Annual Review of Biophysics最新文献

Photosynthesis, the biological process of converting light energy into chemical energy, involves light harvesting, charge separation and electron transport, proton translocation, ATP synthesis, and carbon fixation, among other processes. Adjacent photosynthetic complexes may assemble into supramolecular complexes to couple and regulate their functions. Here, we review the progress of structural biology studies of photosynthetic supramolecular complexes, such as those that have light-harvesting complexes assembled with photosystem II (PSII) or photosystem I (PSI), both PSII and PSI, or bacterial reaction center complexes. The intricate architectures of the NADH dehydrogenase-like (NDH) complex and PSI-NDH supercomplex, revealed through cryo-electron microscopy studies, provide crucial frameworks for understanding the molecular mechanisms of cyclic electron flow in cyanobacteria and plants. Furthermore, structural studies have also yielded detailed insights into the assembly and repair of PSII, regulation of ATP synthase, and carbon fixation. The review concludes with a summary of the emerging directions of structural biology studies of photosynthetic supramolecular complexes.

Chemical signaling underlies many biological processes, and membrane receptors such as G protein-coupled receptors and ligand-gated ion channels represent two of the most pharmacologically important protein families. Advances in single-molecule fluorescence techniques have transformed our understanding of molecular mechanisms, including protein folding, transcription, and ligand binding. Unlike ensemble measurements, which average over populations and obscure molecular heterogeneity, single-molecule approaches enable direct observation of individual events, revealing rare conformational states and distinguishing between mechanisms that are indistinguishable at the ensemble level. This review highlights how single-molecule FRET (smFRET) and single-molecule fluorescence ligand binding (smFLiB) provide complementary insights into ligand-dependent receptor activation and allosteric coupling. smFRET offers structural information by tracking conformational transitions, but limited observation times can hinder detection of slow or infrequent events. In contrast, smFLiB allows long-duration monitoring of ligand-receptor interactions throughout the activation pathway, though with less direct information about structural rearrangements. Through selected case studies, we illustrate how these techniques have been applied to dissect the complexity of ligand-receptor interactions with unprecedented resolution. These advances hold promise for guiding the rational design of more selective and effective therapeutics targeting membrane proteins.

T cells are central to the adaptive immune response, capable of detecting pathogenic antigens while ignoring healthy tissues with remarkable specificity and sensitivity. Quantitatively understanding how T cell receptors discern among antigens requires biophysical models and theoretical analyses of signaling networks. Here, we review current theoretical frameworks of antigen recognition in the context of modern experimental and computational advances. Antigen potency spans a continuum and exhibits nonlinear effects within complex mixtures, challenging discrete classification and simple threshold-based models. This complexity motivates the development of models, such as adaptive kinetic proofreading, that integrate both activating and inhibitory signals. Advances in high-throughput technologies now generate large-scale, quantitative data sets, enabling the refinement of such models through statistical and machine learning approaches. This convergence of theory, data, and computation promises deeper insights into immune decision-making and opens new avenues for rational immunotherapy design.

Voltage-dependent anion channels (VDACs) of the outer mitochondrial membrane carry out bidirectional flux of metabolites and ions and serve as the first line of communication between the cytosol and mitochondria. They are now recognized as indispensable for mitochondrial function and cellular homeostasis, mitochondria-endoplasmic reticulum communication, lipid and cholesterol biogenesis, Ca2+ homeostasis, and mitochondria-mediated apoptosis. The unique structural features of VDACs are also important in redox regulation. VDAC dysregulation by interaction with amyloid-β, α-synuclein, Tau, or tubulin can lead to neurodegeneration. Here, we provide insights into the structures, isoform-specific molecular functions, cellular interactome, variations, and unique regulatory elements of VDACs and their direct implications in widespread burdens like cancer and neurodegeneration in humans. We discuss how deducing isoform-specific structure-function studies of VDACs has the potential for successful development of next-generation diagnostics-guided therapeutics.

Hopfield models, originally developed to study memory retrieval in neural networks, have become versatile tools for modeling diverse biological systems in which function emerges from collective dynamics. In this review, we provide a pedagogical introduction to both classic and modern Hopfield networks from a biophysical perspective. After presenting the underlying mathematics, we build physical intuition through three complementary interpretations of Hopfield dynamics: as noise discrimination, as a geometric construction defining a natural coordinate system in pattern space, and as gradient-like descent on an energy landscape. We then survey applications of Hopfield networks in a variety of biological settings, including cellular differentiation and epigenetic memory, molecular self-assembly, and spatial neural representations.

Over the last decade, proteomics analysis of single cells by mass spectrometry transitioned from an uncertain possibility to a set of robust and rapidly advancing technologies supporting the accurate quantification of thousands of proteins. We review the major drivers of this progress, from establishing feasibility to powerful and increasingly scalable methods. We focus on the trade-offs and synergies of different technological solutions within a coherent conceptual framework, which projects considerable room both for throughput scaling and for extending the analysis scope to functional protein measurements. We highlight the potential of these technologies to support the development of mechanistic biophysical models and to help uncover new principles.

Nanopores have become transformative tools in single-molecule chemical analysis, enabling detailed interrogation of molecular interactions and reaction dynamics. These advancements have revolutionized the characterization of chemical kinetics and stereospecificity, broadening nanopore applications. This review evaluates the principles of nanopore single-molecule chemistry, highlighting breakthroughs in chemically reactive nanopore construction via site-specific mutagenesis, semisynthetic engineering, and orthogonal modifications. Notably, we highlight the innovative strategies enabling precise subunit stoichiometry control to ensure single-molecule reactions, and the integration of machine learning for high-fidelity ionic current analysis. These developments position nanopores as versatile tools for intricate molecular detection in fundamental and applied research. Looking forward, nanopore single-molecule chemistry promises an impact on diagnostics, environmental monitoring, and precision medicine. Integration of molecular dynamics simulations, artificial intelligence-driven protein design frameworks, and microsystems technology may expand detectable species, enhancing robustness and lowering detection limits. Such advancements will deepen our understanding of chemical transformations and support meaningful real-world applications of nanopore technologies.

Pattern formation, or the emergence of spatial order and fate specification from initially homogeneous cell populations, is a fundamental problem in developmental biology and biophysics. In vitro cell culture platforms now provide powerful tools to investigate the mechanisms of pattern formation under controlled conditions. In this review, we present an integrated perspective on recent advances in pattern formation studies across a diverse array of in vitro systems, organized thematically by the experimental platforms they employ and the underlying principles they reveal, ranging from biochemical gradients to dynamic signaling and mechanochemical feedback. We discuss how these systems recapitulate principles such as morphogen gradients and reaction-diffusion dynamics while enabling mechanistic dissection of self-organization. Particular emphasis is placed on stem cell-based models of early human development that provide unique access to early developmental patterning. We further explore how dynamic signaling, collective behavior, and multisignal integration define emergent patterning phenomena. Finally, we outline future challenges and opportunities in combining theoretical and experimental approaches to model and engineer spatial organization in multicellular systems.

Cells process signals by using large and complex networks of molecules that interact and modify one another. Some of these interactions occur among molecules connected by long flexible tethers, often made of intrinsically disordered protein regions. In this review, we present recent research showing that tethered reactions (a) are ubiquitous in cells, (b) are exploited by cell signaling networks, (c) can be qualitatively and quantitatively understood using simple polymer physics, (d) give rise to categorically different features compared with molecular interactions driven by free diffusion, and (e) provide novel avenues for therapeutics and bioengineering. Recent studies have begun to shed light on cases in which the tethers must reach between different molecular assemblies that are not connected by protein scaffolding. We provide an in-depth case study of immune receptors, where such tethered signaling plays a vital role in signal integration and immune cell decisions.

The spatiotemporal organization of intracellular compartments is fundamental to cellular function and to the understanding of the processes underpinning health and disease. Fluorescence microscopy offers a powerful means to observe organelle morphology and dynamics with high specificity. However, no single technique can capture the wide range of relevant spatiotemporal scales due to inherent trade-offs in resolution, speed, field of view, signal-to-noise ratio, and sample viability. In this review, we describe recent developments across high-resolution fluorescence microscopy techniques and associated computational methods, critically evaluating how these advances address key limitations. Through biological examples of organelle dynamics at different scales, we illustrate the impact of these technologies on our understanding of cellular organization and function. Finally, we discuss the current challenges and outline future directions for imaging-based research, highlighting the potential for further innovations to deepen insights into dynamic subcellular processes.