A key question regarding protein evolution is how proteins adapt to the dynamic environment in which they function and how in turn their evolution shapes the protein interaction network. We used extant and resurrected ancestral plant MADS-domain transcription factors to understand how SEPALLATA3, a protein with hub and glue properties, evolved and takes part in network organization. Although the density of dimeric interactions was saturated in the network, many new interactions became mediated by SEPALLATA3 after a whole genome triplication event. By swapping SEPALLATA3 and its ancestors between dimeric networks of different ages, we found that the protein lost the capacity of promiscuous interaction and acquired specificity in evolution. This was accompanied with constraints on conformations through proline residue accumulation, which made the protein less flexible. SHORT VEGETATIVE PHASE on the other hand (non-hub) was able to gain protein-protein interactions due to a C-terminal domain insertion, allowing for a larger interaction interface. These findings illustrate that protein interaction evolution occurs at the level of conformational dynamics, when the binding mechanism concerns an induced fit or conformational selection. Proteins can evolve towards increased specificity with reduced flexibility when the complexity of the protein interaction network requires specificity. Whether it is a receptor on the surface of the cell sensing the environment123678101113S.cerevisiae, AGAMOUS in A.thaliana, DEFICIENS A.majus and SRF in H.sapiens).A valuable tool to further our understanding of protein interactions is to study how they originate and disappear in evolution. This viewpoint allows to answer questions of a different nature. How come some proteins evolve towards molecular promiscuity yet others evolved to be specific15171920MADS-domain transcription factors are involved in numerous developmental processes in eukaryotic organisms. In plants the MIKC clade plays a key role in regulating reproductive development, including transducing environmental temperature and light signals to regulate flowering time, establishing floral organ identity and regulating seed and fruit development222324826272829302930313233343536MADS-domain proteins form both homo- and heterodimers and in the floral quartet model2223213839402637424245In this study we sought to integrate data from the network level down to the structural level to understand how molecular evolution can explain the evolution of specificity of proteins in their protein interaction networks.In two previous studies, we inferred and resurrected ancestral sequences for plant MADS-domain proteins which made it possible to characterize these ancient proteins using current laboratory methods37Arabidopsis thaliana (Ara-PIN) and Solanum lycopersicum (Sol-PIN)To have a better understanding of the origin and evolution of the glue capacity of SEP3 to mediate protein-protein interactions between other MADS-domain proteins, we reconstructed two yeast-three-hybrid (Y3H) protein interaction networks (PIN) at different time points . A firstOur results show that after the gamma triplication many more interactions were mediated by SEP3 and the network became denser , To compArabidopsis were placed in ePIN and pre-PIN networks, pSEP3, pAP3 and pSVP24 ancestors were placed in the more ancient ePIN and the extant Ara-PIN network and finally the ancestor of SEP3 in the ePIN network (ancE) and AP3 in the ePIN network (ancB), were placed in the more recent pre-PIN and Ara-PIN networks , which maintains few interactions and therefore can be considered an island and the other is SHORT VEGETATIVE PHASE (SVP), an intermediate between a hub and an islandnetworks . The mosnetworks . AlthougArabidopsis MADS-domain proteins, similar to pAP3 (seven weak interactions) and AP3 (five interactions). pSVP24 interacted with only one other protein in both pre-PIN and Ara-PIN networks , and the promiscuous-hub ancE (NYGAQETNVSS) for the number of low energy local minima conformations they can assume and we estimated their conformational entropies as flexibility descriptors. As expected, the SEP3 I-domain had the smallest conformational space  and the lowest conformational entropy . The I-domain of ancE showed around 2.5 fold increase in conformational space  and higher conformational entropy , which correlated with the number of interactions observed in our two-hybrid assay. The lowest energy state of ancE might be close to SEP3 state, but it is possible that the energy barriers between the states that bind other partners are lower compared to SEP3, allowing for fast inter conversion between the various binding conformations.We noticed that the M-domain of SEP3 did not change at all throughout its evolution , indicats tested . To furtArabidopsis SEP3 and its paralogs SEP1 and SEP2, which can complement the sep3 mutant5253This accumulation of proline residues was observed in E-lineage proteins along their evolution, in α-helices formed a single continuous α-helix in the crystal structure, the K2 α-helix showed stability both in the monomer and in the tetrameric complex , where protein atoms are allowed to move dynamically under certain physicochemical conditions. Utilizing the recently published SEP3 K-domain crystal structure complex . The K3  complex , again p complex . Finally complex . These l complex , and it Based on protein sequence alignments, the seven amino-acid loop between the K1 and K2 subdomains, seems to be a conserved feature in all type II MADS-domain proteins  and therφi + 1 = −50.ψi + 1 = −51, φi + 2 = −62, ψi + 2 = −20) with a hydrogen bond between i and i + 3 of 2.34 Å. The angles in this turn, though it has a proline with cis configuration at i + 2, are different from regular type VIa1, VIa2 and VIb β-turns56To better understand its role in protein interaction, we first investigated the conformations the loop takes in bound versus unbound states. In the bound state, the last four residues, LGPL form a type IV β-turn . The D119-R113 interaction was lost and it was not replaced with R110. A large hydrophobic surface was formed between the K1 and K2 subdomains. The RMSD calculations of K1 residues 98–111 and K2 residues 129–146 in the molecular dynamics simulations  in comparison to the other two mutants. Interestingly, here the β-turn (LGPL) was rearranged into a pseudo-α-turn (AAAAG: ulations  show thaulations .The SEP3 mutants behaved somewhat differently in electrophoretic mobility shift assays (EMSA) with AGAMOUS, which allowed to test the interaction on DNA . At highTogether, we can conclude from these observations that the loop structure contributes to allow the SEP3 K-domain to take on its energetically most favourable conformation for binding. The presence of positively charged residues at the end of the K1 domain (4 residues away from the loop region) contributes to dimer formation through binding to a negatively charged residue within the loop. This position indeed is very conserved  but significant increase in interaction strength with SEP3, but decreased FLC binding by 44%, and SOC1 binding by 86%. While ins80M had almost no effect on SOC1 binding, it decreased binding of both FLC and SEP3 by ~64%. The K2-subdomain mutation Q127R decreased SEP3 binding by 73%, but increased binding to both FLC and SOC1 by ~40%. The C-domain insertion 220–221 YD had no significant effect on FLC binding, but decreased SEP3 and SOC1 binding by 22% and 28% respectively. This variation between different SVP heterodimers suggests that these mutations are on the protein-protein interaction interface, rather than being mutations that affect an overall tertiary structure fold as in the case of proline mutations in SEP3. By contrast, the deletion of the SVP unique C-domain insertion 176–196 had a global effect, reducing all binding to FLC, SEP3 and SOC1 by ~84–95%. This mutation was not expected to have such a strong effect, since the C-domain was thought not to play a significant role in dimerization, but rather to affect tertiary complex formation. The C-domain contains the activation domain for some of the MADS-domain transcription factors60In this study we investigated the molecular mechanisms and evolutionary trajectories behind the evolution of hub and non-hub proteins in the network of MADS-domain transcription factors. We noticed that when the density of dimeric interactions in the network reached saturation, the glue protein SEP3 had an important role in increasing network density. This persisted after the major whole genome duplication event gamma, suggesting that higher order complex formation provided a way to incorporate more novel interactions from the gamma triplication onwards. We highlighted the importance of structural flexibility in hub proteins, a property that allows them to adopt different structural conformations upon binding to targets (induced fit model), and/or by lowering the energy barrier between different the conformations of the free state proteins, allowing for a higher rate of inter conversion between conformations , boosting their binding capacity, and keeping a degree of interaction promiscuity.This structural flexibility seems to be a key hub property, not only in large hubs but in small hubs as well, rather than surface residues charges as a main contributor to their interaction capacity. We followed the evolutionary history of the hub protein SEP3 and found that its ancestor at 180 mya (ancE) had a very high promiscuity level and was able to bind to all other MADS-domain proteins in every protein network. This universal hub property was largely due to its structural flexibility, as it was lost through its evolution through accumulation of proline residues in linker regions between its subdomains. Also, ancE seems to have undergone subfunctionalization along its evolution, as the number of its interacting partners adds up approximately to the number of interacting partners of SEP1,2,3 and 4 in each network. This evolutionary strategy of having multiple specific hubs instead of super-hubs may provide a higher level of network robustness, since a failure in hubs with lower numbers of interactions will have less effect than a central hub failure. We also illustrated that the high number of interactions of hubs was determined mainly by the hubs themselves, rather than by their interaction partners.It appears that specificity evolved through selection of conformational folding states by controlling the degree of disorder, followed by fine tuning through electrostatic/hydrophobic residue-residue interaction on the interaction interface. We also noticed the presence of proline residues in linker regions between protein domains  in MADS proteins. This may have led to reduced flexibility along their evolution, an observation that seems to be an exception to what has been reported beforein vivo total number of interactions could be lower than the yeast two/three hybrid reported interactions. But the trend of an increasing or decreasing number of interacting partners would not be affected.It is worth mentioning that yeast-two hybrid assays in general might increase the number of observed protein-protein interaction due to unnatural high expression level of the cloned genes, thus the a priori existing in the monomer state (induced fit). After allowing the crystal structure to relax we observed rearrangements in the loop region in addition to the α-helices of K1, K2 and K3 subdomains. The wild type SEP3 escaped the dimer conformation more slowly than the mutants. The second case we studied was SVP, which evolved from not being a hub and gained more protein interactions in evolution. We illustrated that it used a different molecular strategy during its evolution. An additional α-helix C-terminal insertion was gained through its evolution, increasing the number of protein interactions with other MADS-domain proteins in the network. Surprisingly this extra helix seems to be crucial for heterodimerization, since the deletion mutants of this unique 21 residues lost their capacity to dimerize through MIK-Cmut domains, while SEP3 dimerization does not require the C-domain4463Dimerization in MADS-domain proteins requires proper folding at the event. The molecular dynamic simulation showed that this fold is induced (dynamic) rather than et al. (unpublished)et al. (unpublished data)Gunnera maniacata and Pachysandra terminalis RNAseq dataset from 676870S.cerevisiae and A. thaliana and synthesized by Genscript USA.Ancestral genes from ePIN were used from Ruelens Mutations were introduced in the expression vectors through PCR using Phusion polymerase  and the following primers:SEP3 P78,80,83-QTS (TGGAGCACAAGAAACCAATGTGTCTTCAAGAGAGGCCT)SEP3 P122A (GGAGAAGATCTTGGAGCGCTAAGTACAAAGGAG)SEP3 GEDLAAA (GGAGAAGATCTTGCGGCGGCGAGTACAAAGGAG)SEP3 AAAAGPL (CAAAGGAATCTGTTGGCGGCGGCGGCGGGACCTCTAAGTACAAAG)SVP 80 M-insertion (AA AGAACTTGGAGAAGCTTATGGATCAGCCATCTCTTGAG)SVP 89 V-deletion (ATCTCTTGAGTTACAGCTGGAGAACAGTGATCACGCC)SVP 175–195 deletion (CAAGGAACGCAACTAGAATCGGAGAACGCTG)SVP 220 Y-221 D insertion (CCGGAAACTCTTATGATACCGGAGCGCCTGTT)SVP Q127R (GACATTGAAGAGCTTCAGAGACTAGAGAAGGCCCTTGAAACT)® SV Gel and PCR Clean-Up System from which 5ul was transformed in Top10 chemically competent cells. Successful mutagenesis was confirmed by sequencing.After the PCR reaction, 1 μl DpnI enzyme (NEB) was added to each PCR reaction tube and incubated at 37 °C overnight, followed by column purification using WizardArabidopsis thaliana network or TM5 in Solanum lycopersicum network, was co-transformed in addition to the AD and BD vectors, the successful transformation was confirmed by selective growth on Synthetic Defined (SD) 2% agar plates lacking Adenine and essential amino acids Leu and Trp. All yeast transformations were carried out following the LiAc-mediated yeast transformation as described in Clontech Yeast Protocols Handbook. Auto-activation was tested through recombinant BD vector containing MADS-box gene, co-transformed with empty AD vector in Y2H, while in Y3H auto-activation was tested by, co-transformation of recombinant pTFT1 vector containing either SEP3 or an ancestral SEP3 in Arabidopsis thaliana networks, or TM5 in Solanum lycopersicum networks, and empty AD vector. Co-transformation of empty BD vector with empty AD vector in Y2H, and empty BD vector with empty AD vector and empty pTFT1 vector in Y3H, was used as internal reference control of reporter gene leakiness. To analyze the protein-protein interaction quantitatively, β-galactosidase activity was detected using ortho-Nitrophenyl-β-galactoside (ONPG) as a substrateFor yeast two hybrid (Y2H), recombinant pGADT7 AD Vector and pGBKT7 BD vectors (Clontech) containing MADS-box genes, were co-transformed into Y187 yeast strain. Successful transformation was confirmed by selective growth on Synthetic Defined (SD) 2% agar plates lacking essential amino acids Leu and Trp. For yeast three hybrid (Y3H), to detect the ternary complex formation, a third recombinant pTFT1 vectorArabidopsis thalianaA dsDNA oligonucleotide probe was cloned into pGEM-T vector (Promega). The DNA probe containing two CArG boxes, was derived from SEP3 promoter region in Forward: (5′ end-biotin-CATGGCCGCGGGATT-3′),Reverse: (5′ end-biotin-GCGGCCGCACTAGTGATT-3′)EMSA 5′ end-biotin double-labeled Oligonucleotide Probe Sequence (CArG-boxes are underlined):CCATCTTTCTATTTTGGGTAACGAGGTCCCCTTCCCATTACGTCTTGACGTGGACCCTGTCCGTCTATTTTTAGCAGAATCACTAGTGCGGCCGC).(CATGGCCGCGGGATTTTGACGATAACTin vitro translation vector. Proteins synthesis was carried out using TNT® SP6 High-Yield Wheat Germ Protein Expression System (Promega), into a total volume of 10 μl according to the ratio of the reaction components of the manufacturer. It contained a 6 μl TNT® SP6 High-Yield Wheat Germ Master Mix and a total of 1 μg plasmid DNA in each tube. Reaction mix was incubated for 2 hours at 25 °C. The binding reaction mixt has total volume of 12 μl, containing 1.13 mM EDTA (pH 8.0), 0.024% BSA, 7.2 mM HEPES-NaOH (pH 7.3), 0.72 mM DTT, 57.72 μg/ml salmon sperm DNA, 1.27 mM spermidine, 2.5% CHAPS, 8% glycerol, 40 fmol double-labeled oligonucleotide probe and 2 μl in vitro expressed proteins, according to the previous described mixture®-Plus positively charged nylon membrane (Ambion®) overnight. The DNA band shift was detected using chemiluminescent nucleic acid detection module (Thermo Scientific).Then the probe was purified on 1% agarose gel (Promega). Mutant SEP3 genes generated in previously described site-directed mutagenesis step, in addition to AG gene, were amplified via PCR, then cloned downstream of an SP6 RNA polymerase promoter into a pSPUTK SEP3 M-domain was homology modeled based on available  crystal structures, two separate models were obtained for SEP3 M-domain using (SWISS-MODEL Server)7677788082838480SEP3 (GEDLGAL), SEP3 (AAAAGPL) and SEP3 (GEDLAAA) tetramer structures were modeled by manually mutating the residues of wtSEP tetramer experimental structure (PDB ID 4OX0)+ and Cl− ions, which were added until a concentration of 0.15 M was reached. A series of minimizations and short MD simulations were carried out to relax the model systems, by means of a relaxation protocol consisting of six stages: (i) minimization with the solute restrained; (ii) minimization without restraints; (iii) simulation (12 ps) in the NVT ensemble using a Berendsen thermostat (10 K) with non-hydrogen solute atoms restrained; (iv) simulation (12 ps) in the NPT ensemble using a Berendsen thermostat (10 K) and a Berendsen barostat (1 atm) with non-hydrogen solute atoms restrained; (v) simulation (24 ps) in the NPT ensemble using a Berendsen thermostat (300 K) and a Berendsen barostat (1 atm) with non-hydrogen solute atoms restrained; (vi) unrestrained simulation (24 ps) in the NPT ensemble using a Berendsen thermostat (300 K) and a Berendsen barostat (1 atm). At this point, 48 ns long MD simulations were carried out at a temperature of 300° K in the NPT ensemble using a Nose-Hoover chain thermostat and a Martyna-Tobias-Klein barostat (1.01325 bar). Recording intervals for trajectories was set to 4.8 ns. Trajectories were analyzed using Desmond Simulation Event AnalysisMolecular dynamics simulations of WT SEP3, SEP3 (GEDLGAL), SEP3 (AAAAGPL) and SEP3 (GEDLAAA) were run in explicit solvent, using the TIP4P9092NYGAPEPNVPS and NYGAQETNVSS peotides were investigated for their conformational behavior in solution using the MacroModel software98B is the Boltzmann constant, and Pi is the probability of the ith conformer.where kHow to cite this article: Alhindi, T. et al. Protein interaction evolution from promiscuity to specificity with reduced flexibility in an increasingly complex network. Sci. Rep.7, 44948; doi: 10.1038/srep44948 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Dear Editor,SPTB gene (466insG) leading to a frameshift and a premature stop codon 29 codons downstream in the region encoding the C-terminal part of the dimerization domain. Most probably, instability of mutant mRNA results in spectrin deficiency and clinically moderate to serious HS.Hereditary spherocytosis (HS) is the most frequent congenital haemolytic anaemia in Caucasians. β-spectrin defects are typically inherited in an autosomal dominant manner with the clinical presentation of HS , 9. We rThe N-terminal region of the β-spectrin subunit consists of actin-binding domain and located downstream the dimerization domain consisting of the first two spectrin repeats. During our studies on HS cases in the population of Western Poland, we found a new mutation in a family with autosomal dominant HS –4. PatieANK1, SPTB and SLC4A1 genes were amplified by PCR from genomic DNA. During DNA analysis of these genes, several common polymorphisms were encountered . Immunoblotting of erythrocyte membrane ghosts from C14 and C10 patients with this mutation with antibody recognising an actin binding domain of β-spectrin [SPTB gene associated with HS have been previously reported and in some cases the mutant mRNA could not be detected or was detected in decreased levels [SPTB gene takes place which results in spectrin deficiency. This in consequence is a reason for a decreased surface density of the membrane skeleton and disturbed support of the membrane lipid bilayer and hereditary spherocytosis.Next, we assessed the presence of the mutant mRNA transcripts. Loss of mutant allele in the cDNA in relation to the genomic DNA was observed Fig. , indicatd levels , 6–8. Ald levels . As in tESM 1(DOCX 21 kb)Below is the link to the electronic supplementary material.


We observe an unprece- dented, non-linear increase in the domain switching speed caused by the nucleation of new domains within the switching domain. We determine the strength of the electric field to evoke nucleation of new domains and show that the nucleated domains diffuse into nearby favourable domains when the electric field is removed. Furthermore, we discuss the prominence of domain nucleations during ferroelectric switching.Ferroelectric materials contain domains of ordered electric dipoles, separated by domain walls, that can undergo polarisation switching under externally applied electric fields. The domain switching dynamics in ferroelectric materials plays an essential role in their application to electronic and electro-optic de- vices. Previous studies suggest that the switching occurs largely through domain wall motion which is explained from the viewpoint of statistical physics on surface growth as the behaviour of a pinned elas- tic interface. We perform molecular dynamics simulations to investigate the domain switching process and quantitatively estimate the switching speed of anti-parallel 180° domains in ferroelectric, tetragonal BaTiO The electrical hysteresis loops are characteristic of ferroelectric materials as they possess spontaneous electric polarisation, in a definite range of temperature, that can be switched to two or more stable states by application of an external electric field. This change in the polarisation direction is remnant if the electric field is sufficiently high. The critical strength of an electric field in order to change the direction of the polarisation permanently is the coercive field. The coercive field is material-dependent and is influenced by defects, doping, stoichiometry and illumination.Ferroelectric materials are indispensable in a countless number of industrial and scientific applications due to their dielectric, piezoelectric, pyroelectric, electro-optic and electrical hysteresis propertiesClosed regions where the dipole moments have the same directions are the ferroelectric domains. The interface between two domains is the domain wall. The domain structure and its evolution under external electric fields are direct consequences of polarisation switching processes. When an electric field is applied to a sample, domains with polarisation oriented along the field direction become energetically more favourable. Domains with such orientation are the growing domains. They grow at the expense of their neighbours, switching or reversal domains, through the propagation of existing domain walls and/or by the formation and growth of new domains.7 and theoretically10. Experimentally, the domain wall motion is suggested as a non-linear dynamic process resulting from competition between elastic and pinning forces6. Nucleation and growth at the domain wall have been observed and analysed in detail8. It is suggested that the typical ferroelectric switching is largely governed by a simple, universal mechanism of intrinsic domain wall motion and that, even in the absence of defects, the electric field dependence of the domain wall speed can be described with a non-linear creep-like region and a depinning-like region10.180° domain wall motion and domain switching dynamics in single crystals have been studied experimentallyT) close to 0 K remain strongly pinned by local disorders and do not move until the applied electric field (E) crosses a critical value EC0. When E ≥ EC0, the domain wall experiences a pinning-depinning transition and starts to move with a finite speed (v). At finite temperatures, however, thermal activation enables a non-linear dynamic response for fields even below the critical value EC0. The domain wall motion at finite temperatures under relatively weak electric fields can be described by a creep process10:U is a characteristic energy barrier, kB is Boltzmann’s constant, EC0 is a critical field at which depinning occurs at 0 K and μ is the dynamical exponent determined by the nature of the defects in the material sample. Equation 10,Ea = UEC0/(kBT) is the activation field and μ = 1.Ferroelectric domain walls, at temperatures . In particular we investigate the switching processes and the domain wall movement in uncharged anti-parallel 180° domains. We apply a range of electric fields and identify the strength of the electric field (EN) necessary to evoke domain nucleation inside a domain. We find that the ferroelectric switching is characterized not just by the domain wall motion but more importantly by the nucleations and growth of new domains within the reversal domain. We explain the behaviour of domain switching speed in response to the applied electric field.While domain nucleations inside the reversal domain  have been observed in epitaxial PbO3 single crystals, we consider a set-up of anti-parallel domains under periodic boundary condition separated by 180° domain walls as shown in Fig. Z direction and the reversal domain along the −Z direction. The growing domain, as its name suggests, grows at the expense of reversal domain in the presence of an electric field energetically favouring the growing domain. In an atomistic simulation such anti-parallel domains set-up can be created by applying anti-parallel electric fields. Such poling electric fields are removed when the anti-parallel domain set-up is deemed stable. In other words, both the growing and the reversal domains undergo an equilibration process after which the domain walls remain stationary in the absence of external electric fields. It is imperative that the domains created by poling electric fields are stable (see Methods).To investigate the ferroelectric switching processes in BaTiOZ direction, favouring the growing domain. The ferroelectric switching process is then studied under various external electric fields. To visualize the switching processes, a fixed colour map with red to blue gradation is employed to indicate the Z component of the polarisation of each unit cell in the system, as shown in Fig. After the anti-parallel domains set-up is achieved an external electric field is applied along the +3 single crystals), the growing domain increases steadily and in a quantifiable manner. This is also to say that the domain walls gradually move into the reversal domain. The domain patterns obtained from the atomistic simulations at various instants during the ferroelectric switching process are shown in Fig. weak external electric fields, the ferroelectric switching occurs through a clear domain wall propagation. The domain walls approach closer to each other and would eventually merger producing a single domain with polarisation along +Z. Nucleation of new domains inside the reversal domain is not observed.When the magnitude of the externally applied electric field is around or below 20 MV/m , the ferroelectric switching process is significantly different. The switching process under strong electric fields is dominated by nucleation of new domains in the reversal domain. Though the propagation of domain walls is identifiable, the contribution of domain wall propagation towards ferroelectric switching is considerably low. The domain patterns obtained from the atomistic simulations at various instants during the ferroelectric switching process under strong electric fields are shown in Fig. When the magnitude of the externally applied electric field is around or above 25 MV/m  or by pursuing the change in overall polarisation during the switching processes. Figure 22. The KAI model is based on the classical approach of nucleation and subsequent growth of reversed domains23. It describes the time-dependent normalized change in polarisation as a Lorentzian function similar to the time-dependent evolution of the fraction of growing domains obtained in Fig. It can be observed that the switching kinetics under a strong field resembles that of mono-domain switching. Experimentally, the switching kinetics of mono-domains have been explained using the Kolmogorov-Avrami-Ishibashi (KAI) modelThe emergence and the number of domain nucleations increases with an increase in the electric field strength. From the experimental point of view, the emergence of domain nucleations could mean that a large jump in the switching current is observed. Overall, the nucleation of new domains along with the creation of new domain walls drastically reduces the total switching time under strong electric fields.9. In other words, the evolution of domain pattern revealed that the domain walls stop moving when the electric field is turned off and show that the domain wall speed is solely determined by the strength of the electric field at any given time. The simulations performed in this study are in agreement with the above under weak electric fields; on removal of the electric field the domain walls remained stationary. However, the evolution of domain pattern upon application and removal of strong electric fields is slightly different. This is due to the sustained nucleations of new domains.In an earlier theoretical study through atomistic simulations, it is concluded that the ferroelectric domain walls do not exhibit significant intrinsic inertial responseThe nucleated domains grow as long as the (strong) electric field is not removed. When the electric field is removed, the nucleated domains diffuse into the nearby nucleated domain or into the growing domain so as to reduce the total surface area of the domain walls as shown in Fig. To account for the influence of domain nucleations on the switching process under strong electric fields, a better descriptor of the switching process as opposed to the domain wall speed is necessary. Figure It is known that the density of domain nucleations increases with the applied electric field. We have not observed domain nucleations in simulations performed with the reversal domain of less than 20 unit cells . Moreover, the critical electric field of 25 MV/m required to observe nucleations is specific to the reversal domain (30 unit cells) employed in this study. We hypothesize that in atomistic simulations of ferroelectric switching under weak electric fields, nucleation of new domains are unlikely to be observed due to the limited size of the reversal domain. Perhaps, it is possible to observe domain nucleations under a weaker critical field if one considers larger reversal domains for atomistic simulations. In other words the nucleation of new domains has pronounced effects on the polarisation switching dynamics in larger reversal domains. Further atomistic simulations are needed to study the size effects of the reversal domain on the critical field for nucleation of new domains.3 single crystals are in the range of 50–365 MV/m27. Nucleation and growth of ferroelectric domains have been studied up to field strengths of at least 45 MV/m in bulk BaTiO3 single crystals and 1.3 GV/m in thin films25. In this study, we observed domain nucleations at a much weaker field strength of 25 MV/m. We therefore rule out the possibility that the domain nucleations observed with our atomistic simulations are a consequence of a dielectric breakdown.Previously reported experimental values of the electric field to cause dielectric breakdown in BaTiO3 single crystal under various electric fields are computed using molecular dynamics simulations. The domain wall motion under weak electric fields follows Merz’s law which can be generalized as a creep-like process. However, a large jump in the switching speed has been observed at around 25 MV/m. This is attributed to the emergence of domain nucleations and domain walls generated thereby. We establish that the domain nucleations can be observed, even within a limited size of the reversal domain, under strong electric fields using atomistic simulations. If the electric field is removed, the nucleated domains diffuse so as to minimize the total domain wall area. We reason that in atomistic simulations of ferroelectric switching under electric fields less than the established EN nucleation of new domains are unlikely to be observed due to the limited size of the reversal domain.The domain switching speed in ferroelectric BaTiO3 single crystals at atmospheric pressure and close to room temperature (310 K). We use the isotropic, an-harmonic core-shell model potential for BaTiO3 (presented below) that has been fitted to results of first-principles density functional theory calculations with a modified generalized gradient approximation28. All the simulations are performed with 60 × 10 × 10 unit cells under periodic boundary conditions. The thermostat and barostat relaxation times are both set to 0.1 ps. All shells are assigned a mass of 2 a.u. and their respective cores are assigned the remaining atomic mass29. The times step used is 0.4 fs. The growing and the reversal domains are generated through a two step process. The unit cells in the system are first poled using an external electric field for 4 ps. The left half of the system is poled in the +Z direction and the right half is poled in the −Z direction. The external field is then removed and the system is equilibrated for another 4 ps. Now, if the system is kept under no external bias, the growing domain and the reversal domain maintain the same average polarisation and the domain walls remain stationary. This ensures the stability of both the domains and the domain walls generated thereby. After the domains are prepared, the time (t) is set to zero and an external electric field is applied in the +Z direction. We then follow the switching process by computing the polarisation of each lattice unit cell in the system. In this study, we compute the polarisation of a unit cell using the core-shell charges31.We carried out isothermal-isobaric (NPT) molecular dynamics simulations to study the ferroelectric switching process in tetragonal BaTiO35. In the core-shell model every ion is represented in terms of a charged (atom) core and a charged (electron) shell, linked by a harmonic or an-harmonic spring. This introduces electronic Polarizability in the ions. In the conventional core-shell model the shells are assumed to be massless. Because the shells have no mass, they respond instantaneously to the motion of the cores i.e., at any instant during a molecular dynamics simulation the shells are repositioned so as to be in the minimum potential energy configuration with respect to the current cores’ configuration. In this work we adopt an alternative approach by assigning a small mass to the shells and treating their motion dynamically similar to that of the cores since this approach has been shown to be computationally more efficient36. We therefore treat cores and shells as point mass particles interacting classically through potentials.The core-shell model has been used in molecular statics and molecular dynamics simulations to gain insights into the properties of ferroelectric materialsk2 and k4,The interaction between cores and shells is described by three different interaction potentials. The interaction between the core and the shell of an atom is treated exclusively by an isotropic, an-harmonic spring with the potential parameters i and j are given by ri and rj, respectively. The norm of the distance vector between two interacting particles i and j is given by |rij| = |rj − ri|.The position vectors of particles A, ρ, and c denote potential parameters.The interactions between shells of different atoms, which describe the electron cloud repulsion and the Van der Waals attraction, are modelled using the Buckingham potential,Finally, the electrostatic interactions among the charged particles viz., cores and shells are considered through the Coulomb potential,i’s charge is given by qi, ε0 is the electric permittivity of free space and the total number of particles is denoted by N. Note that there is no electrostatic interactions between an atom’s core and its respective shell in the core-shell model. Figure The particle 38. In equation α is the damping coefficient and Rc is the cutoff radius to truncate the pairwise sum. The damping coefficient α and cutoff radius Rc must be carefully chosen and calibrated such that the accumulated electrostatic interactions equals with that of the Ewald summation and that there is no artificial pressure developed in the system. To this end, we have studied the effect of the damping coefficient and the cutoff radius on the average Coulombic energy and pressure per ion, captured in the Fig. Accumulation of the electrostatic interactions is one of the most computationally demanding tasks because of the long-range character of the Coulomb potential. Conventionally, Ewald summation is used to accumulate electrostatic interactions in a system with periodic boundary conditions with high accuracy and precision. For large system sizes, however, the Ewald summation method suffers from high computational costs. We therefore substitute the Coulomb potential by the Wolf summation approach,19 as opposed to the core-shell model potential, in PbTiO3 perfect single crystals. The potential parameters used in this study are taken from ref. It is worth mentioning that the only other molecular dynamics study to quantitatively estimate domain wall speed, to the best of our knowledge, was conducted using the bond-valence model potential28.The core-shell model reproduces the correct sequence of phase transitions: Rhombohedral (0–190 K) → Orthorhombic (190–270 K) → Tetragonal (270–350 K) → Cubic, shown in Fig.


Crenarchaeota, Nanoarchaeota, and Korarchaeota. The archaeal sensor histidine kinases are generally similar to their well-studied bacterial counterparts but are often located in the cytoplasm and carry multiple PAS and/or GAF domains. In contrast, archaeal response regulators differ dramatically from the bacterial ones. Most archaeal genomes do not encode any of the major classes of bacterial response regulators, such as the DNA-binding transcriptional regulators of the OmpR/PhoB, NarL/FixJ, NtrC, AgrA/LytR, and ActR/PrrA families and the response regulators with GGDEF and/or EAL output domains. Instead, archaea encode multiple copies of response regulators containing either the stand-alone receiver (REC) domain or combinations of REC with PAS and/or GAF domains. Therefore, the prevailing mechanism of archaeal TCS signaling appears to be via a variety of protein-protein interactions, rather than direct transcriptional regulation.The two-component signal transduction (TCS) machinery is a key mechanism of sensing environmental changes in the prokaryotic world. TCS systems have been characterized thoroughly in bacteria but to a much lesser extent in archaea. Here, we provide an updated census of more than 2,000 histidine kinases and response regulators encoded in 218 complete archaeal genomes, as well as unfinished genomes available from metagenomic data. We describe the domain architectures of the archaeal TCS components, including several novel output domains, and discuss the evolution of the archaeal TCS machinery. The distribution of TCS systems in archaea is strongly biased, with high levels of abundance in haloarchaea and thaumarchaea but none detected in the sequenced genomes from the phyla IMPORTANCE Although the Archaea represent a separate domain of life, their signaling systems have been assumed to be closely similar to the bacterial ones. A study of the domain architectures of the archaeal two-component signal transduction (TCS) machinery revealed an overall similarity of archaeal and bacterial sensory modules but substantial differences in the signal output modules. The prevailing mechanism of archaeal TCS signaling appears to involve various protein-protein interactions rather than direct transcription regulation. The complete list of histidine kinases and response regulators encoded in the analyzed archaeal genomes is available online at http://www.ncbi.nlm.nih.gov/Complete_Genomes/TCSarchaea.html. All living organisms possess certain means to monitor environmental conditions and react to changes in the environment by adjusting their behavior and/or metabolism. Two-component signal transduction (TCS) systems provide a key mechanism of environmental sensing and intracellular surveillance in most bacteria and some archaea –3. The c–5–Euryarchaeota and Thaumarchaeota but not among members of Crenarchaeota, Korarchaeota, or Nanoarchaeota   domain and an uncharacterized DUF1724 (Pfam ID PF08350) domain in the middle, and FilR2, which consists of a stand-alone REC domain   containsription) , 18, if C domain . Finallyitecture .http://www.p2cs.org/)  domain appear to be quite rare in archaea, and most putative archaeal output domains remain either functionally uncharacterized  or not 21–cs.org/) , 25, proCrenarchaeota . However, several TCSs have been detected in the unfinished genome sequences of putative crenarchaea obtained in metagenomics studies (see below and Table S3). The current selection of completely sequenced genomes is clearly biased. There are currently only single complete genomes from the phyla Korarchaeota and “Ca. Nanohaloarchaeota” and two genomes of the members of Nanoarchaeota; none of them encodes any TCSs. The recently sequenced genome of the first representative of the phylum “Candidatus Micrarchaeota”  and Thaumarchaeota  , which is comparable to their numbers in bacterial genomes . In contrast, members of the classes Halobacteria and Methanomicrobia typically encode 10 or more TCSs, which can comprise up to 3 to 4% of the protein-coding genes.Due to the recent efforts in archaeal genome sequencing, the current collection includes completely sequenced genomes of 218 archaeal species that encode more than half a million protein sequences, of which over 4,000 could be classified as either histidine kinases (HKs) or response regulators (RRs) (chaeota”  encodes chaeota” . Therefode TCSs) . The totde TCSs)  and rang genomes . The divIn contrast to bacterial genomes, which generally encode highly similar numbers of HKs and RRs, both the absolute and the relative numbers of these two components in archaea vary widely, particularly in methanogens  and B. Imethanogen/methylotroph, DcmR sensory domain [Pfam ID PF14417]), PocR (Pfam ID PF10114), and HisKA_7TM (Pfam ID PF16927)  and other methanogen HKs or the HisKA_7TM domain in the H. salinarum protein VNG_2180C (GenBank accession number AAG20315) and many other haloarchaeal HKs. In addition to these widespread sensor domains, there were some found only in specific archaeal lineages, often restricted to a single family or even a single genus. An example of such domains is the HisKA_4TM (Pfam ID PF16926) sensor domain, found exclusively among haloarchaea.In accordance with previous reports , 9, 28, PF16927) –31. The F16927) –, 33 wereArchaeoglobi and Methanomicrobia . Despite lacking the HATPase domain and therefore being devoid of the kinase activity, most of these proteins are currently misannotated as HKs. Although such annotation is misleading, the conserved His residues of these proteins could still have the phosphoacceptor (and even phosphotransfer) function, allowing participation in signal transduction. An intriguing possibility is that such proteins possess the phosphatase activity toward RRs that appears to be the property of the HisKA-type domains , but the ability of their protein products to interact with each other remains to be demonstrated in vitro and/or in vivo.DNA-binding transcriptional regulators comprise more than two-thirds of all bacterial RRs , 6, and Thaumarchaeota and Archaeoglobi  are encoded within the chemotaxis operons, often next to the CheA-type HKs , as well as the Mg2+-coordinating acidic residues Asp/Glu12 and Asp13 and the phosphoryl group-interacting Thr/Ser87 and Lys109 . In thad Lys109 . All theH. salinarum , it might form an HTH (or helix-loop-helix) structure. However, DNA-binding capacity—or any other function—of this domain has not been documented. The current analysis revealed many RR sequences in which HalX domains did not appear in the standard CDD or Pfam/InterPro outputs but could be recognized in iterative searches with PSI-BLAST or jackHMMer. Accordingly, several additional sequences of the HalX domain (Table S4A) were submitted to Pfam, which should result in better recognition of this common haloarchaeal domain.Response regulators containing the HalX output domain have been identified in multiple copies in many haloarchaeal genomes . During Some archaea (mostly methanogens) encode RRs with REC-PAS, REC-PAS-PAS, REC-PAS-GAF, and similar domain combinations that include ligand-binding PAS and/or GAF domains –40 but d–38–Methanomicrobia and comprise ∼5% of their HKs (Table S2). However, in the aceticlastic methanogen Methanothrix soehngenii , all 8 HKs are of the hybrid type. Some of the hybrid HKs contain the second REC domain and/or the Hpt domain (Pfam ID PF01627) at their C termini; such domain architectures can be predicted to allow complex phosphorelays.Almost 20% of the archaeal REC-containing proteins  are histA. fulgidus protein AF_1472, combine the REC domain with PAS and/or GAF domains and the dimerization HisKA domain but lack the HATPase domain. Similarly to the REC- and PAS-/GAF-containing RRs mentioned above, such RRs likely form dimers  and participate in protein-protein interactions. Again, no RR of this class has been studied experimentally, and their interacting partners remain unknown.Several archaeal RRs, exemplified by the Methanococcoides, Methanohalobium, Methanohalophilus, Methanolobus, Methanomethylovorans, and Methanosalsum) of the family Methanosarcinaceae . However, short proteins consisting of the stand-alone HalOD1 . Stand-alone HalOD1 proteins have previously been assigned to the arCOGs arCOG08103, arCOG08928, arCOG08980, arCOG08989, and arCOG09008 . The predicted secondary structure of its N-terminal part is similar to that of the HalX domain. At the C terminus, it contains two highly conserved CXXC motifs, separated by 20 to 25 residues . These cysteines form a predicted Zn finger and might be involved in DNA (or RNA) binding.Methanobacteria and Methanomicrobia (Table S4D). Iterative database searches show that this domain is similar to the heme-NO-binding (HNOB), vinyl 4-reductase (V4R), and l-2-amino-thiazoline-4-carboxylic acid hydrolase (ATC hydrolase) domains . Specifically, MetOD1 shares the predicted secondary structure with the HNOB domain and a cluster of 3 highly conserved Cys residues  with the V4R domain  andMethanocellales and Methanomicrobiales , whereas NitrOD3 is related to the Roadblock/LC7 (Pfam ID PF03259) domain (Table S5). NitrOD2, NitrOD4, and NitrOD5 belong to the archaeal “Death domain-like” family  domain but includes three more cysteines. An insert domain with 8 similarly located cysteines is found in the DNA helicase PriA, where it binds two Zn ions and positions a β-hairpin that likely acts as a DNA-unwinding wedge . Although searching the metagenomic sequence data does not allow accurate accounting of the full gene complements of the respective species, these sequences provide some insight into the signaling mechanisms in the proverbial “microbial dark matter” n-HisK domain combinations. There are also some bacterial-type RRs that have not yet been detected in finished archaeal genomes. These include, among others, the NtrC family RRs (REC-AAA-HTH domain architecture) in two crenarchaea and in “Candidatus Thorarchaeota archaeon SMTZ1-83,” a combination of REC with the protein phosphatase 2C (PP2C)-type protein serine phosphatase domain (RsbU/SpoIIE) in three Arc I group archaea, a WspR-like (fusion of REC with inactivated GGDEF domain) RR in two genomes of “Candidatus Micrarchaeum acidiphilum,” the Mycobacterium-like combination of the REC domain with thioredoxin reductase in two organisms from “Candidatus Heimdallarchaeota,” and the Bacteroides-like combination of REC with pyruvate phosphate dikinase in three members of “Ca. Heimdallarchaeota.” In addition, the genome of Lokiarchaeum sp. strain GC14_75 encodes four proteins with the same Arc-type ribbon-helix-helix-HisKA-REC domain architecture that is also encoded in the marine sediment metagenome. Given that the only GGDEF domain detected so far in any archaeon comes from the uncultured methanogenic archaeon Methanocella arvoryzae (GenBank accession number CAJ37382), it remains to be determined whether these bacterial-type RRs are genuine archaeal proteins or represent contamination of archaeal contigs with bacterial DNA.In most new phyla, there are representatives encoding at least some TCSs. This is the case for all 4 phyla in the Asgard group, 5 of 8 phyla in the DPANN group, and 4 of 6 phyla the in TACK group. Notably, while the available finished genomes of Haloarcula marismortui, Methanococcoides burtonii, and Methanospirillum hungatei), as well as the recently sequenced genomes of such thaumarchaea as “Candidatus Nitrososphaera gargensis,” encode numerous diverse TCSs, on par with any bacteria , which has been appropriately noted in the respective genome descriptions n-HisKA, that include ligand-binding and protein-interacting domains but contain no obvious output domains. These features, viewed together with the biased distribution of HKs and RRs among archaeal phyla  the total contents of HKs and RRs encoded in archaea, which are typically somewhat smaller than in bacteria ; (ii) thal phyla , are genThis study also illuminates the poor state of the genome annotation of the archaeal TCS machinery. The difficulties with the annotation of multidomain proteins have been discussed previously , 68, butCrenarchaeota and “Ca. Nanohaloarchaeota,” and the identification of several new and interesting domain architectures (Table S3). However, most HKs and RRs encoded in unfinished genomes were related to those identified in complete genomes. Thus, the trends outlined here are likely to remain valid even after the substantial expansion of the coverage of archaeal genome diversity that is expected in the near future  (http://www.ebi.ac.uk/genomes/archaea.html) and trimmed to leave only a single representative per species, which resulted in a set of 218 genomes that were available by 1 July 2017 . The taxonomic assignments of the selected organisms were taken from the NCBI Taxonomy database . To accelerate the search by using the existing profiles of HisKA and HATPase domains, the first iteration of PSI-BLAST was run using the DELTA-BLAST tool  and InterPro database. The presence of PAS and/or GAF domains was additionally evaluated using PSI-BLAST  . This lidatabase . The lisdatabase  of the eAST tool . It was SI-BLAST  and CD SSI-BLAST . TransmeSI-BLAST .H. salinarum CheY protein  as the query  and by extracting the REC domain assignments from the NCBI's CDD  and H. salinarum as queries. The PSI-BLAST outputs were formatted using the “Query-anchored with letters for identities” option and edited to remove those residues that did not align with CHEY_ECOLI. Genomic neighborhoods were analyzed using the MicrobesOnline and SEED databases (ftp://ftp.ncbi.nih.gov/pub/wolf/COGs/arCOG/).Sequence logos were generated using the WebLogo program  from theatabases , 79 and atabases , which ahttps://www.ncbi.nlm.nih.gov/Complete_Genomes/TCSarchaea.html. As rationalized in the previous studies .The complete list of genomes analyzed, with the respective numbers of HKs and RRs, is presented in Table S1. An expanded HTML version of that table that includes a listing of all HKs and RRs encoded is provided as Table S2 and is also available online at  studies , 9, 70, The domain architectures of the archaeal RRs were manually checked against the CDD/SPARCLE and InterPro databases , 22. The


Calloselasma rhodostoma) venom. The structure of RC alone reveals a heterotetramer arranged as an αβ and γδ subunit in a cruciform shape. RC specifically binds to the collagen binding A-domain of the integrin α2 subunit, thereby blocking collagen-induced platelet aggregation. However, until now, the molecular basis for this interaction has remained unclear. Here, we present the molecular structure of the RCγδ-α2A complex solved to 3.0 Å resolution. Our findings show that RC undergoes a dramatic structural reorganization upon binding to α2β1 integrin. Besides the release of the nonbinding RCαβ tandem, the RCγ subunit interacts with loop 2 of the α2A domain as result of a dramatic conformational change. The RCδ subunit contacts the integrin α2A domain in the “closed” conformation through its helix C. Combined with epitope-mapped antibodies, conformationally locked α2A domain mutants, point mutations within the α2A loop 2, and chemical modifications of the purified toxin protein, this molecular structure of RCγδ-α2A complex explains the inhibitory mechanism and specificity of RC for α2β1 integrin.The collagen binding integrin α2β1 plays a crucial role in hemostasis, fibrosis, and cancer progression amongst others. It is specifically inhibited by rhodocetin (RC), a C-type lectin-related protein (CLRP) found in Malayan pit viper ( C. rhodostoma). RC specifically binds α2β1 integrin, the key protein required for collagen-mediated platelet aggregation. In this study, we describe the interaction between RC and α2β1 integrin at atomic resolution. This study reveals that RC undergoes a massive structural reorganization upon α2β1 integrin binding, such that RC’s αβ subunit is released from its γδ subunit and a γδ-α2β1 integrin complex is formed. The inhibitory nature of this complex can be readily explained as RC binding along the top surface of the α2β1 integrin and directly above the collagen binding site. As a result, access of collagen to its binding site is blocked, thereby preventing collagen-mediated platelet aggregation.In animals, collagen-mediated platelet aggregation is an essential component of the blood’s clotting response following vascular injury. A small group of snake venom toxins belonging to the C-type lectin protein family exert their harmful effects by directly targeting this pathway. Rhodocetin (RC) is a heterotetrameric protein found in the venom of the Malayan pit viper ( Most cellular processes depend on the formation of interactions between cells and the extracellular matrix (ECM). Key facilitators of these interactions are the integrins. They consist of 2 subunits, α and β, each of which has multiple isoforms ,2. The dThe collagen binding site is located within the α2A domain of α2β1 integrin, which is homologous to the A-domain of von Willebrand factor (vWF). The α2A domain contains a metal ion that is required for collagen binding as it is part of the binding site for the collagen triple helix . In orde4 structures of flavocetin and convulxin -approximation. To compare independent inhibition and binding experiments, the dynamic ranges were normalized to the mAb-free control and to the saturation value of the wild-type α2A domain, respectively.The wells of a half-area microtiter plate (Costar) were coated with 10 μg/ml His-tagged α2A domain in TBS/Mg buffer .IIIG5 was immobilized to cyanogen bromide-activated sepharose according to the manufacturer’s instruction . RCαβγδ-containing fractions from the Mono S purification of ma venom  were red0 of added titrant. Based on a Michaelis–Menten-like binding mechanism, we deduced the following equation to approximate titration curves, if the signal S and the total concentration c0 of added ligand (RC) is known:M and Sm, maximum and minimum signals, respectively; cR, the concentration of ligand binding site ; and K, the dissociations constant Kd. The term B·c0 takes into account a linear change in the signal due to nonspecific binding of the ligand. The 5 parameters SM, Sm, cR, K, and B are calculated by nonlinear regression from titration curves.In titration curves, a signal S, usually the extinction at 405 nm caused by the alkaline phosphatase-catalyzed conversion of pNpp, is measured in response to the total concentration ct test, where the significance level was set at 1% unless otherwise stated.The data from titration and inhibition curves were statistically evaluated using GraphPad Prism software. Values were usually compared with the values obtained for the wild-type α2A or nonmodified RC with Student S1 FigA) Overall view of the asymmetric unit showing six RCγδ-α2A complexes. Individual α2A domains are shown in grey, with the Mn2+ as pink spheres. RCγ subunits are shown in red, whereas RCδ subunits are in yellow. (B) The different heterotrimeric assemblies can be subcategorized in three different interaction modes. Domain-domain contacts are mediated either via the core segment of the CLRP fold of RCγ (top), the distal end of the α2A domain (middle) or the index finger loop segments (bottom). Remarkably, the overall r.m.s.d. in Cα positions for all individual subdomains is 1.1Å demonstrating that the different RCγδ-α2A complexes are identical.((TIF)Click here for additional data file.S2 FigA) Model of K168C-E318C representing the open conformation. (B) Model of K168C-A325C showing the conformation. Residues involved in the formation of helix C are in red. To highlight the difference between the two conformations, amino acid residue positions 318 and 325 are colored blue and green, respectively. Structures were modelled with Pymol using the pdb data sets of α2A domain in its “open” (1DZI) and “closed” (1AOX) conformation.By introducing disulfide bridges at the respective sites, helices 1 and 7 were fixed towards each other. Using this approach, the α2A domain is stabilized in either the “open” or “closed” conformation. ((TIF)Click here for additional data file.S3 Fig(A) Fragmentation scheme for the tryptic peptide, of the RCγ subunit containing the IIIG5 epitope. (B) NanoESI fragment ion spectrum of the RCγ peptide containing the IIIG5 epitope. It was obtained from a CID experiment on the ion mobility-separated doubly charged peptide precursor ions at m/z 942.40. The labelled peaks correspond to the fragment ions of this epitope peptide, as shown in (A).(TIF)Click here for additional data file.S4 Fig214QYGGD is highlighted in yellow and shows a high degree of homology between different species. Multiple sequence alignment was carried out with Clustal Omega Software from EMBL-EBI.Sequence comparison of the integrin α2 A-domain from different vertebrate species. The loop 2 sequence S(TIF)Click here for additional data file.S5 Fig214QYGGD,  and is specific to the integrin α2 chain. The secondary structure elements are indicated by the red (α-helices) and the blue (β-strands) boxes, respectively. The residue numbering refers to the integrin α2 sequence. Multiple sequence alignment was carried out with Clustal Omega Software from EMBL-EBI.A comparison of A-domains from different human integrin α subunits. Integrin alpha subunits 1, 2, 10, and 11 belong to the subset of collagen binding integrins. They possess the characteristic helix C , which is absent in the A-domain of the leukocyte β2 integrins with their alpha subunits L, X, M, and D. Helix C of the integrin α2 subunit is the primary binding site for RCγδ and is only present in the “closed” conformation of its A domain. The secondary RC contact site of α2A is located within the loop 2 sequence S(TIF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


At Ts = 42 K, the compound undergoes a structural phase transition of weakly first-order, from a non-centrosymmetric cubic phase at high temperatures to a polar rhombohedral structure at low temperatures. Below Ts, ferroelectric domains are formed with the electric polarization pointing along any of the four 〈111〉 axes. Although in this material the size and the shape of the ferroelectric-ferroelastic domains may act as important limiting factors in the formation of the Néel-type skyrmion lattice emerging below TC = 13 K, the characteristics of polar domains in GaV4S8 have not been studied yet. Here, we report on the inspection of the local-scale ferroelectric domain distribution in rhombohedral GaV4S8 using low-temperature piezoresponse force microscopy. We observed mechanically and electrically compatible lamellar domain patterns, where the lamellae are aligned parallel to the (100)-type planes with a typical spacing between 100 nm–1.2 μm. Since the magnetic pattern, imaged by atomic force microscopy using a magnetically coated tip, abruptly changes at the domain boundaries, we expect that the control of ferroelectric domain size in polar skyrmion hosts can be exploited for the spatial confinement and manipulation of Néel-type skyrmions.GaV Ferroelectrics find numerous applications, amongst others, in non-linear electronic components, memory elements, electro-optical devices, piezoelectric transducers and actuators24567Besides proper ferroelectrics, in which the primary order parameter is the ferroelectric polarization, ‘improper ferroelectrics’ are also intensively studied. In these materials the electric polarization is often coupled to another degree of freedom, leading to new functionalities3910113, LaMnO3, the distortion does not induce any electric polarization. In contrast, whenever the high-symmetry phase of a compound lacks inversion symmetry, a sizeable electric polarization may develop upon the Jahn-Teller transition. Theory suggests that, in strong contrast to structural transitions in proper ferroelectrics, the Jahn-Teller distortion is not suppressed in materials with a partially filled d shell, which in turn allows for simultaneous magnetic and ferroelectric orders, i.e. the emergence of multiferroic states4S8, upon its ferroelectric phase transition driven by the cooperative Jahn-Teller effect4S8. Additionally, the effect of structural domain boundaries on the magnetic patterns, namely the cycloidal order and the Néel-type skyrmion lattice, were investigated by non-contact mode atomic force microscopy, using a magnetically coated AFM tip. The magnetic structures were imaged by measuring the dissipated power while the oscillation amplitude of the tip was held constant. In addition, the topography information was retrieved from the frequency shift in the AFM signal. Hereafter, we will refer to these measurements as the dissipation and topography channels of the magnetic AFM (m-AFM) signal, respectively.Recent theoretical works have suggested that the Jahn-Teller effect, a symmetry-lowering structural distortion driven by the orbital degeneracy of localized charge carriers, may be responsible for a new type of improper ferroelectricity144S8 belongs to the family of lacunar spinels, characterized by the vacancy of every second Ga-site in the unit cell as compared to the ordinary spinel structure. In an ideal spinel structure, the vanadium atoms would be arranged in a network of corner-sharing tetrahedrons, known as the phyrochore lattice. However, owing to the central cationic void in every second tetrahedron, the lattice decomposes into smaller and larger V4 tetrahedral clusters, hence forming a breathing pyrochlore lattice4 clusters constitute an fcc lattice. Each V4 cluster carries a spin of S = 1/2, since the triply degenerate t2 cluster orbital is occupied by single unpaired electronsTs = 42K through the stretching of the lattice along any of the four 〈111〉-type body diagonals in the pseudocubic system, as presented in 4S8 is not displacive but of disorder-to-order type. Since the d point symmetry is reduced to its subgroup Cv3, the formation of four degenerate structural domains is allowed. The polarization in each structural domain is aligned parallel to one of the four 〈111〉 axes, i.e. the direction of the distortion, which is the only remaining C3 axis of the given domain. The local electric polarization of each V4 tetrahedron points toward the unique corner located on the C3 axis, as shown in GaVNote that throughout this study, the structural domains will be labelled by the direction of the local polarization, such as [111], , VR is expected to be equal for all domains. The electromechanical coupling between the tip and the bottom electrode, however, results in an appreciable background signal (see Methods) affecting both the relative phase and amplitude of the PFM signalsThe Jahn-Teller distortion leads to the appearance of a piezo-response, manifested by the μV, corresponing to displacements of 6–12 pm as determined from the calibration of the z-displacement of the AFM tip. The driving voltage was 10 Vpp, therefore the magnitude of the The measured piezo-response falls within the range of 20–40 γ[001] = 0.58°  plane, the PFM scans reveal stripe domain patterns in the rhombohedral phase, vanishing at temperatures above the structural phase transition.In order to gain insight into the three-dimensional orientation of the domain walls, PFM measurements were carried out also on the (111) surface of GaVample #2  at varioμm, being much broader than those observed on the (001) plane. However, one additional stripe seems to be present within each larger domain, indicating a finer sub-structure. Also note that depending on the orientation of the domain walls the actual width of the domains might be smaller than the values determined from their (111)-sections observed here. The actual orientation of the domain walls will be discussed later.In contrast to the (001)-plane scans, the ries see . TherefoPFM measurements on the (111) plane in the rhombohedral phase are sensitive to the contrast in the converse piezoelectric constants between the unique [111] domain and the three other domains μV for domain [111] and 30–40 μV for the other three domains. The corresponding value of The magnitude of the piezoelectric vibrations measured on the (111) plane is 80–100 γ[111] = 0.55° from the degree of rhombohedral distortion determined by X-ray diffractionThe inclination of the (111) surface between the (111) domain and any of the three other domains is expected to be In a ferroelectric crystal the energy minimum is realized as an equilibrium between the cost of domain wall formation and the gain through the reduction in the stray field with increasing number of domains coexisting in the material27et al.28A global energy minimum is typically achieved by the organization of the structural variants into alternating laminate patterns, as described by Tsou 4S8 reveal lamellar domain structures. In case of Sample #1 -type domain walls is consistent with the compatibility criteria and separates structural domains originating from the same high-temperature inversion variant. In this case, the alternation of [111]- and -type  or - andvidenced  and 5. Sse image  owing to4S8 are parallel to (100)-type planes. Consequently, the actual distances between the (100)-type domain-walls in the bulk crystal are smaller by a factor of We note that (110)-type domain walls could also account for similar patterns both in the (001) and (111) planes. However, the electric neutrality of such domain walls would require a 71° angle between the polarization of the adjacent domains4S8, respectively, captured in the rhombohedral phase close to the ends of the stripe domain patterns. Transitions of lamellar structures into another pair of alternating domains or to a uniform crystal variant may occur whenever the new phase starts developing from several nucleation centers such as structural defects. Epilayer plateaus may also serve as natural endings of lamellar structures. The resulting domain boundaries are prone to mechanical and electrostatic incompatibilities. 4S8 crystal similar in shape and size to Sample #3. In order to visualize the magnetic structures near structural domain boundaries, m-AFM measurements were performed on the (001) surface of another GaV4S8 by m-AFM see ref. The rearrangement of the magnetic structures in the sample with increasing magnetic field is followed at both sides of the structural domain boundary. In agreement with the magnetic phase diagram of the compound reported in ref. 4S8 via simultaneous out-of-plane PFM and AFM topographic measurements. Lamellar domain structures have been observed and identified through alternating pairs of structural domains, belonging to a single inversion variant of the crystal. The magnitude of the piezoresponse measured on (001) and (111) faces of the crystals ranges within ≈1–5 pm/V. Based on the mechanical and electric compatibility criteria we showed that the domain walls are parallel with (100)-type planes. The collection of PFM images supports that the compatibility criteria of non-charged domain walls are generally satisfied and also indicates the lack of antiphase domains in the crystals studied here. Notably, local incompatibilities have been encountered at the endings of the lamellar patterns, claiming for charged domain walls and polytype domain structures.We have detected ferroelectric-ferroelastic domains in the rhombohedral phase of GaVμm  originating exclusively from the converse piezoelectric effect can be estimated from the demodulated PFM amplitude and phase maps via background subtraction in the complex planeVX0 and VY0, are determined as the spatial average values of the measured in-phase (VX) and out-of-phase component (VY) of the PFM signal: In theory, structural domains with opposite signs of VX,Y + and VX,Y − are the average of the maximal and minimal amplitudes in the X and Y channels, respectively.In case of experiments performed on the (111) surfaces, the magnitude of the probed piezo-response component of the [111]-domain, is expected to be three times larger as compared to the other three domains see . TherefoThe PFM phase and amplitude images presented in The instrumentation and methodology of the m-AFM measurements are described in details in ref. How to cite this article: Butykai, Á. et al. Characteristics of ferroelectric-ferroelastic domains in Néel-type skyrmion host GaV4S8. Sci. Rep.7, 44663; doi: 10.1038/srep44663 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Stable single-alpha helices (SAHs) are versatile structural elements in many prokaryotic and eukaryotic proteins acting as semi-flexible linkers and constant force springs. This way SAH-domains function as part of the lever of many different myosins. Canonical myosin levers consist of one or several IQ-motifs to which light chains such as calmodulin bind. SAH-domains provide flexibility in length and stiffness to the myosin levers, and may be particularly suited for myosins working in crowded cellular environments. Although the function of the SAH-domains in human class-6 and class-10 myosins has well been characterised, the distribution of the SAH-domain in all myosin subfamilies and across the eukaryotic tree of life remained elusive. Here, we analysed the largest available myosin sequence dataset consisting of 7919 manually annotated myosin sequences from 938 species representing all major eukaryotic branches using the SAH-prediction algorithm of Waggawagga, a recently developed tool for the identification of SAH-domains. With this approach we identified SAH-domains in more than one third of the supposed 79 myosin subfamilies. Depending on the myosin class, the presence of SAH-domains can range from a few to almost all class members indicating complex patterns of independent and taxon-specific SAH-domain gain and loss. Different amino acids  were introduced into these peptides alone and in all possible combinations at varying distances. The corresponding peptides were synthesised, their α-helicity experimentally determined by, for example, circular dichroism, and stabilization energies were obtained by fitting models to the data. Although poly-alanine peptides adopt α-helical conformations when sparsely interrupted by lysine [i, i+3) and  spacing [i, i+3) and  [i, i+3, i+6), , , or  distances [Helices, which are not buried within globular structures or coiled-coil helical dimers, usually need networks of charge interactions for stabilization in water –5. In thy lysine , argininy lysine  and gluty lysine , helicesand i, i+ spacing istances . In addiistances ,12. HoweStudies on natural proteins known to contain stable single α-helices (SAHs) have shown that the respective sequence regions mainly consist of repeated patterns of negatively and positively charged residues –18. In cMyosins are a diverse protein family characterised by a large motor domain, which in most subfamilies contains an ATP-hydrolysis and an actin-binding site, and extended so-called tail domains located both N- and C-terminal to the motor domain . Many myi, i+3, i+6), , , or  distances. In contrast, networks of hydrophobic residues at distances  would provide a hydrophobic seam around the helix and might cause dimerisation by coiled-coil formation. Such hydrophobic networks are thought to be destabilizing and are accounted for by negative stabilization values. In addition to these values describing interactions, we assign alanines positive stabilization and glycines and prolines negative stabilization values, according to their helix promoting and helix braking characteristics, respectively. All stabilization values within a given sequence window are summed up and the sum is subsequently normalized against an idealised SAH-domain containing only strong interactions to compute an SAH-score for the central amino acid of the sequence window [Because of the different combinations of residues and experimental conditions used in studies determining peptide stabilization energies, it is difficult to obtain and tabulate experimentally validated stabilization energies for all possible combinations of amino acids in all positions. We tried to develop a score reflecting the stability of a single α-helix compared to random coils and helices stabilized by interactions with other structural elements (e.g. within a protein structure or by binding to another protein via a coiled-coil motif). To this end, published stabilization energies –5,10 were window .For comparison, we computed SAH-scores for four different window sizes, specifically 14, 21, 28, and 49 . SmaWe obtained 7919 manually annotated myosin sequences from 938 species from CyMoBase , of which 7675 sequences contain tail regions with varying degree of completeness . The minimum length of a SAH-domain was set to 14 amino acids . Every residue within a SAH-domain should have a SAH-score above a given threshold (here: 0.25). Known coiled-coil regions from muscle myosin heavy chain proteins, tropomyosins, and keratins clearly have lower SAH-scores, while known SAH-domains such as those from class-6 and class-10 myosins, GCP60, M4K4, INCENP, and Caldesmon-1 ,14,16,18SAH-domain-score as the highest average of the SAH-scores of any 14 neighbouring amino acids within each SAH-domain. The SAH-domain-score is dependent on the size of the sequence window for computing each amino acid’s SAH-score  from 25 myosin classes . We also identified SAH-domains in 18 orphan myosins . The evaSaccoglossus kowalevskii Myo9, examples for secondary SAH-domain loss events are the absence of SAH-domains in Platyhelminthes and many nematode class-6 myosins , and a second myosin of unknown class, which most probably had been the ancestor of all other classes . The phy myosins  Figs.The SAH-domains are on average 37–54 (low SAH-domain-score cut-off) / 62–81 (high SAH-domain-score cut-off) amino acids long, depending on the window size for SAH-score computation . There iThe proposed SAH-domains contain only amino acids with high helical propensity , supportThe importance of the SAH-domains as functional modules providing stiffness and modulating length of the lever in myosins has already been discussed in detail ,23,34–36In the following, we present examples for veritable SAH-domains and twilight-zone cases . The AcaThe Kinetoplastid class-1 myosins contain unique tail architectures consisting of, from N- to C-terminus, an IQ motif, a WW, a MyTH1, a FYVE, and a subsequent short SAH-domain. This short SAH-domain is around 28 residues long  and its A short region in the tails of the mammalian Mhc14 class-2 myosins  represents a twilight case . This reDrosophila Myo7A myosin in vitro [Drosophila Myo7B myosins represent twilight cases , releasagga API . The preagga API . We usedSAH-domains are stretches of continuous amino acids with SAH-scores above 0.25. The criterion for a SAH-domain was to have at least 14 amino acids . To not obtain regions with multiple predicted SAH-domains interrupted by only one or two residues with SAH-scores below the threshold, we allowed for 20 percent of the SAH-scores of a SAH-domain to be below the threshold. These lower scores were, however, not allowed to be at the start or at the end of the SAH-domain. To provide a score for each SAH-domain but not have peak values dominate the analysis, we defined the SAH-domain-score as the highest average value of the SAH-scores of 14 neighbouring amino acids. The SAH-domain-score depends on the window size used to compute the individual SAH-scores.For SAH-domain-score based analyses we determined SAH-domain-score cut-offs by sorting the SAH-domain-scores in increasing order, estimating a kernel-regression function to smooth noisy scores, and then determining the cut-off by choosing the SAH-domain-score of the maximum slope change position (the maximum change in the difference between two subsequent SAH-domain-scores).S1 TableOne subtable for each of the four window sizes for computing SAH-scores. In all tables, a SAH length cut-off of 14 amino acids has been applied. One additional subtable for window size of 14 with an SAH length cut-off of ten residues was added for comparison. All subtables contain the name of the myosin sequence, the SAH-domain-score, the SAH-domain sequence, the sequence start and end position of the SAH-domain, the SAH-scores for all residues within the SAH-domains, and the full taxonomy of the species according to NCBI.(XLS)Click here for additional data file.S1 FigHelical net view of a SAH-domain region. Some strong and weak interactions are indicated by arrows. The table lists the score of each interaction taken for computing the SAH-score.(PDF)Click here for additional data file.S2 FigA separate plot is shown for each window size, with the corresponding SAH-domain-score cut-off.(PDF)Click here for additional data file.S3 FigIn contrast to (PDF)Click here for additional data file.S4 FigThis figure is similar to (PDF)Click here for additional data file.S5 FigThis figure is similar to (PDF)Click here for additional data file.S6 FigAlignment of part of the tail region of all human class-2 myosins focusing on the region comprising a predicted short SAH-domain in HsMhc14. For comparison, further mammalian and some other vertebrate Mhc14 homologs are shown indicating that the respective region has evolved after separation of the mammals.(PDF)Click here for additional data file.


EEG studies investigating the neural networks that facilitate action observation (AO) and kinaesthetic motor imagery (KMI) have shown reduced, or desynchronized, power in the alpha (8–12 Hz) and beta (13–30 Hz) frequency bands relative to rest, reflecting efficient activation of task-relevant areas. Functional modulation of these networks through expertise in dance has been established using fMRI, with greater activation among experts during AO. While there is evidence for experience-dependent plasticity of alpha power during AO of dance, the influence of familiarity on beta power during AO, and alpha and beta activity during KMI, remain unclear. The purpose of the present study was to measure the impact of familiarity on confidence ratings and EEG activity during (1) AO of a brief ballet sequence, (2) KMI of this same sequence, and (3) KMI of non-dance movements among ballet dancers, dancers from other genres, and non-dancers.Ballet dancers highly familiar with the genre of the experimental stimulus demonstrated higher individual alpha peak frequency (iAPF), greater alpha desynchronization, and greater task-related beta power during AO, as well as faster iAPF during KMI of non-dance movements. While no between-group differences in alpha or beta power were observed during KMI of dance or non-dance movements, all participants showed significant desynchronization relative to baseline, and further desynchronization during dance KMI relative to non-dance KMI indicative of greater cognitive load.These findings confirm and extend evidence for experience-dependent plasticity of alpha and beta activity during AO of dance and KMI. We also provide novel evidence for modulation of iAPF that is faster when tuned to the specific motor repertoire of the observer. By considering the multiple functional roles of these frequency bands during the same task (AO), we have disentangled the compounded contribution of familiarity and expertise to alpha desynchronization for mediating task engagement among familiar ballet dancers and reflecting task difficulty among unfamiliar non-dance subjects, respectively. That KMI of a complex dance sequence relative to everyday, non-dance movements recruits greater cognitive resources suggests it may be a more powerful tool in driving neural plasticity of action networks, especially among the elderly and those with movement disorders. The three facets of movement processing—action observation (AO), kinaesthetic motor imagery (KMI), and execution—have been shown to recruit a common ‘mirror neuron’ network of brain regions in both humans , 2 and nThese and other evaluations of motor representations for complex movements that cannot physically be performed during neuroimaging have utilized tasks involving AO and/or KMI from an internal first-person perspective. During four functional magnetic resonance imaging (fMRI) scans conducted over a 34-week period, recent investigations conducted by our lab have employed both AO and KMI tasks for a novel piece of choreography that was rehearsed and performed by professional ballet dancers. Consistent with findings from Cross et al. , we founWhile most of the evidence for experience-dependent plasticity has come from fMRI studies, valuable insight into the temporal dynamics of sensorimotor processing has been revealed with electroencephalography (EEG). With respect to action processing, the rolandic ‘mu’ alpha rhythm 9–13 Hz) recorded directly from midline primary motor and somatosensory cortices has been shown to decrease in power relative to rest, or desynchronize, when planning, imagining and executing movements , with co Hz recorInvestigations on the influence of dance expertise on EEG activity are sparse, as most expert studies assess either proficiency in task performance, various types of athletes  or the Expanding upon the existing literature, the current study sought to compare differences in alpha and beta power during AO and KMI of a ballet dance sequence among expert dancers that were both familiar and unfamiliar with the genre, as well as non-dancers, in order to disentangle the effects of familiarity and expertise more generally on action processing of dance movements. Similar to Orgs et al. , we alsoSubjects were recruited through the York University Undergraduate Research Participant Pool (URPP) and compensated with course credit. The research study was approved by the Office of Research Ethics’ Human Participants Review Committee (Certificate # 2013-211) and in accordance with the Declaration of Helsinki. Eligibility requirements included self-reported right-handedness and no uncorrected visual or neurological problems.M = 20.7, SD = 4.22) were analyzed. Participants were divided into three groups based on their self-reported experience: ballet dancers , non-ballet dancers with experience in other genres of dance , and non-dancers .The advertised study called for participants with at least 2–5 years of experience in any genre of dance or related craft including gymnastics, figure skating, or sports. A total of 92 participants were tested, but 31 were excluded for the following reasons: lack of event markers in EEG data (n = 9), demographic information not provided (n = 9), reported neurological problems (n = 1), reported left-handedness (n = 4), poor EEG data quality (n = 6), and no registered alpha peak values (n = 2). A total of 61 participants  between the ages of 18 and 37  action observation (AO) and (2) kinaesthetic motor imagery (KMI) of a ballet dance sequence, and (3) the KMI portion of the Visual and Motor Imagery Questionnaire (VMIQ-2) . In ordeGoldberg Variations 988, Variation 1 to match the cadence and genre of the music in the AO and KMI tasks.Four 15-s resting conditions were performed in random order to facilitate baseline correction of subsequent tasks: eyes open without music (BEO), eyes open with music (BEOM), eyes closed without music (BEC), and eyes closed with music (BECM). Participants were instructed to clear their minds and to follow instructions as they were given. A picture of a closed eye appeared on screen during the eyes closed conditions to remind subjects to keep their eyes closed in the event they opened them during the task , and a central fixation cross  appeared during the eyes open conditions. Audio prompts cued subjects to open and close their eyes at the beginning and end of each baseline condition where appropriate. The music conditions  played the first 15 s of Bach’s Concerto in C Major. Participants were asked to closely observe the choreography performed by the female dancer in order to be able to imagine themselves performing it as accurately and precisely as possible during the next experimental task. Because participants were fit for EEG recording during the experimental session, motor performance was not behaviourally assessed in the laboratory. Instead, participants were asked to rate how confident they were that they could perform the dance with complete accuracy and precision if required to on the following scale modelled after the VMIQ-2 scale  employed a repeated measures analyses of variance (RM-ANOVA) design. For analyses of the AO task evaluating the factor of Time [Blocks 1–5], a mixed model structure was applied to the RM-ANOVA to account for the different number of blocks performed between subjects, which would not be adequately corrected for in a RM-ANOVA. Sphericity was assessed for each within-subject factor with Mauchly’s Test and violations were adjusted by reporting degrees of freedom and significance values with Huynh-Feldt corrections (pHF) . Least-sF = 3.616, p = 0.014, η2 = 0.06] revealed faster iAPF during BEOM relative to BECM only (pBonf = 0.024)  = 2.033, p = 0.050, η2 = 0.03] with no significant Bonferroni-corrected pairwise comparisons. As expected, alpha power was higher during both eyes closed conditions  relative to eyes open  in all electrodes , as was beta power in parietal  and occipital (O1 and O2: pBonf = 0.000) electrodes . No significant between-group differences were observed for iAPF, alpha or beta power (p > 0.05).To evaluate possible between-group differences in baseline iAPF, log alpha peak power, and/or log beta power, three separate RM-ANOVA’s were performed with the factors of Condition (4) × Electrode (8) × Group (3). For iAPF, a main effect of Condition . To evaluate task-related changes to iAPF during eyes closed tasks and conditions, we compared BEC, BECM, KMI and VMIQ-2 iAPF values with a 4 (Task) × 8 (Electrode) × 3 (Group) RM-ANOVA and found faster iAPF during VMIQ-2 relative to BEC (pBonf = 0.027), BECM (pBonf = 0.003), and KMI (pBonf = 0.000) . No other main or interaction effects were significant  × 8 (Electrode) × 3 (Group) RM-ANOVA, which revealed slower iAPF during AO in non-ballet dancers only , and were also less confident in their ability to actually perform the dance if required  compared to ballet  and non-ballet dance groups       than both non-ballet dancers  and non-dancers    . A significant main effect of Time  also showed faster iAPF during Block 1 relative to Block 4 (pBonf = 0.006), and AO iAPF was significantly correlated to iAPF during KMI  and VMIQ-2   , but a significant Group × Time interaction  revealed greater alpha power among non-ballet dancers relative to both groups during Block 1  and relative to ballet dancers during Block 2 (pBonf = 0.010), but greater alpha power among ballet dancers than non-dancers during Block 5 (pBonf = 0.039) . Beta power was significantly greater among ballet dancers relative to both non-ballet dancers (pBonf = 0.003) and non-dancers (pBonf = 0.000) , demonstrating modulation of beta power by familiarity with the observed stimulus  = 4.790, pHF = 0.016] showed higher beta power in bilateral occipital electrodes relative to left premotor cortex .Alpha power was greater among non-ballet dancers relative to non-dancers  = 1.137, p = 0.328] . There were no significant group differences in task-related iAPF , but comparing log alpha power during KMI to baseline revealed significant desynchronization (baseline > KMI) during motor imagery , as well as higher alpha power in O1, O2, P7 and P8 electrodes relative to the temporal and frontal sites   and right (pBonf = 0.013) premotor cortex relative to left superior temporal cortex . Similar to alpha power, when comparing log beta power during KMI and baseline we found significant desynchronization during motor imagery  especially in T7 (pBonf = 0.000), P7 (pBonf = 0.008), O1 (pBonf = 0.026), P8 (pBonf = 0.006), and T8 (pBonf = 0.018)   = 51.496, pHF = 0.000, η2 = 0.470].Analyses of the average motor imagery ratings provided at the end of each block of the KMI task revealed no differences in clarity or vividness between groups   and non-dancers (p = 0.028)   = 5.843, pHF = 0.000, η2 = 0.094]. Alpha power did not differ between electrodes or groups (p > 0.1), and similar to the KMI task beta power was higher in bilateral premotor cortex relative to superior temporal , left occipital  and right parietal cortex (FC5 > P8: p = 0.005) but only at uncorrected significance levels .We confirmed that there were no inherent differences in motor imagery ability between groups with a one-way ANOVA on the average VMIQ-2 scores    = 31.960, p = 0.000, η2 = 0.363]  = 13.101, p = 0.001, η2 = 0.19]  = 2.984, pHF = 0.005, η2 = 0.051] revealed faster iAPF in occipital cortex relative to left premotor cortex , and beta power was higher in premotor cortices than temporal  and right parietal cortex (FC5 > P8: pBonf = 0.006) [Electrode: F x 3 (Group) RM-ANOVA on confidence ratings. It was confirmed that all participants reported significantly clearer and more vivid imagery during the VMIQ-2 task relative to the KMI task3 [F, 58 = 61Based on behavioral and neural indices, we demonstrate modulation of oscillatory brain activity by familiarity with an observed ballet stimulus superseding any general dance expertise effects, but no such differences were observed when subjects performed motor imagery of a ballet dance. However, ballet dancers demonstrated faster iAPF during KMI of non-dance movements. Among all participants, KMI was reportedly clearer and more vivid when imagining non-dance movements relative to a complex dance sequence and also elicited faster iAPF and less desychnronization in both alpha and beta frequency bands. These results expand current understanding of experience-dependent plasticity of alpha and beta activity underlying movement processing in dancers. While our conclusions are not based on oscillatory activity in primary motor and/or supplementary motor areas, they provide novel insights on a network of sensorimotor association areas that are modified by learning  and expeSeveral behavioral indices obtained during the AO task reveal greater task engagement by familiar ballet dancers relative to the unfamiliar non-ballet dancers, who also putatively possess specialized sensorimotor networks arising from dance-related experience but in different genres, and relative to non-dancers, who served as a control group for dance-related expertise and familiarity. Ballet dancers performed significantly fewer blocks of the self-paced task, provided lower subjective ratings reflecting greater confidence in their ability to perform the dance sequence if required Fig. , and a sHigher iAPF is reflected during states of arousal, attention and readiness to perform cognitive tasks including working memory , for rev, we can during task performance, with increased cognitive demand resulting in faster occipital and parietal iAPF. Accordingly, one would expect higher iAPF while visualizing a more demanding action (dance) relative to non-dance movements (VMIQ-2). However, the opposite pattern was observed suggesting all subjects were more engaged during KMI of the less demanding VMIQ-2 task . It is Several important insights are revealed in light of the observed outcomes, including a further understanding of experience-dependent plasticity of the neural mechanisms underlying action observation and KMI of specialized and habitual movements. Specifically, we demonstrate modulation of alpha peak frequency and power in the alpha and beta frequency bands during observation of a familiar dance sequence relative to unfamiliar participants, with further expertise effects resulting in more efficient visuomotor processing during KMI of non-dance movements. While all participants demonstrated alpha and beta desynchronization during KMI, we clarify that familiarity with the imagined stimulus does not result in significant modulation of these signals, unlike action observation. Together, these findings promote the translational benefits of dance practice to attentional and sensorimotor networks, and lend further support for the efficacy of dance and motor imagery as effective BCI source signals and therapeutic interventions for clinical populations with movement disorders.