The emergence and dissemination of antimicrobial resistant pathogens have led to rising concern about the widespread and excessive use of antimicrobials in veterinary medicine. Denmark has implemented several initiatives to reduce antimicrobial use in animals since the 90s, with the Yellow Card scheme implemented in 2010. The aim of the present study is to investigate the effects on antimicrobial use in Danish pig farms of changes in the legislation regarding the Yellow Card, based on analysis of temporal trends in monthly antimicrobial consumption calculated at herd-level from 2010 to 2017. A dynamic linear model with a linear growth component was used to model the data. The percentage of herds with a negative growth component and a significant decline below zero  was assessed for the entire study period. The analysis was run separately for the different age groups within each farm: weaners (up to 30 kg), sows and finishers herds, and categorized according to antimicrobial use before the Yellow Card initiative (2008–2009), as groups of herds with “low,” “moderate,” or “high” consumption. The results evidence a decline of the antimicrobial consumption in Danish pig herds, more pronounced during two periods: with the introduction of the Yellow Card and in relation to the announcements and changes in the legislation in 2014. Weaner herds in the high consumption category had the highest percentage of herds with a negative growth component, reaching 82% of herds in January 2011, followed by weaner herds with moderate antimicrobial consumption reaching 71% of herds in October 2012. For finisher herds in the moderate consumption category, the proportion with a negative growth increased from 38% in January 2010 to 71% in July 2011. A decline in antimicrobial consumption was also evident after announcements and changes in the legislation in 2014, particularly for sows and finishers. Our findings suggest that changes in the Yellow Card scheme promoted a continuous reduction in antimicrobial use in Danish pig herds, particularly in herds with high antimicrobial consumption. The emergence and dissemination of antimicrobial resistant pathogens have led to rising concern about the widespread and excessive use of antimicrobials in veterinary medicine. Since the 90s, Denmark has implemented multiple initiatives to reduce antimicrobial use in animals, such as the legal ban of the growth-promoters (avoparcin in 1994 and avilamycin in 1996), the restriction of veterinarians' profit from sales of prescription medicines since 1994, and a voluntary food industry ban of all antimicrobial growth-promoters by 1999 . VetStatCurrently, Denmark is one of the largest pig producing countries in the European Union with a substantial pig production compared to its relatively small human population. In 2017 (3rd quarter), Denmark had 3,226 pig farms with 12.69 million animals  comparedHowever, to date, no published studies have substantiated the effect of the Yellow Card Scheme by comparison of the antimicrobial consumption in Danish pig farms-level with changes in Yellow card Scheme legislation. This information is important for DVFA and the Danish pig industry to evaluate the implementation of the Yellow Card Scheme and to understand when farmers and veterinarians react to changes in the legislation. Additionally, this information could be useful to other countries considering initiatives to reduce antimicrobial consumption. The aim of the present study is to investigate the effects of changes in the legislation regarding the Yellow Card scheme since its implementation. The objective is to describe temporal changes (in trends) in monthly antimicrobial consumption at pig-farm level from 2010 to 2017 after the implementation of the Yellow Card Scheme.In this study, a farm is defined as a single location with its unique identifier in the Central Husbandry Register (CHR), the CHR number. Each age group within a farm is individually referred to as “a herd” throughout the manuscript.Two databases were used for estimating the antimicrobial usage per animal on the farm level: the Vetstat database and the “Manure and Husbandry Database” (Manure Account).The Manure Account is a national database into which the farmers account for all production of animal manure and use of manure on Danish farmland (hobby husbandry excepted). This includes exact data on the number of animals stabled or produced within specific age groups, including animal weights at entrance and exit from active farms for the juvenile age groups. Data for 2010 to 2017 were provided by the authorities (Landbrugsstyrelsen) for this project, while data for 2008 and 2009 are not stored in the database. From these data, animal biomass was estimated for each herd , for each year from 2010 to 2017.All purchases of antimicrobials made between January 2008 and December 2017 for Danish pig farms were extracted from VetStat to be included in the analysis. Each record comprises the date of purchase, CHR number, target species, age group, and disease group at the time of prescription, and detailed information on the prescribed medicines. Internal validation showed inconsistency between recorded species in the CHR and recorded species in VetStat in <1% of records for the included farms. These errors were corrected when possible, based on a comparison of species registered on the farm and information on the prescription record . The data were further cleaned by deleting the negative records of antimicrobial purchases along with their corresponding positive records because of a lag of weeks or even months occasionally occurs. Negative records occur when prescribed medicines are not collected at the pharmacy.The unit of measurement for antimicrobial usage used in this study was the Animal Daily Doses (ADD) defined (in VetStat) by the Food Administration in 2017 , 10. An *days) at herd level) was calculated for each herd on each farm, in the following two steps:For each date, the average daily use of antimicrobials within the herd was estimated, as the number of ADD's prescribed divided by the number of days until the following prescription. The estimation procedure was based on the assumption that all purchased antimicrobials were used at a constant rate between two consecutive purchases. However, it illegal to prescribe medicine for more than 30 days in most herds (leftover medicine has to be prescribed once again by the veterinarian). When the time between two consecutive purchases was over 90 days, the prescribed medicine was assumed to be used at a constant rate within 90 days of prescription; for each date, the average daily use was calculated by dividing the amount purchased by 90 days.The daily antimicrobial use data were then aggregated per month and divided by the estimated number of pigs in each herd  and by the number of days in the month, thus calculating the amount of antimicrobial usage per pig-day for each herd. For 2008 and 2009, the estimated numbers of animals per herd for 2010 were used, as Manure Account data for 2008 and 2009 were not available.The monthly amount of antimicrobials consumed per 100 animals within a herd (monthly number of ADD/(100 pigit should have an estimated (averaged) antimicrobial consumption at least for all months from January 2008 to June 2010;it should have no longer than 6 consecutive months without any estimated antimicrobial consumption for weaners and no longer than 12 consecutive months without any estimated antimicrobial consumption for sows and finishers and be considered as an active herd according to manure account data; and*days) at herd level for finishers and weaners and ≥110 ADD/(100 pig*days) at herd level for sow herd were deleted.herds with unrealistic values for antimicrobial consumption were omitted from the dataset herds, corresponding to antimicrobial consumption ≥300 ADD/, as well as herds closed down in a long period; the latter comprise satellite herds which only produce animals when justified by the price of the meat.Three subsets (categories) were created based on the antimicrobial consumption in 2008 and 2009 for each herd type: low (<median), moderate , and high (≥3Q) consumption. As an exploratory analysis, each subset was tested for seasonality based on autocorrelation plots (ACF function in R) of individual time-series. Neither autocorrelation nor seasonality was found in these data.t−1 and used to calculate a conditional distribution at a given time t. This condition distribution and the observed data for the given time t are used to calculate the underlying true value, also referred to as filtered mean value.A univariate dynamic linear model (DLM) with a local linear trend component, as described in detail by West and Harrison  and applBriefly, the DLM is defined by a set of two equations, namely the observation equation (Equation 1) and the system equation (Equation 2):t) and Equation (t−1 to t. The variance components (V and Wt) are referred to as the observational variance and system variance, respectively. The observational variance (V) was kept constant, while the system variance (Wt) was estimated continuously. The system variance was approximated using a discount factor (δ) which expresses the decay of information. The transposed design matrix (F′) had the following structure:where Equation  describeEquation  describeG) was given the following structure:while the system matrix  and the filtered local linear trend values obtained from the DLM for the different datasets were visually assessed to determine the burn-in period of the model.V) of the models. Different combinations of values for V and δ were tested to optimize the models. This was done to ensure that the models would be optimized for forecasting the data from 2010 and onwards. Values of V ranging from 1 to 670 in increments of 0.1 were evaluated. Similarly, values of δ ranging from 0.1 to 1 by steps of 0.01 were tested. The combination of δ and V that minimized the sum of the squared forecast errors, and where the standardized forecast most closely followed a standard normal distribution, was chosen as the final variance parameters of the respective models. Deviations between forecasted and observed values are an indication that the herds were changing their antimicrobial consumption compared to their consumption in the past. The models were then applied to data until the end of 2017 to estimate investigate temporal changes on antimicrobial consumption for the different herds and compare them with changes in the Yellow Card scheme legislation. The nine different data sets  were included in the analysis and assumed to be mutually independent.Data from 2008 and 2009 were used to define the initial prior distributions and the variance components  for a month i.where All data management and analysis were carried out in R (version 3.3.3).The decision to implement the Yellow Card scheme was taken by the minister by January 2010, in response to the increasing antimicrobial usage in 2009 and earlier years. In the second quarter of 2010, the Danish Agriculture and Meat Council (SEGES), representing the pig producers, was informed about the decision and the upcoming Yellow Card. On June 30 and the first week of July 2010, letters were sent out to all pig producers with antimicrobial consumption over or close to the first Yellow Card limits, warning about the risk of receiving a Yellow Card, but also providing a chance to correct erroneous records in the databases. The dates when the legislation for the Yellow Card scheme and later changes were announced and implemented are described in A total of 2,310,160 purchases of antimicrobials made between January 2008 and December 2017 for 7,328 Danish pig farms were extracted from VetStat to be included in the analysis. This corresponded to a total of 3,513 weaner herds, 2,617 sow herds, and 6,496 finisher herds from which 1,538 weaner herds, 1,663 sow herds, and 1,999 finisher herds had an estimated (averaged) antimicrobial consumption data for at least all months between January 2008 and June 2010 (inclusion criteria no. 1). A total of 1,294 weaner herds, 1,633 sow herds, and 1,831 finisher herds had no longer than 6 or 12 consecutive months without any estimated antimicrobial consumption and be considered as an active herd according to manure account data (inclusion criteria no. 2). After removing herds with unrealistic values for antimicrobial consumption at herd level (inclusion criteria no. 3), a total of 1,282 weaner herds, 1,161 sow herds, and 1,828 finisher herds were included in the analysis. This study describes the dynamics of antimicrobial usage in Danish pig herds in reaction to the introduction and changes in the Yellow Card scheme.As herds with very low, non-use, or intermitting usage are excluded, any trends in those herds are not described. However, the study sample is large, including the two-thirds of the sow population and almost half of the finisher and weaner population, and is most likely more representative for herds with moderate to high consumption due to the selection criteria. Therefore, as the Yellow Card is directed toward herds with moderate to high antimicrobial usage, the observations in this study may be considered representative of the general effects of the Yellow Card.Antimicrobial usage data were analyzed at herd-level  due to physiological differences , as these result in different amounts of antimicrobials prescribed for the three age groups , 16. It *days), because the average weight of a fully grown sow is about 10% higher than the defined standard body weights . For weaners and finishers, the limit was set at 300 ADD/(100 pig*days), due to two aspects; firstly, if an outbreak occurs at the end of a production circle, the body weight may be almost twice the standard weight for the age group, suggesting a limit at 200 ADD/(100 pig*days); additionally, the number of weaners and finishers are estimated from the yearly production statistics. If the number of animals fluctuates within a year , the number of animals would be underestimated for some months and overestimated in others. Therefore, the threshold was increased by additionally 50% in these age categories.Outliers with extremely high consumption estimates were excluded because they were likely due to data errors. In principle, animals cannot be treated with more than one daily dose per day. For sows, the limit for exclusion was set at 110 ADD/. Consequently, it was possible for the veterinarian to lower the estimated antimicrobial use in a herd as calculated by the authorities, simply by choosing a different brand. In 2011 and 2013, the prescription pattern changed toward products that had less weight in the Yellow Card scheme (data not published). Therefore, in 2014, the definition of the ADD's was changed, so that the ADD (mg/kg) has since been the same for all products with the same active compound and administration route.The herds were categorized based on the antimicrobial usage in 2008 and 2009, and they remain in the same category throughout the study even though the individual herd may have changed the level of antimicrobial usage. This should be kept in mind when interpreting the data, especially late in the study period, when antimicrobial usage level may have changed dramatically in many herds. Thus, antimicrobial usage converges between the three categories of antimicrobial consumption over time. This is most important for the moderate and high consumption groups, where the antimicrobial used (median) has declined importantly over time, also with convergence between the two groups. Regarding the low consumption groups, the results suggest that the vast majority of herds in this category remained at a low level: firstly, the medians were stable throughout the study period and secondly, the negative growth curve remains around or just below 50%, showing that the number of herds with increasing and decreasing use is almost the same the entire study period . Thus, the mean antimicrobial consumption across the herds within the low consumption categories increased only slightly throughout the study period and remained well below the other groups . The medThe results evidence a decline of the antimicrobial consumption in Danish pig herds more pronounced during two periods: the introduction of the Yellow Card, and in relation to the announcements and changes in the legislation in 2014. In general, herds with high antimicrobial consumption presented to have a steep drop (both mean and median) on antimicrobial usage from the 2nd quarter of 2010; this coincides with when the warning letters were sent out to the farmers with the highest consumption, prior to the implementation of the Yellow Card. This was also associated with a large increase in the proportion of herds with negative growth in the high consumption groups for all three age groups. Furthermore, the percentage of herds within the moderate and low antimicrobial consumption categories had an increase in the proportion with negative growth of antimicrobial consumption during 2011, i.e., after the Yellow Card legislation was in place.Our results show an increasing trend in antimicrobial use in 2012–2014, which could be explained, at least in part by the possibilities of increasing the use without increasing the risk of receiving a Yellow Card, by manipulation of the calculated antimicrobial use. This option stopped at the beginning of 2014, at the same time as major down-regulation of the Yellow Card thresholds were published. These were the most prominent changes in the Yellow Card threshold after 2011, which could explain the coinciding increases in negative growth and declines in the median consumption throughout 2014 for the high consumption groups, particularly finishers and sows.After 2015 until the end of 2017, a declining trend in negative growth for all herds is observed, showing that more herds have an increasing use than the number of herds with decreasing use. Regarding the regulations in 2016–2017, the aim was mainly the implementation of different weights to specific antimicrobials , whereasFor herds with low antimicrobial consumption in 2008 and 2009, the median and mean antimicrobial use did not decline during the study period, rather small increases were observed. Nevertheless, both weaners and finisher herd in the low category had a higher percentage of herds with significant declines (based on 95% CI) in the growth component for longer periods of time when compared to any of the other categories of antimicrobial consumption. A likely explanation for this is the lower variance in the data and, consequently, narrowed CI rather than larger decreases on antimicrobial consumption. The declines in the other categories of antimicrobial consumption are higher but, due to larger CI, the percentage of herds with significant declines is lower.For sows, the percentage of herds with significant declines in the group of moderate antimicrobial use was much higher than for the other groups. This reflects that the variance is similar to the variance in the low consumption group, but as the consumption is much higher it left more room for reductions. The percentage of herds with a significant decline is at about 5–10% throughout the entire study period. This means that every month 5–10% of the herds have significant declines. As the median is stable (with a slightly decreasing trend) for this group of sow herds, this indicates that the antimicrobial usage fluctuates very much in this group of sow herds.Our findings suggest that changes in the Yellow card legislation have had a major impact on the decreasing quantitative level of antimicrobial use in Danish pig herds in relation to regulations of the thresholds, mainly in pig herds with high antimicrobial usage. The changes in the legislation of the Yellow card scheme made by the Danish Food and Veterinary Administration have thus promoted a reduction in antimicrobial usage over close to a decade in pig farms.https://www.foedevarestyrelsen.dk/english/Aboutus/Contact/Pages/default.aspx).Data cannot be shared publicly because of legal restrictions on sharing the identified dataset due to sensitive information collected at the farm level. The Danish Veterinary and Food Administration owns the antimicrobial consumption datasets used and analyzed in the current study. Data requests should be sent to the Danish Veterinary and Food Administrations (using the following link: AL contributed to the study design, data management, and analysis and wrote the manuscript. VJ contributed to the study design, data management, and reviewed the manuscript.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.


The MLKL pseudokinase is the terminal effector in the necroptosis cell death pathway. Phosphorylation by its upstream regulator, RIPK3, triggers MLKL’s conversion from a dormant cytoplasmic protein into oligomers that translocate to, and permeabilize, the plasma membrane to kill cells. The precise mechanisms underlying these processes are incompletely understood, and were proposed to differ between mouse and human cells. Here, we examine the divergence of activation mechanisms among nine vertebrate MLKL orthologues, revealing remarkable specificity of mouse and human RIPK3 for MLKL orthologues. Pig MLKL can restore necroptotic signaling in human cells; while horse and pig, but not rat, MLKL can reconstitute the mouse pathway. This selectivity can be rationalized from the distinct conformations observed in the crystal structures of horse and rat MLKL pseudokinase domains. These studies identify important differences in necroptotic signaling between species, and suggest that, more broadly, divergent regulatory mechanisms may exist among orthologous pseudoenzymes. The necroptotic cell death pathway involves signaling through pseudokinases. Here the authors define the structural determinants of species specificity in necroptosis signaling mediated by the essential necroptotic effector pseudokinase, Mixed Lineage Kinase Domain-Like (MLKL). The recent implication of pathway dysregulation in the pathology of many human diseases, including inflammatory diseases8, ischaemic reperfusion injuries10 and inflammatory bowel disease11, has led to increased interest in targeting necroptosis therapeutically. The necroptosis pathway can be triggered by a number of stimuli, including death ligands, such as tumour necrosis factor (TNF), and pathogen-derived stimuli, such as lipopolysaccharides12. In scenarios where the activities of the E3 ligase family, the inhibitors of apoptosis proteins (IAPs), and the apoptotic proteolytic enzyme, Caspase-8, have been compromised, the protein kinases, RIPK1 and RIPK3, are incorporated into a large molecular weight complex termed the “necrosome”15. Within the necrosome, RIPK3 is believed to be activated by autophosphorylation17 before promoting the executioner function of the terminal effector in the pathway, the Mixed Lineage Kinase domain-Like (MLKL) pseudokinase20.The necroptosis cell death pathway is thought to have originated as an altruistic innate immunity mechanism involved in host defense against pathogens21 and is responsible for the plasma membrane permeabilization that characterizes necroptotic cell death25. The 4HB domain executioner function is regulated by the C-terminal pseudokinase domain25, which serves as a receiver for upstream signals, such as activation loop phosphorylation by RIPK327. Pseudokinase domains are catalytically defective owing to the absence of key catalytic motifs known to mediate phosphoryl transfer in conventional protein kinases32. Instead these domains serve functions as signal integrators and/or protein interaction modules32. RIPK3-mediated MLKL phosphorylation has been proposed to provoke a conformational change in the pseudokinase domain that is transmitted to the 4HB domain via a two-helix linker termed the brace helices33. By this mechanism, MLKL undergoes a conversion from the dormant basal, cytoplasmic monomer into higher-order entities that translocate to, and mediate, plasma membrane compromise35.MLKL contains an N-terminal four-helix bundle (4HB) domain, which enables MLKL’s membrane translocation36 indicate that the precise mechanism of MLKL activation by RIPK3, and how they might vary across vertebrates, remains incompletely understood. Mouse MLKL activation relies on transient engagement of RIPK3 to facilitate phosphorylation of the pseudokinase domain27, while it appears that stable recruitment of human MLKL by necrosomal RIPK3 is an additional crucial step in human MLKL activation34. We sought to establish the molecular determinants that govern RIPK3 recognition of MLKL by examining which MLKL orthologues can communicate with the necroptosis machinery in mouse and human cell lines. Although the capacity of pseudokinase orthologues to complement signaling pathways in other species has not been widely examined, here our studies reveal remarkable selectivity among orthologues, with pig MLKL able to be activated by human RIPK3 and horse MLKL by mouse RIPK3. Unexpectedly, despite its high sequence identity to mouse MLKL, rat MLKL was not able to reconstitute necroptotic signaling in mouse cells. The molecular basis for RIPK3 selectivity could be rationalized from knowledge obtained from the crystal structures of the rat and horse MLKL pseudokinase domains. The inability of rat MLKL to complement the mouse necroptosis pathway could be attributed to the respective active kinase-like and inactive kinase-like conformations of the rat and mouse MLKL pseudokinase domains. The capacity of horse MLKL to communicate with mouse RIPK3 is enabled by an additional helix N-terminal to the αC helix in horse MLKL, which spatially occupies the position of the mouse activation loop helix in the mouse MLKL:mouse RIPK3 structure. Additionally, the activation loop of horse MLKL adopts an inactive-like conformation that nestles within the pseudoactive site. These findings raise the possibility that activation loop phosphorylation may promote activation loop mobility, and destabilise inactive MLKL conformations, to facilitate molecular switch interconversion and MLKL dissociation from RIPK3.Here, we sought to shed light on the mechanisms governing necroptotic signaling across the vertebrate phylogenetic tree. Recently revealed differences between necroptosis signaling in mouse and human cellsHomo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), horse , pig (Sus scrofa), chicken , stickleback (Gasterosteus aculeatus), frog  and tuatara (Sphenodon punctatus) MLKL into Mlkl−/− mouse dermal fibroblasts (MDFs) and MLKL−/− human U937 cells. These orthologues were chosen to maximize our sampling of phylogenetic diversity among vertebrate MLKL sequences in nature, ranging in sequence identity from 35%–85% to mouse and 36%–65% to human MLKL . Cells were stimulated with TNF, Smac mimetic and the Caspase inhibitor, IDN-6556/emricasan (TSI), to initiate necroptosis, as described before36, in the presence or absence of dox-induced orthologous MLKL gene expression. Cell death was measured by flow cytometry using propidium iodide (PI)-uptake  upon treatment with the necroptosis stimulus, TSI. Remarkably, given the high sequence identity to mouse MLKL  and the high similarity of rat and mouse RIPK3 sequences . We note that while we could verify expression of the MLKL orthologues by immunoblot  and horse MLKL (residues 188–475), following their expression and purification from insect cells, and solved their crystal structures at 2.2 and 2.7 Å resolution, respectively  in a conventional ion pair with the αC helix Glu (E239). This contrasts the human MLKL pseudokinase domain, which contains the conventional VAIK-Lys:αC helix Glu interaction (K230:E250) and exhibits an intact R-spine to resemble an active protein kinase structure39  modelled using RosettaRemodel. The buried conformation can be characterized by hydrogen bonds between the C284 peptide backbone and the activation loop Q355 carboxamide Fig. , left. W42. Cognate pseudoenzymes are thought to have arisen from gene duplications of active enzymes, where the duplicate is relieved of selective pressures to enable evolution of new, non-catalytic functions, often within the same pathway43. It is typical of pseudokinases, like the TYK2 JH2 domain, KSR2, STRADα and HER3/ERBB3 to bind and regulate the activities of their cognate active kinases: the TYK2 JH1 domain, MEK1, LKB1 and EGFR, respectively47. However, the case of MLKL and its active partner kinase, RIPK3, is unusual among known pseudoenzyme:enzyme pairs. Rather than the pseudokinase MLKL regulating the activity of RIPK3, it is RIPK3 that controls MLKL activation via engagement and phosphorylation of the MLKL pseudokinase domain34. MLKL is also an unusual pseudokinase owing to its rapid evolution49. Sequence divergence among orthologues is thought to have arisen from selective pressures, such as those applied by pathogens to evade necroptotic death as a host defense measure51. Here, we have provided a dramatic illustration of how the pairwise interaction between MLKL’s pseudokinase domain and the RIPK3 protein kinase domain has co-evolved between species. We examined the capacity of human and mouse RIPK3 to activate human, mouse, rat, horse, pig, frog, tuatara, chicken and stickleback MLKL orthologues, and the underlying structural basis for the observed selectivity. Until now, the molecular basis for selectivity among different species was poorly understood, with few determinants of species specificity deduced. In previous studies, human MLKL was unable to reconstitute necroptotic signaling in Mlkl−/− mouse cells36, but this could be overcome by swapping the pseudokinase domain of human MLKL for the mouse counterpart33. Within RIPK3, the αG helix in the C-lobe, which includes the phosphorylated mouse T231/S232 and human S227 residues, is known to be crucial for recognition of their cognate MLKL orthologues37. However, with only the mouse RIPK3 kinase domain structure reported to date40, interpretation of the selectivity of RIPK3 for different MLKL orthologues remains challenging considering similar extents of sequence diversity among orthologues as observed for MLKL , consistent with the notion that pseudokinase C-lobe projection into cognate kinase domain C-lobe cavities have convergently evolved as common interaction and regulatory determinants.Our data revealed a remarkable interspecies divergence of RIPK3:MLKL cognate pairs, such that the mouse and human RIPK3 substrate range for activation of MLKL orthologues is highly restricted. Our data indicate that mouse, horse and, to a lesser extent, pig MLKL could be activated by mouse RIPK3, while only human and pig MLKL could be activated by human RIPK3. Activity in both cell lines was unique to pig MLKL, which showed a strong preference for human RIPK3 activation. While our capacity to interpret the selectivity of human RIPK3 for human and pig MLKL is limited by the unavailability of the human RIPK3 kinase domain and pig MLKL structures, we could rationalize the selectivity of mouse RIPK3 for mouse and horse MLKL, in preference to rat MLKL, based on their structures. It remains to be addressed whether the pig MLKL β3-αC loop adopts a helical structure like that observed in our horse MLKL pseudokinase domain structure. The two MLKL orthologues share similar sequences within this loop—S27. Here, we identified the counterpart in horse MLKL, S233, as an important residue for mouse RIPK3 recognition. This residue is spatially conserved in horse MLKL owing to the occurrence of a short helix in the β3-αC loop that is not observed in other MLKL pseudokinase domain structures. Such structural conservation is absent from the rat MLKL pseudokinase domain structure, which likely accounts for its inability to substitute for mouse MLKL in the mouse necroptosis pathway despite ~88% sequence identity between rat and mouse MLKL pseudokinase domains. We also identified horse MLKL R242 as a potential determinant of mouse RIPK3 binding. This residue occupies a similar position to the mouse MLKL residue, F234, which was previously proposed to participate in the mouse RIPK3 binding interface by inspection of the complex structure40. Unexpectedly, however, Ala substitution of horse MLKL R242 led to its autoactivation, where expression alone induced cell death in the absence of a necroptotic stimulus. There are several possible explanations for the constitutive cell death induced by R242A horse MLKL. One possibility is that the R242A mutation enables constitutive activation via mouse RIPK3-mediated phosphorylation of horse MLKL, although currently this is not possible to test experimentally owing to the unavailability of suitable antibodies for phospho-horse MLKL. Another possibility is that the R242A substitution prompts a conformational change in the horse MLKL pseudokinase domain that unleashes the MLKL’s killer 4HB domain, or facilitates exposure of MLKL to necroptosis co-effectors55, to enable cell death to ensue. In contrast to mouse MLKL, where mutations in the “pseudoactive” site can trigger constitutive cell death, the horse MLKL pseudokinase domain adopts a conventional active-like kinase domain fold similar to human MLKL. Consequently, we expect horse MLKL to behave similarly to human MLKL, where pseudoactive site mutations compromise, rather than promote, killing activity. Collectively, these data implicate the position of the αC helix and adjacent structural elements in the MLKL pseudokinase domain as important factors in dictating the propensity for RIPK3 interaction.While it is clear that the MLKL pseudokinase domain αEF-αF loop aromatic residue is an anchor for RIPK3 recognition, its widespread conservation indicates that selectivity for RIPK3 engagement must be at least partially dictated by other sites within MLKL. Previous mutational analyses of mouse MLKL implicated the αC helix residue, S228, as a contributor to RIPK3 regulation53. Similar substitutions abrogated killing by human MLKL even in the presence of necroptotic stimuli34. This suggests a second function of activation loop phosphorylation: to serve as a cue for disengagement from necrosomal RIPK3. In keeping with the latter idea, viral MLKL proteins inhibit cellular RIPK3 via a constitutive association, which cannot be relieved by RIPK3 phosphorylation owing to their contracted activation loops lacking appropriate RIPK3 substrate residues5. Here, we mutated sites in the activation loops of horse and rat MLKL orthologous to the RIPK3 substrates in human and mouse MLKL, respectively. Introduction of the S345D mutation into rat MLKL modestly triggered its killing function in both mouse and human MLKL-deficient cells, suggesting that rat MLKL activation follows principles similar to mouse MLKL activation. Whether S345D rat MLKL would induce more profound death in rat cells, owing to a reliance on endogenous species-specific co-effectors, remains of outstanding interest. Curiously, mutation of the horse activation loop did not promote constitutive killing, and abrogated its capacity to reconstitute necroptotic signaling in Mlkl−/− mouse cells, indicating activation loop phosphorylation serves a function similar to that attributed to human MLKL. Consistent with this notion, molecular dynamics analyses suggest destabilization of the buried activation loop conformation following phosphorylation could perturb interaction with RIPK3 and may promote MLKL dissociation from the necrosome.The third site we have implicated in RIPK3 interaction is the activation loop of the MLKL pseudokinase domain. Our data indicate that the activation loop can serve two clear, but not mutually exclusive, functions in regulating MLKL killing activity. On the one hand, introduction of acidic residues to mimic RIPK3 phosphorylation within the activation loop of the MLKL pseudokinase domain triggered stimulus-independent activation of mouse MLKL34 are heterogeneous between species, there are unifying principles underlying MLKL activation. Principally, RIPK3-mediated phosphorylation of the MLKL pseudokinase domain activation loop, the assembly into higher-order species, and the translocation of MLKL oligomers to the plasma membrane, are hallmarks of necroptosis. Our data support the idea that the MLKL pseudokinase domain can act variously as an integrator of phosphorylation events, interconvert as a molecular switch, and mediate protein–protein interactions, albeit with differences evident in the underlying mechanisms between species. We anticipate that future studies will reveal more examples of pseudoenzymes that display functional heterogeneity between orthologues to allow them to fulfil signal transduction requirements via variant mechanisms, as we have observed for MLKL.Therefore, while MLKL’s pseudokinase domain structure, mode of RIPK3 interaction, the function of MLKL activation loop phosphorylation and the stoichiometries of MLKL assemblies56 , stickleback , frog , in addition to previously described human and mouse MLKL genes 34. All sequences are available upon request. DNA sequences were introduced into the doxycycline-inducible, puromycin-selectable vector, pF TRE3G PGK puro36. Pig, stickleback, frog, tuatara, human and mouse MLKL genes were cloned into a derivative vector containing an in-frame C-terminal FLAG tag. Mutations were introduced into the wild-type templates using oligonucleotide-directed overlap PCR. All insert sequences were verified by Sanger sequencing .DNA sequences were synthesized to encode MLKL from the following species: horse , rat , pig , chicken , tuatara18 1:1000 dilution; available as MABC604; EMD Millipore, Billerica, MA, USA) for all MLKL blots except for pig MLKL, which was detected with rat anti-MLKL clone 5C4 ; mouse anti-Actin ; rabbit anti-GAPDH ; and rat anti-FLAG . Secondary goat anti-mouse (cat#1010-05), goat anti-rabbit (cat#4030-05) and goat anti-rat Ig-HRP conjugates (cat#3010-05) were supplied by Southern Biotech and used at 1:5000 dilution. Recombinant hTNF-Fc, produced in-house, and the Smac mimetic, Compound A, were used as reported earlier58. The pan-caspase inhibitor, IDN-6556/emricasan, was provided by Tetralogic Pharmaceuticals.Primary antibodies used in this study were rat anti-MLKL  system, using procedures established for other MLKL pseudokinase domain orthologues39. Briefly, expressed proteins were purified by Ni-NTA chromatography (Roche) and the His6 tag cleaved by TEV protease treatment to leave a GAMGS overhang. Following dialysis and further Ni-chromatography, the proteins were eluted from Superdex-200 gel filtration chromatography in 0.2 M NaCl, 20 mM HEPES pH 7.5, 5% glycerol and concentrated by centrifugal ultrafiltration to 5 mg/mL.Horse (residues 188–475) and rat (residues 179–464) MLKL pseudokinase domains were cloned into pFastBac Htb and expressed in 2, 25% v/v PEG400, 0.1 M sodium MES pH 6.5 and crystals flash frozen in liquid N2 using PEG400 in the mother liquor as the cryoprotectant. Horse MLKL was crystallized in 2 M ammonium sulfate, 0.1 M sodium HEPES pH 7.5 and crystals flash frozen following transfer to 2 M sodium malonate pH 7. Diffraction data were collection at the MX2 beamline of the Australian Synchrotron using an Eiger detector, at 100 K, with a wavelength of 0.9537 Å. Data reduction, integration and scaling was performed using XDS59. The human MLKL pseudokinase domain coordinates (PDB 4MWI39) were used as the search model in Phaser-MR60 in Phenix61 for both structures. Refinement was carried out in Phenix.refine iteratively with model building in Coot62. The final model Ramachandran statistics were 96.6% favoured, 3.4% allowed and 0% outliers for the rat MLKL pseudokinase structure, and 96.2% favoured, 3.8% allowed and 0% outliers for the horse MLKL pseudokinase structure. Final statistics for refined structures are presented in Supplementary Table Rat and horse MLKL pseudokinase domain were subjected to sparse matrix screening in sitting drops containing 150 nL protein and 150 nL of reservoir solution at 20 °C . Rat MLKL was crystallized in 20 mM MnCl25. These lentiviruses were used to introduce doxycycline-inducible MLKL constructs into biologically independent MDF lines derived from three Mlkl−/− mice and immortalized using SV40 large T antigen25. MDF cells were cultured in DMEM (Life Technologies) supplemented with 10% (v/v) foetal calf serum (FCS), penicillin (100 U/mL), streptomycin (100 μg/mL) and, during and after selection for vector integration, puromycin (5 μg/mL). MLKL−/− U937 cells34 were cultured in human tonicity RPMI medium (prepared in-house) supplemented with 8–10% v/v FCS and puromycin (5 μg/mL) for lines stably transduced with MLKL expression constructs. Protein expression was validated by western blot using established methods34.pF TRE3G puro pgk vectors encoding MLKL variants were co-transfected into HEK293T cells with pVSVg and pCMV δR8.2 helper plasmids to generate lentiviral particles34. Briefly, MDFs were seeded at 5 × 104 cells/well into 24-well plates, and allowed to adhere overnight. Cells were treated with 10 ng/mL doxycycline, and after 3 h either left untreated, or treated with TNF (100 ng/mL), Smac mimetic  and IDN-6556 (5 μM) (TSI) or Q-VD-OPh (10 μM) (TSQ). After 24 h, cells were harvested and treated with propidium iodide , and PI-positive cells counted by BD FACSCalibur flow cytometry. U937 cells were assayed equivalently, but for the addition of doxycycline (10 ng/mL) immediately after plating.Cell death assays were performed using established methods63. Post-translational modifications were introduced using PyTMs64. Unbiased all-atom molecular dynamics simulations were performed using GROMACS 2016.4 (ref. 65). Structures were parameterized using the CHARMM36 (ref. 66) force field and solvated with the TIP3P water model. Random solvents molecules were replaced with sodium or chloride ions to neutralize the charge of the system. The system was contained in a dodecahedron at least 1 nm larger than the protein from all sides with periodic boundary conditions. Long-range interactions were calculated with particle mesh Ewald. Neighbour lists were maintained using the Verlet cutoff scheme67. The system underwent steepest descent minimization until the maximum force was <100 kJ/mol. Canonical ensemble68 was used to heat the system from 0 to 310 K in 100 ps. Isothermal–isobaric ensemble69  was applied for 100 ps. Positional restraints were applied during equilibration. Production runs used 2 fs time steps. Phosphorylated and dephosphorylated MLKL were simulated for 2882 and 1739 ns, respectively.The solved crystal structure of horse MLKL was used as the starting conformation for molecular dynamics simulations. Disordered residues in the activation loop (358–366) were modelled using RosettaRemodelFurther information on research design is available in the Supplementary InformationPeer ReviewReporting Summary


The functional mechanisms of multidomain proteins often exploit interdomain interactions, or “cross-talk.” An example is human Pin1, an essential mitotic regulator consisting of a Trp–Trp (WW) domain flexibly tethered to a peptidyl-prolyl isomerase (PPIase) domain, resulting in interdomain interactions important for Pin1 function. Substrate binding to the WW domain alters its transient contacts with the PPIase domain via means that are only partially understood. Accordingly, we have investigated Pin1 interdomain interactions using NMR paramagnetic relaxation enhancement (PRE) and molecular dynamics (MD) simulations. The PREs show that apo-Pin1 samples interdomain contacts beyond the range suggested by previous structural studies. They further show that substrate binding to the WW domain simultaneously alters interdomain separation and the internal conformation of the WW domain. A 4.5-μs all-atom MD simulation of apo-Pin1 suggests that the fluctuations of interdomain distances are correlated with fluctuations of WW domain interresidue contacts involved in substrate binding. Thus, the interdomain/WW domain conformations sampled by apo-Pin1 may already include a range of conformations appropriate for binding Pin1's numerous substrates. The proposed coupling between intra-/interdomain conformational fluctuations is a consequence of the dynamic modular architecture of Pin1. Such modular architecture is common among cell-cycle proteins; thus, the WW–PPIase domain cross-talk mechanisms of Pin1 may be relevant for their mechanisms as well. Modular multidomain proteins are common cell-cycle regulators in eukaryotes , 2. Theicis-trans isomerization of the prolyl imide bond. Pin1 substrates include mitotic regulators, such as c-Myc as c-Myc , and Cdcas c-Myc , as wellas c-Myc  and APP as c-Myc .16–Arg21) and the side chain of Trp34, one of the two conserved tryptophans (Trp11 being the other) referred to by the WW moniker. The PPIase domain site for pS/T-P binding includes basic residues within the catalytic surface loop (residues 64–80) that arches over the hydrophobic active-site pocket.Pin1 consists of an N-terminal WW domain (residues 1–39) that is linked by a flexible tether to a larger C-terminal PPIase domain (residues 53–163) . Both doPrevious studies of Pin1 have documented changes in PPIase activity caused by remote perturbations in the WW domain that include substitution mutations , 12, 13 cis-Pro. The interdomain contact is partially stabilized by an interstitial PEG400 molecule. In solution, NMR studies have shown similar interdomain contacts within apo-Pin1, but they are highly transient. The transience is a consequence of the extensive relative motion between the two domains afforded by the flexible intervening linker (residues 40–52) Fig. 1) Ts 40–52) , 17, 18.Xenopus laevis. Specifically, binding of the peptide substrate (pCdc25C henceforth) to Loop 1 of the WW domain (DK = 9 μm at 295 K) decreased the transient interdomain contacts between WW domain Loop 2 and PPIase domain α4/β6 residues highlighted by the 1PIN crystal structure   to the active-site pocket. These dynamic changes and the pCdc25C-induced 15N and 13C chemical shift perturbations agreed well with those of a substitution mutant that caused decreased apo-state interdomain contact while leaving substrate binding intact (I28A)  , 17, 19.t (I28A) , 13. The1H-1H interdomain NOEs, which could in principle map the pairwise contacts defining the interdomain interface. Consequently, our indicators of interdomain contact have been parameters such as chemical shift perturbations and spin relaxation parameters. As valuable as these parameters are, they do not directly address an obvious aspect of domain contact—interdomain separation. Consequently, we have incomplete knowledge of the residues mediating interdomain contact and how those contacts are perturbed by pCdc25C substrate binding on the opposite side of the WW domain.Fleshing out this hypothesis requires a more detailed description of the weakening of interdomain contact. However, gathering the appropriate data has proven to be nontrivial. The main challenge has been the extensive domain mobility. Such mobility has obscured the detection of 1H-1H NOEs by revealing through-space contacts. The key difference is that PREs are based on the intrinsically stronger proton-electron dipolar couplings and can therefore probe longer distances (∼24 Å) (1H-1H NOEs (∼5 Å). As such, PREs are appealing for studying long-range and transient close encounters (We have therefore pursued new investigations of interdomain contact in Pin1 using NMR experiments to measure paramagnetic relaxation enhancements (PREs) and present our findings herein. PRE experiments involve attaching a paramagnetic nitroxide spin label to a specific Pin1 residue. Protein protons proximal to the spin label experience enhanced transverse relaxation rates (line broadening) from dipole-dipole interactions with the unpaired electron of the spin label. These interactions vary with the average of the inverse sixth power of the distance between the proton and spin label , 21. Thu ∼24 Å)  than 1H-counters , 24 such Å  than counters , 26.27 position in Loop 2 of the WW domain. The aims of our measurements were to map the interdomain contacts sampled by apo-Pin1 and then characterize their response to pCdc25C binding. We note that our approach is distinct from the earlier PRE study of Matena et al. , thus allowing for substrate binding to Loop 1 while probing for possible changes in interdomain contact between Loop 2 and the PPIase domain.Here, we measured PREs due to a nitroxide spin label at the Hisa et al.  that putIn the sections below, we first describe the PREs measured for apo-Pin1, and then in the presence of saturating amounts of pCdc25C. Briefly, the PREs show a broader interdomain contact area in apo-Pin1 than previously thought. The PREs also gave direct experimental evidence for increased interdomain separation instigated by pCdc25C binding, accompanied by conformational reorganization within the WW domain. We also describe insights from an all-atom 4.5-μs molecular dynamics (MD) simulation of apo-Pin1. The 4.5-μs MD trajectory suggests that the apo-state of Pin1 already involves correlated inter- and intradomain dynamics supporting substrate-induced interdomain cross-talk. Such dynamics open the possibility that the binding of pCdc25C to the WW domain selects a subset of conformers interrelated by correlated changes of inter- and intradomain conformation and leads to the interdomain allosteric response to pCdc25C binding that we observed previously .27 position in the WW domain as the site for attaching the paramagnetic nitroxide spin label, methanethiolsulfonate (MTSL). His27 is at the beginning of WW domain Loop 2, and its side chain is solvent-exposed. Our previous backbone 15N relaxation studies showed restricted mobility of the local backbone region of His27 relative to the WW domain β-sheet . We also compared the HSQC spectra of 3m-Pin1 (no label) versus DIA 3m-Pin1, to investigate the effects of attaching MTSL to position 27. The main effects were 15N-1H chemical shift perturbations (CSPs) confined to the WW domain . Comparisons of the apo- and pCdc25C-complexed R2-R1/2 values revealed greater independence of domain rotational motion in the pCdc25C-complexed state . Instead, both WT-Pin1 (A) and 3m-Pin1 (B) show domain-specific responses, with WW domain residues fitting to a shallower slope  compared with the PPIase domain residues , indicating enhanced rotational mobility of the WW domain relative to the PPIase domain and thus weaker interdomain contact upon binding of pCdc25C. Critically, A and B) shows the same domain-specific response that we observed in our previous study of WT-Pin1  also shows a handful of residues that deviate strongly from the fitted lines. In fact, these residues correspond to those we identified previously as having amplified 15N R2-R1/2 values indicative of exchange dynamics on the micro-millisecond time scale , binding of pCdc25C quenches the exchange dynamics, lowering the R2-R1/2 value, which causes their “dots” to fall below the fitted line. On the other hand, Ala137 and Thr143 in 3m-Pin1 show deviations above the fitted line, indicating the onset of exchange dynamics caused by pCdc25C binding, which is not apparent for WT. These residues localize to the PPIase α4/β6 interface indicated by the 1PIN crystal structure (C). We previously hypothesized that their distinctive R2-R1/2 behavior reflected exchange broadening from transient interdomain contacts , so their disappearance reflected their co-habitation in the same domain. On the other hand, the other four residues were in the PPIase domain . Their disappearances indicated transient contact with the WW domain.We first investigated interdomain contact in apo-Pin1, by looking for paramagnetic broadening of NH cross-peaks going from the apo-DIA to apo-PARA 3m-Pin1 samples . Some brFig. 2R2 (1HN) for apo-states of PARA and DIA 3m-Pin1  (2(1HN), were the differences Γ2(1HN) = R2, apo-PARA(1HN) − R2, apo-DIA(1HN) and are shown in 2(1HN) values were identified as those deviating from the trimmed mean by more than 2 S.D. values. The largest Γ2(1HN) from curve fitting was 61.9 ± 8.0 rad/s for Asp136. The larger values implicit in the disappearance of the 13 cross-peaks appear as “overflow” bars in For a more complete analysis, we measured amide proton transverse relaxation rate constants edures”) . Sequenc148 and Phe151. It also includes Ala140 and Leu141 at the α4/β6 juncture, residues with 15N/1H chemical shifts that have been shown to be diagnostic of interdomain contact ) and the 1PIN crystal structure (27 position in Loop 2 of the WW domain. contact . The α4/tructure  15), wh, wh148 a2(1HN) values for PPIase residues Glu83, Gln94, and Ser98 in α1, Gly99 and Asp102 in the α1/α2 turn, and Phe103 in α2. This second contact region (referred to as α1/α2 henceforth) includes Ser98 and Phe103, the other two “disappearing” PPIase residues. Significantly, as seen in the 1PIN crystal structure, the relative locations of α1/α2 sites and α4/β6/β7 sites within the PPIase domain are such that proximity to the spin label by one cohort excludes the other (he other  15). Th. Th2 are either hydrophobic or adjacent to a hydrophobic residue, making them potential interacting partners for Ile28.We note that the amino acid content of two PPIase domain contact regions, α4/β6/β7 and α1/α2, bolsters the hypotheses of our previous study of I28A-Pin1, which suggested hydrophobic interactions mediating contact between Loop 2 and the PPIase domain . The fouNext, we investigated the effects of pCdc25C binding on the apo-state interdomain contacts, by measuring PREs for 3m-Pin1 under saturating amounts of pCdc25C substrate.C) and the corresponding DIA spectra (D). Notably, three residues that had been completely broadened out in apo-PARA 3m-Pin1—Phe25 and Ala31 in the WW domain and Ser98 in the PPIase domain—reappeared in Cdc-PARA 3m-Pin1.Some changes induced by pCdc25C were obvious from differences between the PARA spectra from apo- and Cdc-3m Pin1 C and the spectra D. Notabl2(1HN) values) for the Cdc-3m-Pin1 samples, using the same R2 (1HN) experiments described for the apo-state. Fig. 5A shows the resulting profile of Γ2(1HN) versus sequence. The profile shape resembles that of apo-3m-Pin1, albeit with generally smaller Γ2(1HN) magnitudes. The changes induced by pCdc25C binding are more apparent in Fig. 5B, which plots the differences ΔΓ2 = Γ2,APO(1HN) – Γ2,CDC(1HN). The plot reveals reduced PREs (ΔΓ2 > 0) for residues in the two domain contact regions, α4/β6/β7 and α1/α2, in the pCdc25C-complexed form and, therefore, greater distance of these sites from the spin label at position 27. The sites showing the most prominent reductions include Ser98 in the α1/α2 region and Asp136, Arg142, and His157 in the α4/β6/β7 region, indicating pCdc25C-induced increases of the interdomain distances DH27Cα–S98Cα, DH27Cα–D136Cα, DH27Cα–R142Cα, and DH27Cα–H157Cα.We obtained quantitative PREs   < Γ2,CDC(1HN)), indicating substrate-induced decreases of intradomain distances, suggesting perturbations of WW domain conformation. We note that these intradomain conformational perturbations coincide with the increased interdomain distances described above. WW domain residues with negative ΔΓ2 include Leu7, Arg14, and Gly39. Notably, Leu7 and Arg14 are parts of two distinct, conserved hydrophobic cores , and their NH chemical shifts are diagnostic of substrate binding (7 and Arg14 are consistent with the substrate-induced compaction (increased concavity) of WW domain noted by early X-ray and NMR structural studies  distances, DH27Cα–L7Cα, DH27Cα–R14Cα, and DH27Cα–G39Cα.In summary, our PREs gave the following new insights: (i) apo-Pin1 has a larger area of transient interdomain contacts, including the more canonical α4/β6/β7 region and the α1/α2 region revealed herein, and (ii) pCdc25C binding to the WW domain reduces apo-state interdomain contact, the most pronounced changes being increases of interdomain distances D98 and Phe103 in α1/α2 and Gly148 and Phe151 in α4/β6/β7. We wanted to explore plausible Pin1 conformations that could produce these responses. Accordingly, we performed explicit solvent MD simulations of apo-Pin1 using AMBER 16 , we usere (1NMV ) as our The water model was critical for simulating interdomain motion. Specifically, we used the three-charge, four-point rigid water model (OPC) , 33 that27 and residues showing the most prominent PREs in apo-3m-Pin1, namely DH27Cα–D136Cα, DH27Cα–G148Cα, and DH27Cα–H151Cα in the α4/β6/β7 region and DH27Cα-S98Cα and DH27Cα-F103Cα in the α1/α2 region  the domain centers of mass, (ii) H27Cα (MTSL spin label position) and the PPIase center of mass, and (iii) the Cα atoms of His2 region .Figure 61HN PREs are observable for distances up to ∼24 Å from the spin label , whereas positive values indicate that Ser98 is closer. The fluctuations are due mainly to Ser98, given that DH27Cα–S98Cα sampled a somewhat broader range of distances (∼74 Å) compared with DH27Cα–F103Cα (∼63 Å). The time trace  distance fluctuations.r values) between pairs of interdomain/intradomain distances (27 Cα–Leu7 Cα and His27 Cα–Arg14 Cα were negative (anti-correlated), as would be expected from their opposite-sense PRE changes induced by pCdc25C binding. Also, the inter-/intradomain correlation coefficients involving the WW domain distance His27 Cα–Ala31 Cα were positive, consistent with the same-sense changes observed for Ala31 PREs upon pCdc25C binding. Specifically, the Ala31 cross-peak was completely broadened out in the apo-PARA 3m-Pin1 but reappeared in Cdc-PARA 3m-Pin1, indicating that pCdc25C binding increased the average Ala31–His27 distance.We first investigated this possibility by calculating Pearson correlation coefficients , and the WW domain radius of gyration . Each dot in axes represent marginal distributions. The WW domain radius of gyration serves as a measure of intradomain distance, or compactness, whereas ρ is a generalized interdomain distance. If interdomain and intradomain distances were completely uncorrelated, then H27Cα–L7Cα and DH27Cα–R14Cα, decreased, signifying increased compaction (increased concavity) of the WW domain (see above).A known caveat of the Pearson correlation coefficient is the assumption of a We explored the influence of interdomain separation on another class of metrics sensitive to intradomain conformation: interresidue contact numbers. For an arbitrary pair of residues, the interresidue contact number is the number of heavy atom pairs (one atom from each residue) within 4.5 Å of each other . As the H27Cα–S98Cα, DH27Cα–D136Cα, DH27Cα–R142Cα, and DH27Cα–H157Cα. Thus, associated with each of the four distances was a pool of 1386 correlation coefficients, each referring to a particular interresidue contact.We identified 1386 residue pairs with fluctuating contact numbers: 279 in the WW domain and 1107 in the PPIase domain. We then calculated the Pearson correlation coefficients between the time series of the 1386 residue pairs with each of the four interdomain distances most sensitive to pCdc25C binding: Dcommon to all four interdomain distances are displayed in Fig. 9A. They reside predominantly in β-sheet regions in both domains. They also coincide with subregions supporting substrate binding and catalysis, such as the catalytic pocket of the PPIase domain and the substrate-binding site of the WW domain defined by Trp34 and Loop 1 residues Ser16–Arg21. These highlighted locations suggest that changes in interdomain distance perturb the domain regions contributing to the functional mechanism of Pin1.Then for each of the four distances, we identified the correlation coefficients with the largest magnitude (top 5%) and their associated interresidue pairs (contacts). The top 5% includes correlation coefficients with magnitudes in the top 2.5% of the positive and negative coefficients . The di. The paiB provide “spatially resolved” maps of higher versus lower magnitude correlations for each of the four interdomain distances. The WW domain shows a persistent pattern: maximal correlation coefficients localize to the substrate binding Loop 1 and Trp34 and then attenuate in the direction of Loop 2 at the other end of the domain. The attenuation smacks of “signal decay” similar to that observed in our previous study of the Pin1 WW domain with a destabilizing substitution mutation Q33E . Fig. 9B and The four structures in Fig. 9ion Q33E . In that23, a residue important for substrate recognition (Y23–R14(−), CY23–S16(+), CY23–S32(+), CY23–Q33(+), CY23–S16(+), and CS16–W34(+)  (We wanted to explore the significance of the interresidue contacts described above to WW domain conformation. ognition . Notable6–W34(+) , where t3–R14(−) , the onl23 in the WW domain stands out for two reasons. First, the X-ray crystal structure of Pin1 complexed with the doubly phosphorylated peptide representing the C-terminal domain of RNA polymerase II by Verdecia et al.  and another with conformations compatible with an overall extended Pin1 . We previously discussed such exchange as part of a speculative model to explain Pin1 interactions with substrate having multiple pS/T-P sites .27 Cα–Ser98 Cα, the distance showing the greatest quantifiable change in PRE (decrease) upon pCdc25C binding. We used the average-linkage approach in the CPPTRAJ program  displayed more intimate contacts of CS16–W34, CS16–Y23, CY23–Q33, and CY23–S32, and a weaker contact of CR14–Y23, relative to ClusterCOMPACT .We assessed the merit of these two clusters by checking their abilities to reproduce the sensitivity of interresidue contacts to interdomain separation . Indeed,EXTENDEDversus ClusterCOMPACT mirrored the aforementioned changes in interresidue contact numbers , creating a more compact substrate-binding site.To understand the atomic basis of the conformational change in the WW domain, we then compared the hydrogen bond patterns of the two clusters. Our metric was the sum over average occupancies of hydrogen bonds between residue pairs , as in our previous work . We foun numbers . SpecifiThe design of Pin1 illustrates a strategy common among eukaryotic signaling proteins: a single chain folded into discrete domain modules connected by flexible linkers . Linker 34 and Loop 1: Ser16–Arg21)  . Our preactivity . HoweverFirst, the PREs of apo-3m-Pin1 have revealed a new region of transient interdomain contact in the α1/α2 region of the PPIase domain. These PREs expand the domain interaction surface beyond the α4/β6/β7 region suggested by previous Pin1 crystal structures , 29 and Second, comparisons of the PREs from the apo- and pCdc25C-complexed 3m-Pin1 samples revealed an increase in the average interdomain distances between WW domain Loop 2 and the entire PPIase domain–interacting surface. These results unequivocally demonstrate that the loss of transient interdomain contact upon pCdc25C binding to the WW domain includes an increase of domain separation, and not merely more vigorous rotational mobility.Third, the binding-induced changes in the PREs also included decreases of certain intradomain distances within the WW domain. Thus, the binding-induced changes in interdomain conformation (increased separation between the do-mains) are coupled to changes of intradomain conformation that affect local compaction. Such PRE changes are experimental signs of correlations between inter- and intradomain motion.It is well-appreciated that substrate binding by single-domain proteins can involve “conformational selection,” whereby an incoming substrate binds to and stabilizes a subset of preexisting apo-state conformers. In this process, correlated conformational fluctuations within the apo-domain give rise to conformations resembling that of the bound substrate. The Pin1 PREs suggest that we can extend this notion to interdomain degrees of freedom characteristic of multidomain proteins. In other words, correlated fluctuations among the interdomain and intradomain degrees of freedom give rise to multidomain conformations resembling that of the bound substrate.H27Ca–S98Ca, DH27Ca–D136Ca, DH27Ca–R142Ca, and DH27Ca–H157Ca, showing the greatest increases upon pCdc25C binding. We calculated Pearson correlation coefficients between these distances and intradomain distances for 22,400 MD snapshots. Whereas the coefficient magnitudes were modest, their signs were consistent with the PRE changes induced by pCdc25C binding, namely compaction of the WW domain concomitant with increased interdomain distances. These correlations are supported by the scatter plot of The MD trajectory of apo-Pin1 let us explore this hypothesis. For example, PREs highlighted four interdomain distances, DA, common residue pairs occur in the WW domain substrate-binding site (Trp34 and Loop 1) and within the catalytic site of the PPIase domain. These locations bolster the notion of cross-talk between these distal sites via internal “synchronization”: loss or gain of interdomain contact. These contact changes at the PPIase domain surface propagate to the hydrophobic pocket for PPIase activity by local changes in side-chain flexibility highlighted by the dynamic conduit noted in previous side-chain dynamics studies (The intradomain residue pairs with the largest-magnitude correlations (between intradomain contacts and interdomain distances) overlapped significantly for the four interdomain distances, an unsurprising result considering that the distance fluctuations were not independent. As shown in Fig. 9 studies .cf. B). Such variation raises the possibility of multiple, overlapping “passageways” connecting the WW domain substrate-binding site to the distal PPIase active site. These “passageways” are related to the dynamic “conduit” we had proposed earlier  by protein kinase A inhibits substrate binding and nuclear localization  reveals a response resembling pCdc25C binding–chemical shift perturbations to PPIase residues in the α4/β6/β7 region contacting the WW domain . The C57S and C113D substitutions were to prevent off-target spin labeling. Cysin vitro .Escherichia coli BL21 (DE3) cells (Novagen). Cells were first grown at 37 °C in lysogeny broth medium until they reached an A600 of 0.8–1.0. For isotope-enriched protein, cells were harvested and resuspended in M9 minimal medium containing 15NH4Cl and/or [13C]glucose (Cambridge Isotope Laboratories) as the sole nitrogen and carbon sources  for ∼20 h. Overexpression of WT-Pin1 was induced at 26 °C for ∼16 h. Cells expressing protein were harvested and resuspended in 50 mm HEPES buffer (pH 7.5 for WT-Pin1 and pH 6.5 for 3m-Pin1 containing 1 mm EDTA). Both 3m- and WT-Pin1 constructs were purified using a HiTrap SP column followed by size exclusion (HiPrep Sephacryl S-200 HR).Both H27C/C57S/C113D-Pin1 (3m-Pin1) and WT-Pin1 were overexpressed in  sources . Overexp3-pyrroline-3-methyl). Protein was eluted in buffer containing 50 mm sodium phosphate and 300 mm NaCl (pH 6.9) during size exclusion. DTT was added to a molar ratio of DTT/protein = 3:1. Paramagnetic MTSL was dissolved in ethanol and then added to the protein sample to a final molar ratio of MTSL/protein = 30: 1. The mixture was incubated overnight at 4 °C in the dark. Excess (free) label was removed using size-exclusion chromatography (avoiding light). Attachment of diamagnetic acetyl-MTSL was identical except that it did not require dark conditions. NMR samples were exchanged into 30 mm imidazole-d4 (Cambridge Isotope Laboratories) buffer (pH 6.6) containing 30 mm NaCl, 0.03% NaN3, 5 mm DTT-d10, and 90% H2O, 10% D2O using a 10,000 molecular weight cutoff centrifugal filter.Our paramagnetic spin label was MTSL  equipped with a TCI cryogenic probe . Sample concentrations ranged from 80 to 100 μm. The 3m-pin1 backbone assignments were confirmed using established three-dimensional HNCACB . For a nitroxide spin radical like MTSL, the PREs are dominated by direct dipole-dipole interactions per the Solomon–Bloembergen expressions  and the effective electron relaxation time (τelec)  .(Eq. 3)The sum above assumes that electron relaxation is uncoupled from isotropic molecular tumbling.rIS refers to the distance between an amide proton and the unpaired electron of the spin label (approximately at the nitrogen position of MTSL). The symbol µB is the magnetic moment of the free electron (Bohr magneton), S is the electron spin quantum number, g is the electron g factor, γΙ is the gyromagnetic ratio of the amide proton, and ωΗ is its Larmor frequency. In 2(1HN) values) are proportional to the ensemble average of the inverse sixth power of the interspin distance (i.e. 〈rIS−6〉), where the unpaired electron spin location is approximated by the nitrogen of the nitroxide spin label. Under the reasonable assumption that domain reorientational and translational motions are rapid on the chemical shift time scale, we can semiquantitatively interpret the experimental PREs of different amide protons as indicative of their relative proximity to the paramagnetic label (2(1HN) = R2,PARA(1HN) − R2, DIA(1HN). The latter were measured using established 2D 15N-1H pulse schemes  and S.D. (STD1) of all PREs and filtered PREs falling outside of M1 ± STD1; we then calculated the mean (M2) and S.D. (STD2) of the remaining PREs and likewise filtered PREs falling outside of M2 ± STD2. The remaining PREs after the second filter were taken as the core values. We further calculated the mean (M3) and S.D. (STD3) of the core PREs and defined the threshold value as M3 ± 2·STD3.15N spin relaxation rate constants (e.g. R1(N), R2(N)) report on the spectral density functions J(ω) describing the rotational dynamics of 15N–1H bond vectors relative to the laboratory static magnetic field. For slowly tumbling molecules such as proteins at high magnetic field strengths, the combination of rate constants 15N R2-R1/2 is approximately the following IND refers to the 15N-1H heteronuclear dipole-dipole interaction, and NC reflects the anisotropy of the 15N chemical shielding tensor. In the absence of chemical exchange processes and assuming isotropic overall tumbling,c is the effective rotational correlation time of the NH bond.15N R2-R1/2 for apo-DIA 3m-Pin1, Cdc-DIA 3m-Pin1, apo-WT-Pin1, and Cdc WT-Pin1, using a consolidated 2D 15N-1H pulse scheme. The relaxation delays included 4.12 (2×), 8.24 (2× for Cdc-DIA 3m-Pin1), 12.36, 16.48, 24.72, 28.84, 32.96, 37.08, and 41.2 ms . Cross-peak intensity versus relaxation delay were fitted to monoexponential decays with R2-R1/2 as one of the parameters. Uncertainties were estimated using Monte Carlo simulations with noise estimates from the duplicate spectra.We measured the backbone amide 1NMV) (All-atom MD simulations of WT-Pin1 were performed at 300 K using the GPU (CUDA) version , 56, 57 1NMV)  was used2)−1 respectively. Prior to the production run, we implemented hydrogen mass repartitioning  with positional restraint factors of 10, 1, and 0.1 kcal·  , 61 for r values vary from −1 to 1, with 0 indicating no correlation. The variables x and y indicate distances or contact numbers: ix and iy are the individual snapshot values, 〈x〉 and 〈y〉 are averages over the entire trajectory, and N is the total snapshot count . The estimated S.E. of r, denoted as S.E.r, is as follows,r is the correlation coefficient from r values, S.E.r is ∼0.007.The r, we calculated the probability that N independent measurements of two uncorrelated variables would give an |r| ≥ |r0|  is highly unlikely. In other words, a correlation coefficient magnitude  ≥ 4·S.E.r (0.028) is significant. We were more conservative, considering as significant only those correlation coefficients with magnitudes ≥0.05 (∼7·S.E.r).Thus, to the extent that the X and Y by its average occurrence over the entire trajectory (or the fraction of frames the H-bond is present). The distance and angle cutoffs for H-bonds were 3.2 Å and 135˚. Our metric was the sum defined as follows,iO,XY is the specific average occupancy of the ith H-bond over the trajectory, and N is the total number of H-bonds between residues X and Y  at the University of Notre Dame.Data are available upon request. Please contact Jeffrey W. Peng (


N = 320,128). We present structural loadings for all trait facets organized into respective FFM-trait domain . Both hierarchical second-order and bi-factor models showed tolerable model fit indices, using confirmatory factor analysis in a structural equation modeling (SEM) framework. Some facet traits were substantially more representative than others for their respective trait domain, which facilitate further discussions on FFM-construct content. We conclude that IPIP-NEO is sufficiently structurally robust for future use, for the benefit of research and practice in personality assessment.Assessment of individual differences in personality traits is arguably one of the hallmarks of psychological research. Testing the structural validity of trait measurements is paramount in this endeavor. In the current study, we investigated 30 facet traits in one of the accessible and comprehensive public-domain Five Factor Model (FFM) personality inventories, IPIP-NEO-120 , using one of the largest US samples to date ( N = 320,128).Understanding how measurements of individual differences are structured is one of the foundations of psychology. A particular interest for assessment of personality traits has for some time been on the rise, even in fields outside psychology . The argWithout a psychometrically sound understanding of an open-source FFM instrument such as the IPIP-NEO-120, implications concerning use and interpretation may be skewed or erroneous see . Not kno2h = .49). The majority of reliable (error-corrected) trait variance over a life-time (83%) has been shown to be due to stable influences such as genetics and social maturation  of personality, IPIP-NEO-120 . The speN = 320,128). The sample consisted of 40% males  and 60% females , with an average age of 28.1 years (SD = 10.1). The respondents were 19–69 year old. The data was collected through an online survey, in the form of a website offering visitors feedback on their FFM personality after filling out 120 items. The visitors were at large volunteers from all walks of life, nationwide in the US, who could have reached the site by word-of-mouth, Internet search engines, and other informal channels. Every participant had to actively acknowledge that the survey would be time-consuming, used for educational and research purposes, and that careless responding would invalidate the usefulness. No login was required, and no personal, traceable data  was collected, neither data on ethnicity, socio-economic status, or other demographic characteristics. The data-set can be accessed at an open psychology research depository , the item-correlations with and without the missing data imputed showed near perfect convergence (r = .998). The conclusion is that these commonly raised problems may not interfere with the purpose of the present study.Another concern was that online surveys are often known for inconsistent responding or intentional misrepresentations. However, the quality of the present online survey data was analyzed according to guidelines in IPIP-NEO-120 is a publically available representation of the FFM , drawingThe analyses were based on the 30 facet traits in the FFM (IPIP-NEO-120). We first explored the unconstrained factor structure with facet correlations and exploratory factor analysis to see if facets organized into five proposed factors. The main objective was then finalized with testing hierarchical structural CFA models, one for each trait domain. The models were based on established FFM literature , and tesOverall, the contrasting intensity of blue along the diagonal generally shows a high internal coherence within the five trait domains. Equally, the rows and columns of the Neuroticism factor (facets n1-n6) show intensity of red colors, indicating overall negative correlations with the other four trait domains. Apart from these patterns, most facets showed weak or no color with cross-facet traits, indicating fair, but far from perfect, discriminant qualities.2 (295) = 38,877, RMSEA = 0.07, and TLI = 0.84. The first factor extracted 21%, the second 11%, the third 8%, the fourth 6%, and the fifth 5% of total variance, making the total 51%. Overall, the facet traits revealed five clear trait structures  would confirm five factors. Utilizing Maximum Likelihood (ML) for extraction, and oblique rotation, assuming correlations between facets, the summary of loadings  is reported in ructures . HoweverThe main objective was to test the structures of the FFM trait domains in the public IPIP-NEO-120 instrument. All five CFA trait structure models, one for each domain, were conducted in two ways: Second-order models; See 2(df), RMSEA, TLI, and CFI), reported in the trait domain rows, and facet and item loadings, reported in the facet rows. The first observation was that the second-order structures overall showed at best tolerable model fits. Most were above recommended stricter limits (RMSEA > 0.05 and CFI, TLI < 0.90; i These mentioned examples may represent the core of respective general factors but may at the same time be too similar to optimally function as independent facet traits.A last and final complexity was that all the trait domain models may be confounded by common variance bias, such as social desirability or acquiescence responding. This could potentially confound fit indices, as well as loadings (See the extended discussion in We were able to confirm the established five factor structure in the public-domain version of the FFM, IPIP-NEO-120, in one of the largest US public samples to date. The inter-correlation matrix , as wellIn line with previous research, the present study also identified some overall concerns regarding trait domain validity in the IPIP-NEO-120. Despite the FFM being an empirically impressive model, many critics have pointed out apparent limitations, such as lack of robust scope and meaning see . One meaMoreover, one specific finding worth highlighting is that the facet trait Modesty was almost entirely disconnected from trait domain Agreeableness, and may surprisingly be the one facet currently not functioning sufficiently. One explanation could be that Modesty should indeed be viewed as an independent trait apart from Agreeableness. Modesty may be more linked to a proposed sixth factor in an expanded FFM paradigm, Honesty-Humility cf. . AnotherThe other side of the coin is that a few IPIP-NEO facet traits also showed, not weak or disorganized loadings, but extremely strong loadings bordering on identity. Facet traits Self-discipline under Conscientiousness, Friendliness under Extraversion, and Anxiety under Neuroticism reported CFA loadings around and above β = .90. This may be interpreted as the facet trait being identical to the trait domain, and could be considered too convergent for a multifaceted structure. Conversely, this can also be considered as core features of respective FFM trait domains, and can be helpful when creating and choosing items for shorter personality measurements with fewer items.It is clear that there remains a challenge on how to establish conceptualizations of personality trait domains—Are they mostly descriptions of underlying temperaments, or behavioral life-styles, thus yielding different scopes and meanings to the trait domains cf. ? The pre