European Biophysics Journal最新文献

Exocytosis is a fundamental process related to the information exchange in the nervous and endocrine system. Among the various techniques, vesicle impact electrochemical cytometry (VIEC) has emerged as an effective method to mimic the exocytosis process and measure dynamic information about content transfer using nanoscale electrodes. In this article, through analytical models and large scale simulations, we develop scaling laws for the decay time constant $\left(\tau \right)$ for VIEC single-exponential transients. Specifically, our results anticipate a power law dependence of $\tau$ on the geometric and the transport parameters. This model compares very well with large scale simulations exploring the parameter space relevant for VIEC and with experimental results from literature. Remarkably, such physics-based compact models could allow for novel multi-feature-based self consistent strategies for back extraction of geometric and transport parameters and hence could contribute towards better statistical analysis and understanding of exocytosis transients and events.

Moving in a crowded cellular environment, proteins have to recognize and bind to each other with high specificity. This specificity reflects in a combination of geometric and chemical complementarities at the core of interacting regions that ultimately influences binding stability. Exploiting such peculiar complementarity patterns, we recently developed CIRNet, a neural network architecture capable of identifying pairs of protein core interacting residues and assisting docking algorithms by rescaling the proposed poses. Here, we present a detailed analysis of the geometric and chemical descriptors utilized by CIRNet, investigating its decision-making process to gain deeper insights into the interactions governing protein-protein binding and their interdependence. Specifically, we quantitatively assess (i) the relative importance of chemical and physical features in network training and (ii) their interplay at protein interfaces. We show that shape and hydrophobic-hydrophilic complementarities contain the most predictive information about the classification outcome. Electrostatic complementarity alone does not achieve high classification accuracy but is required to boost learning. Ultimately, our findings suggest that identifying the most information-dense features may enhance our understanding of the mechanisms driving protein-protein interactions at core interfaces.

The revival mechanism in dormant bacteria is a puzzling and open issue. We propose a model of information diffusion on a regular grid where agents represent bacteria and their mutual interactions implement quorum sensing. Agents may have different metabolic characteristics corresponding to multiple phenotypes. The intra/inter phenotype cooperation is analyzed under different metabolic and productivity conditions. We study the interactions between rapidly reproducing active bacteria and non-reproducing quiescent bacteria. We highlight the conditions under which the quiescent bacteria may revive. The occurrence of revival is generally related to a change in environmental conditions. Our results support this picture showing that revival can be mediated by the presence of different catalyst bacteria that produce the necessary resources.

The c(s) sedimentation distribution method implemented in the program SEDFIT (Biophys J 78:1606-1619, 2000) is widely used for analyzing sedimentation velocity data, and is particularly useful for detecting low levels of aggregates or other minor components in protein pharmaceuticals. Unfortunately, this method does not provide confidence limits for the area or sedimentation coefficient of each resolved peak, which makes it difficult to assess whether differences from one sample to another are statistically significant. This paper describes a new method to obtain such confidence limits using the program SVEDBERG (Biophys J 72:435-444, 1997) by automatically translating a saved c(s) distribution into a discrete species model where the molar masses of all species are constrained to keep the f/f₀ ratio constant for all species. This approach also then allows relaxing the constant f/f₀ ratio constraint on one or more minor species to determine their true molar masses (independent of assumptions about hydrodynamic shape), and also determining the confidence limits on that molar mass. It is demonstrated that this approach will work for samples containing up to five minor components (six total species), and even when multiple minor species are present at levels of only a few tenths of 1%.

The bioactivity of the short antimicrobial peptides (ssAMPs) UyCT1, CT2, CT3, CT5, Uy17, Uy192, and Uy234 from the scorpion Urodacus yaschenkoi has been well-characterized. The antagonistic effect reported in those studies on some clinical isolates of pathogenic bacteria, including Staphylococcus aureus, Klebsiella pneumoniae, and Escherichia coli was studied with an in silico approach to contrast their bioactivity in molecular terms. The peptides were modeled by generating high-quality structures with AlphaFold2, properly validated, and subjected to dynamic simulations in aqueous systems with the Gromos 43a1 and Charmm 36 force fields. Our analysis indicates that the degree of helicity of these peptides is closely linked to their composition and several physicochemical factors such as the hydrophobicity index, electrostatic potential, intrinsic flexibility, and dipole moment. We also found interesting parallels between the degree of order mentioned and the potency of each peptide with previously studied bacterial strains, specifically S. aureus. We analyzed in more detail of two specific peptides, UyCT1 and UyCT2, whose sequences are almost identical, except for the presence of a G-cap in the former. This subtle difference has a decisive impact on the conformational dynamics of these peptides, making the UyCT2 peptide more prone to disorder and the UyCT1 peptide more stable through the formation of multiple H-bonds. This analysis, based on an exhaustive characterization of the physicochemical properties of these ssAMPs, together with the determination of their conformational dynamics and the correlation with experimental data, could be the basis for the design and optimization of new drugs based on natural peptides found in scorpion venoms.

Currently, there is an increased interest in identifying the characteristics of amyloid aggregates in the initial stages of amyloid formation. The aggregation mechanism of the α-synuclein (Syn) amyloid protein, which has been extensively studied, is still not fully understood. I show that with conventional dynamic light scattering (DLS) technique, the measurements of the dimensions of Syn amyloid precursor forms can be done early in the protein incubation. Additionally, the early aggregation of the Syn protein was initially studied by analyzing autocorrelation functions from fit distributions up to 10⁴ µs in the initial DLS measurements, specifically within the first 21 min. Investigation was conducted on the variation in the pH of the Syn solution throughout time. Based on DLS data, large Syn aggregated species formed from the monomer protein species. Afterward, I generated the autocorrelation functions based on the original DLS data, extending the fit distributions up to 10⁵ µs and noticed the existence of elongated Syn amyloid precursor forms in the protein solutions. Because the length of the elongated Syn amyloid precursor forms closely matches the wavelength of the incident light, the combination of translational diffusion Dt and rotational diffusion Dr in the decay rates enabled the measurement of their geometric dimensions through DLS. The improved precision of the fitted distributions I offered resulted in a new interpretation for the Syn protein aggregation in the initial stages. Overall, the methodology used in this study could be an effective strategy for examining how Syn amyloid precursor forms develop over time.

At present, research on the biomechanical response of the cupula of human semicircular canals (HSCs) has focused on indirect inference through the nystagmus view, which is limited by the participation of the human nervous system. In this study, 3D printing technology and hydrogel modification methods were used to fabricate a one-dimensional bionic semicircular canal (BSC) model with a ratio of 1:1 to the horizontal HSC. Target tracking technology was used to observe the deformation of the cupula. Then, constant angular acceleration stimulation and the other two stimulations were separately applied to the BSC to explore its biomechanical response. The results showed that the BSC had a similar time constant to that of the HSC, its maximum deviation displacement was proportional to the applied angular acceleration, and its amplitude-frequency gain under sinusoidal oscillation stimulation increased, but its phase difference decreased with increasing frequency, which consistent with the conclusions obtained by our theoretical deduction. The BSC model is expected to play a certain role in the mechanistic research and disease diagnosis of HSCs.

Targeted protein degradation (TPD) has garnered appreciable interest in drug discovery due to its unique mechanism of action - degradation of a target in an event-driven manner, instead of traditional occupancy-driven inhibitor-based therapies. This is achieved by employing mono- or hetero-bifunctional small molecules known as degraders to induce the proximity of two proteins: a target protein and an E3 ubiquitin ligase, ultimately resulting in clearance of the target protein by the cell's inherent degradation machinery. A critical step in this pathway is ternary complex formation (TCF) between the ligase, degrader molecule, and the target protein. Although a bevy of biochemical, biophysical, cellular and structural approaches have been used to characterize degrader-induced ternary complexes, several knowledge gaps remain, such as stoichiometry and how much ternary complex is formed in solution. Analytical ultracentrifugation (AUC) is a biophysical method that is uniquely suited to address these questions, yet to this point has been surprisingly overlooked as an ideal method to characterize degrader candidates. In this study, we leveraged sedimentation velocity AUC (SV-AUC) to profile the degrader-induced ternary complex formation between Bruton's tyrosine kinase (BTK) and Cereblon (CRBN), allowing for evaluation of multiple attributes including sample purity, percent ternary complex, binding and kinetic rate constants, and hydrodynamics. We show that sedimentation equilibrium AUC (SE-AUC) can further complement the SV-AUC data with accurate molecular weight estimates of the ternary complex to confirm stoichiometry. This work demonstrates that AUC can be used both as a highly informative platform method for rapid characterization of candidate degrader compounds and as a rigorous method for elucidating additional details of the system.