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

Tips and Tricks in the Modeling of Supramolecular Peptide Assemblies

Supramolecular peptide assemblies (SPAs) hold promise as materials for nanotechnology and biomedicine. Although their investigation often entails adapting experimental techniques from their protein counterparts, SPAs are fundamentally distinct from proteins, posing unique challenges for their study. Computational methods have emerged as indispensable tools for gaining deeper insights into SPA structures at the molecular level, surpassing the limitations of experimental techniques, and as screening tools to reduce the experimental search space. However, computational studies have grappled with issues stemming from the absence of standardized procedures and relevant crystal structures. Fundamental disparities between SPAs and protein simulations, such as the absence of experimentally validated initial structures and the importance of the simulation size, number of molecules, and concentration, have compounded these challenges. Understanding the roles of various parameters and the capabilities of different models and simulation setups remains an ongoing endeavor. In this review, we aim to provide readers with guidance on the parameters to consider when conducting SPA simulations, elucidating their potential impact on outcomes and validity.

HydrogelFinder: A Foundation Model for Efficient Self-Assembling Peptide Discovery Guided by Non-Peptidal Small Molecules

Self-assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self-assembling peptides from scratch. This model explores the self-assembly properties by molecular structure, leveraging 1,377 self-assembling non-peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self-assembly and biophysical characteristics of nine peptides ranging from 1-10 amino acids-all achieved within a 19-day workflow. Notably, the two de novo-designed self-assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self-assembling peptides through non-peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

Advancing Synthetic Hydrogels through Nature-Inspired Materials Chemistry

Synthetic extracellular matrix (ECM) mimics that can recapitulate the complex biochemical and mechanical nature of native tissues are needed for advanced models of development and disease. Biomedical research has heavily relied on the use of animal-derived biomaterials, which is now impeding their translational potential and convoluting the biological insights gleaned from in vitro tissue models. Natural hydrogels have long served as a convenient and effective cell culture tool, but advances in materials chemistry and fabrication techniques now present promising new avenues for creating xenogenic-free ECM substitutes appropriate for organotypic models and microphysiological systems. However, significant challenges remain in creating synthetic matrices that can approximate the structural sophistication, biochemical complexity, and dynamic functionality of native tissues. This review summarizes key properties of the native ECM, and discusses recent approaches used to systematically decouple and tune these properties in synthetic matrices. The importance of dynamic ECM mechanics, such as viscoelasticity and matrix plasticity, is also discussed, particularly within the context of organoid and engineered tissue matrices. Emerging design strategies to mimic these dynamic mechanical properties are reviewed, such as multi-network hydrogels, supramolecular chemistry, and hydrogels assembled from biological monomers.

Enhancing calvarial defects repair with PDGF-BB mimetic peptide hydrogels

Addressing bone defects represents a significant challenge to public health. Localized delivery of growth factor has emerged as promising approach for bone regeneration. However, the clinical application of Platelet-Derived Growth Factor (PDGF) is hindered by its high cost and short half-life. In this work, we introduce the application of PDGF-mimicking peptide (PMP1) hydrogels for calvarial defect restoration, showcasing their remarkable effectiveness. Through osteogenic differentiation assays and q-PCR analyses, we demonstrate PMP1's substantial capacity to enhance osteogenic differentiation of bone marrow mesenchymal stem cell (BMSC), leading to increased expression of crucial osteogenic genes. Further molecular mechanistic investigations reveal PMP1's activation of the PI3K-AKT-mTOR signaling pathway, a key element of its osteogenic effect. In vivo experiments utilizing a rat calvaria critical-sized defect model underscore the hydrogels' exceptional ability to accelerate new bone formation, thereby significantly advancing the restoration of calvaria defects. This research provides a promising bioactive material for bone tissue regeneration.

From cells to subcellular organelles: Next-generation cancer therapy based on peptide self-assembly

Due to the editability, functionality, and excellent biocompatibility of peptides, in situ self-assembly of peptides in cells is a powerful strategy for biomedical applications. Subcellular organelle targeting of peptides assemblies enables more precise drug delivery, enhances selectivity to disease cells, and mitigates drug resistance, providing an effective strategy for disease diagnosis and therapy. This reviewer first introduces the triggering conditions, morphological changes, and intracellular locations of self-assembling peptides. Then, the functions of peptide assemblies are summarized, followed by a comprehensive understanding of the interactions between peptide assemblies and subcellular organelles. Finally, we provide a brief outlook and the remaining challenges in this field.

Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications

Materialomics integrates experiment, theory, and computation in a high-throughput manner, and has changed the paradigm for the research and development of new functional materials. Recently, with the rapid development of high-throughput characterization and machine-learning technologies, the establishment of biomaterialomics that tackles complex physiological behaviors has become accessible. Breakthroughs in the clinical translation of nanoparticle-based therapeutics and vaccines have been observed. Herein, recent advances in biomaterials, including polymers, lipid-like materials, and peptides/proteins, discovered through high-throughput screening or machine learning-assisted methods, are summarized. The molecular design of structure-diversified libraries; high-throughput characterization, screening, and preparation; and, their applications in drug delivery and clinical translation are discussed in detail. Furthermore, the prospects and main challenges in future biomaterialomics and high-throughput screening development are highlighted.

Engineered Bio-Based Hydrogels for Cancer Immunotherapy

Immunotherapy represents a revolutionary paradigm in cancer management, showcasing its potential to impede tumor metastasis and recurrence. Nonetheless, challenges including limited therapeutic efficacy and severe immune-related side effects are frequently encountered, especially in solid tumors. Hydrogels, a class of versatile materials featuring well-hydrated structures widely used in biomedicine, offer a promising platform for encapsulating and releasing small molecule drugs, biomacromolecules, and cells in a controlled manner. Immunomodulatory hydrogels present a unique capability for augmenting immune activation and mitigating systemic toxicity through encapsulation of multiple components and localized administration. Notably, hydrogels based on biopolymers have gained significant interest owing to their biocompatibility, environmental friendliness, and ease of production. This review delves into the recent advances in bio-based hydrogels in cancer immunotherapy and synergistic combinatorial approaches, highlighting their diverse applications. It is anticipated that this review will guide the rational design of hydrogels in the field of cancer immunotherapy, fostering clinical translation and ultimately benefiting patients.

Facile Reactive Oxygen Species-Scavenging Supramolecular Hydrogel to Promote Diabetic Wound Healing

Chronic wound healing impairment is a significant complication in diabetes. Hydrogels that maintain wound moisture and enable sustained drug release have become prominent for enhancing chronic wound care. Particularly, hydrogels that respond to reactive oxygen species (ROS) are sought-after for their dual capacity to mitigate ROS and facilitate controlled drug delivery at the wound site. We have strategically designed an ROS-responsive and scavenging supramolecular hydrogel composed of the simple hexapeptide Glu-Phe-Met-Phe-Met-Glu (EFM). This hydrogelator, composed solely of canonical amino acids without additional ROS-sensitive motifs, forms a hydrogel rapidly upon sonication. Interaction with ROS leads to the oxidation of Met residues to methionine sulfoxide, triggering hydrogel disassembly and consequent payload release. Cellular assays have verified their biocompatibility and efficacy in promoting cell proliferation and migration. In vivo investigations underscore the potential of this straightforward hydrogel as an ROS-scavenger and drug delivery vehicle, enhancing wound healing in diabetic mice. The simplicity and effectiveness of this hydrogel suggest its broader biomedical applications in the future.

Applications of peptide-based nanomaterials in targeting cancer therapy

To meet the demand for precision medicine, researchers are committed to developing novel strategies to reduce systemic toxicity and side effects in cancer treatment. Targeting peptides are widely applied due to their affinity and specificity, and their ability to be high-throughput screened, chemically synthesized and modified. More importantly, peptides can form ordered self-assembled structures through non-covalent supramolecular interactions, which can form nanostructures with different morphologies and functions, playing crucial roles in targeted diagnosis and treatment. Among them, in targeted immunotherapy, utilizing targeting peptides to block the binding between immune checkpoints and ligands, thereby activating the immune system to eliminate cancer cells, is an advanced therapeutic strategy. In this mini-review, we summarize the screening, self-assembly, and biomedical applications of targeting peptide-based nanomaterials. Furthermore, this mini-review summarizes the potential and optimization strategies of targeting peptides.

In situ peptide assemblies for bacterial infection imaging and treatment

Bacterial infections, especially antibiotic-resistant ones, remain a major threat to human health. Advances in nanotechnology have led to the development of numerous antimicrobial nanomaterials. Among them, in situ peptide assemblies, formed by biomarker-triggered self-assembly of peptide-based building blocks, have received increasing attention due to their unique merits of good spatiotemporal controllability and excellent disease accumulation and retention. In recent years, a variety of "turn on" imaging probes and activatable antibacterial agents based on in situ peptide assemblies have been developed, providing promising alternatives for the treatment and diagnosis of bacterial infections. In this review, we introduce representative design strategies for in situ peptide assemblies and highlight the bacterial infection imaging and treatment applications of these supramolecular materials. Besides, current challenges in this field are proposed.

Self-assembly and hydrogelation of a potential bioactive peptide derived from quinoa proteins

In this work the identification of peptides derived from quinoa proteins which could potentially self-assemble, and form hydrogels was carried out with TANGO, a statistical mechanical based algorithm that predicts β-aggregate propensity of peptides. Peptides with the highest aggregate propensity were subjected to gelling screening experiments from which the most promising bioactive peptide with sequence KIVLDSDDPLFGGF was selected. The self-assembling and hydrogelation properties of the C-terminal amidated peptide (KIVLDSDDPLFGGF-NH2) were studied. The effect of concentration, pH, and temperature on the secondary structure of the peptide were probed by circular dichroism (CD), while its nanostructure was studied by transmission electron microscopy (TEM) and small-angle neutron scattering (SANS). Results revealed the existence of random coil, α-helix, twisted β-sheet, and well-defined β-sheet secondary structures, with a range of nanostructures including elongated fibrils and bundles, whose proportion was dependant on the peptide concentration, pH, or temperature. The self-assembly of the peptide is demonstrated to follow established models of amyloid formation, which describe the unfolded peptide transiting from an α-helix-containing intermediate into β-sheet-rich protofibrils. The self-assembly is promoted at high concentrations, elevated temperatures, and pH values close to the peptide isoelectric point, and presumably mediated by hydrogen bond, hydrophobic and electrostatic interactions, and π-π interactions (from the F residue). At 15 mg/mL and pH 3.5, the peptide self-assembled and formed a self-supporting hydrogel exhibiting viscoelastic behaviour with G' (1 Hz) ~2300 Pa as determined by oscillatory rheology measurements. The study describes a straightforward method to monitor the self-assembly of plant protein derived peptides; further studies are needed to demonstrate the potential application of the formed hydrogels in food and biomedicine.

Masking Strategy Constructed Metal Coordination Hydrogels with Improved Mechanical Properties for Flexible Electronic Sensors

Metal coordination hydrogels (MC-HGs) that introduce dynamically coordinate bonds together with metal ionic conduction have attracted considerable attention in flexible electronics. However, the traditional soaking method alleged to have technical scalability faces the challenge of forming MC-HGs with a "core-shell" structure, which undoubtedly reduces the whole mechanical properties and ionic stimulation responsiveness required for flexible electronics materials. Herein, a novel strategy referred to as "masking" has been proposed based on the theory of the valence bond and coordination chemistry. By regulating the masking agents and their concentrations as well as pairing mode with the metal ions, the whole mechanical properties of the resulting composites (MC-HGsMasking) show nearly double the values of their traditional soaking samples (MC-HGsSoaking). For example, the fracture stress and toughness of Fe-HGsMasking(SA, 5.0 g/L) are 1.55 MPa and 2.14 MJ/m3, almost twice those of Fe-HGsSoaking (0.83 MPa and 0.93 MJ/m3, respectively). Microstructure characterization combined with finite element analysis, molecular dynamics, and first-principles simulations demonstrates that the masking strategy first facilitating interfacial permeation of metal complexes and then effective coordination with functional ligands (carboxylates) of the hydrogels is the mechanism to strengthen the mechanical properties of composites MC-HGsMasking, which has the potential to break through the limitations of current MC-HGs in flexible electronic sensor applications.

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.

Oriented Self-assembly of Flexible MOFs Nanocrystals into Anisotropic Superstructures with Homogeneous Hydrogels Behaviors

Building of metal-organic frameworks (MOFs) homogeneous hydrogels made by spontaneous crystallization remains a significant challenge. Inspired by anisotropically structured materials in nature, an oriented super-assembly strategy to construct micro-scale MOFs superstructure is reported, in which the strong intermolecular interactions between zirconium-oxygen (Zr─O) cluster and glutamic acid are utilized to drive the self-assembly of flexible nanoribbons into pumpkin-like microspheres. The confined effect between water-flexible building blocks and crosslinked hydrogen networks of superstructures achieved a mismatch transformation of MOFs powders into homogeneous hydrogels. Importantly, the elastic and rigid properties of hydrogels can be simply controlled by precise modulation of coordination and self-assembly for anisotropic superstructure. Experimental results and theoretical calculations demonstrates that MOFs anisotropic superstructure exhibits dynamic double networks with a superior water harvesting capacity (119.73 g g-1 ) accompanied with heavy metal removal (1331.67 mg g-1 ) and strong mechanical strength (Young's modulus of 0.3 GPa). The study highlights the unique possibility of tailoring MOFs superstructure with homogeneous hydrogel behavior for application in diverse fields.

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.

Exploring the Potential of Artificial Intelligence for Hydrogel Development-A Short Review

AI and ML have emerged as transformative tools in various scientific domains, including hydrogel design. This work explores the integration of AI and ML techniques in the realm of hydrogel development, highlighting their significance in enhancing the design, characterisation, and optimisation of hydrogels for diverse applications. We introduced the concept of AI train hydrogel design, underscoring its potential to decode intricate relationships between hydrogel compositions, structures, and properties from complex data sets. In this work, we outlined classical physical and chemical techniques in hydrogel design, setting the stage for AI/ML advancements. These methods provide a foundational understanding for the subsequent AI-driven innovations. Numerical and analytical methods empowered by AI/ML were also included. These computational tools enable predictive simulations of hydrogel behaviour under varying conditions, aiding in property customisation. We also emphasised AI's impact, elucidating its role in rapid material discovery, precise property predictions, and optimal design. ML techniques like neural networks and support vector machines that expedite pattern recognition and predictive modelling using vast datasets, advancing hydrogel formulation discovery are also presented. AI and ML's have a transformative influence on hydrogel design. AI and ML have revolutionised hydrogel design by expediting material discovery, optimising properties, reducing costs, and enabling precise customisation. These technologies have the potential to address pressing healthcare and biomedical challenges, offering innovative solutions for drug delivery, tissue engineering, wound healing, and more. By harmonising computational insights with classical techniques, researchers can unlock unprecedented hydrogel potentials, tailoring solutions for diverse applications.