Biophysical reviews最新文献

In this work, we analyze the information on the protein intermolecular interactions obtained from macromolecular diffusion. We have shown that the most hopeful results are given by our approach based on analysis of protein translational self-diffusion and collective diffusion obtained by dynamic light scattering and pulsed-field gradient NMR (PFG NMR) spectroscopy with the help of Vink's approach to analyze diffusion motion of particles by frictional formalism of non-equilibrium thermodynamics and the usage of the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory of colloid particles interactions in electrolyte solutions. Early we have shown that integration of Vink's theory with DLVO provides a reliable basis for uniform interpreting of PFG NMR and DLS experiments on concentration dependence of diffusion coefficients. Basic details of theoretical and mathematical procedures and a broad analysis of experimental attestation of proposed conception on proteins of various structural form, size, and shape are presented. In the present review, the main capabilities of our approach obtain the details of intermolecular interactions of proteins with different shapes, internal structures, and mass. The universality of Vink's approach is experimentally shown, which gives the appropriate description of experimental results for proteins of complicated structure and shape.

Despite the fact that the investigation of the structural and functional properties of hemoglobin dates back more than 150 years, the topic has not lost its relevance today. The most important component of these studies is the development of mathematical models that formalize and generalize the mechanisms determining the cooperative binding of ligands based on data on the structural and functional state of the protein. In this work, we review the mathematical relationships describing oxygen binding by hemoglobin, ranging from the classical Hüfner, Hill, and Adair equations to the Szabo-Karplus and tertiary two-state mathematical models based on the Monod-Wyman-Changeux and Koshland-Némethy-Filmer concepts. The generality of the considered equations as mathematical functions, bearing in their basis a power dependence, is demonstrated. The problems and possible solutions related to approximation of experimental data by the oxygenation equations with correlated fitting parameters are noted. Attention is paid to empirical equations, extended versions of the Hill equation, where the coefficient of cooperation is modulated by Gauss and Lorentz distributions as functions of partial oxygen pressure.

Under macromolecular crowding (MC) conditions such as cellular, extracellular, food and other environments of biotechnological interest, the thermodynamic activity of the different macromolecules present in the system is several orders of magnitude higher than in dilute solutions. In this state, the diffusion rates are affected by the volume exclusion induced by the crowders. Immiscible liquid phases, which may arise in MC by liquid-liquid phase separation, may induce a dynamic confinement of reactants, products and/or enzymes, tuning reaction rates. In cellular environments and other crowding conditions, membranes and macromolecules provide, on the whole, large surfaces that can perturb the solvent, causing its immobilisation by adsorption in the short range and also affecting the solvent viscosity in the long range. The latter phenomenon can affect the conformation of a protein and/or the degree of association of its protomers and, consequently, its activity. Changes in the water structure can also alter the enzyme-substrate interaction, and, in the case of hydrolytic enzymes, where water is one of the substrates, it also affects the reaction mechanism. Here, we review the evidence for how macromolecular crowding affects the catalysis induced by hydrolytic enzymes, focusing on the structure and dynamics of water.

Over the past decade, myriads of studies have highlighted the central role of protein condensation in subcellular compartmentalization and spatiotemporal organization of biological processes. Conceptually, protein condensation stands at the highest level in protein structure hierarchy, accounting for the assembly of bodies ranging from thousands to billions of molecules and for densities ranging from dense liquids to solid materials. In size, protein condensates range from nanocondensates of hundreds of nanometers (mesoscopic clusters) to phase-separated micron-sized condensates. In this review, we focus on protein nanocondensation, a process that can occur in subsaturated solutions and can nucleate dense liquid phases, crystals, amorphous aggregates, and fibers. We discuss the nanocondensation of proteins in the light of general physical principles and examine the biophysical properties of several outstanding examples of nanocondensation. We conclude that protein nanocondensation cannot be fully explained by the conceptual framework of micron-scale biomolecular condensation. The evolution of nanocondensates through changes in density and order is currently under intense investigation, and this should lead to the development of a general theoretical framework, capable of encompassing the full range of sizes and densities found in protein condensates.

I am Ivan Krasnyakov, a senior lecturer at the Department of Applied Physics at Perm National Research Polytechnic University. I hold a degree of Candidate of Physical and Mathematical Sciences. My main area of research revolves around the biophysics of multicellular tissues. One of my initial research projects focused on creating a mathematical model to understand the growth of carcinoma. What intrigued me was the fresh perspective from clinical medicine researchers regarding the spatial structure of tumors. It was discovered that tumors display cellular heterogeneity and undergo self-organization, forming diverse architectural patterns during their development. At the moment, I continue to develop our model.

Abstract: Cubosomes are nanostructured lipid-based particles that have gained significant attention in the field of drug delivery and nanomedicine. These unique structures consist of a three-dimensional cubic lattice formed by the self-assembly of lipid molecules. The lipids used to construct cubosomes are typically nonionic surfactants, such as monoolein, which possess both hydrophilic and hydrophobic regions, allowing them to form stable, water-dispersible nanoparticles. One of the key advantages of cubosomes is their ability to encapsulate and deliver hydrophobic as well as hydrophilic drugs. The hydrophobic regions of the lipid bilayers provide an ideal environment for incorporating lipophilic drugs, while the hydrophilic regions can encapsulate water-soluble drugs. This versatility makes cubosomes suitable for delivering a wide range of therapeutic agents, including small molecules, proteins, peptides, and nucleic acids. The unique structure of cubosomes also offers stability and controlled release benefits. The lipid bilayers provide a protective barrier, shielding the encapsulated drugs from degradation and improving their stability. Moreover, the cubic lattice arrangement enables the modulation of drug release kinetics by varying the lipid composition and surface modifications. This allows for the development of sustained or triggered drug release systems, enhancing therapeutic efficacy and reducing side effects. Furthermore, cubosomes can be easily modified with targeting ligands or surface modifications to achieve site-specific drug delivery, enhancing therapeutic selectivity and reducing off-target effects. In conclusion, cubosomes offer a versatile and promising platform for the delivery of therapeutic agents. In this manuscript, we will highlight some of these applications.

Graphical abstract:

Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.

Learning how multicellular organs are developed from single cells to different cell types is a fundamental problem in biology. With the high-throughput scRNA-seq technology, computational methods have been developed to reveal the temporal dynamics of single cells from transcriptomic data, from phenomena on cell trajectories to the underlying mechanism that formed the trajectory. There are several distinct families of computational methods including Trajectory Inference (TI), Lineage Tracing (LT), and Gene Regulatory Network (GRN) Inference which are involved in such studies. This review summarizes these computational approaches which use scRNA-seq data to study cell differentiation and cell fate specification as well as the advantages and limitations of different methods. We further discuss how GRNs can potentially affect cell fate decisions and trajectory structures.

Supplementary information: The online version contains supplementary material available at 10.1007/s12551-023-01090-5.

A comparative analysis between two problems-apparently unrelated-which are solved in a period of ~400 years, viz., the accurate prediction of both the planetary orbits and the protein structures, leads to inferred conjectures that go far beyond the existence of a common path in their resolution, i.e., observation → pattern recognition → modeling. The preliminary results from this analysis indicate that complementary science, together with a new perspective on protein folding, may help us discover common features that could contribute to a more in-depth understanding of still-unsolved problems such as protein folding.

Mechanical forces drive and modulate a wide variety of processes in eukaryotic cells including those occurring in the nucleus. Relevantly, forces are fundamental during development since they guide lineage specifications of embryonic stem cells. A sophisticated macromolecular machinery transduces mechanical stimuli received at the cell surface into a biochemical output; a key component in this mechanical communication is the cytoskeleton, a complex network of biofilaments in constant remodeling that links the cell membrane to the nuclear envelope. Recent evidence highlights that forces transmitted through the cytoskeleton directly affect the organization of chromatin and the accessibility of transcription-related molecules to their targets in the DNA. Consequently, mechanical forces can directly modulate transcription and change gene expression programs. Here, we will revise the biophysical toolbox involved in the mechanical communication with the cell nucleus and discuss how mechanical forces impact on the organization of this organelle and more specifically, on transcription. We will also discuss how live-cell fluorescence imaging is producing exquisite information to understand the mechanical response of cells and to quantify the landscape of interactions of transcription factors with chromatin in embryonic stem cells. These studies are building new biophysical insights that could be fundamental to achieve the goal of manipulating forces to guide cell differentiation in culture systems.