Current opinion in structural biology最新文献

Riboswitches are specialized RNA structures that orchestrate gene expression in response to sensing specific metabolite or ion ligands, mostly in bacteria. Upon ligand binding, these conformationally dynamic RNA motifs undergo structural changes that control critical gene expression processes such as transcription termination and translation initiation, thereby enabling cellular homeostasis and adaptation. Because RNA folds rapidly and co-transcriptionally, riboswitches make use of the low complexity of RNA sequences to adopt alternative, transient conformations on the heels of the transcribing RNA polymerase (RNAP), resulting in kinetic partitioning that defines the regulatory outcome. This review summarizes single molecule microscopy evidence that has begun to unveil a sophisticated network of dynamic, kinetically balanced interactions between riboswitch architecture and the gene expression machinery that, together, integrate diverse cellular signals.

Necroptosis is a lytic form of programmed cell death implicated in inflammatory pathologies, leading to intense interest in the underlying mechanisms and therapeutic prospects. Here, we review our current structural understanding of how the terminal executioner of the pathway, the dead kinase, mixed lineage kinase domain-like (MLKL), is converted from a dormant to killer form by the upstream regulatory kinase, RIPK3. RIPK3-mediated phosphorylation of MLKL's pseudokinase domain toggles a molecular switch that induces dissociation from a cytoplasmic platform, assembly of MLKL oligomers, and trafficking to the plasma membrane, where activated MLKL accumulates and permeabilises the lipid bilayer to induce cell death. We highlight gaps in mechanistic knowledge of MLKL's activation, how mechanisms diverge between species, and the power of modelling in advancing structural insights.

Ion-driven membrane motors, essential across all domains of life, convert a gradient of ions across a membrane into rotational energy, facilitating diverse biological processes including ATP synthesis, substrate transport, and bacterial locomotion. Herein, we highlight recent structural advances in the understanding of two classes of ion-driven membrane motors: rotary ATPases and 5:2 motors. The recent structure of the human F-type ATP synthase is emphasised along with the gained structural insight into clinically relevant mutations. Furthermore, we highlight the diverse roles of 5:2 motors and recent mechanistic understanding gained through the resolution of ions in the structure of a sodium-driven motor, combining insights into potential unifying mechanisms of ion selectivity and rotational torque generation in the context of their function as part of complex biological systems.

Protein kinases are dynamic enzymes that display complex regulatory mechanisms. Although they possess a structurally conserved catalytic domain, significant conformational dynamics are evident both within a single kinase and across different kinases in the kinome. Here, we highlight methods for exploring this conformational space and its dynamics using kinase domains from ABL1 (Abelson kinase), PKA (protein kinase A), AurA (Aurora A), and PYK2 (proline-rich tyrosine kinase 2) as examples. Such experimental approaches combined with AI-driven methods, such as AlphaFold, will yield discoveries about kinase regulation, the catalytic process, substrate specificity, the effect of disease-associated mutations, as well as new opportunities for structure-based drug design.

Cre recombinase is a phage-derived enzyme that has found utility for precise manipulation of DNA sequences. Cre recognizes and recombines pairs of loxP sequences characterized by an inverted repeat and asymmetric spacer. Cre cleaves and religates its DNA targets such that error-prone repair pathways are not required to generate intact DNA products. Major obstacles to broader applications are lack of knowledge of how Cre recognizes its targets, and how its activity is controlled. The picture emerging from high resolution methods is that the dynamic properties of both the enzyme and its DNA target are important determinants of its activity in both sequence recognition and DNA cleavage. Improved understanding of the role of dynamics in the key steps along the pathway of Cre-loxP recombination should significantly advance our ability to both redirect Cre to new sequences and to control its DNA cleavage activity in the test tube and in cells.

The rapid advancement in computational power available for research offers to bring not only quantitative improvements, but also qualitative changes in the field of biomolecular simulation. Here, we review the state of biomolecular dynamics simulations at the threshold to exascale resources becoming available. Both developments in parallel and distributed computing will be discussed, providing a perspective on the state of the art of both. A main focus will be on obtaining binding and conformational free energies, with an outlook to macromolecular complexes and (sub)cellular assemblies.

RNA molecules fold to form complex internal structures. Many of these RNA structures populate ensembles with rheostat-like properties, with each state having a distinct function. Until recently, analysis of RNA structures, especially within cells, was limited to modeling either a single averaged structure or computationally-modeled ensembles. These approaches obscure the intrinsic heterogeneity of many structured RNAs. Single-molecule correlated chemical probing (smCCP) strategies are now making it possible to measure and deconvolute RNA structure ensembles based on efficiently executed chemical probing experiments. Here, we provide an overview of fundamental single-molecule probing principles, review current ensemble deconvolution strategies, and discuss recent applications to diverse biological systems. smCCP is enabling a revolution in understanding how the plasticity of RNA structure is exploited in biological systems to respond to stimuli and alter gene function. The energetics of RNA ensembles are often subtle and a subset can likely be targeted to modulate disease-associated biological processes.

The cellular process by which the protein ubiquitin (Ub) is covalently attached to a protein substrate involves Ub activating (E1s) and conjugating enzymes (E2s) that work together with a large variety of E3 ligases that impart substrate specificity. The largest family of E3s is the Cullin-RING ligase (CRL) family which utilizes a wide variety of substrate receptors, adapter proteins, and cooperating ligases. Cryo-electron microscopy (cryoEM) has revealed a wide variety of structures which suggest how Ub transfer occurs. Hydrogen deuterium exchange mass spectrometry (HDXMS) has revealed the role of dynamics and expanded our knowledge of how covalent NEDD8 modification (neddylation) activates the CRLs, particularly by facilitating cooperation with additional RING-between-RING ligases to transfer Ub.

Protein-protein interactions (PPIs) play a crucial role in cellular function and disease manifestation, with dysfunctions in PPI networks providing a direct link between stressors and phenotype. The dysfunctional Protein-Protein Interactome (dfPPI) platform, formerly known as epichaperomics, is a newly developed chemoproteomic method aimed at detecting dynamic changes at the systems level in PPI networks under stressor-induced cellular perturbations within disease states. This review provides an overview of dfPPIs, emphasizing the novel methodology, data analytics, and applications in disease research. dfPPI has applications in cancer research, where it identifies dysfunctions integral to maintaining malignant phenotypes and discovers strategies to enhance the efficacy of current therapies. In neurodegenerative disorders, dfPPI uncovers critical dysfunctions in cellular processes and stressor-specific vulnerabilities. Challenges, including data complexity and the potential for integration with other omics datasets are discussed. The dfPPI platform is a potent tool for dissecting disease systems biology by directly informing on dysfunctions in PPI networks and holds promise for advancing disease identification and therapeutics.

Adopting computational tools for analyzing extensive biological datasets has profoundly transformed our understanding and interpretation of biological phenomena. Innovative platforms have emerged, providing automated analysis to unravel essential insights about proteins and the complexities of their interactions. These computational advancements align with traditional studies, which employ experimental techniques to discern and quantify physical and functional protein-protein interactions (PPIs). Among these techniques, tandem mass spectrometry is notably recognized for its precision and sensitivity in identifying PPIs. These approaches might serve as important information enabling the identification of PPIs with potential pharmacological significance. This review aims to convey our experience using computational tools for detecting PPI networks and offer an analysis of platforms that facilitate predictions derived from experimental data.