Biophysical reports最新文献

Calcium ions (Ca²⁺) enter mitochondria via the mitochondrial Ca²⁺ uniporter, driven by electrical and concentration gradients. In this regard, transgenic mouse models, such as calsequestrin knockout (CSQ-KO) mice, with higher mitochondrial Ca²⁺ concentrations ([Ca²⁺]_mito), should display higher cytosolic Ca²⁺ concentrations ([Ca²⁺]_cyto). However, repeated measurements of [Ca²⁺]_cyto in quiescent CSQ-KO fibers never showed a difference between WT and CSQ-KO. Starting from the consideration that fluorescent Ca²⁺ probes (Fura-2 and Indo-1) measure averaged global cytosolic concentrations, in this report we explored the role of local Ca²⁺ concentrations (i.e., Ca²⁺ microdomains) in regulating mitochondrial Ca²⁺ in resting cells, using a multicompartmental diffusional Ca²⁺ model. Progressively including the inward and outward fluxes of sarcoplasmic reticulum (SR), extracellular space, and mitochondria, we explored their contribution to the local Ca²⁺ distribution within the cell. The model predicts Ca²⁺ concentration gradients with hot spots or microdomains even at rest, minor but similar to those of evoked Ca²⁺ release. Due to their specific localization close to Ca²⁺ release units (CRU), mitochondria could take up Ca²⁺ directly from high-concentration microdomains, thus sensibly raising [Ca²⁺]_mito, despite minor, possibly undetectable, modifications of the average [Ca²⁺]_cyto.

Quantifying biomolecular dynamics has become a major task of single-molecule fluorescence spectroscopy methods. In single-molecule Förster resonance energy transfer (smFRET), kinetic information is extracted from the stream of photons emitted by attached donor and acceptor fluorophores. Here, we describe a time-resolved version of burst variance analysis that can quantify kinetic rates at microsecond to millisecond timescales in smFRET experiments of diffusing molecules. Bursts are partitioned into segments with a fixed number of photons. The FRET variance is computed from these segments and compared with the variance expected from shot noise. By systematically varying the segment size, dynamics at different timescales can be captured. We provide a theoretical framework to extract kinetic rates from the decay of the FRET variance with increasing segment size. Compared to other methods such as filtered fluorescence correlation spectroscopy, recurrence analysis of single particles, and two-dimensional lifetime correlation spectroscopy, fewer photons are needed to obtain reliable timescale estimates, which reduces the required measurement time.

Viruses have a profound influence on all forms of life, motivating the development of rapid and minimally invasive methods for virus detection. In this study, we present a novel methodology that enables quantitative measurement of the interaction between individual biotic nanoparticles and antibodies in solution. Our approach employs a label-free, full-field common-path interferometric technique to detect and track biotic nanoparticles and their interactions with antibodies. It is based on the interferometric detection of light scattered by viruses in aqueous samples for the detection of individual viruses. We employ single-particle tracking analysis to characterize the size and properties of the detected nanoparticles, and to monitor the changes in their diffusive mobility resulting from interactions. To validate the sensitivity of our detection approach, we distinguish between particles having identical diffusion coefficients but different scattering signals, using DNA-loaded and DNA-devoid capsids of the Escherichia coli T5 virus phage. In addition, we have been able to monitor, in real time, the interaction between the bacteriophage T5 and purified antibodies targeting its major capsid protein pb8, as well as between the phage SPP1 and nonpurified anti-SPP1 antibodies present in rabbit serum. Interestingly, these virus-antibody interactions are observed within minutes. Finally, by estimating the number of viral particles interacting with antibodies at different concentrations, we successfully quantify the dissociation constant $K_d$ of the virus-antibody reaction using single-particle tracking analysis.

The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome. During the progression of the disease, some individual patients acquire drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the "flap region" of the HIV-1 protease, which is a critical dynamic region involved in nonspecific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning-assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and drug binding of wild-type and common drug-resistant versions of the main protease. We also use a multiagent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs. We find that a key conserved functional site in the flap region, a solvent-exposed isoleucine (Ile50) that controls flap dynamics is functionally targeted by drug resistance mutations, leading to amplified molecular dynamics affecting the functional ability of the flap region to hold the drugs. We conclude that better long-term patient outcomes may be achieved by designing drugs that target protease regions that are less dependent upon single sites with large functional binding effects.

Fluorescence lifetime imaging microscopy (FLIM) is a popular modality to create additional contrast in fluorescence images. By carefully analyzing pixel-based nanosecond lifetime patterns, FLIM allows studying complex molecular populations. At the single-molecule or single-particle level, however, image series often suffer from low signal intensities per pixel, rendering it difficult to quantitatively disentangle different lifetime species, such as during Förster resonance energy transfer (FRET) analysis in the presence of a significant donor-only fraction. In this article we investigate whether an object localization strategy and the phasor approach to FLIM have beneficial effects when carrying out FRET analyses of single particles. Using simulations, we first showed that an average of ∼300 photons, spread over the different pixels encompassing single fluorescing particles and without background, is enough to determine a correct phasor signature (SD < 5% for a 4-ns lifetime). For immobilized single- or double-labeled dsDNA molecules, we next validated that particle-based phasor-FLIM-FRET readily allows estimating fluorescence lifetimes and FRET from single molecules. Thirdly, we applied particle-based phasor-FLIM-FRET to investigate protein-protein interactions in subdiffraction HIV-1 viral particles. To do this, we first quantitatively compared the fluorescence brightness, lifetime, and photostability of different popular fluorescent protein-based FRET probes when genetically fused to the HIV-1 integrase enzyme in viral particles, and conclude that eGFP, mTurquoise2, and mScarlet perform best. Finally, for viral particles coexpressing FRET-donor/acceptor-labeled IN, we determined the absolute FRET efficiency of IN oligomers. Available in a convenient open-source graphical user interface, we believe that particle-based phasor-FLIM-FRET is a promising tool to provide detailed insights in samples suffering from low overall signal intensities.

Single-molecule localization microscopy achieves nanometer spatial resolution by localizing single fluorophores separated in space and time. A major challenge of single-molecule localization microscopy is the long acquisition time, leading to low throughput, as well as to a poor temporal resolution that limits its use to visualize the dynamics of cellular structures in live cells. Another challenge is photobleaching, which reduces information density over time and limits throughput and the available observation time in live-cell applications. To address both challenges, we combine two concepts: first, we integrate the neural network DeepSTORM to predict super-resolution images from high-density imaging data, which increases acquisition speed. Second, we employ a direct protein label, HaloTag7, in combination with exchangeable ligands (xHTLs), for fluorescence labeling. This labeling method bypasses photobleaching by providing a constant signal over time and is compatible with live-cell imaging. The combination of both a neural network and a weak-affinity protein label reduced the acquisition time up to ∼25-fold. Furthermore, we demonstrate live-cell imaging with increased temporal resolution, and capture the dynamics of the endoplasmic reticulum over extended time without signal loss.

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction dynamics that can be embedded in the GRN as weights, converting a GRN to gene regulatory neural network (GRNN). Focusing on Pseudomonas aeruginosa, we extracted the GRNN associated with a well-known virulence factor, pyocyanin production, using an introduced weight extraction technique based on transcriptomic data and proving its computing accuracy using wet-lab experimental data. As part of our analysis, we evaluated the structural changes in the GRNN based on mutagenesis to determine its varying computing behavior. Furthermore, we model the ecosystem-wide cell-cell communications to analyze its impact on computing based on environmental as well as population signals, where we determine the impact on the computing reliability. Subsequently, we establish that the individual GRNNs can be clustered to collectively form computing units with similar behaviors to single-layer perceptrons with varying sigmoidal activation functions spatio-temporally within an ecosystem. We believe that this will lay the groundwork toward molecular machine learning systems that can see artificial intelligence move toward non-silicon devices, or living artificial intelligence, as well as giving us new insights into bacterial natural computing.

Kinetic and thermodynamic models of biological systems are commonly used to connect microscopic features to system function in a bottom-up multiscale approach. The parameters of such models-free energy differences for equilibrium properties and in general rates for equilibrium and out-of-equilibrium observables-have to be measured by different experiments or calculated from multiple computer simulations. All such parameters necessarily come with uncertainties so that when they are naively combined in a full model of the process of interest, they will generally violate fundamental statistical mechanical equalities, namely detailed balance and an equality of forward/backward rate products in cycles due to Hill. If left uncorrected, such models can produce arbitrary outputs that are physically inconsistent. Here, we develop a maximum likelihood approach (named multibind) based on the so-called potential graph to combine kinetic or thermodynamic measurements to yield state-resolved models that are thermodynamically consistent while being most consistent with the provided data and their uncertainties. We demonstrate the approach with two theoretical models, a generic two-proton binding site and a simplified model of a sodium/proton antiporter. We also describe an algorithm to use the multibind approach to solve the inverse problem of determining microscopic quantities from macroscopic measurements and, as an example, we predict the microscopic ${pK}_a$ values and protonation states of a small organic molecule from 1D NMR data. The multibind approach is applicable to any thermodynamic or kinetic model that describes a system as transitions between well-defined states with associated free energy differences or rates between these states. A Python package multibind, which implements the approach described here, is made publicly available under the MIT Open Source license.

In this paper we present a transistor circuit model for cystic fibrosis transmembrane conductance regulator (CFTR) that seeks to map the functional form of CFTR both in wild type and mutants. The circuit architecture is configured so that the function, and as much as possible the form, faithfully represents what is known about CFTR from cryo-electron microscopy and molecular dynamics. The model is a mixed analog-digital topology with an AND gate receiving the input from two separate ATP-nucleotide-binding domain binding events. The analog portion of the circuit takes the output from the AND gate as its input. The input to the circuit model and its noise characteristics are extracted from single-channel patch-clamp experiments. The chloride current predicted by the model is then compared with single-channel patch-clamp recordings for wild-type CFTR. We also consider the patch-clamp recordings from CFTR with a G551D point mutation, a clinically relevant mutant that is responsive to therapeutic management. Our circuit model approach enables bioengineering approaches to CFTR and allows biophysicists to use efficient circuit simulation tools to analyze its behavior.

Thermodenaturation (melting) curves of macromolecules are used to determine folding thermodynamic parameters. Notably, this insight into RNA and DNA stability underlies nearest neighbor theory and diverse structure prediction tools. Analysis of UV-detected absorbance melting curves is complex and multivariate, requiring many data preprocessing, regression, and error analysis steps. The absorbance melting curve-fitting software MeltWin, introduced in 1996, provided a consistent and facile melting curve analysis platform used in a generation of folding parameters. Unfortunately, MeltWin software is not maintained and relies on idiosyncratic choices of baselines by the user. Herein, we provide MeltR, an open-source, curve-fitting package for analysis of macromolecular thermodynamic data. The MeltR package provides the facile conversion of melting curve data to parameters provided by MeltWin while offering additional features including global fitting of data, auto-baseline generation, and two-state melting analysis. MeltR should be a useful tool for analyzing the next generation of DNA, RNA, and nonnucleic acid macromolecular melting data.