Woods VA, Sharma S, Lemberikman AM
… +1 more, Keedy DA
Curr Opin Struct Biol
· 2025 Oct · PMID 40752367
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Protein tyrosine phosphatases (PTPs) are a family of enzymes that play critical roles in intracellular signaling and regulation. PTPs are conformationally dynamic, exhibiting motions of catalytic loops and additional reg...Protein tyrosine phosphatases (PTPs) are a family of enzymes that play critical roles in intracellular signaling and regulation. PTPs are conformationally dynamic, exhibiting motions of catalytic loops and additional regions of the structurally conserved catalytic domain. However, many questions remain about how dynamics contribute to catalysis and allostery in PTPs, how these behaviors vary among evolutionarily divergent PTP family members, and how mutations and ligands reshape dynamics to modulate PTP function. Recently, our understanding in these areas has expanded significantly, thanks to novel applications of existing methods and emergence of new approaches in structural biology and biophysics. Here we review exciting advances in this realm from the last few years. We organize our commentary both by experimental and computational methodologies, including solution techniques, avant-garde crystallography, molecular dynamics simulations, and bioinformatics, and also by scientific focus, including regulatory mechanisms, mutations and protein engineering, and small-molecule ligands such as allosteric modulators.
Since the publication of the first papers in the early 1990s, molecular simulation as a reliable biophysical tool in the area of membrane biophysics has come a long way. Advances in simulation algorithms, coupled with ex...Since the publication of the first papers in the early 1990s, molecular simulation as a reliable biophysical tool in the area of membrane biophysics has come a long way. Advances in simulation algorithms, coupled with exascale computing have pushed the size and time scales of biomolecular membrane simulations to scales where connections to experiments are made with higher fidelity. When integrated with experimental data in a theoretically well-grounded manner, current biomolecular simulations are providing indispensable insights that cannot be obtained through experiments alone. Here, I summarize some recent developments where simulations have allowed a deeper understanding in membrane spatiotemporal organization. I also discuss the need for transformative method developments to meet recent breakthroughs in experimental measurements at molecular scales.
Curr Opin Struct Biol
· 2025 Oct · PMID 40737735
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Major progress has been made in recent years in terms of strategies for regulating enzyme activities. Novel high-throughput enzyme kinetic assays and efficient computational methodologies enabled a deeper understanding o...Major progress has been made in recent years in terms of strategies for regulating enzyme activities. Novel high-throughput enzyme kinetic assays and efficient computational methodologies enabled a deeper understanding of molecular mechanisms that dictate the activity of enzymes, which provide guidance to rational modulation of enzyme catalysis. Continued development of efficient screening, directed evolution technologies, and machine learning-driven protein engineering tools make it possible to tune enzyme activities without having to understand the detailed mechanism of catalysis regulation. By combining these two limiting approaches, the efficiency of enzyme regulation can be substantially improved as a mechanistic understanding can help reduce the size of design space before the 'brute-force' engineering approach takes over. We briefly discuss relevant advances in both experiment and computation and comment on future developments that can further enhance mechanistic understanding and engineering capability for broad applications.
It has been a longstanding dream of the structural biology and molecular biophysics communities to determine protein functions directly from the amino acid sequences. Most methods available today, however, are homology-...It has been a longstanding dream of the structural biology and molecular biophysics communities to determine protein functions directly from the amino acid sequences. Most methods available today, however, are homology- or library-based and often undermine determining divergent functions from comparable sequences or vice versa. The sequence-to-function relationship is intrinsically dependent on the biophysical space of protein dynamics, which can be potentially exploited to annotate function. But, despite three decades of active research, the space of molecular dynamics data remains grossly underpopulated. By employing surveys of the existing literature, we highlight this gray area in the context of machine learning methods. Thereafter, we share examples that point toward learning biophysical representations-or signatures-and combining them with integrative models as means to robustly associate sequence with function. The aim is to avoid having to compute protein dynamics for an impossible thousand years to achieve data completeness and generalization.
Förster resonance energy transfer (FRET) is a powerful tool for studying protein conformations, interactions, and dynamics at the single-molecule level. Multicolor FRET extends conventional two-color FRET by incorporatin...Förster resonance energy transfer (FRET) is a powerful tool for studying protein conformations, interactions, and dynamics at the single-molecule level. Multicolor FRET extends conventional two-color FRET by incorporating three or more fluorophores and thereby enabling a more comprehensive view of complex biomolecular processes. This technique allows for the simultaneous tracking of multiple structural changes, detecting intermediate states, and resolving heterogeneous population distributions. In this review, we discuss the recent advancements in fluorophore labeling strategies and data analysis methods that have significantly improved the precision and applicability of multicolor FRET in protein studies. We then end this review by showcasing recent applications for investigating protein folding and processes involved in gene regulation.
Biomolecular condensates play crucial roles in cellular organisation, regulating diverse biological functions as well as contributing to disease pathologies when phase separation is dysregulated. Computer simulations and...Biomolecular condensates play crucial roles in cellular organisation, regulating diverse biological functions as well as contributing to disease pathologies when phase separation is dysregulated. Computer simulations and theoretical approaches have emerged as powerful tools to probe the molecular mechanisms governing phase transitions in these systems. This review highlights recent advancements in computational methods, focusing on coarse-grained and all-atom molecular dynamics simulations, to elucidate the driving forces underlying protein and RNA condensate formation and how their stability and material properties can be regulated and tuned. Additionally, we address new strategies for designing synthetic condensates with tunable properties and leveraging predictive models to guide experimental studies. The integration of molecular simulations, with data-driven approaches and theory, has expanded our understanding of biomolecular condensates, offering novel insights into both fundamental biology and physics, as well as potential practical applications.
Cryo-electron tomography provides an unprecedented view of cellular architecture, yet extracting meaningful biological insights remains challenging. Segmentation is a crucial step in this process through its ability to i...Cryo-electron tomography provides an unprecedented view of cellular architecture, yet extracting meaningful biological insights remains challenging. Segmentation is a crucial step in this process through its ability to identify structural relationships between subcellular components visible in cryo-electron tomography data. While segmentation pipelines were historically low throughput, recent advancements in deep learning have significantly improved their automation, accuracy, and scalability. This review explores how these innovations redefine best practices for segmentation and accelerate biological discovery. This article highlights the critical role of segmentation in unlocking the full potential of cryo-electron tomography-not only for resolving macromolecular structures but also for quantifying their impact on subcellular organization and function.
The covalent attachment of oligosaccharides to asparagine side chains on protein surfaces (N-linked glycosylation) is a ubiquitous modification that is critical to protein stability and function. Experimental 3D structur...The covalent attachment of oligosaccharides to asparagine side chains on protein surfaces (N-linked glycosylation) is a ubiquitous modification that is critical to protein stability and function. Experimental 3D structures of glycoproteins in which the N-linked glycans are well resolved are rare due to both the presumed flexibility of the N-linked glycan and to glycan microheterogeneity. To surmount these limitations, computational modeling is often applied to glycoproteins, particularly to generate an ensemble of 3D shapes for the N-linked glycans. While the number of glycoprotein modelling tools continues to expand, the available experimental data against which the predictions can be validated remains extremely limited. Here, we present our current understanding of the dynamic properties of N-linked glycans, with a particular focus on features that impact their presentation (orientation) relative to the protein surface. Additionally, we review the limits of experimental and theoretical studies of glycoproteins, and ask the question, "Are N-linked glycans intrinsically disordered?".
Cryo-electron tomography (cryo-ET) and subtomogram averaging have emerged as powerful techniques for investigating cellular structures and their spatial organization. However, the exact localization of proteins in the cr...Cryo-electron tomography (cryo-ET) and subtomogram averaging have emerged as powerful techniques for investigating cellular structures and their spatial organization. However, the exact localization of proteins in the crowded and noisy environment of cellular tomograms is challenging. This review provides a comprehensive overview of existing deep learning-based particle-picking procedures, which were proposed to overcome these challenges. We evaluate both annotation-based and annotation-free methods, highlighting their respective strengths, weaknesses, and ideal use cases. Furthermore, we assess these methodologies based on various criteria, such as the effort required to generate the necessary input data, inference runtime, and filament support. Additionally, we consider practical factors such as the availability of documentation and tutorials to guide researchers in selecting the most appropriate approach for their needs.
Curr Opin Struct Biol
· 2025 Aug · PMID 40633127
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tMachine learning has advanced protein structure prediction to deliver accurate but mostly static models. Capturing protein dynamics as conformational ensembles remains a significant challenge. Recent developments, espec...tMachine learning has advanced protein structure prediction to deliver accurate but mostly static models. Capturing protein dynamics as conformational ensembles remains a significant challenge. Recent developments, especially generative models, are enabling the prediction of structural ensembles beyond traditional simulations. This review examines emerging machine learning approaches for modeling protein dynamics, in terms of generating PDB-like ensembles, accelerating molecular simulations, modeling non-globular protein ensembles, and integrating experimental data. General-purpose and system-specific models are discussed, particularly in terms of conformational coverage, transferability, and responsiveness to environmental conditions. Hybrid models, which combine experimental and simulation data, represent a promising direction. Nonetheless, key challenges remain, including generating states with correct probabilities, modeling unseen conformations, and integrating experimental constraints rigorously.
Curr Opin Struct Biol
· 2025 Aug · PMID 40633126
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Cryo-electron tomography (cryo-ET) enables 3D visualization of complex biological environments without the need for purification, thereby preserving the native biological context of the specimen. For determining macromol...Cryo-electron tomography (cryo-ET) enables 3D visualization of complex biological environments without the need for purification, thereby preserving the native biological context of the specimen. For determining macromolecular structures, repeating molecules can be localized in tomograms and subjected to subtomogram averaging, the 3D analog to single particle analysis. In addition to molecular structure, tomograms have a wealth of other information that can be accessed through image processing, including the analysis of membrane surfaces, cytoskeletal filaments, and the relationships between molecules of interest. Here, we provide an overview of recent developments in cryo-ET image processing with the goal of clarifying key considerations to help new users obtain novel biological findings.
Since the Hadean-Eoarchaean era of Earth's history, peptides/proteins and RNA have undergone a complex evolutionary trajectory. Originating from simple monomeric units, these molecules evolved abiotically under various b...Since the Hadean-Eoarchaean era of Earth's history, peptides/proteins and RNA have undergone a complex evolutionary trajectory. Originating from simple monomeric units, these molecules evolved abiotically under various biochemical and biophysical constraints into functional biomolecules that contributed to the emergence of the first living cells. Within these cells, their interactions could then evolve through Darwinian selection. In this review, we examine current understanding of how protein-RNA interactions emerged under prebiotic conditions and developed into today's iconic biomolecular machines such as the ribosome. Particular emphasis is placed on the types of physicochemical interactions accessible to early protein-RNA complexes. Special attention is given to how the limited prebiotic amino acid repertoire influenced these interactions and their roles in driving spatial organization and compartmentalization in protocellular environments.
Many cellular processes involve assemblies of diverse biological molecules acting in concert. Single-molecule force spectroscopy offers a powerful approach for deciphering how the components of such complexes interact dy...Many cellular processes involve assemblies of diverse biological molecules acting in concert. Single-molecule force spectroscopy offers a powerful approach for deciphering how the components of such complexes interact dynamically. By applying mechanical forces to individual molecules within the complex, multiple features can be explored, including the conformation of these molecules, the strength of their interactions with other members of the complex, and association/dissociation rates. We discuss recent advances from force spectroscopy studies of complexes involving protein-nucleic acid, protein-protein, and protein-lipid interactions, which provide insight into processes relevant for both biological function and disease.
Cryo-electron tomography (cryo-ET) is revolutionizing in situ structural analysis of single-cell specimens, yet its application to tissues has been hindered, primarily due to challenges posed by tissue thickness. Advance...Cryo-electron tomography (cryo-ET) is revolutionizing in situ structural analysis of single-cell specimens, yet its application to tissues has been hindered, primarily due to challenges posed by tissue thickness. Advances in sample vitrification, cryo-focused ion beam (cryo-FIB) milling, and lift-out techniques have substantially improved tissue preparation, enabling thin, electron microscopy-compatible samples. Furthermore, the integration of automation, complementary imaging modalities, and AI has streamlined imaging workflows and data analysis. This review highlights these technological developments, their implications for tissue analysis, and the future potential of cryo-ET in advancing structural biology and biomedical research.
The endosomal sorting complex required for transport-III (ESCRT-III) system is an ancient protein family involved in membrane remodelling. Recent phylogenetic and structural analyses reveal its conservation across the tr...The endosomal sorting complex required for transport-III (ESCRT-III) system is an ancient protein family involved in membrane remodelling. Recent phylogenetic and structural analyses reveal its conservation across the tree of life, including bacteria and archaea, suggesting an evolutionary origin predating the last universal common ancestor. These findings underscore the importance of the ESCRT-III superfamily to our origins, particularly with the recognition of their contribution to eukaryogenesis through the Asgard archaea lineage. Bacterial systems, often with a single ESCRT-III-like protein, offer a simple model for understanding how ESCRT-III can function as both membrane sensor and sculptor. This review explores the structural dynamics, evolutionary trajectories, and biological significance of ESCRT-III in bacteria and archaea. We describe how ESCRT-III polymerises and assembles conserved filaments with the coating of flat or positively curved membranes prevalent, at least in vitro. Finally, we highlight common mechanistic principles and unique adaptations that enable ESCRT-III systems to support diverse cellular processes across evolutionary domains.
The increasing importance of RNA as a prime player in biology can hardly be overstated. Problems in RNA, such as folding and RNA-RNA interactions that drive phase separation, require cations. Because experiments alone ca...The increasing importance of RNA as a prime player in biology can hardly be overstated. Problems in RNA, such as folding and RNA-RNA interactions that drive phase separation, require cations. Because experiments alone cannot reveal the dynamics of cation-RNA interactions, well calibrated theory and computations are needed to predict how ions control the behavior of RNA. The perspective describes the development and use of coarse-grained models at different resolutions. We focus on single- and three-interaction site models, in which electrostatic interactions are treated using a combination of explicit and implicit representations. Applications to the folding of ribozymes and riboswitches are discussed, with emphasis on the role of monovalent and divalent cations. We also discuss phase separation in low-complexity sequences. Challenges in the simulation of complex problems such as ribosome assembly and RNA chaperones, requiring developments of models for RNA-protein interactions, are pointed out.
Curr Opin Struct Biol
· 2025 Aug · PMID 40616977
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Crosslinking mass spectrometry has emerged as a powerful tool in structural biology. This technology utilizes chemical crosslinkers to capture spatial proximities between protein residues to probe the organization, stoic...Crosslinking mass spectrometry has emerged as a powerful tool in structural biology. This technology utilizes chemical crosslinkers to capture spatial proximities between protein residues to probe the organization, stoichiometry, and flexibility of protein assemblies under near-native conditions. Photo-crosslinking reagents have become increasingly used in crosslinking MS, with chemical properties that offer significant advantages when studying dynamic protein structures. This review explores the fundamentals, applications, and future potential of photo-crosslinkers in crosslinking mass spectrometry.
Membrane proteins play pivotal roles in cellular signaling, transport, and immune responses. Dysregulation of these proteins frequently underlies diverse disease states, making them appealing targets for drug development...Membrane proteins play pivotal roles in cellular signaling, transport, and immune responses. Dysregulation of these proteins frequently underlies diverse disease states, making them appealing targets for drug development, including therapeutic antibodies. Traditionally, the extraction and stabilization of membrane proteins involve detergents, which may compromise the protein's native conformation, thus impeding antibody discovery. The shift toward detergent-free formulations using membrane protein nanodiscs formed by membrane scaffold proteins (MSPs), copolymers, saposins, or peptides has opened new avenues in membrane protein research and antibody discovery. They allow for the stabilization of membrane proteins in a more native-like environment, preserving structural integrity and function. This review discusses various membrane protein nanodiscs, and their applications in antibody discovery, alongside current advancements and challenges.