Curr Opin Struct Biol
· 2025 Aug · PMID 40614690
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Biomolecular condensates shape a wide spectrum of physiological and pathological processes, yet the molecular mechanisms underlying their formation and activity are still to be fully understood. Molecular simulations can...Biomolecular condensates shape a wide spectrum of physiological and pathological processes, yet the molecular mechanisms underlying their formation and activity are still to be fully understood. Molecular simulations can provide valuable insights into the structure and dynamics of such condensates, and coarse-grained simulations have been widely used to characterize phenomena related to their phase equilibrium. All-atom simulations provide a complementary picture-while too expensive to readily study equilibrium between dense and dilute phases, they offer molecular detail on the dense phase that is missing from coarse-grained models, as well as accurate dynamical information. We provide an overview of this nascent application of atomistic simulations to condensates and the insights they have yielded on their structure and dynamics.
Recent breakthroughs in single-particle cryogenic electron microscopy (cryo-EM) and protein structure prediction have transformed our ability to resolve molecular structures. Since we have now experimentally determined,...Recent breakthroughs in single-particle cryogenic electron microscopy (cryo-EM) and protein structure prediction have transformed our ability to resolve molecular structures. Since we have now experimentally determined, or can confidently predict, the structures of a significant portion of the proteome, and since workflows for imaging in cells are established, the stage is set for applying cryo-EM to understand the molecular basis of complex cellular functions. This review explores a spectrum of data collection strategies-from 2D approaches to tomography-used for in situ cryo-EM. We discuss their relative merits in addressing key biological questions and the need to tailor them towards experimental goals. Improvements in theoretical and practical understanding of the challenges for in situ cryo-EM are necessary for optimizing data collection strategies and pushing the boundaries of structural cell biology.
Curr Opin Struct Biol
· 2025 Aug · PMID 40580813
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Intrinsically disordered proteins/regions (IDPs/IDRs) frequently engage in dynamic charge:charge interactions, commonly referred to as 'fuzzy' interactions. These fuzzy interactions play critical roles in enzymatic regul...Intrinsically disordered proteins/regions (IDPs/IDRs) frequently engage in dynamic charge:charge interactions, commonly referred to as 'fuzzy' interactions. These fuzzy interactions play critical roles in enzymatic regulation and substrate recruitment, especially for protein kinases and protein phosphatases. Here, we review recent advances that demonstrate how inter- and intramolecular fuzzy interactions among kinases and phosphatases and their cognate regulators and substrates allow for enzyme assembly, activation and substrate recruitment. We also highlight a unique mechanism of protein inhibition, where a protein phosphatase is inhibited by dynamic fuzzy interactions with its active site metals.
Curr Opin Struct Biol
· 2025 Oct · PMID 40580801
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G protein-coupled receptors (GPCRs) function within cellular membranes, complex and dynamic environments. Rather than serving as a passive background, lipid membranes actively influence GPCR drug responses and signaling....G protein-coupled receptors (GPCRs) function within cellular membranes, complex and dynamic environments. Rather than serving as a passive background, lipid membranes actively influence GPCR drug responses and signaling. Studies utilizing nuclear magnetic resonance (NMR) spectroscopy have revealed key insights into receptor-lipid interactions, enabled by the compatibility of NMR experiments with many different membrane systems and physiological temperature, conditions more closely reflecting the native cellular environment. NMR data have revealed new mechanistic insights that explain how specific lipids regulate GPCR activation, how bulk membrane properties influence receptor dynamics, and how different membrane mimetics affect GPCR behavior. These findings establish a framework for bridging in vitro structural studies with in vivo biological and pharmacological data.
Curr Opin Struct Biol
· 2025 Oct · PMID 40580800
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Determining the rules of transcriptional regulation, associated with a complex transcription factor grammar, is fundamental to understand the control of most biological processes, disease mechanisms, and evolution. High-...Determining the rules of transcriptional regulation, associated with a complex transcription factor grammar, is fundamental to understand the control of most biological processes, disease mechanisms, and evolution. High-throughput reporter assays, such as MPRAs and STARR-seq, have enabled systematic functional annotations of genomes and have provided large substrates for training machine learning models to determine these rules, predict the activity of native and synthetic elements, and design elements for different applications. This review provides an overview of high-throughput reporter assays and their applications for the study of enhancers, promoters, silencers and insulators. We discuss how these assays help identify causal disease-associated non-coding variants and design synthetic elements with desired features for functional studies or therapeutic purposes.
Unlike conventional biochemical and cell biological methods, in-cell NMR enables direct observation of intracellular protein structures and dynamics at atomic resolution in their native cellular environment. However, a k...Unlike conventional biochemical and cell biological methods, in-cell NMR enables direct observation of intracellular protein structures and dynamics at atomic resolution in their native cellular environment. However, a key challenge is maintaining cell viability during prolonged measurements, as nutrient depletion leads to rapid cell death and protein degradation. To address this, bioreactor systems have been developed to perfuse culture media, enabling long-term NMR experiments. Bioreactor-based in-cell NMR has enabled real-time observation of function-related structural information of proteins, which are involved in various intracellular events, such as signal transduction, oxidative stress responses, and glycolytic regulation. Additionally, in-cell NMR has been applied to drug discovery, allowing assessment of drug binding kinetics, intracellular permeability, and target engagement within living cells.
This review highlights cutting-edge techniques for modeling peptide-protein interactions and advancing computer-aided peptide-drug design. We examine significant progress in generating peptide poses through docking and a...This review highlights cutting-edge techniques for modeling peptide-protein interactions and advancing computer-aided peptide-drug design. We examine significant progress in generating peptide poses through docking and artificial intelligence (AI), assessing peptide flexibility via enhanced molecular dynamics simulations, and analyzing binding interactions through free energy calculations. Additionally, we discuss how these insights can inform the rational design of therapeutic peptides by utilizing free energy metrics and strategic modifications to enhance their binding affinity and therapeutic potential. Looking forward, further integrating AI will be crucial for optimizing peptide design and enhancing drug development efforts.
In the few years since AlphaFold 2 revolutionized protein structure prediction, AI technologies have demonstrated strong potential for practical application in therapeutic antibody development, a key area in the pharmace...In the few years since AlphaFold 2 revolutionized protein structure prediction, AI technologies have demonstrated strong potential for practical application in therapeutic antibody development, a key area in the pharmaceutical industry. This mini-review provides a concise overview of AI-driven approaches designed to precisely optimize antibody properties critical for successful therapeutics. In particular, protein structure prediction-based antibody design AI is advancing rapidly, facilitating the effective targeting of protein hotspots, as demonstrated in a few reported cases. These advancements are expected to streamline experimental workflows, reduce reliance on trial-and-error screening, and enable the efficient discovery of novel molecules that would be challenging to identify through traditional methods. Additionally, this review explores emerging AI methodologies aimed at optimizing Fc function, immunogenicity, and developability, offering insights into future directions in the field.
Computational prediction of DNA-binding residues (DBRs) and the RNA-binding residues (RBRs) in protein sequences is an active area of research, with about 90 predictors and 20 that were published over the last two years....Computational prediction of DNA-binding residues (DBRs) and the RNA-binding residues (RBRs) in protein sequences is an active area of research, with about 90 predictors and 20 that were published over the last two years. The new predictors rely on sophisticated deep neural networks and protein language models, produce accurate predictions, and are conveniently available as code and/or web servers. However, we identified shortage of tools that predict these interactions in intrinsically disordered regions and tools capable of predicting residues that interact with specific RNA and DNA types. Moreover, cross-predictions between RBRs and DBRs should be quantified and minimized to ensure that future tools accurately differentiate between these two distinct types of nucleic acids.
Curr Opin Struct Biol
· 2025 Aug · PMID 40544581
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All-atom molecular dynamics (MD) simulations of intact virus capsids provide unparalleled insights into the functional motions of these complex macromolecular assemblies. Despite the computational challenges of simulatin...All-atom molecular dynamics (MD) simulations of intact virus capsids provide unparalleled insights into the functional motions of these complex macromolecular assemblies. Despite the computational challenges of simulating multimillion-atom systems, these simulations uniquely reveal the structural basis for emergent properties, including collective motions, allostery, selective permeability, and mechanical responses that are inaccessible through experimental methods. Capsid simulations also drive technological advancements in MD methodologies, analysis tools, and multiscale modeling, fostering broader innovations in structural biology and biophysics. Given next-generation computational resources, MD simulations will continue to illuminate virus biology, support antiviral drug discovery, and enhance preparedness for emerging viral diseases. Here, atomistic simulations of complete capsid assemblies are reviewed, and their role in elucidating fundamental principles of virus function and therapeutic targeting is discussed. Altogether, MD of intact capsids is a computational challenge worth the payoff.
Proteins are central to biological complexity as their ligand binding processes, shaped by thermodynamics, have driven evolutionary adaptation throughout Earth's history. Despite extensive research into protein-ligand in...Proteins are central to biological complexity as their ligand binding processes, shaped by thermodynamics, have driven evolutionary adaptation throughout Earth's history. Despite extensive research into protein-ligand interactions, the evolution of their binding thermodynamics, particularly regarding enthalpy-entropy trade-offs, remains underexplored. This review compares experimental and computational findings to illustrate how the balance of thermodynamics influences protein structure and function over time. We hypothesize that ancient proteins likely exhibit entropically favored, flexible binding modes, while modern proteins increasingly rely on enthalpically driven specificity. Evolutionary trajectories, including those from ancestral sequence reconstruction studies and modern viral evolution, reveal that thermodynamic trade-offs allow proteins to adapt to diverse functions. Our evolutionary perspective on the existing research demonstrates that binding thermodynamics not only govern ligand affinity and specificity but also fundamentally shape protein evolution and inform potential protein engineering strategies.
Curr Opin Struct Biol
· 2025 Oct · PMID 40527155
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Icosahedral double stranded DNA (dsDNA) virus assembly first necessitates the formation of a precursor capsid (procapsid) into which the DNA is packaged. Direct interactions between the major capsid protein (MCP) and a s...Icosahedral double stranded DNA (dsDNA) virus assembly first necessitates the formation of a precursor capsid (procapsid) into which the DNA is packaged. Direct interactions between the major capsid protein (MCP) and a scaffolding protein promote proper procapsid assembly. The scaffolding protein can be an independent protein or a scaffolding-like domain covalently attached to the MCP that is comparable in structure and function. A full understanding of scaffolding protein structures has been limited by their intrinsically disordered nature. Advances in cryogenic electron microscopy (cryoEM) data processing techniques have provided new methodologies to help solve the structures of scaffolding proteins within procapsids. These structural insights further our understanding of how scaffolding proteins interact with the other assembly proteins to correctly construct the procapsid.
Proteins within a family sharing sequence and structure similarity due to a common evolutionary origin often also share functional similarities. Clustering of proteins therefore offers valuable insights, enabling the tra...Proteins within a family sharing sequence and structure similarity due to a common evolutionary origin often also share functional similarities. Clustering of proteins therefore offers valuable insights, enabling the transfer of features and annotations from well-studied proteins to less-investigated ones. On a local scale, clustering helps identify patterns within specific protein families. On a larger scale, it provides insights into the entire protein universe, showcasing relationships that may not be immediately apparent. Traditionally, this was done at the sequence level or with the use of experimentally resolved protein structures, but the advent of deep learning in protein bioinformatics has brought new options to the table, increasing the breadth, depth, and diversity of similarity metrics and clustering approaches.
Machine learning has revolutionized protein structure prediction and design. This review discusses current methods for protein folding and inverse folding challenges. Models like AlphaFold2 (AF2), RoseTTAFold, and ESMFol...Machine learning has revolutionized protein structure prediction and design. This review discusses current methods for protein folding and inverse folding challenges. Models like AlphaFold2 (AF2), RoseTTAFold, and ESMFold excel at leveraging evolutionary information to accurately predict protein structures while still struggling to capture the physics of protein folding. Their repurposing for protein design has led to innovations such as RFdiffusion, AF2-design, and relaxed sequence optimization. ProteinMPNN and ESM-IF design sequences based on structure, so they are frequently referred to as "inverse folding' methods. By examining the potential and limitations of current protein design methods and metrics, we provide perspectives on developing models that fully characterize energy landscapes associated with amino acid sequences. Such advances would enable more accurate structure prediction and the design of proteins with specified conformational dynamics, potentially transforming our ability to engineer novel proteins for biotechnological applications.
Structural data on protein-DNA and protein-RNA interactions are indispensable in molecular biology research. In this article, we review available databases and other web-based resources devoted to 3D structures of protei...Structural data on protein-DNA and protein-RNA interactions are indispensable in molecular biology research. In this article, we review available databases and other web-based resources devoted to 3D structures of protein-nucleic acid complexes. First, we describe the core databases that collect and disseminate experimental data. We then review derivative databases focused specifically on structural data on protein-nucleic acid interactions. Finally, we provide an overview of several useful web servers for structure prediction, analysis and comparison. Tools for investigating protein-nucleic acid complexes are relatively scarce. This is primarily because the methods that integrate structural information from both proteins and nucleic acids are in short supply. However, the emerging AI-driven techniques for structure prediction are expected to boost the development of such methods.
Curr Opin Struct Biol
· 2025 Aug · PMID 40494166
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In the current age of protein structure prediction and determination, resolving the time dependence of structural transitions represents an exciting frontier. Time-resolved biophysical techniques possess the capability t...In the current age of protein structure prediction and determination, resolving the time dependence of structural transitions represents an exciting frontier. Time-resolved biophysical techniques possess the capability to directly observe dynamic structural changes of biomolecules in real time. Here, we review applications of site-directed spin labeling (SDSL) coupled with electron paramagnetic resonance (EPR) spectroscopy that cover a broad range of protein dynamics, from backbone fluctuations on the ps-ns timescale to protein complex assembly formation on the ms-s timescale. Recent developments in SDSL EPR methods allow for direct investigation of protein conformational exchange kinetics on the important μs-ms timescale, providing the time axis for structural transitions needed to define molecular mechanisms of complex biological phenomena.
Curr Opin Struct Biol
· 2025 Aug · PMID 40494165
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Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful technique for investigating the structure and dynamics of biomolecules. This review focuses on recent advances in quantitative methods to analyze f...Single-molecule Förster Resonance Energy Transfer (smFRET) is a powerful technique for investigating the structure and dynamics of biomolecules. This review focuses on recent advances in quantitative methods to analyze freely diffusing molecules in smFRET. The methods include traditional approaches of analyzing FRET efficiency and advanced photon-by-photon techniques based on maximum likelihood estimation without binning photon sequences. More recently, methods explicitly accounting for molecular diffusion have been developed, addressing biases arising from variations in brightness and diffusivity among molecular states and species. Applications of these tools include studies of protein folding, DNA dynamics, and oligomerization processes of neurodegenerative proteins. These advancements expand the ability of free diffusion-based smFRET to elucidate the dynamic behavior of biomolecules on the timescales relevant to their biological processes.