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We recently used in situ Hi-C to create kilobase-resolution 3D maps of mammalian genomes. Here, we combine these maps with new Hi-C, microscopy, and genome-editing experiments to study the physical structure of chromatin fibers, domains, and loops. We find that the observed contact domains are inconsistent with the equilibrium state for an ordinary condensed polymer. Combining Hi-C data and novel mathematical theorems, we show that contact domains are also not consistent with a fractal globule. Instead, we use physical simulations to study two models of genome folding. In one, intermonomer attraction during polymer condensation leads to formation of an anisotropic “tension globule.” In the other, CCCTC-binding factor (CTCF) and cohesin act together to extrude unknotted loops during interphase. Bothmodels are consistent with the observed contact domains and with the observation that contact domains tend to form inside loops. However, the extrusion model explains a far wider array of observations, such as why loops tend not to overlap and why the CTCF-binding motifs at pairs of loop anchors lie in the convergent orientation. Finally, we perform 13 genome-editing experiments examining the effect of altering CTCF-binding sites on chromatin folding. The convergent rule correctly predicts the affected loops in every case. Moreover, the extrusion model accurately predicts in silico the 3D maps resulting from each experiment using only the location of CTCF-binding sites in the WT. Thus, we show that it is possible to disrupt, restore, and move loops and domains using targeted mutations as small as a single base pair.

Gene activator proteins comprise distinct DNA-binding and transcriptional activation domains (ADs). Because few ADs have been described, we tested domains tiling all yeast transcription factors for activation in vivo and identified 150 ADs. By mRNA display, we showed that 73% of ADs bound the Med15 subunit of Mediator, and that binding strength was correlated with activation. AD-Mediator interaction in vitro was unaffected by a large excess of free activator protein, pointing to a dynamic mechanism of interaction. Structural modeling showed that ADs interact with Med15 without shape complementarity (‘fuzzy’ binding). ADs shared no sequence motifs, but mutagenesis revealed biochemical and structural constraints. Finally, a neural network trained on AD sequences accurately predicted ADs in human proteins and in other yeast proteins, including chromosomal proteins and chromatin remodeling complexes. These findings solve the longstanding enigma of AD structure and function and provide a rationale for their role in biology. Cells adapt and respond to changes by regulating the activity of their genes. To turn genes on or off, they use a family of proteins called transcription factors. Transcription factors influence specific but overlapping groups of genes, so that each gene is controlled by several transcription factors that act together like a dimmer switch to regulate gene activity. The presence of transcription factors attracts proteins such as the Mediator complex, which activates genes by gathering the protein machines that read the genes. The more transcription factors are found near a specific gene, the more strongly they attract Mediator and the more active the gene is. A specific region on the transcription factor called the activation domain is necessary for this process. The biochemical sequences of these domains vary greatly between species, yet activation domains from, for example, yeast and human proteins are often interchangeable. To understand why this is the case, Sanborn et al. analyzed the genome of baker’s yeast and identified 150 activation domains, each very different in sequence. Three-quarters of them bound to a subunit of the Mediator complex called Med15. Sanborn et al. then developed a machine learning algorithm to predict activation domains in both yeast and humans. This algorithm also showed that negatively charged and greasy regions on the activation domains were essential to be activated by the Mediator complex. Further analyses revealed that activation domains used different poses to bind multiple sites on Med15, a behavior known as ‘fuzzy’ binding. This creates a high overall affinity even though the binding strength at each individual site is low, enabling the protein complexes to remain dynamic. These weak interactions together permit fine control over the activity of several genes, allowing cells to respond quickly and precisely to many changes. The computer algorithm used here provides a new way to identify activation domains across species and could improve our understanding of how living things grow, adapt and evolve. It could also give new insights into mechanisms of disease, particularly cancer, where transcription factors are often faulty.

Activation of the μ-opioid receptor (μOR) is responsible for the efficacy of the most effective analgesics. To shed light on the structural basis for μOR activation, here we report a 2.1 Å X-ray crystal structure of the murine μOR bound to the morphinan agonist BU72 and a G protein mimetic camelid antibody fragment. The BU72-stabilized changes in the μOR binding pocket are subtle and differ from those observed for agonist-bound structures of the β 2 -adrenergic receptor (β 2 AR) and the M2 muscarinic receptor. Comparison with active β 2 AR reveals a common rearrangement in the packing of three conserved amino acids in the core of the μOR, and molecular dynamics simulations illustrate how the ligand-binding pocket is conformationally linked to this conserved triad. Additionally, an extensive polar network between the ligand-binding pocket and the cytoplasmic domains appears to play a similar role in signal propagation for all three G-protein-coupled receptors. X-ray crystallography and molecular dynamics simulations of the μ-opioid receptor reveal the conformational changes in the extracellular and intracellular domains of this G-protein-coupled receptor that are associated with its activation. Activation of the μ-opioid receptor The μ-opioid receptor is a G-protein-coupled receptor (GPCR) activated by various analgesics, endogenous endorphins and drugs of abuse such as heroin and opium. Our understanding of the mechanism by which agonist binding leads to recognition, coupling, and activation of a particular G protein subtype is incomplete. In two papers in this issue of Nature , the authors used X-ray crystallography, molecular dynamics simulations, and NMR spectroscopy to probe the structural basis for receptor activation. As well as revealing the conformational changes in the extracellular and intracellular domains of this GPCR associated with receptor activation, these studies help explain why the allosteric coupling between the agonist-binding pocket and the cytoplasmic G-protein-coupling interface of this receptor is relatively weak.

During interphase, the inactive X chromosome (Xi) is largely transcriptionally silent and adopts an unusual 3D configuration known as the “Barr body.” Despite the importance of X chromosome inactivation, little is known about this 3D conformation. We recently showed that in humans the Xi chromosome exhibits three structural features, two of which are not shared by other chromosomes. First, like the chromosomes of many species, Xi forms compartments. Second, Xi is partitioned into two huge intervals, called “superdomains,” such that pairs of loci in the same superdomain tend to colocalize. The boundary between the superdomains lies near DXZ4, a macrosatellite repeat whose Xi allele extensively binds the protein CCCTC-binding factor. Third, Xi exhibits extremely large loops, up to 77 megabases long, called “superloops.” DXZ4 lies at the anchor of several superloops. Here, we combine 3D mapping, microscopy, and genome editing to study the structure of Xi, focusing on the role of DXZ4. We show that superloops and superdomains are conserved across eutherian mammals. By analyzing ligation events involving three or more loci, we demonstrate that DXZ4 and other superloop anchors tend to colocate simultaneously. Finally, we show that deleting DXZ4 on Xi leads to the disappearance of superdomains and superloops, changes in compartmentalization patterns, and changes in the distribution of chromatin marks. Thus, DXZ4 is essential for proper Xi packaging.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Activation of the [mu]-opioid receptor ([mu]OR) is responsible for the efficacy of the most effective analgesics. To shed light on the structural basis for [mu]OR activation, here we report a 2.1 Å X-ray crystal structure of the murine [mu]OR bound to the morphinan agonist BU72 and a G protein mimetic camelid antibody fragment. The BU72-stabilized changes in the [mu]OR binding pocket are subtle and differ from those observed for agonist-bound structures of the [beta].sub.2-adrenergic receptor ([beta].sub.2AR) and the M2 muscarinic receptor. Comparison with active [beta].sub.2AR reveals a common rearrangement in the packing of three conserved amino acids in the core of the [mu]OR, and molecular dynamics simulations illustrate how the ligand-binding pocket is conformationally linked to this conserved triad. Additionally, an extensive polar network between the ligand-binding pocket and the cytoplasmic domains appears to play a similar role in signal propagation for all three G-protein-coupled receptors.

Understanding how a subset of expressed genes dictates cellular phenotype is a considerable challenge owing to the large numbers of molecules involved, their combinatorics and the plethora of cellular behaviours that they determine 1 , 2 . Here we reduced this complexity by focusing on cellular organization—a key readout and driver of cell behaviour 3 , 4 —at the level of major cellular structures that represent distinct organelles and functional machines, and generated the WTC-11 hiPSC Single-Cell Image Dataset v1, which contains more than 200,000 live cells in 3D, spanning 25 key cellular structures. The scale and quality of this dataset permitted the creation of a generalizable analysis framework to convert raw image data of cells and their structures into dimensionally reduced, quantitative measurements that can be interpreted by humans, and to facilitate data exploration. This framework embraces the vast cell-to-cell variability that is observed within a normal population, facilitates the integration of cell-by-cell structural data and allows quantitative analyses of distinct, separable aspects of organization within and across different cell populations. We found that the integrated intracellular organization of interphase cells was robust to the wide range of variation in cell shape in the population; that the average locations of some structures became polarized in cells at the edges of colonies while maintaining the ‘wiring’ of their interactions with other structures; and that, by contrast, changes in the location of structures during early mitotic reorganization were accompanied by changes in their wiring. A dataset of 3D images from more than 200,000 human induced pluripotent stem cells is used to develop a framework to analyse cell shape and the location and organization of major intracellular structures.

Gene activator proteins comprise distinct DNA-binding and transcriptional activation domains (ADs). Because few ADs have been described, we tested domains tiling all yeast transcription factors for activation in vivo and identified 150 ADs. By mRNA display, we showed that 73% of ADs bound the Med15 subunit of Mediator, and that binding strength was correlated with activation. AD-Mediator interaction in vitro was unaffected by a large excess of free activator protein, pointing to a dynamic mechanism of interaction. Structural modeling showed that ADs interact with Med15 without shape complementarity (‘fuzzy’ binding). ADs shared no sequence motifs, but mutagenesis revealed biochemical and structural constraints. Finally, a neural network trained on AD sequences accurately predicted ADs in human proteins and in other yeast proteins, including chromosomal proteins and chromatin remodeling complexes. These findings solve the longstanding enigma of AD structure and function and provide a rationale for their role in biology.

RNA-based medicines and RNA-targeting drugs are emerging as promising new approaches for treating disease. Optimizing these therapeutics by naive experimental screening is a time-consuming and expensive process, while rational design requires an accurate understanding of the structure and function of RNA. To address this design challenge, we present ATOM-1, the first RNA foundation model trained on chemical mapping data, enabled by data collection strategies purposely developed for machine learning training. Using small probe neural networks on top of ATOM-1 embeddings, we demonstrate that this model has developed rich internal representations of RNA. Trained on limited amounts of additional data, these small networks achieve state-of-the-art accuracy on key RNA prediction tasks, suggesting that this approach can enable the design of therapies across the RNA landscape.Competing Interest StatementAll authors are current or former employees of Atomic AI. There is a pending patent application in relation to this work.