Modeling the self-assembly of peptide amphiphiles into fibers using coarse-grained molecular dynamics

Towards food-derived self-assembling peptide-based hydrogels: Insights into preparation, characterization and mechanism

Food proteins represent a vital source of self-assembling peptides, with hydrogels constructed through peptide self-assembly exhibiting widespread utility in the food sector. This review aims to provide a recent research progress in preparation and characterization of hydrogels from food-derived peptides. Also, the self-assembly mechanisms and the impact of factors are discussed. Presently, food-derived self-assembling peptide-based hydrogels can be synthesized using either physical or chemical methodologies and evaluated through methodologies such as microscopic, spectroscopic, and rheological assessment. The self-assembly of food-derived peptides is hierarchically formed by non-covalent interactions, including hydrogen bond and hydrophobic interactions, where variables such as temperature and pH intricately modulate the assembly mechanism. The association between peptide sequence and hydrogel structure in the self-assembly mechanism is also discussed, which remains to be further explored. The present review contributes to application of food-derived peptide-based hydrogels in the fields of food, nutrition and material sciences.

Recent advances in computational prediction of molecular properties in food chemistry

The combination of food chemistry and computational simulation has brought many impacts to food research, moving from experimental chemistry to computer chemistry. This paper will systematically review in detail the important role played by computational simulations in the development of the molecular structure of food, mainly from the atomic, molecular, and multicomponent dimension. It will also discuss how different computational chemistry models can be constructed and analyzed to obtain reliable conclusions. From the calculation principle to case analysis, this paper focuses on the selection and application of quantum mechanics, molecular mechanics and coarse-grained molecular dynamics in food chemistry research. Finally, experiments and computations of food chemistry are compared and summarized to obtain the best balance between them. The above review and outlook will provide an important reference for the intersection of food chemistry and computational chemistry, and is expected to provide innovative thinking for structural research in food chemistry.

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.

Advanced computational approaches to understand protein aggregation

Protein aggregation is a widespread phenomenon implicated in debilitating diseases like Alzheimer's, Parkinson's, and cataracts, presenting complex hurdles for the field of molecular biology. In this review, we explore the evolving realm of computational methods and bioinformatics tools that have revolutionized our comprehension of protein aggregation. Beginning with a discussion of the multifaceted challenges associated with understanding this process and emphasizing the critical need for precise predictive tools, we highlight how computational techniques have become indispensable for understanding protein aggregation. We focus on molecular simulations, notably molecular dynamics (MD) simulations, spanning from atomistic to coarse-grained levels, which have emerged as pivotal tools in unraveling the complex dynamics governing protein aggregation in diseases such as cataracts, Alzheimer's, and Parkinson's. MD simulations provide microscopic insights into protein interactions and the subtleties of aggregation pathways, with advanced techniques like replica exchange molecular dynamics, Metadynamics (MetaD), and umbrella sampling enhancing our understanding by probing intricate energy landscapes and transition states. We delve into specific applications of MD simulations, elucidating the chaperone mechanism underlying cataract formation using Markov state modeling and the intricate pathways and interactions driving the toxic aggregate formation in Alzheimer's and Parkinson's disease. Transitioning we highlight how computational techniques, including bioinformatics, sequence analysis, structural data, machine learning algorithms, and artificial intelligence have become indispensable for predicting protein aggregation propensity and locating aggregation-prone regions within protein sequences. Throughout our exploration, we underscore the symbiotic relationship between computational approaches and empirical data, which has paved the way for potential therapeutic strategies against protein aggregation-related diseases. In conclusion, this review offers a comprehensive overview of advanced computational methodologies and bioinformatics tools that have catalyzed breakthroughs in unraveling the molecular basis of protein aggregation, with significant implications for clinical interventions, standing at the intersection of computational biology and experimental research.

Multiscale Simulations of Self-Assembling Peptides: Surface and Core Hydrophobicity Determine Fibril Stability and Amyloid Aggregation

Assemblies of peptides and proteins through specific intermolecular interactions set the basis for macroscopic materials found in nature. Peptides provide easily tunable hydrogen-bonding interactions, which can lead to the formation of ordered structures such as highly stable β-sheets that can form amyloid-like supramolecular peptide nanofibrils (PNFs). PNFs are of special interest, as they could be considered as mimics of various fibrillar structures found in nature. In their ability to serve as supramolecular scaffolds, they could mimic certain features of the extracellular matrix to provide stability, interact with pathogens such as virions, and transduce signals between the outside and inside of cells. Many PNFs have been reported that reveal rich bioactivities. PNFs supporting neuronal cell growth or lentiviral gene transduction have been studied systematically, and their material properties were correlated to bioactivities. However, the impact of the structure of PNFs, their dynamics, and stabilities on their unique functions is still elusive. Herein, we provide a microscopic view of the self-assembled PNFs to unravel how the amino acid sequence of self-assembling peptides affects their secondary structure and dynamic properties of the peptides within supramolecular fibrils. Based on sequence truncation, amino acid substitution, and sequence reordering, we demonstrate that peptide-peptide aggregation propensity is critical to form bioactive β-sheet-rich structures. In contrast to previous studies, a very high peptide aggregation propensity reduces bioactivity due to intermolecular misalignment and instabilities that emerge when fibrils are in close proximity to other fibrils in solution. Our multiscale simulation approach correlates changes in biological activity back to single amino acid modifications. Understanding these relationships could lead to future material discoveries where the molecular sequence predictably determines the macroscopic properties and biological activity. In addition, our studies may provide new insights into naturally occurring amyloid fibrils in neurodegenerative diseases.

Assessment of the MARTINI 3 Performance for Short Peptide Self-Assembly

The coarse-grained MARTINI force field, initially developed for membranes, has proven to be an exceptional tool for investigating supramolecular peptide assemblies. Over the years, the force field underwent refinements to enhance accuracy, enabling, for example, the reproduction of protein-ligand interactions and constant pH behavior. However, these protein-focused improvements seem to have compromised its ability to model short peptide self-assembly. In this study, we assess the performance of MARTINI 3 in reproducing peptide self-assembly using the well-established diphenylalanine (FF) as our test case. Unlike its success in version 2.1, FF does not even exhibit aggregation in version 3. By systematically exploring parameters for the aromatic side chains and charged backbone beads, we established a parameter set that effectively reproduces tube formation. Remarkably, these parameter adjustments also replicate the self-assembly of other di- and tripeptides and coassemblies. Furthermore, our analysis uncovers pivotal insights for enhancing the performance of MARTINI in modeling short peptide self-assembly. Specifically, we identify issues stemming from overestimated hydrophilicity arising from charged termini and disruptions in π-stacking interactions due to insufficient planarity in aromatic groups and a discrepancy in intermolecular distances between this and backbone-backbone interactions. This investigation demonstrates that strategic modifications can harness the advancements offered by MARTINI 3 for the realm of short peptide self-assembly.

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.

Enhanced Neuron Growth and Electrical Activity by a Supramolecular Netrin-1 Mimetic Nanofiber

Neurotrophic factors are essential not only for guiding the organization of the developing nervous system but also for supporting the survival and growth of neurons after traumatic injury. In the central nervous system (CNS), inhibitory factors and the formation of a glial scar after injury hinder the functional recovery of neurons, requiring exogenous therapies to promote regeneration. Netrin-1, a neurotrophic factor, can initiate axon guidance, outgrowth, and branching, as well as synaptogenesis, through activation of deleted in colorectal cancer (DCC) receptors. We report here the development of a nanofiber-shaped supramolecular mimetic of netrin-1 with monomers that incorporate a cyclic peptide sequence as the bioactive component. The mimetic structure was found to activate the DCC receptor in primary cortical neurons using low molar ratios of the bioactive comonomer. The supramolecular nanofibers enhanced neurite outgrowth and upregulated maturation as well as pre- and postsynaptic markers over time, resulting in differences in electrical activity similar to neurons treated with the recombinant netrin-1 protein. The results suggest the possibility of using the supramolecular structure as a therapeutic to promote regenerative bioactivity in CNS injuries.

Aggregation-Induced Asymmetric Charge States of Amino Acids in Supramolecular Nanofibers

Electrostatic interactions contribute critically to the kinetic pathways and thermodynamic outcomes of peptide self-assembly involving one or more than one charged amino acids. While it is well understood in protein folding that those amino acids with acidic/basic side chains could shift their pKas when placed in a hydrophobic microenvironment, to what extent aggregation of monomeric peptide units from the bulk solution could alter their charged status and how this change in pKa values would reciprocally impact their assembly outcomes. Here, we design and analyze two solution systems containing peptide amphiphiles with hydrocarbon chains of different lengths to determine the factor of deprotonation on assembly. Our results suggest that models of supramolecular nanofibers with uniformly distributed, fully charged amino acids are oversimplified. We demonstrate, with molecular dynamics simulations, and validate with experimental results that asymmetric, different protonation states of the peptides lead to distinct nanostructures after self-assembly. The results give estimates on the electrostatic interactions in peptide amphiphiles required for their self-assembly and shed light on modeling molecular assembly systems containing charged amino acids.

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.

Diverging conformations guide dipeptide self-assembly into crystals or hydrogels

The prediction of dipeptide assembly into crystals or gels is challenging. This work reveals the diverging conformational landscape that guides self-organization towards different outcomes. In silico and experimental data enabled deciphering of the electronic circular dichroism (ECD) spectra of self-assembling dipeptides to reveal folded or extended conformers as key players.

Development, Validation, and Applications of Nonbonded Interaction Parameters between Coarse-Grained Amino Acid and Water Models

Interactions between amino acids and water play an important role in determining the stability and folding/unfolding, in aqueous solution, of many biological macromolecules, which affects their function. Thus, understanding the molecular-level interactions between water and amino acids is crucial to tune their function in aqueous solutions. Herein, we have developed nonbonded interaction parameters between the coarse-grained (CG) models of 20 amino acids and the one-site CG water model. The nonbonded parameters, represented using the 12-6 Lennard Jones (LJ) potential form, have been optimized using an artificial neural network (ANN)-assisted particle swarm optimization (PSO) (ANN-assisted PSO) method. All-atom (AA) molecular dynamics (MD) simulations of dipeptides in TIP3P water molecules were performed to calculate the Gibbs hydration free energies. The nonbonded force-field (FF) parameters between CG amino acids and the one-site CG water model were developed to accurately reproduce these energies. Furthermore, to test the transferability of these newly developed parameters, we calculated the hydration free energies of the analogues of the amino acid side chains, which showed good agreement with reported experimental data. Additionally, we show the applicability of these models by performing self-assembly simulations of peptide amphiphiles. Overall, these models are transferable and can be used to study the self-assembly of various biomaterials and biomolecules to develop a mechanistic understanding of these processes.

Computation meets experiment: identification of highly efficient fibrillating peptides

Self-assembling peptides are of huge interest for biological, medical and nanotechnological applications. The enormous chemical variety that is available from the 20 amino acids offers potentially unlimited peptide sequences, but it is currently an issue to predict their supramolecular behavior in a reliable and cheap way. Herein we report a computational method to screen and forecast the aqueous self-assembly propensity of amyloidogenic pentapeptides. This method was found also as an interesting tool to predict peptide crystallinity, which may be of interest for the development of peptide based drugs.

Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

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.

Structurally Induced Chirality of an Achiral Chromophore on Self-Assembled Nanofibers: A Twist Makes It Chiral

The surface domains of self-assembled amphiphiles are well-organized and can perform many physical, chemical, and biological functions. Here, we present the significance of chiral surface domains of these self-assemblies in transferring chirality to achiral chromophores. These aspects are probed using l- and d-isomers of alkyl alanine amphiphiles which self-assemble in water as nanofibers, possessing a negative surface charge. When bound on these nanofibers, positively charged cyanine dyes (CY524 and CY600), each having two quinoline rings bridged by conjugated double bonds, show contrasting chiroptical features. Interestingly, CY600 displays a bisignated circular dichroic (CD) signal with mirror-image symmetry, while CY524 is CD silent. Molecular dynamics simulations reveal that the model cylindrical micelles (CM) derived from the two isomers exhibit surface chirality and the chromophores are buried as monomers in mirror-imaged pockets on their surfaces. The monomeric nature of template-bound chromophores and their binding reversibility are established by concentration- and temperature-dependent spectroscopies and calorimetry. On the CM, CY524 displays two equally populated conformers with opposite sense, whereas CY600 is present as two pairs of twisted conformers in each of which one is in excess, due to differences in weak dye-amphiphile hydrogen bonding interactions. Infrared and NMR spectroscopies support these findings. Reduction of electronic conjugation caused by the twist establishes the two quinoline rings as independent entities. On-resonance coupling between the transition dipoles of these units generates bisignated CD signals with mirror-image symmetry. The results presented herein provide insight on the little-known structurally induced chirality of achiral chromophores through transfer of chiral surface information.

Monitoring of dairy farm management determinants and production performance using structural equation modelling in the Amhara region, Ethiopia

BACKGROUND

Models have been presented to evaluate the link between dairy farm production factors and their degree of association with production determinants. Studies have found causal relationships between production parameters (dairy farm facility, farm hygiene and waste management, feed and nutrition, reproduction performance, health and extension services, mode of transportation, education level and gross revenue) as well as farm efficiency parameters. Furthermore, structural equation modelling (SEM) allows for the estimation of parameters that are not directly quantifiable, known as latent variables.

OBJECTIVE

The research was designed to identify the dairy management determinants and evaluate farm production performance using an SEM approach in the selected areas of the Amhara region, Ethiopia.

METHODOLOGY

In-person survey using a semi-structured pre-tested questionnaire was employed in 2021 to collect primary data on 117 randomly selected commercial dairy producers keeping cross-breed Holstein Frisian cows in the Amhara region. SEM was used to study the complexity of influences on efficiency measures in milk production utilizing the combined data.

RESULTS

The model result revealed that the relationship between construct reliabilities and farm facilities was significantly varied (p < 0.01). The model analysis showed that the level of education has also a positive and statistically significant correlation with the reproduction performance of the dairy farms, (ρ = 0.337) and the gross revenue of the farm showed as (p = 0.849). Farm gross revenue articulated a positive, strong statistically significant association with feed and nutrition values (ρ = 0.906), dairy farm facilities (ρ = 0.934), and hygiene and waste management (ρ = 0.921). Consequently, the predictors of dairy farm facility's feed and nutrition and hygiene and waste management explained 93.40%, 84.0%, 80.20%, and 88.50% of the variance.

CONCLUSION

The proposed model was scientifically valid, and training and education have an effect on management practices, subsequently affecting the production performance of the dairy farms.

Artificial extracellular matrix scaffolds of mobile molecules enhance maturation of human stem cell-derived neurons

Human induced pluripotent stem cell (hiPSC) technologies offer a unique resource for modeling neurological diseases. However, iPSC models are fraught with technical limitations including abnormal aggregation and inefficient maturation of differentiated neurons. These problems are in part due to the absence of synergistic cues of the native extracellular matrix (ECM). We report on the use of three artificial ECMs based on peptide amphiphile (PA) supramolecular nanofibers. All nanofibers display the laminin-derived IKVAV signal on their surface but differ in the nature of their non-bioactive domains. We find that nanofibers with greater intensity of internal supramolecular motion have enhanced bioactivity toward hiPSC-derived motor and cortical neurons. Proteomic, biochemical, and functional assays reveal that highly mobile PA scaffolds caused enhanced β1-integrin pathway activation, reduced aggregation, increased arborization, and matured electrophysiological activity of neurons. Our work highlights the importance of designing biomimetic ECMs to study the development, function, and dysfunction of human neurons.

Computational Biomaterials: Computational Simulations for Biomedicine

With the flourishing development of material simulation methods (quantum chemistry methods, molecular dynamics, Monte Carlo, phase field, etc.), extensive adoption of computing technologies (high-throughput, artificial intelligence, machine learning, etc.), and the invention of high-performance computing equipment, computational simulation tools have sparked the fundamental mechanism-level explorations to predict the diverse physicochemical properties and biological effects of biomaterials and investigate their enormous application potential for disease prevention, diagnostics, and therapeutics. Herein, the term "computational biomaterials" is proposed and the computational methods currently used to explore the inherent properties of biomaterials, such as optical, magnetic, electronic, and acoustic properties, and the elucidation of corresponding biological behaviors/effects in the biomedical field are summarized/discussed. The theoretical calculation of the physiochemical properties/biological performance of biomaterials applied in disease diagnosis, drug delivery, disease therapeutics, and specific paradigms such as biomimetic biomaterials is discussed. Additionally, the biosafety evaluation applications of theoretical simulations of biomaterials are presented. Finally, the challenges and future prospects of such computational simulations for biomaterials development are clarified. It is anticipated that these simulations would offer various methodologies for facilitating the development and future clinical translations/utilization of versatile biomaterials.

Molecular communications in complex systems of dynamic supramolecular polymers

Supramolecular polymers are composed of monomers that self-assemble non-covalently, generating distributions of monodimensional fibres in continuous communication with each other and with the surrounding solution. Fibres, exchanging molecular species, and external environment constitute a sole complex system, which intrinsic dynamics is hard to elucidate. Here we report coarse-grained molecular simulations that allow studying supramolecular polymers at the thermodynamic equilibrium, explicitly showing the complex nature of these systems, which are composed of exquisitely dynamic molecular entities. Detailed studies of molecular exchange provide insights into key factors controlling how assemblies communicate with each other, defining the equilibrium dynamics of the system. Using minimalistic and finer chemically relevant molecular models, we observe that a rich concerted complexity is intrinsic in such self-assembling systems. This offers a new dynamic and probabilistic (rather than structural) picture of supramolecular polymer systems, where the travelling molecular species continuously shape the assemblies that statistically emerge at the equilibrium.

The dynamic structure of supramolecular polymers is challenging to determine both in experiments and in simulations. Here the authors use coarse-grained molecular models to provide a comprehensive analysis of the molecular communication in these complex molecular systems.

Acid-Base Equilibrium and Dielectric Environment Regulate Charge in Supramolecular Nanofibers

Peptide amphiphiles are a class of molecules that can self-assemble into a variety of supramolecular structures, including high-aspect-ratio nanofibers. It is challenging to model and predict the charges in these supramolecular nanofibers because the ionization state of the peptides are not fixed but liable to change due to the acid-base equilibrium that is coupled to the structural organization of the peptide amphiphile molecules. Here, we have developed a theoretical model to describe and predict the amount of charge found on self-assembled peptide amphiphiles as a function of pH and ion concentration. In particular, we computed the amount of charge of peptide amphiphiles nanofibers with the sequence C₁₆ − V₂A₂E₂. In our theoretical formulation, we consider charge regulation of the carboxylic acid groups, which involves the acid-base chemical equilibrium of the glutamic acid residues and the possibility of ion condensation. The charge regulation is coupled with the local dielectric environment by allowing for a varying dielectric constant that also includes a position-dependent electrostatic solvation energy for the charged species. We find that the charges on the glutamic acid residues of the peptide amphiphile nanofiber are much lower than the same functional group in aqueous solution. There is a strong coupling between the charging via the acid-base equilibrium and the local dielectric environment. Our model predicts a much lower degree of deprotonation for a position-dependent relative dielectric constant compared to a constant dielectric background. Furthermore, the shape and size of the electrostatic potential as well as the counterion distribution are quantitatively and qualitatively different. These results indicate that an accurate model of peptide amphiphile self-assembly must take into account charge regulation of acidic groups through acid–base equilibria and ion condensation, as well as coupling to the local dielectric environment.

Modeling Interactions within and between Peptide Amphiphile Supramolecular Filaments

Many peptides are able to self-assemble into one-dimensional (1D) nanostructures, such as cylindrical fibers or ribbons of variable widths, but the relationship between the morphology of 1D objects and their molecular structure is not well understood. Here, we use coarse-grained molecular dynamics (CG-MD) simulations to study the nanostructures formed by self-assembly of different peptide amphiphiles (PAs). The results show that ribbons are hierarchical superstructures formed by laterally assembled cylindrical fibers. Simulations starting from bilayer structures demonstrate the formation of filaments, whereas other simulations starting from filaments indicate varying degrees of interaction among them depending on chemical structure. These interactions are verified by observations using atomic force microscopy of the various systems. The interfilament interactions are predicted to be strongest in supramolecular assemblies that display hydrophilic groups on their surfaces, while those with hydrophobic ones are predicted to interact more weakly as confirmed by viscosity measurements. The simulations also suggest that peptide amphiphiles with hydrophobic termini bend to reduce their interfacial energy with water, which may explain why these systems do not collapse into superstructures of bundled filaments. The simulations suggest that future experiments will need to address mechanistic questions about the self-assembly of these systems into hierarchical structures, namely, the preformation of interactive filaments vs equilibration of large assemblies into superstructures.

Machine Learning-Driven Biomaterials Evolution

Biomaterials is an exciting and dynamic field, which uses a collection of diverse materials to achieve desired biological responses. While there is constant evolution and innovation in materials with time, biomaterials research has been hampered by the relatively long development period required. In recent years, driven by the need to accelerate materials development, the applications of machine learning in materials science has progressed in leaps and bounds. The combination of machine learning with high-throughput theoretical predictions and high-throughput experiments (HTE) has shifted the traditional Edisonian (trial and error) paradigm to a data-driven paradigm. In this review, each type of biomaterial and their key properties and use cases are systematically discussed, followed by how machine learning can be applied in the development and design process. The discussions are classified according to various types of materials used including polymers, metals, ceramics, and nanomaterials, and implants using additive manufacturing. Last, the current gaps and potential of machine learning to further aid biomaterials discovery and application are also discussed.

A hybrid coarse-grained model for structure, solvation and assembly of lipid-like peptides

Reconstituted photosynthetic proteins which are activated upon exposure to solar energy hold enormous potential for powering future solid state devices and solar cells. The functionality and integration of these proteins into such devices has been successfully enabled by lipid-like peptides. Yet, a fundamental understanding of the organization of these peptides with respect to the photosynthetic proteins and themselves remains unknown and is critical for guiding the design of such light-activated devices. This study investigates the relative organization of one such peptide sequence V₆K₂ (V: valine and K: lysine) within assemblies. Given the expansive spatiotemporal scales associated with this study, a hybrid coarse-grained (CG) model which captures the structure, conformation and aggregation of the peptide is adopted. The CG model uses a combination of iterative Boltzmann inversion and force matching to provide insight into the relative organization of V₆K₂ in assemblies. The CG model reproduces the structure of a V₆K₂ peptide sequence along with its all atom (AA) solvation structure. The relative organization of multiple peptides in an assembly, as captured by CG simulations, is in agreement with corresponding results from AA simulations. Also, a backmapping procedure reintroduces the AA details of the peptides within the aggregates captured by the CG model to demonstrate the relative organization of the peptides. Furthermore, a large number of peptides self-assemble into an elongated micelle in the CG simulation, which is consistent with experimental findings. The coarse-graining procedure is tested for transferability to longer peptide sequences, and hence can be extended to other amphiphilic peptide sequences.

Molecular Insight into the β-Sheet Twist and Related Morphology of Self-Assembled Peptide Amphiphile Ribbons

Self-assembly of high-aspect-ratio filaments containing β-sheets has attracted much attention due to potential use in bioengineering and biomedicine. However, precisely predicting the assembled morphologies remains a grand challenge because of insufficient understanding of the self-assembly process. We employed an atomistic model to study the self-assembly of peptide amphiphiles (PAs) containing valine-glutamic acid (VE) dimeric repeats. By changing of the sequence length, the assembly morphology changes from flat ribbon to left-handed twisted ribbon, implying a relationship between β-sheet twist and strength of interstrand hydrogen bonds. The calculations are used to quantify this relationship including both magnitude and sign of the ribbon twist angle. Interestingly, a change in chirality is observed when we introduce the RGD epitope into the C-terminal of VE repeats, suggesting arginine and glycine's role in suppressing right-handed β-sheet formation. This study provides insight into the relationship between β-sheet twist and self-assembled nanostructures including a possible design rule for PA self-assembly.

Bioactive scaffolds with enhanced supramolecular motion promote recovery from spinal cord injury

[Figure: see text].

Recent advances in design and applications of biomimetic self-assembled peptide hydrogels for hard tissue regeneration

Abstract

The development of natural biomaterials applied for hard tissue repair and regeneration is of great importance, especially in societies with a large elderly population. Self-assembled peptide hydrogels are a new generation of biomaterials that provide excellent biocompatibility, tunable mechanical stability, injectability, trigger capability, lack of immunogenic reactions, and the ability to load cells and active pharmaceutical agents for tissue regeneration. Peptide-based hydrogels are ideal templates for the deposition of hydroxyapatite crystals, which can mimic the extracellular matrix. Thus, peptide-based hydrogels enhance hard tissue repair and regeneration compared to conventional methods. This review presents three major self-assembled peptide hydrogels with potential application for bone and dental tissue regeneration, including ionic self-complementary peptides, amphiphilic (surfactant-like) peptides, and triple-helix (collagen-like) peptides. Special attention is given to the main bioactive peptides, the role and importance of self-assembled peptide hydrogels, and a brief overview on molecular simulation of self-assembled peptide hydrogels applied for bone and dental tissue engineering and regeneration.

Graphic abstract

Computational Tools to Rationalize and Predict the Self-Assembly Behavior of Supramolecular Gels

Supramolecular gels form a class of soft materials that has been heavily explored by the chemical community in the past 20 years. While a multitude of experimental techniques has demonstrated its usefulness when characterizing these materials, the potential value of computational techniques has received much less attention. This review aims to provide a complete overview of studies that employ computational tools to obtain a better fundamental understanding of the self-assembly behavior of supramolecular gels or to accelerate their development by means of prediction. As such, we hope to stimulate researchers to consider using computational tools when investigating these intriguing materials. In the concluding remarks, we address future challenges faced by the field and formulate our vision on how computational methods could help overcoming them.

Dynamics in supramolecular nanomaterials

Self-assembly of amphiphilic small molecules in water leads to nanostructures with customizable structure-property relationships arising from their tunable chemistries. Characterization of these assemblies is generally limited to their static structures -e.g. their geometries and dimensions - but the implementation of tools that provide a deeper understanding of molecular motions has recently emerged. Here, we summarize recent reports showcasing dynamics characterization tools and their application to small molecule assemblies, and we go on to highlight supramolecular systems whose properties are substantially affected by their conformational, exchange, and water dynamics. This review illustrates the importance of considering dynamics in rational amphiphile design.

The Martini Model in Materials Science

The Martini model, a coarse-grained force field initially developed with biomolecular simulations in mind, has found an increasing number of applications in the field of soft materials science. The model's underlying building block principle does not pose restrictions on its application beyond biomolecular systems. Here, the main applications to date of the Martini model in materials science are highlighted, and a perspective for the future developments in this field is given, particularly in light of recent developments such as the new version of the model, Martini 3.

Molecular Simulations Guidelines for Biological Nanomaterials: From Peptides to Membranes

In studying biological processes and focusing on the molecular mechanisms at the basis of these, molecular dynamics (MD) simulations have demonstrated to be a very useful tool for the past 50 years. This suite of computational methods calculates the time-dependent evolution of a molecular system using physics-based first principles. In this chapter, we give a brief introduction to the theory and practical use of molecular dynamics simulations, highlighting the different models and algorithms that have been developed to tackle specific problems, with a special focus on classical force fields. Some examples of how simulations have been used in the past will help the reader in discerning their power, limitations, and significance.

Molecular Basis for the Morphological Transitions of Surfactant Wormlike Micelles Triggered by Encapsulated Nonpolar Molecules

Surfactant wormlike micelles are prone to experience morphological changes, including the transition to spherical micelles, upon the addition of nonpolar additives. These morphological transitions have profound implications in diverse technological areas, such as the oil and personal-care industries. In this work, additive-induced morphological transitions in wormlike micelles were studied using a molecular theory that predicts the equilibrium morphology and internal molecular organization of the micelles as a function of their composition and the molecular properties of their components. The model successfully captures the transition from wormlike to spherical micelles upon the addition of a nonpolar molecule. Moreover, the predicted effects of the concentration, molecular structure, and degree of hydrophobicity of the nonpolar additive on the wormlike-to-sphere transition are shown to be in good agreement with experimental trends in the literature. The theory predicts that the location of the additive in the micelle (core or hydrophobic-hydrophilic interface) depends on the additive hydrophobicity and content, and the morphology of the micelles. Based on the results of our model, simple molecular mechanisms were proposed to explain the morphological transitions of wormlike micelles upon the addition of nonpolar molecules of different polarities.

3D Printing of Supramolecular Polymer Hydrogels with Hierarchical Structure

Liquid crystalline hydrogels are an attractive class of soft materials to direct charge transport, mechanical actuation, and cell migration. When such systems contain supramolecular polymers, it is possible in principle to easily shear align nanoscale structures and create bulk anisotropic properties. However, reproducibly fabricating and patterning aligned supramolecular domains in 3D hydrogels remains a challenge using conventional fabrication techniques. Here, a method is reported for 3D printing of ionically crosslinked liquid crystalline hydrogels from aqueous supramolecular polymer inks. Using a combination of experimental techniques and molecular dynamics simulations, it is found that pH and salt concentration govern intermolecular interactions among the self-assembled structures where lower charge densities on the supramolecular polymers and higher charge screening from the electrolyte result in higher viscosity inks. Enhanced hierarchical interactions among assemblies in high viscosity inks increase the printability and ultimately lead to greater nanoscale alignment in extruded macroscopic filaments when using small nozzle diameters and fast print speeds. The use of this approach is demonstrated to create materials with anisotropic ionic and electronic charge transport as well as scaffolds that trigger the macroscopic alignment of cells due to the synergy of supramolecular self-assembly and additive manufacturing.

Peptide-Based Nanomaterials for Tumor Immunotherapy

With the increasing understanding of tumor immune circulation mechanisms, tumor immunotherapy including immune checkpoint blockade has become a research hotspot, which requires the development of more accurate and more efficient drugs with fewer side effects. In line with this requirement, peptides with good biocompatibility, targeting, and specificity become favorable theranostic reagents, and a series of promising candidates for tumor immunotherapy based on peptides have been developed. Additionally, the advantages of nanomaterials as drug carriers such as higher affinity have been demonstrated, providing possibilities of combination therapy. In this review, we summarize the development of peptide-based nanomaterials in tumor immunotherapy from the two aspects of functionalization and self-assembly. Furthermore, new methods for peptide screening, especially machine-learning-related strategies, is also a topic we were interested in, as this forms the basis for the construction of peptide-based platforms. Peptides provide broad prospects for tumor immunotherapy and we hope that this summary can provide insight into possible avenues for future exploration.

A combined experimental and computational approach reveals how aromatic peptide amphiphiles self-assemble to form ion-conducting nanohelices

Reported here is a combined experimental-computational strategy to determine structure-property-function relationships in persistent nanohelices formed by a set of aromatic peptide amphiphile (APA) tetramers with the general structure K S XEK S , where KS= S-aroylthiooxime modified lysine, X = glutamic acid or citrulline, and E = glutamic acid. In low phosphate buffer concentrations, the APAs self-assembled into flat nanoribbons, but in high phosphate buffer concentrations they formed nanohelices with regular twisting pitches ranging from 9-31 nm. Coarse-grained molecular dynamics simulations mimicking low and high salt concentrations matched experimental observations, and analysis of simulations revealed that increasing strength of hydrophobic interactions under high salt conditions compared with low salt conditions drove intramolecular collapse of the APAs, leading to nanohelix formation. Analysis of the radial distribution functions in the final self-assembled structures led to several insights. For example, comparing distances between water beads and beads representing hydrolysable KS units in the APAs indicated that the KS units in the nanohelices should undergo hydrolysis faster than those in the nanoribbons; experimental results verified this hypothesis. Simulation results also suggested that these nanohelices might display high ionic conductivity due to closer packing of carboxylate beads in the nanohelices than in the nanoribbons. Experimental results showed no conductivity increase over baseline buffer values for unassembled APAs, a slight increase (0.4 × 102 μS/cm) for self-assembled APAs under low salt conditions in their nanoribbon form, and a dramatic increase (8.6 × 102 μS/cm) under high salt conditions in their nanohelix form. Remarkably, under the same salt conditions, these self-assembled nanohelices conducted ions 5-10-fold more efficiently than several charged polymers, including alginate and DNA. These results highlight how experiments and simulations can be combined to provide insight into how molecular design affects self-assembly pathways; additionally, this work highlights how this approach can lead to discovery of unexpected properties of self-assembled nanostructures.

Supramolecular-covalent hybrid polymers for light-activated mechanical actuation

The development of synthetic structures that mimic mechanical actuation in living matter such as autonomous translation and shape changes remains a grand challenge for materials science. In living systems the integration of supramolecular structures and covalent polymers contributes to the responsive behaviour of membranes, muscles and tendons, among others. Here we describe hybrid light-responsive soft materials composed of peptide amphiphile supramolecular polymers chemically bonded to spiropyran-based networks that expel water in response to visible light. The supramolecular polymers form a reversibly deformable and water-draining skeleton that mechanically reinforces the hybrid and can also be aligned by printing methods. The noncovalent skeleton embedded in the network thus enables faster bending and flattening actuation of objects, as well as longer steps during the light-driven crawling motion of macroscopic films. Our work suggests that hybrid bonding polymers, which integrate supramolecular assemblies and covalent networks, offer strategies for the bottom-up design of soft matter that mimics living organisms.

Solvent-induced morphological transitions in methacrylate-based block-copolymer aggregates

Poly(ethylene oxide)-b-poly(butylmethacrylate) (PEO-b-PBMA) copolymers have recently been identified as excellent building blocks for the synthesis of hierarchical nanoporous materials. Nevertheless, while experiments have unveiled their potential to form bicontinuous phases and vesicles, a general picture of their phase and aggregation behavior is still missing. By performing Molecular Dynamics simulations, we here apply our recent coarse-grained model of PEO-b-PBMA to investigate its self-assembly in water and tetrahydrofuran (THF) and unveil the occurrence of a wide spectrum of mesophases. In particular, we find that the morphological phase diagram of this ternary system incorporates bicontinuous and lamellar phases at high copolymer concentrations, and finite-size aggregates, such as dispersed sheets or disk-like aggregates, spherical vesicles and rod-like vesicles, at low copolymer concentrations. The morphology of these mesophases can be controlled by tuning the THF/water relative content, which has a striking effect on the kinetics of self-assembly as well as on the resulting equilibrium structures. Our results disclose the fascinating potential of PEO-b-PBMA copolymers for the templated synthesis of nanostructured materials and offer a guideline to fine-tune their properties by accurately selecting the THF/water ratio.

Martini Coarse-Grained Model of Hyaluronic Acid for the Structural Change of Its Gel in the Presence of Monovalent and Divalent Salts

Hyaluronic acid (HA) has a wide range of biomedical applications including the formation of hydrogels, microspheres, sponges, and films. The modeling of HA to understand its behavior and interaction with other biomolecules at the atomic level is of considerable interest. The atomistic representation of long HA polymers for the study of the macroscopic structural formation and its interactions with other polyelectrolytes is computationally demanding. To overcome this limitation, we developed a coarse grained (CG) model for HA adapting the Martini scheme. A very good agreement was observed between the CG model and all-atom simulations for both local (bonded interactions) and global properties (end-to-end distance, a radius of gyration, RMSD). Our CG model successfully demonstrated the formation of HA gel and its structural changes at high salt concentrations. We found that the main role of CaCl₂ is screening the electrostatic repulsion between chains. HA gel did not collapse even at high CaCl₂ concentrations, and the osmotic pressure decreased, which agrees well with the experimental results. This is a distinct property of HA from other proteins or polynucleic acids which ensures the validity of our CG model. Our HA CG model is compatible with other CG biomolecular models developed under the Martini scheme, which allows for large-scale simulations of various HA-based complex systems.

Molecular Modeling and Simulations of Peptide–Polymer Conjugates

Peptide–polymer conjugates are a class of soft materials composed of covalently linked blocks of protein/polypeptides and synthetic/natural polymers. These materials are practically useful in biological applications, such as drug delivery, DNA/gene delivery, and antimicrobial coatings, as well as nonbiological applications, such as electronics, separations, optics, and sensing. Given their broad applicability, there is motivation to understand the molecular and macroscale structure, dynamics, and thermodynamic behavior exhibited by such materials. We focus on the past and ongoing molecular simulation studies aimed at obtaining such fundamental understanding and predicting molecular design rules for the target function. We describe briefly the experimental work in this field that validates or motivates these computational studies. We also describe the various models (e.g., atomistic, coarse-grained, or hybrid) and simulation methods (e.g., stochastic versus deterministic, enhanced sampling) that have been used and the types of questions that have been answered using these computational approaches.

Theoretical and computational advances in protein misfolding

Misfolded proteins escape the cellular quality control mechanism and fail to fold properly or remain correctly folded leading to a loss in their functional specificity. Thus misfolding of proteins cause a large number of very different diseases ranging from errors in metabolism to various types of complex neurodegenerative diseases. A theoretical and computational perspective of protein misfolding is presented with a special emphasis on its salient features, mechanism and consequences. These insights quantitatively analyze different determinants of misfolding, that may be applied to design disease specific molecular targets.

Molecular Model for the Self-Assembly of the Cyclic Lipodepsipeptide Pseudodesmin A

Self-assembly of peptides into supramolecular structures represents an active field of research with potential applications ranging from material science to medicine. Their study typically involves the application of a large toolbox of spectroscopic and imaging techniques. However, quite often, the structural aspects remain underexposed. Besides, molecular modeling of the self-assembly process is usually difficult to handle, since a vast conformational space has to be sampled. Here, we have used an approach that combines short molecular dynamics simulations for peptide dimerization and NMR restraints to build a model of the supramolecular structure from the dimeric units. Experimental NMR data notably provide crucial information about the conformation of the monomeric units, the supramolecular assembly dimensions, and the orientation of the individual peptides within the assembly. This in silico/in vitro mixed approach enables us to define accurate atomistic models of supramolecular structures of the bacterial cyclic lipodepsipeptide pseudodesmin A.

Encapsulation of an endostatin peptide in liposomes: Stability, release, and cytotoxicity study

The endostatin protein is a potent inhibitor of angiogenesis and tumor growth. The anti-angiogenic and antitumor properties of full-length endostatin can be mimicked by its N-terminal segment, including residues 1-27. Therefore, our previous studies have shown that a mutant N-terminal peptide which the Zn-binding loop was replaced by a disulfide loop (referred to as the ES-SS peptide) has preserved antiangiogenic and antitumor properties compared to the native peptide. To increase stability and plasma half-life of the ES-SS peptide, the nano-sized liposomal formulations of the peptide with different ratio of phosphocholine (PC) were synthesized. The liposomal peptide formulations possessed an average size of around 100 nm with (-4 to -36 mv) in zeta potential. The encapsulation efficiency of the ES-SS peptide was in the range of 24-54% with different lipid: peptide molar ratios. In vitro release of the peptide from liposomes indicated a complete peptide release after 7 days. Cytotoxicity assay was evaluated using the human umbilical vein endothelial cells (HUVECs) for various concentrations of the liposomal peptide. The results depicted the gradual release of the peptide through liposomes. By comparing with the free peptide, the liposomal peptide formulations have indicated higher cell viability with IC₅₀ value about 0.1 μM. The peptide-liposome interactions, as well as the peptide effect on the liposome structure, were also investigated through coarse-grained molecular dynamics (CG-MD) simulation. The results revealed that the peptides were assembled in the hydrophilic core of the liposome. The peptide behavior in liposome can stabilize the liposome structure and be a response to the observed low peptide release rate. The investigation is promising for designing a liposome-based anti-angiogenesis peptide delivery system.

A comparison of peptide amphiphile nanofiber macromolecular assembly strategies

Supramolecular peptide nanofibers that are composed of peptide amphiphile molecules have been widely used for many purposes from biomedical applications to energy conversion. The self-assembly mechanisms of these peptide nanofibers also provide convenient models for understanding the self-assembly mechanisms of various biological supramolecular systems; however, the current theoretical models that explain these mechanisms do not sufficiently explain the experimental results. In this study, we present a new way of modeling these nanofibers that better fits with the experimental data. Molecular dynamics simulations were applied to create model fibers using two different layer models and two different tilt angles. Strikingly, the fibers which were modeled to be tilting the peptide amphiphile molecules and/or tilting the plane were found to be more stable and consistent with the experiments.

Design of self-assembly dipeptide hydrogels and machine learning via their chemical features

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.