T. molitor) to produce protein-fortified bread. Bread containing mealworm powder showed similar density and water activity compared to the control wheat bread. The addition of mealworm powder did not negatively affect the properties of bread. The total color difference increased significantly (p < 0.05) with the insect flour share in bread formulation and ranged between 2.27 for M5, 4.00 for M10, and 4.50 for M15. The protein content in bread fortified with 5–15% mealworm powder increased by 15–59% compared to the control bread, whereas fat content increased by 35% to 113%. Results of sensory evaluation revealed that modification of the recipe, depending on the mealworm powder addition level, significantly (p < 0.05) affected bread color, odor, flavor, and overall sensory quality. The research showed that the optimal enrichment level is using 5% mealworm flour in the bread recipe. Moreover, the obtained variants of bread were characterized by good microbiological quality after baking. In bread M10, no yeasts and molds were found during a period of 2 days of storage. The number of yeasts and molds in the other bread variants was relatively low. To conclude, the results confirmed the usefulness of insect powder in making protein-fortified bread of good quality comparable to traditional wheat bread. Entomophagy, that is, the consumption of insects, is gaining more and more popularity. The research carried out so far on the use of edible insects in the food industry has shown that they are a valuable source of protein, and do not significantly affect the functional and sensory properties of food. Edible insects also contribute to sustainable, environment friendly food production. Taking the above into account, the influence of adding insect powder on the physicochemical properties, sensory characteristics, and microbiological qualities of wheat bread was evaluated. This study aimed to partially replace wheat flour  in bread with mealworm powder ( T. molitor larva as a novel food [In recent years, insects have gained more attention in Europe as a sustainable source of protein. Since 2003, the Food and Agriculture Organization (FAO) recognized the potential of using edible insects for food and feed. This supports several topics related to edible insects, including knowledge generation and sharing through publications and awareness raising on the role of insects in many countries worldwide . More sivel food ,5. Edible insects are a good source of essential amino acids, polyunsaturated fatty acids, certain vitamins, and minerals . ProteinThe major challenge for the food industry is making edible insects more acceptable for consumers. Insects can be consumed at different stages of their development, such as eggs, larvae, pupae, and adults, but most of the registered species are consumed in the form of larvae or pupa. In general, Western society may be reluctant to accept insects as a protein source because they have never played an essential role in their food culture . One of Acheta domesticus L., Alphitobius diaperinus P., T. molitor L.), and affect not only the nutritional value, but also the technological parameters and sensory properties in final product [T.molitor) can be used as a meat substitute in hamburger formulations or for obtaining oils [T. molitor), available as a finely ground flour-like powder, are a source of nutrients, this work investigated the effect of partially replace wheat flour in bread with mealworms powder (T. molitor). The study investigated the impact of some flour with insect powder could influence bread’s sensory, physicochemical and microbiological properties. The used substitution was to demonstrate the potential use of mealworm powder in food technology and at the same time to obtain bread enriched with nutrients from mealworm (mainly wholesome protein).Recently, there has been a growing number of papers discussing the use of insects in common food products, and some reformulations have been described, ,17,18,19 product ,22,23,24 product  confirmeing oils ,27. Accoing oils  cereal-bp < 0.05) in pH, color coordinates, and water activity, while they were similar in water and fat absorption. Wheat flour was characterized by a lower pH, amounting to approx. 6.78, compared to the mealworm powder. The water activity was characterized by the higher value, amounting to approx. 0.44, when compared to powder, where it was 0.33. The degree of water and fat absorption was similar for both preparations; it was 1.47–1.63 g water/g powder and 0.26–0.40 g fat/of g powder, respectively. As in the present study, in the work of Roncolini et al., 2019 [l., 2019 , no signw/w). Substitution of wheat flour with mealworm powder did not significantly affect the characteristics of the raw dough. The obtained bread doughs did not differ from one another in the homogeneity of the samples. Dough with the addition of mealworm powder showed higher viscosity ; the higher the powder addition, the higher the viscosity. Nevertheless, it did not cause great difficulties in forming the loaves. In the studies of Roncolini et al., (2019) [Tenebrio molitor L.) powder to bread dough was investigated at the level of substitution of 5% and 10% of wheat flour. In that study, the addition of powdered mealworm did not adversely affect the features of both the raw dough and the finished bread. All the trials made showed the same acidifying capacity, and the bread containing 5% addition of mealworm powder showed the highest volume and the lowest hardness. The addition of mealworm powder negatively influenced the instrumentally determined dough strength; the greater the amount of added preparation, the lower the strength obtained, which was probably due to the reduced amount of total gluten in the dough [Three different amounts of mealworm powder were used in addition to traditional wheat bread to produce experimental bread. In , (2019) , the poshe dough .Edible insects are considered one of the most promising alternative sources of protein to solve the global problem of protein production. Insects also present large amounts of fats and minerals, especially potassium, calcium, iron, and magnesium ,28. Furtp < 0.05) in L*, a*, b*, browning index, and hardness.The color, texture, and volume of bakery products influence consumer choice and acceptance. The values of CIE L*a*b* coordinates, the density of bread loaf, water activity, and hardness are presented in T. molitor addition; significant differences (p ≤ 0.05) were observed between crumbs of bread. The darker color of bread in both M5, M10, and M15 addition of insect powder was attributed to the higher protein content compared to control. These results were confirmed by studies by Khuenpet et al. [T. molitor addition compared to the control. The results obtained by Garcia-Segovia et al. [T. molitor) powders in bread and muffins. Most likely, this was caused by the color of the bread flour and its dark tonality. The yellow color indicated by b* value was significantly reduced for M10 and M15 compared to the control. This trend for change might be attributed to the color of insect powder which was confirmed by Cabuk’s [The lightness (L*) values of the bread ranged between 44.87 for control and 40.07 for bread with 15% of t et al.  and Cabut et al.  on breada et al.  and Cabua et al.  were dif Cabuk’s  researchThe best way to interpret L*a*b* values is to determine the total color difference (ΔE) and browning index (BI). The total color difference increased significantly with the increase of insect powder share in the bread formulation. Previous studies pointed out that color difference is visually noticeable for consumers when ΔE ˃ 3.0 . In our Bread with mealworm powder showed a significantly higher BI value than the control, and it can be assumed that the share of the raw material with a higher protein content contributed to the intensity of the Maillard reaction, which takes place during the baking process. This was also confirmed by Gonzales et al.  and Kowa3 for 10% insect powder addition and 0.43 ± 0.02 g/cm3 for control, wheat flour bread. There were no significant differences in this parameter depending on the proportion of mealworm powder and wheat flour. Khuenpet et al. [3) to loaf weight (g) and observed a decrease in the parameter value with an increase share of insect powder. In the case of the control sample (only wheat flour bread), the highest value of specific volume was determined. This could be related to the type of wheat flour used and may be caused by the weakening of the gluten strength by adding another flour of a different origin [According to the obtained results, no significant differences in terms of water activity were observed among the analyzed samples and was 0.96–0.97. The density ranged between 0.36 ± 0.00 g/cmt et al.  measuredt origin . Thus, iA significant effect on the increase in the hardness of slices was observed only with a 15% share of mealworm powder. The breads with 5% and 10% addition of mealworm powder did not differ significantly in this parameter compared to control bread (wheat flour). González et al.  did not As expected, reformulating wheat bread with mealworm increased protein and fat . More spSensory evaluation showed that the body of the tested bread was characterized by a light brown color, the intensity of which increased with a higher mealworm powder addition . The bodNo significant influence of changes in the bread recipe on pore size was found. In terms of the odor attributes, the bread odor dominated in all the samples, the intensity of which decreased with the higher level of mealworm flour addition—in the M10 and M15 samples, the bread smell intensity was statistically significantly lower compared to the control sample (C) and M5. The bread also had a noticeable sweet and nutty smell of significantly higher intensity in the M15 test. The examined bread did not differ significantly regarding texture attributes assessed in the oral cavity. The body of the bread was quite soft, moderately moist, and adhesive. Concerning the flavor descriptors, it was found that the higher addition of mealworms (10% and 15%) significantly reduced the intensity of the bread flavor. Recipe modification also influenced the intensity of bitter taste and nutty flavor—bread with the highest mealworm flour addition (15%) was characterized by a significantly higher intensity of these attributes compared to the control sample.The conducted research showed that the optimal enrichment level is using 5% of mealworm flour in the bread recipe. The overall sensory quality of this bread, understood as an appropriate harmonization of all sensory attributes, was the highest—it did not differ statistically significantly from the control sample. At the same time, it was statistically significantly higher than the breads with a higher addition of mealworm powder (10% and 15%).Schistocerca gregaria) powder adding at the level 200 g/kg negatively affected wheat bread sensory quality, mainly due to its distinctive odor, which was not accepted by consumers, whereas the odor liking scores of the bread enriched at the level 100 g/kg was comparable to the control sample. Literature data also indicate a significant effect of the insect powder addition on the color of bakery products, which was also shown in our research. The color of wheat bakery products turns brownish with increasing levels of insect powder addition [The results of our research are in line with the literature data which show that edible insect powders are useful in the production of bakery goods. Their use improves nutritional value and affects the product’s properties including sensory attributes. The quality of the enriched products depends, inter alia, on the insect species, method of powder production and the bakery product recipe; therefore, the level of the enrichment must not be too high and must be determined in studies ,40. Simiaddition . Howeveraddition , whole wBread belongs to easy spoilage products, and adverse changes begin to appear in it immediately after baking. These undesirable processes are associated with moisture loss, the staling process, and the development of bacteria, molds, and yeasts ,43,44. T−1 for the control and M15 bread, respectively. There was no presence of yeasts and molds in any tested bread. The total viable counts of bacteria, yeasts, and molds may be an indicator of estimate the shelf life of storage bread [p < 0.05) in the total viable counts between breads with the addition of insect powder. On the second day of storage, there was an increase in TVC of about 2–3 logarithmic orders in the studied breads. Sagar and Pareek [−1) during storage at 28 °C and 70% relative humidity. Ravimannan et. al. (2016) [Total viable counts (TVC) and yeasts and molds of the bread are given in ge bread ,52. Therd Pareek  also obs. (2016)  found anT.molitor) powder was produced by ZRIP insects . According to the information on the label, insects were roasted before griding to enhance the palatability of the powder. In Bread wheat flour type 1850 was purchased on the local market . Mealworm . The pH values were measured in a 5% wheat flour and mealworm powder solution. The values were measured in triplicate. To measure the water activity, 5g of wheat flour or mealworm powder was placed in a measuring cup in the device , and the measurement was performed in triplicate.The water holding capacity (WHC) analysis was carried out on 1 g of the protein powder or wheat flour. The sample was homogenized using an ultrasonic homogenizer  with 30 mL of distilled water and then centrifuged for 15 min  in a centrifuge . After flooding with complementary water, samples with sediment were left upside down for 10 min and then weighed. The WHC was determined in triplicate for each sample by weighting the samples at the beginning and after the centrifugation of sampling and calculated based on mass difference. The measurement was expressed as a gram of absorbed water per one gram of flour or powder. The fat absorption capacity (FAC) was determined using a similar procedure but rapeseed oil. The FAC was expressed as the amount of oil per one of flour or powder and determined in triplicate.The bread was prepared in three variants with different amounts of added mealworm flour, partially replacing the wheat flour with 5,10, and 15%. Control bread was prepared without the addition of insect flour. Water, yeasts, salt, and sugar were placed in a mixing bowl and then mixed for 3 min at 37 °C. Then the flour was added, and the dough was mixed for 3 min. The bread dough was put aside for 1 h in a warm place to rise. A table in results section present the complex combination of investigated samples. The bread was obtained through a one-step fermentation process followed by 60 min oven baking at 200 °C . The bread was then allowed to cool at room temperature and subjected to physical and color measurements. Next, the bread was stored in room temperature for one week, to perform additional analyses.The color of the bread was measured using the artificial eye Visual Analyzer IRIS . The color measurement was performed in triplicate and bread was randomly selected from the batch. The color was recorded in the CIE L*a*b* scale in terms of lightness (L*) and color . Between the reference sample and the test sample the total color difference (ΔE) was calculated using the following equation:The browning index was calculated using following eq.:The density of bread was determined by pouring into a measuring cup filled with rape seeds. A slice of bread was placed in the center. By observing the increase in the volume of the mixture and knowing the mass of the sample, the density of the tested samples was determined. The measurement was performed with three repetitions for each bread sample. Density of bread was calculated following equation: Mechanical features of the crumb, expressed as hardness, was measured on 20 mm thick slices using a texture analyzer . The applied settings were: 25 mm diametric aluminum cylindrical probe, 50% deformation, 5 s intermission between the measurements and 5 mm/s probe movement speed. Measurements were done with six repetitions, at room temperature. Three slices from each loaf (from the center) were measured. To measure water activity, 5 g of the bread samples were weighed, then ground and placed in a measuring cup in the device , the measurement was performed in triplicate.The proximate composition of bread was carried out in accordance with the Atwater system and parameters such as: protein, fat, and fiber content were calculated based on the nutritional value of products.−1). The plate culture technique was used to determine the number of microorganisms. For the enumeration of total number of microorganism Nutrient agar  was used according to ISO 4833-1:2013-12/A1:2022-06 . For theSensory characteristics of the bread samples were evaluated using the scaling method according to the ISO standard 4121:2003 . The intp < 0.05. Statistica 13.0  software was used for all statistical processing. One-way analysis of variance (ANOVA) was used for dependent groups with a post hoc analysis of Duncan’s test at a significance level T.molitor for the protein enrichment of bread. However, further research is needed to investigate the effects of adding edible insects to other food matrices and increase consumer acceptance and awareness.The results of this study demonstrated the possibility of using mealworm powder in baked products. Obtained results showed that it is an adequate ingredient for bread fortification. Added edible insects to the bread dough caused increases in the protein and fat content compared to traditional wheat bread. Increased levels of mealworm powder led to a darker appearance and higher total color change. All variants of bread were characterized by good microbiological quality after baking and during 7 days of storage. Overall results indicated that the enrichment level using 5% of mealworm powder in the bread recipe was optimal. Thus, the results confirm the usefulness of


TP53 mutations predispose carriers to a wide spectrum of cancers, which is known as Li–Fraumeni syndrome (LFS). To date, the association of melanomas with LFS remains unestablished. No melanomas have been reported in any P53‐modified mouse models either. In this study, we show that targeted disruption of P53 at the DNA‐binding domain in Xenopus tropicalis recapitulates LFS, with the formation of soft‐tissue sarcomas and pancreatic ductal adenocarcinoma. Interestingly, 19% of the 14‐month‐old tp53Δ7/Δ7 homozygotes and 18% of tp53+/Δ7 heterozygotes spontaneously developed small nevi and non‐invasive melanomas. Large invasive melanomas were also observed in other older homozygous mutants, with about 7.9% penetrance. Our data suggest that more dermatologic investigation of LFS patients should be able to settle the association of melanoma with LFS in epidemiology. Our model is also valuable for further investigation of the molecular mechanism underlying melanoma progression upon germline alteration of the tp53 locus.In humans, germline  TP53 mutations remains unsettled. Melanomas have not been observed in any Trp53‐modified mouse models either. Here, we show that targeted disruption of P53 at the DNA binding domain in Xenopus tropicalis led to nevus and melanoma formation, which recapitulates the process of human cutaneous melanoma development.In humans, the association of melanomas and germline  H&EHematoxylin–EosinHCGhuman chorionic gonadotropinLFSLi–Fraumeni syndromeLOHloss of heterozygositySDstandard deviationWTwild‐type1TP53 gene in human, contains two transactivation domains, a conserved proline‐rich domain, a central DNA‐binding domain, a nuclear localization signal, and an oligomerization domain in succession from N terminus to C terminus. To date, nine TP53 mRNAs are found in humans resulted from alternative promoter usage (promoter 1 and promoter 2) at the TP53 locus and alternative splicing of intron 2 and intron 9, which further encode 12 different P53 protein isoforms via translation initiation at different start codons   antibody was used to detect wild‐type P53 in X. tropicalis, and anti‐β‐tubulin  antibody was used as a loading control. Goat anti‐rabbit or goat anti‐mouse HRP‐conjugated  antibodies were used as secondary antibodies. For RT‐PCR analyses, total RNA was extracted with the TransZol Up Plus RNA Kit . cDNA was prepared with the TransScript® Uni One‐Step gDNA Removal and cDNA Synthesis SuperMix . The sequences of RT‐PCR primers are listed in Table 33.1TP53 mutations occur in the highly conserved DNA‐binding domain in humans, we chose to design a gRNA targeting X. tropicalis tp53 DNA‐binding domain coding region for Leu111 to Val119  probably due to mRNA accumulation  compromised transcriptional activation of ion Fig. . A low lded Fig. . Consistded Fig. . Of noteion Fig. . In gastnts Fig. . Thus, t3.2tp53 mutant line, we noticed that small hypermelanotic lesions started to appear in both the hetero‐ and homozygous mutant frogs by 6 months, but not in wild‐type siblings. Among 115 14‐month‐old tp53Δ7/Δ7 frogs (Clutch 3), 22 (19%) displayed the nevus‐like lesions without other discernable anomalies. Four out of 22 tp53+/Δ7siblings (18%) showed similar lesions. We randomly selected eight lesion‐positive homozygotes and biopsied all their lesions for histological analyses. Based on the histological data, the lesions can be categorized into three types  and 34  months, respectively. Histological data revealed clear cancerous melanophore invasion into the entire stratum compactum of dermis, the epidermis, and the hypodermis  lesions appeared in two moribund frogs from two clutches of older mis Fig. . These lin situ samples for RNA extraction were biopsied from the Clutch 3 tp53Δ7/Δ7 homozygotes, now 19‐month‐old with average diameter of about 2 mm for dysplastic nevi , of which expression was decreased in invasive melanomas [X. tropicalis.Given the paucity of antibody availability in frogs, to further verify our histology‐based classification of nevi and melanomas, we carried out RT‐PCR analysis instead of immunohistochemical analysis. Benign nevus, dysplastic nevus, and melanoma evi Fig. . The invevi Fig. . Each dievi Fig.  and thenevi Fig.  and the evi Fig. . The graevi Fig. . For RT‐ig. pmel  and mitfel [mitf , neural elanomas , melanomas [bcl2  and axl cl2 [axl , and cdhelanomas . The traelanomas , 33, 34,3.3TP53 loss of heterozygosity (LOH) can be frequently detected in tumor samples of human LFS patients [Trp53 [tp53 [tp53+/Δ7 frogs, pigmented nevus cells and neighboring stratum compactum cells were isolated by laser capture microdissection from a dysplastic nevus with a diameter of 2 mm for DNA extraction, PCR amplification, and Sanger DNA sequencing  developed conspicuous subcutaneous sarcomas  is sign5tp53 at the DNA binding domain in X. tropicalis predisposed the frogs to nevi and melanomas in addition to soft tissue sarcomas and pancreatic ductal adenocarcinoma. The latter belongs to the cancer types seen in LFS patients. Our data suggest that more dermatologic control should be given to LFS patients, which likely will settle the association of melanoma with LFS in epidemiology. The tp53 Δ7 mutants are also valuable for further investigation of the molecular mechanism underlying melanoma progression upon germline alteration of the tp53 locus.In this study, we show that targeted disruption of The authors declare no conflict of interest.tp53 knockout founder frogs. RR and YC wrote the manuscript with input from all the authors.RR, ZS, and YC conceived the project. RR and LL performed the experiments and analyzed the data together with GL, HJ, JH, WC, LF, and TX. ZS generated the https://publons.com/publon/10.1002/1878‐0261.13301.The peer review history for this article is available at Fig. S1. Invasive melanoma developed in a 20‐month‐old tp53Δ7/Δ7 frog, as shown in Fig. Fig. S2. Gradually increase of the average size of dysplastic nevi over time.Fig. S3. Sanger DNA sequencing data for three potential CRISPR/Cas9 off‐target sites.Click here for additional data file.Table S1. The primers (from 5′ to 3′ end) used in this project.Table S2. Statistics on X‐ray irradiation.Table S3. Potential off‐target sites predicted by CRISPOR.Table S4. cDNA sequencing data for four different tp63 mRNAs from tp53Δ7/Δ7 nevi.Click here for additional data file.


An elastomeric class of flexible skin-based polymorphing wings changes its configuration to maximize performance at radically different flight conditions. One of the key design challenges for such an aircraft technology is the multiaxial deformation characterization and modeling of nonlinear elastomeric skins of polymorphing wings. In the current study, three elastomeric materials, Latex, Oppo, and Ecoflex, are experimentally characterized and modeled under all possible deformation modes such as uniaxial, pure shear, biaxial, and equibiaxial relevant for flexible skin-based morphing wing applications. Additionally, a novel material model with four material constants is proposed to model the considered elastomers-based morphing wings keeping all the material parameters constant for all the possible deformation modes. The present experimental and theoretical study provides a concise comparative study of the three elastomers used in the morphing wings tested in all possible deformation modes. In a traditional aircraft, only one or two flight conditions are optimized rather than optimizing the entire flight envelope. At the same time, the advancement of research is moving closer to nature, mimicking the flight of birds. In particular, birds reshape their wing and adjust their profiles to obtain optimal performance in all flight conditions ,2,3,4. RResearchers have implemented elastomers in the morphing wing, and their mechanical characterization, simulation, and material modeling are investigated. For this purpose, they either purchased different elastomers or prepared them in the laboratory before implementing them on the morphing structures of the wing. Kikuta  is a pioThe above literature studies show that flexible elastomers are currently used in morphing wings along with rigid structures such as ribs. At the same time, the hyperelastic material modeling under multiaxial deformation modes is of utmost importance for simulation and modeling the entire wing structure. On the contrary, modeling such hyperelastic materials is generally done using only one mode of deformation at one time. For example, the uniaxial mode of deformation is selected to understand the hyperelastic behavior of elastomers through different hyperelastic mathematical models ,24,25,26Motivated by the literature, the current research aims to characterize three categories of elastomeric materials experimentally, Latex (widely applicable), Oppo (highly durable), and Ecoflex (less viscous) used in aircraft morphing wings by developing a novel material model keeping all the material parameters constant for all the possible modes of deformations. The present study is further organized as follows: In the present section, details of the multiaxial testing set up to conduct the experiments such as uniaxial (UX), pure shear (PS), biaxial (BX) and equibiaxial (EB) at a particular condition is elaborated. Moreover, the geometry of the specimen and the fabrication method of Ecoflex are discussed in a separate section.The experimental setup to conduct the multiaxial test on the three elastomers Latex, Oppo and Ecoflex is shown in www.radicalrubber.co.uk, accessed on 10 March 2022), while Oppo is purchased from Oppo Medical Inc., Seattle, WA, USA. The thickness of both sheets is 0.25 mm. Latex and Oppo exhibit high-quality finish with translucent and blue colors, respectively. Oppo is selected as it is already used for medical purposes in physiotherapy. Therefore, its durability is already proven and can undergo multiple cycles without failure [The specimens of Latex, Oppo, and Ecoflex are cut into a square shape of side 7 mm using a scissor. Grips are fixed from all four sides keeping the end-to-end distance of 6 mm as shown in  failure . EcoflexDifferent modes of deformations are elaborated in The CCD camera, as shown in Under the pure shear mode of deformation, the strains developed in the X and Y direction at different specimen positions are shown in tion (X) ,43. TheyThe strain maps for both the X and Y directions under biaxial deformation modes are shown in This section summarizes the material modeling of an incompressible isotropic hyperelastic elastomeric material class of skins for morphing wing applications subjected to different modes of deformations by defining the state variables in line with the literature ,45,46.O and an orthonormal vector space Consider a body Further, if s and S denote the arc length parameters. Then, the above Equation I1From the theory of hyperelasticity ,50, the p denotes the indeterminate pressure to be determined from boundary conditions. In the current study, experiments are carried out in a displacement-driven setup wherein the nominal stresses are readily available due to the experimental lack of actual cross-section. In line with that, the above expression (The derivatives of the invariants  with reselations  and 9)  B are gilations ,,10), theIn this deformation case, the material is again stretched in the x-direction Using the above expressions  in 10),,10), theIn this deformation case, the material is stretched unequally in two directions given byF=λ000λ+αIn this deformation case, the material is stretched equally in two directions Equation , the noma, b, and c are the material parameters. All together, the exponential term There are so many existing material models  that area explicitly linked with the limiting chain extensibility of monomers in polymeric chains, b accounts for material micro-voids, porosity, and molecular chain breakage, and c physically linked with the strength of intermolecular forces between chain molecules.Moreover, the proposed “Exp-ln-ln” type of new strain energy function  satisfiefunction , the matThis section firstly validates the analytical findings of a newly proposed material model  in previExperimental validation of any material model is commonly examined based on how accurately the model works in different regions of the stress-strain curve. In the related studies ,37, the In practice, there is a possibility that the Gent material model may accurately fit the tested data after varying a single set of material constants fitted from an uniaxial deformation case. However, in actual practical conditions, this should not be allowed. Hence, the proposed material model  is the oIt is evident that the material for morphing wings cannot be wholly rigid but should be flexible enough to morph its shape during flight operations. To meet such requirements of morphing wings, the materials must be elastic and flexible enough for easy deformation with adequate strength to bear aerodynamic loads. Additionally, the materials should easily recover to its original state with no plastic deformation as excess material causes drag that reduces efficiency markedly . Elastomsitivity , larger sitivity , low hyssitivity  that besThe present work provides a comprehensive multiaxial experimental and theoretical study of three potential elastomers  relevant for morphing wing application. In addition, a novel nonlinear hyperelastic constitutive model “Exp-ln-ln” with four material parameters is proposed to reasonably predict multiaxial modes of deformations  mimicking polymorphing wing. The unique feature of the proposed model is that it reasonably fits all modes of deformations using a single set of material parameters. Limitations of the applicability of the proposed material model apply only to a few elastomers in biaxial transverse stress states (


Pin1 is a two-domain cell regulator that isomerizes peptidyl-prolines. The catalytic domain (PPIase) and the other ligand-binding domain (WW) sample extended and compact conformations. Ligand binding changes the equilibrium of the interdomain conformations, but the conformational changes that lead to the altered domain sampling were unknown. Prior evidence has supported an interdomain allosteric mechanism. We recently introduced a magnetic resonance-based protocol that allowed us to determine the coupling of intra- and interdomain structural sampling in apo Pin1. Here, we describe ligand-specific conformational changes that occur upon binding of pCDC25c and FFpSPR. pCDC25c binding doubles the population of the extended states compared to the virtually identical populations of the apo and FFpSPR-bound forms. pCDC25c binding to the WW domain triggers conformational changes to propagate via the interdomain interface to the catalytic site, while FFpSPR binding displaces a helix in the PPIase that leads to repositioning of the PPIase catalytic loop. Born et al. describe interdomain allostery in the two domain peptidyl-prolyl isomerase Pin1 upon binding of two ligands. These ligands couple population shifts of extended and compact states to changes in the catalytic site of Pin1. Many proteins are organized into multiple domains with the potential for interdomain crosstalk. The function and activity of one domain can be modulated through the structure and dynamics of another domain, what we thus term “interdomain allostery”. In addition, the structure and dynamics of the entire system may change upon environmental change . While interdomain allostery can be demonstrated by comparing the activity of the individual domains to the whole multi-domain system, it is challenging to directly probe the cascades of conformational changes leading to allostery.7. Pin1 regulates other proteins through isomerization of the peptide bonds of prolines that leads to the recycling of phosphoproteins, regulating other post-translational modification, and enhancing or preventing protein degradation8. Pin1 consists of the WW binding domain (residues 1–39) connected to the peptidyl-prolyl isomerase (PPIase) domain (residues 50–163) via a flexible linker  interface as well as a dispersed extended state where the two domains can tumble semi-independently as displayed in Fig. 9.Extensive work has focused on investigating interdomain allostery through the two-domain mitotic regulator Pin1ker Fig. . Pin1’s 10. The pCDC25c ligand is a peptide derivative from the pT48-P49 site of the mitotic phosphatase CDC25c11. An early study of interdomain mobility compared tumbling times (τc) of apo Pin1 and Pin1 complexed to various ligands. The study concluded that Pintide restricts the flexibility of the two domains , while pCDC25c increases the flexibility7. Further NMR studies involving chemical shift perturbations (CSPs) and order parameters quantifying the motional restriction of covalent bonds have supported the reduced interdomain contact caused by pCDC25c12, but the proposed compaction due to FFpSPR binding is not supported by published data6  and pCDC25c (with peptide sequence EQPLpTPVTDL). FFpSPR is a more soluble variant of another artificial ligand (Pintide with peptide sequence WFYpSPR) that has high PPIase efficiency with minimal sequenceta6 Fig. . As the 2. While only the PPIase domain is catalytically active, mutagenesis studies have shown that the WW domain is essential in vivo13. Yet, the isolated PPIase domain displays higher catalytic activity than the full-length protein for both FFpSPR and pCDC25c ligands2. Therefore, WW domain contact alters the properties of the PPIase catalytic site, which is located on the opposite side of the PPIase domain than the interface with the WW domain. Overall, these observations support interdomain allostery coupled to intradomain allostery. Interdomain communication and dynamic allostery are also supported by the fact that ligand-binding reduces methyl group flexibility along a conduit linking the ID interface to the catalytic site5. The exact conduit residues and degree of change shows some variance depending on the ligand and its effect on the interdomain contact6. More evidence of interdomain allostery comes from the expansive studies on the I28A mutation in the WW domain interface. This mutation in the WW domain increases the activity of the PPIase with evidence supporting the stabilization of the extended state14. A conformational selection-driven allosteric model has been proposed that explains why PPIase activity is negatively regulated by interdomain contact2. Both extended and compact states exist in apo Pin1; shifting the equilibrium to favor the extended state by either mutating the interface, binding to pCDC25c, or through deleting the WW domain causes an increase in catalysis1. In addition, a recent study shows that pCDC25c binding causes compaction within the WW domain while the interdomain distance increases15. In contrast, other ligands, including FFpSPR, do not have the same effect on the interdomain contact16. All this data establishes that ligand binding changes the intradomain structure and interdomain orientation, and that the specific ligand sequence has distinct effects on Pin1.It has been established that the WW domain can allosterically regulate the activity of the PPIase4. Upon addition of FFpSPR, Path 2 extends into the WW domain’s ligand-binding pocket via the bound substrate4. In the following, we adapt this nomenclature , α2, and the catalytic loop17, they show Pin1 only occupying a compact configuration. We recently published a protocol for elucidating the coupling of inter- and intradomain spatial sampling of multi-domain proteins using magnetic resonance9: first, we calculated multi-state structural ensembles of apo Pin1 from exact Nuclear Overhauser Enhancements (eNOE) and scalar couplings restraining the intradomain structure, whereas paramagnetic relaxation enhancement (PRE), interdomain NOEs and residue dipolar couplings (RDC) define the domains’ positions. In a second step, our ensemble was cross-validated against experimental double electron-electron resonance (DEER) electron paramagnetic resonance (EPR) measurements. Our two-state ensemble (PDB ID: 7SA5) simultaneously describes the compact and extended states present in solution and shows coupling between intradomain conformation and interdomain positions through rearrangement of hydrophobic residues in the interdomain interface extending to the catalytic pocket9. Here, we apply the same method9 as summarized in Fig. Previous work has extensively described signatures of allostery in Pin1, yet to date the structure of ligand-bound, full-length Pin1 in solution has not been determined. Though ligand-bound crystal structures exist7, we observe that upon pCDC25c addition the domain-specific tumbling time (τc) of the WW domain decreases relative to that of the PPIase domain (but not FFpSPR). As shown in Supplementary Table In agreement with previous reports of Pin1We utilize DEER to directly measure the distance distribution between the two domains. These EPR measurements involved flash-freezing the samples, allowing for the detection of all distances and their populations at the temperature where the sample vitrifies. Using the double-mutant MTSL-labeled constructs 15–90, 15–98, and 15–131, we measured distances between the two domains for apo and ligand-bound Pin1, while construct 90–131 with both labels in the PPIase domain served as a control Fig. . We perf9. Our measurements show that ligand FFpSPR does not considerably alter this population of the compact state (p1) as it changes by less than ±5% for both 15–90 and 15–98 constructs compared to apo Pin1  across domains upon addition of pCDC25c (but not FFpSPR), which also corresponds to an increased distance between the domains. From R2sp, we extracted population-averaged interdomain distances (up to 25 Å) that are used as distance restraints in our subsequent structure calculations.Regardless of ligand presence or absence, for constructs 15–90 and 15–98, we see a narrow distance distribution centered around short distances of 22 and 24 Å, respectively, corresponding to a compact conformation Fig. . A longein1 Fig. . Conversp1 value. Nevertheless, we observe two major populations centered around 38 and 43 Å. For apo and FFpSPR-bound Pin1, it appears that the 43 Å peak is populated twice as often as the 38 Å peak, which is a similar ratio (66:33) that we see for the compact and extended populations with the other DEER constructs. This population near 43 Å decreases upon addition of pCDC25c. Due to the correlation of populations between the 15–90 and 15–131 constructs, we believe that the peaks centered around 43 and 38 Å originate from the compact and extended states, respectively. Upon pCDC25c addition, we also see an increase in smaller distances between 20 and 35 Å and at larger distances around 55 Å that may balance the decrease in intensity around 43 Å. Based on our DEER measurements, we conclude that FFpSPR-binding does not significantly change the apo equilibrium (70:30), while pCDC25c increases the extended population between 7 and 20%.For the 15–131 construct, the bi-Gaussian model provides a poor description of the distance distribution. For this reason, we are unable to provide a quantitative 19. As multiple exchange peaks exist, we are unable to conclusively assign specific conformations to specific exchange peaks. Nevertheless, the populations of the compact and extended states from the 15–90 and 15–98 DEER distance distributions are similar to the major and most separate minor slow-exchange peak populations from the N90C (~0.66:0.34) and S98C PRE mutant constructs (~0.78:0.22). Thus, we suggest that these major and minor peaks emanate predominantly from the compact and extended conformations, respectively. This is further supported by the absence of interdomain NOESY peaks for the minor exchange peaks, as expected for extended states. Because the extended state does not contribute appreciably to the interdomain NOEs, we cannot discount the possibility that though the major peaks stem primarily from the compact state also the extended state contributes. Furthermore, we also cannot discount that the relevant timescales for interdomain mobility also cover fast exchange which does not result in separate exchange peaks. Therefore, the most separate slow-exchange minor peaks set a lower limit to the extended population. We note that the relative intensities of these peaks also appear to be sensitive to mutations. In apo and FFpSPR-bound WT Pin1, the average population of the minor peaks is about 10%. Importantly, we consistently see increases in the minor, extended state population upon addition of pCDC25c in both the PRE mutants and WT Pin1 . While the populations must be interpreted with caution, we can confidently conclude that the NMR data confirms the DEER data in that pCDC25c, but not FFpSPR, increases the population of the extended state.Independent qualitative assessment of these population shifts comes from slow-exchange NMR peaks observed for residues located in the interdomain interface  at the interface likely stabilizes the compact and extended states in apo Pin1 due to a hydrophobic effect. In addition, the extended and compact states feature distinct conformations around loops encompassing residues 98–102, 125–128, and 152–154 and the α4 helix9.Next, we determined the subtle, intradomain conformational changes that induce the large-scale, interdomain rearrangements. As for apo Pin1For the pCDC25c-bound form, we can see a clear difference between extended and compact states on both sides of the interface . Subsequently, the PPIase interdomain residues are also further occluded with pCDC25c, while the residues adopt an intermediate position with FFpSPR. The pCDC25c- bound structure shows large conformational changes that originate from the interdomain interface and propagate to the catalytic site through the core β-sheet and α4 helix, in the previously proposed Path 1 from MD simulationscis/trans-locked isosteres, we report that changes due to pCDC25c binding also fall within these clusters, which suggests that these allosteric paths are inherent to Pin1.Inter- and intradomain spatial sampling may be coupled in many multi-domain proteins. We have used our recently introduced NMR- and EPR-based approach to demonstrate such coupling in the two-domain protein Pin1, which rationalizes ligand-specific interdomain allostery. Specifically, FFpSPR binding does not change Pin1’s interdomain equilibrium while pCDC25c stabilizes the extended state. We have shown that the conformation of the hydrophobic residues in the interface controls the population of the extended and compact states. Our structural ensembles demonstrate that pCDC25c and FFpSPR cause distinct conformational changes in Pin1 primarily through previously proposed Path 1 and Path 2, respectively. While the MD study only examined FFpSPR and the 20. Until the structural ensemble of the isolated PPIase is solved with pCDC25c bound (no solution structure of the isolated PPIase bound to any ligand has been deposited in the PDB to date), we can only hypothesize which perturbations link the WW domain to the PPIase catalytic site.Although we see conformational changes that appear to propagate from the interdomain interface to the PPIase catalytic site, we are unable to ultimately conclude that the changes are due to the contact (or lack-there-of) with the WW domain as the PPIase itself binds/isomerizes pCDC25c as well. We note, however that the binding affinity is at least one order of magnitude lower than for the WW domain and the CSPs are also relatively weak in the active site upon ligand binding27. Some of the substrates interact with Pin1 only at one site, including β-catenin and NFkB29. For such cases, a role of the coexistence of compact and extended states that immediately emerges from this study is a continuous rather than a binary control of the enzymatic activity of Pin1 through ligand binding to the WW domain. Pin1 typically targets intrinsically disordered regions of substrate proteins, frequently involving multiple pS/T-P sites that vary in distance from 5 to 30 + residues30. Some examples of multivalent Pin1 ligands include tau, full-length CDC25c, RNA Pol II, and IRAK131. Here, an additional role of the coexistence of these states may be a specific accommodation of ligands with multiple binding sites. The function may be directly dependent on the separation of two such binding sites, which then fine-tunes the interaction with Pin1. Alternatively, this coexistence of states may provide a mechanism to bind two different molecules, each to one Pin1 domain, which in turns repositions the two bound molecules by a shift from extended to compact state of Pin1. In these scenarios, interdomain flexibility allows Pin1 to interact with a higher variety of multivalent ligands as proposed through a “fly-casting” model32. We expect the detailed structures of ligand-bound Pin1 to be utilized to understand what peptide sequence determinants lead to differences in the interdomain equilibrium. Recent work has described the residues preceding pS/pT as most critical, with polar residues (as in peptide pCDC25c) stabilizing the extended state more than hydrophobic residues (FFpSPR)19. Further exploration of substrate sequence determinants and the Pin1 in-cell interactome will aid in determining the functional consequences of Pin1’s malleable interdomain equilibrium.The conformational flexibility described here likely promotes the substrate interaction promiscuity in Pin1. Pin1 is known to have a large interactome; within cell cycle progression alone, Pin1 is known to interact with CDK1, CDC25c, Wee1, CENP-F, p53, and p279.The protein expression and purification, the measurements of NOE buildups, scalar couplings, structure calculations, relaxation rates, PRE, and DEER experiments were extensively described in our recent publication on apo Pin1Ligands FFpSPR and pCDC25c (sequence EQPLpTPVTDL) were synthesized by the Peptide and Protein Chemistry core at Univ. of Colorado Anschutz using Fmoc solid-phase synthesis. The N-termini were acetylated while the C-termini were amidylated to protect the peptides against degradation. HPLC was performed to purify the peptides, and samples were analyzed by LC/MS which confirmed purity >96%. The samples were lyophilized and resuspended in NMR buffer  dependent on their solubility. Ligand pCDC25c was more soluble (40 mM stock) than FFpSPR (4 mM stock) in this buffer, likely due to the hydrophobic phenylalanines in the latter. No DMSO was used.15N,13C-labeled Pin1 in NMR buffer at pH 6.5 with 3% D2O (low D2O to reduce H-D exchange). While the apo sample contained ~2 mM Pin1, the amount of protein had to be reduced for the ligand-bound samples due to low concentration of FFpSPR, and to reduce aggregation with pCDC25c. Therefore, the FFpSPR-saturated sample contained 600 μM Pin1 and 3.6 mM FFpSPR and the pCDC25c-saturated sample contained 750 μM Pin1 and 4 mM pCDC25c. Apo Pin1 was assigned as described previously9. Using 15N-HSQC, 13C-resolved aliphatic and aromatic CT-HSQC, and a 15N/13C-resolved [1H-1H] NOESY experiment, we assigned the chemical shifts of the FFpSPR- and pCDC25c-bound Pin1 based on the apo Pin1 assignment18. While chemical shift changes occur, the NOESY towers remain relatively intact and characteristic of each atom. The chemical shifts of FFpSPR-bound and pCDC25c-bound Pin1 have been deposited in the BMRB under accession codes 51043 and 51034, respectively. NOE buildup series were run as previously described1 with mixing times of 24, 32, 40, 48, and 56 ms. Scalar coupling  and relaxation (R1 and R1ρ for determination of tumbling times) experiments were also recorded on these ligand-bound samples, as previously described9.For assignment, scalar coupling, relaxation, and NOESY experiments, all samples contained 9 were also used for ligand-bound measurements. The samples were expressed, purified, and spin-labeled as previously published9. For PRE, the ligands were added to be 8x the concentration of Pin1. R2 PRE rates were measured on the 15N-labeled MTSL-conjugated (paramagnetic) and quenched (diamagnetic) samples M15C, N90C, S98C, and Q131C that all also contained C57A and C113D mutations. The relaxation enhancement due to the paramagnetic labels (R2sp) were obtained from the R2 difference between the paramagnetic and diamagnetic samples34. The DEER samples M15C-N90C, M15C-S98C, M15C-Q131C, and N90C-Q131C were all measured with 8x ligand. All PRE and DEER constructs maintained catalytic activity, as previously published9.The same cysteine-mutant constructs for PRE and DEER that were developed to measure the interdomain orientation of apo Pin19Data fitting and analysis for the ligand-bound samples were performed as previously published for apo Pin1, using TopSpin (Bruker) version 7 and VNMRJ version 4.2 Revision A (Agilent), CCPnmr version 2.4.2 (CCP), and NMRpipe Version 10.9 (NIST IBBR).35 and an incoherent arbitrary waveform generator pulse channel using Xepr. A total of 40 µL of 30–80 µM MTSL-labeled Pin1 in buffer:glycerol-d8 1:1 v:v were filled in 3 mm o.d. quartz capillaries, flash frozen and stored in liquid nitrogen between measurements. Echo-detected fieldsweeps were recorded using π/2−τ − π − τ with pulse lengths tπ/2 = tπ/2 = 12 ns and an interpulse delay of τ = 400 ns. DEER-EPR data were acquired either with the 4-pulse DEER (4pDEER) sequence π/2obs − τ1 − πobs − (τ1 + t) − πpump − (τ2 − t) − πobs − τ236 or the 5-pulse DEER (5pDEER) sequence π/2obs − (τ/2-t0) − πpump − t0 − πobs − t − πpump − (τ − t + δ)−πobs − (τ/2+δ)37 featuring a time shift δ = 120 ns to separate the refocused from the stimulated echo38. 4pDEER measurements were performed with monochromatic, rectangular pulses of length tπ,pump = 12 ns, applied at the maximum of the nitroxide Q-band spectrum and observer pulses with tπ/2,obs = tπ,obs = 16 ns, offset 100 MHz from the pump frequency. 5pDEER measurements were recorded with HS{1,6} pump pulses of 150 MHz width and monochromatic, rectangular observer pulses with tπ/2,obs = tπ,obs = 32 ns, placed 70 MHz away from the pump position. In each case, nuclear modulations were averaged using an eight-step phase cycle with 16 ns steps.Pulsed Double Electron-Electron Resonance (DEER) Q-band EPR measurements were recorded at 50 K with a Bruker Elexsys E580 spectrometer equipped with a home-built resonator39  by modelling the background decay by a stretched exponential function  = exp(-κ|t|d)) with the decay rate κ and the stretch factor d restraint to 0.02–1 µs−1 and 0.9–1.2, respectively. 4pDEER and artefact corrected 5pDEER data40 were analyzed using the single-pathway 4pDEER experiment model (ex_4pdeer) including the modulation depth λ as a fit parameter. For the analysis of the primary 5pDEER data, the multi-pathway 5pDEER model (ex_5pdeer) was applied. This model consists of an unmodulated pathway, the 5pDEER pathway and 4pDEER pathway (artefact that refocused at time T0(2)) with amplitudes Λ0, λ1 and λ2, respectively. Parameter-free and Gaussian distance distributions P(r) were computed via Tikhonov regularization, using generalized cross-validation (GCV) to select the optimal regularization parameter. Bootstrapping with 200 samples produced converged 95% confidence intervals (CI), listed in Supplementary Tables All DEER data were analyzed using an earlier version of DeerLab (DL) based on Matlab7SA5), we combined all PRE, eNOE, and scalar coupling restraints and calculated multi-state ensembles of ligand-bound Pin1. To account for the time- and ensemble-averaged nature of the probes, these restraints must be fulfilled by the average of the back-calculated contributions from each individual state. To allow large spatial sampling between the two domains, we (1) calculated the structure of the WW domain and then (2) froze the WW angles and used them as an input to determine the PPIase structure and interdomain positions9. In contrast to our original protocol, we omit RDC restraints from our ligand-bound ensembles as we saw that RDCs had a negligible effect on our resulting apo Pin1 ensemble. This has the advantage that we can exclude potential interactions between ligands and alignment media, or that the relative domain orientations are impacted by the induced alignment. The PDB IDs for FFpSPR- and pCDC25c-bound two-state ensembles are 7SUQ and 7SUR, respectively.As with apo Pin1  but decreased significantly upon addition of a second state to fulfill all the structure restraints  was high for the single state structures ints see , we obtaFurther information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Multidomain proteins composed of individual domains connected by flexible linkers pose a challenge for structural studies due to their intrinsic conformational dynamics. Integrated modelling approaches provide a means to characterise protein flexibility by combining experimental measurements with molecular simulations. In this study, we characterise the conformational dynamics of the catalytic RBR domain of the E3 ubiquitin ligase HOIP, which regulates immune and inflammatory signalling pathways. Specifically, we combine small angle X-ray scattering experiments and molecular dynamics simulations to generate weighted conformational ensembles of the HOIP RBR domain using two different approaches based on maximum parsimony and maximum entropy principles. Both methods provide optimised ensembles that are instrumental in rationalising observed differences between SAXS-based solution studies and available crystal structures and highlight the importance of interdomain linker flexibility. Their modular architecture is often maintained by interdomain linkers that provide the required flexibility for domains to rearrange themselves relative to each other5. The ability to adopt a variety of conformational states underlies the versatile functional repertoire of multidomain proteins. A key cellular process involving multidomain proteins is protein ubiquitination, a post-translational modification that regulates many aspects of cellular behaviour6. Protein ubiquitination is mediated by a cascade of three enzymes, E1 activating, E2 conjugating and E3 ligating enzymes that function in a sequential manner. E3 ligases confer substrate specificity and catalyse attachment of a ubiquitin (Ub) molecule to a lysine residue in substrate proteins often followed by ubiquitin chain extension7. The work presented here focuses on the E3 ligase HOIP, which belongs to the RING-in-Between-RING (RBR) subclass of E3 ubiquitin ligases that function via a covalent E3 ~ Ub intermediate. HOIP, together with its accessory binding partners HOIL and SHARPIN, forms the linear ubiquitin chain assembly complex (LUBAC) which synthesizes linear polyubiquitin chains that regulate immune and inflammatory signalling pathway, as well as apoptosis10. RBR E3 ligases contain a conserved catalytic RBR domain that consists of three sequential zinc-binding subdomains, RING1 (E2 interacting site), IBR (in-between RING) and RING2 . The subdomains are separated by two flexible linkers: linker 1 (L1) bridging RING1 and IBR domains and linker 2 (L2) connecting IBR and RING2 subdomains12.Multidomain proteins are comprised of two or more domains that fold and function independently. These proteins are highly prevalent in eukaryotic proteomes and regulate numerous cellular processes, including signal transduction, gene expression or protein folding12. The presence of flexible interdomain linkers allows RBR subdomains to adopt multiple conformations relative to each other and is crucial for orchestrating ubiquitin transfer from E2 to substrate. These linkers and their flexibility are central to the function of RBR E3 ligases: (i) in crystal structures, linkers often have higher temperature factors or are disordered, suggesting conformational flexibility17; (ii) linkers allow relative rearrangement of RBR subdomains as indicated by comparison of structures of autoinhibited and active RBR conformations, where the overall fold of RBR subdomains is conserved, but their relative orientations are distinct22 (iii) solution experiments indicated that RBR/E2∼Ub complexes existed in a conformational equilibrium23; (iv) linkers are found to act as protein–protein interaction sites for binding ubiquitin molecules24.Substrate ubiquitination by RBR E3 ligases is a dynamic multistep process that often requires release of an autoinhibitory conformation to establish the active enzyme. Catalytic activity is initiated upon binding of the E2 ~ Ub conjugate to RING1, followed by ubiquitin transfer onto the active site cysteine in RING2 to form a thioester intermediate, and final ubiquitin transfer onto a target substrate18. The extended conformer is formed by a single polypeptide chain, whilst the closed conformer is formed by two polypeptide fragments that interact with the same E2 ~ Ub conjugate, and which represents the ubiquitin transfer complex (described in more detail below). Based on this structural arrangement we hypothesized that the two conformational states of the RBR domain observed in this crystal structure represent the apo inactive and E2 ~ Ub bound active conformations18.Despite HOIP being one of the most extensively studied RBR proteins, a detailed description of the conformational dynamics of its catalytic RBR domain in solution is currently missing. First structural insight into the dynamic nature of the HOIP RBR domain was provided by the crystal structure of a HOIP RBR/E2 ~ Ub (UbcH5B ~ Ub) complex. In this complex, the HOIP RBR domain adopts two distinct conformational states, an “extended” and a “closed” conformation (5EDV)23. Surprisingly, this study further suggested that in solution even in the presence of the E2 ~ Ub conjugate the HOIP RBR retains its high flexibility and preference for extended conformations. Based on these apparently contradictory observations we wondered whether the closed E3/E2 ~ Ub complex observed in the crystal structure may be short-lived in solution and the HOIP RBR domain preferentially populates extended conformational states. Due to flexibility of interdomain linkers, structural characterisation of multidomain proteins can be challenging. To overcome such challenges, a number of integrated structural biology methods were developed28. Integrative modelling methods provide a way to combine experimental methods and computer simulations to provide atomistic insight into global and local flexibility properties of a biomolecule and produce an ensemble of conformations that is consistent with the experimental data.A previous SEC-SAXS-based analysis indicated that the isolated HOIP RBR domain populates mainly extended conformations in solutionIn this study, we combined small angle X-ray scattering (SAXS) experiments and molecular dynamics (MD) simulations to investigate preferred conformational states of the HOIP RBR domain in isolation. We applied maximum parsimony  reweighing, to generate a minimal structural ensemble of the RBR domain in accordance with experimental data. Furthermore, we compared MaxPars ensemble with maximum entropy  reweighted ensemble and found a qualitative agreement between the two approaches, with the MaxEnt ensemble producing a better fit to the experimental data. The ensembles identified a conformational preference of the isolated HOIP RBR domain towards more compact states though without a single preferential arrangement of the domains with respect to one another. This study provides further insights into the dynamic nature of RBR domains in solution and sheds light on the importance of the linkers in this conformational behaviour. Furthermore, it highlights the power of integrative modelling in characterising multi-domain protein conformational dynamics and interpreting experimental data.23 (Supplementary Table 2). From the Guinier approximation, the radius of gyration Rg was 30.3 ± 0.1 Å. Construct flexibility was assessed using dimensionless Kratky analysis29. At low qRg, the HOIP RBR domain conferred a broad bell-shaped Kratky profile with a maximum peak close to the peak of Guinier approximation for globular molecules  of the RBR domain was 120 Å, which corresponds to the longest dimension of the extended HOIP RBR conformation observed in the crystal structure (119 Å). Furthermore, the tail of P(r) pair distribution suggested the presence of extended conformations with long intramolecular distances  coupled with SAXS (SEC-SAXS) was used Fig. A. We pre18 18 Fig. A. The asThe final starting model of the extended conformation spans residues 699–1071, comprising RING1 (699–751), L1 752–795), IBR (796–841), L2 (842–868) and RING2 (869–1071) . The conformational sampling was analysed through a set of collective variables, including distances between the center-of-mass (COM) of three domains, DRING1−IBR and DIBR−RING2 and radius of gyration Rg. The COMs for RING, IBR and RING2 domains were defined based on the 699–751, 796–841 and 869–934 regions, respectively. The initial inter-domain distances (DRING1−IBR and DIBR−RING2) and Rg for the starting extended model  are 42.8 Å, 58.7 Å and 39.6 Å, respectively . The inter-domain distances of the MD ensemble were projected onto a two dimensional free-energy surface -based values, we chose P(r)-based as a more representative value of the radius of gyration of a molecule in solution, because the pair distribution P(r) function takes into account all of the SAXS curve and is not limited to the sole Guinier region. The average Rg from the MD ensemble was 29.1 +/− 1.1 Å, which was only slightly lower than the P(r)-derived value (31.2 +/− 0.1 Å), indicating the good fit of the MD ensemble. To compare the agreement between simulations and experiments, we computed a single scattering profile by averaging over all MD trajectory frames  and examined the fit to the experimental SAXS data. The MD ensemble had a relatively good fit, with a χ2red value of 2.3 . These time-course plots show overall a consistency, for all simulated trajectories, in converging towards a more compact state with lower Rg values after equilibration.Next, we compared the average radius of gyration Rg values below or above 30 Å, respectively. The group with Rg values below 30 Å represented largely structures with shorter interdomain distances  that employs a MaxPars reweighing frameworkRg = 25.2 Å) contributed the largest weight of approximately ~ 44% to the final fit  and MP7 (Rg = 33.8 Å) made contributions of ~ 18% and ~ 26%, respectively, , the most extended conformation, made the lowest contribution to the fit of just ~ 11%  were clustered and the top four representative cluster members were extracted  and ME2 (Rg = 26.9 Å) had an abundance 34 and 24%, respectively, while the other two clusters, ME3 (Rg = 29.1 Å) and ME4 (Rg = 40.1 Å) contributed only ∼10% and ∼6%, respectively  represented by cluster ME4 was up-weighed in the optimised ensemble  and several random combinations for 2-sized, 3-sized and 5-sized ensembles . The scattering profiles of individual ensembles indicated highly variable (good to poor) fitting to the experimental data with reduced χ2 values ranging from ~ 1.3 to ~ 6.3 . Instead, to achieve a reliably good fit with experimental data, it was necessary to include multiple simulations in the ensemble . Our calculations show that at least 3, but better 5, simulations were needed to achieve good to excellent agreement with the experimental data, showing reduced χ2 values of ~ 1.1–1.2 .g values of the top 4 representative cluster members with their associated MaxPars-inferred weights as a discrete distribution alongside the continuous distribution of the MaxEnt ensemble  to the fit of the MaxPars ensemble -derived Rg value of 31.2 +/− 0.1 Å than the MD ensemble average (29.1 +/− 1.1 Å), thus indicating an improved fit . To compare ensembles, we plotted Rble Fig. A. There ble Fig. A. The 25bution ~ 5% to thebution ~ 5% to thebution ~ 5% to the21. The E2 ~ Ub bound structure shows two different arrangements of the RBR domain which we refer to as the extended and closed forms, while the dAb bound complex also adopts an elongated RBR arrangement, which however is very different from the E2-bound arrangement due to linker twisting induced by dAb binding. Crucially, the structures of individual subdomains are maintained in these different conformations, and the changes observed occur in the linker regions. In contrast, a solution-based SAXS analysis of the HOIP RBR domain, on its own and in complex with an E2 conjugate, indicated that the RBR domain remains flexible and populates mainly elongated conformations, even when bound to an E2 ~ Ub conjugate. To interrogate these differences, we employed integrative modelling which combines experimental and theoretical data to produce consistent models of dynamic biomolecular structures. Here, we have applied a posteriori ensemble reweighing to integrate MD simulations and solution scattering data to reconcile the apparently conflicting data from crystal structures and solution-based experiments. In a posteriori reweighing, the statistical weights of ensemble conformers generated from unrestrained simulations are modified to obtain a better matching ensemble average to the experimental data40. The usefulness of this approach is its simplicity in specifying a forward model for computing experimental observables and low computational costs associated with ensemble refinement. Specifically, we applied two distinct a posteriori ensemble reweighing frameworks: maximum parsimony (MaxPars) and maximum entropy (MaxEnt). MaxPars reweighing follows the principle of Occam’s razor. The optimal MaxPars solution is the ensemble containing the minimum number of structures needed to explain experimental data26. In MaxEnt, the optimal distribution is achieved by minimally perturbing the initial distribution while imposing certain constraints, where the magnitude of perturbation is measured by the amount of relative entropy needed to bias the distribution42. To avoid biasing ourselves towards a particular method, we compared the ability of both approaches to describe the conformational dynamics of the HOIP RBR domain and interpret experimental SAXS-derived data.RBR ligases transfer ubiquitin in a multi-step reaction that requires significant rearrangements of the three subdomains with respect to one another and is enabled by the flexible nature of the linkers connecting the subdomains. For HOIP, only two crystal structures of the entire RBR domain are available, the active form bound to an E2 ~ Ub conjugate, and in complex with a dAb (domain antibody) 26. Further investigation of this method, highlighted that multiple MD simulations are needed to obtain reliably good fits but that combination sets of 5 simulations are already, in this case, sufficient to obtain excellent agreement with the experimental data.The initial MD simulation analysis of the HOIP RBR domain highlighted that most of the ensemble is populated with compact conformers with shorter interdomain distances that coexist with a smaller proportion of extended conformations Fig. B. Even a23 which concluded that the RBR domain primarily populates elongated conformational states in solution overestimated their proportion within the overall conformation ensemble and emphasizes the power of integrated modelling approaches to investigate multi-domain protein conformational dynamics and help interpret experimental data43. Furthermore, our MD simulations highlight the inherent flexibility of RBR interdomain linkers that allow the subdomains to twist and rearrange and adopt multiple different conformational states. Such flexibility is likely important to enable RBR ligases to respond to a multitude of cellular regulatory signals including association with E2 ~ Ub conjugates, phosphorylation or allosteric activation by ubiquitin and possibly other molecules and modulate catalytic activity according to the specific input received.In summary, we have combined SEC-SAXS experiments and MD simulations to investigate the conformational behaviour of the catalytic HOIP RBR domain in solution. This study shows that the multi-domain RBR domain exists in a conformational equilibrium that is dominated by conformational states with shorter interdomain distances between RING1 and RING2, however with highly varied relative orientations of the subdomains to each other due to twisting of the linker regions. More elongated conformations make only a smaller contribution to the overall ensemble. This observation indicates that our previous SAXS-based ab-initio modelling44. Protein was expressed in LB medium supplemented with 200 µM ZnCl2 in BL21(Gold)DE3 Escherichia coli cells and induced with 0.5 mM IPTG at 18 °C for 14–16 h. Protein purification followed a 3-step purification protocol consisting of an affinity step using Glutathione Sepharose 4 Fast Flow, followed by GST-tag removal with with 3C-protease and ion-exchange chromatography using a HiTrap Q Fast Flow column. The protein was further purified by size-exclusion chromatography (SEC) using a Superdex 200 column in 25 mM HEPES, 150 mM NaCl, 0.5 mM TCEP, pH 7.5. Protein concentrations were determined by UV spectroscopy at 280 nm using ExPASy ProtParam calculated extinction coefficients (https://web.expasy.org/protparam/). Concentrated proteins were flash frozen in liquid nitrogen. Protein molecular mass was verified by electrospray ionization mass spectrometry.The HOIP-RBR (aa 697–1072) construct used in this study was described previouslyhttps://www.synchrotron-soleil.fr/en/beamlines/swing). Data frames for averaging to produce final scattering profiles were selected using CORMAP. Data processing was carried out with ATSAS software suite (2.8.4-1v)45  to obtain the radius of gyration (Rg), the maximum particle dimension (Dmax), the Porod volume (Vp) and the pair distribution function (Pr).SEC-SAXS data were collected at SOLEIL Light Source on beamline SWING (Supplementary Table 1). In-line SEC-SAXS was performed using an Agilent 1200 HPLC system equipped with 3 ml Agilent Bio SEC-3, 300 Å, 4.6 × 300 mm, 3 µm, HPLC column. Samples were loaded onto size-exclusion column previously equilibrated with 25 mM HEPES, 150 mM NaCl, 0.5 mM TCEP and pH 7.5. Frames were collected for the duration fractionation run, starting at 7 min. Frames collected before the void volume were averaged and subtracted from the signal of the elution profile to account for background scattering. The primary data reduction of collected frames was performed using SWING beamline software Foxtrot (3.5.2-3645v)  in the RING1 domain of 5EDV chain B was replaced by an identical region of the 5EDV chain A. The closed HOIP RBR (699–1071) model was prepared using the following steps: (i) RING1-IBR-L2 (699–852) and L2-RING2 (853–1071) substructures from the previously extended HOIP RBR domain model were extracted and superimposed onto the 5EDV chain B region of RING1-IBR-L2 (699–852) and 5EDV chain A region of L2-RING2 (853–936), respectively; (ii) Loop between 852–853 was closed and refined using RosettaCommons modelling suite47 (2017.26.59567 source bundle) with Generalized Kinematic Closure and remodel protocols, respectively49.The extended HOIP RBR (699–1071) conformation Fig. C was pre8  Fig. . However32 using GROMACS 5.1.350 under periodic boundary conditions in a rectangular box. The solute molecules were solvated with SPC water and system charge was neutralised by replacing random waters with Cl- counter ions. Before running production simulations, the system was subjected to two-stage energy minimization, followed by two-stage equilibration. The steepest-descent algorithm was applied during a two-stage energy minimization, where the system was minimized for 2,000 steps, while restraining heavy solute atoms to their initial positions using a harmonic potential with a force constant of 1,000 kJ/mol/n2. Followed by unrestrained energy minimization for 30,000 steps. During a two-stage equilibration procedure, the system was equilibrated for 100 ps at 50, 100, 200 and 300 K in the NVT ensemble using 10,000, 5,000, 4,000 and 1,000 kJ/mol/nm2 harmonic restraints on atom positions, respectively. This was followed by additional equilibration for 200 ps at 50, 100, 200 and 300 K in the NPT ensemble with the same harmonic restraints on heavy atom positions. Following equilibration, production run was initialized with generated velocities according to a Maxwell distribution at temperature of 300 K and simulated under NPT ensemble. Temperature was maintained at 300 K using velocity rescaling with a stochastic term (modified Berendsen thermostat) algorithm51 at coupling constant τT of 0.1 ps. Pressure was maintained isotropically at 1 bar using Parrinello-Rahman algorithm53 at coupling constant τP of 2 ps. Equations of motion were integrated using leap-frog algorithm with a 2 fs time step and neighbour search was performed using Verlet algorithm. The van der Waals interactions were cut-off at 0.9 nm. Electrostatics were calculated with the particle mesh Ewald method with a 0.9 nm cut-off. All covalent bonds were constrained using LINCS algorithm. The geometry of the simple point charge water molecules was constrained using SETTLE. Removal of center-of-mass translational and rotational velocity was done every 10 fs. Zn2+ ions were restrained to their respective Zn2+-coordinating residue pairs using distance restraints. The system was simulated 10 times for 100 ns, resulting in a cumulative simulation time of 1 µs. For all trajectories, the coordinates for analysis were saved every 20 ps, resulting in 50,001 frames.The extended HOIP RBR domain (699–1071) was simulated using GROMOS 54A8 forcefieldhdbscan (0.8.22v)55 with the parameter min_size_cluster set to a value of 5, while the rest of input parameters were set to their respective default values. To capture information about relative domain orientations and avoid bias in roto-translational alignment of trajectory frames, all pairwise distances between C-α atoms of RING1 (699–751), IBR (796–841) and RING2 (869–934) domains were computed and provided as input for hdbscan clustering. After cluster assignment, frames with assigned ids were extracted and noise (frames with cluster id of -1) was discarded. Representative conformer, or a centroid of a cluster, was computed based on the pairwise root-mean squared deviations (RMSD) between members of the cluster using the previously defined pairwise C-α distances of RBR domains. The pairwise RMSD matrix was transformed into a set of scores as a sum of matrix values along a single cluster member:R is the pairwise RMSD matrix, i and j represent matrix member indices and N is the size of the cluster. The member with the lowest RMSD score is selected as the representative conformer of the cluster.Clustering of trajectory frames was performed using 56 was used to calculate scattering intensities I(q) as a function of momentum transfer q defined as 3. The order of Fibonacci grid was set to a default value of 17. The maximum order of harmonics value was set to 25. The quality of the fit was calculated using the following metric:Icomp(q) is the computed scattering intensities, Iexp(q) is the experimental scattering intensities and σexp is the experimental error. The interpretation of value depends on the number of data points (N) used in calculation, thus a reduced CRYSOL (2.8.4v)Trajectory Clustering section  are the scattering intensities of a state K and wk is an associated weight. To determine relative populations of wk, Bayesian Monte Carlo is used to map a posterior probability of weights defined using Bayes’ Theorem:P(w) is a prior uniform distribution of weights and P(data|w) is a likelihood function. To account for uncorrelated and normally distributed errors, the likelihood function is defined as:2 is defined as:c is a scaling constant defined as57:The number of clusters was defined using distance threshold of 0.15. The two-step clustering protocol yields a set K of conformationally unique and experimentally distinguishable states, which is termed basis-set. In the last step, a weighted average of theoretical scattering profiles within the basis-set is computed:k and associated uncertainties as the mean and standard deviation of the observed posterior distribution, respectively.To map the posterior distribution, we conducted a Metropolis sampling for 51,000 steps, where the initial 1,000 steps were discarded as a burn-in period. We report fractional weights whttps://github.com/bio-phys/BioEN) package to determine statistical ensemble weights in accordance with MaxEnt principle36. Using CRYSOL, we computed scattering profiles for each sampled MD conformation , given experimental SAXS data,We used Bayesian Inference of Ensembles (BioEn)  is a prior and P(data|w) is a likelihood function. Prior is given by SKL is Kullback–Leibler divergence:θ defined our confidence in the reference ensemble. The larger θ values, the closer refined χ2 formulation differs by a factor of ½, which is equivalent to rescaling θ value. Instead of maximising a posterior distribution, BioEn performs an optimisation of a negative log-posterior given by:The θ values using a GSL optimiser. To account for nuisance parameters, such as unknown scaling parameter, BioEn performs an iterative weight optimisation, which was set to 10 iterations. To select an optimum θ value, the L-curve analysis was performed for SKL vs. χ2 values. Once the optimum θ value was selected, the corresponding optimised weights The multiple minimisations were performed over a set of python (3.7v). Plots were generated with matplotlib (3.1.0v). Protein trajectories were analysed using MDAnalysis (0.19.2v) and mdtraj (1.9.3v). Error estimation was determined using block analysis 58. Protein structures were visualized with PyMOL (1.7.2v).Data analysis was performed with Supplementary Information.