Current opinion in structural biology最新文献

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.

By incorporating full flexibility and enabling the quantification of crucial parameters such as binding free energies and residence times, methods for investigating protein-ligand binding and unbinding via molecular dynamics provide details on the involved mechanisms at the molecular level. While these advancements hold promise for impacting drug discovery, a notable drawback persists: their relatively time-consuming nature limits throughput. Herein, we survey recent implementations which, employing a blend of enhanced sampling techniques, a clever choice of collective variables, and often machine learning, strive to enhance the efficiency of new and previously reported methods without compromising accuracy. Particularly noteworthy is the validation of these methods that was often performed on systems mirroring real-world drug discovery scenarios.

The expansion of the chemical space to tangible libraries containing billions of synthesizable molecules opens exciting opportunities for drug discovery, but also challenges the power of computer-aided drug design to prioritize the best candidates. This directly hits quantum mechanics (QM) methods, which provide chemically accurate properties, but subject to small-sized systems. Preserving accuracy while optimizing the computational cost is at the heart of many efforts to develop high-quality, efficient QM-based strategies, reflected in refined algorithms and computational approaches. The design of QM-tailored physics-based force fields and the coupling of QM with machine learning, in conjunction with the computing performance of supercomputing resources, will enhance the ability to use these methods in drug discovery. The challenge is formidable, but we will undoubtedly see impressive advances that will define a new era.

The nucleolus functions as a multi-layered regulatory hub for ribosomal RNA (rRNA) biogenesis and ribosome assembly. Long noncoding RNAs (lncRNAs) in the nucleolus, originated from transcription by different RNA polymerases, have emerged as critical players in not only fine-tuning rRNA transcription and processing, but also shaping the organization of the multi-phase nucleolar condensate. Here, we review the diverse molecular mechanisms by which functional lncRNAs operate in the nucleolus, as well as their profound implications in a variety of biological processes. We also highlight the development of emerging molecular tools for characterizing and manipulating RNA function in living cells, and how application of such tools in the nucleolus might enable the discovery of additional insights and potential therapeutic strategies.

Approximately 11% of human genes are transcribed by a bidirectional promoter (BDP), defined as two genes with <1 kb between their transcription start sites. Despite their evolutionary conservation and enrichment for housekeeping genes and oncogenes, the regulatory role of BDPs remains unclear. BDPs have been suggested to facilitate gene coregulation and/or decrease expression noise. This review discusses these potential regulatory functions through the context of six prospective underlying mechanistic models: a single nucleosome free region, shared transcription factor/regulator binding, cooperative negative supercoiling, bimodal histone marks, joint activation by enhancer(s), and RNA-mediated recruitment of regulators. These molecular mechanisms may act independently and/or cooperatively to facilitate the coregulation and/or decreased expression noise predicted of BDPs.

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.