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Homologous recombination (HR) is a major pathway to repair DNA double-strand breaks (DSB). HR uses an undamaged homologous DNA sequence as a template for copying the missing information, which requires identifying a homologous sequence among megabases of DNA within the crowded nucleus. In eukaryotes, the conserved Rad51–single-stranded DNA nucleoprotein filament (NPF) performs this homology search. Although NPFs have been extensively studied in vitro by molecular and genetic approaches, their in vivo formation and dynamics could not thus far be assessed due to the lack of functional tagged versions of Rad51. Here we develop and characterize in budding yeast the first fully functional, tagged version of Rad51. Following induction of a unique DSB, we observe Rad51–ssDNA forming exceedingly long filaments, spanning the whole nucleus and eventually contacting the donor sequence. Emerging filaments adopt a variety of shapes not seen in vitro and are modulated by Rad54 and Srs2, shedding new light on the function of these factors. The filaments are also dynamic, undergoing rounds of compaction and extension. Our biophysical models demonstrate that formation of extended filaments, and particularly their compaction–extension dynamics, constitute a robust search strategy, allowing DSB to rapidly explore the nuclear volume and thus enable efficient HR.

Eukaryotic genomes are compacted into loops and topologically associating domains (TADs)1–3, which contribute to transcription, recombination and genomic stability4,5. Cohesin extrudes DNA into loops that are thought to lengthen until CTCF boundaries are encountered6–12. Little is known about whether loop extrusion is impeded by DNA-bound machines. Here we show that the minichromosome maintenance (MCM) complex is a barrier that restricts loop extrusion in G1 phase. Single-nucleus Hi-C (high-resolution chromosome conformation capture) of mouse zygotes reveals that MCM loading reduces CTCF-anchored loops and decreases TAD boundary insulation, which suggests that loop extrusion is impeded before reaching CTCF. This effect extends to HCT116 cells, in which MCMs affect the number of CTCF-anchored loops and gene expression. Simulations suggest that MCMs are abundant, randomly positioned and partially permeable barriers. Single-molecule imaging shows that MCMs are physical barriers that frequently constrain cohesin translocation in vitro. Notably, chimeric yeast MCMs that contain a cohesin-interaction motif from human MCM3 induce cohesin pausing, indicating that MCMs are ‘active’ barriers with binding sites. These findings raise the possibility that cohesin can arrive by loop extrusion at MCMs, which determine the genomic sites at which sister chromatid cohesion is established. On the basis of in vivo, in silico and in vitro data, we conclude that distinct loop extrusion barriers shape the three-dimensional genome.

Despite the well-established role of nuclear organization in the regulation of gene expression, little is known about the reverse: how transcription shapes the spatial organization of the genome. Owing to the small sizes of most previously studied genes and the limited resolution of microscopy, the structure and spatial arrangement of a single transcribed gene are still poorly understood. Here we study several long highly expressed genes and demonstrate that they form open-ended transcription loops with polymerases moving along the loops and carrying nascent RNAs. Transcription loops can span across micrometres, resembling lampbrush loops and polytene puffs. The extension and shape of transcription loops suggest their intrinsic stiffness, which we attribute to decoration with multiple voluminous nascent ribonucleoproteins. Our data contradict the model of transcription factories and suggest that although microscopically resolvable transcription loops are specific for long highly expressed genes, the mechanisms underlying their formation could represent a general aspect of eukaryotic transcription.

The relationships between chromosomal compartmentalization, chromatin state and function are poorly understood. Here by profiling long-range contact frequencies in HCT116 colon cancer cells, we distinguish three silent chromatin states, comprising two types of heterochromatin and a state enriched for H3K9me2 and H2A.Z that exhibits neutral three-dimensional interaction preferences and which, to our knowledge, has not previously been characterized. We find that heterochromatin marked by H3K9me3, HP1α and HP1β correlates with strong compartmentalization. We demonstrate that disruption of DNA methyltransferase activity greatly remodels genome compartmentalization whereby domains lose H3K9me3-HP1α/β binding and acquire the neutrally interacting state while retaining late replication timing. Furthermore, we show that H3K9me3-HP1α/β heterochromatin is permissive to loop extrusion by cohesin but refractory to CTCF binding. Together, our work reveals a dynamic structural and organizational diversity of the silent portion of the genome and establishes connections between the regulation of chromatin state and chromosome organization, including an interplay between DNA methylation, compartmentalization and loop extrusion.

Chromosome conformation capture (3C) assays are used to map chromatin interactions genome-wide. Chromatin interaction maps provide insights into the spatial organization of chromosomes and the mechanisms by which they fold. Hi-C and Micro-C are widely used 3C protocols that differ in key experimental parameters including cross-linking chemistry and chromatin fragmentation strategy. To understand how the choice of experimental protocol determines the ability to detect and quantify aspects of chromosome folding we have performed a systematic evaluation of 3C experimental parameters. We identified optimal protocol variants for either loop or compartment detection, optimizing fragment size and cross-linking chemistry. We used this knowledge to develop a greatly improved Hi-C protocol (Hi-C 3.0) that can detect both loops and compartments relatively effectively. In addition to providing benchmarked protocols, this work produced ultra-deep chromatin interaction maps using Micro-C, conventional Hi-C and Hi-C 3.0 for key cell lines used by the 4D Nucleome project.

Chromosome organization mediated by structural maintenance of chromosomes (SMC) complexes is vital in many organisms. SMC complexes act as motors that extrude DNA loops, but it remains unclear what happens when multiple complexes encounter one another on the same DNA in living cells and how these interactions may help to organize an active genome. We therefore created a crash-course track system to study SMC complex encounters in vivo by engineering defined SMC loading sites in the Bacillus subtilischromosome. Chromosome conformation capture (Hi-C) analyses of over 20 engineered strains show an amazing variety of chromosome folding patterns. Through three-dimensional polymer simulations and theory, we determine that these patterns require SMC complexes to bypass each other in vivo, as recently seen in an in vitro study. We posit that the bypassing activity enables SMC complexes to avoid traffic jams while spatially organizing the genome.

The nucleus of mammalian cells displays a distinct spatial segregation of active euchromatic and inactive heterochromatic regions of the genome1,2. In conventional nuclei, microscopy shows that euchromatin is localized in the nuclear interior and heterochromatin at the nuclear periphery1,2. Genome-wide chromosome conformation capture (Hi-C) analyses show this segregation as a plaid pattern of contact enrichment within euchromatin and heterochromatin compartments3, and depletion between them. Many mechanisms for the formation of compartments have been proposed, such as attraction of heterochromatin to the nuclear lamina2,4, preferential attraction of similar chromatin to each other1,4–12, higher levels of chromatin mobility in active chromatin13–15and transcription-related clustering of euchromatin16,17. However, these hypotheses have remained inconclusive, owing to the difficulty of disentangling intra-chromatin and chromatin–lamina interactions in conventional nuclei18. The marked reorganization of interphase chromosomes in the inverted nuclei of rods in nocturnal mammals19,20provides an opportunity to elucidate the mechanisms that underlie spatial compartmentalization. Here we combine Hi-C analysis of inverted rod nuclei with microscopy and polymer simulations. We find that attractions between heterochromatic regions are crucial for establishing both compartmentalization and the concentric shells of pericentromeric heterochromatin, facultative heterochromatin and euchromatin in the inverted nucleus. When interactions between heterochromatin and the lamina are added, the same model recreates the conventional nuclear organization. In addition, our models allow us to rule out mechanisms of compartmentalization that involve strong euchromatin interactions. Together, our experiments and modelling suggest that attractions between heterochromatic regions are essential for the phase separation of the active and inactive genome in inverted and conventional nuclei, whereas interactions of the chromatin with the lamina are necessary to build the conventional architecture from these segregated phases. Attractions between heterochromatic regions are essential for phase separation of the active and inactive genome in inverted and conventional nuclei, whereas chromatin–lamina interactions are necessary to build the conventional genomic architecture from these segregated phases.

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.ioand is also available as a containerized application that can be run on any platform.

Imaging and chromosome conformation capture studies have revealed several layers of chromosome organization, including segregation into megabase-sized active and inactive compartments, and partitioning into sub-megabase domains (TADs). It remains unclear, however, how these layers of organization form, interact with one another and influence genome function. Here we show that deletion of the cohesin-loading factor Nipbl in mouse liver leads to a marked reorganization of chromosomal folding. TADs and associated Hi-C peaks vanish globally, even in the absence of transcriptional changes. By contrast, compartmental segregation is preserved and even reinforced. Strikingly, the disappearance of TADs unmasks a finer compartment structure that accurately reflects the underlying epigenetic landscape. These observations demonstrate that the three-dimensional organization of the genome results from the interplay of two independent mechanisms: cohesin-independent segregation of the genome into fine-scale compartments, defined by chromatin state; and cohesin-dependent formation of TADs, possibly by loop extrusion, which helps to guide distant enhancers to their target genes.

The 4D Nucleome Network aims to develop and apply approaches to map the structure and dynamics of the human and mouse genomes in space and time with the goal of gaining deeper mechanistic insights into how the nucleus is organized and functions. The project will develop and benchmark experimental and computational approaches for measuring genome conformation and nuclear organization, and investigate how these contribute to gene regulation and other genome functions. Validated experimental technologies will be combined with biophysical approaches to generate quantitative models of spatial genome organization in different biological states, both in cell populations and in single cells.

Structural maintenance of chromosomes (SMC) protein complexes are key determinants of chromosome conformation. Using Hi-C and polymer modelling, we study how cohesin and condensin, two deeply conserved SMC complexes, organize chromosomes in the budding yeast Saccharomyces cerevisiae. The canonical role of cohesin is to co-align sister chromatids, while condensin generally compacts mitotic chromosomes. We find strikingly different roles for the two complexes in budding yeast mitosis. First, cohesin is responsible for compacting mitotic chromosome arms, independently of sister chromatid cohesion. Polymer simulations demonstrate that this role can be fully accounted for through cis-looping of chromatin. Second, condensin is generally dispensable for compaction along chromosome arms. Instead, it plays a targeted role compacting the rDNA proximal regions and promoting resolution of peri-centromeric regions. Our results argue that the conserved mechanism of SMC complexes is to form chromatin loops and that distinct SMC-dependent looping activities are selectively deployed to appropriately compact chromosomes.

Chromatin is reprogrammed after fertilization to produce a totipotent zygote with the potential to generate a new organism. The maternal genome inherited from the oocyte and the paternal genome provided by sperm coexist as separate haploid nuclei in the zygote. How these two epigenetically distinct genomes are spatially organized is poorly understood. Existing chromosome conformation capture-based methods are not applicable to oocytes and zygotes owing to a paucity of material. To study three-dimensional chromatin organization in rare cell types, we developed a single-nucleus Hi-C (high-resolution chromosome conformation capture) protocol that provides greater than tenfold more contacts per cell than the previous method. Here we show that chromatin architecture is uniquely reorganized during the oocyte-to-zygote transition in mice and is distinct in paternal and maternal nuclei within single-cell zygotes. Features of genomic organization including compartments, topologically associating domains (TADs) and loops are present in individual oocytes when averaged over the genome, but the presence of each feature at a locus varies between cells. At the sub-megabase level, we observed stochastic clusters of contacts that can occur across TAD boundaries but average into TADs. Notably, we found that TADs and loops, but not compartments, are present in zygotic maternal chromatin, suggesting that these are generated by different mechanisms. Our results demonstrate that the global chromatin organization of zygote nuclei is fundamentally different from that of other interphase cells. An understanding of this zygotic chromatin ‘ground state’ could potentially provide insights into reprogramming cells to a state of totipotency.

Topologically associating domains (TADs) are fundamental structural and functional building blocks of human interphase chromosomes, yet the mechanisms of TAD formation remain unclear. Here, we propose that loop extrusion underlies TAD formation. In this process, cis-acting loop-extruding factors, likely cohesins, form progressively larger loops but stall at TAD boundaries due to interactions with boundary proteins, including CTCF. Using polymer simulations, we show that this model produces TADs and finer-scale features of Hi-C data. Each TAD emerges from multiple loops dynamically formed through extrusion, contrary to typical illustrations of single static loops. Loop extrusion both explains diverse experimental observations—including the preferential orientation of CTCF motifs, enrichments of architectural proteins at TAD boundaries, and boundary deletion experiments—and makes specific predictions for the depletion of CTCF versus cohesin. Finally, loop extrusion has potentially far-ranging consequences for processes such as enhancer-promoter interactions, orientation-specific chromosomal looping, and compaction of mitotic chromosomes.

During cell division, chromosomes are compacted in length by more than a 100-fold. A wide range of experiments demonstrated that in their compacted state, mammalian chromosomes form arrays of closely stacked consecutive ∼100 kb loops. The mechanism underlying the active process of chromosome compaction into a stack of loops is unknown. Here we test the hypothesis that chromosomes are compacted by enzymatic machines that actively extrude chromatin loops. When such loop-extruding factors (LEF) bind to chromosomes, they progressively bridge sites that are further away along the chromosome, thus extruding a loop. We demonstrate that collective action of LEFs leads to formation of a dynamic array of consecutive loops. Simulations and an analytically solved model identify two distinct steady states: a sparse state, where loops are highly dynamic but provide little compaction; and a dense state, where there are more stable loops and dramatic chromosome compaction. We find that human chromosomes operate at the border of the dense steady state. Our analysis also shows how the macroscopic characteristics of the loop array are determined by the microscopic properties of LEFs and their abundance. When the number of LEFs are used that match experimentally based estimates, the model can quantitatively reproduce the average loop length, the degree of compaction, and the general loop-array morphology of compact human chromosomes. Our study demonstrates that efficient chromosome compaction can be achieved solely by an active loop-extrusion process.

The relationship between the host and its microbiota is challenging to understand because both microbial communities and their environments are highly variable. We have developed a set of techniques based on population dynamics and information theory to address this challenge. These methods identify additional bacterial taxa associated with pediatric Crohn disease and can detect significant changes in microbial communities with fewer samples than previous statistical approaches required. We have also substantially improved the accuracy of the diagnosis based on the microbiota from stool samples, and we found that the ecological niche of a microbe predicts its role in Crohn disease. Bacteria typically residing in the lumen of healthy individuals decrease in disease, whereas bacteria typically residing on the mucosa of healthy individuals increase in disease. Our results also show that the associations with Crohn disease are evolutionarily conserved and provide a mutual information-based method to depict dysbiosis.

Metazoan genomes are spatially organized at multiple scales, from packaging of DNA around individual nucleosomes to segregation of whole chromosomes into distinct territories. At the intermediate scale of kilobases to megabases, which encompasses the sizes of genes, gene clusters and regulatory domains, the three-dimensional (3D) organization of DNA is implicated in multiple gene regulatory mechanisms, but understanding this organization remains a challenge. At this scale, the genome is partitioned into domains of different epigenetic states that are essential for regulating gene expression. Here we investigate the 3D organization of chromatin in different epigenetic states using super-resolution imaging. We classified genomic domains in Drosophila cells into transcriptionally active, inactive or Polycomb-repressed states, and observed distinct chromatin organizations for each state. All three types of chromatin domains exhibit power-law scaling between their physical sizes in 3D and their domain lengths, but each type has a distinct scaling exponent. Polycomb-repressed domains show the densest packing and most intriguing chromatin folding behaviour, in which chromatin packing density increases with domain length. Distinct from the self-similar organization displayed by transcriptionally active and inactive chromatin, the Polycomb-repressed domains are characterized by a high degree of chromatin intermixing within the domain. Moreover, compared to inactive domains, Polycomb-repressed domains spatially exclude neighbouring active chromatin to a much stronger degree. Computational modelling and knockdown experiments suggest that reversible chromatin interactions mediated by Polycomb-group proteins play an important role in these unique packaging properties of the repressed chromatin. Taken together, our super-resolution images reveal distinct chromatin packaging for different epigenetic states at the kilobase-to-megabase scale, a length scale that is directly relevant to genome regulation.

Eukaryotic chromosomes are huge DNA polymers organized in space and time. Chromatin looping compacts these polymers, helps genome organization by the formation of enhancer–promoter and other interactions, and emerges when genes are highly expressed. Thus, several types of chromatin loops exist, which vary in structure, maintenance and function.

Tumor-propagating cells in acute leukemia maintain a stem/progenitor-like immature phenotype and proliferative capacity. Acute myeloid leukemia (AML) and acute T-lymphoblastic leukemia (T-ALL) originate from different lineages through distinct oncogenic events such as MLL fusions and Notch signaling, respectively. We found that Zfx, a transcription factor that controls hematopoietic stem cell self-renewal, controls the initiation and maintenance of AML caused by MLL-AF9 fusion and of T-ALL caused by Notch1 activation. In both leukemia types, Zfx prevents differentiation and activates gene sets characteristic of immature cells of the respective lineages. In addition, endogenous Zfx contributes to gene induction and transformation by Myc overexpression in myeloid progenitors. Key Zfx target genes include the mitochondrial enzymes Ptpmt1and Idh2, whose overexpression partially rescues the propagation of Zfx-deficient AML. These results show that distinct leukemia types maintain their undifferentiated phenotype and self-renewal by exploiting a common stem-cell-related genetic regulator.

A study reveals that a process called loop extrusion, which is central to the folding and function of chromosomes, also seems to play a key part in the repair of double-strand DNA breaks.

Metaphase spindles are steady-state ensembles of microtubules that turn over rapidly and slide poleward in some systems. Since the discovery of dynamic instability in the mid-1980s, models for spindle morphogenesis have proposed that microtubules are stabilized by the spindle environment. We used single molecule imaging to measure tubulin turnover in spindles, and nonspindle assemblies, in Xenopus laevis egg extracts. We observed many events where tubulin molecules spend only a few seconds in polymer and thus are difficult to reconcile with standard models of polymerization dynamics. Our data can be quantitatively explained by a simple, phenomenological model—with only one adjustable parameter—in which the growing and shrinking of microtubule ends is approximated as a biased random walk. Microtubule turnover kinetics did not vary with position in the spindle and were the same in spindles and nonspindle ensembles nucleated by Tetrahymena pellicles. These results argue that the high density of microtubules in spindles compared with bulk cytoplasm is caused by local enhancement of nucleation and not by local stabilization. It follows that the key to understanding spindle morphogenesis will be to elucidate how nucleation is spatially controlled.

Covalent modification cycles (e.g., phosphorylation-dephosphorylation) underlie most cellular signaling and control processes. Low molecular copy number, arising from compartmental segregation and slow diffusion between compartments, potentially renders these cycles vulnerable to intrinsic chemical fluctuations. How can a cell operate reliably in the presence of this inherent stochasticity? How do changes in extrinsic parameters lead to variability of response? Can cells exploit these parameters to tune cycles to different ranges of stimuli? We study the dynamics of an isolated phosphorylation cycle. Our model shows that the cycle transmits information reliably if it is tuned to an optimal parameter range, despite intrinsic fluctuations and even for small input signal amplitudes. At the same time, the cycle is sensitive to changes in the concentration and activity of kinases and phosphatases. This sensitivity can lead to significant cell-to-cell response variability. It also provides a mechanism to tune the cycle to transmit signals in various amplitude ranges. Our results show that signaling cycles possess a surprising combination of robustness and tunability. This combination makes them ubiquitous in eukaryotic signaling, optimizing signaling in the presence of fluctuations using their inherent flexibility. On the other hand, cycles tuned to suppress intrinsic fluctuations can be vulnerable to changes in the number and activity of kinases and phosphatases. Such trade-offs in robustness to intrinsic and extrinsic fluctuations can influence the evolution of signaling cascades, making them the weakest links in cellular circuits.

Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly disputed hypothesis. Although structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g., flux balance analysis, are more successful in predicting phenotypes of knockout strains. We reconcile these seemingly conflicting results by showing that the topology of the metabolic networks of both Escherichia coli and Saccharomyces cerevisiae are, in fact, sufficient to predict the viability of knockout strains with accuracy comparable to flux balance analysis on large, unbiased mutant data sets. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics such as node degree, graph diameter, and node usage (betweenness) fail to predict the viability of E. coli mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Our results strongly support a link between the topology and function of biological networks and, in agreement with recent genetic studies, emphasize the minimal role of flux rerouting in providing robustness of mutant strains.

Understanding the relationships between the structure (topology) and function of biological networks is a central question of systems biology. The idea that topology is a major determinant of systems function has become an attractive and highly-disputed hypothesis. While the structural analysis of interaction networks demonstrates a correlation between the topological properties of a node (protein, gene) in the network and its functional essentiality, the analysis of metabolic networks fails to find such correlations. In contrast, approaches utilizing both the topology and biochemical parameters of metabolic networks, e.g. flux balance analysis (FBA), are more successful in predicting phenotypes of knock-out strains. We reconcile these seemingly conflicting results by showing that the topology of E. coli's metabolic network is, in fact, sufficient to predict the viability of knock-out strains with accuracy comparable to FBA on a large, unbiased dataset of mutants. This surprising result is obtained by introducing a novel topology-based measure of network transport: synthetic accessibility. We also show that other popular topology-based characteristics like node degree, graph diameter, and node usage (betweenness) fail to predict the viability of mutant strains. The success of synthetic accessibility demonstrates its ability to capture the essential properties of the metabolic network, such as the branching of chemical reactions and the directed transport of material from inputs to outputs. Our results (1) strongly support a link between the topology and function of biological networks; (2) in agreement with recent genetic studies, emphasize the minimal role of flux re-routing in providing robustness of mutant strains.

Recognition and binding of specific sites on DNA by proteins is central for many cellular functions such as transcription, replication, and recombination. In the process of recognition, a protein rapidly searches for its specific site on a long DNA molecule and then strongly binds this site. Here we aim to find a mechanism that can provide both a fast search (1–10s) and high stability of the specific protein-DNA complex (Kd=10−15–10−8M). Earlier studies have suggested that rapid search involves sliding of the protein along the DNA. Here we consider sliding as a one-dimensional diffusion in a sequence-dependent rough energy landscape. We demonstrate that, despite the landscape’s roughness, rapid search can be achieved if one-dimensional sliding is accompanied by three-dimensional diffusion. We estimate the range of the specific and nonspecific DNA-binding energy required for rapid search and suggest experiments that can test our mechanism. We show that optimal search requires a protein to spend half of its time sliding along the DNA and the other half diffusing in three dimensions. We also establish that, paradoxically, realistic energy functions cannot provide both rapid search and strong binding of a rigid protein. To reconcile these two fundamental requirements we propose a search-and-fold mechanism that involves the coupling of protein binding and partial protein folding. The proposed mechanism has several important biological implications for search in the presence of other proteins and nucleosomes, simultaneous search by several proteins, etc. The proposed mechanism also provides a new framework for interpretation of experimental and structural data on protein-DNA interactions.

In recent years discussion of nuclear hormone receptors, transporters, and drug-metabolizing enzymes has begun to take place as our knowledge of the overlapping ligand specificity of each of these proteins has deepened. This ligand specificity is potentially valuable information for influencing future drug design, as it is important to avoid certain enzymes or transporters in order to circumvent potential drug–drug interactions. Similarly, it is critical that the induction of these same proteins via nuclear hormone receptors is avoided, as this can result in further toxicities. Using a ligand-based approach in this review we describe new and previously published computational models for PXR, CAR, FXR, LXRα, and LXRβ that may help in understanding the complexity of interactions between transporters and enzymes. The value of these types of models is that they may enable us to design molecules to selectively modulate pathways for therapeutic effect and in addition predict the potential for drug interactions more reliably. Simultaneously, we might learn which came first: the transporter, the enzyme, or the nuclear hormone receptor?

Concepts of orthology and paralogy are become increasingly important as whole-genome comparison allows their identification in complete genomes. Functional specificity of proteins is assumed to be conserved among orthologs and is different among paralogs. We used this assumption to identify residues which determine specificity of protein-DNA and protein-ligand recognition. Finding such residues is crucial for understanding mechanisms of molecular recognition and for rational protein and drug design. Assuming conservation of specificity among orthologs and different specificity of paralogs, we identify residues which correlate with this grouping by specificity. The method is taking advantage of complete genomes to find multiple orthologs and paralogs. The central part of this method is a procedure to compute statistical significance of the predictions. The procedure is based on a simple statistical model of protein evolution. When applied to a large family of bacterial transcription factors, our method identified 12 residues that are presumed to determine the protein-DNA and protein-ligand recognition specificity. Structural analysis of the proteins and available experimental results strongly support our predictions. Our results suggest new experiments aimed at rational re-design of specificity in bacterial transcription factors by a minimal number of mutations. While sets of orthologous and paralogous proteins can be easily derived from complete genomic sequences, our method can identify putative specificity determinants in such proteins.

Here, we present statistical analysis of conservation profiles in families of homologous sequences for nine proteins whose folding nucleus was determined by protein engineering methods. We show that in all but one protein (AcP) folding nucleus residues are significantly more conserved than the rest of the protein. Two aspects of our study are especially important: (i) grouping of amino acid residues into classes according to their physical-chemical properties and (ii) proper normalization of amino acid probabilities that reflects the fact that evolutionary pressure to conserve some amino acid types may itself affect concentration of various amino acid types in protein families. Neglect of any of those two factors may make physical and biological “signals” from conservation profiles disappear.

Two methods were proposed recently to derive energy parameters from known native protein conformations and corresponding sets of decoys. One is based on finding, by means of a perceptron learning scheme, energy parameters such that the native conformations have lower energies than the decoys. The second method maximizes the difference between the native energy and the average energy of the decoys, measured in terms of the width of the decoys' energy distribution (Z‐score). Whereas the perceptron method is sensitive mainly to “outlier” (i.e., extremal) decoys, the Z‐score optimization is governed by the high density regions in decoy‐space. We compare the two methods by deriving contact energies for two very different sets of decoys: the first obtained for model lattice proteins and the second by threading. We find that the potentials derived by the two methods are of similar quality and fairly closely related. This finding indicates that standard, naturally occurring sets of decoys are distributed in a way that yields robust energy parameters (that are quite insensitive to the particular method used to derive them). The main practical implication of this finding is that it is not necessary to fine‐tune the potential search method to the particular set of decoys used. Proteins 2000;41:192–201. © 2000 Wiley‐Liss, Inc.

Two methods were proposed recently to derive energy parameters from known native protein conformations and corresponding sets of decoys. One is based on finding, by means of a perceptron learning scheme, energy parameters such that the native conformations have lower energies than the decoys. The second method maximizes the difference between the native energy and the average energy of the decoys, measured in terms of the width of the decoys' energy distribution (Z-score). Whereas the perceptron method is sensitive mainly to “outlier” (i.e., extremal) decoys, the Z-score optimization is governed by the high density regions in decoy-space. We compare the two methods by deriving contact energies for two very different sets of decoys: the first obtained for model lattice proteins and the second by threading. We find that the potentials derived by the two methods are of similar quality and fairly closely related. This finding indicates that standard, naturally occurring sets of decoys are distributed in a way that yields robust energy parameters (that are quite insensitive to the particular method used to derive them). The main practical implication of this finding is that it is not necessary to fine-tune the potential search method to the particular set of decoys used. Proteins 2000;41:192–201. © 2000 Wiley-Liss, Inc.

A lattice model with side chains was used to investigate protein folding with computer simulations. In this model, we rigorously demonstrate the existence of a specific folding nucleus. This nucleus contains specific interactions not present in the native state that, when weakened, slow folding but do not change protein stability. Such a decoupling of folding kinetics from thermodynamics has been observed experimentally for real proteins. From our results, we conclude that specific non-native interactions in the transition state would give rise to φ-values that are negative or larger than unity. Furthermore, we demonstrate that residue Ile 34 in src SH3, which has been shown to be kinetically, but not thermodynamically, important, is universally conserved in proteins with the SH3 fold. This is a clear example of evolution optimizing the folding rate of a protein independent of its stability and function.

Here, we provide an analysis of molecular evolution of five of the most populated protein folds: immunoglobulin fold, oligonucleotide-binding fold, Rossman fold, alpha/beta plait, and TIM barrels. In order to distinguish between “historic”, functional and structural reasons for amino acid conservations, we consider proteins that acquire the same fold and have no evident sequence homology. For each fold we identify positions that are conserved within each individual family and coincide when non-homologous proteins are structurally superimposed. As a baseline for statistical assessment we use the conservatism expected based on the solvent accessibility. The analysis is based on a new concept of “conservatism-of-conservatism”. This approach allows us to identify the structural features that are stabilized in all proteins having a given fold, despite the fact that actual interactions that provide such stabilization may vary from protein to protein. Comparison with experimental data on thermodynamics, folding kinetics and function of the proteins reveals that such universally conserved clusters correspond to either: (i) super-sites (common location of active site in proteins having common tertiary structures but not function) or (ii) folding nuclei whose stability is an important determinant of folding rate, or both (in the case of Rossman fold). The analysis also helps to clarify the relation between folding and function that is apparent for some folds.

We developed a novel Monte Carlo threading algorithm which allows gaps and insertions both in the template structure and threaded sequence. The algorithm is able to find the optimal sequence-structure alignment and sample suboptimal alignments. Using our algorithm we performed sequence-structure alignments for a number of examples for three protein folds (ubiquitin, immunoglobulin and globin) using both “ideal” set of potentials (optimized to provide the best Z-score for a given protein) and more realistic knowledge-based potentials. Two physically different scenarios emerged. If a template structure is similar to the native one (within 2 Å RMS), then (i) the optimal threading alignment is correct and robust with respect to deviations of the potential from the “ideal” one; (ii) suboptimal alignments are very similar to the optimal one; (iii) as Monte Carlo temperature decreases a sharp cooperative transition to the optimal alignment is observed. In contrast, if the template structure is only moderately close to the native structure (RMS greater than 3.5 Å), then (i) the optimal alignment changes dramatically when an “ideal” potential is substituted by the real one; (ii) the structures of suboptimal alignments are very different from the optimal one, reducing the reliability of the alignment; (iii) the transition to the apparently optimal alignment is non-cooperative. In the intermediate cases when the RMS between the template and the native conformations is in the range between 2 Å and 3.5 Å, the success of threading alignment may depend on the quality of potentials used.

As epidemiologists search for smaller and smaller effects, the statistical uncertainty in their studies can be dwarfed by biases and systematic uncertainty. We here suggest that Monte Carlo techniques are very useful to estimate some of these biases and uncertainties, and perhaps to avoid them entirely. We illustrate this by two simple Monte Carlo simulations. First, we show how often false positive findings, and sometimes false negative findings, can result from 33 differential misclassification of the exposure status. Secondly, we show how a bias, that we call “the binning bias,” can be caused if the investigator chooses bin boundaries after he has seen the data. We show how an allowance might be made for such a bias by increasing the uncertainty bounds. This would put the presentation of the results on a par with the presentation in physical sciences where a quantitative estimate of systematic errors is routinely included with the final result. Finally, we suggest how similar Monte Carlo simulations carried out before and during the study can be used to avoid the biases entirely.

In this paper we introduce a novel method of deriving a pairwise potential for protein folding. The potential is obtained by an optimization procedure that simultaneously maximizes thermodynamic stability for all proteins in the database. When applied to the representative dataset of proteins and with the energy function taken in pairwise contact approximation, our potential scored somewhat better than existing ones. However, the discrimination of the native structure from decoys is still not strong enough to make the potential useful forab initiofolding. Our results suggest that the problem lies with pairwise amino acid contact approximation and/or simplified presentation of proteins rather than with the derivation of potential. We argue that more detail of protein structure and energetics should be taken into account to achieve energy gaps. The suggested method is general enough to allow us to systematically derive parameters for more sophisticated energy functions. The internal control of validity for the potential derived by our method is convergence to a unique solution upon addition of new proteins to the database. The method is tested on simple model systems where sequences are designed, using the preset “true” potential, to have low energy in a dataset of structures. Our procedure is able to recover the potential with correlationr≃91% with the true one and we were able to fold all model structures using the recovered potential. Other statistical knowledge-based approaches were tested using this model and the results indicate that they also can recover the true potential with high degree of accuracy.

The primary sequence of esterases from Acinetobacter lwoffiiRAG-1 and A. calcoaceticusBD413 were compared with linearized structural sequences of two hundred proteins selected from Brookhaven Protein DataBank using a modified version of the Bowie et al. algorithm [3]. Significant structural homology was found to α/β proteins and specifically to those with the α/β-hydrolase fold for which the crystal structure was reported. No such homology was detected using common primary sequence alignment programs such as FASTA or BLAST.

The p53 protein, a transcription factor of key importance in tumorigenesis, is suggested to diffuse one-dimensionally along DNA via its C-terminal domain, a process that is proposed to regulate gene activation both positively and negatively. There has been no direct observation of p53 moving along DNA, however, and little is known about the mechanism and rate of its translocation. Here, we use single-molecule techniques to visualize, in real time, the one-dimensional diffusion of p53 along DNA. The one-dimensional diffusion coefficient is measured to be close to the theoretical limit, indicative of movement along a free energy landscape with low activation barriers. We further investigate the mechanism of translocation and determine that p53 is capable of sliding—moving along DNA while in continuous contact with the duplex, rather than through a series of hops between nearby bases.

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This corrects the article DOI: 10.1038/nature23884

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We report on equilibrium properties of a concentrated solution of non-concatenated ring polymers by Molecular dynamics simulations using HooMD-blue, a fast implementation on graphics processor units (GPUs). We are able to identify the intermediate scaling regime for the radius of gyration Rg ∝N0.4 as well as indication for a crossover to Rg ∝N1/3 for rings with chain length N in our fully flexible off-lattice polymer model. This crossover takes place between a ring size of 2500 and 7500 monomers for monomer density ρ=0.5. Our results are in agreement with recent studies for lattice and stiff off-lattice models and show once again that this scaling is not model dependent at all. Furthermore this study demonstrates that GPUs are suited to tackle large scale problems in computational polymer science. To this end we present performance measurements relating the computing performance of a single NVidias GTX580 consumer graphic card to conventional state of the art supercomputers.