Current opinion in structural biology最新文献

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.

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.