Cryo-electron tomography has emerged as the premier technique for ultrastructural analysis of natively preserved biological specimens and in situ structure determination. Each tomogram of a cell contains valuable informa...Cryo-electron tomography has emerged as the premier technique for ultrastructural analysis of natively preserved biological specimens and in situ structure determination. Each tomogram of a cell contains valuable information on the imaged molecular assemblies, leading to potential discoveries, but it also contains enormous amounts of noise. This noise, in combination with the typical low contrast in raw cryo-electron tomograms, hampers the discovery process. To overcome this impairment on the levels of tomogram reconstruction and the reconstructed tomogram, the field has employed a variety of image processing techniques, ranging from binning and low-pass filters removing the typically noisier high-frequency Fourier components to neural network-based denoisers. Here, we provide an overview of the approaches that are used in current research studies and an outlook on a set of newly developed strategies leveraging neural networks to raise the visual interpretability of tomograms and thereby, hopefully, increase the rate of new discoveries.
DNA-protein interactions underlie genome activity, governing gene expression as well as the physical organisation of DNA. Until recently, DNA-protein complexes were predominantly described at atomic resolution using shor...DNA-protein interactions underlie genome activity, governing gene expression as well as the physical organisation of DNA. Until recently, DNA-protein complexes were predominantly described at atomic resolution using short DNA fragments, concealing how proteins recognise and manipulate the long, supercoiled DNA present in cells. Now single-molecule imaging and cryo-electron microscopy (cryo-EM) are showing how longer DNA sequences are recognised by proteins and computations are predicting how these elements influence larger-scale genomic structures. Here we discuss how the polymeric nature of DNA influences its atomic-level structure and dynamics, as well as the implications for DNA recognition and ultimately biological function. We emphasise how theory and simulation help interpret these effects, which are difficult to replicate using conventional experimental settings.
AlphaFold-like models have transformed monomer structure prediction yet reliable, generalizable prediction of protein-protein interactions (PPIs) remains challenging, particularly for antibody-antigen docking. These limi...AlphaFold-like models have transformed monomer structure prediction yet reliable, generalizable prediction of protein-protein interactions (PPIs) remains challenging, particularly for antibody-antigen docking. These limitations often stem from reliance on pattern memorization under severe data scarcity. Here, we review the emerging transition toward physics-integrated machine learning to address these gaps. We categorize recent efforts to improve generalization into three complementary approaches: (I) enriching inputs with physics-based sampling (e.g., molecular dynamics/fast Fourier transform ensembles); (II) designing architectures with strict geometric inductive biases (e.g., SE(3)-equivariance); and (III) constraining the generative process using physical energy functions or potentials. While standard models often struggle on out-of-distribution targets, these hybrid strategies aim to enforce physical plausibility at different stages of the prediction pipeline, offering a path from memorization to true physical generalization.
Single-molecule fluorescence spectroscopy encompasses a range of techniques to probe dynamic conformational ensembles of intrinsically disordered regions (IDRs). By resolving subpopulation-specific properties and spannin...Single-molecule fluorescence spectroscopy encompasses a range of techniques to probe dynamic conformational ensembles of intrinsically disordered regions (IDRs). By resolving subpopulation-specific properties and spanning timescales from nanoseconds to seconds, these measurements provide observables often inaccessible with ensemble measurements. Here, we contextualize key challenges and recent advances, including integrating experiments, simulations, and theory to quantify sequence-encoded effects, as well as photon statistic analyses to characterize protein dynamics. We also summarize extensions of single-molecule fluorescence approaches to decode IDR conformations, dynamics, and spatial organizations within biomolecular condensates. Together, these technical advances make single-molecule fluorescence spectroscopy increasingly accessible for studying IDR function, while the complexity of IDR biology and physics continues to drive further methodological innovation.
Protein function emerges from ensembles of interconverting conformations, presenting challenges beyond static structure prediction. Although recent advances in generative artificial intelligence (AI) have transformed nat...Protein function emerges from ensembles of interconverting conformations, presenting challenges beyond static structure prediction. Although recent advances in generative artificial intelligence (AI) have transformed native-fold prediction, capturing conformational landscapes and binding-associated structural transitions remains a central limitation for mechanistic biology and structure-based drug discovery. Emerging generative models now aim to learn dynamic conformational distributions directly, enabling ensemble generation, ligand-responsive receptor sampling, and pathway-level inference. However, predictive fidelity is still constrained by limited physical grounding, incomplete kinetic realism, data imbalance, and uncertain calibration. This review summarizes key developments from 2023 to 2025, examines their methodological and practical limitations, and discusses how generative AI may evolve into a reliable framework for dynamic structural biology and mechanism-guided drug design.
Advances in cryo-electron microscopy and tomography (cryoEM/ET) allow us to visualize cellular ultrastructure at molecular resolution in a near-native state. By bridging the resolution between cell and structural biology...Advances in cryo-electron microscopy and tomography (cryoEM/ET) allow us to visualize cellular ultrastructure at molecular resolution in a near-native state. By bridging the resolution between cell and structural biology, in situ cryoEM/ET unravels unprecedented molecular details of macromolecular complexes, as well as their spatial organization, within intact cells and organelles. Beyond structural determination, emerging image processing pipelines combine subtomogram averaging with contextual, quantitative, and multimodal analyses to reveal how molecules are coordinated in cellular space - what might be termed the "molecular sociology" of cells. Here, we review recent progress in specific cellular organelles, highlight key conceptual trends, and discuss current limitations and future perspectives for integrative in situ structural biology.
Cryo-electron tomography (cryo-ET) is emerging as a transformative tool for structural biology. Unlike methods based on purified molecules, cryo-ET enables visualisation of macromolecules directly within intact cells and...Cryo-electron tomography (cryo-ET) is emerging as a transformative tool for structural biology. Unlike methods based on purified molecules, cryo-ET enables visualisation of macromolecules directly within intact cells and tissues, preserving their native interactions and physiological context. By providing high-resolution views of healthy and diseased cells, cryo-ET offers a powerful means to understand infection and disease mechanisms. Moreover, cryo-ET combined with subtomogram averaging can resolve macromolecular structures beyond 3 Å resolution, making it a promising approach for computational drug discovery. This article highlights the recent contributions from cryo-ET in understanding human disease and examines future perspectives of this rapidly evolving technique in structure-based drug discovery. We propose a roadmap for the developments required for its widespread adoption in pharmacological research.
Cryo-electron microscopy (cryo-EM) is transitioning from determining structures of isolated proteins in vitro to visualizing macromolecular architecture directly in situ. Conventional in situ approaches, primarily relyin...Cryo-electron microscopy (cryo-EM) is transitioning from determining structures of isolated proteins in vitro to visualizing macromolecular architecture directly in situ. Conventional in situ approaches, primarily relying on cryo-electron tomography combined with subtomogram averaging, are often limited in resolution due to complex workflows, cumulative errors in processing, and low data throughput. Emerging in situ single-particle cryo-EM methods address these limitations by collecting high-dose, untilted images of cellular lamellae. Using high-resolution templates for particle identification and refinement, these methods have significantly advanced both data throughput and achievable resolution. This review systematically outlines the principles, workflow, and advantages of in situ single-particle methods, highlights their key applications, and discusses future perspectives.
Base excision repair (BER) in eukaryotes must navigate a crowded nuclear environment to locate rare lesions embedded in diverse chromatin contexts, such as nucleosomes, replication forks, and active promoters. Central to...Base excision repair (BER) in eukaryotes must navigate a crowded nuclear environment to locate rare lesions embedded in diverse chromatin contexts, such as nucleosomes, replication forks, and active promoters. Central to this sophisticated search are intrinsically disordered regions (IDRs) flanking the conserved catalytic cores of enzymes like OGG1, APE1, TDG, NEIL1, UNG2, and XRCC1. These IDRs act as biophysical tuners that govern BER dynamics by (i) regulating multi-state DNA target search through a non-linear balance of 3D encounters and 1D facilitated diffusion, (ii) modulating sampling across heterogeneous chromatin environments, and (iii) modulating the nucleation and decay of transient repair assemblies. While the chemistry of catalytic cores is well-defined, how IDRs quantitatively tune these kinetic search modes and scaffolded assemblies remains enigmatic. Recent single-molecule and live-cell imaging now allow for a transition from static, end-point readouts toward a high-resolution kinetic description of BER as a dynamic, hierarchical genomic surveillance system.
Nuclear magnetic resonance (NMR) has a long history of addressing mechanistic questions in membrane biology. Its unique capabilities include the non-perturbing nature of isotope labels, the ability of NMR signals to prov...Nuclear magnetic resonance (NMR) has a long history of addressing mechanistic questions in membrane biology. Its unique capabilities include the non-perturbing nature of isotope labels, the ability of NMR signals to provide atomic-level structural and dynamic information in complex samples, including living systems, and the ability to detect very weak binding through chemical shift changes. Solution and solid-state NMR methods are highly complementary and enable a wide range of biomolecular complexity and dynamic timescales to be analyzed. Here we describe recent advances that push the envelope of current technology and offer new opportunities for targeting membrane protein assemblies to gain fundamental human health insights.
Bio-macromolecules rely on dynamic conformational changes for their function. While static 3D structures have provided invaluable insights into the spatial architecture of biomolecules, they lack temporal resolution. Com...Bio-macromolecules rely on dynamic conformational changes for their function. While static 3D structures have provided invaluable insights into the spatial architecture of biomolecules, they lack temporal resolution. Complementarily, single molecule Förster resonance energy transfer (smFRET) enables real-time recording of nanoscale motions, providing a unique link between structure and function. This review highlights recent biological insights and methodological advances of the past two years. First, with examples from across the central dogma of molecular biology, we highlight mechanisms that became accessible through smFRET, complementing traditional structural biology techniques. Second, we review technological innovations, namely smFRET-specific progress, in situ integration of smFRET with other experiments, and ex situ complementary studies. Altogether, these advances demonstrate that the era of dynamic structural biology has truly arrived.
Cryo-electron tomography (cryo-ET) has revolutionized visualization of bacterial nanomachines by revealing molecular structures in situ under near-native conditions. This review highlights recent advances extending cryo-...Cryo-electron tomography (cryo-ET) has revolutionized visualization of bacterial nanomachines by revealing molecular structures in situ under near-native conditions. This review highlights recent advances extending cryo-ET from isolated cells to host-microbe interactions within intact tissues. We discuss technical solutions for large-volume imaging such as cryo-focused ion beam milling, correlative light and electron microscopy, and serial lift-out workflows that preserve native structural context. Computational developments in AI-driven denoising, segmentation, and sub-tomogram averaging enhance interpretability of low-dose tomograms. Using examples from predatory bacteria, intracellular pathogens, and squid symbionts, we demonstrate how cryo-ET elucidates mechanistic details of microbial interactions.
Macromolecular complexes are cells' functional units, and their correct and efficient assembly is critical to life's processes. Complex assembly was classically described as the encounter of fully synthesized, mature pro...Macromolecular complexes are cells' functional units, and their correct and efficient assembly is critical to life's processes. Complex assembly was classically described as the encounter of fully synthesized, mature protein subunits, yet an amalgam of current studies shows that cotranslational assembly is prevalent, in which nascent proteins vectorially form interfaces with their partners during translation. In this review, we examine the advances in this emerging field. We discuss the thermodynamic and kinetic principles underlying different modes of assembly and highlight how the specific structural/biophysical features of the corresponding complexes enable them. We propose that cotranslational assembly produces kinetically stable oligomeric states that resist dissociation and stochastic conformational changes, thereby conferring functionality amid molecular crowding or environmental stresses.
Curr Opin Struct Biol
· 2026 Jun · PMID 42096888
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Viral envelope glycoproteins catalyze membrane fusion during entry into cells. Envelope glycoprotein function has traditionally been viewed through the lens of kinetic control, where environmental cues like pH trigger ir...Viral envelope glycoproteins catalyze membrane fusion during entry into cells. Envelope glycoprotein function has traditionally been viewed through the lens of kinetic control, where environmental cues like pH trigger irreversible refolding from the pre-fusion conformation to the post-fusion conformation. Single-molecule Förster resonance energy transfer (smFRET) imaging has revealed an additional layer of thermodynamic control that governs the conformational dynamics of envelope glycoproteins in their pre-fusion form. smFRET studies of the envelope glycoproteins from HIV-1, SARS-CoV-2, MERS-CoV, Ebola virus, and influenza A virus demonstrate that these glycoproteins dynamically sample an ensemble of pre-fusion conformations whose relative stabilities respond to pH, receptor binding, ions, and host proteases. This thermodynamic tuning precedes the kinetically controlled transition that promotes membrane fusion. Collectively, smFRET imaging has transformed our understanding of viral entry, illustrating how the pre-fusion energy landscape of envelope glycoproteins is tuned by the host environment to maintain viral fitness.
Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful tool to probe macromolecular dynamics. Two-color smFRET, however, provides only one-dimensional distance information, limiting its use in comp...Single-molecule fluorescence resonance energy transfer (smFRET) is a powerful tool to probe macromolecular dynamics. Two-color smFRET, however, provides only one-dimensional distance information, limiting its use in complex systems. Multicolor smFRET with three or more fluorophores can overcome this, though three is often the practical limit due to the narrow selection of dyes with suitable spectral properties, the challenges of labeling with multiple fluorophores, and the complexity of data interpretation. Here, we survey recent investigations of the dynamics of macromolecules and their complexes based on three-color smFRET. In this review, three-color smFRET refers to the use of three fluorophores in a FRET system where two or all three fluorophores may participate in FRET at a given time point. We aim to highlight structural biology problems particularly well suited for three-color smFRET and to provide a brief overview of designing and implementing such experiments in light of their benefits and challenges.
In eukaryotes, chromatin state and nuclear architecture are tightly linked to the replication timing (RT) program. Here, we consider how chromatin features operate across all scales of genome organization in mammals, fro...In eukaryotes, chromatin state and nuclear architecture are tightly linked to the replication timing (RT) program. Here, we consider how chromatin features operate across all scales of genome organization in mammals, from the local nucleosomal level, with histone variants and their posttranslational modifications, to the global scale that comprises compartments and topologically associated domains (TADs). We discuss recent findings that tie these features with DNA replication; we examine how histone variants and modifications shape replication initiation, how 3D genome architecture may contribute to temporal regulation, and how these local and global scales operate during early mammalian development, when RT emerges de novo. This overview will encourage dissection of principles that establish and maintain RT.
Artificial intelligence (AI) has reshaped protein design by enabling models trained on large-scale sequence and structure data to generate proteins with specified functions. These models are best understood in the contex...Artificial intelligence (AI) has reshaped protein design by enabling models trained on large-scale sequence and structure data to generate proteins with specified functions. These models are best understood in the context of an end-to-end pipeline that includes data curation, model development, candidate generation and filtering, and experimental validation. Here, we review AI-driven protein design methods that span this full pipeline. We begin with a primer on AI-driven protein design and then outline the key components of the pipeline and assess performance across three major application areas: binders, antibodies, and enzymes. By consolidating experimental outcomes across diverse approaches, we provide a practical reference for methods that currently succeed in the lab and highlight the ongoing importance of experimental feedback in advancing AI-driven protein design.
Advances in cryogenic electron microscopy (cryo-EM) data modeling and deep learning are reshaping our ability to interrogate and engineer genome-editing systems. Their synergistic integration enables high-resolution stru...Advances in cryogenic electron microscopy (cryo-EM) data modeling and deep learning are reshaping our ability to interrogate and engineer genome-editing systems. Their synergistic integration enables high-resolution structural interpretation, quantitative mapping of conformational landscapes, and rational design across diverse CRISPR-Cas architectures. By coupling molecular dynamics with cryo-EM refinement, we uncover functionally relevant dynamic ensembles, while quantum mechanical methods resolve ambiguous features in low-resolution density maps. Emerging deep-learning frameworks including graph neural networks, extract interpretable communication pathways from large-scale simulations and provide methods that are broadly transferable across biomolecular systems. These advances propel the field beyond static structural snapshots toward a dynamic, predictive, and data-driven approach for understanding and designing genome-editing systems.