Annual Review of Biophysics最新文献

Low-molecular-weight oligomers formed from amyloidogenic peptides and proteins have been identified as key cytotoxins across a range of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease. Developing therapeutic strategies that target oligomers is therefore emerging as a promising approach for combating protein misfolding diseases. As such, there is a great need to understand the fundamental properties, dynamics, and mechanisms associated with oligomer formation. In this review, we discuss how chemical kinetics provides a powerful tool for studying these systems. We review the chemical kinetics approach to determining the underlying molecular pathways of protein aggregation and discuss its applications to oligomer formation and dynamics. We discuss how this approach can reveal detailed mechanisms of primary and secondary oligomer formation, including the role of interfaces in these processes. We further use this framework to describe the processes of oligomer conversion and dissociation, and highlight the distinction between on-pathway and off-pathway oligomers. Furthermore, we showcase on the basis of experimental data the diversity of pathways leading to oligomer formation in various in vitro and in silico systems. Finally, using the lens of the chemical kinetics framework, we look at the current oligomer inhibitor strategies both in vitro and in vivo.

Double electron-electron resonance (DEER) combined with site-directed spin labeling can provide distance distributions between selected protein residues to investigate protein structure and conformational heterogeneity. The utilization of the full quantitative information contained in DEER data requires effective protein and spin label modeling methods. Here, we review the application of DEER data to protein modeling. First, we discuss the significance of spin label modeling for accurate extraction of protein structural information and review the most popular label modeling methods. Next, we review several important aspects of protein modeling with DEER, including site selection, how DEER restraints are applied, common artifacts, and the unique potential of DEER data for modeling structural ensembles and conformational landscapes. Finally, we discuss common applications of protein modeling with DEER data and provide an outlook.

Over the last 30 years, fluorescence microscopy, renowned for its sensitivity and specificity, has undergone a revolution in resolving ever-smaller details. This advancement began with stimulated emission depletion (STED) microscopy and progressed with techniques such as photoactivatable localization microscopy and stochastic optical reconstruction microscopy (STORM). Single-molecule localization microscopy (SMLM), which encompasses methods like direct STORM, has significantly enhanced image resolution. Even though its speed is slower than that of STED, SMLM achieves higher resolution by overcoming photobleaching limitations, particularly through DNA point accumulation for imaging in nanoscale topography (DNA-PAINT), which continuously renews fluorescent labels. Additionally, cryo-fluorescence microscopy and advanced techniques like minimal photon fluxes imaging (MINFLUX) have pushed the boundaries toward molecular resolution SMLM. This review discusses the latest developments in SMLM, highlighting methods like resolution enhancement by sequential imaging (RESI) and PAINT-MINFLUX and exploring axial localization techniques such as supercritical angle fluorescence and metal-induced energy transfer. These advancements promise to revolutionize fluorescence microscopy, providing resolution comparable to that of electron microscopy.

Studies of membrane protein folding have progressed from simple systems such as bacteriorhodopsin to complex structures such as ATP-binding cassette transporters and voltage-gated ion channels. Advances in techniques such as single-molecule force spectroscopy and in vivo force profiling now allow for the detailed examination of membrane protein folding pathways at amino acid resolutions. These proteins navigate rugged energy landscapes partly shaped by the absence of hydrophobic collapse and the viscous nature of the lipid bilayer, imposing biophysical limitations on folding speeds. Furthermore, many transmembrane (TM) helices display reduced hydrophobicity to support functional requirements, simultaneously increasing the energy barriers for membrane insertion, a manifestation of the evolutionary trade-off between functionality and foldability. These less hydrophobic TM helices typically insert and fold as helical hairpins, following the protein synthesis direction from the N terminus to the C terminus, with assistance from endoplasmic reticulum (ER) chaperones like the Sec61 translocon and the ER membrane protein complex. The folding pathways of multidomain membrane proteins are defined by allosteric networks that extend across various domains, where mutations and folding correctors affect seemingly distant domains. A common evolutionary strategy is likely to be domain specialization, where N-terminal domains enhance foldability and C-terminal domains enhance functionality. Thus, despite inherent biophysical constraints, evolution has finely tuned membrane protein sequences to optimize foldability, stability, and functionality.

Like their prokaryotic counterparts, eukaryotic transcription factors must recognize specific DNA sites, search for them efficiently, and bind to them to help recruit or block the transcription machinery. For eukaryotic factors, however, the genetic signals are extremely complex and scattered over vast, multichromosome genomes, while the DNA interplay occurs in a varying landscape defined by chromatin remodeling events and epigenetic modifications. Eukaryotic factors are rich in intrinsically disordered regions and are also distinct in their recognition of short DNA motifs and utilization of open DNA interaction interfaces as ways to gain access to DNA on nucleosomes. Recent findings are revealing the profound, unforeseen implications of such characteristics for the mechanisms of DNA interplay. In this review we discuss these implications and how they are shaping the eukaryotic transcription control paradigm into one of promiscuous signal recognition, highly dynamic interactions, heterogeneous DNA scanning, and multiprong conformational control.

Living systems are characterized by controlled flows of matter, energy, and information. While the biophysics community has productively engaged with the first two, addressing information flows has been more challenging, with some scattered success in evolutionary theory and a more coherent track record in neuroscience. Nevertheless, interdisciplinary work of the past two decades at the interface of biophysics, quantitative biology, and engineering has led to an emerging mathematical language for describing information flows at the molecular scale. This is where the central processes of life unfold: from detection and transduction of environmental signals to the readout or copying of genetic information and the triggering of adaptive cellular responses. Such processes are coordinated by complex biochemical reaction networks that operate at room temperature, are out of equilibrium, and use low copy numbers of diverse molecular species with limited interaction specificity. Here we review how flows of information through biochemical networks can be formalized using information-theoretic quantities, quantified from data, and computed within various modeling frameworks. Optimization of information flows is presented as a candidate design principle that navigates the relevant time, energy, crosstalk, and metabolic constraints to predict reliable cellular signaling and gene regulation architectures built of individually noisy components.

The growing appreciation of native state conformational fluctuations mediating protein function calls for critical reevaluation of protein evolution and adaptation. If proteins are ensembles, does nature select solely for ground state structure, or are conformational equilibria between functional states also conserved? If so, what is the mechanism and how can it be measured? Addressing these fundamental questions, we review our investigation into the role of local unfolding fluctuations in the native state ensembles of proteins. We describe the functional importance of these ubiquitous fluctuations, as revealed through studies of adenylate kinase. We then summarize elucidation of thermodynamic organizing principles, which culminate in a quantitative probe for evolutionary conservation of protein energetics. Finally, we show that these principles are predictive of sequence compatibility for multiple folds, providing a unique thermodynamic perspective on metamorphic proteins. These research areas demonstrate that the locally unfolded ensemble is an emerging, important mechanism of protein evolution.

Advances in imaging technology enable striking views of life's most minute details. A missing piece of the puzzle, however, is the direct atomic observation of biomolecules in action. Liquid-phase transmission electron microscopy (liquid-EM) is the room-temperature correlate to cryo-electron microscopy, which is leading the resolution revolution in biophysics. This article reviews current challenges and opportunities in the liquid-EM field while discussing technical considerations for specimen enclosures, devices and systems, and scientific data management. Since liquid-EM is gaining traction in the life sciences community, cross talk among the disciplines of materials and life sciences is needed to disseminate knowledge of best practices along with high-level user engagement. How liquid-EM technology is inspiring the real-time revolution in molecular microscopy is also discussed. Looking ahead, the new movement can be better supported through open resource sharing and partnerships among academic, industry, and federal organizations, which may benefit from the scientific equity foundational to the technique.

Mass photometry (MP) is a technology for the mass measurement of biological macromolecules in solution. Its mass accuracy and resolution have transformed label-free optical detection into a quantitative measurement, enabling the identification of distinct species in a mixture and the characterization of their relative abundances. Its applicability to a variety of biomolecules, including polypeptides, nucleic acids, lipids, and sugars, coupled with the ability to quantify heterogeneity, interaction energies, and kinetics, has driven the rapid and widespread adoption of MP across the life sciences community. These applications have been largely orthogonal to those traditionally associated with microscopy, such as detection, imaging, and tracking, instead focusing on the constituents of biomolecular complexes and their change with time. Here, we present an overview of the origins of MP, its current applications, and future improvements that will further expand its scope.

The rate of protein synthesis is slower than many folding reactions and varies depending on the synonymous codons encoding the protein sequence. Synonymous codon substitutions thus have the potential to regulate cotranslational protein folding mechanisms, and a growing number of proteins have been identified with folding mechanisms sensitive to codon usage. Typically, these proteins have complex folding pathways and kinetically stable native structures. Kinetically stable proteins may fold only once over their lifetime, and thus, codon-mediated regulation of the pioneer round of protein folding can have a lasting impact. Supporting an important role for codon usage in folding, conserved patterns of codon usage appear in homologous gene families, hinting at selection. Despite these exciting developments, there remains few experimental methods capable of quantifying translation elongation rates and cotranslational folding mechanisms in the cell, which challenges the development of a predictive understanding of how biology uses codons to regulate protein folding.