A comparative overview of dual experimental and computational advancements is provided and how the integration of generative diffusion models could facilitate the real-time simulation of conditional, multi-state structural ensembles across the redox proteome is highlighted.
Abstract
Thiol-based redox switches utilize the unique nucleophilicity of cysteine and selenocysteine to dynamically link real-time cellular redox fluctuations to metabolic regulation and signaling pathways. Capturing the precise atomic-level thermodynamic and kinetic mechanisms driving these oxidative modifications has long been limited by the chemical instability of transient intermediates and the immense computational costs of classical molecular dynamics simulations. However, recent advancements in chemoselective small-molecule probes now allow for the high-purity trapping and enrichment of specific sulfenic and sulfinic acid states. In parallel, a paradigm shift toward machine learning, graph neural networks, and protein language models has bypassed traditional computational bottlenecks, enabling high-throughput, proteome-wide predictions of redox switches in seconds. Furthermore, emerging data reveal that these redox modifications do not merely alter well-structured proteins, but actively dictate conditional folding transitions and structural transformations within intrinsically disordered proteins and biomolecular condensates. Here, we provide a comparative overview of these dual experimental and computational advancements and highlight how the integration of generative diffusion models could facilitate the real-time simulation of conditional, multi-state structural ensembles across the redox proteome.
Proteins are intrinsically dynamic molecules that continuously explore conformational ensembles to execute biological functions. Conventional structural biology methods rely on in vitro reconstitution of purified components and therefore capture predominantly static snapshots, often overlooking the regulatory roles of the cellular microenvironment, such as molecular crowding, weak interaction networks, and post-translational modifications. This limitation has driven an urgent need to transition from in vitro reconstruction to in vivo characterization within living cells. Nuclear magnetic resonance (NMR) spectroscopy provides atomic-resolution insights into structure and motions spanning multiple timescales, yet its application is constrained by molecular weight limits, isotopic labeling requirements, and inherently low throughput. Cross-linking mass spectrometry (XL-MS) complements NMR by delivering sparse but long-range spatial restraints without an upper molecular weight limit. The integration of NMR and XL-MS establishes a powerful synergistic framework that bridges atomic-resolution local structures and large-scale interaction topologies, thereby enabling comprehensive characterization of protein dynamic conformations and interaction networks in native environments. Here, we review how this integrative strategy advances the understanding of intrinsically disordered proteins, multi-domain proteins, and dynamic protein-protein interaction networks in native cellular environments. We further discuss emerging technological frontiers, including hyperpolarized NMR, photo-cross-linking, organelle-resolved analysis, and artificial intelligence-guided integrative modeling, which together promise to transform our ability to resolve the true functional states of proteins inside cells.
Zhou Gong, Qun Zhao, Min Sun et al.· Magnetic Resonance Letters· 0 citations
Covalent drug discovery and chemical proteomics have historically relied on a nucleophilic logic, where electrophilic "warheads" react with nucleophilic amino acid side chains. While powerful, this paradigm probes only a single dimension of the protein's chemical surface. In contrast, biology leverages a second axis: redox potential. This is exemplified by the regulated redox proteome, where specific residues undergo reversible oxidation and reduction as functional post-translational modifications. Inspired by this natural machinery, researchers are developing redox-activated probes to label proteins at oxidizable residues and deploying similar chemistry to selectively perturb protein function. This review highlights recent advances in redox-activated covalent chemistry and explores its burgeoning potential for the development of next-generation targeted therapeutics.
Qingtao Zeng, Jingcheng Yang, Dan He· Current Opinion in Chemical...· 0 citations
Biomolecular condensation has emerged as a central mechanism of cellular organization, regulating fundamental processes from transcription to stress responses. Its dysregulation - often involving transitions from dynamic condensates to more solid or aggregated states - has been linked to human disease and thus represents a growing therapeutic opportunity. Yet, despite substantial progress, precisely relating protein sequence to condensate behavior, function, and dysregulation remains a largely unresolved challenge. Recent advances in high-throughput approaches are beginning to address this gap. By combining large-scale mutagenesis to fitness, fluorescence- or imaging-based selections, and deep sequencing, these methods enable systematic interrogation of different types of protein self-assembly across vast sequence spaces. However, most of the currently available assays measure indirect readouts such as solubility, stability, or cellular fitness and differ in the way they capture different parameters of the self-assembly process. Here, we review emerging high-throughput strategies to study protein condensation and aggregation at scale, emphasizing what they truly measure, their limitations, and how the cross-talk among these complementary approaches can provide a more accurate and mechanistic mapping of sequence-to-assembly relationships.
Mariano Martín, Alice Lissmatz, Benedetta Bolognesi· Current Opinion in Structura...· 0 citations
Structural biology is undergoing a transformative era driven by advances in artificial intelligence (AI)-based protein structure prediction and cryo-electron microscopy. Predictive approaches have dramatically expanded structural coverage across proteomes and are increasingly integrated into experimental workflows. However, protein function frequently depends on dynamic molecular processes including ligand-dependent conformational remodeling, transient interactions, cooperative assembly, and transport-state transitions that remain difficult to define from static computational models alone. These challenges are particularly evident in plants, where signaling pathways often involve environmentally responsive receptor complexes, lineage-expanded regulatory proteins, and transient assemblies. Here, we discuss how experimental structural biology continues to advance plant biology by revealing mechanisms underlying hormone perception, immune receptor activation, transporter function, and enzymatic regulation. From early landmark discoveries such as the crystallization of urease to recent cryo-electron microscopy studies of dynamic signaling complexes, plant systems have repeatedly uncovered molecular architectures, chemically modified intermediates, and regulatory principles that require direct structural and biochemical characterization. Plant proteins also remain markedly underrepresented in structural databases, leaving many plant-specific pathways structurally unresolved. Together, these observations highlight the continuing importance of experimental structural biology for defining biologically relevant molecular states and enabling structure-guided strategies for crop improvement and agricultural biotechnology.
M. Palayam, N. Shabek· Current opinion in plant bio...· 0 citations
The advent of machine learning structural prediction tools has largely accelerated bottom-up structural biology and provides static Anfinsenian models with near-experimental accuracy, complementing X-ray crystallography and cryogenic electron microscopy. However, these techniques often overlook two fundamental aspects of biomolecular structure-function relationships: (i) conformational equilibria and (ii) transient noncovalent interactions. Indeed, these considerations are particularly important for integral membrane proteins (MPs), frequently serving mechanistic roles, such as conformational plasticity in ion channels facilitating heterogeneous state transitions, or dynamic binding partners modulating signal transduction in receptors and small-molecule transporters. In this vein, electron paramagnetic resonance (EPR) spectroscopy, an ensemble method, provides a powerful toolbox to guide high-resolution structural data by directly reporting on conformational subensembles and noncovalent binding events. Herein, we highlight recent EPR applications to integral MPs and emerging synergies with bottom-up atomistic approaches to enhance insights into structure-dynamics-function relationships.
Joshua L. Wort, Xinyu Liu, James Pullen et al.· Current Opinion in Structura...· 0 citations
Conformational transitions are not accidents; they are the currency of regulation. Cells actively spend energy through adenosine triphosphate (ATP) turnover, mechanical work, targeted post-translational modification to bias proteins toward specific metastable states in space and time, rather than stabilizing a single 'active' structure. This mini-review summarizes recent computational advances that resolve and, increasingly, design these biased ensembles across three mechanisms: (i) ligand- and allosteric-driven redistribution of signaling proteins, (ii) force-gated unfolding and refolding in mechanosensitive scaffolds, and (iii) disorder↔order reweighting in intrinsically disordered regions. We further outline emerging deep-learning frameworks that aim not only to observe such transitions, but also to program them, suggesting that rational control of ensemble occupancy is becoming an achievable design target.
Lijin Wang, Nazmul Shuzan, Jie Zheng· Current Opinion in Structura...· 0 citations