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Synaptic vesicles are small membrane-enclosed organelles that store neurotransmitters at presynaptic terminals. The uniform morphology of synaptic vesicles is important for brain function, because it enables the storage of well-defined amounts of neurotransmitters and thus reliable synaptic transmission. Here, we show that the synaptic vesicle membrane protein synaptogyrin cooperates with the lipid phosphatidylserine to remodel the synaptic vesicle membrane. Using NMR spectroscopy, we determine the high-resolution structure of synaptogyrin and identify specific binding sites for phosphatidylserine. We further show that phosphatidylserine binding changes the transmembrane structure of synaptogyrin and is critical for membrane bending and the formation of small vesicles. Cooperative binding of phosphatidylserine to both a cytoplasmic and intravesicular lysine-arginine cluster in synaptogyrin is required for the formation of small vesicles. Together with other synaptic vesicle proteins, synaptogyrin thus can sculpt the membrane of synaptic vesicles.

Carbon–carbon dipolar recoupling sequences are frequently used building blocks in routine magic-angle spinning NMR experiments. While broadband homonuclear first-order dipolar recoupling sequences mainly excite intra-residue correlations, selective methods can detect inter-residue transfers and long-range correlations. Here, we present the great offset difference internuclear selective transfer (GODIST) pulse sequence optimized for selective carbonyl or aliphatic recoupling at fast magic-angle spinning, here, 55 kHz. We observe a 3- to 5-fold increase in intensities compared with broadband RFDR recoupling for perdeuterated microcrystalline SH3 and for the membrane protein influenza A M2 in lipid bilayers. In 3D (H)COCO(N)H and (H)CO(CO)NH spectra, inter-residue carbonyl–carbonyl correlations up to about 5 Å are observed in uniformly 13C-labeled proteins.

The voltage-dependent anion channel (VDAC), the most abundant protein in the outer mitochondrial membrane, is responsible for the transport of all ions and metabolites into and out of mitochondria. Larger than any of the beta-barrel structures determined to date by magic-angle spinning (MAS) NMR, but smaller than the size limit of cryo-electron microscopy (cryo-EM), VDAC1's 31 kDa size has long been a bottleneck in determining its structure in a near-native lipid bilayer environment. Using a single two-dimensional (2D) crystalline sample of human VDAC1 in lipids, we applied proton-detected fast magic-angle spinning NMR spectroscopy to determine the arrangement of beta strands. Combining these data with long-range restraints from a spin-labeled sample, chemical shift-based secondary structure prediction, and previous MAS NMR and atomic force microscopy (AFM) data, we determined the channel's structure at a 2.2 angstrom root-mean-square deviation (RMSD). The structure, a 19-stranded beta-barrel, with an N-terminal alpha-helix in the pore is in agreement with previous data in detergent, which was questioned due to the potential for the detergent to perturb the protein's functional structure. Using a quintuple mutant implementing the channel's closed state, we found that dynamics are a key element in the protein's gating behavior, as channel closure leads to the destabilization of not only the C-terminal barrel residues but also the alpha 2 helix. We showed that cholesterol, previously shown to reduce the frequency of channel closure, stabilizes the barrel relative to the N-terminal helix. Furthermore, we observed channel closure through steric blockage by a drug shown to selectively bind to the channel, the Bcl2-antisense oligonucleotide G3139.

Homonuclear dipolar recoupling is routinely used for magic-angle spinning NMR-based structure determination. In fully protonated samples, only short proton-proton distances are accessible to broadband recoupling approaches because of high proton density. Selective methods allow detection of longer distances by directing polarization to a subset of spins. Here we introduce the selective pulse sequence MODIST, which recouples spins that have a modest chemical shift offset difference, and demonstrate it to selectively record correlations between amide protons. The sequence was selected for good retention of total signal, leading to up to twice the intensity for proton-proton correlations compared with other selective methods. The sequence is effective across a range of spinning conditions and magnetic fields, here tested at 55.555 and 100 kHz magic-angle spinning and at proton Larmor frequencies from 600 to 1200 MHz. For influenza A M2 in lipid bilayers, cross-peaks characteristic of a helical conformation are observed.

Amyloid-beta (Aβ) deposition as senile plaques is a pathological hallmark of Alzheimer’s disease (AD). AD is characterized by a large level of heterogeneity in amyloid pathology, whose molecular origin is poorly understood. Here, we employ NMR spectroscopy and MD simulation at ambient and high pressures and investigate how AD-related mutations in Aβ peptide influence the stability of Aβ aggregates. The pressure-induced monomer dissociation from Aβ aggregates monitored by NMR demonstrated that the Iowa (D23N), Arctic (E22G), and Osaka (ΔE22) mutations altered the pressure stability of Aβ40 aggregates in distinct manners. While the NMR data of monomeric Aβ40 showed only small localized effects of mutations, the MD simulation of mutated Aβ fibrils revealed their distinct susceptibility to elevated pressure. Our data propose a structural basis for the distinct stability of various Aβ fibrils and highlights “stability” as a molecular property potentially contributing to the large heterogeneity of amyloid pathology in AD.

A detailed experimental study on the aerodynamic performance and noise emission of airfoils and fan blades with permeable leading edges under disturbed inflow conditions was performed. The airfoils...

Sound generation during voiced speech remains an open research topic because the underlying process within the human larynx is hardly accessible for direct measurements. In the present study, harmonic sound generation during phonation was investigated with a model that replicates the fully coupled fluid-structure-acoustic interaction (FSAI). The FSAI was captured using a multi-modal approach by measuring the flow and acoustic source fields based on particle image velocimetry, as well as the surface velocity of the vocal folds based on laser vibrometry and high-speed imaging. Strong harmonic sources were localized near the glottis, as well as further downstream, during the presence of the supraglottal jet. The strongest harmonic content of the vocal fold surface motion was verified for the area near the glottis, which directly interacts with the glottal jet flow. Also, the acoustic back-coupling of the formant frequencies onto the harmonic oscillation of the vocal folds was verified. These findings verify that harmonic sound generation is the result of a strong interrelation between the vocal fold motion, modulated flow field, and vocal tract geometry.

The integration of rotating machineries in human-populated environments requires to limit noise emissions, with multiple aspects impacting on control of amplitude and frequency of the acoustic signature. This is a key issue to address and when combined with compliance of minimum efficiency grades, further complicates the design of axial fans. The aim of this research is to assess the capability of unsupervised learning techniques in unveiling the mechanisms that concur to the sound generation process in axial fans starting from high-fidelity simulations. To this aim, a numerical dataset was generated by means of large Eddy simulation (LES) simulation of a low-speed axial fan. The dataset is enriched with sound source computed solving a-posteriori the perturbed convective wave equation (PCWE). First, the instantaneous flow features are associated with the sound sources through correlation matrices and then projected on latent basis to highlight the features with the highest importance. This analysis in also carried out on a reduced dataset, derived by considering two surfaces at 50% and 95% of the blade span. The sampled features on the surfaces are then exploited to train three cluster algorithms based on partitional, density and Gaussian criteria. The cluster algorithms are optimized and their results are compared, with the Gaussian Mixture one demonstrating the highest similarity (>80%). The derived clusters are analyzed, and the role of statistical distribution of velocity and pressure gradients is underlined. This suggests that design choices that affect these aspects may be beneficial to control the generation of noise sources.

Natural compounds with anti-aggregation capacity are increasingly recognized as viable candidates against neurodegenerative diseases. Recently, the polyphenolic fraction of propolis (PFP), a complex bee product, has been shown to inhibit amyloid aggregation of a model protein especially in the nanosheet form. Here, we examine the aggregation-modulating effects of the PFP nanosheets on α-synuclein (α-syn), an intrinsically disordered protein involved in the pathogenesis of Parkinson's disease. Based on a range of biophysical data including intrinsic and extrinsic fluorescence, circular dichroism (CD) data, and nuclear magnetic resonance spectroscopy, we propose a model for the interaction of α-syn with PFP nanosheets, where the positively charged N-terminal and the middle non-amyloid component regions of α-syn act as the main binding sites with the negatively charged PFP nanosheets. The Thioflavin T (ThT) fluorescence, Congo red absorbance, and CD data reveal a prominent dose-dependent inhibitory effect of PFP nanosheets on α-syn amyloid aggregation, and the microscopy images and MTT assay data suggest that the PFP nanosheets redirect α-syn aggregation toward nontoxic off-pathway oligomers. When preformed α-syn amyloid fibrils are present, fluorescence images show co-localization of PFP nanosheets and ThT, further confirming the binding of PFP nanosheets with α-syn amyloid fibrils. Taken together, our results demonstrate the binding and anti-aggregation activity of PFP nanosheets in a disease-related protein system and propose them as potential nature-based tools for probing and targeting pathological protein aggregates in neurodegenerative diseases.

Atomic details of structured water molecules are indispensable to understand the thermodynamics of important biological processes including the proton conduction mechanism of the M2 protein. Despite the expectation of structured water molecules based on crystal structures of Influenza A M2, only two water populations have been observed by NMR in reconstituted lipid bilayer samples. These are the bulk‐ and lipid‐associated water populations typically seen in membrane samples. Here, we detect a bound water molecule at a chemical shift of 11 ppm, located near the functional histidine 37 residue in the M2 conductance domain, which comprises residues 18 to 60. Combining 100 kHz magic‐angle spinning NMR, dynamic nuclear polarization and density functional theory calculations, we show that the bound water forms a hydrogen bond to the δ1 nitrogen of histidine 37., Water structure impacts biological processes. Within the Influenza A M2 proton channel, a bound water molecule proton was detected at a chemical shift of 11 ppm. Combining magic‐angle spinning NMR, dynamic nuclear polarization and quantum chemical calculations, the water was found hydrogen bonded to Nδ1 of the functional histidine 37 residue.

Solid-state NMR (ssNMR) is a versatile technique that can be used for the characterization of various materials, ranging from small molecules to biological samples, including membrane proteins. ssNMR can probe both the structure and dynamics of membrane proteins, revealing protein function in a near-native lipid bilayer environment. The main limitation of the method is spectral resolution and sensitivity, however recent developments in ssNMR hardware, including the commercialization of 28 T magnets (1.2 GHz proton frequency) and ultrafast MAS spinning (, Solid-state NMR (ssNMR) is a versatile technique that can be used for the characterization of various materials, ranging from small molecules to biological samples, including membrane proteins, as reviewed here.

The amyloid cascade hypothesis proposes that amyloid‐beta (Aβ) aggregation is the initial triggering event in Alzheimer's disease. Here, we utilize NMR spectroscopy and monitor the structural dynamics of two variants of Aβ, Aβ40 and Aβ42, as a function of temperature. Despite having identical amino acid sequence except for the two additional C‐terminal residues, Aβ42 has higher aggregation propensity than Aβ40. As revealed by the NMR data on dynamics, including backbone chemical shifts, intra‐methyl cross‐correlated relaxation rates and glycine‐based singlet‐states, the C‐terminal region of Aβ, especially the G33‐L34‐M35 segment, plays a particular role in the early steps of temperature‐induced Aβ aggregation. In Aβ42, the distinct dynamical behaviour of C‐terminal residues at higher temperatures is accompanied with marked changes in the backbone dynamics of residues V24‐K28. The distinctive role of the C‐terminal region of Aβ42 in the initiation of aggregation defines a target for the rational design of Aβ42 aggregation inhibitors., Amyloid‐beta (Aβ) aggregation, the pathological process believed to be a key triggering event for Alzheimer's, is studied using atomic‐scale probes of peptide dynamics. All the probes point to a segment in the C‐terminal region of Aβ acting as a hotspot in the initiation of Aβ aggregation, particularly in the more toxic Aβ42 variant.

Paramagnetic NMR constraints are very useful to study protein interdomain motion, but their interpretation is not always straightforward. On the example of the particularly flexible complex Calmodulin/Munc13-1, we present a new approach to characterize this motion with pseudocontact shifts and residual dipolar couplings. Using molecular mechanics, we sampled the conformational space of the complex and used a genetic algorithm to find ensembles that are in agreement with the data. We used the Bayesian information criterion to determine the ideal ensemble size. This way, we were able to make an accurate, unambiguous, reproducible model of the interdomain motion of Calmodulin/Munc13-1 without prior knowledge about the domain orientation from crystallography.