Current opinion in structural biology最新文献

The combination of cryo-electron tomography and subtomogram analysis affords 3D high-resolution views of biological macromolecules in their native cellular environment, or in situ. Streamlined methods for acquiring and processing these data are advancing attainable resolutions into the realm of drug discovery. Yet regardless of resolution, structure prediction driven by artificial intelligence (AI) combined with subtomogram analysis is becoming powerful in understanding macromolecular assemblies. Automated and AI-assisted data mining is increasingly necessary to cope with the growing wealth of tomography data and to maximize the information obtained from them. Leveraging developments from AI and single-particle analysis could be essential in fulfilling the potential of in situ cryo-EM. Here, we highlight new developments for in situ cryo-EM and the emerging potential for AI in this process.

Co-fractionation mass spectrometry (CF-MS) uses biochemical fractionation to isolate and characterize macromolecular complexes from cellular lysates without the need for affinity tagging or capture. In recent years, this has emerged as a powerful technique for elucidating global protein-protein interaction networks in a wide variety of biospecimens. This review highlights the latest advancements in CF-MS experimental workflows including machine learning-guided analyses, for uncovering dynamic and high-resolution protein interaction landscapes with enhanced sensitivity, accuracy and throughput, enabling better biophysical characterization of endogenous protein complexes. By addressing challenges and emergent opportunities in the field, this review underscores the transformative potential of CF-MS in advancing our understanding of functional protein interaction networks in health and disease.

Pioneering transcription factors (TFs) can drive cell fate changes by binding their DNA motifs in a repressive chromatin environment. Recent structures illustrate emerging rules for nucleosome engagement: TFs distort the nucleosomal DNA to gain access or employ alternative DNA-binding modes with smaller footprints, they preferentially access solvent-exposed motifs near the entry/exit sites, and frequently interact with histones. The extent of TF–histone interactions, in turn, depends on the motif location on the nucleosome, the type of DNA-binding fold, and adjacent domains present. TF–histone interactions can phase TF motifs relative to nucleosomes, and we discuss how these complex and surprisingly diverse interactions between nucleosomes and TFs contribute to function.

Network biology is a powerful framework for studying the structure, function, and dynamics of biological systems, offering insights into the balance between health and disease states. The field is seeing rapid progress in all of its aspects: data availability, network synthesis, network analytics, and impactful applications in medicine and drug development. We review the most recent and significant results in network biomedicine, with a focus on the latest data, analytics, software resources, and applications in medicine. We also discuss what in our view are the likely directions of impactful development over the next few years.

To initiate DNA replication, it is essential to properly assemble a pair of replicative helicases at each replication origin. While the general principle of this process applies universally from prokaryotes to eukaryotes, the specific mechanisms governing origin selection, helicase loading, and subsequent helicase activation vary significantly across different species. Recent advancements in cryo-electron microscopy (cryo-EM) have revolutionized our ability to visualize large protein or protein-DNA complexes involved in the initiation of DNA replication. Complemented by real-time single-molecule analysis, the available high-resolution cryo-EM structures have greatly enhanced our understanding of the dynamic regulation of this process at origin DNA. This review primarily focuses on the latest structural discoveries that shed light on the key molecular machineries responsible for driving replication initiation, with a particular emphasis on the assembly of pre-replication complex (pre-RC) in eukaryotes.

Interactions between thousands of proteins define cells' protein–protein interaction (PPI) network. Some of these interactions lead to the formation of protein complexes. It is challenging to identify a protein complex in a haystack of protein–protein interactions, and it is even more difficult to predict all protein complexes of the complexome. Simulations and machine learning approaches try to crack these problems by looking at the PPI network or predicted protein structures. Clustering of PPI networks led to the first protein complex predictions, while most recently, atomistic models of protein complexes and deep-learning-based structure prediction methods have also emerged. The simulation of PPI level interactions even enables the quantitative prediction of protein complexes. These methods, the required data sources, and their potential future developments are discussed in this review.

Many critical biological processes depend on protein complexes that exist as ensembles of subcomplexes rather than a discrete complex. The subcomplexes dynamically interconvert with one another, and the ability to accurately resolve the composition of the diverse molecular species in the ensemble is crucial for understanding the contribution of each subcomplex to the overall function of the protein complex. Advances in computational programs have made it possible to predict the various molecular species in these ensembles, but experimental approaches to identify the pool of subcomplexes and associated stoichiometries are often challenging. This review highlights some experimental approaches that can be used to resolve the diverse molecular species in protein complexes that exist as ensembles of sub complexes.

Cell states result from the ordered activation of gene expression by transcription factors. Transcription factors face opposing design constraints: they need to be dynamic to trigger rapid cell state transitions, but also stable enough to maintain terminal cell identities indefinitely. Recent progress in live-cell single-molecule microscopy has helped define the biophysical principles underlying this paradox. Beyond transcription factor activity, single-molecule experiments have revealed that at nearly every level of transcription regulation, control emerges from multiple short-lived stochastic interactions, rather than deterministic, stable interactions typical of other biochemical pathways. This architecture generates consistent outcomes that can be rapidly choreographed. Here, we highlight recent results that demonstrate how order in transcription regulation emerges from the apparent molecular-scale chaos and discuss remaining conceptual challenges.

The intrinsically disordered, lipid-modified membrane anchor of small GTPases is emerging as a critical modulator of function through its ability to sort lipids in a conformation-dependent manner. We reviewed recent computational and experimental studies that have begun to shed light on the sequence-ensemble-function relationship in this unique class of lipidated intrinsically disordered regions (LIDRs).

Structural information on protein–protein interactions (PPIs) is essential for improved understanding of regulatory interactome networks that confer various physiological and pathological responses. Additionally, maladaptive PPIs constitute desirable therapeutic targets due to inherently high disease state specificity. Recent advances in chemical cross-linking strategies coupled with mass spectrometry (XL-MS) have positioned XL-MS as a promising technology to not only elucidate the molecular architecture of individual protein assemblies, but also to characterize proteome-wide PPI networks. Moreover, quantitative in vivo XL-MS provides a new capability for the visualization of cellular interactome dynamics elicited by drug treatments, disease states, or aging effects. The emerging field of XL-MS based complexomics enables unique insights on protein moonlighting and protein complex remodeling. These techniques provide complimentary information necessary for in-depth structural interactome studies to better comprehend how PPIs mediate function in living systems.