Biophysical journal最新文献

Intrinsically disordered proteins (IDPs) show structural changes stimulated by changes in external conditions. This study aims to reveal the temperature dependence of the structure and the dynamics of the intrinsically disordered region of the helicase-associated endonuclease for fork-structured DNA, one of the typical IDPs, using an integrative approach. Small-angle X-ray scattering (SAXS) and circular dichroism (CD) studies revealed that the radius of gyration and ellipticity at 222 nm remained constant up to 313-323 K, followed by a decline above this temperature range. NMR studies revealed the absence of a promotion of the α helix. As a result, SAXS, CD, and NMR data strongly suggest that these temperature-dependent structural changes were primarily due to a reduction in the content of the polyproline II (PPII) helix. Moreover, quasielastic neutron scattering studies revealed a slight change in the activation energy in a similar temperature range. Considering the concept of glass transition, it is posited that dynamical cooperativity between the PPII helix and water may play a significant role in these structural changes. The findings suggest that internal dynamics are crucial for regulating the structure of IDPs, highlighting the importance of considering dynamical cooperativity in future studies of protein behavior under varying temperature conditions.

Cholesterol-enriched plasma membrane domains are known to serve as signaling platforms in a diverse array of cellular processes. However, the link between cholesterol homeostasis and mutant APC-KRas-associated colorectal tumorigenesis remains to be established. Thus, we investigated the impact of Apc-Kras on 1) colonocyte plasma membrane cholesterol homeostasis, order, and receptor nanoclustering, 2) colonocyte cell proliferation, and 3) whether these effects are modulated by select membrane active dietaries (MADs). We observed that oncogenic APC-KRas increased membrane order by perturbing cholesterol homeostasis when cell proliferation is upregulated, in part by altering the expression of genes associated with cholesterol influx, export and de novo synthesis in mouse colorectal cancer (CRC) models and CRC patients. In addition, oncogene-induced loss of cholesterol homeostasis altered Fzd7, LRP6, and KRas cluster structure/organization. Notably, we show that the combination of chemoprotective MADs, i.e., n-3 PUFAs and curcumin, reduced colonic membrane free cholesterol, order, receptor cluster size, cell proliferation, and the number of dysplastic foci in mutant APC-KRas models. This work highlights the dynamic shaping of plasma membrane organization during colon tumorigenesis and the utility of membrane-targeted cancer therapy.

Plant leaf tissues are difficult to image via fluorescence microscopy due to the presence of chlorophyll and other pigments, which provide large background fluorescence. Lattice light-sheet microscopy offers the advantage of using Bessel beams to illuminate a thin focal region of interest for microscopy, allowing for the excitation of fluorescent molecules within this region without surrounding chlorophyll-like objects outside of the region of interest. Here, we apply STORM super-resolution techniques to observe receptor-like kinases in Arabidopsis thaliana leaf cells. By applying this technique with lattice light-sheet microscopy, we can localize immune-response proteins at sub-100-nm length scales and reconstruct three-dimensional locations of proteins within individual leaf cells. Using this technique, we observed the effect of the ATP and flg22 elicitors, where we observed a significant degree of internalization of cognate receptors P2K1 and FLS2. We were also able to similarly observe differences in colocalization due to stimulation with these elicitors, whereby we observe proteins on the membrane becoming less colocalized as a result of stimulation, suggesting an immune-response mechanism involving receptor internalization via distinct pathways. These data show lattice light-sheet microscopy's capabilities for imaging tissue with problematic background fluorescence that otherwise makes super-resolution fluorescence microscopy difficult.

Advanced deep learning and statistical methods can predict structural models for RNA molecules. However, RNAs are flexible, and it remains difficult to describe their macromolecular conformations in solutions where varying conditions can induce conformational changes. Small-angle x-ray scattering (SAXS) in solution is an efficient technique to validate structural predictions by comparing the experimental SAXS profile with those calculated from predicted structures. There are two main challenges in comparing SAXS profiles to RNA structures: the absence of cations essential for stability and charge neutralization in predicted structures and the inadequacy of a single structure to represent RNA's conformational plasticity. We introduce a solution conformation predictor for RNA (SCOPER) to address these challenges. This pipeline integrates kinematics-based conformational sampling with the innovative deep learning model, IonNet, designed for predicting Mg²⁺ ion binding sites. Validated through benchmarking against 14 experimental data sets, SCOPER significantly improved the quality of SAXS profile fits by including Mg²⁺ ions and sampling of conformational plasticity. We observe that an increased content of monovalent and bivalent ions leads to decreased RNA plasticity. Therefore, carefully adjusting the plasticity and ion density is crucial to avoid overfitting experimental SAXS data. SCOPER is an efficient tool for accurately validating the solution state of RNAs given an initial, sufficiently accurate structure and provides the corrected atomistic model, including ions.

It is interesting to find pathologically that leukocytes, especially neutrophils, tend to adhere in the liver sinusoids dominantly but not in the postsinusoidal venules. While both views of receptor-ligand interactions and physical trapping are proposed for mediating leukocyte adhesion in liver sinusoids, integrated investigations for classifying their respective contributions are poorly presented. With a combination of Monte Carlo simulation and immersed boundary method, this study explored numerically the effects of molecular interaction kinetics and sinusoidal mechanical properties on leukocyte adhesion in liver sinusoid jointly. Results showed that, within the range of biological limitations, the lumen stenosis ratio, leukocyte stiffness, Disse space stiffness and endothelium permeability regulate the comprehensive adhesion process in a descending order of significance in the presence of receptor-ligand interactions. While leukocyte adhesions could be mutually promoted with proper combinations of leukocyte stiffness, lumen stenosis, and molecular interaction, the binding affinity is insensitive under the conditions with low leukocyte stiffness in normal lumen stenosis and high leukocyte stiffness in high lumen stenosis. This work deepens the understanding of recruitment mechanism of leukocyte in liver sinusoids.

In the circulatory system, the microenvironment surrounding cancer cells is complex and involves multiple coupled factors. We selected two core physical factors, shear stress and hydraulic resistance, and constructed a microfluidic device with dual negative inputs to study the trade-off movement behavior of cancer cells when facing coupled factors. We detected significant shear stress escape phenomena in the MDA-MB-231 cell line and qualitatively explained this behavior using a cellular force model. Through the dual validation of substrate anti-cell-adhesion modification and employment of the MCF-7 cell line, we further substantiated the predictability and feasibility of our model. This study provides an explanation for the trade-off underlying the direction-choosing mechanism of cancer cells when facing environmental selection.

In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on simplified molecular input line entry system representations for molecular input data, which can limit the diversity and novelty of generated molecules. To address this, we present the transformer graph variational autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus capturing the complex structural relationships within molecules more effectively than string models. To enhance molecular generation capabilities, TGVAE combines a transformer, graph neural network (GNN), and VAE. Additionally, we address common issues like over-smoothing in training GNNs and posterior collapse in VAEs to ensure robust training and improve the generation of chemically valid and diverse molecular structures. Our results demonstrate that TGVAE outperforms existing approaches, generating a larger collection of diverse molecules and discovering structures that were previously unexplored. This advancement not only brings more possibilities for drug discovery but also sets a new level for the use of AI in molecular generation.

Synaptic morphology plays a critical role in modulating the dynamics of neurotransmitter diffusion and receptor activation in interneuron communication. Central physical aspects of synaptic geometry, such as the curvature of the synaptic cleft, the distance between the presynaptic and postsynaptic membranes, and the surface-area-to-volume ratio of the cleft, crucially influence glutamate diffusion and N-methyl-D-aspartate receptor (NMDAR) opening probabilities. In this study, we developed a stochastic model for receptor activation using realistic synaptic geometries. Our simulations revealed substantial variability in NMDAR activation, showing a significant impact of synaptic structure on receptor activation. Next, we designed a theoretical study with idealized cleft geometries to understand the impact of different biophysical properties on receptor activation. Specifically, we found that increasing the curvature of the synaptic membranes could compensate for reduced NMDAR activation when the synaptic cleft width was large. Additionally, nonparallel membrane configurations, such as convex presynapses or concave postsynaptic densities, maximize NMDAR activation by increasing the surface-area-to-volume ratio, thereby increasing glutamate residence time and reducing glutamate escape. Furthermore, clustering NMDARs within the postsynaptic density significantly increased receptor activation across different geometric conditions and mitigated the effects of synaptic morphology on NMDAR opening probabilities. These findings highlight the complex interplay between synaptic geometry and receptor dynamics and provide important insights into how structural modifications can influence synaptic efficacy and plasticity. By considering the major physical factors that affect neurotransmitter diffusion and receptor activation, our work offers a comprehensive understanding of how variations in synaptic geometry may regulate neurotransmission.

Membrane fusion is central to fundamental cellular processes such as exocytosis, when an intracellular machinery fuses membrane-enclosed vesicles to the plasma membrane for content release. The core machinery components are the SNARE proteins. SNARE complexation pulls the membranes together, but the fusion mechanism remains unclear. A common view is that the complexation energy drives fusion, but how this energy is harvested for fusion is unexplained. Moreover, SNAREs likely fully assemble before fusion. Computer simulation is challenging, as even fast neurotransmitter release at neuronal synapses involves fusion on ms timescales, beyond the scope of atomistic or mildly coarse-grained approaches. Here, we used highly coarse-grained representations, allowing simulation of the ms timescales of physiological SNARE-driven fusion under physiological conditions. Due to constant collisions, the rod-like SNARE complexes spontaneously generated entropic forces ∼8 pN per SNARE that cleared the fusion site and squeezed the membranes with forces ∼19 pN per SNARE, catalyzing a hemifused stalk connection. Regrouping, five or more SNARE complexes exerted entropic tensions 2.5 pN/nm or greater, expanding the stalk into a hemifusion diaphragm (HD), followed by HD rupture and fusion. The entropic forces generated tensions ∼17-21 pN in the SNARE linker domains (LDs). Previous optical tweezer measurements suggest that, on the ms timescales of fusion, these LD tensions are sufficient to unzipper the LDs while leaving the C-terminal domain (CTD) marginally intact, which are both required for fusion. Consistent with a recent magnetic tweezers study, we propose that the CTD may be further stabilized by complexin for robust fusion. Our results explain how SNARE-generated forces fuse membranes and predict that more SNARE complexes exert higher net force so that fusion is faster, consistent with experimental electrophysiological studies at neuronal synapses. Thus, entropic forces evolve SNARE complexes into a fusogenic, partially unzippered state, squeeze membranes for hemifusion, and expand hemifusion connections for fusion.

G-protein-coupled receptors (GPCRs) represent one of the largest classes of therapeutic targets. However, developing successful therapeutics to target GPCRs is a challenging endeavor, with many molecules failing during in vivo clinical trials due to a lack of efficacy. The in vitro identification of drug-target residence time (1/k_off) has been suggested to improve predictions of in vivo success. Here, a ligand binding assay using fluorescence anisotropy was implemented to successfully determine on rates (k_on) and off rates (k_off) of labeled and unlabeled ligands binding to the adenosine A_2A receptor (A_2AR) purified into nanodiscs (A_2AR-NDs). The kinetic assay was used to determine the optimal storage conditions of A_2AR-NDs, where they were found to be stable for more than 6 months at -80°C. The binding assay was implemented to further understand receptor function by determining the effects of charged lipids on agonist binding kinetics, how sodium levels allosterically modulate A_2AR function, and how A_2AR protonation affects agonist binding.