocytosis of synthetic peptides for intracellular self-assembly.

Selective Induction of Molecular Assembly to Tissue-Level Anisotropy on Peptide-Based Optoelectronic Cardiac Biointerfaces

The conduction efficiency of ions in excitable tissues and of charged species in organic conjugated materials both benefit from having ordered domains and anisotropic pathways. In this study, a photocurrent-generating cardiac biointerface is presented, particularly for investigating the sensitivity of cardiomyocytes to geometrically comply to biomacromolecular cues differentially assembled on a conductive nanogrooved substrate. Through a polymeric surface-templated approach, photoconductive substrates with symmetric peptide-quaterthiophene (4T)-peptide units assembled as 1D nanostructures on nanoimprinted polyalkylthiophene (P3HT) surface are developed. The 4T-based peptides studied here can form 1D nanostructures on prepatterned polyalkylthiophene substrates, as directed by hydrogen bonding, aromatic interactions between 4T and P3HT, and physical confinement on the nanogrooves. It is observed that smaller 4T-peptide units that can achieve a higher degree of assembly order within the polymeric templates serve as a more efficient driver of cardiac cytoskeletal anisotropy than merely presenting aligned -RGD bioadhesive epitopes on a nanotopographic surface. These results unravel some insights on how cardiomyocytes perceive submicrometer dimensionality, local molecular order, and characteristics of surface cues in their immediate environment. Overall, the work offers a cardiac patterning platform that presents the possibility of a gene modification-free cardiac photostimulation approach while controlling the conduction directionality of the biotic and abiotic components.

Bioinspired Flexible Hydrogelation with Programmable Properties for Tactile Sensing

Tactile sensing requires integrated detection platforms with distributed and highly sensitive haptic sensing capabilities along with biocompatibility, aiming to replicate the physiological functions of the human skin and empower industrial robotic and prosthetic wearers to detect tactile information. In this regard, short peptide-based self-assembled hydrogels show promising potential to act as bioinspired supramolecular substrates for developing tactile sensors showing biocompatibility and biodegradability. However, the intrinsic difficulty to modulate the mechanical properties severely restricts their extensive employment. Herein, by controlling the self-assembly of 9-fluorenylmethoxycarbonyl-modifid diphenylalanine (Fmoc-FF) through introduction of polyethylene glycol diacrylate (PEGDA), wider nanoribbons are achieved by untwisting from well-established thinner nanofibers, and the mechanical properties of the supramolecular hydrogels can be enhanced 10-fold, supplying bioinspired supramolecular encapsulating substrate for tactile sensing. Furthermore, by doping with poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) and 9-fluorenylmethoxycarbonyl-modifid 3,4-dihydroxy-l-phenylalanine (Fmoc-DOPA), the Fmoc-FF self-assembled hydrogels can be engineered to be conductive and adhesive, providing bioinspired sensing units and adhesive layer for tactile sensing applications. Therefore, the integration of these modules results in peptide hydrogelation-based tactile sensors, showing high sensitivity and sustainable responses with intrinsic biocompatibility and biodegradability. The findings establish the feasibility of developing programmable peptide self-assembly with adjustable features for tactile sensing applications.

Self-assembled peptide nanotubes (SPNTs)/SnO2nanocomposites for high-performance NO2sensing at room temperature

Nitrogen dioxide (NO2) is a major pollutant that poses significant risks to sustainable human life. As a result, a growing focus has been placed on the development of highly selective and sensitive gas sensors for NO2. Traditional cutting-edge non-organic NO2gas detectors often necessitate stringent production conditions and potentially harmful materials, which are not environmentally friendly, and these shortcomings have limited their widespread practical use. To overcome these challenges, we synthesized self-assembled peptide nanotubes (SPNTs) through a molecular self-assembly process. The SPNTs were then combined with SnO2in varying proportions to construct NO2gas sensors. The design of this sensor ensured efficient electron transfer and leverage the extensive surface area of the SPNTs for enhanced gas adsorption and the effective dispersion of SnO2nanoparticles. Notably, the performance of the sensor, including its sensitivity, response time, and recovery rate, along with a lower detection threshold, could be finely tuned by varying the SPNTs content. This approach illustrated the potential of bioinspired methodologies, using peptide self-assemblies, to develop integrated sensors for pollutant detection, providing a significant development in environmentally conscious sensor technology.

Two-Dimensional Layered Nanomaterials Steering Self-Assembly of Dodecapeptides with Three Building Blocks

Self-assembly of peptides on layered nanomaterials such as graphite and MoS2 in the formation of long-range ordered two-dimensional nanocrystal patterns leading to its potential applications for biosensing and bioelectronics has attracted significant interest in nanoscience and nanotechnology. However, controlling the self-assembly of peptides on nanomaterials is still challenging due to the unclear role of nanomaterials in steering self-assembly. Here, we used the in-situ AFM technique to capture different changes of peptide coverage as well as lengthening and widening rates depending on peptide concentrations, show the distinct boundary dynamics of two stabilized peptide domains, and resolve the molecular resolution structural differences and specific orientation of peptide on both nanomaterials. Moreover, ex-situ results showed that the nanomaterial layers tuned the opposite changes of nanowire heights and densities and displayed the different water-resistance stabilities on both nanomaterials. This work provides a basis for understanding nanomaterials steering peptide self-assembly and using hybrid bionanomaterials as a scaffold, enabling for potential biosensing and bioelectronics applications.

Metal-Assisted Carbohydrate Assembly

The sequence-controlled assembly of nucleic acids and amino acids into well-defined superstructures constitutes one of the most revolutionary technologies in modern science. The elaboration of such superstructures from carbohydrates, however, remains elusive and largely unexplored on account of their intrinsic constitutional and configurational complexity, not to mention their inherent conformational flexibility. Here, we report the bottom-up assembly of two classes of hierarchical superstructures that are formed from a highly flexible cyclo-oligosaccharide─namely, cyclofructan-6 (CF-6). The formation of coordinative bonds between the oxygen atoms of CF-6 and alkali metal cations (i) locks a myriad of flexible conformations of CF-6 into a few rigid conformations, (ii) bridges adjacent CF-6 ligands, and (iii) gives rise to the multiple-level assembly of three extended frameworks. The hierarchical superstructures present in these frameworks have been shown to modulate their nanomechanical properties. This research highlights the unique opportunities of constructing convoluted superstructures from carbohydrates and should encourage future endeavors in this underinvestigated field of science.

Futuristic Alzheimer's therapy: acoustic-stimulated piezoelectric nanospheres for amyloid reduction

The degeneration of neurons due to the accumulation of misfolded amyloid aggregates in the central nervous system (CNS) is a fundamental neuropathology of Alzheimer's disease (AD). It is believed that dislodging/clearing these amyloid aggregates from the neuronal tissues could lead to a potential cure for AD. In the present work, we explored biocompatible polydopamine-coated piezoelectric polyvinylidene fluoride (DPVDF) nanospheres as acoustic stimulus-triggered anti-fibrillating and anti-amyloid agents. The nanospheres were tested against two model amyloidogenic peptides, including the reductionist model-based amyloidogenic dipeptide, diphenylalanine, and the amyloid polypeptide, amyloid beta (Aβ42). Our results revealed that DPVDF nanospheres could effectively disassemble the model peptide-derived amyloid fibrils under suitable acoustic stimulation. In vitro studies also showed that the stimulus activated DPVDF nanospheres could efficiently alleviate the neurotoxicity of FF fibrils as exemplified in neuroblastoma, SHSY5Y, cells. Studies carried out in animal models further validated that the nanospheres could dislodge amyloid aggregates in vivo and also help the animals regain their cognitive behavior. Thus, these acoustic stimuli-activated nanospheres could serve as a novel class of disease-modifying nanomaterials for non-invasive electro-chemotherapy of Alzheimer's disease.

Controlled Assembly and Disassembly of Higher-Order Peptide Nanotubes

The controlled peptide self-assembly and disassembly are not only implicated in many cellular processes but also possess huge application potential in a wide range of biotechnology and biomedicine. β-sheet peptide assemblies possess high kinetic stability, so it is usually hard to disassemble them rapidly. Here, we reported that both the self-assembly and disassembly of a designed short β-sheet peptide IIIGGHK could be well harnessed through the variations of concentration, pH, and mechanical stirring. Microscopic imaging, neutron scattering, and infrared spectroscopy were used to track the assembly and disassembly processes upon these stimuli, especially the interconversion between thin, left-handed protofibrils and higher-order nanotubes with superstructural right-handedness. The underlying rationale for these controlled disassembly processes mainly lies in the fact that the specific His-His interactions between protofibrils were responsive to these stimuli. By taking advantage of the peptide self-assembly and disassembly, the encapsulation of the hydrophobic drug curcumin and its rapid release upon stimuli were achieved. Additionally, the peptide hydrogels facilitated the differentiation of neural cells while maintaining low cell cytotoxicity. We believe that such dynamic and reversible structural transformation in this work provides a distinctive paradigm for controlling the peptide self-assembly and disassembly, thus laying a foundation for practical applications of peptide assemblies.

Stimuli-responsive peptide hydrogels for biomedical applications

Stimuli-responsive hydrogels can respond to external stimuli with a change in the network structure and thus have potential application in drug release, intelligent sensing, and scaffold construction. Peptides possess robust supramolecular self-assembly ability, enabling spontaneous formation of nanostructures through supramolecular interactions and subsequently hydrogels. Therefore, peptide-based stimuli-responsive hydrogels have been widely explored as smart soft materials for biomedical applications in the last decade. Herein, we present a review article on design strategies and research progress of peptide hydrogels as stimuli-responsive materials in the field of biomedicine. The latest design and development of peptide hydrogels with responsive behaviors to stimuli are first presented. The following part provides a systematic overview of the functions and applications of stimuli-responsive peptide hydrogels in tissue engineering, drug delivery, wound healing, antimicrobial treatment, 3D cell culture, biosensors, etc. Finally, the remaining challenges and future prospects of stimuli-responsive peptide hydrogels are proposed. It is believed that this review will contribute to the rational design and development of stimuli-responsive peptide hydrogels toward biomedical applications.

Peptide Self-assembly: From Ordered to Disordered

ConspectusBiomolecular self-assembly is a ubiquitous occurrence in nature that gives rise to sophisticated superstructures that enable the implementation of complex biological functions. It encompasses both ordered structures, such as the DNA double helix, and disordered structures, such as the nucleolus and other nonmembranous organelles. In contrast to these highly organized ordered structures, which exhibit specific patterns or symmetry, disordered structures are characterized by their flexible and randomized molecular organization, which provides versatility, dynamicity, and adaptability to biological systems and contributes to the complexity and functionality of living organisms. However, these disordered structures usually exist in a thermodynamically metastable state. This means that these disordered structures are unstable and difficult to observe due to their short existence time. Achieving disordered structures through precise control of the assembly process and ensuring their stability and integrity pose significant challenges. Currently, ongoing research efforts are focused on the self-assembly of proteins with intrinsically disordered regions (IDRs). However, the structural complexity and instability of proteins present prohibitive difficulties in elucidating the multiscale self-assembly process. Therefore, simple peptides, as a segment of proteins, hold great promise in constructing self-assembly systems for related research. Since our finding on droplet-like disordered structures that occur transiently during the peptide self-assembly (PSA), our research is centered around the dynamic evolution of peptide supramolecular systems, particularly the modulation of a variety of assembled structures ranging from ordered to disordered.In this Account, we narrate our recent research endeavors on supramolecular structures formed by PSA, spanning from ordered structures to disordered structures. We delve into the mechanisms of structural regulation, shedding light on how these peptide-based structures can be controlled more precisely. Moreover, we emphasize the functional applications that arise from these structures. To begin, we conduct a comprehensive overview of various types of ordered structures that emerge from PSA, showcasing their diverse applications. Following, we elaborate on the discovery and development of droplet-like disordered structures that arise during PSA. A mechanistic study on multistep self-assembly processes mediated by liquid-liquid phase separation (LLPS) is critically emphasized. Ordered structures with different morphologies and functions can be obtained by subtly controlling and adjusting the metastable liquid droplets. In particular, we have recently developed solid glasses with long-range disorder, including noncovalent biomolecular glass based on amino acid and peptide derivatives, as well as high-entropy glass based on cyclic peptides. This demonstrates the great potential of using biologically derived molecules to create green and sustainable glassy materials.

Computational Design of Peptide Assemblies

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Bioinspired Amino Acid Based Materials in Bionanotechnology: From Minimalistic Building Blocks and Assembly Mechanism to Applications

Inspired by natural hierarchical self-assembly of proteins and peptides, amino acids, as the basic building units, have been shown to self-assemble to form highly ordered structures through supramolecular interactions. The fabrication of functional biomaterials comprised of extremely simple biomolecules has gained increasing interest due to the advantages of biocompatibility, easy functionalization, and structural modularity. In particular, amino acid based assemblies have shown attractive physical characteristics for various bionanotechnology applications. Herein, we propose a review paper to summarize the design strategies as well as research advances of amino acid based supramolecular assemblies as smart functional materials. We first briefly introduce bioinspired reductionist design strategies and assembly mechanism for amino acid based molecular assembly materials through noncovalent interactions in condensed states, including self-assembly, metal ion mediated coordination assembly, and coassembly. In the following part, we provide an overview of the properties and functions of amino acid based materials toward applications in nanotechnology and biomedicine. Finally, we give an overview of the remaining challenges and future perspectives on the fabrication of amino acid based supramolecular biomaterials with desired properties. We believe that this review will promote the prosperous development of innovative bioinspired functional materials formed by minimalistic building blocks.

Titanium dioxide nanoparticles embedded in assembled dipeptide hydrogels for microfluidic photodegradation

Dipeptides can be self-assembled via non-covalent bonds towards functional nanostructures for diverse applications in nanotechnology. Here, we introduce a convenient microfluidics-guided dipeptide design as a platform for photodegradation of contaminants in water. Titanium dioxide (TiO2) nanoparticles (NPs) are chosen as photocatalysts due to their vastly studied properties. By using a well-defined microchannel architecture, the dipeptide N-fluorenylmethoxycarbonyl diphenylalanine (Fmoc-FF) and TiO2 NPs are efficiently mixed leading to a self-assembled Fmoc-FF hydrogel with embedded TiO2. Owing to shear-thinning and rapid self-healing of Fmoc-FF hydrogels, we can transfer and inject Fmoc-FF/TiO2 hydrogels into any other microdevice for specific applications, where these low-molecular-weight-gelator- (LMWG-)based Fmoc-FF hydrogels fill out the microchannel volume. Different morphologies of Fmoc-FF/TiO2 hydrogels are obtained by simple concentration screening of TiO2 NPs and Fmoc-FF. Owing to the density of the three-dimensionally twined Fmoc-FF nanofibers, solutions swelling the dipeptide hydrogel can be exchanged without leaching out TiO2 NPs. By further analysis, our hydrogel-filled flow cell can be employed for continuous-flow photodegradation in water under light irradiation. Especially, compared to the TiO2 NPs suspension, Fmoc-FF/TiO2 hydrogels with relatively low concentrations of TiO2 exhibit enhanced photodegradation capabilities due to better dispersion of nanoparticles. Such strategy provides a versatile platform for embedment of small inorganic catalysts or enzymes for (bio-)chemical conversion of solutes passing through the hydrogel network.

New Insight into the Structural Nature of Diphenylalanine Nanotube through Comparison with Amyloid Assemblies

Diphenylalanine (FF) nanotubes are a star material in the field of peptide self-assembly and have demonstrated numerous intriguing applications. Due to its resemblance to amyloid assembly, the FF nanotube is widely regarded as a simplified mimic of amyloids. Yet, whether FF nanotube truly possesses amyloid structure remains an open question. To better understand the structural nature of FF nanotube, we herein performed a comparative structural investigation between FF nanotube and typical amyloid systems by Aβ1-40, Aβ1-42, Aβ16-22, Aβ13-23, α-synuclein, and lysozyme using Fourier transform infrared spectroscopy. Through this comparative investigation, we obtained clear evidence to support that the FF nanotube does not possess a β-sheet structure, a key structural characteristic of amyloid assembly, thus revealing the non-amyloid structural nature of the FF nanotube. At last, in light of our new finding, we further discussed the unique self-assembly behaviors of FF during nanotube formation and the implications of our work for FF nanotube related applications.

Enzymatic Dimerization-Induced Self-Assembly of Alanine-Tyramine Conjugates into Versatile, Uniform, Enzyme-Loaded Organic Nanoparticles

Herein, we report a novel enzymatic dimerization-induced self-assembly (e-DISA) procedure that converts alanine-tyramine conjugates into highly uniform enzyme-loaded nanoparticles (NPs) or nanocontainers by the action of horseradish peroxidase (HRP) in an aqueous medium under ambient conditions. The NP formation was possible with both enantiomers of alanine, and the average diameter could be varied from 150 nm to 250 nm (with a 5-12 % standard deviation of as-prepared samples) depending on the precursor concentration. About 60 % of the added HRP enzyme was entrapped within the NPs and was subsequently utilized for post-synthetic modification of the NPs with phenolic compounds such as tyramine or tannic acid. One-pot multi-enzyme entrapment of glucose oxidase (GOx) and peroxidase (HRP) within the NPs was also achieved. These GOx-HRP loaded NPs allowed multimodal detection of glucose, including that present in human saliva, with a limit of detection (LoD) of 740 nM through fluorimetry. The NPs exhibited good cytocompatibility and were stable to changes in pH (acidic to basic), temperature, ultrasonication, and even the presence of organic solvent (EtOH) to a certain extent, since they are stabilized by intermolecular hydrogen bonding, π-π, and CH-π interactions. The proposed e-DISA procedure can be widely expanded through the design of diverse enzyme-responsive precursors.

A repertoire of nanoengineered short peptide-based hydrogels and their applications in biotechnology

Peptide nanotechnology has currently bridged the gap between materials and biological worlds. Bioinspired self-assembly of short-peptide building blocks helps take the leap from molecules to materials by taking inspiration from nature. Owing to their intrinsic biocompatibility, high water content, and extracellular matrix mimicking fibrous morphology, hydrogels engineered from the self-assembly of short peptides exemplify the actualization of peptide nanotechnology into biomedical products. However, the weak mechanical property of these hydrogels jeopardizes their practical applications. Moreover, their functional diversity is limited since they comprise only one building block. Nanoengineering the networks of these hydrogels by incorporating small molecules, polymers, and inorganic/carbon nanomaterials can augment the mechanical properties while retaining their dynamic supramolecular nature. These additives interact with the peptide building blocks supramolecularly and may enhance the branching of the networks via coassembly or crystallographic mismatch. This phenomenon expands the functional diversity of these hydrogels by synergistically combining the attributes of the individual building blocks. This review highlights such nanoengineered peptide hydrogels and their applications in biotechnology. We have included exemplary works on supramolecular modification of the peptide hydrogel networks by integrating other small molecules, synthetic/biopolymers, conductive polymers, and inorganic/carbon nanomaterials and shed light on their various utilities focusing on biotechnology. We finally envision some future prospects in this highly active field of research.

Piezoactive Bioorganic Diphenylalanine Films: Mechanism of Phase Formation

Self-organized peptides are unique materials with various applications in biology, medicine, and nanotechnology. Many of these applications require fabrication of homogeneous thin films having high piezoelectric effect and sufficiently low roughness. Recently, a facile method for the controlled deposition of flat solid films of the most studied peptide, diphenylalanine (FF), has been proposed, which is based on the crystallization of FF in the amorphous phase under the action of water vapor. This method is very advantageous compared with crystallization from a liquid phase reported previously. Here, we thoroughly investigate the mechanism of solid-state transformation from the amorphous to crystalline phase. The study revealed that the process proceeds in two distinct stages, maintaining clamped condition of self-assembling building blocks that preserve the films' morphology and high piezoelectric activity. We emphasize the critical role of water diffusion that governs two-dimensional growth of crystalline domains in FF films, merging in very dense, flat, and homogeneous films. These findings open a wide perspective for using this methodology for the direct fabrication of biofilms from the amorphous phase. We thus expect the application of these films to various nanotechnological applications of self-assembled structures.

Neutron reflection and scattering in characterising peptide assemblies

Self-assemblies of de novo designed short peptides at interface and in bulk solution provide potential platforms for developing applications in many medical and technological areas. However, characterising how bioinspired supramolecular nanostructures evolve with dynamic self-assembling processes and respond to different stimuli remains challenging. Neutron scattering technologies including small angle neutron scattering (SANS) and neutron reflection (NR) can be advantageous and complementary to other state-of-the-art techniques in tracing structural changes under different conditions. With more neutron sources now available, SANS and NR are becoming increasingly popular in studying self-assembling processes of diverse peptide and protein systems, but the difficulty in experimental manipulation and data analysis can deter beginners. This review will introduce the basic theory, general experimental setup and data analysis of SANS and NR, followed by provision of their applications in characterising interfacial and solution self-assemblies of representative peptides and proteins. SANS and NR are remarkably effective in determining the morphological features self-assembled short peptides, especially size and shape transitions as a result of either sequence changes or in response to environmental stimuli, demonstrating the unique capability of NR and SANS in unravelling the interactive processes. These examples highlight the potential of NR and SANS in supporting the development of novel short peptides and proteins as biopharmaceutical candidates in the fight against many diseases and infections that share common features of membrane interactive processes.

Peptide-Based Optical/Electronic Materials: Assembly and Recent Applications in Biomedicine, Sensing, and Energy Storage

The unique optical and electronic properties of living systems are impressive. Peptide-based supramolecular self-assembly systems attempt to mimic these properties by preparation optical/electronic function materials with specific structure through simple building blocks, rational molecular design, and specific kinetic stimulation. From the perspective of building blocks and assembly strategies, the unique optical and electronic properties of peptide-based nanostructures, including peptides self-assembly and peptides regulate the assembly of external function subunits, are systematically reviewed. Additionally, their applications in biomedicine, sensing, and energy storage are also highlighted. This bioinspired peptide-based function material is one of the hot candidates for the new generation of green intellect materials, with many advantages such as biocompatibility, environmental friendliness, and adjustable morphology.

Deep Learning Empowers the Discovery of Self-Assembling Peptides with Over 10 Trillion Sequences

Self-assembling of peptides is essential for a variety of biological and medical applications. However, it is challenging to investigate the self-assembling properties of peptides within the complete sequence space due to the enormous sequence quantities. Here, it is demonstrated that a transformer-based deep learning model is effective in predicting the aggregation propensity (AP) of peptide systems, even for decapeptide and mixed-pentapeptide systems with over 10 trillion sequence quantities. Based on the predicted AP values, not only the aggregation laws for designing self-assembling peptides are derived, but the transferability relation among the APs of pentapeptides, decapeptides, and mixed pentapeptides is also revealed, leading to discoveries of self-assembling peptides by concatenating or mixing, as consolidated by experiments. This deep learning approach enables speedy, accurate, and thorough search and design of self-assembling peptides within the complete sequence space of oligopeptides, advancing peptide science by inspiring new biological and medical applications.

Supramolecular Nanoarchitectonics Based on Antagonist Peptide Self-Assembly for Treatment of Liver Fibrosis

Therapeutic peptides have attracted increasing attention as anti-fibrotic drug candidates. However, the rapid degradation and insufficient liver accumulation of therapeutic peptides have seriously hampered their clinical translation. Here, the use of supramolecular nanoarchitectonics is reported to fabricate nanodrugs from therapeutic peptides for treating liver fibrosis. Self-assembling antagonist peptides are rationally designed and manipulated into uniform peptide nanoparticles with well-defined nanostructures and uniform sizes. Significantly, the peptide nanoparticles show enhanced accumulation in liver sites and limited distribution in other tissues. In vivo results show that the peptide nanoparticles exhibit greatly enhanced anti-fibrotic activity compared to the pristine antagonist along with good biocompatibility. These results indicate that self-assembly is a promising nanoarchitectonics approach to enhance the anti-fibrotic activity of therapeutic peptides for treating liver fibrosis.

Geometric Transformations Afforded by Rotational Freedom in Aramid Amphiphile Nanostructures

Molecular self-assembly in water leads to nanostructure geometries that can be tuned owing to the highly dynamic nature of amphiphiles. There is growing interest in strongly interacting amphiphiles with suppressed dynamics, as they exhibit ultrastability in extreme environments. However, such amphiphiles tend to assume a limited range of geometries upon self-assembly due to the specific spatial packing induced by their strong intermolecular interactions. To overcome this limitation while maintaining structural robustness, we incorporate rotational freedom into the aramid amphiphile molecular design by introducing a diacetylene moiety between two aramid units, resulting in diacetylene aramid amphiphiles (D-AAs). This design strategy enables rotations along the carbon-carbon sp hybridized bonds of an otherwise fixed aramid domain. We show that varying concentrations and equilibration temperatures of D-AA in water lead to self-assembly into four different nanoribbon geometries: short, extended, helical, and twisted nanoribbons, all while maintaining robust structure with thermodynamic stability. We use advanced microscopy, X-ray scattering, spectroscopic techniques, and two-dimensional (2D) NMR to understand the relationship between conformational freedom within strongly interacting amphiphiles and their self-assembly pathways.

Machine Learning-Guided Adaptive Parametrization for Coupling Terms in a Mixed United-Atom/Coarse-Grained Model for Diphenylalanine Self-Assembly in Aqueous Ionic Liquids

Precise regulation of the peptide self-assembly into ordered nanostructures with intriguing properties has attracted intense attention. However, predicting peptide assembly at atomic resolution is a challenge due to both the structural flexibility of peptides and the associated huge computational costs. A machine learning-guided adaptive parametrization method was proposed for developing a mixed atomic and coarse-grained (CG) model through a multiobjective optimization strategy. Our model incorporates the united-atom (UA) model for diphenylalanine (P) and the polarizable electrostatic-variable coarse-grained (VaCG) model for aqueous ionic liquid [BMIM]+[BF4]- solution. In this mixed model, the coupling van der Waals (vdW) interaction is addressed by introducing virtual sites (VS) in the UA model to interact with solvent CG beads. The coupling parameters, including the electrostatic parameter and vdW parameters, are automatically optimized through ML-guided adaptive parametrization. The performance of this model was tested by some microstructural properties, e.g., the average number of P-P intermolecular hydrogen bonds (HBs) and radius distribution functions (RDFs) between P and different fragments of IL, in comparison with all-atom (AA) simulations. The computational cost is significantly reduced using such a parametrization scheme, which could search tens of thousands of force-field parameter sets, while needing only a small fraction of them to be assessed with molecular dynamics (MD) simulations. We used such a mixed resolution model to investigate the self-assembly in IL-water mixtures with variants of IL concentration (X). The long-range-ordered fibril structure is formed in a pure water system (X = 0). With an increase of IL concentrations, the formation of an ordered self-assembly nanostructure is prohibited, instead forming branched fibril at X = 2 mol % or amorphous aggregates when X > 10 mol %, resulting from the interplay between π-stacking and HB interactions between P and IL. The qualitative agreement between the simulated structures and the observed morphologies in experiments indicates the applicability of ML-guided parametrization strategy in the study of complex systems, such as polymers, lipid bilayers, and polysaccharides.

Pathways of hLL-3717-29 Aggregation Give Insight into the Mechanism of α-Amyloid Formation

α-amyloids present a novel self-assembly principle that can be utilized to prepare functional biomaterials. Evidence of α-amyloid formation in the active core of the human LL-37 protein (comprising residues 17 to 29) was associated with this peptide's membranolytic property. Though mechanistic pathways of β-amyloid formation are known, such studies are scarce in α-amyloids. Modern computational techniques allow such mechanistic studies in molecular detail. Here, we propose aggregation pathways in hLL-3717-29 through molecular dynamics simulations. We first identified oligomers among peptides based on a distance criterion. The distribution of oligomers was then used to build Markov state models from which pathways were obtained using the framework of transition path theory. We checked the structural stability of the peptides during oligomerization, which is crucial from their functional point of view. We also investigated the key residues that participate in oligomer formation, the interactions between them, and the effect of residue mutations on the binding free energy of the peptides. Our findings suggest that larger oligomers are produced from the association of smaller and intermediate oligomers. The peptides retain their helical structure during aggregation with transient occurrences of 3-10 helix and turns. Hydrophobic interactions are vital in the aggregation of these peptides with Ile24 playing a crucial role. Mutation of this residue to alanine decreases the peptides' binding free energy, resulting in reduced aggregation tendency.

Efficient prediction of peptide self-assembly through sequential and graphical encoding

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Histidine modulates amyloid-like assembly of peptide nanomaterials and confers enzyme-like activity

Amyloid-like assembly is not only associated with pathological events, but also leads to the development of novel nanomaterials with unique properties. Herein, using Fmoc diphenylalanine peptide (Fmoc-F-F) as a minimalistic model, we found that histidine can modulate the assembly behavior of Fmoc-F-F and induce enzyme-like catalysis. Specifically, the presence of histidine rearranges the β structure of Fmoc-F-F to assemble nanofilaments, resulting in the formation of active site to mimic peroxidase-like activity that catalyzes ROS generation. A similar catalytic property is also observed in Aβ assembled filaments, which is correlated with the spatial proximity between intermolecular histidine and F-F. Notably, the assembled Aβ filaments are able to induce cellular ROS elevation and damage neuron cells, providing an insight into the pathological relationship between Aβ aggregation and Alzheimer's disease. These findings highlight the potential of histidine as a modulator in amyloid-like assembly of peptide nanomaterials exerting enzyme-like catalysis.

Single-molecular protein-based bioelectronics via electronic transport: fundamentals, devices and applications

Biomolecular electronics is a rapidly growing multidisciplinary field that combines biology, nanoscience, and engineering to bridge the two important fields of life sciences and molecular electronics. Proteins are remarkable for their ability to recognize molecules and transport electrons, making the integration of proteins into electronic devices a long sought-after goal and leading to the emergence of the field of protein-based bioelectronics, also known as proteotronics. This field seeks to design and create new biomolecular electronic platforms that allow for the understanding and manipulation of protein-mediated electronic charge transport and related functional applications. In recent decades, there have been numerous reports on protein-based bioelectronics using a variety of nano-gapped electrical devices and techniques at the single molecular level, which are not achievable with conventional ensemble approaches. This review focuses on recent advances in physical electron transport mechanisms, device fabrication methodologies, and various applications in protein-based bioelectronics. We discuss the most recent progress of the single or few protein-bridged electrical junction fabrication strategies, summarise the work on fundamental and functional applications of protein bioelectronics that enable high and dynamic electron transport, and highlight future perspectives and challenges that still need to be addressed. We believe that this specific review will stimulate the interdisciplinary research of topics related to protein-related bioelectronics, and open up new possibilities for single-molecule biophysics and biomedicine.

Hydrogen-Bond-Driven Peptide Nanotube Formation: A DFT Study

DFT calculations were carried out to examine geometries and binding energies of H-bond-driven peptide nanotubes. A bolaamphiphile molecule, consisting of two N-α amido glycylglycine head groups linked by either one CH2 group or seven CH2 groups, is used as a building block for nanotube self-assembly. In addition to hydrogen bonds between adjacent carboxy or amide groups, nanotube formation is also driven by weak C-H· · ·O hydrogen bonds between a methylene group and the carboxy OH group, and between a methylene group and an amide O=C group. The intratubular O-H· · ·O=C hydrogen bonds account for approximately a third of the binding energies. Binding energies calculated with the wB97XD/DGDZVP method show that the hydrocarbon chains play a stabilizing role in nanotube self-assembly. The shortest nanotube has the length of a single monomer and a diameter than increases with the number of monomers. Lengthening of the tubular structure occurs through intertubular O-H· · ·O=C hydrogen bonds. The average intertubular O-H· · ·O=C hydrogen bond binding energy is estimated to change with the size of the nanotubes, decreasing slightly towards some plateau value near 15 kcal/mol according to the wB97XD/DGDZVP method.

Nanostructured Electrospun Fibers with Self-Assembled Cyclo-L-Tryptophan-L-Tyrosine Dipeptide as Piezoelectric Materials and Optical Second Harmonic Generators

The potential use of nanostructured dipeptide self-assemblies in materials science for energy harvesting devices is a highly sought-after area of research. Specifically, aromatic cyclo-dipeptides containing tryptophan have garnered attention due to their wide-bandgap semiconductor properties, high mechanical rigidity, photoluminescence, and nonlinear optical behavior. In this study, we present the development of a hybrid system comprising biopolymer electrospun fibers incorporated with the chiral cyclo-dipeptide L-Tryptophan-L-Tyrosine. The resulting nanofibers are wide-bandgap semiconductors (bandgap energy 4.0 eV) consisting of self-assembled nanotubes embedded within a polymer matrix, exhibiting intense blue photoluminescence. Moreover, the cyclo-dipeptide L-Tryptophan-L-Tyrosine incorporated into polycaprolactone nanofibers displays a strong effective second harmonic generation signal of 0.36 pm/V and shows notable piezoelectric properties with a high effective coefficient of 22 pCN-1, a piezoelectric voltage coefficient of geff=1.2 VmN-1 and a peak power density delivered by the nanofiber mat of 0.16μWcm-2. These hybrid systems hold great promise for applications in the field of nanoenergy harvesting and nanophotonics.

Accelerating the prediction and discovery of peptide hydrogels with human-in-the-loop

The amino acid sequences of peptides determine their self-assembling properties. Accurate prediction of peptidic hydrogel formation, however, remains a challenging task. This work describes an interactive approach involving the mutual information exchange between experiment and machine learning for robust prediction and design of (tetra)peptide hydrogels. We chemically synthesize more than 160 natural tetrapeptides and evaluate their hydrogel-forming ability, and then employ machine learning-experiment iterative loops to improve the accuracy of the gelation prediction. We construct a score function coupling the aggregation propensity, hydrophobicity, and gelation corrector Cg, and generate an 8,000-sequence library, within which the success rate of predicting hydrogel formation reaches 87.1%. Notably, the de novo-designed peptide hydrogel selected from this work boosts the immune response of the receptor binding domain of SARS-CoV-2 in the mice model. Our approach taps into the potential of machine learning for predicting peptide hydrogelator and significantly expands the scope of natural peptide hydrogels.

Molecular Dynamics Simulation of Self-Assembly Processes of Diphenylalanine Peptide Nanotubes and Determination of Their Chirality

In this work, we further developed a new approach for modeling the processes of the self-assembly of complex molecular nanostructures using molecular dynamics methods; in particular, using a molecular dynamics manipulator. Previously, this approach was considered using the example of the self-assembly of a phenylalanine helical nanotube. Now, a new application of the algorithm has been developed for implementing a similar molecular dynamic self-assembly into helical structures of peptide nanotubes (PNTs) based on other peptide molecules-namely diphenylalanine (FF) molecules of different chirality L-FF and D-FF. In this work, helical nanotubes were assembled from linear sequences of FF molecules with these initially different chiralities. The chirality of the obtained nanotubes was calculated by various methods, including calculation by dipole moments. In addition, a statistical analysis of the results obtained was performed. A comparative analysis of the structures of nanotubes was also performed using the method of visual differential analysis. It was found that FF PNTs obtained by the MD self-assembly method form helical nanotubes of different chirality. The regimes that form nanotubes of right chirality D from initial L-FF dipeptides and nanotubes of left chirality L from D-FF dipeptides are revealed. This corresponds to the law of changing the sign of the chirality of molecular helical structures as the level of their hierarchical organization becomes more complicated.

Three-dimensional (3D) bioprinting of medium toughened dipeptide hydrogel scaffolds with Hofmeister effect

Short peptide self-assembled hydrogels as 3D bioprinting inks show excellent biocompatibility and diverse functional expansion, and have broad application prospects in cell culture and tissue engineering. However, the preparation of biological hydrogel inks with adjustable mechanical strength and controllable degradation for 3D bioprinting still faces big challenges. Herein, we develop dipeptide bio-inks that can be gelled in-situ based on Hofmeister sequence, and prepare hydrogel scaffold by using a layer-by-layer 3D printing strategy. Excitingly, after the introduction of Dulbecco's Modified Eagle's medium (DMEM), which is necessary for cell culture, the hydrogel scaffolds show an excellent toughening effect, which matches the needs of cell culture. It's notable that in the whole process of preparation and 3D printing of hydrogel scaffolds, no cross-linking agent, ultraviolet (UV), heating or other exogenous factors are involved, ensuring high biosafety and biocompatibility. After two weeks of 3D culture, millimeter-sized cell spheres are obtained. This work provides an opportunity for the development of short peptide hydrogel bioinks without exogenous factors in 3D printing, tissue engineering, tumor simulant reconstruction and other biomedical fields.

Peptide-derived coordination frameworks for biomimetic and selective separation

Peptide-derived metal-organic frameworks (PMOFs) have emerged as a class of biomimetic materials with attractive performances in analytical and bioanalytical chemistry. The incorporation of biomolecule peptides gives the frameworks conformational flexibility, guest adaptability, built-in chirality, and molecular recognition ability, which greatly accelerate the applications of PMOFs in enantiomeric separation, affinity separation, and the enrichment of bioactive species from complicated samples. This review focuses on the recent advances in the engineering and applications of PMOFs in selective separation. The unique biomimetic size-, enantio-, and affinity-selective performances for separation are discussed along with the chemical structures and functions of MOFs and peptides. Updates of the applications of PMOFs in adaptive separation of small molecules, chiral separation of drug molecules, and affinity isolation of bioactive species are summarized. Finally, the promising future and remaining challenges of PMOFs for selective separation of complex biosamples are discussed.

A selectivity-enhanced fluorescence imprinted sensor based on yellow-emission peptide nanodots for sensitive and visual smart detection of λ-cyhalothrin

The development of precise and efficient detection technologies to recognize λ-cyhalothrin (LC) in agricultural products has attracted attention worldwide due to its widespread use and notable toxic effects on humans. Herein, a novel fluorescence biomimetic nanosensor was elaborately designed based on Zn(II)-doped cyclo-ditryptophan (c-WW)-type peptide nanodots and incorporating molecularly imprinted polymer (c-WW/Zn-PNs@MIP) for LC assays. C-WW/Zn-PNs obtained by self-assembly with aromatic cyclic dipeptides as basic building blocks and coordination with Zn(II) have low-toxicity, photostability, and bright yellow fluorescence emission, as a sensitive signal transducer. High-affinity imprinting sites further endow c-WW/Zn-PNs@MIP with superior selectivity and reusability. Based on prominent merits, c-WW/Zn-PNs@MIP demonstrated a good linear range (1-360 μg/L) with a low limit of detection (LOD) (0.93 μg/L), fast kinetics in target capture (10 min), and strong practicability in the capture of LC from real samples (spiked recovery of 81.0-107.7%). Additionally, to attain onsite profiling of LC, a visual platform was developed by integrating c-WW/Zn-PNs@MIP with a smartphone-assisted optical device. This smart evaluation system can capture concentration-dependent fluorescent images and accurately digitize them, enabling quantitative analysis of LC. This study developed a fluorescent c-WW/Zn-PNs@MIP-based smart evaluation system as a novel platform for LC monitoring applications, which not only has enormous economic value but also great environmental health significance.

Fmoc-diphenylalanine gelating nanoarchitectonics: A simplistic peptide self-assembly to meet complex applications

9-fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF), has been has been extensively explored due to its ultrafast self-assembly kinetics, inherent biocompatibility, tunable physicochemical properties, and especially, the capability of forming self-sustained gels under physiological conditions. Consequently, various methodologies to develop Fmoc-FF gels and their corresponding applications in biomedical and industrial fields have been extensively studied. Herein, we systemically summarize the mechanisms underlying Fmoc-FF self-assembly, discuss the preparation methodologies of Fmoc-FF hydrogels, and then deliberate the properties as well as the diverse applications of Fmoc-FF self-assemblies. Finally, the contemporary shortcomings which limit the development of Fmoc-FF self-assembly are raised and the alternative solutions are proposed, along with future research perspectives.

Engineering Multi-Scale Organization for Biotic and Organic Abiotic Electroactive Systems

Multi-scale organization of molecular and living components is one of the most critical parameters that regulate charge transport in electroactive systems-whether abiotic, biotic, or hybrid interfaces. In this article, an overview of the current state-of-the-art for controlling molecular order, nanoscale assembly, microstructure domains, and macroscale architectures of electroactive organic interfaces used for biomedical applications is provided. Discussed herein are the leading strategies and challenges to date for engineering the multi-scale organization of electroactive organic materials, including biomolecule-based materials, synthetic conjugated molecules, polymers, and their biohybrid analogs. Importantly, this review provides a unique discussion on how the dependence of conduction phenomena on structural organization is observed for electroactive organic materials, as well as for their living counterparts in electrogenic tissues and biotic-abiotic interfaces. Expansion of fabrication capabilities that enable higher resolution and throughput for the engineering of ordered, patterned, and architecture electroactive systems will significantly impact the future of bioelectronic technologies for medical devices, bioinspired harvesting platforms, and in vitro models of electroactive tissues. In summary, this article presents how ordering at multiple scales is important for modulating transport in both the electroactive organic, abiotic, and living components of bioelectronic systems.

Identification of heterochirality-mediated stereochemical interactions in peptide architectures

In this work, we illustrate a strategy for constructing heterochiral peptide architectures with distinct structural, mechanical and thermal characteristics. A series of nanotube structures based on diphenylalanine (FF) and its chiral derivatives were examined. Pronounced effects relating to heterochirality on mechanostability and thermal stability can be identified. The homochiral peptide FF and its enantiomer ff formed nanotubes with high thermal and mechanical stabilities (Young's modulus: 20.3 ± 5.9 GPa for FF and 21.2 ± 4.7 GPa for ff). In contrast, heterochiral nanotubes formed by Ff and fF manifest superstructures along the axial direction with differed thermal and mechanical strength (Young's modulus: 7.3 ± 2.4 GPa for Ff and 8.3 ± 2.1 GPa for fF). Combining their single-crystal XRD structure and in silico results, it was demonstrated that the spatial orientations of aromatic moieties were subtly changed by heterochirality of peptide building blocks, which led to intramolecular face-to-face interactions. As the result, both intermolecular axial and interchannel interactions in heterochiral nanotubes were weakened as reflected in the strikingly deteriorated mechanical and thermal stabilities. Conversely, two aromatic side chains of the homochiral peptides were staggered and formed interdigitated steric zippers, which served as strong glues that secured the robustness of nanotubes in both axial and radial orientation. Furthermore, the generality of the heterochiral-mediated stereochemical effects was demonstrated in other "FF class" dipeptides, including fluorinated Ff, FW and FL. Our results unequivocally revealed the relationship between amino acid chirality, peptide molecule packing, and physical stabilities of "FF class" dipeptide self-assembled materials and provide valuable molecular insights into chirality-mediated stereochemical interactions in determining the properties of peptide architectures.

Technology Roadmap for Flexible Sensors

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

Supramolecular self-assembly of glycosaminoglycan mimetic nanostructures for cell proliferation and 3D cell culture application

Glycosaminoglycans (GAGs), such as heparin, heparan sulfate and chondroitin sulfate, are playing important roles in various biological processes. Due to the laborious work of organic or enzymatic total synthesis of GAGs, different approaches, including glycopolymers, dendrimers, etc., have been developed to mimic the structures and bioactivities of GAGs, but the syntheses can still be difficult. In the current study, a new format of GAG mimetic structure, supramolecularly assembled polymers, have been easily prepared by mixing fluorenylmethoxycarbonyl-diphenylalanine (Fmoc-FF) and sulfated glyco-modified fluorenylmethoxy derivatives (FGS and FG3S). The self-assembly behavior of these polymers into different structural formats of nanoparticles, nanofibers and macroscopic hydrogels upon adjusted concentrations and composite ratios have been detailed studied. The nanofibers modified with highly sulfated glycol groups (FG3S/Fmoc-FF) showed strong promotion effect for cell proliferation, which efficiency was even similar to that of natural heparin, higher than nanoparticles or non-/low-sulfated glyco-modified nanofibers. Moreover, the supramolecular polymers were further made into hydrogels that capable of 3D cell culture. This study provided a novel and efficient approach for GAG mimicking, showing great potential for tissue engineering related applications.

Amyloid-Gold Nanoparticle Hybrids for Biocompatible Memristive Devices

Biomolecular materials offer tremendous potential for the development of memristive devices due to their low cost of production, environmental friendliness, and, most notably, biocompatibility. Herein, biocompatible memristive devices based on amyloid-gold nanoparticle hybrids have been investigated. These memristors demonstrate excellent electrical performance, featuring an ultrahigh R_off/R_on ratio (>10⁷), a low switching voltage (<0.8 V), and reliable reproducibility. Additionally, the reversible transition from threshold switching to resistive switching mode was achieved in this work. The arrangement of peptides in amyloid fibrils endows the surface polarity and phenylalanine packing, which provides channels for the migration of Ag ions in the memristors. By modulating voltage pulse signals, the study successfully imitates the synaptic behavior of excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and the transition from short-term plasticity (STP) to long-term plasticity (LTP). More interestingly, Boolean logic standard cells were designed and simulated using the memristive devices. The fundamental and experimental results of this study thus offer insights into the utilization of biomolecular materials for advanced memristive devices.

Identification of biomolecule-based electronic materials from a first-principles study of aliphatic amino acids

Biomolecule-based electronic materials can enable health innovations by virtue of their intrinsic bioactivity and physical properties. However, the ultra-wide bandgap and limited piezoelectric properties of most biomaterials prevent them from reaching their full potential. Herein, the electronic structures and electromechanical properties of aliphatic amino acid crystals are investigated based on density functional theory. L-Met is found to be a wide bandgap p-type semiconductor, and the much-reduced bandgap of 2.88 eV is ascribed to the sulphur atoms in L-Met. L-Leu has a shear piezoelectric voltage constant of 2.706 V mN^-1 that is over an order of magnitude higher than that of lead zirconate titanate, and good toughness and ductility are also revealed in L-Leu from mechanical property investigations. This study illustrates a computational approach to find smart and multifunctional biomaterials and inspire their growth and applications.

Racemic Amino Acid Assembly Enables Supramolecular β-Sheet Transition with Property Modulations

Amino acids are the most simplistic bio-building blocks and perform a variety of functions in metabolic activities. Increasing publications report that amino acid-based superstructures present amyloid-like characteristics, arising from their supramolecular β-sheet secondary structures driven by hydrogen-bonding-connected supramolecular β-strands, which are formed by head-to-tail hydrogen bonds between terminal amino and carboxyl groups of the adjacent residues. Therefore, the establishment of the structure-function relationships is critical for exploring the properties and applications of amino acid assemblies. Among the naturally encoded self-assembling amino acids, tyrosine (Y)-based superstructures have been found to show diverse properties and functions including high rigidity, promoting melanin formations, mood regulations, and preventing anxiety, thus showing promising potential as next-generation functional biomaterials for biomedical and bio-machine interface applications. However, the development of Y-based organizations of functional features is severely limited due to the intrinsic difficulty of modulating the energetically stable supramolecular β-sheet structures. Herein, we report that by the racemic assembly of l-Y and d-Y, the supramolecular secondary structures are modulated from the antiparallel β-sheets in the enantiomeric assemblies to the parallel ones in the racemate counterparts, thus leading to higher degrees of freedom, which finally induce distinct organization kinetics and modulation of the physicochemical properties including the optical shifts, elastic softening, and the piezoelectric outputs of the superstructures.

Self-Assembling Peptide-Based Functional Biomaterials

Peptides can self-assemble into various hierarchical nanostructures through noncovalent interactions and form functional materials exhibiting excellent chemical and physical properties, which have broad applications in bio-/nanotechnology. The self-assembly mechanism, self-assembly morphology of peptide supramolecular architecture and their various applications, have been widely explored which have the merit of biocompatibility, easy preparation, and controllable functionality. Herein, we introduce the latest research progress of self-assembling peptide-based nanomaterials and review their applications in biomedicine and optoelectronics, including tissue engineering, anticancer therapy, biomimetic catalysis, energy harvesting. We believe that this review will inspire the rational design and development of novel peptide-based functional bio-inspired materials in the future.

Non-Conventional Peptide Self-Assembly into a Conductive Supramolecular Rope

Structures composed of alternating α and β amino acids can give rise to peculiar secondary structural motifs, which could self-assemble into complex structures of controlled geometries. This work describes the self-assembly properties of an α,β-peptide, containing three units of syn H2-(2-F-Phe)-h-PheGly-OH, able to self-organize on surfaces into a fascinating supramolecular rope. This material was characterized by AFM, electronic conduction and fluorescence measurements. Molecular dynamics simulations showed that this hexapeptide can self-assemble into an antiparallel β-sheet layer, stabilized by intermolecular H-bonds, which, in turn, can self-assemble into many side-by-side layers, due to π-π interactions. As a matter of fact, we demonstrated that in this system, the presence of aromatic residues at the intramolecular interface promoted by the alternation of α,β-amino-acids in the primary sequence, endorses the formation of a super-secondary structure where the aromatic groups are close to each other, conferring to the system good electron conduction properties. This work demonstrates the capability and future potential of designing and fabricating distinctive nanostructures and efficient bioelectronic interfaces based on an α,β-peptide, by controlling structure and interaction processes beyond those obtained with α- or β-peptides alone.

CO2 Induces Symmetry Breaking in Layered Dipeptide Crystals

Control of symmetry breaking of materials provides large opportunities to regulate their properties and functions. Herein, we report breaking the symmetry of layered dipeptide crystals by utilizing CO₂ to induce the adjacent monomolecular layers to stack from the opposite to the same direction. The role of CO₂ is to cover the interlayer interaction sites and force the dipeptides to adsorb at asymmetric positions. Further, the dipeptide crystals exhibit far superior piezoelectricity after symmetry breaking and the piezoelectric voltage generated from the dipeptide-based generators becomes more than 500 % higher than before. This work reveals a potential route to engineer structures and properties of layered materials and provides a deep insight into the control of non-covalent interactions.

Hierarchical Materials from High Information Content Macromolecular Building Blocks: Construction, Dynamic Interventions, and Prediction

Hierarchical materials that exhibit order over multiple length scales are ubiquitous in nature. Because hierarchy gives rise to unique properties and functions, many have sought inspiration from nature when designing and fabricating hierarchical matter. More and more, however, nature's own high-information content building blocks, proteins, peptides, and peptidomimetics, are being coopted to build hierarchy because the information that determines structure, function, and interfacial interactions can be readily encoded in these versatile macromolecules. Here, we take stock of recent progress in the rational design and characterization of hierarchical materials produced from high-information content blocks with a focus on stimuli-responsive and "smart" architectures. We also review advances in the use of computational simulations and data-driven predictions to shed light on how the side chain chemistry and conformational flexibility of macromolecular blocks drive the emergence of order and the acquisition of hierarchy and also on how ionic, solvent, and surface effects influence the outcomes of assembly. Continued progress in the above areas will ultimately usher in an era where an understanding of designed interactions, surface effects, and solution conditions can be harnessed to achieve predictive materials synthesis across scale and drive emergent phenomena in the self-assembly and reconfiguration of high-information content building blocks.

Unidirectional diphenylalanine nanotubes for dynamically guiding neurite outgrowth

Neural networks have been culturedin vitroto investigate brain functions and diseases, clinical treatments for brain damage, and device development. However, it remains challenging to form complex neural network structures with desired orientations and connectionsin vitro. Here, we introduce a dynamic strategy by using diphenylalanine (FF) nanotubes for controlling physical patterns on a substrate to regulate neurite-growth orientation in cultivating neural networks. Parallel FF nanotube patterns guide neurons to develop neurites through the unidirectional FF nanotubes while restricting their polarization direction. Subsequently, the FF nanotubes disassemble and the restriction of neurites disappear, and secondary neurite development of the neural network occurs in other direction. Experiments were conducted that use the hippocampal neurons, and the results demonstrated that the cultured neural networks by using the proposed dynamic approach can form a significant cross-connected structure with substantially more lateral neural connections than static substrates. The proposed dynamic approach for neurite outgrowing enables the construction of oriented innervation and cross-connected neural networksin vitroand may explore the way for the bio-fabrication of highly complex structures in tissue engineering.

Structural control of self-assembled peptide nanostructures to develop peptide vesicles for photodynamic therapy of cancer

Vesicles such as liposomes, polymersomes, and exosomes have been widely used as drug delivery carriers; however, peptide vesicles (peptidesomes) despite their potential utility are far less well developed. Peptidesomes are distinctive because peptides play dual roles as a self-assembly building block and a bioactive functional unit. In order for peptidesomes to become successful nanodrugs, the issues related to differences in nanostructural properties between in vitro and in vivo conditions should be addressed. Here, we delineate a multivariate approach to feedback control the structures of peptide building blocks, nanoparticle size, drug loading process, nanoparticle aggregation, cytotoxicity, cell targeting capability, endosome disruption function, protease resistance, and in vivo performance, which eventually enabled the successful development of a highly efficacious peptidesome for in vivo cancer therapy. This study lays the groundwork for the successful in vivo translation of peptide nanodrugs.

Machine learning overcomes human bias in the discovery of self-assembling peptides

Peptide materials have a wide array of functions, from tissue engineering and surface coatings to catalysis and sensing. Tuning the sequence of amino acids that comprise the peptide modulates peptide functionality, but a small increase in sequence length leads to a dramatic increase in the number of peptide candidates. Traditionally, peptide design is guided by human expertise and intuition and typically yields fewer than ten peptides per study, but these approaches are not easily scalable and are susceptible to human bias. Here we introduce a machine learning workflow-AI-expert-that combines Monte Carlo tree search and random forest with molecular dynamics simulations to develop a fully autonomous computational search engine to discover peptide sequences with high potential for self-assembly. We demonstrate the efficacy of the AI-expert to efficiently search large spaces of tripeptides and pentapeptides. The predictability of AI-expert performs on par or better than our human experts and suggests several non-intuitive sequences with high self-assembly propensity, outlining its potential to overcome human bias and accelerate peptide discovery.