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A vesicle microrheometer for high-throughput viscosity measurements of lipid and polymer membranes
Viscosity is a key property of cell membranes that controls mobility of embedded proteins and membrane remodeling. Measuring it is challenging because existing approaches involve complex experimental designs and/or models, and the applicability of some is limited to specific systems and membrane compositions. As a result there is scarcity of data and the reported values for membrane viscosity vary by orders of magnitude for the same system. Here, we show how viscosity of bilayer membranes can be obtained from the transient deformation of giant unilamellar vesicles. The approach enables a non-invasive, probe-independent and high-throughput measurement of the viscosity of bilayers made of lipids or polymers with a wide range of compositions and phase state. Pure lipid and single-phase mixed bilayers are found to behave as Newtonian fluids with strain-rate independent viscosity, while phase-separated and diblock-copolymers systems exhibit shear-thinning in the explored range of strain rates 1-2000 s−1. The results also reveal that electrically polarized bilayers can be significantly more viscous than charge-neutral bilayers. These findings suggest that biomembrane viscosity is a dynamic property that can be actively modulated not only by composition but also by membrane polarization, e.g., as in action potentials.
Paper
Continuum dynamics and statistical correction of compositional heterogeneity in multivalent IDP oligomers resolved by single-particle EM
Multivalent intrinsically disordered protein (IDP) complexes are prevalent in biology and control diverse cellular functions, including tuning levels of transcription, coordinating cell-signaling events, and regulating the assembly and disassembly of complex macromolecular architectures. These systems pose a significant challenge to structural investigation, due to the continuum dynamics imparted by the IDP and compositional heterogeneity resulting from characteristic low-affinity interactions. Traditional single-particle electron microscopy (EM) is a powerful tool for visualizing IDP complexes. However, the IDPs themselves are typically “invisible” by EM, undermining methods of image analysis and structural interpretation. To overcome these challenges, we developed a pipeline for automated analysis of common ‘beads-on-a-string’ type of assemblies, composed of IDPs bound at multivalent sites to the ubiquitous ~20 kDa cross-linking hub protein LC8. This approach quantifies conformational and compositional heterogeneity on a single-particle basis, and statistically corrects spurious observations arising from random proximity of bound and unbound LC8. After careful validation of the methodology, the approach was applied to the nuclear pore IDP Nup159 and the transcription factor ASCIZ. The analysis unveiled significant compositional and conformational diversity in both systems that could not be obtained from traditional single particle EM class-averaging strategies, and shed new light on how these architectural properties contribute to their physiological roles in supramolecular assembly and transcriptional regulation. Ultimately, we expect that this approach may be adopted to many other intrinsically disordered systems that have evaded traditional methods of structural characterization.
Intrinsically disordered proteins (IDPs) or protein regions (IDRs) represent >30% of the human proteome, but mechanistically remain some of the most poorly understood classes of proteins in biology. This dearth in understanding stems from these very same intrinsic and dynamic properties, which make them difficult targets for quantitative and structural characterization. Here, we present an automated approach for extracting quantitative descriptions of conformational and compositional heterogeneity present in a common ‘beads-on-a-string’ type of multivalent IDP system from single-particle images in electron micrographs. This promising approach may be adopted to many other intrinsically disordered systems that have evaded traditional ensemble methods of characterization.
Paper
Estimating the Probability of Early Afterdepolarization and Predicting Arrhythmic Risk associated with Long QT Syndrome Type 1 Mutations
Early after-depolarizations (EADs) are action potential (AP) repolarization abnormalities that can trigger lethal arrhythmias. Simulations using biophysically-detailed cardiac myocyte models can reveal how model parameters influence the probability of these cellular arrhythmias, however such analyses can pose a huge computational burden. We have previously developed a highly simplified approach in which logistic regression models (LRMs) map parameters of complex cell models to the probability of ectopic beats (EBs). Here, we extend this approach to predict the probability of early after-depolarizations (P(EAD)). We use the LRM to investigate how changes in parameters of the slow-activating delayed rectifier current (IKs) affect P(EAD) for 17 different Long QT syndrome type 1 (LQTS1) mutations. We compare P(EAD) for these 17 LQTS1 mutations with two other recently proposed model-based arrhythmia risk metrics. These three model-based risk metrics yield similar prediction performance; however, they all fail to predict relative clinical risk for a significant number of the 17 studied LQTS1 mutations. The consistent successes and failures of all three risk metrics suggest that important functional characteristics of LQTS1 mutations may not yet be fully known.
An early after-depolarization (EAD) is an abnormal cellular electrical event which can trigger dangerous arrhythmias in the heart. We use our previously developed method to build a simple logistic regression model (LRM) that estimates the probability of EAD (P(EAD)) as a function of myocyte model parameters. Using this LRM along with two other recently published model-based arrhythmia risk predictors, we estimate risk of arrhythmia for 17 Long QT syndrome type 1 (LQTS1) mutations. Results show that all approaches have similar prediction performance in that there are a set of mutations whose relative clinical risk for arrhythmia are well estimated using these metrics, but that relative risk is consistently over- or under-estimated across all approaches for a significant number of other mutations. We believe this indicates that the functional characterization of the LQTS1 phenotype is incomplete.
Paper
Putting ourselves back in the equation : why physicists are studying human consciousness and AI to unravel the mysteries of the universe
\"An exploration of the interconnections between quantum mechanics, cosmology, and neuroscience\"-- Provided by publisher.
BOOK
Sensitive Detection of Structural Differences using a Statistical Framework for Comparative Crystallography
Chemical and conformational changes underlie the functional cycles of proteins. Comparative crystallography can reveal these changes over time, over ligands, and over chemical and physical perturbations in atomic detail. A key difficulty, however, is that the resulting observations must be placed on the same scale by correcting for experimental factors. We recently introduced a Bayesian framework for correcting (scaling) X-ray diffraction data by combining deep learning with statistical priors informed by crystallographic theory. To scale comparative crystallography data, we here combine this framework with a multivariate statistical theory of comparative crystallography. By doing so, we find strong improvements in the detection of protein dynamics, element-specific anomalous signal, and the binding of drug fragments.
Journal Article