De novo design and experimental characterization of ultrashort self-associating peptides

Molecular Modeling of Self-Assembling Peptides

Peptide epitopes mediate as many as 40% of protein-protein interactions and fulfill signaling, inhibition, and activation roles within the cell. Beyond protein recognition, some peptides can self- or coassemble into stable hydrogels, making them a readily available source of biomaterials. While these 3D assemblies are routinely characterized at the fiber level, there are missing atomistic details about the assembly scaffold. Such atomistic detail can be useful in the rational design of more stable scaffold structures and with improved accessibility to functional motifs. Computational approaches can in principle reduce the experimental cost of such an endeavor by predicting the assembly scaffold and identifying novel sequences that adopt said structure. Yet, inaccuracies in physical models and inefficient sampling have limited atomistic studies to short (two or three amino acid) peptides. Given recent developments in machine learning and advances in sampling strategies, we revisit the suitability of physical models for this task. We use the MELD (Modeling Employing Limited Data) approach to drive self-assembly in combination with generic data in cases where conventional MD is unsuccessful. Finally, despite recent developments in machine learning algorithms for protein structure and sequence predictions, we find the algorithms are not yet suited for studying the assembly of short peptides.

Calming the Nerves via the Immune Instructive Physiochemical Properties of Self-Assembling Peptide Hydrogels

Current therapies for the devastating damage caused by traumatic brain injuries (TBI) are limited. This is in part due to poor drug efficacy to modulate neuroinflammation, angiogenesis and/or promoting neuroprotection and is the combined result of challenges in getting drugs across the blood brain barrier, in a targeted approach. The negative impact of the injured extracellular matrix (ECM) has been identified as a factor in restricting post-injury plasticity of residual neurons and is shown to reduce the functional integration of grafted cells. Therefore, new strategies are needed to manipulate the extracellular environment at the subacute phase to enhance brain regeneration. In this review, potential strategies are to be discussed for the treatment of TBI by using self-assembling peptide (SAP) hydrogels, fabricated via the rational design of supramolecular peptide scaffolds, as an artificial ECM which under the appropriate conditions yields a supramolecular hydrogel. Sequence selection of the peptides allows the tuning of these hydrogels' physical and biochemical properties such as charge, hydrophobicity, cell adhesiveness, stiffness, factor presentation, degradation profile and responsiveness to (external) stimuli. This review aims to facilitate the development of more intelligent biomaterials in the future to satisfy the parameters, requirements, and opportunities for the effective treatment of TBI.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Towards rational computational peptide design

Peptides are prevalent in biology, mediating as many as 40% of protein-protein interactions, and involved in other cellular functions such as transport and signaling. Their ability to bind with high specificity make them promising therapeutical agents with intermediate properties between small molecules and large biologics. Beyond their biological role, peptides can be programmed to self-assembly, and they are already being used for functions as diverse as oligonuclotide delivery, tissue regeneration or as drugs. However, the transient nature of their interactions has limited the number of structures and knowledge of binding affinities available-and their flexible nature has limited the success of computational pipelines that predict the structures and affinities of these molecules. Fortunately, recent advances in experimental and computational pipelines are creating new opportunities for this field. We are starting to see promising predictions of complex structures, thermodynamic and kinetic properties. We believe in the following years this will lead to robust rational peptide design pipelines with success similar to those applied for small molecule drug discovery.

Vaccine building ‘kit’: combining peptide bricks to elicit a desired immune response without adding an adjuvant

Protein nanoparticles (NPs) can be used as vaccine platforms for target antigen presentation. Aim: To conduct a proof-of-concept study to demonstrate that an effective NP platform can be built based on a short self-assembling peptide (SAP) rather than a large self-assembling protein. Materials & methods: SUMO-based protein fusions (SFs) containing an N-terminal SAP and a C-terminal antigen were designed, expressed in Escherichia coli and purified. The structure was investigated by electron microscopy. The antibody response was tested in mice after two adjuvant-free immunizations. Results: Renatured SFs form fiber-like NPs with the antigen exposed on the surface and induce a significant antibody response with a remarkably high target-to-platform ratio. Conclusion: The platform is effective and has considerable potential for modification toward various applications, including vaccine development.

Enhancing Peptide Biomaterials for Biofabrication

Biofabrication using well-matched cell/materials systems provides unprecedented opportunities for dealing with human health issues where disease or injury overtake the body’s native regenerative abilities. Such opportunities can be enhanced through the development of biomaterials with cues that appropriately influence embedded cells into forming functional tissues and organs. In this context, biomaterials’ reliance on rigid biofabrication techniques needs to support the incorporation of a hierarchical mimicry of local and bulk biological cues that mimic the key functional components of native extracellular matrix. Advances in synthetic self-assembling peptide biomaterials promise to produce reproducible mimics of tissue-specific structures and may go some way in overcoming batch inconsistency issues of naturally sourced materials. Recent work in this area has demonstrated biofabrication with self-assembling peptide biomaterials with unique biofabrication technologies to support structural fidelity upon 3D patterning. The use of synthetic self-assembling peptide biomaterials is a growing field that has demonstrated applicability in dermal, intestinal, muscle, cancer and stem cell tissue engineering.

Computational Modeling as a Tool to Investigate PPI: From Drug Design to Tissue Engineering

Protein-protein interactions (PPIs) mediate a large number of important regulatory pathways. Their modulation represents an important strategy for discovering novel therapeutic agents. However, the features of PPI binding surfaces make the use of structure-based drug discovery methods very challenging. Among the diverse approaches used in the literature to tackle the problem, linear peptides have demonstrated to be a suitable methodology to discover PPI disruptors. Unfortunately, the poor pharmacokinetic properties of linear peptides prevent their direct use as drugs. However, they can be used as models to design enzyme resistant analogs including, cyclic peptides, peptide surrogates or peptidomimetics. Small molecules have a narrower set of targets they can bind to, but the screening technology based on virtual docking is robust and well tested, adding to the computational tools used to disrupt PPI. We review computational approaches used to understand and modulate PPI and highlight applications in a few case studies involved in physiological processes such as cell growth, apoptosis and intercellular communication.

Peptide-Protein Interactions: From Drug Design to Supramolecular Biomaterials

The self-recognition and self-assembly of biomolecules are spontaneous processes that occur in Nature and allow the formation of ordered structures, at the nanoscale or even at the macroscale, under thermodynamic and kinetic equilibrium as a consequence of specific and local interactions. In particular, peptides and peptidomimetics play an elected role, as they may allow a rational approach to elucidate biological mechanisms to develop new drugs, biomaterials, catalysts, or semiconductors. The forces that rule self-recognition and self-assembly processes are weak interactions, such as hydrogen bonding, electrostatic attractions, and van der Waals forces, and they underlie the formation of the secondary structure (e.g., α-helix, β-sheet, polyproline II helix), which plays a key role in all biological processes. Here, we present recent and significant examples whereby design was successfully applied to attain the desired structural motifs toward function. These studies are important to understand the main interactions ruling the biological processes and the onset of many pathologies. The types of secondary structure adopted by peptides during self-assembly have a fundamental importance not only on the type of nano- or macro-structure formed but also on the properties of biomaterials, such as the types of interaction, encapsulation, non-covalent interaction, or covalent interaction, which are ultimately useful for applications in drug delivery.

Peptide-Peptide Co-Assembly: A Design Strategy for Functional Detection of C-peptide, A Biomarker of Diabetic Neuropathy

Diabetes-related neuropathy is a debilitating condition that may be averted if it can be detected early. One possible way this can be achieved at low cost is to utilise peptides to detect C-peptide, a biomarker of diabetic neuropathy. This depends on peptide-peptide co-assembly, which is currently in a nascent stage of intense study. Instead, we propose a bead-based triple-overlay combinatorial strategy that can preserve inter-residue information during the screening process for a suitable complementary peptide to co-assemble with C-peptide. The screening process commenced with a pentapeptide general library, which revealed histidine to be an essential residue. Further screening with seven tetrapeptide focused libraries led to a table of self-consistent peptide sequences that included tryptophan and lysine at high frequencies. Three complementary nonapeptides (9mer com-peptides), wpkkhfwgq (Trp-D), kwkkhfwgq (Lys-D), and KWKKHFWGQ (Lys-L) (as a negative control) were picked from this table for co-assembly studies with C-peptide. Attenuated total reflectance Fourier transform infrared (ATR-FTIR) and circular dichroism (CD) spectroscopies were utilized to study inter-peptide interactions and changes in secondary structures respectively. ATR-FTIR studies showed that there is indeed inter-peptide interaction between C-peptide and the tryptophan residues of the 9mer com-peptides. CD studies of unaggregated and colloidal C-peptide with the 9mer com-peptides suggest that the extent of co-assembly of C-peptide with Trp-D is greatest, followed by Lys-D and Lys-L. These results are promising and indicate that the presented strategy is viable for designing and evaluating longer complementary peptides, as well as complementary peptides for co-assembly with other polypeptides of interest and importance. We discuss the possibility of designing complementary peptides to inhibit toxic amyloidosis with this approach.

Sequence-Dependent Nanofiber Structures of Phenylalanine and Isoleucine Tripeptides

The molecular design of short peptides to achieve a tailor-made functional architecture has attracted attention during the past decade but remains challenging as a result of insufficient understanding of the relationship between peptide sequence and assembled supramolecular structures. We report a hybrid-resolution model to computationally explore the sequence–structure relationship of self-assembly for tripeptides containing only phenylalanine and isoleucine. We found that all these tripeptides have a tendency to assemble into nanofibers composed of laterally associated filaments. Molecular arrangements within the assemblies are diverse and vary depending on the sequences. This structural diversity originates from (1) distinct conformations of peptide building blocks that lead to different surface geometries of the filaments and (2) unique sidechain arrangements at the filament interfaces for each sequence. Many conformations are available for tripeptides in solution, but only an extended β-strand and another resembling a right-handed turn are observed in assemblies. It was found that the sequence dependence of these conformations and the packing of resulting filaments are determined by multiple competing noncovalent forces, with hydrophobic interactions involving Phe being particularly important. The sequence pattern for each type of assembly conformation and packing has been identified. These results highlight the importance of the interplay between conformation, molecular packing, and sequences for determining detailed nanostructures of peptides and provide a detailed insight to support a more precise design of peptide-based nanomaterials.

Designing peptide nanoparticles for efficient brain delivery

The targeted delivery of therapeutic compounds to the brain is arguably the most significant open problem in drug delivery today. Nanoparticles (NPs) based on peptides and designed using the emerging principles of molecular engineering show enormous promise in overcoming many of the barriers to brain delivery faced by NPs made of more traditional materials. However, shortcomings in our understanding of peptide self-assembly and blood-brain barrier (BBB) transport mechanisms pose significant obstacles to progress in this area. In this review, we discuss recent work in engineering peptide nanocarriers for the delivery of therapeutic compounds to the brain: from synthesis, to self-assembly, to in vivo studies, as well as discussing in detail the biological hurdles that a nanoparticle must overcome to reach the brain.

Multi-scale structural analysis of proteins by deep semantic segmentation

MOTIVATION

Recent advances in computational methods have facilitated large-scale sampling of protein structures, leading to breakthroughs in protein structural prediction and enabling de novo protein design. Establishing methods to identify candidate structures that can lead to native folds or designable structures remains a challenge, since few existing metrics capture high-level structural features such as architectures, folds and conformity to conserved structural motifs. Convolutional Neural Networks (CNNs) have been successfully used in semantic segmentation-a subfield of image classification in which a class label is predicted for every pixel. Here, we apply semantic segmentation to protein structures as a novel strategy for fold identification and structure quality assessment.

RESULTS

We train a CNN that assigns each residue in a multi-domain protein to one of 38 architecture classes designated by the CATH database. Our model achieves a high per-residue accuracy of 90.8% on the test set (95.0% average per-class accuracy; 87.8% average per-structure accuracy). We demonstrate that individual class probabilities can be used as a metric that indicates the degree to which a randomly generated structure assumes a specific fold, as well as a metric that highlights non-conformative regions of a protein belonging to a known class. These capabilities yield a powerful tool for guiding structural sampling for both structural prediction and design.

AVAILABILITY AND IMPLEMENTATION

The trained classifier network, parser network, and entropy calculation scripts are available for download at https://git.io/fp6bd, with detailed usage instructions provided at the download page. A step-by-step tutorial for setup is provided at https://goo.gl/e8GB2S. All Rosetta commands, RosettaRemodel blueprints, and predictions for all datasets used in the study are available in the Supplementary Information.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Short to ultrashort peptide-based hydrogels as a platform for biomedical applications

Short peptides have attracted significant attention from researchers in the past few years due to their easy design, synthesis and characterization, diverse functionalisation possibilities, low cost, possibility to make a large range of hierarchical nanostructures and most importantly their high biocompatibility and biodegradability. Generally, short peptides are also relatively more stable than their longer variants, non-immunogenic in nature and many of them self-assemble to provide an exciting range of nanostructures, including hydrogels. Thus, the development of short peptide-based hydrogels has become an area of intense investigation. Although these hydrogels have a water content of greater than 90%, they are surprisingly highly stable structures, and thus have been used for various biomedical applications, including cell therapeutics, drug delivery, tissue engineering and regeneration, contact lenses, biosensors, and wound healing, by different researchers. Herein, we review the progress of research in the rapidly expanding field of short to ultrashort peptide-based hydrogels and their possible applications. Special attention is paid to address and review this field with regard to the stability of peptide-based hydrogels, particularly to enzymatic degradation.

Advancement of Peptide Nanobiotechnology via Emerging Microfluidic Technology

Peptide nanotechnology has experienced a long and enduring development since its inception. Many different applications have been conceptualized, which depends on the functional groups present on the peptide and the physical shape/size of the peptide nanostructures. One of the most prominent nanostructures formed by peptides are nanoparticles. Until recently, however, it has been challenging to engineer peptide nanoparticles with low dispersity. An emerging and promising technique involves the utility of microfluidics to produce a solution of peptide nanoparticles with narrow dispersity. In this process, two or more streams of liquid are focused together to create conditions that are conducive towards the formation of narrowly dispersed samples of peptide nanoparticles. This makes it possible to harness peptide nanoparticles for the myriad of applications that are dependent on nanoparticle size and uniformity. In this focus review, we aim to show how microfluidics may be utilized to (1) study peptide self-assembly, which is critical to controlling nanostructure shape and size, and peptide-interface interactions, and (2) generate self-assembling peptide-based microgels for miniaturized cell cultures. These examples will illustrate how the emerging microfluidic approach promises to revolutionize the production and application of peptide nanoparticles in ever more diverse fields than before.

Applications of self-assembling ultrashort peptides in bionanotechnology

Peptides are intriguing building blocks for a variety of applications in bionanotechnology. Peptides can self-assemble into well-ordered nanostructures. Among the various nanomorphology forms, peptide nanofibers and nanotubes are relevant in biomedical applications. In this review, their applications as tissue engineering scaffolds, drug delivery vehicles, three-dimensional printing bioinks and bioimaging nanoprobes will be illustrated. This review article describes di-, tri-, tetra-peptides because they are cost-effective, simple to prepare, and amenable to production on a large scale.

Ultrashort peptide applications in bionanotechnology.

C-Terminal Residue of Ultrashort Peptides Impacts on Molecular Self-Assembly, Hydrogelation, and Interaction with Small-Molecule Drugs

Single molecular changes on a tripeptide can have dramatic effects on their self-assembly and hydrogelation. Herein, we explore C-terminal residue variation on two consistent ultrashort peptide backbones, i.e. acetylated-Leu-Ile-Val-Ala-Gly-Xaa and acetylated-Ile-Val-Xaa (Xaa = His, Arg, Asn). The objective of this study is to identify candidates that can form hydrogels for small-molecule drug (SMD) delivery. Haemolysis and cytotoxicity (with human adipose-derived mesenchymal stem cells) assays showed that the new soluble peptides (Xaa = His, Arg) are cytocompatible. Gelation studies showed that all but acetylated-Ile-Val-Arg could gel under physiological conditions. Longer peptidic backbones drive self-assembly more effectively as reflected in field emission scanning electron microscopy (FESEM) and circular dichroism spectroscopy studies. Rheological studies revealed that the resultant hydrogels have varying stiffness and yield stress, depending on the backbone and C-terminal residue. Visible spectroscopy-based elution studies with SMDs (naltrexone, methotrexate, doxorubicin) showed that besides the C-terminal residue, the shape of the SMD also determines the rate and extent of SMD elution. Based on the elution assays, infrared spectroscopy, and FESEM, we propose models for the peptide fibril-SMD interaction. Our findings highlight the importance of matching the molecular properties of the self-assembling peptide and SMD in order to achieve the desired SMD release profile.

Molecular simulations of self-assembling bio-inspired supramolecular systems and their connection to experiments

In bionanotechnology, the field of creating functional materials consisting of bio-inspired molecules, the function and shape of a nanostructure only appear through the assembly of many small molecules together. The large number of building blocks required to define a nanostructure combined with the many degrees of freedom in packing small molecules has long precluded molecular simulations, but recent advances in computational hardware as well as software have made classical simulations available to this strongly expanding field. Here, we review the state of the art in simulations of self-assembling bio-inspired supramolecular systems. We will first discuss progress in force fields, simulation protocols and enhanced sampling techniques using recent examples. Secondly, we will focus on efforts to enable the comparison of experimentally accessible observables and computational results. Experimental quantities that can be measured by microscopy, spectroscopy and scattering can be linked to simulation output either directly or indirectly, via quantum mechanical or semi-empirical techniques. Overall, we aim to provide an overview of the various computational approaches to understand not only the molecular architecture of nanostructures, but also the mechanism of their formation.

Tunable Pentapeptide Self-Assembled β-Sheet Hydrogels

Oligopeptide‐based supramolecular hydrogels hold promise in a range of applications. The gelation of these systems is hard to control, with minor alterations in the peptide sequence significantly influencing the self‐assembly process. We explored three pentapeptide sequences with different charge distributions and discovered that they formed robust, pH‐responsive hydrogels. By altering the concentration and charge distribution of the peptide sequence, the stiffness of the hydrogels could be tuned across two orders of magnitude (2–200 kPa). Also, through reassembly of the β‐sheet interactions the hydrogels could self‐heal and they demonstrated shear‐thin behavior. Using spectroscopic and cryo‐imaging techniques, we investigated the relationship between peptide sequence and molecular structure, and how these influence the mechanical properties of the hydrogel. These pentapeptide hydrogels with tunable morphology and mechanical properties have promise in tissue engineering, injectable delivery vectors, and 3D printing applications.

Systematic Moiety Variations of Ultrashort Peptides Produce Profound Effects on Self-Assembly, Nanostructure Formation, Hydrogelation, and Phase Transition

Self-assembly of small biomolecules is a prevalent phenomenon that is increasingly being recognised to hold the key to building complex structures from simple monomeric units. Small peptides, in particular ultrashort peptides containing up to seven amino acids, for which our laboratory has found many biomedical applications, exhibit immense potential in this regard. For next-generation applications, more intricate control is required over the self-assembly processes. We seek to find out how subtle moiety variation of peptides can affect self-assembly and nanostructure formation. To this end, we have selected a library of 54 tripeptides, derived from systematic moiety variations from seven tripeptides. Our study reveals that subtle structural changes in the tripeptides can exert profound effects on self-assembly, nanostructure formation, hydrogelation, and even phase transition of peptide nanostructures. By comparing the X-ray crystal structures of two tripeptides, acetylated leucine-leucine-glutamic acid (Ac-LLE) and acetylated tyrosine-leucine-aspartic acid (Ac-YLD), we obtained valuable insights into the structural factors that can influence the formation of supramolecular peptide structures. We believe that our results have major implications on the understanding of the factors that affect peptide self-assembly. In addition, our findings can potentially assist current computational efforts to predict and design self-assembling peptide systems for diverse biomedical applications.

Probing Gelation and Rheological Behavior of a Self-Assembled Molecular Gel

Molecular gels have been investigated over the last few decades; however, mechanical behavior of these self-assembled gels is not well understood, particularly how these materials fail at large strain. Here, we report the gelation and rheological behavior of a molecular gel formed by self-assembly of a low molecular weight gelator (LMWG), di-Fmoc-l-lysine, in 1-propanol/water mixture. Gels were prepared by solvent-triggered technique, and gelation was tracked using Fourier transform infrared (FTIR) spectroscopy and shear rheology. FTIR spectroscopy captures the formation of hydrogen bonding between the gelator molecules, and the change in IR spectra during the gelation process correlates with the gelation kinetics results captured by rheology. Self-assembly of gelator molecules leads to a fiber-like structure, and these long fibers topologically interact to form a gel-like material. Stretched-exponential function can capture the stress-relaxation data. Stress-relaxation time for these gels have been found to be long owing to long fiber dimensions, and the stretching exponent value of 1/3 indicates polydispersity in fiber dimensions. Cavitation rheology captures fracture-like behavior of these gels, and critical energy release rate has been estimated to be of the order 0.1 J/m². Our results provide new understanding of the rheological behavior of molecular gels and their structural origin.

Limiting the valence: advancements and new perspectives on patchy colloids, soft functionalized nanoparticles and biomolecules

Artistic representation of limited valance units consisting of a soft core (in blue) and a small number of flexible bonding patches (in orange).

Dissecting Electrostatic Contributions to Folding and Self-Assembly Using Designed Multicomponent Peptide Systems

We investigate formation of nano- to microscale peptide fibers and sheets where assembly requires association of two distinct collagen mimetic peptides (CMPs). The multicomponent nature of these designs allows the decoupling of amino acid contributions to peptide folding versus higher-order assembly. While both arginine and lysine containing CMP sequences can favor triple-helix folding, only arginine promotes rapid supramolecular assembly in each of the three two-component systems examined. Unlike lysine, the polyvalent guanidyl group of arginine is capable of both intra- and intermolecular contacts, promoting assembly. This is consistent with the supramolecular diversity of CMP morphologies observed throughout the literature. It also connects CMP self-assembly with a broad range of biomolecular interaction phenomena, providing general principles for modeling and design.

BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design

Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

Supramolecular Hydrogelators and Hydrogels: From Soft Matter to Molecular Biomaterials

In this review we intend to provide a relatively comprehensive summary of the work of supramolecular hydrogelators after 2004 and to put emphasis particularly on the applications of supramolecular hydrogels/hydrogelators as molecular biomaterials. After a brief introduction of methods for generating supramolecular hydrogels, we discuss supramolecular hydrogelators on the basis of their categories, such as small organic molecules, coordination complexes, peptides, nucleobases, and saccharides. Following molecular design, we focus on various potential applications of supramolecular hydrogels as molecular biomaterials, classified by their applications in cell cultures, tissue engineering, cell behavior, imaging, and unique applications of hydrogelators. Particularly, we discuss the applications of supramolecular hydrogelators after they form supramolecular assemblies but prior to reaching the critical gelation concentration because this subject is less explored but may hold equally great promise for helping address fundamental questions about the mechanisms or the consequences of the self-assembly of molecules, including low molecular weight ones. Finally, we provide a perspective on supramolecular hydrogelators. We hope that this review will serve as an updated introduction and reference for researchers who are interested in exploring supramolecular hydrogelators as molecular biomaterials for addressing the societal needs at various frontiers.

De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy

Despite efforts for over 25 years, de novo protein design has not succeeded in achieving the TIM-barrel fold. Here we describe the computational design of four-fold symmetrical (β/α)8 barrels guided by geometrical and chemical principles. Experimental characterization of 33 designs revealed the importance of side chain-backbone hydrogen bonds for defining the strand register between repeat units. The X-ray crystal structure of a designed thermostable 184-residue protein is nearly identical to that of the designed TIM-barrel model. PSI-BLAST searches do not identify sequence similarities to known TIM-barrel proteins, and sensitive profile-profile searches indicate that the design sequence is distant from other naturally occurring TIM-barrel superfamilies, suggesting that Nature has sampled only a subset of the sequence space available to the TIM-barrel fold. The ability to design TIM barrels de novo opens new possibilities for custom-made enzymes.

Self-Assembled Proteins and Peptides as Scaffolds for Tissue Regeneration

Self-assembling proteins and peptides are increasingly gaining interest for potential use as scaffolds in tissue engineering applications. They self-organize from basic building blocks under mild conditions into supramolecular structures, mimicking the native extracellular matrix. Their properties can be easily tuned through changes at the sequence level. Moreover, they can be produced in sufficient quantities with chemical synthesis or recombinant technologies to allow them to address homogeneity and standardization issues required for applications. Here. recent advances in self-assembling proteins, peptides, and peptide amphiphiles that form scaffolds suitable for tissue engineering are reviewed. The focus is on a variety of motifs, ranging from minimalistic dipeptides, simplistic ultrashort aliphatic peptides, and peptide amphiphiles to large "recombinamer" proteins. Special emphasis is placed on the rational design of self-assembling motifs and biofunctionalization strategies to influence cell behavior and modulate scaffold stability. Perspectives for combination of these "bottom-up" designer strategies with traditional "top-down" biofabrication techniques for new generations of tissue engineering scaffolds are highlighted.

Exploring the sequence space for (tri-)peptide self-assembly to design and discover new hydrogels

Peptides that self-assemble into nanostructures are of tremendous interest for biological, medical, photonic and nanotechnological applications. The enormous sequence space that is available from 20 amino acids probably harbours many interesting candidates, but it is currently not possible to predict supramolecular behaviour from sequence alone. Here, we demonstrate computational tools to screen for the aqueous self-assembly propensity in all of the 8,000 possible tripeptides and evaluate these by comparison with known examples. We applied filters to select for candidates that simultaneously optimize the apparently contradicting requirements of aggregation propensity and hydrophilicity, which resulted in a set of design rules for self-assembling sequences. A number of peptides were subsequently synthesized and characterized, including the first reported tripeptides that are able to form a hydrogel at neutral pH. These tools, which enable the peptide sequence space to be searched for supramolecular properties, enable minimalistic peptide nanotechnology to deliver on its promise.