Design of parallel 𝛽-sheet nanofibrils using Monte Carlo search, coarse-grained simulations, and experimental testing.

Peptide self-assembly into amyloid fibrils provides numerous applications in drug delivery and biomedical engineering applications. We augment our previously-established computational screening technique along with experimental biophysical characterization to discover 7-mer peptides that self-assemble into "parallel ß-sheets", that is, ß-sheets with N-terminus-to-C-terminus 𝛽-strand vectors oriented in parallel. To accomplish the desired ß-strand organization, we applied the PepAD amino acid sequence design software to the Class-1 cross-ß spine defined by Sawaya et al. This molecular configuration includes two layers of parallel ß-sheets stacked such that N-terminus-to-C-terminus vectors are oriented antiparallel for molecules on adjacent ß-sheets. The first cohort of PepAD identified peptides were examined for their fibrillation behavior in DMD/PRIME20 simulations, and the top performing sequence was selected as a prototype for a subsequent round of sequence refinement. The two rounds of design resulted in a library of eight 7-mer peptides. In DMD/PRIME20 simulations, five of these peptides spontaneously formed fibril-like structures with a predominantly parallel 𝛽-sheet arrangement, two formed fibril-like structure with <50% in parallel 𝛽-sheet arrangement and one remained a random coil. Among the eight candidate peptides produced by PepAD and DMD/PRIME20, five were synthesized and purified. All five assembled into amyloid fibrils composed of parallel ß-sheets based on Fourier transform infrared spectroscopy, circular dichroism, electron microscopy, and thioflavin-T fluorescence spectroscopy measurements.

Spatially Confined Assembly and Immobilization of Hierarchical Nanoparticle Architectures inside Microdroplets in Magnetic Fields.

Magnetic field-directed colloidal interactions offer facile tools for assembly of structures that range from linear chains to multidimensional hierarchical architectures. While the field-driven assembly of colloidal particles has commonly been investigated in unbounded media, a knowledge gap remains concerning such assembly in confined microenvironments. Here, we investigate how confinement of ferromagnetic nanoparticles in microspheres directs their magnetic assembly into hierarchical architectures. Microdroplets from polydimethylsiloxane (PDMS) liquid precursor containing dispersed iron oxide magnetic nanoparticles (MNPs) were placed in a static magnetic field leading to the formation of organized assemblies inside the host droplets. By changing the MNP concentrations, we revealed a sequence of microstructures inside the droplets, ranging from linear chains at a low MNP loading, transitioning to a combination of chains and networked bundles, to solely 3D bundles at high MNP loading. These experimental results were analyzed with the aid of COMSOL simulations where we calculated the potential energy to identify the preferred assembly conformations. The chains at high MNP loading minimized their energy by aggregating laterally to form bundles with their MNP dipoles being out-of-registry. We cured these PDMS droplets to immobilize the assemblies by forming soft microbeads. These microbeads constitute an "interaction toolbox" with different magnetic macroscale responses, which are governed by the structuring of the MNPs and their magnetic polarizability. We show that thanks to their ability to rotate by field-induced torque under a rotating field, these microbeads can be employed in applications such as optical modulators and microrollers.

Programming co-assembled peptide nanofiber morphology via anionic amino acid type: Insights from molecular dynamics simulations.

Co-assembling peptides can be crafted into supramolecular biomaterials for use in biotechnological applications, such as cell culture scaffolds, drug delivery, biosensors, and tissue engineering. Peptide co-assembly refers to the spontaneous organization of two different peptides into a supramolecular architecture. Here we use molecular dynamics simulations to quantify the effect of anionic amino acid type on co-assembly dynamics and nanofiber structure in binary CATCH(+/-) peptide systems. CATCH peptide sequences follow a general pattern: CQCFCFCFCQC, where all C's are either a positively charged or a negatively charged amino acid. Specifically, we investigate the effect of substituting aspartic acid residues for the glutamic acid residues in the established CATCH(6E-) molecule, while keeping CATCH(6K+) unchanged. Our results show that structures consisting of CATCH(6K+) and CATCH(6D-) form flatter ß-sheets, have stronger interactions between charged residues on opposing ß-sheet faces, and have slower co-assembly kinetics than structures consisting of CATCH(6K+) and CATCH(6E-). Knowledge of the effect of sidechain type on assembly dynamics and fibrillar structure can help guide the development of advanced biomaterials and grant insight into sequence-to-structure relationships.

Using Enhanced Sampling Simulations to Study the Conformational Space of Chiral Aromatic Peptoid Monomers.

Peptoids, or N-substituted glycines, are peptide-like materials that form a wide variety of secondary structures owing to their enhanced flexibility and a diverse collection of possible side chains. Compared to that of peptides, peptoids have a substantially more complex conformational landscape. This is mainly due to the ability of the peptoid amide bond to exist in both cis- and trans-conformations. This makes conventional molecular dynamics simulations and even some enhanced sampling approaches unable to sample the complete energy landscapes. In this article, we present an extension to the CGenFF-NTOID peptoid atomistic forcefield by adding parameters for four side chains to the previously available collection. We employ explicit solvent well-tempered metadynamics simulations to optimize our forcefield parameters and parallel bias metadynamics to study the cis-trans isomerism for SN1-phenylethyl (s1pe) and SN1-naphthylethyl (s1ne) peptoid monomers, the free energy minima generated from which are validated with available experimental data. In the absence of experimental data, we supported our atomistic simulations with ab initio calculations. This work represents an important step toward the computational design of peptoid-based materials.

Side-Chain Chemistry Governs Hierarchical Order of Charge-Complementary ß-sheet Peptide Coassemblies.

Self-assembly of proteinaceous biomolecules into functional materials with ordered structures that span length scales is common in nature yet remains a challenge with designer peptides under ambient conditions. This report demonstrates how charged side-chain chemistry affects the hierarchical co-assembly of a family of charge-complementary ß-sheet-forming peptide pairs known as CATCH(X+/Y-) at physiologic pH and ionic strength in water. In a concentration-dependent manner, the CATCH(6K+) (Ac-KQKFKFKFKQK-Am) and CATCH(6D-) (Ac-DQDFDFDFDQD-Am) pair formed either ß-sheet-rich microspheres or ß-sheet-rich gels with a micron-scale plate-like morphology, which were not observed with other CATCH(X+/Y-) pairs. This hierarchical order was disrupted by replacing D with E, which increased fibril twisting. Replacing K with R, or mutating the N- and C-terminal amino acids in CATCH(6K+) and CATCH(6D-) to Qs, increased observed co-assembly kinetics, which also disrupted hierarchical order. Due to the ambient assembly conditions, active CATCH(6K+)-green fluorescent protein fusions could be incorporated into the ß-sheet plates and microspheres formed by the CATCH(6K+/6D-) pair, demonstrating the potential to endow functionality.

In Silico Design and Analysis of Plastic-Binding Peptides.

Peptides that bind to inorganic materials can be used to functionalize surfaces, control crystallization, or assist in interfacial self-assembly. In the past, inorganic-binding peptides have been found predominantly through peptide library screening. While this method has successfully identified peptides that bind to a variety of materials, an alternative design approach that can intelligently search for peptides and provide physical insight for peptide affinity would be desirable. In this work, we develop a computational, physics-based approach to design inorganic-binding peptides, focusing on peptides that bind to the common plastics polyethylene, polypropylene, polystyrene, and poly(ethylene terephthalate). The PepBD algorithm, a Monte Carlo method that samples peptide sequence and conformational space, was modified to include simulated annealing, relax hydration constraints, and an ensemble of conformations to initiate design. These modifications led to the discovery of peptides with significantly better scores compared to those obtained using the original PepBD. PepBD scores were found to improve with increasing van der Waals interactions, although strengthening the intermolecular van der Waals interactions comes at the cost of introducing unfavorable electrostatic interactions. The best designs are enriched in amino acids with bulky side chains and possess hydrophobic and hydrophilic patches whose location depends on the adsorbed conformation. Future work will evaluate the top peptide designs in molecular dynamics simulations and experiment, enabling their application in microplastic pollution remediation and plastic-based biosensors.

Design of 8-mer peptides that block Clostridioides difficile toxin A in intestinal cells.

Infections by Clostridioides difficile, a bacterium that targets the large intestine (colon), impact a large number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can be delivered to the gut and inhibit the biocatalytic activity of these toxins represent a promising therapeutic strategy to prevent and treat C. diff. infection. We describe an approach that combines a Peptide Binding Design (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in colon epithelial cells. One peptide, SA1, is found to block TcdA toxicity in primary-derived human colon (large intestinal) epithelial cells. SA1 binds TcdA with a KD of 56.1 ± 29.8 nM as measured by surface plasmon resonance (SPR).

Computational investigation of the phase behavior of colloidal squares with offset magnetic dipoles.

Colloidal particles with anisotropic shapes and interactions display rich phase behavior and have potential as structural bases for materials with controllable properties. In this paper, we explore the self-assembling characteristics of a new class of particles that have been shown experimentally to form reconfigurable structures: microscopic cube-shaped colloids with a magnetic dipole that is transversely offset from the particle's center of mass. We have performed in silico simulations of the dynamics of large numbers of dipolar squares in two-dimensions using discontinuous molecular dynamics (DMD). We use a coarse-grain method where the dipolar microcubes are represented by a group of four hard circles bonded together to create a rigid square in two-dimensions and two opposite charges are embedded within the square to represent a magnetic dipole. Annealing, or "slow-cooling", simulations are conducted to determine the equilibrium structures. Systems of dipolar squares tend to assemble into one of two different types of conformations: either single- or double-stranded assemblies, each with unique structures and phase diagrams in the temperature-density plane. Single-stranded assemblies form highly interconnected percolated, or gel-like, networks. In contrast, double stranded assemblies tend to form globally-aligned nematic states at high densities, although this is not seen consistently in all runs. The phase behavior of systems of dipolar squares depends not only on the system's temperature and density, but also on the type of dipole embedded within the square and on the relative number of squares with an opposite "handedness" that are present within the system.

Design of 8-mer Peptides that Block Clostridioides difficile Toxin A in Intestinal Cells.

Clostridioides difficile ( C. diff .) is a bacterium that causes severe diarrhea and inflammation of the colon. The pathogenicity of C. diff . infection is derived from two major toxins, toxins A (TcdA) and B (TcdB). Peptide inhibitors that can be delivered to the gut to inactivate these toxins are an attractive therapeutic strategy. In this work, we present a new approach that combines a pep tide b inding d esign algorithm (PepBD), molecular-level simulations, rapid screening of candidate peptides for toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block the glucosyltransferase activity of TcdA by targeting its glucosyltransferase domain (GTD). Using PepBD and explicit-solvent molecular dynamics simulations, we identified seven candidate peptides, SA1-SA7. These peptides were selected for specific TcdA GTD binding through a custom solid-phase peptide screening system, which eliminated the weaker inhibitors SA5-SA7. The efficacies of SA1-SA4 were then tested using a trans-epithelial electrical resistance (TEER) assay on monolayers of the human gut epithelial culture model. One peptide, SA1, was found to block TcdA toxicity in primary-derived human jejunum (small intestinal) and colon (large intestinal) epithelial cells. SA1 bound TcdA with a K D of 56.1 ± 29.8 nM as measured by surface plasmon resonance (SPR). Significance Statement: Infections by Clostridioides difficile , a bacterium that targets the large intestine (colon), impact a significant number of people worldwide. Bacterial colonization is mediated by two exotoxins: toxins A and B. Short peptides that can inhibit the biocatalytic activity of these toxins represent a promising strategy to prevent and treat C. diff . infection. We describe an approach that combines a Peptide B inding D esign (PepBD) algorithm, molecular-level simulations, a rapid screening assay to evaluate peptide:toxin binding, a primary human cell-based assay, and surface plasmon resonance (SPR) measurements to develop peptide inhibitors that block Toxin A in small intestinal and colon epithelial cells. Importantly, our designed peptide, SA1, bound toxin A with nanomolar affinity and blocked toxicity in colon cells.

Computational Design and Experimental Validation of ACE2-Derived Peptides as SARS-CoV-2 Receptor Binding Domain Inhibitors.

The COVID-19 pandemic has caused significant social and economic disruption across the globe. Cellular entry of SARS-CoV-2 into the human body is mediated via binding of the Receptor Binding Domain (RBD) on the viral Spike protein (SARS-CoV-2 RBD) to Angiotensin-Converting Enzyme 2 (ACE2) expressed on host cells. Molecules that can disrupt ACE2:RBD interactions are attractive therapeutic candidates to prevent virus entry into human cells. A computational strategy that combines our Peptide Binding Design (PepBD) algorithm with atomistic molecular dynamics simulations was used to design new inhibitory peptide candidates via sequence iteration starting with a 23-mer peptide, referred to as SBP1. SBP1 is derived from a region of the ACE2 Peptidase Domain α1 helix that binds to the SARS-CoV-2 RBD of the initial Wuhan-Hu-1 strain. Three peptides demonstrated a solution-phase RBD-binding dissociation constant in the micromolar range during tryptophan fluorescence quenching experiments, one peptide did not bind, and one was insoluble at micromolar concentrations. However, in competitive ELISA assays, none of these peptides could outcompete ACE2 binding to SARS-CoV-2-RBD up to concentrations of 50 µM, similar to the parent SBP1 peptide which also failed to outcompete ACE2:RBD binding. Molecular dynamics simulations suggest that P4 would have a good binding affinity for the RBD domain of Beta-B.1.351, Gamma-P.1, Kappa-B.1.617.1, Delta-B.1.617.2, and Omicron-B.1.1.529 variants, but not the Alpha variant. Consistent with this, P4 bound Kappa-B.1.617.1 and Delta-B.1.617.2 RBD with micromolar affinity in tryptophan fluorescence quenching experiments. Collectively, these data show that while relatively short unstructured peptides can bind to SARS-CoV-2 RBD with moderate affinity, they are incapable of outcompeting the strong interactions between RBD and ACE2.

De novo discovery of peptide-based affinity ligands for the fab fragment of human immunoglobulin G.

Antibody fragments and their engineered variants show true potential as next-generation therapeutics as they combine excellent targeting with superior biodistribution and blood clearance. Unlike full antibodies, however, antibody fragments do not yet have a standard platform purification process for large-scale production. Short peptide ligands are viable alternatives to protein ligands in affinity chromatography. In this work, an integrated computational and experimental scheme is described to de novo design 9-mer peptides that bind to Fab fragments. The first cohort of designed sequences was tested experimentally using human polyclonal Fab, and the top performing sequence was selected as a prototype for a subsequent round of ligand refinement in silico. The resulting peptides were conjugated to chromatographic resins and evaluated via equilibrium and dynamic binding studies using human Fab-κ and Fab-λ. The equilibrium studies returned values of binding capacities up to 32 mg of Fab per mL of resin with mild affinity (KD ∼ 10-5 M) that are conducive to high product capture and recovery. Dynamic studies returned values of product yield up to ∼90%. Preliminary purification studies provided purities of 83-93% and yields of 11-89%. These results lay the groundwork for future development of these ligands towards biomanufacturing translation.

In Silico Identification and Experimental Validation of Peptide-Based Inhibitors Targeting Clostridium difficile Toxin A.

Clostridium difficile infection is mediated by two major exotoxins: toxins A (TcdA) and B (TcdB). Inhibiting the biocatalytic activities of these toxins with targeted peptide-based drugs can reduce the risk of C. difficile infection. In this work, we used a computational strategy that integrates a peptide binding design (PepBD) algorithm and explicit-solvent atomistic molecular dynamics simulation to determine promising toxin A-targeting peptides that can recognize and bind to the catalytic site of the TcdA glucosyltransferase domain (GTD). Our simulation results revealed that two out of three in silico discovered peptides, viz. the neutralizing peptides A (NPA) and B (NPB), exhibit lower binding free energies when bound to the TcdA GTD than the phage-display discovered peptide, viz. the reference peptide (RP). These peptides may serve as potential inhibitors against C. difficile infection. The efficacy of the peptides RP, NPA, and NPB to neutralize the cytopathic effects of TcdA was tested in vitro in human jejunum cells. Both phage-display peptide RP and in silico peptide NPA were found to exhibit strong toxin-neutralizing properties, thereby preventing the TcdA toxicity. However, the in silico peptide NPB demonstrates a relatively low efficacy against TcdA.

Sequence patterns and signatures: Computational and experimental discovery of amyloid-forming peptides.

Screening amino acid sequence space via experiments to discover peptides that self-assemble into amyloid fibrils is challenging. We have developed a computational peptide assembly design (PepAD) algorithm that enables the discovery of amyloid-forming peptides. Discontinuous molecular dynamics (DMD) simulation with the PRIME20 force field combined with the FoldAmyloid tool is used to examine the fibrilization kinetics of PepAD-generated peptides. PepAD screening of ∼10,000 7-mer peptides resulted in twelve top-scoring peptides with two distinct hydration properties. Our studies revealed that eight of the twelve in silico discovered peptides spontaneously form amyloid fibrils in the DMD simulations and that all eight have at least five residues that the FoldAmyloid tool classifies as being aggregation-prone. Based on these observations, we re-examined the PepAD-generated peptides in the sequence pool returned by PepAD and extracted five sequence patterns as well as associated sequence signatures for the 7-mer amyloid-forming peptides. Experimental results from Fourier transform infrared spectroscopy (FTIR), thioflavin T (ThT) fluorescence, circular dichroism (CD), and transmission electron microscopy (TEM) indicate that all the peptides predicted to assemble in silico assemble into antiparallel ß-sheet nanofibers in a concentration-dependent manner. This is the first attempt to use a computational approach to search for amyloid-forming peptides based on customized settings. Our efforts facilitate the identification of ß-sheet-based self-assembling peptides, and contribute insights towards answering a fundamental scientific question: "What does it take, sequence-wise, for a peptide to self-assemble?".

Engineering ß-Sheet Peptide Coassemblies for Biomaterial Applications.

Peptide coassembly, wherein at least two different peptides interact to form multicomponent nanostructures, is an attractive approach for generating functional biomaterials. Current efforts seek to design pairs of peptides, A and B, that form nanostructures (e.g., ß-sheets with ABABA-type ß-strand patterning) while resisting self-assembly (e.g., AAAAA-type or BBBBB-type ß-sheets). To confer coassembly behavior, most existing designs have been based on highly charged variants of known self-assembling peptides; like-charge repulsion limits self-assembly while opposite-charge attraction promotes coassembly. Recent analyses using solid-state NMR and coarse-grained simulations reveal that preconceived notions of structure and molecular organization are not always correct. This perspective highlights recent advances and key challenges to understanding and controlling peptide coassembly.

De novo design of peptides that coassemble into ß sheet-based nanofibrils.

Peptides' hierarchical coassembly into nanostructures enables controllable fabrication of multicomponent biomaterials. In this work, we describe a computational and experimental approach to design pairs of charge-complementary peptides that selectively coassemble into ß-sheet nanofibers when mixed together but remain unassembled when isolated separately. The key advance is a peptide coassembly design (PepCAD) algorithm that searches for pairs of coassembling peptides. Six peptide pairs are identified from a pool of ~106 candidates via the PepCAD algorithm and then subjected to DMD/PRIME20 simulations to examine their co-/self-association kinetics. The five pairs that spontaneously aggregate in kinetic simulations selectively coassemble in biophysical experiments, with four forming ß-sheet nanofibers and one forming a stable nonfibrillar aggregate. Solid-state NMR, which is applied to characterize the coassembling pairs, suggests that the in silico peptides exhibit a higher degree of structural order than the previously reported CATCH(+/−) peptides.

CATCH Peptides Coassemble into Structurally Heterogeneous ß-Sheet Nanofibers with Little Preference to ß-Strand Alignment.

Coassembling peptides offer an additional degree of freedom in the design of nanostructured biomaterials when compared to analogous self-assembling peptides. Yet, our understanding of how amino acid sequences encodes coassembled nanofiber structure is limited. Prior work on a charge-complementary pair, CATCH+ and CATCH- peptides, detected like-peptide nearest neighbors (CATCH+:CATCH+ and CATCH-:CATCH-) within coassembled ß-sheet nanofibers; these self-associated peptide pairs marked a departure from an "ideal" coassembled structure. In this work, we employ solid-state NMR, isotope-edited FTIR, and coarse-grained molecular dynamics simulations to evaluate the alignment of ß-strands within CATCH peptide nanofibers. Both experimental and computational results suggest that CATCH molecules coassemble into structurally heterogeneous nanofibers, which is consistent with our observations in another coassembling system, the King-Webb peptides. Within ß-sheet nanofibers, ß-strands were found to have nearest neighbors aligned in-register parallel, in-register antiparallel, and out-of-register. In comparison to the King-Webb peptides, CATCH nanofibers exhibit a greater degree of structural heterogeneity. By comparing the amino acid sequences of CATCH and King-Webb peptides, we can begin to unravel sequence-to-structure relationships, which may encode more precise coassembled ß-sheet nanostructures.

Molecular simulation study of 3,4-dihydroxyphenylalanine in the context of underwater adhesive design.

Adhesives that can stick to multiple surface types in underwater and high moisture conditions are critical for various applications such as marine coatings, sealants, and medical devices. The analysis of natural underwater adhesives shows that L-3,4-dihydroxyphenylalanine (DOPA) and functional amyloid nanostructures are key components that contribute to the adhesive powers of these natural glues. The combination of DOPA and amyloid-forming peptides into DOPA-amyloid(-forming peptide) conjugates provides a new approach to design generic underwater adhesives. However, it remains unclear how the DOPA monomers may interact with amyloid-forming peptides and how these interactions may influence the adhesive ability of the conjugates. In this paper, we investigate the behavior of DOPA monomers, (glycine-DOPA)3 chains, and a KLVFFAE and DOPA-glycine chain conjugate in aqueous environments using molecular simulations. The DOPA monomers do not aggregate significantly at concentrations lower than 1.0M. Simulations of (glycine-DOPA)3 chains in water were done to examine the intra-molecular interactions of the chain, wherein we found that there were unlikely to be interactions detrimental to the adhesion process. After combining the alternating DOPA-glycine chain with the amyloid-forming peptide KLVFFAE into a single chain conjugate, we then simulated the conjugate in water and saw the possibility of both intra-chain folding and no chain folding in the conjugate.

On the liquid demixing of water + elastin-like polypeptide mixtures: bimodal re-entrant phase behaviour.

Water + elastin-like polypeptides (ELPs) exhibit a transition temperature below which the chains transform from collapsed to expanded states, reminiscent of the cold denaturation of proteins. This conformational change coincides with liquid-liquid phase separation. A statistical-thermodynamics theory is used to model the fluid-phase behavior of ELPs in aqueous solution and to extrapolate the behavior at ambient conditions over a range of pressures. At low pressures, closed-loop liquid-liquid equilibrium phase behavior is found, which is consistent with that of other hydrogen-bonding solvent + polymer mixtures. At pressures evocative of deep-sea conditions, liquid-liquid immiscibility bounded by two lower critical solution temperatures (LCSTs) is predicted. As pressure is increased further, the system exhibits two separate regions of closed-loop of liquid-liquid equilibrium (LLE). The observation of bimodal LCSTs and two re-entrant LLE regions herald a new type of binary global phase diagram: Type XII. At high-ELP concentrations the predicted phase diagram resembles a protein pressure denaturation diagram; possible "molten-globule"-like states are observed at low concentration.

Structural insights into peptide self-assembly using photo-induced crosslinking experiments and discontinuous molecular dynamics.

Determining the structure of the (oligomeric) intermediates that form during the self-assembly of amyloidogenic peptides is challenging because of their heterogeneous and dynamic nature. Thus, there is need for methodology to analyze the underlying molecular structure of these transient species. In this work, a combination of fluorescence quenching, photo-induced crosslinking (PIC) and molecular dynamics simulation was used to study the assembly of a synthetic amyloid-forming peptide, Aß16-22. A PIC amino acid containing a trifluormethyldiazirine (TFMD) group-Fmoc(TFMD)Phe-was incorporated into the sequence (Aß*16-22). Electrospray ionization ion-mobility spectrometry mass-spectrometry (ESI-IMS-MS) analysis of the PIC products confirmed that Aß*16-22 forms assemblies with the monomers arranged as anti-parallel, in-register ß-strands at all time points during the aggregation assay. The assembly process was also monitored separately using fluorescence quenching to profile the fibril assembly reaction. The molecular picture resulting from discontinuous molecule dynamics simulations showed that Aß16-22 assembles through a single-step nucleation into a ß-sheet fibril in agreement with these experimental observations. This study provides detailed structural insights into the Aß16-22 self-assembly processes, paving the way to explore the self-assembly mechanism of larger, more complex peptides, including those whose aggregation is responsible for human disease.

Membrane morphologies induced by mixtures of arc-shaped particles with opposite curvature.

Biological membranes are shaped by various proteins that either generate inward or outward membrane curvature. In this article, we investigate the membrane morphologies induced by mixtures of arc-shaped particles with coarse-grained modeling and simulations. The particles bind to the membranes either with their inward, concave side or their outward, convex side and, thus, generate membrane curvature of opposite sign. We find that small fractions of convex-binding particles can stabilize three-way junctions of membrane tubules, as suggested for the protein lunapark in the endoplasmic reticulum of cells. For comparable fractions of concave-binding and convex-binding particles, we observe lines of particles of the same type, and diverse membrane morphologies with grooves and bulges induced by these particle lines. The alignment and segregation of the particles is driven by indirect, membrane-mediated interactions.