Histones are small basic proteins that form the proteinaceous core of the nucleosome, the repeating building block of chromatin in all eukaryotes. Long thought to be exclusive to eukaryotes, histones are now increasingly appreciated for their roles in organizing genomes across all domains of life, namely in archaea, bacteria, and even viruses. We survey recent advances in our understanding of the imaginative uses of histones in disparate biological entities, ranging from nucleosome-like metastable particles in giant viruses to slinky-like hypernucleosomes in archaea to bacterial histones that bind DNA in decidedly unorthodox ways. Across these different contexts, we examine how DNA compaction and conformation emanate from evolutionarily conserved aspects of histone structure, including how the oligomeric states of histones dictate their capacity to contort DNA in different conformations. It appears that relatively small tweaks to the amino acid sequences of histones can result in structural and functional variations in DNA binding. As such, nucleosomes in eukaryotes sample only a narrow range of possible structures.

While the role of water in soluble protein structure and function is well-established, the analogous role of lipids as a solvent for membrane proteins is less understood. Bacterial membranes exhibit extraordinary lipid diversity, with synthesizing over 1,800 distinct glycerophospholipids. This lipid diversity gives rise to bulk membrane properties and specific lipid-lipid and lipid-protein interactions that directly affect α-IMP folding, assembly, and function. In this review, we use the same thermodynamic framework for understanding the solvation of soluble proteins to examine bacterial α-helical integral membrane protein (α-IMP) interactions with chemically diverse lipid environments. We propose that preferential solvent interactions were essential evolutionary drivers that enabled lipids to evolve as protein cofactors and substrates, with lipid chemical diversity creating unique evolutionary pressures distinct from those of aqueous systems.

This review focuses on the use of high-pressure nuclear magnetic resonance (HP NMR) to map local protein stability and conformational landscapes, with an emphasis on the population and characteristics of protein excited states. Section 1 discusses the volumetric properties of proteins in the pressure-temperature plane, highlighting the underlying mechanisms of pressure effects, the magnitude of the volume changes upon unfolding, their temperature dependence, and the nature of the unfolded state at high pressure. In Section 2, NMR-detected, pressure-induced equilibrium unfolding of proteins is discussed. Section 3 covers how HP NMR can reveal the complexity of protein conformational landscapes, the population of excited states, and the local stability distribution across the structure. Studies exploring the sequence determinants of these landscapes are presented. Of particular interest are the sequence determinants that define the excited states implicated in functional dynamics, one of the most important unresolved issues in protein science.

Globular proteins are expected to assume folds with fixed secondary structures, α-helices and β-sheets. Fold-switching proteins challenge this expectation by remodeling their secondary and/or tertiary structures in response to cellular stimuli. Though these shape-shifting proteins were once thought to be haphazard evolutionary by-products with little intrinsic biological relevance, recent work has shown that evolution has selected for their dual-folding behavior, which plays critical roles in biological processes across all kingdoms of life. The widening scope of fold switching draws attention to the ways it challenges conventional wisdom, raising fundamental unanswered questions about protein structure, biophysics, and evolution. Here we discuss the progress being made to answer these questions and suggest future directions for the field.

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.

It is now clear that membrane association of intrinsically disordered proteins or intrinsically disordered regions regulates many cellular processes, such as membrane targeting of Src family kinases and ion channel gating. Residue-specific characterization by nuclear magnetic resonance spectroscopy, molecular dynamics simulations, and other techniques has shown that polybasic motifs and amphipathic helices are the main drivers of membrane association; sequence-based prediction of residue-specific membrane association propensity has become possible. Membrane association facilitates protein-protein interactions and protein aggregation-these effects are due to reduced dimensionality but are similar to those afforded by condensate formation via liquid-liquid phase separation (LLPS). LLPS at the membrane surface provides a powerful means for recruiting and clustering proteins, as well as for membrane remodeling.

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.

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

Microbial cell factories have been developed to produce various compounds in a sustainable and economically viable manner. The yeast has been used as a platform cell factory in industrial biotechnology with numerous advantages, including ease of operation, rapid growth, and tolerance for various industrial stressors. Advances in synthetic biology and metabolic models have accelerated the design-build-test-learn cycle in metabolic engineering, significantly facilitating the development of yeast strains with complex phenotypes, including the redirection of metabolic fluxes to desired products, the expansion of the spectrum of usable substrates, and the improvement of the physiological properties of strain. Strains with enhanced titer, rate, and yield are now competing with traditional petroleum-based industrial approaches. This review highlights recent advances and perspectives in the metabolic engineering of yeasts for the production of a variety of compounds, including fuels, chemicals, proteins, and peptides, as well as advancements in synthetic biology tools and mathematical modeling.

The cytoskeleton comprises networks of different biopolymers, which serve various cellular functions. To accomplish these tasks, their mechanical properties are of particular importance. Understanding them requires detailed knowledge of the mechanical properties of the individual filaments that make up these networks, in particular, microtubules, actin filaments, and intermediate filaments. Far from being homogeneous beams, cytoskeletal filaments have complex mechanical properties, which are directly related to the specific structural arrangement of their subunits. They are also versatile, as the filaments' mechanics and biochemistry are tightly coupled, and their properties can vary with the cellular context. In this review, we summarize decades of research on cytoskeletal filament mechanics, highlighting their most salient features and discussing recent insights from this active field of research.

Evolutionary processes have transformed simple cellular life into a great diversity of forms, ranging from the ubiquitous eukaryotic cell design to the more specific cellular forms of spirochetes, cyanobacteria, ciliates, heliozoans, amoeba, and many others. The cellular traits that constitute these forms require an evolutionary explanation. Ultimately, the persistence of a cellular trait depends on its net contribution to fitness, a quantitative measure. Independent of any positive effects, a cellular trait exhibits a baseline energetic cost that needs to be accounted for when quantitatively examining its net fitness effect. Here, we explore how the energetic burden introduced by a cellular trait quantitatively affects cellular fitness, describe methods for determining cell energy budgets, summarize the costs of cellular traits across the tree of life, and examine how the fitness impacts of these energetic costs compare to other evolutionary forces and trait benefits.

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.

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.

To ensure survival and reproduction, individual animals navigating the world must regularly sense their surroundings and use this information for important decision-making. The same is true for animals living in groups, where the roles of sensing, information propagation, and decision-making are distributed on the basis of individual knowledge, spatial position within the group, and more. This review highlights key examples of temporal and spatiotemporal dynamics in animal group decision-making, emphasizing strong connections between mathematical models and experimental observations. We start with models of temporal dynamics, such as reaching consensus and the time dynamics of excitation-inhibition networks. For spatiotemporal dynamics in sparse groups, we explore the propagation of information and synchronization of movement in animal groups with models of self-propelled particles, where interactions are typically parameterized by length and timescales. In dense groups, we examine crowding effects using a soft condensed matter approach, where interactions are parameterized by physical potentials and forces. While focusing on invertebrates, we also demonstrate the applicability of these results to a wide range of organisms, aiming to provide an overview of group behavior dynamics and identify new areas for exploration.

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.

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.

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.

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.

Cryo-electron microscopy (cryo-EM) is the method of choice for investigating the structures of membrane protein complexes at high resolution under near-native conditions. This review focuses on recent cryo-EM work on mitochondrial complex I and ATP synthase. Single-particle cryo-EM structures of complex I from mammals, plants, and fungi extending to a resolution of 2 Å show different functional states, indicating consistent conformational changes of loops near the Q binding site, clusters of internal water molecules in the membrane arm, and an α-π transition in a membrane-spanning helix that opens and closes the proton transfer path. Cryo-EM structures of ATP synthase dimers from mammalian, yeast, and mitochondria show several rotary states at a resolution of 2.7 to 3.5 Å. The new structures of complex I and ATP synthase are important steps along the way toward understanding the detailed molecular mechanisms of both complexes. Cryo-electron tomography and subtomogram averaging have the potential to resolve their high-resolution structures in situ.

In this article I review mechanisms that underpin epigenetic inheritance of CpG methylation and histone H3 lysine 9 methylation (H3K9me) in chromatin in fungi and mammals. CpG methylation can be faithfully inherited epigenetically at some sites for a lifetime in vertebrates and, remarkably, can be propagated for millions of years in some fungal lineages. Transmission of methylation patterns requires maintenance-type DNA methyltransferases (DNMTs) that recognize hemimethylated CpG DNA produced by replication. DNMT1 is the maintenance enzyme in vertebrates; we recently identified DNMT5 as an ATP-dependent CpG maintenance enzyme found in fungi and protists. In vivo, CpG methylation is coupled to H3K9me. H3K9me is itself reestablished after replication via local histone H3-H4 tetramer recycling involving mobile and nonmobile chaperones, de novo nucleosome assembly, and read-write mechanisms that modify naive nucleosomes. Additional proteins recognize hemimethylated CpG or fully methylated CpG-containing motifs and enhance restoration of methylation by recruiting and/or activating the maintenance methylase.

All biological systems are subject to perturbations arising from thermal fluctuations, external environments, or mutations. Yet, while biological systems consist of thousands of interacting components, recent high-throughput experiments have shown that their response to perturbations is surprisingly low dimensional: confined to only a few stereotyped changes out of the many possible. In this review, we explore a unifying dynamical systems framework-soft modes-to explain and analyze low dimensionality in biology, from molecules to ecosystems. We argue that this soft mode framework makes nontrivial predictions that generalize classic ideas from developmental biology to disparate systems, namely phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.