Double-stranded DNA viruses actively package their genomes into pre-assembled protein capsids using energy derived from virus-encoded ASCE ATPase ring motors. Single molecule experiments in the aughts and early 2010s demonstrated that these motors are some of the most powerful molecular motors in nature, and that the activities of individual subunits around the ATPase ring motor are highly coordinated to ensure efficient genome encapsidation. While these studies provided a comprehensive kinetic scheme describing the events that occur during packaging, the physical basis of force generation and subunit coordination remained elusive. This article reviews recent structural and computational results that have begun to illuminate the molecular basis of force generation and DNA translocation in these powerful molecular motors.

Core regulatory circuitry refers to the network of lineage-specific transcription factors regulating expression of both their own coding genes, and that of other transcription factors. Such autoregulatory feedback loops coordinate the transcriptome and epigenome during development and cell fate decisions. This circuitry is hijacked during oncogenesis resulting in cancer cell fate being maintained by lineage-specific transcription factors. Major advances in functional genomics and chemical biology are paving the way for a new generation of cancer therapeutics aimed at disrupting this circuitry through both direct and indirect means. Here we review these critical advances in mechanistic understanding of transcription factor addiction in cancer and how the advent of proteolysis targeting chimeras and CRISPR screen assays are leading the way for a new paradigm in targeted cancer treatments.

The energy cost accompanying changes in the structures of nucleic acids when they bind partner molecules is a significant but underappreciated thermodynamic contribution to binding affinity and specificity. This review highlights recent advances in measuring conformational penalties and determining their contribution to the recognition, folding, and regulatory activities of nucleic acids. Notable progress includes methods for measuring and structurally characterizing lowly populated conformational states, obtaining ensemble information in high throughput, for large macromolecular assemblies, and in complex cellular environments. Additionally, quantitative and predictive thermodynamic models have been developed that relate conformational penalties to nucleic acid-protein association and cellular activity. These studies underscore the crucial role of conformational penalties in nucleic acid recognition.

Since genome sequencing became accessible, determining how specific differences in genotypes lead to complex phenotypes such as disease has become one of the key goals in biomedicine. Predicting effects of sequence variants on cellular or organismal phenotype faces several challenges. First, variants simultaneously affect multiple protein properties and predicting their combined effect is complex. Second, effects of changes in a single protein propagate through the cellular network, which we only partially understand. In this review, we emphasize the importance of both biochemistry and genetics in addressing these challenges. Moreover, we highlight work that blurs the distinction between biochemistry and genetics fields to provide new insights into the genotype-to-phenotype relationships.

Intrinsically disordered proteins (IDPs) lack a stable three-dimensional structure under physiological conditions, challenging traditional structure-based prediction methods. This review explores how modern deep learning approaches, which have revolutionized structure prediction for globular proteins, have impacted protein disorder predictions. We highlight the role of community-driven efforts in curating data and assessing state-of-the-art, which have been crucial in advancing the field. We also review state-of-the-art methods utilizing deep learning techniques, highlighting innovative approaches. We also address advancements in characterizing protein conformational ensembles directly from sequence data using novel machine learning methods.

Protein aggregation is a complex process, consisting of a large number of pathways connecting monomers and mature amyloid fibrils. Recent advances in structure determination techniques, such as solid-state NMR and cryoEM, have allowed the determination of atomic resolution structures of fibril polymorphs, but most of the intermediate stages of the process including oligomer formation remain unknown. Proper characterization of the heterogeneity of the process is critical not only for physical and chemical understanding of the aggregation process but also for elucidation of the disease mechanisms and identification of therapeutic targets. This article reviews recent developments in the characterization of heterogeneity in amyloid formation processes.

Deepening our understanding of RNA biology and accelerating development of RNA-based therapeutics go hand-in-hand-both requiring a transition from qualitative descriptions of RNA structure to quantitative models capable of predicting RNA behaviors, and from a static to an ensemble view. Ensembles are determined from their free energy landscapes, which define the relative populations of conformational states and the energetic barriers separating them. Experimental determination of RNA ensembles over the past decade has led to powerful predictive models of RNA behavior in vitro. It has also been shown during this time that the cellular environment redistributes RNA ensembles, changing the abundances of functionally relevant conformers relative to in vitro contexts with subsequent functional RNA consequences. However, recent studies have demonstrated that testing models built from in vitro ensembles with highly quantitative measurements of RNA cellular function, aided by emerging computational methodologies, enables predictive modelling of cellular activity and biological discovery.

RNA conformational dynamics is pivotal for functional regulations in biology. RNA can function as versatile as protein but adopts multiple distinct structures. In this review, we provide a focused review of the recent advances in studies of RNA conformational dynamics and address some of the misconceptions about RNA structure and its conformational dynamics. We discuss why the traditional methods for structure determination come up short in describing RNA conformational space. The examples discussed provide illustrations of the structure-based mechanisms of RNAs with diverse roles, including viral, long noncoding, and catalytic RNAs, one of which focuses on the debated area of conformational heterogeneity of an RNA structural element in the HIV-1 genome.

Intracellular DNA primarily serves as the cellular genetic material both in eukaryotes and prokaryotes. This function is often regulated by alterations in the DNA structure to accommodate transcription, recombination, and DNA replication. Extracellularly, both eukaryotic and prokaryotic cells take advantage of DNA plenty in addition to a permissive environment and create novel structures to fulfill multiple new roles. As often occurs intracellularly, extracellular DNA requires proteins to facilitate and stabilize these important structures. Here I review, both host and eubacterial nucleoprotein structures, their composition, their functions, and how these distinct structures can interact. Even at this early stage of study, it is clear that extracellular chromatin plays important biological roles in the survival of both prokaryotic and eukaryotic organisms.

Gene expression regulation requires both diversity and specificity. How can these two contradictory conditions be reconciled? Dynamic DNA recognition mechanisms lead to heterogeneous bound conformations, which can be shifted by the cellular cues. Here we summarise recent experimental evidence on how fuzzy interactions contribute to chromatin remodelling, regulation of DNA replication and repair and transcription factor binding. We describe how the binding mode continuum between DNA and regulatory factors lead to variable, multisite contact patterns; polyelectrolyte competitions; on-the-fly shape readouts; autoinhibition controlled by posttranslational modifications or dynamic oligomerisation mechanisms. Increasing experimental evidence supports the rugged energy landscape of the bound protein-DNA assembly, modulation of which leads to distinct functional outcomes. Recent results suggest the evolutionary conservation of these combinatorial mechanisms with moderate sequence constraints in the malleable transcriptional machinery.

Correlative light and electron microscopy (CLEM) pipelines serve to integrate the imaging modalities of fluorescence light microscopy (FLM) and cryogenic electron microscopy (cryo-EM) to produce contextually relevant high-resolution structural snapshots of biological systems. Innovations in sample preparation, instrumentation, imaging, and data processing have advanced the field of cryo-EM. This review focuses on prior work and recent developments in the field of cryo- EM that support further integration of technologies for correlative microscopy workflows.

Thermal proteome profiling (TPP) is an innovative technique that uses the principle of protein thermal stability to identify potential protein interaction partners. Employing quantitative mass spectrometry, TPP measures protein stability across the proteome, offering a comprehensive snapshot of protein interactions in a single experiment. When studying protein-protein interactions (PPI), TPP leverages changes in apparent protein melting temperatures to identify transient and weak interactions that most traditional PPI detection methodologies struggle to measure. This review discusses current TPP methodologies, the challenges of interpreting the resulting complex datasets, and opportunities to deepen and improve PPI networks. By advancing our grasp of intricate protein interactions, TPP promises to illuminate the molecular basis of diseases and drive the discovery of novel therapeutic targets.

Protein kinase inhibitors designed to compete with ATP as a primary mode of action turn out to have considerable effects that go beyond their interference of nucleotide binding. New research shows how kinase activation and sometimes noncatalytic functions of protein kinases can be controlled by allosteric properties of kinase inhibitors, communicating perturbations from the active site to distal regulatory regions.

Natural proteins are fragile entities, intrinsically sensitive to perturbations both at the level of sequence and their immediate environment. Here, we highlight the diverse strategies available for engineering function through mutations influencing backbone conformational entropy, charge-charge interactions, and in the loops and hinge regions, many of which are located far from the active site. It thus appears that there are potentially numerous ways to microscopically vary the identity of residues and the constituent interactions to tune function. Functional modulation could occur via changes in native-state stability, altered thermodynamic coupling extents within the folded structure, redistributed dynamics, or through modulation of the population of conformational substates. As these mechanisms are intrinsically linked and given the pervasive long-range effects of mutations, it is crucial to consider the interaction network as a whole and fully map the native conformational landscape to place mutational effects in the context of allostery and protein evolution.

Membrane proteins remain challenging targets for conventional structural biology techniques because they need to reside within complex hydrophobic lipid environments to maintain proper structure and function. Magnetic resonance combined with site-directed spin labeling is an alternative method that provides atomic-level structural and dynamical information from effects introduced by an electron- or nuclear-based spin label. With the advent of bioorthogonal click chemistries and genetically engineered non-canonical amino acids (ncAAs), options for linking spin probes to biomolecules have substantially broadened outside the conventional cysteine-based labeling scheme. Here, we highlight current strategies to spin-label membrane proteins through ncAAs for nuclear and electron paramagnetic resonance applications. Such advances are critical for developing bioorthogonal spin labeling schemes to achieve in-cell labeling and in-cell measurements of membrane protein conformational dynamics.

RNA's inherent flexibility and dynamics pose great challenges to characterize its structure and dynamics using conventional techniques including X-ray crystallography, nuclear magnetic resonance (NMR) spectroscopy and cryo-electron microscopy. Three complementary molecular ruler techniques, the electron paramagnetic resonance (EPR) spectroscopy, X-ray scattering interferometry (XSI) and Förster resonance energy transfer (FRET) which measure intramolecular and intermolecular pair-wise distance distributions in the nanometer range in a solution, have become increasingly popular and been widely used to explore RNA structure and dynamics. The prerequisites for successful application of such techniques are to achieve site-specific labeling of RNAs with spin labels, fluorescent tags, or gold nanoparticles, respectively, which are however, challenging, especially to large RNAs (generally >200 nts). Here, we briefly review the basics of these molecular rulers, how the NaM-TPT3 unnatural base pair system empower them, and their applications to explore conformational dynamics of large RNAs, especially in the context of flavivirus RNA genome.

Maturation of pre-mRNA into fully functional mRNA involves a series of highly coordinated steps that are essential for eukaryotic gene expression. RNA structure has been found to play regulatory roles in many of these steps, including cleavage, polyadenylation, and termination. Recent advances in structure probing techniques have been instrumental in revealing how nascent transcript conformation contributes to these dynamic, co-transcriptional processes. In this review, we present examples where RNA structure affects accessibility and/or function of key processing enzymes, thereby influencing the efficiency and precision of 3' end processing machinery. We also discuss emerging technologies that could further enhance our understanding of RNA structure mediated regulation of 3' end processing.