Single-molecule microscopy has transformed our view of biomolecular condensates-membraneless organelles that organize cellular biochemistry and are frequently dysregulated in disease-revealing them not as simple liquid d...Single-molecule microscopy has transformed our view of biomolecular condensates-membraneless organelles that organize cellular biochemistry and are frequently dysregulated in disease-revealing them not as simple liquid droplets, but as spatially heterogeneous and percolated networks that can undergo time-dependent physical aging and gelation. Here, we summarize how single-particle tracking, single-molecule-fluorescence resonance energy transfer and super-resolution microscopy resolve molecular motion, confinement, and conformational dynamics to link nanoscale behaviors to mesoscale condensate material properties and biological function. In vitro reconstitution affords mechanistic control, whereas emerging live-cell imaging probes physiological context. Photobleaching, phototoxicity, and autofluorescence remain challenges that are increasingly mitigated by optimized fluorophore and label-free approaches. Concurrently, deep-learning pipelines automate analysis and expose hidden heterogeneities. Further integrating artificial intelligence and imaging advances will be essential for decoding condensate structure-function relationships in health and disease.
Curr Opin Struct Biol
· 2026 Jun · PMID 41980520
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Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such appr...Generative artificial intelligence models learn probability distributions from data and produce novel samples that capture the salient properties of their training sets. Proteins are particularly attractive for such approaches given their abundant data and the versatility of their representations, ranging from sequences to structures and functions. This versatility has motivated the rapid development of generative models for protein design, enabling the generation of functional proteins and enzymes with unprecedented success. However, because these models mirror their training distribution, they tend to sample from its most probable modes, while low-probability regions, often encoding valuable properties, remain underexplored. To address this challenge, recent work has proposed strategies for steering generative models toward user-specified properties. In this review, we survey and categorize these strategies, distinguishing approaches that modify model parameters, such as reinforcement learning or supervised fine-tuning, from those that keep the model's parameters fixed, including conditional generation, retrieval-augmented strategies, Bayesian guidance, and tailored sampling methods. Together, these developments are beginning to enable the steering of generative models toward proteins with desired properties.
While potency of drugs has been considered the main criterion defining therapeutic utility in vivo, target residence time (RT, the time the ligand associates with the target in vivo) is also important, and studies have s...While potency of drugs has been considered the main criterion defining therapeutic utility in vivo, target residence time (RT, the time the ligand associates with the target in vivo) is also important, and studies have shown that in some cases, it is more important than potency. New data in this area have contributed toward the substantive detection and manipulation of RT for therapeutic increased value on the following two fronts: (1) new and better techniques to assess ligand-target kinetics and (2) new appreciation of ligand types (i.e. allosteric modulators) and mechanisms of ligand binding including the temporal implications of binding to cryptic pockets on GPCRs. These data will be discussed.
Single-molecule force spectroscopy (SMFS) techniques initially emerged as a new method to probe protein biophysics, often providing complementary insights to biochemical bulk experiments. Over time, however, advances in...Single-molecule force spectroscopy (SMFS) techniques initially emerged as a new method to probe protein biophysics, often providing complementary insights to biochemical bulk experiments. Over time, however, advances in instrumentation and the growing recognition that mechanical forces are integral to biological function have progressively redirected its use toward exploring protein systems operating in mechanically active environments. In this review, we highlight recent applications of SMFS that shed light on how force regulates protein function, spanning diverse biological systems like cotranslational folding, protein degradation, and cellular adhesion proteins. Beyond allowing us to manipulate individual molecules, SMFS uniquely recreates the mechanical conditions under which many proteins operate, revealing mechanistic details inaccessible to traditional protein characterization methods. Looking ahead, ongoing innovation, both in instrumentation and in the integration of SMFS with complementary techniques, is bringing the field closer to mimicking physiologically relevant conditions. These developments are opening new avenues for recognizing mechanical force as a central regulator of biological systems.
Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is...Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is a complex exercise, which has recently been the focus of approaches relying on deep learning. Here, we survey such artificial-intelligence-based approaches and compare them with classical methods for smFRET analysis. The use of deep learning has shown potential to enhance precision, accuracy, and speed in analysing massive smFRET datasets.
Curr Opin Struct Biol
· 2026 Jun · PMID 41932145
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G-quadruplexes (G4) are non-canonical nucleic acid structures formed by stacked Hoogsteen base-paired guanine (G) quartets, stabilized by monovalent cations. Due to the inherent propensity of G-rich sequences to fold int...G-quadruplexes (G4) are non-canonical nucleic acid structures formed by stacked Hoogsteen base-paired guanine (G) quartets, stabilized by monovalent cations. Due to the inherent propensity of G-rich sequences to fold into G4 structures, both DNA and RNA G4s are widely detected in cells. Previous studies have demonstrated that the unique structure and exceptional stability of G4s can act as physical barriers to translocating enzymes and modulate nucleic acid topology. In addition, G4-binding proteins (G4BPs), including G4 helicases, interact with G4 structures and modulate their stability, thereby contributing to the regulation of transcription and translation. This review highlights single-molecule approaches for probing G4-G4BP interactions and recent advances elucidating G4 functions in the R-loop and transcription.
Advances in deep learning have opened an era of abundant and accurately predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review highlights several recent research di...Advances in deep learning have opened an era of abundant and accurately predicted protein structures; however, similar progress in protein ensembles has remained elusive. This review highlights several recent research directions towards AI-based predictions of protein ensembles, including coarse-grained force fields, generative models, multiple sequence alignment perturbation methods, and modeling of ensemble descriptors. An emphasis is placed on realistic assessments of the technological maturity of current methods, the strengths and weaknesses of broad families of techniques, and promising machine learning frameworks at an early stage of development. We advocate for 'closing the loop' between model training, simulation, and inference to overcome challenges in training data availability and to enable the next generation of models.
Molecular dynamics simulations allow the investigation of the time-resolved mechanics of large, complex biological systems such as the Gram-negative bacterial cell envelope. Such simulations pose challenges due to their...Molecular dynamics simulations allow the investigation of the time-resolved mechanics of large, complex biological systems such as the Gram-negative bacterial cell envelope. Such simulations pose challenges due to their chemical diversity and crowded environments. We review artificial intelligence-based approaches that can support simulations of large biological systems, focussing on trajectory analysis and propagation whilst highlighting the difficulties of feature representation. Integrating trajectory analysis and propagation is powerful, but we also consider the serious data and resource requirements involved. In this context, we summarise the current state of cell envelope simulations and then ask a practical question: where can such approaches be applied to understand these crowded, chemically diverse environments?
Curr Opin Struct Biol
· 2026 Jun · PMID 41924828
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Structure-based drug design (SBDD) is an evolving paradigm that leverages protein structural information to improve small molecule therapeutic design. Building on more than 50 years of data curation from the Protein Data...Structure-based drug design (SBDD) is an evolving paradigm that leverages protein structural information to improve small molecule therapeutic design. Building on more than 50 years of data curation from the Protein Data Bank, the recent emergence of protein structure prediction models (PSPMs) promises to enable new computationally driven approaches for therapeutic discovery. However, it is critical to assess the limitations of these models using blind challenges and to expand existing datasets to better reflect real-world drug design tasks. Here, we discuss recent efforts to benchmark existing PSPMs and identify their limitations. We offer a hierarchical framework for parsing which tasks the current models perform well, and which tasks remain challenging or unexplored. Finally, we emphasize the need for systematic dataset generation to support the development of frontier models and highlight recent efforts to generate experimental and physics-based datasets for challenging tasks in drug discovery.
The conformational ensemble of a protein and its corresponding probabilities and dynamics are crucial determinants of its function, but are difficult to access with traditional experimental and computational technologies...The conformational ensemble of a protein and its corresponding probabilities and dynamics are crucial determinants of its function, but are difficult to access with traditional experimental and computational technologies. This review examines the landscape of machine learning for modeling protein conformational ensembles. We categorize computational methods into three classes: AlphaFold-based approaches that modify the input multiple sequence alignment, score-based generative models that use diffusion or flow-matching algorithms, and protein language models that link sequence evolution with sequential dynamics. We discuss the data available for training and benchmarking, including molecular dynamics simulations and experimental repositories. We highlight current limitations in the field, including the lack of standardization in benchmarking and the high variability of mechanisms and environmental conditions that challenge current methods. Drawing lessons from the success of AlphaFold, we identify key opportunities for further improvement, including accurate modeling of kinetics and thermodynamics, and linking model uncertainty with targeted collection of new data.
Intrinsically disordered proteins (IDPs) lack a stable tertiary structure but are fundamental to cellular function and disease, making them a compelling yet challenging class of drug targets. While their inherent flexibi...Intrinsically disordered proteins (IDPs) lack a stable tertiary structure but are fundamental to cellular function and disease, making them a compelling yet challenging class of drug targets. While their inherent flexibility poses a significant hurdle to conventional structure-based design methods, recent breakthroughs in computational protein design have enabled the creation of specific binders for these dynamic targets. In this review, we dissect the principles, advantages, and limitations of the design strategies, from those based on physical principles to those driven by deep learning. We then survey current applications of IDP binders and conclude by exploring the key challenges and future directions in this rapidly advancing field.
Chemical chaperones are conserved cellular modulators that developed early in evolution. These small molecules allow cells to maintain homeostasis under changing environmental conditions. Numerous organisms including bac...Chemical chaperones are conserved cellular modulators that developed early in evolution. These small molecules allow cells to maintain homeostasis under changing environmental conditions. Numerous organisms including bacteria, archaea, plants, and animals, synthesize or accumulate small molecules to protect macromolecules from stress-induced alterations. While previous studies clearly show the key roles of these chaperones in maintaining proteostasis, it has recently shown that their influence extends beyond proteins and that they can also affect the homeostasis of metabolites. These insights position chemical chaperones at the interface of proteostasis and metabolostasis, two quality-control mechanisms. Evolution might have selected these small molecules as natural guardians of intracellular stability to buffer protein folding and metabolite aggregation, thereby maintaining a functional chemical environment under stress.
Adenosine triphosphate-binding cassette (ABC) transporters form one of the most ancient and functionally diverse protein superfamilies, mediating the translocation of substrates that span an expansive range of sizes, che...Adenosine triphosphate-binding cassette (ABC) transporters form one of the most ancient and functionally diverse protein superfamilies, mediating the translocation of substrates that span an expansive range of sizes, chemistries, and physiological roles. This diversity poses a challenge to unifying their mechanisms within a single conceptual framework. In this review, we examine recent advances that demonstrate how substrate properties, energetic constraints, and evolutionary pressures shape the molecular design and operating principles of ABC transporters. We discuss emerging insights into substrate recognition and selectivity in exporters and importers, revisit the physiological relevance of so-called 'futile' adenosine triphosphate (ATP) hydrolysis, and explore the role of stoichiometry as a regulatory and evolutionary variable. Together, these perspectives highlight common design principles that link molecular architecture to the functional demands of transport across the ABC family, offering broader insights for how protein systems adapt structure and energetics to diverse cellular challenges.
Curr Opin Struct Biol
· 2026 Jun · PMID 41849864
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During eukaryotic translation initiation, initiation factor proteins and the ribosomal small subunit undergo binding and dissociation reactions and conformational rearrangements that properly assemble a ribosome at the s...During eukaryotic translation initiation, initiation factor proteins and the ribosomal small subunit undergo binding and dissociation reactions and conformational rearrangements that properly assemble a ribosome at the start codon of a messenger RNA. Building on extensive genetic and biochemical studies, single-molecule fluorescence experiments are revealing the time-dependent pathways of factor binding to, and dissociation from, the ribosomal small subunit and messenger RNA during initiation. Nonetheless, essential binding and/or dissociation events, conformational rearrangements, and the coupling between binding and conformational changes remain kinetically uncharacterized. Here, we summarize the status of single-molecule investigations of initiation and advocate for integrating single-molecule microscopy, structural biology, and molecular simulations to enable a time-dependent, molecular description of this fundamental step in gene expression.
The ultimate mission of de novo enzyme design methodology is to develop strategies that produce new-to-nature enzymes that match the efficiency and versatility of natural ones. Until recent years, design methods yielded...The ultimate mission of de novo enzyme design methodology is to develop strategies that produce new-to-nature enzymes that match the efficiency and versatility of natural ones. Until recent years, design methods yielded enzymes with low catalytic efficiencies even for simple reactions, underscoring the need for tighter control over backbone structure and active-site preorganization. Two approaches have recently emerged to address these problems: artificial intelligence-driven design of de novo folds and evolution-guided atomistic design of natural folds. These strategies have produced catalysts with efficiencies approaching natural enzymes, but achieving high catalytic rates and complex, multistep mechanisms remains challenging. We argue that progress toward high-performance enzymes for novel reactions requires more precise control over complex natural folds and close collaboration between designers and computational chemists.
Curr Opin Struct Biol
· 2026 Jun · PMID 41844067
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Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules,...Recent advances in artificial intelligence have introduced novel methods for high-accuracy prediction of protein tertiary structures, protein complex structures, and interactions between proteins and other biomolecules, such as small molecules and nucleic acids. Such advancements are accelerating biomedical research and the development of new protein design and bioengineering methods among many other important biotechnology applications. In this review, we outline the recent advances in protein-centric biomolecular structure and interaction prediction, highlight some major challenges in the field, and discuss potential directions to address them.
Curr Opin Struct Biol
· 2026 Jun · PMID 41830661
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Amyloid fibrils are involved in devastating conditions such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and systemic amyloidosis. They exhibit polymorphism, meaning that a single protein sequence c...Amyloid fibrils are involved in devastating conditions such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and systemic amyloidosis. They exhibit polymorphism, meaning that a single protein sequence can adopt different amyloid folds that vary with time and self-assembly conditions. Polymorphism confounds structure-based drug design and raises fundamental questions regarding why particular fibril structures form and how they cause disease. Here, we highlight the latest advances in our understanding of amyloid polymorphism, including its structural basis, thermodynamic origins, kinetic influences, and significance for disease. The next frontier will be to predict fibril structures, disentangle the dynamic mechanisms that guide the progression of fibril polymorphs, and illuminate how cofactors and the physiological milieu select for particular polymorphs in disease.
Membrane proteins (MPs) play essential roles in a wide range of cellular processes and represent major therapeutic targets. Nevertheless, their structural and functional characterization remains challenging due to inhere...Membrane proteins (MPs) play essential roles in a wide range of cellular processes and represent major therapeutic targets. Nevertheless, their structural and functional characterization remains challenging due to inherent difficulties in production, extraction, and stabilization outside their native lipid environment. The rise of cryogenic electron microscopy (cryo-EM) has markedly accelerated the structure determination of MPs through single-particle analysis (SPA). A systematic examination of high-resolution cryo-EM SPA structures deposited in the Protein Data Bank (PDB) over the past two years provides a comprehensive overview of the most frequently used amphipathic environments. We discuss the strengths and limitations of each approach and underscore the ongoing need to develop near-native environment strategies to improve the interpretation of MP structures under membrane-like conditions.
Nucleocytoplasmic transport relies on targeting signals within cargo polypeptides, typically as short linear motifs but sometimes as folded domains. These signals are recognized by the Karyopherin-β (Kap) family of impor...Nucleocytoplasmic transport relies on targeting signals within cargo polypeptides, typically as short linear motifs but sometimes as folded domains. These signals are recognized by the Karyopherin-β (Kap) family of importins, exportins, and biportins. Despite the number of Kaps, only a few linear signal classes are well-defined: the classical nuclear localization signal (cNLS) recognized by importin-α (IMPα), which in turn binds IMPβ to form the IMPα/β heterodimer, the IMPβ-binding domain, the Pro-Tyr NLS of transportin-1 (TNPO1/Kapβ2), the IK-NLS of Kap121/importin-5, and the RS/E- and RSY-NLSs of TNPO3, along with the classical nuclear export signal (NES) of exportin-1 (XPO1/CRM1) and the phosphorylated NES of yeast Msn5. This review summarizes recent structural and biochemical advances that define these signals and their recognition rules and highlights the remaining gaps in our understanding of linear signals across the Kap family.