Current opinion in structural biology最新文献

Cryogenic-focused ion beam (cryo-FIB) instruments became essential for high-resolution imaging in cryo-preserved cells and tissues. Cryo-FIBs use accelerated ions to thin samples that would otherwise be too thick for cryo-electron microscopy (cryo-EM). This allows visualizing cellular ultrastructures in near-native frozen hydrated states. This review describes the current state-of-the-art capabilities of cryo-FIB technology and its applications in structural cell and tissue biology. We discuss recent advances in instrumentation, imaging modalities, automation, sample preparation protocols, and targeting techniques. We outline remaining challenges and future directions to make cryo-FIB more precise, enable higher throughput, and be widely accessible. Further improvements in targeting, efficiency, robust sample preparation, emerging ion sources, automation, and downstream electron tomography have the potential to reveal intricate molecular architectures across length scales inside cells and tissues. Cryo-FIB is poised to become an indispensable tool for preparing native biological systems in situ for high-resolution 3D structural analysis.

Cryogenic electron tomography (cryo-ET), a method that enables the viewing of biomolecules in near-native environments at high resolution, is rising in accessibility and applicability. Over the past several years, once slow sample preparation and data collection procedures have seen innovations which enable rapid collection of the large datasets required for attaining high resolution structures. Increased data availability has provided a driving force for exciting improvements in cryo-ET data processing methodologies throughout the entire processing pipeline and the development of accessible graphical user interfaces (GUIs) that enable individuals inexperienced in computational fields to convert raw tilt series into 3D structures. These advances in data processing are enabling cryo-ET to attain higher resolution and extending its applicability to more complex samples.

Cell fate changes require rewiring of transcriptional programs to generate functionally specialized cell states. Reconfiguration of transcriptional networks requires overcoming epigenetic barriers imposed by silenced heterochromatin in order to activate lineage-specific genes. Further, cell fate decisions are made in a tissue-specific context, where cells are physically linked to each other as well as to the connective tissue environment. Here, cells are continuously exposed to a multitude of mechanical forces emanating from cellular dynamics in their local microenvironments, for example through cell movements, cell divisions, tissue contractions, or fluid flow. Through their ability to deform cellular structures and activate receptors, mechanical forces can be sensed at the plasma membrane, but also at the nuclear periphery through direct or cytoskeleton-mediated deformation of the nuclear envelope. This deformation and the associated signaling is capable of triggering changes in the mechanical state of the nuclear membranes, the organization and rigidity of the underlying nuclear lamina, compaction state of chromatin, and ultimately transcription. This review focuses on the role of nuclear architecture, particularly the nuclear lamina-chromatin interface, and its mechanical regulation in cell fate decisions as well as its physiological role in development and cellular reprogramming.

Defining the molecular mechanisms by which genome maintenance proteins dynamically associate with and process DNA is essential to understand the potential avenues by which these stabilizing mechanisms are disrupted. Single-molecule fluorescence imaging (SMFI) of protein dynamics on extended DNA has greatly expanded our ability to accomplish this, as it captures singular biomolecular interactions – in all their complexity and diversity – without relying on ensemble-averaging of bulk protein activity as most traditional biochemical techniques must do. In this review, we discuss how SMFI studies with extended DNA have substantially contributed to genome stability research over the past two years.

Double helical DNA winds around nucleosomes, forming a beads-on-a-string array that further contributes to the formation of high-order chromatin structures. The regulatory components of the chromatin, interacting intricately with DNA, often exploit the topological tension inherent in the DNA molecule. Recent findings shed light on, and simultaneously complicate, the multifaceted roles of DNA topology (also known as DNA supercoiling) in various aspects of chromatin regulation. Different studies may emphasize the dynamics of DNA topological tension across different scales, interacting with diverse chromatin factors such as nucleosomes, nucleic acid motors that propel DNA-tracking processes, and DNA topoisomerases. In this review, we consolidate recent studies and establish connections between distinct scientific discoveries, advancing our current understanding of chromatin regulation mediated by the supercoiling tension of the double helix. Additionally, we explore the implications of DNA topology and DNA topoisomerases in human diseases, along with their potential applications in therapeutic interventions.

Proteins execute numerous cell functions in concert with one another in protein–protein interactions (PPI). While essential in each cell, such interactions are not identical from cell to cell. Instead, PPI heterogeneity contributes to cellular phenotypic heterogeneity in health and diseases such as cancer. Understanding cellular phenotypic heterogeneity thus requires measurements of properties of PPIs such as abundance, stoichiometry, and kinetics at the single-cell level. Here, we review recent, exciting progress in single-cell PPI measurements. Novel technology in this area is enabled by microscale and microfluidic approaches that control analyte concentration in timescales needed to outpace PPI disassembly kinetics. We describe microscale innovations, needed technical capabilities, and methods poised to be adapted for single-cell analysis in the near future.

Since the discovery of G-quadruplex (G4) participation in vital cellular processes, the regulation of the interaction of naturally occurring G4s with the relative target proteins has emerged as a promising approach for therapeutic development. Additionally, a synthetic strategy has produced several oligonucleotide aptamers, embodying a G4 module, which exhibit relevant biological activity by binding selectively to a target protein. In this context, the G4-protein structures available in the Protein Data Bank represent a valuable molecular view of the different G4 topologies involved in protein interaction. Interestingly, recent results have showed the co-existence of G4s with other structural domains such as duplexes. Overall, these findings allow a better understanding of the mechanisms that regulate intricate biological functions and suggest new design for innovative medical treatments.

Structure-based virtual screening aims to find molecules forming favorable interactions with a biological macromolecule using computational models of complexes. The recent surge of commercially available chemical space provides the opportunity to search for ligands of therapeutic targets among billions of compounds. This review offers a compact overview of structure-based virtual screens of vast chemical spaces, highlighting successful applications in early drug discovery for therapeutically important targets such as G protein-coupled receptors and viral enzymes. Emphasis is placed on strategies to explore ultra-large chemical libraries and synergies with emerging machine learning techniques. The current opportunities and future challenges of virtual screening are discussed, indicating that this approach will play an important role in the next-generation drug discovery pipeline.

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer’s disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.