Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials . Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed. Multi-dwelling residential buildings are currently being constructed in areas with high population densities since these buildings can offer many houses within a limited urban space. This type of building allows residents access to workplaces and conveniences as well as economic benefits to society. Though multi-dwelling residential buildings have various advantages, they also have inherent problems due to their structural characteristics: shared floor, ceiling, and walls. In multi-dwelling residential buildings, any source of noise generated within a unit can be transmitted to adjacent units through those shared components. Specifically, the floor impact sound is generally more severe than others and can lead to troubles or even lawsuits between neighbors. Many countries have established standards for quantifying floor impact sounds. The U.S. Department of Housing and Urban Development (HUD)  has cateTo mitigate the floor impact sounds, the use of resilient materials within floor slabs has recently become popular in research and construction practice ,6. TheseExperiment-based studies are intrinsically data-driven. Classically, researchers gather the experimental data and extract an empirical formula or a model that can best describe the behavior of the material under question within the collected information. However, as the behavior of the material becomes highly nonlinear, the formula becomes more complex and harder to use. To ease the usage and eventually be free of a visible formula, we can utilize the emerging machine learning approach to estimate the material behavior. The data-driven property of material behavior study also makes it an excellent candidate to apply the machine learning strategy.Applications of machine learning algorithms have shown reasonably good results in various engineering fields. Artificial neural networks (ANN) and linear regression have been used to predict the performance of a diesel engine . The useIt must be noted here that the machine learning approach used for this purpose still falls into the regression analysis. Regression analysis is a statistical process to estimate the relationship between dependent variables and independent variables. The machine learning approach is doing the regression analysis that the conventional way—non-computer-based—does, except for the computational efficiency.This paper presents a technique to predict the long-term compressive deformation of resilient materials using novel machine learning algorithms: K-nearest neighbors (KNN), regression tree (RT), and artificial neural networks (ANN). A compressive creep test results over 350 and 500 days were used to train and evaluate the machine learning algorithms. First, the above-mentioned machine learning algorithms were trained using the actual data set. Then, the predictions obtained from the trained algorithms were compared to the prediction by ISO 20392 procedure. ISO 20392 provides a means to evaluate the compressive creep of resilient material . This stThe two estimations are compared with the measured data points, and the long-term deformation prediction ability will be evaluated.3 for EPS1, EPS2, and P.E., and it was 59.3 kg/m3 for EVA-59-E. As shown in Six types of resilient materials widely used in construction practice were selected to investigate their long-term compressive deformation performance. The test was performed following ISO 20392. The specimens were expanded polystyrene 1 (EPS1), expanded polystyrene 2 (EPS2), polyethylene (P.E.), and ethylene-vinyl acetate (EVA). The densities of the specimens ranged from 12 to 25 kg/mA universal testing machine (UTM) was used to measure the elastic modulus of the resilient materials. A steel plate was placed on the resilient materials to prevent eccentricity. The deformation-controlled load was monotonically applied to the specimens at a speed of 0.02 mm/s. The specimens were loaded until the deformation reached 50% of the initial thickness. The load-deformation relationship of the specimens showed near-linear responses, whereas EVA-59-E showed bilinear responses. The reason for the bilinear responses was due to the shape of the EVA-59-E. In the case of EVA-59-E, unlike other specimens, EVA-59-E consisted of two types of cross-sections: flat and embossed. To evaluate the elastic modulus of each resilient material, the tangent moduli measured at a specific strain of 0.30 was defined as the elastic moduli of the respective material. It should be noted that the elastic moduli of EVA-59-E were evaluated at specific strains of 0.17 and 0.30, because of an obvious bilinear response of the load-deformation relationship.To measure the long-term compressive deformation of the resilient materials under sustained loads, the load levels of 40, 80, 250, and 500 N were applied to 24 specimens over 350- and 500-day periods. These four loading conditions were selected to represent various loading conditions of residential buildings. The load levels of 40 and 80 N, representing the permanent loads , were applied to the specimens over 350-day periods. The load levels of 250 N and 500 N, which induce 0.011 and 0.022 MPa of stresses on the resilient materials, respectively, are representatives of heavy appliances or furniture such as refrigerators and pianos in the household. In the cases of 250 N and 500 N, the loads were applied to the specimens over 500-day periods.As shown in The long-term deformations of the resilient materials were measured for more than 500 days. The first measurement was taken at 1 min after loading, then consecutive measurements were made at 10 min, 30 min, 1 h, 5 h, 10 h, 1 day, 2 days, and 3 days after the specimens were loaded. Between 3 and 30 days, the deformation was measured every 3 days. After 30 days, the deformation was measured every ten days. The measuring intervals are summarized in In this section, the learning algorithms used in this article are briefly introduced. Three algorithms are considered, namely, distance-weighted k-nearest-neighbor regression (KNN), regression tree (RT), and artificial neural network (ANN).i-th input features and scalar k nearest neighbors of k is a preselected parameter. In other words,The k-nearest neighbor (KNN) algorithm is intrinsically suitable for such increasing relationships because it involves neighboring data points and, therefore, sensitive to the local slope. For a given set of data S in the computer’s memory space. Then, in the later querying step, the algorithm loads the k number of nearest neighbors of the query point S to find the nearest k neighbors in the worst case, and may have large memory costs since the prediction must inquire of S. Additional regression details such as equations for KNN can be found in Kim et al. [KNN is a type of instance-based learning algorithm. The learning in this algorithm is done by simply storing the provided training set m et al. . S. Starting from the root node, the algorithm tracks down the branches of the upside-down tree with the given query point S and ultimately gives no meaningful information. It is recommended to either sweep through every leaf size to seek an optimal leaf size or implement some strategy such as bagging or boosting to introduce regularity. Learning in RT is implemented as building the tree with given S, and S can be forgotten after the learning is finished since the querying is done with the RT, not with S. Such implementation expedites the query time compare to KNN; however, the whole tree must be rebuilt if one wants to add additional data points.The regression tree (RT) algorithm if the ranges are adequately divided since the local slopes will be better expressed. RT builds a binary decision tree with the given training set Step 1 Let d vectors where j-th feature of i-th instance among N instances in S. The corresponding label is a scalar value j in j that has the highest correlation coefficient.Step 2 Let j be the highest correlated feature to c.Step 3 Training set S has now been split into two subsets with respect to c, namely In this article, RT with the highest correlation coefficient is implemented, and the algorithm is as follows:Once the tree is built, for a given query point m et al. .The artificial neural network (ANN) algorithm is robust when the data points have random change over the entire range, such as abrupt jumps. The ANN algorithm possesses a mechanism to include all patterns appearing in the whole range. Thus, the ANN is presumed to be less suitable for the present application than the other two methods. ANN superficially mimics the biological nerve system, although many details are neglected in implementation. When there is an input S at best, the direction of the data flow, and other detailed implementation specifics, ANN can be categorized into several different types. In this article, we relied on by far the most popular method, called the feed-forward, backpropagation method, where the data moves in one-way from the input to the output through hidden layers, and the error terms at the end of each trial, also known as an epoch, propagate back to the lower layer, and the weights are iteratively adjusted in every epoch until they converge. A schematic of this is shown in The training in this algorithm is the process of finding an optimal set of weights that connect the neurons in layers. Depending on the method of optimizing the weights to fit the given dataset For this particular application, the model landed at two hidden layers, where each layer consists of 50 neurons. Sigmoid activation function was used that gave the highest fidelity compared to the others, which were also chosen by trial and error. The dataset was divided into ten subsets, where nine were used for training, and one was used for validation. There are ten possible combinations to group the training set and validation set in this approach, and all ten combinations were used for training the ANN model. . AdditioIn this section, the predictions of long-term compressive deformation responses using three machine learning algorithms are presented. Those algorithms were trained using the experimental test results presented in The long-term compressive deformation tests on six soundproofing resilient materials were conducted. Their deformations were monitored for periods of 350 days (for the specimens loaded at both 40 and 80 N) and 500 days (for the specimens loaded at 250 and 500 N). The long-term deformation varied depending on the mechanical properties, specimen dimensions, and loading situations of resilient materials. The larger the applied load, the greater the increment in long-term deformation was observed; however, they were not proportional to the applied loads.PE-24-F deflected the most in all loading cases, while EPS 2 deflected more than EPS 1. In particular, the PE-24-F specimen showed deformations of 23.9 and 27.2 mm under 250 and 500 N loads, respectively, which is more than 80% deformation as compared to the initial thickness (30 mm). The deformation in the specimens with the corrugated bottom shape was larger than that of flat bottom shape. In the resilient materials with identical materials and bottom shape, the deformation was larger for the specimen with lower density. Across all specimens, deformations were observed when the applied load was 80, 250, and 500 N; however, no deformation could be recognized as long-term deformation when they were lightly loaded (40 N).The deformation trend in all loading cases was similar across all specimens. The increment in deformations was large at the initial stage of loading and gradually decreased until they sufficiently reached their asymptotes. This trend was more apparent when the specimens were subject to heavier loads: 250 and 500 N.Here, the long-term deformations of resilient materials estimated by ISO 20392 and by our trained learning algorithms are compared to the actual measured data, and their estimation quality and accuracy are discussed.ISO 20392 calculates the long-term compressive deformation using Equation (1). The input parameters required for the calculation are brought from the experiment settings. The equation uses information on a period up to the first 90 days only.tX indicates the deformation at time t, X0 is the initial deformation, mx and my are averages of tx and ty, respectively, tx is time (logt), and ty = creep .In this equation, k values for KNN, tree depth, and leaf size for RT, and hidden layer configuration and choice of activation functions were considered to identify the best-trained models with the minimum root mean squared error (RMSE). KNN utilized three different distance weighting: No distance weighting, weighting by 1/distance, and weight by 1-distance. The minimum total weight of the instances in a leaf of RT was two. In the case of KNN, the value of the weighted KNN was four, with one over distance weighting. The tree depth of RT was limited to 10, and two hidden layers where each layer consists of 50 neurons were used for ANN and iterated for 1000 epochs.Various The correlation coefficients and various errors between the test data and trained models plus ISO 20392 are tabulated in KNN returned the most accurate result among the trained models, including ISO 20392; the correlation coefficient increased by 6.1%, and the RMSE decreased by 43.4% with respect to ISO 20392. The evaluation of ISO 20392 was also done with a full range of test data, while the equation takes only the first 90 days of observation for the calculation. Thus, the rest of the data by ISO 20392 up to 350 or 500 days is extrapolated, and the assessment is not strictly an in-sample test. Taking note of this fact, the accuracy assessment of ISO 20392 in terms of correlation coefficient and other error measurements gives a good sense of the long-term deformation prediction ability of ISO 20392. It turns out that ISO 20392 is the worst compared to the trained models, which indicates that ISO 20392 does not accurately predict the long-term behavior of the resilient materials, and the use of a learning algorithm is justified.The test results under 250 and 500 N loadings are plotted with the ISO 20392 equation, and the predictions by each of the three learning algorithms are presented in k in KNN can alleviate this trend; however, doing so increases RMSE. Therefore, the value of N.N. is set to one in this study. In the case of the ANN algorithm (multilayer perceptron), the algorithm predicts the test results well within the range of the actual test data. However, the prediction deviated sharply after 500 days.In the case of the prediction curve using ISO 20392, the predicted curve before 90 days was nearly the same as the test results, except for PE-24-F. Since the ISO 20392 equation depends on the test results of 90 days, large deformations occurring after 90 days are not reflected. The step-wise shape of the predictions observed in KNN and RT is due to the sparsely distributed test data, as the gradient of the deformation in time is very steep in the early stage of loadings. Increasing the parameter The long-term deformation after 500 days, as calculated using ISO 20392, tends to increase gradually. In PE-24-F, the deformation estimated to occur after ten years was more than the initial thickness. Therefore, it is difficult to predict the long-term deformation of all of the resilient materials by the ISO equation. The long-term deformation using the KNN model did not increase after a certain point and converged. The tendency of the long-term deformation was nearly the same as the actual test results. This observation implies that the deformation prediction of the resilient materials using the KNN model is valid.Whenever a new building material is considered in the design, it is important to understand their mechanical properties, structural performances, and long-term behaviors to ensure the safety of the structure during its service life. This article measured the long-term deformation of acoustically resilient material over 500 days. It then adopted a machine learning technique to build models that can accurately predict the resilient material’s asymptotic behavior over more extended periods.Four loading conditions were considered, and six resilient material specimens were prepared to test the long-term deformations of the new materials. The measured data were then fed into three different machine learning algorithms, KNN, RT, and ANN, to train the models. Then, the predictions from each of the trained models were compared to the ISO 20392 prediction and the experimental test data to see how closely the trained models could estimate the long-term behaviors of resilient materials, and in turn, their potential for supplementing the shortcoming inherent in ISO 20392.The main scope of this experimental study was the comparative analysis of the regression algorithms that could be applied to resilient materials among construction materials. Among the three different algorithms, the KNN algorithm was determined to predict the long-term behavior of the resilient materials usefully.(1)The compressive deformation of resilient materials varied depending on the material type, base shape, and density. Under the same long-term loading conditions and bottom patterns, both EPS 1 and EPS 2 deformed more than the others. The specimens with a corrugated or embossed bottom shape experienced more long-term compressive deformation. Also, the results of resilient materials with a higher density show that the amount of compressive deformation was less in denser resilient material.(2)The predicted deformation responses using all three algorithms – KNN, RT, and ANN –could reasonably predict the actual experimental data, regardless of material properties. However, the predicted long-term responses using ANN showed significant deviations. Meanwhile, KNN and RT showed reasonable predictions for long-term deformation, which could supplement ISO 20392.(3)The ISO 20392 model underestimates the long-term deformation response of PE-24-F. Such underestimation is because Findley’s equations  were developed based on compressive creep tests on plastic laminates. As such, the ISO 20392 model is inappropriate for resilient materials which have variable shapes and nonhomogeneous material properties. However, machine learning techniques trained using an appropriate set of experimental test data are capable of accurately predicting long-term compressive deformation.The conclusions of this study are summarized as follows:Extending the result of this literature can lead us to approach other novel constructional material’s behavior estimation using machine learning. We can also assess the results of structural analyses in two different paths: one with the structural member’s behavior calculated by the classical approach and the other with machine learning algorithms. Such comparison can provide us the confidence on the answers given by the machine learning approach, and ultimately provide the grounds for it to be used in practice.


Domains are more than 100 kb in size; complexes less than 100 kb. They are present in the genomes of organisms ranging from fission yeast to human, with an expansion in size and number in mammals. Some of the likely functions of domains/complexes include silencing of the donor mating type region in fission yeast, preservation of DNA methylation at imprinted germline differentially methylated regions (gDMRs) and regulation of the phylotypic progression during vertebrate development. Far cis- and trans-contacts between micro-phase separated domains/complexes in mammalian nuclei contribute to the emergence of epigenetic compartmental domains (ECDs) detected in Hi-C maps. A thermodynamic description of micro-phase separation of heterochromatin-like domains/complexes may require a gestalt shift away from the monomer as the “unit of incompatibility” that determines the sign and magnitude of the Flory–Huggins parameter, χ. Instead, a more dynamic structure, the oligo-nucleosomal “clutch”, consisting of between 2 and 10 nucleosomes is both the long sought-after secondary structure of chromatin and its unit of incompatibility. Based on this assumption we present a simple theoretical framework that enables an estimation of χ for domains/complexes flanked by euchromatin and thereby an indication of their tendency to phase separate. The degree of phase separation is specified by χN, where N is the number of “clutches” in a domain/complex. Our approach could provide an additional tool for understanding the biophysics of the 3D genome.The hallmarks of constitutive heterochromatin, HP1 and H3K9me2/3, assemble heterochromatin- Drosophila . iews see ,7,8,9,10iews see ,12,13. Tiews see  . HP1 aniews see ,17 that to human ,23,24,25to human . Co-locato human ; buddingto human .N-terminal CD and a sequence-related domain towards the C-terminus called the chromo shadow domain (CSD) . As. As43]. or (LEF) ,165. LEFor (LEF) ,165,166.or (LEF) . Mixing like domain. In loop 2 is a smaller heterochromatin-like complex. In wt cells, extrusion of the large heterochromatin-like domain in loop 1 by the cohesin complex (green rings on left in cis- and trans-contacts (red arrows) that will be detected as contact enrichments in ECDs. Extrusion of the smaller complex in loop 2 by the cohesin complex results in extensive mixing with euchromatin leading to its dissolution in wt cells (depicted by the red dots on blue line). As a consequence, the smaller complex makes contacts (blue arrows) that will be detected as contact enrichments in A-type compartments. On the right in like complex is reconstituted (red line in loop 2 on right of cis- and trans-contacts (red arrows) that cause the finer, more defined, ECDs observed in SCC1 deleted cells  resulting in mixing of domains/complexes with euchromatin. Unrestrained loop extrusion eventually leads to an overall loss of interphase chromatin organisation where interphase chromatin takes on condensed mitotic-like chromatin state termed vermicelli [Mixing of larger heterochromatin-rmicelli . This lolike domain/complexes with euchromatin. Specifically, it was observed that the Pds5 mutation in fission yeast alleviates heterochromatin-mediated silencing at the donor mating-type region [Pds5 mutation can be reversed by introduction of the eso1 mutation [like complex assembled at the silenced mating-type region with flanking euchromatin. Accordingly, loss of Pds5p activity enhances cohesin-driven loop extrusion leading to disruption of HP1-mediated bridging of H3K9me3-marked nucleosomes resulting in mixing with euchromatin  between i and j segments, each consisting of (dissimilar) monomer repeating units, could be quantified by the equation:For di-BCPs, χ can be described by (1) where interactions between the hydrocarbon ijr is the segment-segment separation, α and I are the segment polarisability and the first ionisation potential, respectively. If there is neither a change in volume (Equation (1) assumes an incompressible lattice; see also given in , containjI = iI ≡ I; the latter is valid to within 10% for most hydrocarbons in a di-BCP. The volume of the cubic lattice site is also assumed as V, where V = 3r (the 6r term is represented as 2V in (3)). Further, defining the number of segments that interact with each other by the co-ordination number z places all segments surrounding a particular segment within a single parameter, with an average interaction given by I and (αA − αB)2. A more rigorous treatment that removes the assumption of an incompressible (cubic) lattice [6 dependence . Despite being an approximation, Equation (3) provides qualitative predictions, for example, for a bulk di-BCP governed solely by dispersive interactions a value of χ ≥ 0 is obtained indicating that the di-BCP will have a tendency to phase separate. In this  treatment the magnitude of χ is fundamentally dependent upon the choice of repeating unit, the monomer. The monomer is the unit of incompatibility from which the “blocks” or “segments” of a di-BCP are made.Equation (3) is an approximation for χ owing to a number of assumptions (see ). A cubi lattice ,163 woulAs might be expected, the assumptions required to derive Equation (3) are rarely satisfied in practice. Because of this, χ is usually determined empirically using a much simpler general formula:like domain/complex vs. euchromatin once the unit of incompatibility has been defined. This is the subject of our next section.χ retains the temperature dependency. α and β are experimentally determined coefficients for enthalpy and excess entropy for a particular composition of a BCP. We believe that the utility of Equation (4) can be extended to enable an estimation of χ for a heterochromatin-like domains/complexes flanked by euchromatin is likely to employ a different physics to that which developed for determination of χ for di-BCPs (Equations (1)–(3)). An obvious reason for this is that heterochromatin-like domains/complexes are assembled from the 11 nm “beads-on-a-string” nucleosome fibre “polymer” that is the primary structure of chromatin [monomer repeating unit, is the nucleosome, a multicomponent structure very different to the simple hydrocarbon monomer found in BCPs ). Anunit of incompatibility” from which the micro-phase separated “blocks” or “segments” are composed  HP1 proteins drive the incompatibility rather than the H3K9me3-marked nucleosomes per se and (ii) the unit of incompatibility is a larger than the mono-nucleosome. For one, recent liquid Hi-C experiments directed towards identifying factors that generate and maintain compartmental domains indicated that ECDs  enriched in HP1α and β were found to be equally stable after chromatin fragmentation followed by HP1γ with ECDs enriched in the Polycomb CBX8 homologue being the most unstable [like domains/complexes requires at least two H3K9me3-marked nucleosomes to be “bridged” by HP1  that determines the magnitude of χ. This value will indicate whether a there is a tendency for a heterochromatin-like domain/complex to micro-phase separate from the surrounding euchromatin. We now describe a theoretical model for the determination of χ for a heterochromatin-like “clutch” (HC) vs. euchromatic “clutch” based on Equation (4).Based on the evidence we suggest that the “r top of  that is r top of B,C. For “clutch” B,C. It iunit of incompatibility”) of a heterochromatin-like domain/complex; likewise, for the euchromatic clutch and euchromatin. Both clutches are equally miscible in the nucleoplasm (a concentrated solution of chromatin fibres) reducing the problem to incompatibility between the clutches.A symmetric pair of “clutches”, a euchromatic “clutch” and heterochromatin-like “clutch” (HC) that are connected by linker DNA. Each clutch contains six nucleosomes, a value that lies in the middle of the range 2–10. The HC consisting of H3K9me3-marked nucleosomes represents the repeating unit  zig-zag geometry.As shown C the modUsing Equation (4), χ for HC vs. euchromatic “clutch” can be written as:CD-H3K9meH is the free energy contribution from the binding of the HP1 CD to H3K9me3-marked histone H3. CD-H3K9me3 binding is best understood for HP1β [N-terminal extension of HP1β with the H3K9me3 tail [N-terminal region to draw upwards and wrap around the peptide and, as a consequence, an aromatic “cage” is formed from three conserved aromatic residues: Tyr21, Trp42 and Phe45  elasticity of linker DNA  and (ii) steric exclusion between the nucleosomes and linker DNA [COMP is a consequence of the “bridging” of H3K9me3-marked nucleosomes by HP1 dimers, which stabilises the zig-zag geometry and promotes compaction of nucleosomes in the “clutch”.nker DNA . HCOMP iTLH is elastic potential energy of the terminal linker DNA  that connects a heterochromatic-like clutch to a euchromatic clutch. For a heterochromatin-like domain/complex flanked by euchromatic segments the term would be 2TLH.COMPS is the excess entropy given up to the nucleoplasm after “bridging” of H3K9me3-marked nucleosomes by HP1 dimers, which stabilises the zig-zag geometry and promotes compaction.macro-phase separation of cytologically-visible constitutive heterochromatin. There are additional factors that may need to be included that are together severally necessary and jointly sufficient in causing macro-phase separation of constitutive heterochromatin. They include repetitive DNAs, ncRNAs, proteins that bind (modified) DNA/histones [HP1 CSD dimer with the H2Bα1 helix that results in the deformation of the nucleosome core that is thought to drive phase separation of constitutive heterochromatin in fission yeast [micro-phase separation of heterochromatin-like domains/complexes outside constitutive heterochromatin.In the form presented the model may not be applicable to histones . Notablyon yeast . HP1 alson yeast , which mon yeast . These fH3K9me3 epigenetic modification acts as an HP1 binding site only. Absent HP1 an H3K9me3-marked “clutch” is thermodynamically equivalent to the euchromatic “clutch”. Micro-phase separation results only after HP1 “bridging” between H3K9me3-marked nucleosomes. Experimentally, a reference “clutch” consisting of six H3K9me3-marked nucleosomes could be used to measure changes in free energy and excess entropy generated after addition of HP1 to the system. The H3K9me3-marked clutch would need to be tethered to mimic the connection through linker DNA to flanking euchromatic “clutches”.HHP1-HP1)HC to Equation (5).HP1 dimer binding is modelled as “bridging” two H3K9-methylated histone H3 molecules in separate nucleosomes . Accordiwrite-and-read” activities that ensure their inheritance from one cell generation to the next [Histone acetylation is not included in the model. Histone acetylation is an epigenetic modification generally associated with euchromatin and is known to neutralise the positive charge on the histone tails . Inclusithe next . Histonethe next .TLH term reduces the magnitude of χ and acts against phase separation. Accordingly, release of a domain/complex from the constraints of the chromatin fibre by severing linker DNA that connects the domain/complex to flanking euchromatin would remove the HTL term and enhance phase separation. Liquid Hi-C experiments have provided evidence that this is the case: chromatin fragmentation that releases domains/complexes from the chromatin fibre results in stronger compartmental segregation [The regation .any pair of clutches e.g., a HP1α/β/γ-containing clutch (H3K9me3-marked nucleosomes) vs. a euchromatic clutch; a Pc-containing clutch (H3K27me3-marked nucleosomes) vs. a euchromatic clutch; a HP1β-containing clutch vs. a HP1γ-containing clutch, and any other pair-wise combinations. This is analogous to the approach for BCPs, where it is the degree of incompatibility between monomers (the “unit of incompatibility”) that determines the magnitude and sign of χ  the number of heterochromatin-like clutches in a heterochromatic-like domain/complex and (ii) the number of Pc-containing clutches in Pc-G domain/complex. The magnitude for χN will determine the character of the micro-phase separation actually observed  and this is our subject in the next section.Using (5), χ can be calculated for degree of incompatibility between dissimilar monomers that make up the blocks in a symmetrical di-BCP and indicates whether the di-BCP has a tendency to micro-phase separate. The degree of phase separation of a symmetric di-BCP is specified by the segregation product χN, where N is the number of repeat units (monomers) that make up the polymer chain [ODT ≈ 10.5) where a disordered phase that is entropically favoured yet energetically costly is replaced by a periodic lamellae mesophase , where er chain . Given tχN ≥ 10; . The chaODT distinct from ≈10.5 for bulk di-BCPs that behave as flexible chains governed by Gaussian statistics [like and Pc-G domains/complexes are termed χNODT_HC and χNODT_PC, respectively.The original SCFT approach to micro-phase separation of bulk BCPs treats polymers as flexible chains that coarse-grain as Gaussian random walks at sufficiently large (infinite) length scales ,219. Howatistics ,222. Acclike domains/complexes involves a multicomponent system that takes place in the context of a concentrated solution of chromatin fibres where other domains/complexes roam, to a lesser or greater extent, the nuclear space as part of those fibres. Segregation of domains/complexes in interphase nuclei will, therefore, involve a different physics to that which has been described for bulk symmetrical di-BCPs ([HC—quantified by Equation (5)—will indicate the tendency of a heterochromatin-like domain/complex to micro-phase separate. The degree of phase separation of the domain/complex is specified by the product χNHC (where NHC is the number of “clutches” in the heterochromatin-like domain/complex). When χNHC is greater than χNODT_HC the domain/complex will be become enriched owing to “bridging” by HP1 of H3K9me3-marked nucleosomes within and between clutches of the domain/complex ), they have less entropy to lose when they come into contact with each other compared to contact with euchromatic segments of chromatin fibres [cis- and trans-contacts that emerge as ECDs is Hi-C experiments . Notably, the character of phase separated Pc bodies that contain Pc-G domains/complexes has been reported to be “liquid-like” [PC ≥ χNODT_PC resulting in wavy, liquid-like, interfaces between the Pc-G domains/complexes and euchromatin. This would be similar to what is observed for di-BCPs when χN is close to χNODT . This would in turn determine the magnitude of χNHC and χNPC and thereby the character (degree) of phase separation  of the respective domains/complexes.The different degrees of phase separation observed with heterochromatin-H3K27me3 . Given Elike domains/complexes that share the hallmarks, HP1 and H3K9me3, with constitutive heterochromatin but lie outside the canonical constitutively heterochromatic territories [like complexes is that which encompasses the 20 kb donor mating type region in fission yeast [like domains/complexes represent an ancient mechanism for epigenetically regulating chromatin template-dependent processes in euchromatic regions of the genome outside canonical constitutive heterochromatin [The hallmarks of constitutive heterochromatin, HP1 and H3K9me3, were present in the common ancestor of fission yeast and human around one billion years ago ,17. Bothritories ,65. Our on yeast , indicathromatin .like domains/complexes have expanded in mammals where the number in the human genome is, conservatively, around 163–855 heterochromatin-like domains (>0.1 Mb) and around 18,853–32,292 complexes (<0.1 Mb)  and their binding to TEs is thought assemble local heterochromatin-like complexes that repress transposon expression [like domains. Other genomic sites include imprinted gDMRs where heterochromatin-like complexes are assembled through binding of ZFP57/445 to their methylation-sensitive recognition sequences. Assembly of complexes at imprinted gDMRs preserves parent-of-origin-specific 5mC against the global DNA demethylation that characterises pre-implantation development in mouse and human  . How het domains ,70. Brie domains . Targeti domains , indicatpression ,230. It pression . It is tnd human  [102,123gression . As explgression , during Another aspect of cellular identity is age. In this context, it will be of interest to investigate the relationship between ECDs  and the A key question concerns the contribution of DNA methylation to the generation of ECDs. DNA methylation is an essential epigenetic mechanism that regulates the cell-to-cell inheritance of gene repression patterns . Its rollike domains/complexes flanked by euchromatin. A recent attempt at determining the degree of incompatibility between A- and B-type homo-polymers (euchromatin vs. heterochromatin) estimated χ as 0.03 ± 0.01/nucleosome [unit of incompatibility to be a monomer (pl); the fundamental unit remains the monomer nonetheless. Instead, we suggest that the unit of incompatibility for chromatin is a larger more dynamic structure that probably represents the secondary structure of chromatin, namely the oligo-nucleosomal “clutch”, where a clutch contains from 2 to 10 nucleosomes (see like “clutch” and a Pc-G “clutch”. For large domains/complexes consisting of N number of clutches the magnitude of χN will determine the character (degree) of phase separation (ODT) and when χN >> χNODT the interfaces between the domain/complex and euchromatin will be sharp and well-defined as observed for heterochromatin-like complexes [ODT the interfaces between the domain/complex and euchromatin will be wavy, “liquid-like”, as observed for Pc bodies that contain Pc-G domains/complexes [BCP theory has been used to provide plausible explanations for changes in compartmentalisation seen in Hi-C maps when chromatin-associated cohesin activity is experimentally manipulated ,164 Fig. Implicicleosome . This vacleosome , contrarcleosome ,163 usedomes see . In our omes see . Our appomplexes ,188,223.omplexes ,225.like clutch this would be (CD-H3K9meH)HC and (CSD-CSDH)HC in Equation (5)) and potential energies (COMPH and TLH in Equation (5)) could generate, in the dynamic environment of the interphase nucleus, oligo-nucleosomal “clutches” that vary from 2 to 10 nucleosomes. In this context, a simple application of Equation (5) to constitutive heterochromatin, where HP1 and H3K9me3-marked nucleosomal fibres are highly enriched [The mesoscale organisation of chromatin as oligo-nucleosomal “clutches” containing 2–10 nucleosomes with a variable zig-zag organisation of nucleosomes appears to be a ubiquitous motif of interphase chromatin . What caenriched , predictenriched ,202.unit of incompatibility” of chromatin.The oligo-nucleosomal “clutch” is the “When a domain/complex assembles from “clutches” consisting of “bridged” nucleosomes with zig-zag geometry it will have a tendency, specified by χ, for micro-phase separation from flanking euchromatin.qualitative prediction of the degree of micro-phase separation (wavy vs. sharp interfaces) is specified by the magnitude of χN compared to χNODT for a given domain/complex.A Segregation of micro-phase separated domains/complexes facilitates folding of the genome into the confines of the nucleus while retaining an environment that enables (regulation of) chromatin template-dependent processes.


Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase with key roles in the regulation of cell adhesion migration, proliferation and survival. In cancer FAK is a major driver of invasion and metastasis and its upregulation is associated with poor patient prognosis. FAK is autoinhibited in the cytosol, but activated upon localisation into a protein complex, known as focal adhesion complex. This complex forms upon cell adhesion to the extracellular matrix (ECM) at the cytoplasmic side of the plasma membrane at sites of ECM attachment. FAK is anchored to the complex via multiple sites, including direct interactions with specific membrane lipids and connector proteins that attach focal adhesions to the actin cytoskeleton. In migrating cells, the contraction of actomyosin stress fibres attached to the focal adhesion complex apply a force to the complex, which is likely transmitted to the FAK protein, causing stretching of the FAK molecule. In this review we discuss the current knowledge of the FAK structure and how specific structural features are involved in the regulation of FAK signalling. We focus on two major regulatory mechanisms known to contribute to FAK activation, namely interactions with membrane lipids and stretching forces applied to FAK, and discuss how they might induce structural changes that facilitate FAK activation. Focal adhesion kinase (FAK) was discovered almost three decades ago as a kinase that is highly phosphorylated in response to cell adhesion and localisation into the focal adhesion complex ,2. Its iFAK is today recognised as a hub in the interactome of focal adhesions ,20,21, aSuper-resolution optical microscopy has defined a layered focal adhesion architecture with a signalling layer closest to the plasma membrane, a central force transduction layer and an actin regulatory layer, the site of actin attachment to the focal adhesion complex . In addiFAK contains an N-terminal FERM  domain a central kinase domain followed by a 220-residue proline-rich low-complexity region and a C-terminal focal adhesion targeting (FAT) domain a. IndiviThe FERM domain (residues 33–361) of FAK adopts a three lobed structure , with the three lobes arranged in a clover-leaf shaped assembly  Figure b. The F1Crystallised FERM domains contain most of the linker residues following the FERM domain . This lThe FAK kinase domain adopts a typical two lobed fold seen in all protein kinases with the ATP binding site sandwiched between the two lobes c. Within712PKP) interacts with the SH3 domain of p130Cas [874PKKPP) with the SH3 domains of the small GTPase activating proteins GRAF and ASAP [The region C-terminal to the kinase (residues 686–917) is proline rich and predicted to be unstructured. It contains two PxxP motives, site I  FATIn addition to FERM-kinase rupture, there are additional features in the structure of FAK that are compatible with force induced structural changes. As mentioned in Another intriguing thought is how force might affect the structure and function of the FAT domain. As discussed in High resolution structures of FAK regions, including all portions predicted to adopt globular domains, have provided important insights into the architecture and function of FAK at atomic level. These structures have defined the mechanism of FAK autoinhibition in the cytosol  and idenOn the other hand, studying the FAK structure in the context of the focal adhesion ultrastructure in cells by cryo-electron tomography will also entail major challenges. A past study has identified donut shaped structures in focal adhesions . However


Protein kinases and GTPases are the two major molecular switches that regulate much of biology, and both of these domains are embedded within the large multi-domain Leucine-Rich Repeat Kinase 2 (LRRK2). Mutations in LRRK2 are the most common cause of familial Parkinson’s disease (PD) and are also implicated in Crohn’s disease. The recent Cryo-Electron Microscopy (Cryo-EM) structure of the four C-terminal domains [ROC COR KIN WD40 (RCKW)] of LRRK2 includes both of the catalytic domains. Although the important allosteric N-terminal domains are missing in the Cryo-EM structure this structure allows us to not only explore the conserved features of the kinase domain, which is trapped in an inactive and open conformation but also to observe the direct allosteric cross-talk between the two domains. To define the unique features of the kinase domain and to better understand the dynamic switch mechanism that allows LRRK2 to toggle between its inactive and active conformations, we have compared the LRRK2 kinase domain to Src, BRaf, and PKA. We also compare and contrast the two canonical glycine-rich loop motifs in LRRK2 that anchor the nucleotide: the G-Loop in protein kinases that anchors ATP and the P-Loop in GTPases that anchors GTP. The RCKW structure also provides a template for the cross-talk between the kinase and GTPase domains and brings new mechanistic insights into the physiological function of LRRK2 and how the kinase domain, along with key phosphorylation sites, can serve as an allosteric hub for mediating conformational changes. Dictyostelium and C. tepidium, respectively  is a large multi-domain kinase that is linked through numerous mutations to Parkinson’s disease -Spine residues in the N-Lobe (V1893 and A1904 in LRRK2), and these residues provide a hydrophobic cap for the buried adenine ring of ATP. There are also three “Shell” residues in the N-lobe that contribute to the hydrophobic core architecture  which in most kinases contains a key phosphorylation site (Johnson and Lewis, In the open and inactive conformation of LRRK2, as seen in the recent RCKW structure (Deniston et al., Dictyostelium Roco4 kinase (Gilsbach et al., Although the recent apo RCKW structure represents an open and inactive conformation, Deniston et al. , in collLRRK2 is highly unusual in that it has both a P-Loop in the ROC/GTPase domain and a classic G-Loop in the kinase domain. Binding of nucleotides is the key mechanism that allows both of these switches to function and it is important to appreciate the distinction between them (Saraste et al., Another key hydrophobic residue in the G-Loop immediately precedes the third Glycine . This reDicytostelium and C. tepidium ROC:COR domains from the LRRK2 homologs, there is no Threonine or Serine. Instead, this position is preceded by an acidic residue, and this acidic residue is conserved in many GTPase domains. The Threonine in LRRK2 may be a feature of the more highly evolved mammalian LRRK2 structures. While the biological significance of this Threonine remains to be determined, it is positioned in a strategically important region. The donor of this phosphate may be the GTP that is bound to the ROC domain. There would also likely be functional consequences of this phosphorylation event. This phosphorylated form of RCKW could not, for example, form the dimer that is seen in the earlier ROC:COR structures. A similar phosphorylated residue has not been previously reported in other GTPases so it could be highly dynamic and not usually trapped. It is perhaps a unique feature of cryo-EM that allows one to trap different conformational states that might otherwise be washed out by averaging in a crystal structure.The P-Loop in the ROC/GTPase domain corresponds to “Switch I” in the GTPase terminology (Yao et al., With structures now in hand, we are poised to explore some of the detailed mechanisms that allow LRRK2 to toggle between its active and inactive states and most importantly to understand how PD mutations interfere with this finely tuned regulatory switch. How do multiple phosphorylation sites as well as nucleotide-binding influence the structure and function of LRRK2? How does the binding of 14–3–3 influence the conformation, activity, and localization of LRRK2? and most importantly how do PD mutations disrupt the normal finely tuned functioning and lead to pathogenic states? These are our next exciting challenges. From these first publications of human LRRK2 structures, however, it is already clear that the kinase domain will be a major driver of these conformational transitions. It is also clear that the N-Lobe of the kinase domain will be regulated not only by its hydrophobic core and by nucleotide-binding but also by the domains that flank it. The CORB domain will influence the αC-Helix while the C-terminal helix will communicate with the C-lobe and the αC-β4 loop. Most intriguingly in this structure, we see for the first time how the activation loop of a kinase comes close to a GTPase domain. We also see, how key phosphorylation sites, strategically positioned around the kinase core, are poised to influence the structure, function, and cellular location of LRRK2.Our thinking about the kinase domain of LRRK2, as summarized in this review article, is based on our collective discussions and deliberations over the past year. It is based on our knowledge of the activation of other kinases and our analysis of the cryoEM structure of the RCKW domain, which provides for the first time a template for human LRRK2. ST and PK-S contributed to writing the manuscript while J-HW, PA, SS, and FH edited the drafts and the revisions. SK and SM not only reviewed and edited the manuscript but also provided purified RCKW protein to Leschziner and his colleagues for the cryoEM work. This protein led to the eventual structure solution by CryoEM. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Mitochondria are the powerhouses of eukaryotic cells. Pairs of molecular machines with a rotary action, called ATP synthase, are embedded in their inner membranes and produce adenosine triphosphate, ATP, the fuel of life. These dimers form long rows on cristae tips, helping to endow them with their characteristic and mobile tubular shape. Our structural analyses of bovine dimers show that some structural changes depend on catalysis and others are independent. The monomers pivot and translate at wedge-shaped structures in their membrane domains. The structures suggest how dimeric ATP synthases might interact and fashion themselves to the range of oligomeric arrangements observed in mitochondria, whilst allowing the ATP synthase to produce ATP under a wide range of physiological conditions. The ATP synthase complexes in mitochondria make the ATP required to sustain life by a rotary mechanism. Their membrane domains are embedded in the inner membranes of the organelle, and they dimerize via interactions between their membrane domains. The dimers form extensive chains along the tips of the cristae with the two rows of monomeric catalytic domains extending into the mitochondrial matrix at an angle to each other. Disruption of the interface between dimers by mutation affects the morphology of the cristae severely. By analysis of particles of purified dimeric bovine ATP synthase by cryo-electron microscopy, we have shown that the angle between the central rotatory axes of the monomeric complexes varies between ca. 76 and 95°. These particles represent active dimeric ATP synthase. Some angular variations arise directly from the catalytic mechanism of the enzyme, and others are independent of catalysis. The monomer–monomer interaction is mediated mainly by j subunits attached to the surface of wedge-shaped protein-lipid structures in the membrane domain of the complex, and the angular variation arises from rotational and translational changes in this interaction, and combinations of both. The structures also suggest how the dimeric ATP synthases might be interacting with each other to form the characteristic rows along the tips of the cristae via other interwedge contacts, molding themselves to the range of oligomeric arrangements observed by tomography of mitochondrial membranes, and at the same time allowing the ATP synthase to operate under the range of physiological conditions that influence the structure of the cristae. The membrane extrinsic part of bH3 is supported and largely rigidified by other α-helices in the d and F6 subunits and in the membrane extrinsic region of subunit ATP8, all bound approximately parallel to the membrane extrinsic region of bH3. The upper part of the rod is attached via the C-terminal α-helix of subunit b, bH4, to the C-terminal domain of the OSCP subunit, and the N-terminal domain of this subunit binds to the N-terminal regions of the three α-subunits. The two domains of the OSCP are joined by a flexible linker, which provides the PS with a universal joint. During the rotary catalytic cycle, the α3β3-domain rocks from side to side, as noted before (The ATP synthase complexes embedded in the inner membranes of mitochondria (IMM) are rotary machines that make the ATP required to sustain life . Energy e domain , 3. Three domain . The cate domain , 5, 6. Td before –10, withd before , and thed before . The memd before , with bHd before . Subunitd before –13. How d before , 14. If d before –21. Thusd before . The fusd before , 17, 23,As shown here, the interaction between the two wedges linking the monomers in the dimeric ATP synthase is not a constant feature. It changes to accommodate both the rocking motions of the membrane extrinsic catalytic domain associated with catalysis and other motions that are independent of catalysis observed in the isolated dimeric ATP synthase. These pivoting motions and sliding translations between the monomeric complexes are intrinsic features of the dimeric ATP synthase. They allow it to operate in the ever-changing ultrastructure of the IMM and also to contribute to their ultrastructure.1, the inhibitor has trapped dimeric states where each monomeric catalytic domain contains the same three rotational points in the active catalytic cycle. The positions of these three rotational points relative to the peripheral stalk give rise to three states of the intact monomeric ATP synthase named s1, s2, and s3. Complete structures of the monomeric complex were built , models of dimeric complexes were constructed in a range of combinations of catalytic states and substates . The substates arise from changes that are independent of catalysis in the relative disposition of the two monomers to each other . By hierarchical classification of the earlier dataset  and subsequent refinement, 154,130 particles were resolved into nine discrete classes of the dimeric assembly representing rotational states [s1:s1], [s1:s2], [s1:s3], [s2:s1], [s2:s2], [s2:s3], [s3:s1], [s3:s2], and [s3:s3] at resolutions of 9.2, 11.9, 9.0, 9.4, 8.5, 10.7, 9.7, 11.4, and 13.1 Å, respectively. Composite dimer models were created by rigid body fitting of the maps and associated atomic coordinates  revealed 59 additional substates of the dimer, at resolutions ranging from 13.8 to 23.8 Å. The substates are distinguished from discrete classes of the dimeric assembly in defined rotational states by sequential lettering and are grouped according to catalytic state. For example, the first, second, and third substates of the ATP synthase dimer in which the right and left monomers are in catalytic states 1 and 3, respectively, are referred to as dimer substates [s1:s3a], [s1:s3b], and [s1:s3c].As described before, three datasets of 4,267, 2,238, and 4,096 dose-fractionated exposures of the purified dimeric bovine ATP synthase were collected, and structures of the monomeric enzyme were produced by single-particle analysis of 176,710 particle coordinates . From residues 1–20, subunit j is folded into an amphipathic α-helix jH1, which lies in the lipid head group region on the matrix side of the IMM where it interacts with the C-terminal region of transmembrane α-helix A6LH1 and CDL1. The negatively charged headgroup of CDL1 is bound to jK8, and to residues fQ38 and fY42 of the f subunit in the wedge and residues aT33, and A6LK27 and A6LK30 . Transmembrane α-helix jH2 (with residues 22–39 forming the transmembrane span) lies adjacent to A6LH1 on the opposite side of aH1 to the wedge and is associated with subunit a via interactions between its N-terminal region and residues 107–108, 110–111, and 114–115 of aH4 . The C-terminal region of jH2 (residues 40–49) protrudes into the IMS. Residues j50–60 were not modeled as they were not well-defined in the cryo-electron microscopy reconstructions. They are predicted to have an extended structure  that interacts with the same region in the adjacent j subunit, as indicated by the structures of the dimers in various rotational states and many of their substates, where density beyond the modeled residues can be observed . However, the interactions between the two j subunits are mobile, and the observed angle between the central axes of the central stalk or the PS of the monomers ranges from ca. 75 to 86° in the nine rotational catalytic states (Movies S1 and S2). This range of angles arises by the rigid body of the wedge and the rest of the membrane domain pivoting about contact points between the amphipathic α-helices jH1 (residues 1–20), provided by residues 3, 7, 10, 11, and 14 . The two jH1 α-helices are oriented in the plane of the IMM on the matrix side as the enzyme contorts during the rotational cycle (Movie S3). Residues 3, 7, 10, 11, and 14 in jH1 project toward the interface, possibly interacting via an intervening lipid. This pivoting reduces the net side-to-side displacement of the catalytic domain arising from the rotation of the asymmetrical central stalk and accommodates other changes in the structure of the dynamic mitochondrial cristae.In the dimeric complex, the monomer-monomer interface in the membrane domain consists of contacts between the two j subunits each on the external surface of the wedge (Movie S4) and others where the angle is greater than 90° (Movies S5 and S6). The motions in the former category are similar to those that occur during catalysis in that the relative dispositions of the membrane domains change by a pivoting motion about the interface between j subunits, with the C-terminal IMS protrusions remaining in contact. The flexibility of this pivot is an intrinsic property of the interface, and it has other roles beyond that described above of dissipating the rocking of the catalytic domain in the mitochondrial cristae. Most notably, it provides a mechanism to accommodate the general fluidity of the membrane in the region of curvature associated with the dimeric ATP synthases and the rearrangements that occur. The second category of motions between substates, where the angle between rotatory axes exceeds 90°, arises from sliding and twisting along the monomer-monomer interface presented by the a and j subunits and the membrane wedge, thereby effecting a compound rotation of one monomer relative to the other (Movies S4 and S5). For example, in Movies S5 and S6). In substate [s2:s2c], the C-terminal residues of subunit j are in contact in the IMS as in Movies S5 and S6). In the IMM, this transformation could be part of a mechanism for the dimers and oligomeric rows to adapt to the convolutions of the cristae, with the two contact positions representing a simple steric mechanism that restrains the rotation to upper and lower limits. In this mechanism, the wide angle conformation . The highly dynamic nature of the dimeric complex is demonstrated by the compilation of all the observed conformers in Movie S7.Other variations in motion occurring within groups of particles of a defined catalytic state are substates that are independent of catalysis. They fall into two categories, namely those where the angle between rotatory axes is less than 90° . Although it is not certain whether these very wide rotatory axis angle substates occur in native membranes, as the analysis was performed on isolated dimers, the data strongly suggest that the interfaces between monomers are significantly dynamic and that this dynamism is a general property of the interface, linked to motions of the enzyme that are both dependent and independent of catalysis. Indeed, this dynamism, which manifests as pseudoc2 symmetry about the dimer interface, necessitated specific refinement strategies to prevent the rotation of particle orientations about the pseudoc2-axis in order to isolate and subsequently maintain catalytically homogenous particle subsets during further particle realignment . Therefore, during image processing, each monomer was given the arbitrary designation “left or “right” with respect to a single viewing direction, and, for example, the state [s1:s2] and state [s2:s1] dimers are distinct and do not represent the same molecular state.It cannot be excluded that the wide angle substates with rotatory axis angles greater than ca. 95°, which depart significantly from the consensus structures with rotatory axis angles of ca. 76–86°, arise from the influence of the detergent micelle and the lack of the interactions between dimers that occur in native membranes. In curved membranes, an estimated tension of ca. 10 mN/m is exerted on mechanosensitive channels , 25, andMovies S5 and S6, places adjacent catalytic domains in closer proximity and, therefore, limits the degree of lateral curvature of the oligomeric row. Such a mobile contact also accounts for the positive and negative curvature along the cristae rows , gH2 (residues 42–60), and gH3 (residues 69–93), with transmembranous gH3 augmenting the skeleton of wedge, and amphipathic gH1 and gH2 associated with the top of wedge on the matrix side of the IMM. In the dimeric complex, the two gH2s have the capability to contact each other across the dimer-dimer interface and link the dimers together, as depicted in tae rows C and D atae rows , 17, 23.Movies S5 and S6, narrows the membrane footprint of the dimer, thereby bringing adjacent dimers closer.It is also possible that subunit k could be involved in linking dimers together. During the process of assembly of the closely related human enzyme, this subunit is the last to be incorporated into complex, and in human cells grown under glycolytic conditions, subunit k turned over more rapidly than the other subunits of the enzyme, which turned over together at a slower rate . These o1 molecules spanning between the globular F1 domains across the interface between the two dimeric ATP synthases. However, the membrane domains of the porcine tetramer were misinterpreted . Also, the tethering of adjacent catalytic domains by IF1 induces a torsion in the F1 and PS domains, tilting the rotatory axes accompanied by narrowing of the membrane domain in the dimeric unit . This change in the membrane domains could have arisen from the effects of extraction of the complexes from the membrane with detergents and the loss of endogenous lipids . Thus, it is possible that the binding of the two IF1 dimers to form the ATP synthase tetramers occurs during this extraction procedure, and that the formation of tetramers is an artifact. Relaxation of the tilted geometry of adjacent dimers and the dimer-dimer interface in the porcine and ovine tetramers is not possible because of the close proximity of the catalytic domains, and this prevents the formation of planar oligomeric rows. In contrast, the models of oligomerization of active dimers proposed here permit the formation of the variety of oligomeric arrangements needed to produce the geometry of the IMM. Also, they are compatible with the fluidity of the dimer interface observed in the structures of the bovine dimers (Movies S4–S7). These models of oligomerization of active dimers are incompatible with formation of inactive tetramers . The changes to the architecture of the membrane domains in the tetrameric structures produce a rigid curvature along the axis of oligomerization that is incompatible with the ultrastructures of the IMM observed by tomography .Independent of these physiological considerations, the structures of free dimers and equivalent dimers in tetramers differ substantially, and, for example, the angle between the rotatory axes of monomers in each dimer in tetramers is 112–116°, whereas it is ca. 76–95° in free dimers. This wider angle between monomers in dimers within tetramers is accompanied by an outward bending of each rotatory axis and a significant rearrangement of the interface between monomers in the dimers . The membrane domain of the tetramer has undergone a structural rearrangement where the individual monomeric membrane domains appear to have collapsed inwards: See the change in the inclination of the cmography , 23, and1 that has not been considered so far in the context of the possible influence of the inhibitor protein on the topography of the cristae is that one of its two active inhibitory regions could bind to an ATP synthase complex in hydrolytic mode, leaving the second inhibitory region unbound and unable to insert itself into a catalytic site in a second copy of the enzyme. Indeed, such a state might be quite common as the inhibitor could bind to all three of the possible states of the enzyme, s1, s2, and s3, and not be restricted to cross-linking adjacent s1 and s3 states. In contrast to the cross-linking of two dimers discussed above, such a binding mode would be unlikely to have a major impact on the ultrastructure of the cristae.One possible mode of binding of dimeric IFMammalian ATP synthases are highly conserved in the composition and sequences of their subunits, and, therefore, the nature of the association between monomers in the dimeric units will also be conserved. Likewise, the compositions and structures of the ATP synthases in yeasts and fungi resemble the mammalian enzymes in many respects , 31. In 32SI Appendix.The purification of dimeric ATP synthase from bovine heart mitochondria, the preparation of cryo-em grids, data collection, preprocessing of micrographs, automated particle picking, and initial particle selection by two-dimensional classification has been described before . ParticlSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File


N), which consists of both the TBC and rhodanese domains. We show that the rhodanese domain is unlikely to be an active sulfurtransferase or phosphatase, despite containing a putative catalytic site. Instead, it packs against the TBC domain and forms part of the platform to interact with golgin-97/245. Using the zebrafish model, we show that impacting golgin-97/245-binding, but not the putative catalytic site, impairs neuronal growth and brain development. Altogether, our studies provide structural and functional insights into an essential protein that is required for organelle-specific trafficking and brain development.Members of the Tre2-Bub2-Cdc16 (TBC) family often function to regulate membrane trafficking and to control signaling transductions pathways. As a member of the TBC family, TBC1D23 is critical for endosome-to-Golgi cargo trafficking by serving as a bridge between Golgi-bound golgin-97/245 and the WASH/FAM21 complex on endosomal vesicles. However, the exact mechanisms by which TBC1D23 regulates cargo transport are poorly understood. Here, we present the crystal structure of the N-terminus of TBC1D23 (D23 The TBC1D23 protein functions in membrane trafficking and is critical for human neuronal development. The crystal structure of the N-terminus of TBC1D23 shows that its rhodanase domain is catalytically inactive and positioned close to the adjacent TBC domain, which together provide a binding interface for golgin-97/245. This interaction is essential for neuronal growth and brain development in zebrafish. In eukaryotic cells, membrane vesicle trafficking involves movement of molecules (cargoes) between membrane organelles, in the form of membrane-bound microsized vesicles ,2. VesicTBC1D23 is a recently characterized member of the TBC family and is conserved in many eukaryotic taxa . Unlike Golgins are a large class of tethering proteins found at the Golgi ,25. Two N), which encompasses both the TBC and rhodanese domains. The two domains tightly pack against each other, forming a platform to interact with golgin-97/245. Both structural and biochemical analysis reveal that the rhodanese domain is unlikely to be an active sulfurtransferase or phosphatase, with C399 deeply buried in the structure. Consistent with biochemical and cellular observations, we demonstrate that golgin-97/245-interacting residues, but not C399, are essential for neuronal growth and brain development in zebrafish.Despite these advances, there is no structural information on the TBC and rhodanese domains of TBC1D23. The molecular mechanism underlying recognition of golgin-97/245 by TBC1D23 is unknown. Furthermore, although the rhodanese domain of TBC1D23 contains a putative active-site cysteine residue (C399), which is required for enzymatic activity of a rhodanese domain, it is unclear whether it functions as an active enzyme. We set to address these questions by utilizing multidisciplinary approaches, including biochemical, structural, cellular, and zebrafish studies. Here, we present the crystal structure of the N-terminus of TBC1D23 (D23N) consisting of residues 1–460 and encompassing both the TBC and rhodanese domains. The crystal structure of D23N was determined by selenium single-wavelength anomalous diffraction (Se-SAD) and refined to a resolution of 2.5 Å  and residues 316–460 , connected by a short loop , which adopts a 310-helix within its N-terminus  within its rhodanese domain; however, it is unknown whether this domain possesses any enzymatic activities [The rhodanese domain is a small domain (about 120 residues) defined by the sequence and structural similarity and can be found within all three major evolutionary phyla . Proteinc pocket . TBC1D23tivities .5R motif is within a loop connecting the central β5 strand of the β-sheet and helix α4  or TSTD1 (Z-score = 9.0), we chose substrates and used the experimental conditions that have been established for these two enzymes ,42. To aPrevious studies have provided definitive functions for the TBC and the C-terminal PH domain of TBC1D23 ,33; in cTo determine whether the rhodanese domain contributes to the cellular functions of TBC1D23, we chose TGN46 as a model cargo, which recycles between endosomes and TGN in a TBC1D23-dependent manner ,33. ConsN, or to soak the peptides with preformed crystals. Unfortunately, we were not able to obtain the complex structure through either approach. We suspected that the present crystallographic packing disfavored the interaction between TBC1D23 and golgin-97 or golgin-245. D23N possesses several deep pockets lining the center of its surface, with pocket 1 and 2 being predominately hydrophobic and pocket 3 consisting of both aromatic and negative charged residues (s (aa301–308) from the neighboring TBC1D23 molecule (D23N2) makes multiple contacts with residues forming these pockets (N2) is deeply inserted into hydrophobic pocket formed by I236, L237, and L275. Similarly, the side chain of I305 (D23N2) is inserted to pocket 2, which is formed by I236, V239, and Y282. Furthermore, K306 of D23N2 interacts with Y281 of D23N1 through cation-π interaction.In order to understand the mechanism of recognition of golgin-97 and golgin-245 by TBC1D23, we attempted to co-crystallize various golgin-97 peptides with D23residues . Interes pockets . Specifi10-helical structure. Similarly, the N-terminal 14 residues of golgin-97 and golgin-245 are predicted to adopt an α-helical structure   pull-down and isothermal titration calorimetry (ITC) assays . Whereas≈ 12 μM) . The 12-≈ 12 μM) . Thus, wN2 binds analogously to TBC1D23, we introduced a number of mutations in residues forming the three pockets of TBC1D23   . The bin≈ 18 μM) . Similar≈ 18 μM) . Finally charged . AltogetN proteins were retained by immobilized GST-golgin-97, but not by GST, similar to their human ortholog P, are likely involved in TBC1D23 membrane targeting ,18,33. THomozygous mutations of TBC1D23 have been recently linked with PCH ,20. We aWe have previously reported that TBC1D23 is critical for the development of CaP motor neuron axons during embryonic neurogenesis . To probThe Rab family of small GTPases plays a central role in vesicle-mediated membrane trafficking, and members of the TBC family often function in membrane trafficking through regulating the activation states of Rabs. Unlike many other members of the TBC family, TBC1D23 is unable to promote GTP hydrolysis by Rabs . HoweverThe rhodanese domain is an ancient protein fold that can be found in all three kingdoms. Based on the presence of other domains, the rhodanese-containing proteins can be classified as (1) single domain, (2) tandem domains, or (3) combined with other protein domain ,31. BiocTogether with previous studies ,26–28,33DNA constructs and antibodies used in this paper were listed in N) was cloned into a modified pGEX-4T1 vector to express a protein with an N-terminal cleavable GST fusion. Protein was expressed in Escherichia coli BL21 (DE3) strain at 20 °C for 12 h. The protein was first captured by glutathione-Sepharose beads. After extensive washes, the bead-bound protein was subjected to on-column TEV cleavage. The eluate was collected, concentrated, and further purified by size exclusion chromatography using a Superdex 200 column  in 20 mM Tris (pH 8.0), 200 mM NaCl, and 5 mM DTT. The seleno-methionine (SeMet) substituted D23N was purified using an identical protocol, which yielded about one-third of the native protein.The DNA sequence encoding human TBC1D23 aa1–460 . The eluate was collected and dialyzed against a buffer without 300 mM imidazole.N, and the GST fusion was removed for the enzymatic assay.CDC25B was cloned into a modified pGEX-4T1 vector, yielding an N-terminal cleavable GST fusion. The protein was purified similar to D23N were grown using the hanging-drop vapor-diffusion methods at 18 °C, by mixing equal amounts of D23N protein solution (12 mg/mL) and reservoir buffer containing 0.1 M Tris (pH 8.5), 0.4 M magnesium chloride, 25% (w/v) polyethylene glycol 3350. All crystals were cryo-protected using reservoir buffer supplemented with 20% (v/v) glycerol and flash-frozen in liquid nitrogen. X-ray diffraction data was collected at Shanghai Synchrotron Radiation facility beamline BL17U1. The data collection statistics are given in Crystals of both the native and the SeMet-derivative proteins of D23N molecule through the program Autobuild [Structure was solved by Se-SAD. The resulting phase was used to allow modeling of one D23utobuild  and manuutobuild . Refinemutobuild . CoordinN protein (50 μM) at 25 °C. Each experiment was performed at least two times.ITC experiments were performed on a Microcal iTC200 instrument, as previously described ,48. All GST pull-down experiments were carried out as previously described ,49. The For pull-down assays using cell lysate, HEK 293T cells (ATCC) were transfected with plasmids encoding GST or GST-tagged golgin-97. Whole-cell lysate was prepared by adding lysis buffer  and 1% protein inhibitor . Cells were lysed by ultrasonication and then centrifuged at 10,000 rpm for 5 min. Supernatant was incubated with glutathione-Sepharose beads at 4 °C for 3 h, and beads were washed with 1 mL of lysis buffer four times. Bead-bound samples were detected by immunoblotting using the indicated antibodies (summarized in The GAP assay was carried out using the EnzChek Phosphate Assay Kit (Invitrogen), similar to previous studies ,50. Brie2S under the catalysis of sulfurtransferase. H2S was further converted into lead sulfide, which has absorption at 390 nm. The reaction mixture contained GSH (50 mM), thiosulfate (20 mM), and lead acetate (0.4 mM) in 100 mM HEPES (pH 7.5), 150 mM NaCl . The reaction was initiated by the addition of enzyme (10 μg). After incubation at 37 °C for 5 min, absorbance at 390 nm was recorded.The sulfurtransferase assays were performed as described previously . The firThe second assay used contained thiosulfate and potassium cyanide as substrates. The reaction mixture contained thiosulfate (20 mM), potassium cyanide (20 mM), and purified TSTD1 protein (10 μg) in 100 mM HEPES (pH 7.5), 150 mM NaCl, 100 mM HEPES (pH 7.5). After reaction at 37 °C for 20 min, the reaction was quenched by the addition of formaldehyde, followed by a ferric nitrate solution, as descripted in previous studies . AbsorbapNPP was utilized as a substrate in the phosphatase assay, similar to previous studies ,51. The HEK 293T and HeLa cell lines were obtained from ATCC and cultured in DMEM supplemented with 10% fetal bovine serum (BI) at 37 °C. The PEI transfection system (Invitrogen) was used for transfection. TBC1D23 KO cell lines were generated similar to the previous study, using the target guide RNA 5′-CTGCCAACGTCGAGCGGCGA-3′ ,33.Immunofluorescence experiments were performed as previously described ,52. SampDanio rerio) experiments were performed in accordance with the guidelines of the animal ethical committee of West China Hospital [All zebrafish  Detailed view of the interactions between the TBC and rhodanese domains of TBC1D23. The TBC domain is shown in electrostatic potential surface representation , and rhodanese domain (yellow) is shown in ribbon representation. The complex is shown in the same orientation as that of the left molecule in (TIF)Click here for additional data file.S2 FigSequence alignments were performed with ClustalW, with protein secondary structure listed above and consensus sequence listed below. ●, putative catalytic residues of the rhodanese domain; ▲, golgin-97/245-binding.(TIF)Click here for additional data file.S3 Figkcat/KM) relative to the intrinsic rate constant (kintr) for GTP hydrolysis was calculated from two replicate experiments (mean ± S.D.), and the numerical data are included in Catalytic efficiency ((TIF)Click here for additional data file.S4 FigAbsorbance at 460 nm was monitored. Data are from three replicate experiments (mean ± S.D.), and the numerical data are included in (TIF)Click here for additional data file.S5 FigThree residues critical for contacting TBC1D23 are indicated: F in position 2, L in position 5, and I or L in position 9.(JPG)Click here for additional data file.S6 FigN. After incubation with soluble protein(s), the resin was extensively washed. The purified proteins used in the assays (left) and resin-bound proteins (right) were then subjected to SDS-PAGE and Coomassie Blue staining. (C) ITC experiments for the binding of the golgin-97-25aa, -12aa, or -8aa peptide with D23N. Top and bottom panels show raw and integrated heat from injections, respectively. The solid curves in the bottom panel represent a fit of the integrated data to a single-site binding model. (D) GST pull-down assays performed with GST-golgin-245-25aa, and purified D23N WT, E179K (“179”), L278A/Y281A/Y282A (“278/281/282”), I236A/I237A/V239A (“236/237/239”), E396K (“396”), or TBC1D23-E425K/Y426A (“425/426”). After incubation with soluble protein(s), the resin was extensively washed. The resin-bound proteins were then subjected to SDS-PAGE and Coomassie Blue staining. (E) GST pull-down assays performed with GST-golgin-97-25aa, 25aa-K6E/K8E, or GST, and purified D23N. After incubation with soluble protein(s), the resin was extensively washed. The resin-bound proteins were then subjected to SDS-PAGE and Coomassie Blue staining. GST pull-down assays performed with GST-golgin-97-25aa, or GST, and purified human, fly, and worm D23N WT or mutants . After incubation with soluble protein(s), the resin was extensively washed. The purified proteins used in the assays (left) and resin-bound proteins (right) were then subjected to SDS-PAGE and Coomassie Blue staining. D23N, N-terminus of TBC1D23; GST, glutathione S-transferase; ITC, isothermal titration calorimetry.(A) aa sequences of golgin-97 peptides used for ITC experiments in (B) and for the GST fusion (C). (B) GST pull-down assays performed with GST-golgin-97-25aa, -12aa, -8aa, or GST, and purified D23(TIF)Click here for additional data file.S7 FigGST pull-down analyses of HEK 293T cells transfected with vectors expressing GST, GST-TBC1D23-FL (“FL”), GST-TBC1D23-ΔTBC, GST-TBC1D23-ΔRhod1, or GST-TBC1D23-TBC+Rhod1, and Myc-tagged golgin-97. Cell lysates were precipitated with glutathione-Sepharose beads and probed with anti-GST, Myc (to detect golgin-97), or GAPDH (control) antibodies. Top: input samples; bottom: bound samples. FL, full-length; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GST, glutathione S-transferase; HEK, human embryonic kidney.(TIF)Click here for additional data file.S8 FigP values were calculated using unpaired t test. ***P < 0.0001. Experiments were triplicated, and the numerical data are included in (A) Confocal immunofluorescence of HeLa cells transfected with siRNA targeting Arl1 or control siRNA. Cells were fixed 72 h after transfection and labeled with anti-CI-MPR (green) and GM130 (white) antibodies. Scale bar: 10 μm. (B) Quantitation of TBC1D23 colocalization with GM130 in cells as treated in (A); each point represents one cell. (TIF)Click here for additional data file.S9 FigP values were calculated using one-way ANOVA, post hoc Tukey’s test. ***P < 0.0001. Experiments were triplicated, and the numerical data are included in (A) Confocal immunofluorescence of TBC1D23 knockout HeLa cells transfected with vectors expressing mCherry, mCherry-TBC1D23-FL (“FL”), TBC1D23-L278A/Y281A/Y282A (“278/281/282”), TBC1D23-I236A/I237A/V239A (“236/237/239”), or TBC1D23-E425K/Y426A (“425/426”). The cells were fixed and labeled with anti-CI-MPR (green) and ZFPL1 (white) antibodies. Scale bar: 10 μm. (B) Quantitation of Golgi-localized CI-MPR over its total amount in cells treated as in (A). Each dot represents result from one cell. (TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TablesiRNA, short interfering RNA.(DOCX)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


The human immunodeficiency virus (HIV-1) polyprotein Gag (Group-specific antigen) plays a central role in controlling the late phase of the viral lifecycle. Considered to be only a scaffolding protein for a long time, the structural protein Gag plays determinate and specific roles in HIV-1 replication. Indeed, via its different domains, Gag orchestrates the specific encapsidation of the genomic RNA, drives the formation of the viral particle by its auto-assembly (multimerization), binds multiple viral proteins, and interacts with a large number of cellular proteins that are needed for its functions from its translation location to the plasma membrane, where newly formed virions are released. Here, we review the interactions between HIV-1 Gag and 66 cellular proteins. Notably, we describe the techniques used to evidence these interactions, the different domains of Gag involved, and the implications of these interactions in the HIV-1 replication cycle. In the final part, we focus on the interactions involving the highly conserved nucleocapsid (NC) domain of Gag and detail the functions of the NC interactants along the viral lifecycle. The human immunodeficiency virus HIV-1) genome encodes three major polyproteins: Gag (group-specific antigen), Pol (polymerase) and Env (envelope), two regulatory proteins: Tat (transactivator of transcription) and Rev (regulator of virion expression) as well as four accessory proteins: Vif (virion infectivity factor), Vpu , Vpr  and Nef (negative regulatory factor). In this review, we will focus on the Gag precursor that is synthesized in the cytoplasm as a multidomain protein of 55 kDa. Gag is composed of four major structural domains (from the N- to the C-terminus): MA (matrix), CA (capsid), NC (nucleocapsid) and p6 as well as two small peptides SP1 and SP2 (spacer peptides 1 and 2) linking respectively the CA-NC and NC-p6 domains . The mat genome eThe viral budding and release are synchronized with the activation of the viral protease which cleaves Gag-Pol and Gag in a well-defined order and marks the onset of the maturation process. The successive cleavages of Gag polyprotein lead to particle rearrangements and dramatic modifications in the global virion morphology ,13,14. CThe role of Gag in viral assembly involves a set of interactions with a variety of different biomolecules, including different types of RNAs (transfer RNA (tRNA)), messenger RNA (mRNA)), gRNA), lipids, and viral and cellular proteins. In this review, these latter are specifically discussed in detail.The first identification of the proteins present in HIV-1 virions took place in 1996 . Ten yeaIn this review, we will summarize the interactions between Gag and host cell factors, describing the domain of Gag implicated in the binding and the possible role, if identified, of the complex in the HIV-1 cell cycle. We will then focus on the interactions specifically occurring between Gag’s NC domain and cellular proteins and describe the putative functions of the complexes in the HIV-1 replication cycle. To our knowledge, this is the first exhaustive review of the interactions between Gag and cellular proteins.The matrix (MA) domain of Gag is composed of 132 amino acids and is post-translationally myristoylated at its N-terminus. Its functions in Gag assembly have been discussed in multiple reviews ,48,49,50lys3 on Gag membrane targeting. They showed that by interacting with the conserved basic patch of MA, tRNALys3 could block the association of MA to liposomes including those enriched in PIP2. In contrast, the myristoyl group exposure inhibits tRNALys3 binding to MA ,11149,11The importance of NC structure conservation explains its high sequence conservation in HIV-1 subtypes and isolates from treated patients and the low probability of mutations found in treatment-resistant strains ,127.The nucleocapsid is essential for several viral activities, which rely on its ability to bind NAs and to chaperone them. This NA chaperone activity allows the NAs to reach among their various possible conformations the most energetically and functionally favorable one ,119. ThiLys3 [During viral assembly, Gag-NC interacts with several cellular partners such as tRNALys3 , actin mLys3  and ESCRLys3 .In addition to the specific selection of gRNA, NC interaction with NAs helps to promote Gag oligomerization in the cytoplasm. The two ZFs are important for Gag oligomerization (in addition to CA–CA interactions) and its traffic through the cytoplasm to reach the PM . NC can Gag p6 is a multifunctional domain that is crucial in the late phase of the viral cycle . It corrThe p6 domain plays a critical role during the scission (pinching off) of the nascent virions from the cell surface by the cellular ESCRT machinery. Besides viral budding, the members of the ESCRT family are involved in various, topologically similar membrane remodeling processes such as the abscission step of cytokinesis, autophagy, wound-healing or exosome production The ESCRT components bind specifically to the PTAP and LYPXnL sequences of p6, so-called late (L)-domains. Mutations in PTAP motif, located near the N-terminus of p6, ,176 resuPTAP recruits the ESCRT machinery by binding to the ubiquitin E2 variant (UEV) domain of the tumor susceptibility gene 101 (Tsg101) protein ,178,179.The second L-domain of the p6 protein, LYPXnL serves as a docking site for the apoptosis-linked gene 2-interacting protein X (ALIX/AIPI) ,187,188.A recent study depicting the HIV-release kinetics brought a new perspective on the role of ESCRT-p6 interactions in the release of infectious virions. Rather than a complete budding arrest, the L-domain mutations would induce a transient delay of the budding process which leads to a situation when the viral protease is activated before the VLP neck closure and in consequence the Pol products quit the viral particles and diffuse back into the cytosol. Interestingly the role p6 L-domains in the recruitment of ESCRT members is related to the size of “cargo” to be incorporated into the VLPs. Small cargoes rely mostly on PTAP/Tsg101 pathway, while bigger complexes require activation of both PTAP-Tsg101 and LYPXnL-ALIX pathways for the proper VLPs budding and release .41LXXLF and 15FRFG sequences of p6 [36YPLTSL sequence plays a role for the incorporation of Env [The p6 domain of Gag is also critical for the packaging of several viral proteins into the nascent virions. The best-known example is the p6 driven incorporation of Vpr, via its binding to es of p6 ,194,195.es of p6 , but PM es of p6 . The 36Yn of Env  and the n of Env ,199. Then of Env .Finally, a recent study has revealed an unexpected role of p6 domain in the specific recognition and encapsidation of the viral gRNA .6). Upon protease cleavage, IP6 interactions also promote capsid maturation [The SP1 domain is composed of 14 amino acids and is located between the CA and the NC domains of Gag . The CA-turation . Thus, bturation ,210,211.turation .The SP2 domain is composed of 16 amino acids and separates the NC and p6 Gag domains . The rolAlthough Gag is able to interact with HIV-1 viral proteins like Vpr, Vif, Env or itself, this review focuses on Gag-interacting cellular proteins which are listed and described in The interactions of Gag NC domain with cellular proteins  will be Thanks to its NAs chaperone activity, NCp7 is implicated in the RTion process by helping the NAs remodeling. In consequence, the minus-strand DNA exists for a shorter time as ssDNA substrate for the NC-binding restriction factor APOBEC3G (A3G) .APOBEC proteins and especially A3G are incorporated in the virions via their interaction with the NC domain of Gag ,324,325.A3G binds to ssRNA and ssDNA with the same affinity even if the latter is the sole substrate of deamination. The A3G-induced C-to-U deamination of the ss minus-stranded DNA leads to G-to-A hypermutations in the vDNA positive strand, leading to a viral replication failure called error catastrophe. In parallel, the C-to-U hypermutation is thought to trigger the degradation of the reverse transcripts by cellular uracil DNA glycosylases (UDGs) . UracilaLys3 annealing efficiency, but other groups did not observe this effect [In parallel, A3G also exerts a deamination-independent effect. Guo et al. ,331 props effect ,333. By The human genome encodes seven members of the APOBEC3 family. Several of them interact with Vif  and restrict HIV-1 infection via deamination-dependent and/or independent process. Other members, such as A3A, also affect HIV infection but do not seem to interact with the NC domain of Gag, which explains why they are not incorporated in HIV virions .The moloney leukemia virus 10 homolog (MOV10) is a member of the super-family-1 RNA helicase expressed in a variety of cell types and incorporated in HIV virions via its interaction with the NC domain of Gag ,285. MOVCimarelli and colleagues  reportedThe NC domain of Gag is also acting on global host cell translation by modulating stress granule (SG) formation. Two types of SGs have been described that differ in their mechanism of assembly, localization, morphology, and composition. Type-I SGs such as those induced by sodium arsenite include eIF3 and promote cell survival, whereas type-II SGs are induced by selenite, or nitric oxide excludes eIF3, and enhance cell death. The formation of both types of SG depends on global translation inhibition. Both SGs contain stalled translation initiation complexes and thus a number of mRNA and eukaryotic initiation factors. Gag interacts via its CA domain with the eEF2 translation elongation factor and thus blocks the assembly of SG to the benefit of the formation of large detergent-insoluble HIV-1-dependent ribonucleoprotein complexes (SHRNP) containing the chaperone protein dsRNA-binding Staufen homolog 1 . SHRNPs are composed not only of Gag but also of other HIV-1 viral proteins and multiple cellular factors like Upf1 and IMP1 . This SHOn another side, NCp7 is thought to promote the formation of SGs, which resemble type-I SGs. NCp7 induces PKR activation, which leads to eIF2α phosphorylation, a known inducer of SG formation . The NCpInterestingly, several Gag partners mentioned in Lys synthetase interacts with the CA domain, helping the recruitment of tRNALys into HIV-1 virions [A number of other cellular factors interact with Gag and play various roles in virus production . For ins virions ,281,282. virions ,252,253  virions ,235,236.During the course of HIV-1 infection, Gag also interacts (via MA and NC domains) with Lyric . Lyric fThe cellular ATP-binding protein ABCE1 , is another partner of Gag ,218,352 A study from Mély’s group showed an interaction between Gag and the ribosomal protein L7 (RPL7), a protein involved in ribosome biogenesis and regulation of mRNA translation . This inAs mentioned above, Gag interacts with Stau1, a protein involved in the transport and cellular localization of RNA ,304,305.HIV-1 Gag also interacts with Ubc9, an E2 SUMO-conjugating enzyme that post-translationally modifies target proteins and alters their function by the addition of SUMO. This interaction involves the NC-SP1-p6 domain , in lineThe suppressor of cytokine signaling protein 1 (SOCS1), induced upon HIV-1 infection, also plays a role in the late stage of HIV-1 replication ,301. SOCS. cerevisiae transcription factor GCN4) resulted in a slower budding of VLPs, and the absence of actin remodeling; confirming the importance of NC domain in binding actin. Moreover, a star-shaped actin filament emanating from the budding site was observed [The involvement of the cytoskeleton in the HIV-cell cycle was also investigated. Early studies indicated that several cytoskeletal proteins  are present in HIV-1 virions ,220,355.observed . Furtherobserved . Treatinobserved . Howeverobserved , clearlyobserved . Therefoobserved .A major cytoskeleton regulator IQ motif-containing GTPase activating protein 1 (IQGAP1) ubiquitously expressed in many cell types (including HIV-1 target cells) interacts with the NC and p6 domains of Gag in an RNA independent manner . The intThe insulin-like growth factor II mRNA binding protein 1 (IMP1), an evolutionary regulatory protein involved in RNA transport and translation, interacts via its KH3 and KH4 domains (hnRNP K homology) with the NC domain of Gag at the rim of the cell . IMP1 isFinally, the drosophila discs large protein (Dlg1/hDlg/SAP97), another negative regulator targeting a very late stage of infection, was described . Dlg1 isThus, Dlg1, IQGAP1 or IMP1 are Gag-binding proteins that negatively regulate the late stage of infection, making them promising targets to block HIV-1 infection.The viral budding and release of nascent viral particles are mainly driven by the p6 domain of Gag. However, the NC domain is essential for this process by favoring the recruitment of the ESCRT machinery and by interacting with different cellular proteins that increase the virus release efficacy.Transmission EM images of 293T, HeLa and T cells transfected with HIV-1 coding plasmid (pNL4-3) have shown that the Gag mutants in which the NC domain was replaced by a leucine zipper (conserving the gag multimerization capacity) form immature budding particles that remain attached to the cell surface in a similar way to those observed for L-domains mutants. Interestingly, the budding defects of NC mutants can be rescued by ectopic expression of Nedd4.2, an ubiquitin E3 ligase which binds to Gag and recruits the ESCRT III members, or by expression in trans of Gag carrying NC domain but lacking all L-domains. Thus, the NC domain is essential for the recruitment of ESCRT members by Gag . In addiIn the work of ElMeshri et al.  confocalAs for the PTAP L-domain, the function of the LYPXnL motif depends on the NC domain of Gag ,229,231.PTAP mutant budding phenotype is dependent on the Gag NC domain [28,49S and Δ15–39 mutations of the NC domain, which prevent its binding to ALIX. In addition, Dussupt et al. [The interaction between ALIX and the NC domain of Gag has been confirmed in the cellular context by experiments showing that overexpressed ALIX is incorporated into VLPs by WT Gag, but not by a Gag mutant where the NC domain is replaced by a LZ. Moreover, the ALIX/LYPXnP-mediated rescue of the HIV-1ΔC domain ,229. Thit et al.  have shot et al. . Interest et al.  have fout et al. . In concThe role of NC in virus budding seems to be conserved in other lentiviruses (SIVcpzGAB2 and SIVsmmE543) whose release depends on interactions of PTAP and LyPXnL L-domains with Tsg101 and ALIX, respectively. Similarly, a functional NC domain has been shown to condition the viral release of EIAV that relies solely on ALIX binding to the LYPXnL domain .PTAP mutants. In the case of Nedd4-1, this rescue occurs via the ALIX/LYPXnL pathway and requires the basic residues in the NC domain [The NC domain of Gag is also involved in alternative recruitment of the ESCRT machinery, via ubiquitination. Ubiquitination is a regulation mechanism of the protein transport between different intracellular vesicular compartments. Several ubiquitin ligases play a role in HIV-1 budding and release. Nedd4-like ubiquitin ligase family is recruited by many retroviruses via their PPPY-type L-domain ,368. TheC domain , while NC domain ,288,369.+/CXCR4+/CCR5+ HeLa cells, the expression of NSF-DN perturbs the binding of WT Gag as well as of MA-deficient and HIV-1∆PTAP Gag mutants to the PM. Taken together, these results indicate that the SNARE machinery perturbs the binding of Gag to the PM via its NC domain, which leads to defects in virus release [The NC domain of Gag also plays a role in the activation of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) machinery. The soluble SNARE proteins regulate several membrane remodeling events involved in membrane vesicle trafficking and cytokinesis. In 293T cells, the siRNA knock-down of NSF, a critical SNARE component, leads to disruption of Gag processing and HIV-1 virus release. The same phenotype is observed by expressing a dominant-negative NSF-DN mutant, defective in ATP hydrolysis. The viral target of NSF seems to be the NC domain since NC-lacking, but not MA-lacking Gag mutants are insensitive to SNARE perturbations. In CD4 release ,370.Other cellular proteins, notably several RNA binding and ribosomal proteins, likely intervene in NC-dependent events in late budding and release of HIV-1 viruses . Among tThis review highlights the multiple roles of the structural protein Gag in HIV-1 replication, through specific interactions with cellular components, notably during the late phase of the replication . We focu