Biophysics and Physicobiology最新文献

Protein functions associated with biological activity are precisely regulated by both tertiary structure and dynamic behavior. Thus, elucidating the high-resolution structures and quantitative information on in-solution dynamics is essential for understanding the molecular mechanisms. The main experimental approaches for determining tertiary structures include nuclear magnetic resonance (NMR), X-ray crystallography, and cryogenic electron microscopy (cryo-EM). Among these procedures, recent remarkable advances in the hardware and analytical techniques of cryo-EM have increasingly determined novel atomic structures of macromolecules, especially those with large molecular weights and complex assemblies. In addition to these experimental approaches, deep learning techniques, such as AlphaFold 2, accurately predict structures from amino acid sequences, accelerating structural biology research. Meanwhile, the quantitative analyses of the protein dynamics are conducted using experimental approaches, such as NMR and hydrogen-deuterium mass spectrometry, and computational approaches, such as molecular dynamics (MD) simulations. Although these procedures can quantitatively explore dynamic behavior at high resolution, the fundamental difficulties, such as signal crowding and high computational cost, greatly hinder their application to large and complex biological macromolecules. In recent years, machine learning techniques, especially deep learning techniques, have been actively applied to structural data to identify features that are difficult for humans to recognize from big data. Here, we review our approach to accurately estimate dynamic properties associated with local fluctuations from three-dimensional cryo-EM density data using a deep learning technique combined with MD simulations.

Small-angle scattering (SAS) is a powerful tool for the detailed structural analysis of objects at the nanometer scale. In contrast to techniques such as electron microscopy, SAS data are presented as reciprocal space information, which hinders the intuitive interpretation of SAS data. This study presents a workflow: (1) creating objects, (2) 3D scanning, (3) the representation of the object as point clouds on a laptop, (4) computation of a distance distribution function, and (5) computation of SAS, executed via the computer program Phone2SAS. This enables us to realize SAS and perform the interactive modeling of SAS of the object of interest. Because 3D scanning is easily accessible through smartphones, this workflow driven by Phone2SAS contributes to the widespread use of SAS. The application of Phone2SAS for the structural assignment of SAS to Y-shaped antibodies is reported in this study.

Nuclear speckles are nuclear bodies consisting of populations of small and irregularly shaped droplet-like molecular condensates that contain various splicing factors. Recent experiments have revealed the following structural features of nuclear speckles: (I) Each molecular condensate contains SON and SRRM2 proteins, and MALAT1 non-coding RNA surrounds these condensates; (II) During normal interphase of the cell cycle in multicellular organisms, these condensates are broadly distributed throughout the nucleus. In contrast, when cell transcription is suppressed, the condensates fuse and form strongly condensed spherical droplets; (III) SON is dispersed spatially in MALAT1 knocked-down cells and MALAT1 is dispersed in SON knocked-down cells because of the collapse of the nuclear speckles. However, the detailed interactions among the molecules that are mechanistically responsible for the structural variation remain unknown. In this study, a coarse-grained molecular dynamics model of the nuclear speckle was developed by considering the dynamics of SON, SRRM2, MALAT1, and pre-mRNA as representative components of the condensates. The simulations reproduced the structural changes, which were used to predict the interaction network among the representative components of the condensates.

Progress of molecular biology resulted in the accumulation of information on biomolecular interactions, which are complex enough to be termed as networks. Dynamical behavior generated by complex network systems is considered to be the origin of the biological functions. One of the largest missions in modern life science is to obtain logical understanding for the dynamics of complex systems based on experimentally identified networks. However, a network does not provide sufficient information to specify dynamics explicitly, i.e. it lacks information of mathematical formulae of functions or parameter values. One has to develop mathematical models under assumptions of functions and parameter values to know the detail of dynamics of network systems. In this review, on the other hand, we introduce our own mathematical theory to understand the behavior of biological systems from the information of regulatory networks alone. Using the theory, important aspects of dynamical properties can be extracted from networks. Namely, key factors for observing/controlling the whole dynamical system are determined from network structure alone. We also show an application of the theory to a real biological system, a gene regulatory network for cell-fate specification in ascidian. We demonstrate that the system was completely controllable by experimental manipulations of the key factors identified by the theory from the information of network alone. This review article is an extended version of the Japanese article, Controlling Cell-Fate Specification System Based on a Mathematical Theory of Network Dynamics, published in SEIBUTSU BUTSURI Vol. 60, p. 349-351 (2020).

Mesenchymal stem cells (MSCs) have the potential for self-renewal and multipotency to differentiate into various lineages. Thus, they are of great interest in regenerative medicine as a cell source for tissue engineering. Substrate stiffness is one of the most extensively studied exogenous physical factors; however, consistent results have not always been reported for controlling MSCs. Conventionally used stiff culture substrates, such as tissue-culture polystyrene and glass, enhance nuclear localization of a mechanotransducer YAP and a pre-osteogenic transcription factor RUNX2, and bias MSCs towards the osteogenic lineage, even without osteogenic-inducing soluble factors. The mechanosensitive nature and intrinsic heterogeneity present challenges for obtaining reproducible results. This review summarizes the heterogeneity in human MSC response, specifically, nuclear/cytoplasmic localization changes in the mechanotransducer yes-associated protein (YAP) and the osteogenic transcription factor RUNX2, in response to substrate stiffness. In addition, a perspective on the intracellular factors attributed to response heterogeneity is discussed. The optimal range of stiffness parameters, Young's modulus, for MSC expansion culture to suppress osteogenic differentiation bias through the suppression of YAP and RUNX2 nuclear localization, and cell cycle progression is likely to be surprisingly narrow for a cell population from an identical donor and vary among cell populations from different donors. We believe that characterization of the heterogeneity of MSCs and understanding their biological meaning is an exciting research direction to establish guidelines for the design of culture substrates for the sophisticated control of MSC properties.

Cooking with fire produces foods containing carbohydrates that are not naturally occurring, such as α-d-fructofuranoside found in caramel. Each of the hundreds of compounds produced by caramelization reactions is considered to possess its own characteristics. Various studies from the viewpoints of biology and biochemistry have been conducted to elucidate some of the scientific characteristics. Here, we review the composition of caramelized sugars and then describe the enzymatic studies that have been conducted and the physiological functions of the caramelized sugar components that have been elucidated. In particular, we recently identified a glycoside hydrolase (GH), GH172 difructose dianhydride I synthase/hydrolase (αFFase1), from oral and intestinal bacteria, which is implicated in the degradation of oligosaccharides in caramel. The structural basis of αFFase1 and its ligands provided many insights. This discovery opened the door to several research fields, including the structural and phylogenetic relationship between the GH172 family enzymes and viral capsid proteins and the degradation of cell membrane glycans of acid-fast bacteria by some αFFase1 homologs. This review article is an extended version of the Japanese article, Identification and Structural Basis of an Enzyme Degrading Oligosaccharides in Caramel, published in SEIBUTSU BUTSURI Vol. 62, p. 184-186 (2022).

The evaluation of the inhibitory activities of drugs on multiple cardiac ion channels is required for the accurate assessment of proarrhythmic risks. Moreover, the in silico prediction of such inhibitory activities of drugs on cardiac channels can improve the efficiency of the drug-development process. Here, we performed molecular docking simulations to predict the complex structures of 25 reference drugs that were proposed by the Comprehensive in vitro Proarrhythmia Assay consortium using two cardiac ion channels, the human ether-a-go-go-related gene (hERG) potassium channel and human Na_V1.5 (hNa_V1.5) sodium channel, with experimentally available structures. The absolute binding free energy (ΔG_bind) values of the predicted structures were calculated by a molecular dynamics-based method and compared with the experimental half-maximal inhibitory concentration (IC₅₀) data. Furthermore, the regression analysis between the calculated values and negative of the common logarithm of the experimental IC₅₀ values (pIC₅₀) revealed that the calculated values of four and ten drugs deviated significantly from the regression lines of the hERG and hNa_V1.5 channels, respectively. We reconsidered the docking poses and protonation states of the drugs based on the experimental data and recalculated their ΔG_bind values. Finally, the calculated ΔG_bind values of 24 and 19 drugs correlated with their experimental pIC₅₀ values (coefficients of determination=0.791 and 0.613 for the hERG and hNa_V1.5 channels, respectively). Thus, the regression analysis between the calculated ΔG_bind and experimental IC₅₀ data ensured the realization of an increased number of reliable complex structures.

Microbial rhodopsins are photoreceptive transmembrane proteins that transport ions or regulate other intracellular biological processes. Recent genomic and metagenomic analyses found many microbial rhodopsins with unique sequences distinct from known ones. Functional characterization of these new types of microbial rhodopsins is expected to expand our understanding of their physiological roles. Here, we found microbial rhodopsins having a DSE motif in the third transmembrane helix from members of the Actinobacteria. Although the expressed proteins exhibited blue-green light absorption, either no or extremely small outward H⁺ pump activity was observed. The turnover rate of the photocycle reaction of the purified proteins was extremely slow compared to typical H⁺ pumps, suggesting these rhodopsins would work as photosensors or H⁺ pumps whose activities are enhanced by an unknown regulatory system in the hosts. The discovery of this rhodopsin group with the unique motif and functionality expands our understanding of the biological role of microbial rhodopsins.