Atypical cofilin signaling drives dendritic cell migration through the extracellular matrix via nuclear deformation.

To mount an adaptive immune response, dendritic cells must migrate to lymph nodes to present antigens to T cells. Critical to 3D migration is the nucleus, which is the size-limiting barrier for migration through the extracellular matrix. Here, we show that inflammatory activation of dendritic cells leads to the nucleus becoming spherically deformed and enables dendritic cells to overcome the typical 2- to 3-µm diameter limit for 3D migration through gaps in the extracellular matrix. We show that the nuclear shape change is partially attained through reduced cell adhesion, whereas improved 3D migration is achieved through reprogramming of the actin cytoskeleton. Specifically, our data point to a model whereby the phosphorylation of cofilin-1 at serine 41 drives the assembly of a cofilin-actomyosin ring proximal to the nucleus and enhances migration through 3D collagen gels. In summary, these data describe signaling events through which dendritic cells deform their nucleus and enhance their migratory capacity.

PMIpred: a physics-informed web server for quantitative protein-membrane interaction prediction.

MOTIVATION: Many membrane peripheral proteins have evolved to transiently interact with the surface of (curved) lipid bilayers. Currently, methods to quantitatively predict sensing and binding free energies for protein sequences or structures are lacking, and such tools could greatly benefit the discovery of membrane-interacting motifs, as well as their de novo design. RESULTS: Here, we trained a transformer neural network model on molecular dynamics data for >50 000 peptides that is able to accurately predict the (relative) membrane-binding free energy for any given amino acid sequence. Using this information, our physics-informed model is able to classify a peptide's membrane-associative activity as either non-binding, curvature sensing, or membrane binding. Moreover, this method can be applied to detect membrane-interaction regions in a wide variety of proteins, with comparable predictive performance as state-of-the-art data-driven tools like DREAMM, PPM3, and MODA, but with a wider applicability regarding protein diversity, and the added feature to distinguish curvature sensing from general membrane binding. AVAILABILITY AND IMPLEMENTATION: We made these tools available as a web server, coined Protein-Membrane Interaction predictor (PMIpred), which can be accessed at https://pmipred.fkt.physik.tu-dortmund.de.

When Data Are Lacking: Physics-Based Inverse Design of Biopolymers Interacting with Complex, Fluid Phases.

Biomolecular research traditionally revolves around comprehending the mechanisms through which peptides or proteins facilitate specific functions, often driven by their relevance to clinical ailments. This conventional approach assumes that unraveling mechanisms is a prerequisite for wielding control over functionality, which stands as the ultimate research goal. However, an alternative perspective emerges from physics-based inverse design, shifting the focus from mechanisms to the direct acquisition of functional control strategies. By embracing this methodology, we can uncover solutions that might not have direct parallels in natural systems, yet yield crucial insights into the isolated molecular elements dictating functionality. This provides a distinctive comprehension of the underlying mechanisms.In this context, we elucidate how physics-based inverse design, facilitated by evolutionary algorithms and coarse-grained molecular simulations, charts a promising course for innovating the reverse engineering of biopolymers interacting with intricate fluid phases such as lipid membranes and liquid protein phases. We introduce evolutionary molecular dynamics (Evo-MD) simulations, an approach that merges evolutionary algorithms with the Martini coarse-grained force field. This method directs the evolutionary process from random amino acid sequences toward peptides interacting with complex fluid phases such as biological lipid membranes, offering significant promises in the development of peptide-based sensors and drugs. This approach can be tailored to recognize or selectively target specific attributes such as membrane curvature, lipid composition, membrane phase (e.g., lipid rafts), and protein fluid phases. Although the resulting optimal solutions may not perfectly align with biological norms, physics-based inverse design excels at isolating relevant physicochemical principles and thermodynamic driving forces governing optimal biopolymer interaction within complex fluidic environments. In addition, we expound upon how physics-based evolution using the Evo-MD approach can be harnessed to extract the evolutionary optimization fingerprints of protein-lipid interactions from native proteins. Finally, we outline how such an approach is uniquely able to generate strategic training data for predictive neural network models that cover the whole relevant physicochemical domain. Exploring challenges, we address key considerations such as choosing a fitting fitness function to delineate the desired functionality. Additionally, we scrutinize assumptions tied to system setup, the targeted protein structure, and limitations posed by the utilized (coarse-grained) force fields and explore potential strategies for guiding evolution with limited experimental data. This discourse encapsulates the potential and remaining obstacles of physics-based inverse design, paving the way for an exciting frontier in biomolecular research.

Phase-Separated Lipid-Based Nanoparticles: Selective Behavior at the Nano-Bio Interface.

The membrane-protein interface on lipid-based nanoparticles influences their in vivo behavior. Better understanding may evolve current drug delivery methods toward effective targeted nanomedicine. Previously, the cell-selective accumulation of a liposome formulation in vivo is demonstrated, through the recognition of lipid phase-separation by triglyceride lipases. This exemplified how liposome morphology and composition can determine nanoparticle-protein interactions. Here, the lipase-induced compositional and morphological changes of phase-separated liposomes-which bear a lipid droplet in their bilayer- are investigated, and the mechanism upon which lipases recognize and bind to the particles is unravelled. The selective lipolytic degradation of the phase-separated lipid droplet is observed, while nanoparticle integrity remains intact. Next, the Tryptophan-rich loop of the lipase is identified as the region with which the enzymes bind to the particles. This preferential binding is due to lipid packing defects induced on the liposome surface by phase separation. In parallel, the existing knowledge that phase separation leads to in vivo selectivity, is utilized to generate phase-separated mRNA-LNPs that target cell-subsets in zebrafish embryos, with subsequent mRNA delivery and protein expression. Together, these findings can expand the current knowledge on selective nanoparticle-protein communications and in vivo behavior, aspects that will assist to gain control of lipid-based nanoparticles.

CGCompiler: Automated Coarse-Grained Molecule Parametrization via Noise-Resistant Mixed-Variable Optimization.

Coarse-grained force fields (CG FFs) such as the Martini model entail a predefined, fixed set of Lennard-Jones parameters (building blocks) to model virtually all possible nonbonded interactions between chemically relevant molecules. Owing to its universality and transferability, the building-block coarse-grained approach has gained tremendous popularity over the past decade. The parametrization of molecules can be highly complex and often involves the selection and fine-tuning of a large number of parameters (e.g., bead types and bond lengths) to optimally match multiple relevant targets simultaneously. The parametrization of a molecule within the building-block CG approach is a mixed-variable optimization problem: the nonbonded interactions are discrete variables, whereas the bonded interactions are continuous variables. Here, we pioneer the utility of mixed-variable particle swarm optimization in automatically parametrizing molecules within the Martini 3 coarse-grained force field by matching both structural (e.g., RDFs) as well as thermodynamic data (phase-transition temperatures). For the sake of demonstration, we parametrize the linker of the lipid sphingomyelin. The important advantage of our approach is that both bonded and nonbonded interactions are simultaneously optimized while conserving the search efficiency of vector guided particle swarm optimization (PSO) methods over other metaheuristic search methods such as genetic algorithms. In addition, we explore noise-mitigation strategies in matching the phase-transition temperatures of lipid membranes, where nucleation and concomitant hysteresis introduce a dominant noise term within the objective function. We propose that noise-resistant mixed-variable PSO methods can both improve and automate parametrization of molecules within building-block CG FFs, such as Martini.

Pure Graphene Acts as an "Entropic Surfactant" at the Octanol-Water Interface.

Graphene oxide (GO) has proved itself as a nanomaterial capable of acting as a surfactant by lowering the interfacial tension of the oil-water interface due to its polar oxygen groups. However, the surfactant behavior of the pure graphene sheet─since prevention of edge oxidation in experimental setups is nontrivial─is still an unresolved issue in graphene research despite significant progress in the field in recent years. Here, we conduct both atomistic and coarse-grained simulations to demonstrate that─surprisingly─even pristine graphene, which only consists of hydrophobic carbon atoms, is attracted to the octanol-water interface and consequently reduces its surface tension by 2.3 kBT/nm2 or about 10 mN/m. Interestingly, the location of the free energy minimum is not precisely at the oil-water interface itself but is rather buried about two octanol layers into the octanol phase, being about 0.9 nm from the water phase. We demonstrate that the observed surfactant behavior is purely entropically driven and can be attributed to the unfavorable lipid-like structuring of octanol molecules at the free octanol-water interface. In essence, graphene enhances the inherent lipid-like behavior of octanol at the water interface rather than directly acting as a surfactant. Importantly, graphene does not display surfactant-like behavior in corresponding Martini coarse-grained simulations of the octanol-water system since the free liquid-liquid interface loses essential structure at the lower coarse-grained resolution. However, a similar surfactant behavior is recovered in coarse-grained simulations of longer alcohols such as dodecan-1-ol and hexadecan-1-ol. The observed discrepancies at different model resolutions enable us to construct a comprehensive model of the surfactant behavior of graphene at the octanol-water interface. The here-gained insights may facilitate the broader utilization of graphene in numerous domains of nanotechnology. Furthermore, since a drug's octanol-water partition coefficient is a crucial physicochemical parameter in rational drug discovery, we also believe that the universality of the here-illustrated entropic surfactant behavior of planar molecules deserves special attention in the drug design and development field.

Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism.

The self-assembly of peptides into supramolecular structures has been linked to neurodegenerative diseases but has also been observed in functional roles. Peptides are physiologically exposed to crowded environments of biomacromolecules, and particularly cellular membrane lipids. Previous research has shown that membranes can both accelerate and inhibit peptide self-assembly. Here, we studied the impact of membrane models that mimic cellular oxidative stress and compared this to mammalian and bacterial membranes. Using molecular dynamics simulations and experiments, we propose a model that explains how changes in peptide-membrane binding, electrostatics, and peptide secondary structure stabilization determine the nature of peptide self-assembly. We explored the influence of zwitterionic (POPC), anionic (POPG) and oxidized (PazePC) phospholipids, as well as cholesterol, and mixtures thereof, on the self-assembly kinetics of the amyloid ß (1-40) peptide (Aß40), linked to Alzheimer's disease, and the amyloid-forming antimicrobial peptide uperin 3.5 (U3.5). We show that the presence of an oxidized lipid had similar effects on peptide self-assembly as the bacterial mimetic membrane. While Aß40 fibril formation was accelerated, U3.5 aggregation was inhibited by the same lipids at the same peptide-to-lipid ratio. We attribute these findings and peptide-specific effects to differences in peptide-membrane adsorption with U3.5 being more strongly bound to the membrane surface and stabilized in an α-helical conformation compared to Aß40. Different peptide-to-lipid ratios resulted in different effects. We found that electrostatic interactions are a primary driving force for peptide-membrane interaction, enabling us to propose a model for predicting how cellular changes might impact peptide self-assembly in vivo.

Physics-based generative model of curvature sensing peptides; distinguishing sensors from binders.

Proteins can specifically bind to curved membranes through curvature-induced hydrophobic lipid packing defects. The chemical diversity among such curvature "sensors" challenges our understanding of how they differ from general membrane "binders" that bind without curvature selectivity. Here, we combine an evolutionary algorithm with coarse-grained molecular dynamics simulations (Evo-MD) to resolve the peptide sequences that optimally recognize the curvature of lipid membranes. We subsequently demonstrate how a synergy between Evo-MD and a neural network (NN) can enhance the identification and discovery of curvature sensing peptides and proteins. To this aim, we benchmark a physics-trained NN model against experimental data and show that we can correctly identify known sensors and binders. We illustrate that sensing and binding are phenomena that lie on the same thermodynamic continuum, with only subtle but explainable differences in membrane binding free energy, consistent with the serendipitous discovery of sensors.

Azobenzene-Based Amino Acids for the Photocontrol of Coiled-Coil Peptides.

Coiled-coil peptides are high-affinity, selective, self-assembling binding motifs, making them attractive components for the preparation of functional biomaterials. Photocontrol of coiled-coil self-assembly allows for the precise localization of their activity. To rationally explore photoactivity in a model coiled coil, three azobenzene-containing amino acids were prepared and substituted into the hydrophobic core of the E3/K3 coiled-coil heterodimer. Two of the non-natural amino acids, APhe1 and APhe2, are based on phenylalanine and differ in the presence of a carboxylic acid group. These have previously been demonstrated to modulate protein activity. When incorporated into peptide K3, coiled-coil binding strength was affected upon isomerization, with the two variants differing in their most folded state. The third azobenzene-containing amino acid, APgly, is based on phenylglycine and was prepared to investigate the effect of amino acid size on photoisomerization. When APgly is incorporated into the coiled coil, a 4.7-fold decrease in folding constant is observed upon trans-to-cis isomerization─the largest difference for all three amino acids. Omitting the methylene group between azobenzene and α-carbon was theorized to both position the diazene of APgly closer to the hydrophobic amino acids and reduce the possible rotations of the amino acid, with molecular dynamics simulations supporting these hypotheses. These results demonstrate the ability of photoswitchable amino acids to control coiled-coil assembly through disruption of the hydrophobic interface, a strategy that should be widely applicable.

Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data.

Nanostructured surfaces are widespread in nature and are being further developed in materials science. This makes them highly relevant for biomolecules, such as peptides. In this data article, we present a curvature model and molecular dynamics (MD) simulation data on the influence of nanoparticle size on the stability of amyloid peptide fibrils related to our research article entitled "Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation" (John et al., 2022) [1]. We provide the code to perform MD simulations in GROMACS 4.5.7 software of arbitrarily chosen biomolecule oligomers adsorbed on a curved surface of chosen nanoparticle size. We also provide the simulation parameters and data for peptide oligomers of Aß40, NNFGAIL, GNNQQNY, and VQIYVK. The data provided allows researchers to further analyze our MD simulations and the curvature model allows for a better understanding of oligomeric structures on surfaces.

TatA and TatB generate a hydrophobic mismatch important for the function and assembly of the Tat translocon in Escherichia coli.

The twin-arginine translocation (Tat) system serves to translocate folded proteins across energy-transducing membranes in bacteria, archaea, plastids, and some mitochondria. In Escherichia coli, TatA, TatB, and TatC constitute functional translocons. TatA and TatB both possess an N-terminal transmembrane helix (TMH) followed by an amphipathic helix. The TMHs of TatA and TatB generate a hydrophobic mismatch with the membrane, as the helices comprise only 12 consecutive hydrophobic residues; however, the purpose of this mismatch is unclear. Here, we shortened or extended this stretch of hydrophobic residues in either TatA, TatB, or both and analyzed effects on translocon function and assembly. We found the WT length helices functioned best, but some variation was clearly tolerated. Defects in function were exacerbated by simultaneous mutations in TatA and TatB, indicating partial compensation of mutations in each by the other. Furthermore, length variation in TatB destabilized TatBC-containing complexes, revealing that the 12-residue-length is important but not essential for this interaction and translocon assembly. To also address potential effects of helix length on TatA interactions, we characterized these interactions by molecular dynamics simulations, after having characterized the TatA assemblies by metal-tagging transmission electron microscopy. In these simulations, we found that interacting short TMHs of larger TatA assemblies were thinning the membrane and-together with laterally-aligned tilted amphipathic helices-generated a deep V-shaped membrane groove. We propose the 12 consecutive hydrophobic residues may thus serve to destabilize the membrane during Tat transport, and their conservation could represent a delicate compromise between functionality and minimization of proton leakage.

Efficient Quantification of Lipid Packing Defect Sensing by Amphipathic Peptides: Comparing Martini 2 and 3 with CHARMM36.

In biological systems, proteins can be attracted to curved or stretched regions of lipid bilayers by sensing hydrophobic defects in the lipid packing on the membrane surface. Here, we present an efficient end-state free energy calculation method to quantify such sensing in molecular dynamics simulations. We illustrate that lipid packing defect sensing can be defined as the difference in mechanical work required to stretch a membrane with and without a peptide bound to the surface. We also demonstrate that a peptide's ability to concurrently induce excess leaflet area (tension) and elastic softening─a property we call the "characteristic area of sensing" (CHAOS)─and lipid packing sensing behavior are in fact two sides of the same coin. In essence, defect sensing displays a peptide's propensity to generate tension. The here-proposed mechanical pathway is equally accurate yet, computationally, about 40 times less costly than the commonly used alchemical pathway (thermodynamic integration), allowing for more feasible free energy calculations in atomistic simulations. This enabled us to directly compare the Martini 2 and 3 coarse-grained and the CHARMM36 atomistic force fields in terms of relative binding free energies for six representative peptides including the curvature sensor ALPS and two antiviral amphipathic helices (AH). We observed that Martini 3 qualitatively reproduces experimental trends while producing substantially lower (relative) binding free energies and shallower membrane insertion depths compared to atomistic simulations. In contrast, Martini 2 tends to overestimate (relative) binding free energies. Finally, we offer a glimpse into how our end-state-based free energy method can enable the inverse design of optimal lipid packing defect sensing peptides when used in conjunction with our recently developed evolutionary molecular dynamics (Evo-MD) method. We argue that these optimized defect sensors─aside from their biomedical and biophysical relevance─can provide valuable targets for the development of lipid force fields.

Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation.

The aggregation of peptides into amyloid fibrils has been linked to ageing-related diseases, such as Alzheimer's and type 2 diabetes. Interfaces, particularly those with large nanostructured surfaces, can affect the kinetics of peptide aggregation, which ranges from complete inhibition to strong acceleration. While a number of physiochemical parameters determine interfacial effects, we focus here on the role of nanoparticle (NP) size and curvature. We used thioflavin T (ThT) fluorescence assays to demonstrate the size-dependent effects of NPs on amyloid fibril formation for the peptides Aß40, NNFGAIL, GNNQQNY and VQIYVK. While 5 nm gold NPs (AuNP-5) retarded or inhibited the aggregation of all peptides except NNFGAIL, larger 20 nm gold NPs (AuNP-20) tended to accelerate or not influence peptide aggregation. Differences in the NP effects for the peptides resulted from the different peptide properties (size, tendency to aggregate) and associated surface binding affinities. Additional dynamic light scattering (DLS), electron microscopy, and atomic force microscopy (AFM) experiments with the Aß40 peptide confirmed size-dependent NP effects on peptide aggregation, and also suggested a structural influence on the formed fibrils. NPs can serve as a surface for the adsorption of peptide monomers and enable nucleation to oligomers and fibril formation. However, molecular dynamics (MD) simulations showed that peptide oligomers were less stable at smaller NPs. High surface curvatures destabilized prefibrillar structures, which provides a possible explanation for inhibitory effects on fibril growth, provided that peptide-NP surface binding was relevant for fibril formation. These mechanistic insights can support the design of future nanostructured materials.

Quantifying Membrane Curvature Sensing of Peripheral Proteins by Simulated Buckling and Umbrella Sampling.

Membrane curvature plays an essential role in the organization and trafficking of membrane associated proteins. Comparison or prediction of the experimentally resolved protein concentrations adopted at different membrane curvatures requires direct quantification of the relative partitioning free energy. Here, we present a highly efficient and simple to implement a free-energy calculation method which is able to directly resolve the relative partitioning free energy of proteins as a direct function of membrane curvature, i.e., a curvature sensing profile, within (coarse-grained) molecular dynamics simulations. We demonstrate its utility by resolving these profiles for two known curvature sensing peptides, namely ALPS and α-synuclein, for a membrane curvature ranging from -1/6.5 to +1/6.5 nm-1. We illustrate that the difference in relative partitioning (binding) free energy between these two extrema is only about 13 kBT for both peptides, illustrating that the driving force of curvature sensing is subtle. Furthermore, we illustrate that ALPS and α-synuclein sense curvature via a contrasting mechanism, which is differentially affected by membrane composition. In addition, we demonstrate that the intrinsic spontaneous curvature of both of these peptides lies beyond the range of membrane curvature accessible in micropipette aspiration experiments, being about 1/7 nm -1. Our approach offers an efficient and simple to implement in silico tool for exploring and screening the membrane curvature sensing mechanisms of proteins.

Where are those lipid nano rings?

Highly curved toroidal micelles with diameters as small as 100 nm have been successfully constructed by self-assembly of amphiphilic block copolymers. These structures may have potential applications in gene or drug delivery. Experimental observations suggest that toroidal micelles likely originate from spherical or disc-like micelles which are tricked into forming toroidal micelles upon external stimuli ('smart' materials). Since self-assembly of polymeric and lipid surfactants is guided by the same physical principles, we hypothesize that 'smart' lipid surfactants can be equivalently tricked into forming highly curved toroidal micelles that are tenfold smaller (≃10 nm diameter). Paradoxically, these 'nano rings' have never been observed. Using coarse-grained molecular dynamics (MD) simulations in conjunction with a state-of-the-art free energy calculation method (a string method), we illustrate how a thermo-responsive lipid surfactant is able to form toroidal micelles. These micelles originate from disc-like micelles that are spontaneously perforated upon heat shocking, thereby supporting a longstanding hypothesis on the possible origin of polymeric toroidal micelle phases observed in experiments. We illustrate that kinetically stable 'nano rings' are substantially shorter lived than their tenfold larger polymeric analogs. The estimated life-time (milliseconds) is in fact similar to the characteristic breaking time of the corresponding worm-like micelle. Finally, we resolve the characteristic finger print which 'nano rings' leave in time-resolved X-ray spectra and illustrate how the uptake of small DNA fragments may enhance their stability. Despite a shared kinetics of self-assembly, length scale dependent differences in the life-time of surfactant phases can occur when phases are kinetically rather than thermodynamically stable. This results in the apparent absence or presence of toroidal micelle phases on different length scales. Our theoretical work precisely illustrates that the universality of surfactants nevertheless remains conserved even at different length scales.

Density Field Thermodynamic Integration (DFTI): A "Soft" Approach to Calculate the Free Energy of Surfactant Self-Assemblies.

Thermodynamic integration is one of the most established methods to quantify excess free energies between different metastable states. Excess intermolecular interactions in surfactant assemblies are on the scale of the energy of thermal fluctuations. Therefore, these materials can be deformed and topologically altered via relatively small mechanical stresses. It is thus intuitive to design reaction paths and associated order parameters that exploit the "soft" nature of these materials to mechanically rather than alchemically morph surfactant assemblies from state to state. Here, we propose a novel method coined "density field thermodynamic integration" (DFTI) that adopts the universality and transferability of alchemical methods while simultaneously exploiting the soft excess interactions between surfactant molecules. DFTI was designed for a rapid quantification of the free energy differences between different metastable structures in soft fluid materials. The DFTI method uses an external field coupled to the local density to mechanically morph the system between metastable states of interest. Here, we explored the capability of the DFTI method to swiftly and accurately calculate free energy differences between states. To this aim, we studied two different coarse-grained lipidic surfactant systems: (i) a fusion stalk and (ii) a worm-like micelle. Our results illustrate that DFTI can provide an efficient, versatile, and rather reliable method to calculate the free energy differences between surfactant assemblies.

Membrane Thinning Induces Sorting of Lipids and the Amphipathic Lipid Packing Sensor (ALPS) Protein Motif.

Heterogeneities (e.g., membrane proteins and lipid domains) and deformations (e.g., highly curved membrane regions) in biological lipid membranes cause lipid packing defects that may trigger functional sorting of lipids and membrane-associated proteins. To study these phenomena in a controlled and efficient way within molecular simulations, we developed an external field protocol that artificially enhances packing defects in lipid membranes by enforcing local thinning of a flat membrane region. For varying lipid compositions, we observed strong thinning-induced depletion or enrichment, depending on the lipid's intrinsic shape and its effect on a membrane's elastic modulus. In particular, polyunsaturated and lysolipids are strongly attracted to regions high in packing defects, whereas phosphatidylethanolamine (PE) lipids and cholesterol are strongly repelled from it. Our results indicate that externally imposed changes in membrane thickness, area, and curvature are underpinned by shared membrane elastic principles. The observed sorting toward the thinner membrane region is in line with the sorting expected for a positively curved membrane region. Furthermore, we have demonstrated that the amphipathic lipid packing sensor (ALPS) protein motif, a known curvature and packing defect sensor, is effectively attracted to thinner membrane regions. By extracting the force that drives amphipathic molecules toward the thinner region, our thinning protocol can directly quantify and score the lipid packing sensing of different amphipathic molecules. In this way, our protocol paves the way toward high-throughput exploration of potential defect- and curvature-sensing motifs, making it a valuable addition to the molecular simulation toolbox.

SNAREs, tethers and SM proteins: how to overcome the final barriers to membrane fusion?

Physiological membrane vesicles are built to separate reaction spaces in a stable manner, even when they accidentally collide or are kept in apposition by spatial constraints in the cell. This requires a natural resistance to fusion and mixing of their content, which originates from substantial energetic barriers to membrane fusion [1]. To facilitate intracellular membrane fusion reactions in a controlled manner, proteinaceous fusion machineries have evolved. An important open question is whether protein fusion machineries actively pull the fusion reaction over the present free energy barriers, or whether they rather catalyze fusion by lowering those barriers. At first sight, fusion proteins such as SNARE complexes and viral fusion proteins appear to act as nano-machines, which mechanically transduce force to the membranes and thereby overcome the free energy barriers [2,3]. Whether fusion proteins additionally alter the free energy landscape of the fusion reaction via catalytic roles is less obvious. This is a question that we shall discuss in this review, with particular focus on the influence of the eukaryotic SNARE-dependent fusion machinery on the final step of the reaction, the formation and expansion of the fusion pore.

Growth, Polymorphism, and Spatially Controlled Surface Immobilization of Biotinylated Variants of IAPP21-27 Fibrils.

The islet amyloid polypeptide (IAPP) is a regulatory peptide that can aggregate into fibrillar structures associated with type 2 diabetes. In this study, the IAPP21-27 segment was modified with a biotin linker at the N-terminus (Btn-GNNFGAIL) to immobilize peptide fibrils on streptavidin-coated surfaces. Key residues for fibril formation of the N-terminal biotinylated IAPP21-27 segment were identified by using an alanine scanning approach combined with molecular dynamics simulations, thioflavin T fluorescence measurements, and scanning electron microscopy. Significant contributions of phenylalanine (F23), leucine (L27), and isoleucine (I26) for the fibrillation of the short peptide segment were identified. The fibril morphologies of the peptide variants differed depending on their primary sequence, ranging from flexible and semiflexible to stiff and crystal-like structures. These insights could advance the design of new functional hybrid bionanomaterials and fibril-engineered surface coatings using short peptide segments. To validate this concept, the biotinylated fibrils were immobilized on streptavidin-coated surfaces under spatial control.