Aug 2026· Current Opinion in Structural Biology· Vol 101, pp.
103353
· 0 citations· 52 references
Medicine
TL;DR
Emerging high-throughput strategies to study protein condensation and aggregation at scale are reviewed, 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.
Abstract
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.
Biomolecular condensates formed through phase separation have emerged as a central principle of cellular organization, enabling the dynamic regulation of gene expression, signaling, metabolism, and stress responses. While early conceptual advances in condensate biology have largely originated from animal and in vitro systems, plant cells present a unique set of biological and technical challenges, including rigid cell walls, turgor pressure, plastid autofluorescence, complex endomembrane organization, and acute environmental responsiveness. These distinctive features impede the direct transfer of existing methodologies and drive the development of heterogeneous experimental practices. In this community comment, we present a comprehensive methodological framework for studying biomolecular condensates in plants, spanning in silico prediction, in vitro reconstitution, molecular dynamics simulations, live-cell and super-resolution imaging, material property measurements, membrane-associated condensates, and synthetic condensate engineering. We highlight best practices, common pitfalls, and plant-specific considerations, emphasizing the need for orthogonal validation, quantitative interpretation, and physiological relevance. By consolidating current methodologies and articulating shared principles, this review aims to establish a foundation for rigorous, reproducible, and conceptually coherent research in condensate biology of plants and beyond, with emerging implications for crop genetic improvement and synthetic biology applications.
Jiaxuan Peng, J. Agudo-Canalejo, Monika Chodasiewicz et al.· Science China Life Sciences· 0 citations
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
Protein–protein interactions underpin virtually all biological processes in plants, from signal transduction and immune responses to development and stress adaptation. Despite their fundamental importance, the plant interactome remains far from complete, and existing maps are systematically biased by the technical limitations inherent to conventional detection platforms. This review critically traces the evolution of protein–protein interaction methodologies, from foundational approaches to advance in vivo and quantitative platforms. Classical techniques such as the yeast two-hybrid system and in vitro pull-down assays operate outside physiological cellular environments and are poorly suited to capturing transient or condition-dependent interactions. Affinity purification coupled with mass spectrometry improves throughput but remains vulnerable to artifacts introduced during cell lysis and to the preferential loss of weak interactors. To address these shortcomings, proximity labeling with engineered biotin ligases, most notably the fast-acting variant TurboID, has emerged as a powerful strategy, enabling covalent biotinylation of protein neighborhoods within living cells prior to lysis and thereby preserving associations that conventional methods routinely miss. Because TurboID reports proximity rather than direct binding, its output requires downstream binary validation. Complementary in planta validation tools are equally critical for moving beyond discovery. Split-luciferase complementation assays based on the NanoLuciferase reporter provide exceptional sensitivity for binary interaction detection under native expression conditions, while Förster Resonance Energy Transfer measured through fluorescence lifetime imaging microscopy offers quantitative biophysical evidence of molecular proximity at endogenous expression levels, serving as a high-confidence validation approach. Emerging technologies, including high-throughput protein microarrays and optogenetically controlled dimerization systems, further expand the methodological repertoire available to the plant biology community. We propose a practical, integrative three-tier framework, combining proximity labeling for broad in vivo discovery, split-luciferase complementation for sensitive binary validation, and fluorescence lifetime imaging microscopy for quantitative confirmation, that systematically funnels candidate interactions from initial identification to physiologically rigorous verification. This framework synthesizes established best practices into a structured workflow applicable to mapping dynamic plant interactomes, though its optimal implementation will depend on the biological question, target protein class, and available resources.
Muhammad Ans Hussain, A. H. Hafeez, Iqra Noor et al.· Plant Methods· 0 citations
Protein self-interaction to form homodimers and higher-order homo-oligomers is a ubiquitous phenomenon fundamental to living organisms. Recent structural, system-level, and computational insights reveal that self-interacting proteins dictate the specificity, rewiring, and topological complexity of cellular signaling networks and macromolecular assemblies. Beyond their physiological roles, aberrant or dysregulated homotypic interactions disrupt cellular proteostasis, driving the formation of toxic non-native oligomers, pathological amyloid fibrillization, or aberrant liquid–liquid phase separation transitions linked to neurodegenerative and systemic diseases. This review provides a comprehensive overview of the state-of-the-art experimental methodologies, including proximity labeling, as well as the advanced computational frameworks, such as deep learning architectures and protein language models, used to map the structural dynamics of SIPs. Furthermore, we dissect the evolutionary trajectories of SIPs within protein–protein interaction networks, which are underpinned by dosage-balance constraints, and highlight their diverse functional advantages, ranging from allosteric modulation to biomolecular condensation. Finally, we summarize the molecular mechanisms linking pathological self-associations to human disorders, underscoring the emerging paradigm of targeting homotypic interfaces as a promising frontier for precision therapeutics.
Yuanxiao Gao, Wenyu Zhang, Guang Hu· International Journal of Mol...· 0 citations
Protein assemblies, such as fibers, cages, and sheets, are essential components of biological systems, with versatile functions that make them attractive engineering targets for biotechnological applications. Understanding the complex sequence–structure–function relationships that govern these assemblies is critical for both basic science and the engineering of novel nanomaterials. Deep mutational scanning (DMS) has emerged as a powerful technique for mapping these relationships across large sections of protein sequence space. Specifically, DMS couples high‐throughput assays with next‐generation sequencing technologies to create datasets that report on how changes to protein sequence alter protein function. This review provides an overview of protein assemblies and the basic principles of DMS, followed by a discussion of how DMS has been applied to protein assemblies, and what unique considerations arise when performing such studies. We aim to provide a comprehensive foundation for researchers across biochemistry and chemical biology looking to leverage such high‐throughput approaches to understand and engineer the next generation of protein‐based assemblies.
Jenna B. Wolfanger, Shoili Banerjee, Carolyn E. Mills· Chemistry–Methods· 0 citations
This Perspective proposes dynamic, reversible assembly as a framework for understanding the mechanisms of RNA processing and gene regulation, and shows how molecular interactions are governed by rates rather than by equilibrium affinities, providing a foundation for time-integrated structure-function studies.
Alexander Johnson-Buck, Adrien Chauvier, A. Abidi et al.· Nature reviews. Molecular ce...· 0 citations
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