Rix7 is an essential type II AAA-ATPase required for the formation of the large ribosomal subunit. Rix7 has been proposed to utilize the power of ATP hydrolysis to drive the removal of assembly factors from pre-60S particles, but the mechanism of release is unknown. Rix7’s mammalian homolog, NVL2 has been linked to cancer and mental illness disorders, highlighting the need to understand the molecular mechanisms of this essential machine. Here we report the cryo-EM reconstruction of the tandem AAA domains of Rix7 which form an asymmetric stacked homohexameric ring. We trapped Rix7 with a polypeptide in the central channel, revealing Rix7’s role as a molecular unfoldase. The structure establishes that type II AAA-ATPases lacking the aromatic-hydrophobic motif within the first AAA domain can engage a substrate throughout the entire central channel. The structure also reveals that Rix7 contains unique post-α7 insertions within both AAA domains important for Rix7 function. Rix7 is a type II AAA-ATPase that is required for the assembly of the large ribosomal subunit. Here the authors present the 4.5 Å cryo-EM structure of the Rix7 homohexamer with a polypeptide fragment bound in its central channel and provide insights into the function of Rix7 as a molecular unfoldase. Three AAA (ATPases associated with various cellular activities)-ATPases are required for the assembly of the large ribosomal subunit including Rea1, Rix7, and Drg16. Their proposed function is to utilize the power of ATP hydrolysis to drive the release of specific ribosome assembly factors from pre-60S particles during the 60S assembly pathway6. Rix7, the earliest acting ATPase, is proposed to drive release of the essential ribosome assembly factor Nsa1 from nucleolar pre-60S particles7. Drg1, the latest acting ATPase, is required for the removal of Rlp24 from cytoplasmic pre-60S particles8. Rea1 is involved in several pre-ribosome intermediates, and is required for release of part of the Nop7 complex from early pre-60S particles and release of Rsa4 from nucleoplasmic pre-60S particles10. Structural snapshots of Rea1 have provided invaluable insight into its roles as a checkpoint during processing of the large ribosomal subunit11. Meanwhile, a lack of structural information on Drg1 and Rix7 has hindered our understanding of how these motors regulate ribosome assembly.Eukaryotic ribosome assembly is a complex pathway that is dependent upon the aid of hundreds of assembly factors such as ATPases, GTPases, helicases, nucleases, chaperones, and transportersSaccharomyces cerevisiae (S. cerevisiae) RIX7 is an essential gene required for maturation of the large ribosomal subunit12. Truncation of the first 14 residues of the N-terminal domain (NTD) of Rix7 leads to the accumulation of ribosome assembly factor Nsa1 on polysomes suggesting a link between Rix7 and release of Nsa1 from pre-60S particles7. The mammalian homolog of Rix7 is known as NVL2 (nuclear VCP Like). NVL, the gene which encodes for NVL2, has been identified as a prognostic outlier gene for high-risk prostate cancer and shown to confer a risk for mental illness disorders14, signifying a need to understand NVL2’s molecular function. NVL2 is a nucleolar protein that associates with pre-60S particles15. Overexpression of an ATP-hydrolysis deficient mutant leads to defects in maturation of the large ribosomal subunit16. NVL2 has been shown to interact with WDR7418, the mammalian homolog of Nsa1, as well as a number of additional binding partners including the RNA helicase Mtr419, nucleolar protein Nucleolin20, ribosomal protein RPL519, and ribonucleoprotein polymerase tERT21. Recent studies revealed that WDR74 and NVL2 function together in an early nucleolar pre-rRNA processing step22. However it remains unclear if WDR74 is a direct target for remodeling by NVL2 or simply a co-factor.28. Despite sharing a similar architecture within the D1 and D2 AAA modules these enzymes can utilize different mechanisms to couple ATP hydrolysis with substrate remodeling30. For example, NSF facilitates remodeling of its substrates through conformational changes of its NTD that are driven by nucleotide binding and hydrolysis coupled with partial substrate threading of the D1 domain31. In contrast recent cryo-EM and in vitro reconstitution studies suggest that Cdc48, Pex1/Pex6, VAT, and ClpB/Hsp100 remodel substrates through a processive threading mechanism, whereby substrates are pulled/unfolded through the central pore formed by the D1 and D2 domains34.One of the most important questions about Rix7’s molecular function is how does Rix7 pull on substrates to drive their removal from pre-60S particles. Rix7 is a type II AAA-ATPase, composed of a unique NTD followed by tandem well conserved AAA modules known as the D1 and D2 domains respectively. The D1 and D2 domains of Rix7 share homology with other members of the type II AAA-ATPase family, including p97 , NSF, the Pex1/Pex6 complex, and the ClpB/Hsp100 chaperone family. A number of two-cassette AAA-ATPase structures have been solved, which revealed a common architecture of stacked hexameric AAA rings, while the position of the distinct NTDs varies and can be dependent upon nucleotide and/or substrate bindingHere we report the cryo-EM reconstruction of the Rix7 homohexamer at 4.5 Å resolution. The structure reveals an asymmetric configuration of both the D1 and D2 AAA domains. Through use of a Rix7 variant unable to hydrolyze ATP , we captured Rix7 in the pre-ATP hydrolysis state with an unexpected polypeptide fragment engaged in the central channel. Five of the six Rix7 protomers grip the polypeptide suggesting a hand-over-hand mechanism for substrate unfolding, providing the first insight into the function of Rix7 as a molecular unfoldase. Despite the absence of the aromatic-hydrophobic motif found in pore loops of unfoldases within the D1 domain, the Rix7 pore loops from both the D1 and D2 domains grip the polypeptide through distinct motifs which are essential for Rix7 function in vivo. The structure also unveils that both the D1 and D2 domains from Rix7 contain structurally distinct post α7 insertions important for Rix7 function.S. cerevisiae Rix7 is composed of 837 residues including the NTD, and the D1 and D2 AAA modules  between β2 and α2  upstream of endogenous RIX7. Using this strain expression of endogenous Rix7 can be suppressed by addition of doxycycline (DOX). Yeast which express WT Rix7 (including Flag and Strep tags for detection/purification purposes) from the ARS1-CEN4 YCplac vector36 grow well in the presence of DOX at 25 °C; however, the empty YCplac vector could not restore growth in the presence of DOX  and arginine finger  motifs in both the D1 and D2 domains also caused a severe growth defect  there was no structural information for the Rix7 D1 domain or the assembled homohexamer. To determine the full-length structure, we utilized single particle cryo-EM to obtain an electron scattering map of a Chaetomium thermophilum Rix7 double Walker B mutant (CT Rix7E303Q/E602Q) and derived a structural model of the hexamer. The EM data were classified and refined without imposing symmetry which resulted in one dominant conformation at 4.5 Å resolution, that contained well resolved density for all 12 AAA domains  CT Rix7E03Q/E602Q6. Disorder prediction with IUPred38 suggests that the entire NTD of Rix7 has an average disorder tendency score of 0.69 (scores above 0.5 indicate disorder) and is predicted to be intrinsically disordered. This is in contrast to VAT/Cdc48/p97 homologs which contain well-ordered NTD regulatory domains28 but draws parallels with the ClpB/Hsp100 family in which the NTD appears more flexible and dynamic39. A recent structure of Cdc48 in complex with its cofactor the Ufd1-Npl4 heterodimer suggests that both ATP and cofactor binding move the NTD into the up conformation40. We therefore hypothesize that Rix7 specific cofactors are likely required for regulating the intrinsically unstable region of the NTD.The cryo-EM reconstruction lacks density for the entire NTD of Rix7 (amino acids 1-192), suggesting that this domain is flexible and dynamic. Aside from a helical bundle at the very N-terminus the rest of the Rix7 NTD includes a disordered linker sequence and a nuclear localization sequenceThe six protomers within the D1 and D2 domains are arranged in a spiral staircase like pattern Fig. . The P1 34, or it could be a mixture of non-specific substrates that accumulated during protein expression in E. coli. Analysis of our recombinant Rix7 by mass spectrometry did not reveal the presence of any peptides in high abundance other than those arising from Rix7, with the very C-terminus of Rix7 being well represented. While the polypeptide trapped by the Rix7 double Walker B mutant does not represent a real substrate of Rix7, the presence of the polypeptide provides insight into the mechanism of substrate threading and processing by Rix7. Several recent cryo-EM structures of other ATPases that adopt asymmetric spiral ring configurations also contain ambiguous substrate density in their central pores, suggesting that substrates stabilize the asymmetric ring structure41.During the fitting of the model we unexpectedly observed clear density for a polypeptide threading the central pore of Rix7 Figs. c, 4a. Th34. There are two pore loops, pore loop 1 (PL-I) and pore loop 2 (PL-II), that line the central channel within each AAA domain of Rix7  grips the polypeptide. PL-I within the D2 domain makes extensive contacts with the polypeptide and the K-Y-V-G motif is well ordered in protomers P1-P5  within both the D1 and D2 domains grip the polypeptide through their pore loops, while P6 does not contact the peptide. The pore loops are arranged in a clockwise spiral configuration around the polypeptide Fig. , likely x7 Figs. . Proteinain Fig. . This ha-P5 Fig. . In addi-P5 Fig. .48. Recent studies of the double ring unfoldase Hsp100 also revealed a similar asymmetric architecture between the D1 and D2 domain and biochemical studies of this enzyme suggest that the D2 domain is the main ATP hydrolysis motor32.The cryo-EM reconstruction revealed that the arrangement of the pore loops is different in the D1 and D2 domains Fig. . The sepS. cerevisiae  and found that this caused a severe growth defect in iae Fig. . Then weiae Fig. . The higiae Fig. . Express34. We defined the P6 protomer as the seam subunit because it does not contact the polypeptide or closely pack with the neighboring AAA domain thereby creating a seam or break in the hexameric ring34. The P6 seam protomer within both D1 and D2 domains of Rix7 breaks the helical arrangement and detaches from the bound polypeptide , associates with the D1 AAA domain of Rix7  and not the NTD, suggesting that Rix7 engages substrates in a different manner than other type II AAA-ATPases18. While the NTD of Rix7 is dispensable for Nsa1 binding, it is essential for Rix7 function, Nsa1 release from pre-60S particles, and nuclear/nucleolar localization20. The molecular role of the NTD remains undefined but we speculate that it functions as a molecular gate to allow access for the substrate to bind the D1 domain and thread through the central pore.Rix7 is a poorly characterized type II AAA-ATPase that plays an essential role in the assembly of the large ribosomal subunit. While related to other type II AAA-ATPases, how Rix7 utilizes its molecular AAA motors to extract proteins from pre-60S particles has remained unknown. Through use of a double Walker B mutant we trapped Rix7 with a polypeptide threading the entire central channel formed by the D1 and D2 domains. While the identity of this polypeptide is unclear, the peptide mimics the presence of a substrate in the central channel. In addition to the D1 and D2 AAA domains, type II AAA-ATPases typically contain N-terminal domains important for substrate recruitment51.Our cryo-EM reconstruction establishes that Rix7 is a molecular unfoldase that utilizes conserved pore loops lining the central channel to pull on substrates. The central pore loop residues of Rix7 adopt a clockwise staircase-like arrangement, in which five of the six protomers grip the substrate in the channel while one protomer, the P6/Seam, does not contact the substrate Fig. . The P6 43. VAT contains the canonical aromatic-hydrophobic residues in both its D1 and D2 domains while, Rix7 and p97 lack the aromatic-hydrophobic motif in the D1 domain and NSF lacks this motif in the D2 domain, suggesting that the pore loops of these motor proteins evolved for different functions. Our data revealed the importance of a well-conserved serine residue from the D1 PL-I signature motif . Moreover, we found that the aromatic-hydrophobic residues from the D2 PL-I are essential and adopt a more extended spiral arrangement than those within the D1. This leads to a more extensive interface between the D2 pore loops and the substrate and suggests that the D2 domain is the major motor driving translocation of the unfolding substrate through successive rounds of ATP binding/hydrolysis.While our structure revealed that Rix7 draws many parallels with other molecular unfoldases our studies also unveiled several unique distinctions between the D1 and D2 AAA domains of Rix7, including the arrangement and sequence of the central pore loops, the requirements for ATP hydrolysis, and the Rix7 specific post α7 helix insertions. The conserved residues in PL-I within Rix7 contribute to the distinction between the D1 and D2 domains. Rix7, p97, and NSF likely evolved from a common ancestor, the archaeal unfoldase VAT29. Yeast variants of Cdc48 with D1 Walker B mutants are also viable but show a reduced growth rate52, similar to what we observe with Rix7. Therefore, while ATP hydrolysis within the D1 domain of Rix7 is not essential it still supports Rix7 function in vivo.The requirements for ATP hydrolysis also differ between the D1 and D2 domains. Through yeast complementation assays we show that the Walker A, arginine finger, and sensor 1 motifs are essential in vivo in both the D1 and D2 domains while the Walker B motif, which is required for ATP hydrolysis is only essential in the D2 domain. In vitro reconstitution experiments with Cdc48 and its substrates have revealed that substrate binding decreases ATP hydrolysis in the D1 domain and stimulates ATP hydrolysis in the D2 domain leading to the unfolding/translocation of the substrate through the central pore25. Based upon the cryo-EM reconstruction, Rix7 contains a specific post α7 insertion within the D1 domain that makes additional contact with the neighboring AAA domain, suggesting it could be important for stabilization of the hexameric ring. This is in contrast to the post α7 insertion within the D2 domain which does not make as extensive interactions with the neighboring AAA domain. Taken together, our results suggest that the D1 and D2 AAA domains of Rix7 play distinct molecular roles in vivo. The D1 domain is important for hexamer formation, substrate recruitment, and initial threading of the substrate into the channel, while the D2 domain is the driver of ATP hydrolysis and unravels the substrate through successive rounds of ATP hydrolysis , however this region of the AAA module can be variable and other ATPases such as p97 and ClpB have insertions in this regionS. cerevisiae even in the absence of Nsa1, suggesting that Rix7 has additional molecular targets37. Does Rix7 fully unfold its substrates or simply pull on disordered/unfolded regions to drive removal from pre-60S particles? Our previous work established that Nsa1 contains a flexible C-terminal tail, thus one intriguing possibility is that Rix7 could pull on the tail of Nsa1 to remove it from pre-60S particles18. This work lays the foundation for future studies on determining the molecular substrates of Rix7 and the molecular mechanisms that link protein unraveling by Rix7 with assembly of the large ribosomal subunit.In summary, our work establishes Rix7 as a nucleolar molecular unfoldase that utilizes the D1 and D2 AAA domains to thread substrates through its central pore via a processive hand-over-hand mechanism. While our work provides a wealth of information about Rix7 unfoldase activity there are numerous unanswered questions which await further discovery. For example, Rix7 mutants display a dominant phenotype in S. cerevisiae Rix7 along with its endogenous promoter and terminator were cloned into the YCplacIII yeast centromeric plasmid36. A list of all primers used in this study is shown in Supplementary Table C. thermophilum Rix7 was codon optimized for E. coli expression (GenScript) and inserted into the pet24b expression plasmid containing a C-terminal hexa-histidine tag (Supplementary Table S. cerevisiae strain encoding a tetracycline-titratable promoter (tetO7) upstream of the endogenous RIX7 gene was obtained from Open Biosystems (GE Dharmacon). A 5xFlag tag was added onto the C-terminus of endogenous NSA1 in the tetO7-RIX7 strain for downstream detection purposes with the pFA6a-5FLAG-natMX6 vector53. Expression of endogenous RIX7 was suppressed by supplementing the YPD media with doxycycline . The tetO7-RIX7_NSA1-5xFlag strain was transformed with plasmids encoding Rix7 mutants (Supplementary Table tetO7-RIX7 strain. We tested the complementation of the Rix7 mutants by suppressing endogenous WT Rix7 expression with DOX following established protocols55. The growth curves were generated from YPD cultures (100 µl) with and without DOX inoculated at an OD600 of 0.05 and incubated at 25 °C for 25 h. Growth was monitored by measuring the absorbance at 595 nm every 15 min using an Infinite 200 Pro (Tecan). All growth assays were performed in triplicate.The −1) to an OD600 of 0.2. Cells were then harvested and protein extraction was performed as described in Zhang et al.56. Western blots were then performed as described in Romes et al.57 using either anti-Flag  antibody (1:1000 dilution), or anti-alpha Tubulin Yol1/34  antibody (1:500 dilution) as primary antibodies. Secondary antibodies used for visualization were either goat anti-rat IgG  or goat anti-rabbit IgG  conjugated HRP antibodies at manufacturer recommended dilutions. Protein bands were visualized with ECL Plus Western blotting detection reagent .Transformed yeast cultures (Supplementary Table tetO7-RIX7 (Supplementary Table −1) for 16 h. 1 L cultures of YPD and DOX (120 μg ml−1) were inoculated to an OD of 0.05 and then grown at 30 °C to an of OD of ~0.6. Cycloheximide (0.1 mg ml−1) was added to the cultures and the cultures were incubated for 5 min on ice before harvesting cells by centrifugation. Cells were resuspended in extraction buffer  and then disrupted with glass beads on a Disruptor Genie (Scientific Industries) for 5 min at 4 °C. Lysate was clarified at 8000 × g for 5 min at 4 °C. RNA was quantitated using a Qubit fluorometer (Invitrogen) and 0.1 mg of total RNA was loaded onto a 7–47% sucrose gradient. Gradients were subjected to 260,110 × g for 2.5 h at 4 °C prior to fractionation (Brandel). All sucrose gradient experiments were performed in triplicate and a representative trace is shown in Fig. Starter cultures of transformed and DOX 10 μg ml−1CT Rix7 expression plasmid was transformed into BL21(DE3)* cells. Cells were grown at 37 °C to mid log phase (OD600 = 1.0), induced with 1 mM IPTG and then harvested 3.5 h after induction. Harvested cells were resuspended in Lysis buffer  and then lysed by sonication. After centrifugation at 26,916 × g for 45 min, the supernatant was loaded onto TALON metal affinity resin (Clontech) equilibrated in Lysis buffer. Rix7 was eluted with Elution buffer  after washing with 10 column volumes of Lysis buffer. The eluate was then further purified on a Superdex 200 column  equilibrated with Buffer A . Column fractions were analyzed by SDS-PAGE; those containing CT Rix7 were pooled and concentrated to 10 ml then injected onto a MonoQ column . Elution was performed by gradient increase in salt concentration (150–500 mM NaCl). To confirm the presence of hexamer, CT Rix7 was further purified using a Superose 6 increase column  in Buffer A. Fractions were analyzed by SDS-PAGE and then used for making cryo-EM grids.The TM 1.2/1.3-4Cu-50, Protochips) using standard preparation techniques. Following SEC fractionation protein concentration was adjusted to ~0.6 mg ml−1 and a 5 μl aliquot was applied to the carbon side of a freshly glow-discharged grid  in an environmentally controlled chamber . Excess liquid was blotted for 3 s with filter paper (Whatman #1) from the same side as applied and plunged into liquid ethane (91 K). Specimens were imaged in a Titan Krios transmission electron microscope (Thermo-Fisher) operated at 300 keV and equipped with a Falcon 3EC detector (Thermo-Fisher). Data was collected using EPU at a nominal magnification of ×59,000 corresponding to a physical pixel size at specimen level of 1.39 Å pixel−1. Images were collected under focus in the range of 1.2–2.7 μm. A total of 40 electrons Å−1 were recorded in counting mode at the dose rate of 0.8 electrons pixel−1 s−1, and stored as movies consisting of 45 fractions of equal dose , all processing steps, 2D and 3D classification and 3D refinement were performed with RELION61. A total of 693,095 putative molecular images were automatically picked in RELION using a Gaussian blob as reference. A first round of reference free 2D classification was used to remove contamination and to separate the particle set into top and side views . Further rounds of 2D classification were performed separately for top and side views . An additional round of 3D classification was performed to further remove contaminating images and in a fruitless attempt to detect potential conformational variants. A subset of 64,386 molecular images was extracted from a well-behaved class which showed a complete double layer of hexameric rings. The final electron scattering density map, obtained by 3D refinement of the selected particles in Class 1, has a resolution estimate of 4.5 Å based on the 0.143 cutoff for the Fourier Shell Correlation between two half-maps independently refined in RELION , NCS restraints among chains for 644 residues in 8 subgroups, and secondary structure restraints for α helices and β sheets. The model includes side chains only for completeness, and are restrained to the reference model where residues are identical. The density for the polypeptide in the channel is ambiguous and was fit was a poly-alanine chain. The presence of nucleotides was determined from a difference map between the nucleotide free hexamer model and the cryo-EM map. The geometry of ATP in the nucleotide binding sites was restrained to the reference structure. Molprobity68 was used to evaluate the model and the model statistics are listed in Table The Rix7 cryo-EM reconstruction has an estimated resolution of 4.5 Å. At this resolution we observe clear density for all the major features of the 12 AAA domains of Rix7. We also observe some side chain density for well-ordered bulky residues. To build an initial model of Rix7, we took the coordinates from the D1 and D2 domains from a recent p97 structure (PDB ID 5C1A) and manually fit the α/β and α subdomains into each AAA protomer using COOTSupplementary InformationPeer Review FileSource DataReporting Summary Checklist


They strongly increase the accuracy of predicting pairs of related protein domains beyond simpler correlation-based approaches like phylogenetic profiling (80% vs. 30–50% positives out of the 1000 highest-scoring pairs). Combined with the direct coupling analysis of inter-protein residue-residue coevolution, we provide multi-scale evidence for direct but unknown interaction between protein families. An in-depth discussion shows these to be biologically sensible and directly experimentally testable. Negative phyletic couplings highlight alternative solutions for the same functionality, including documented cases of convergent evolution. Thereby our work proves the strong potential of global statistical modeling approaches to genome-wide coevolutionary analysis, far beyond the established use for individual protein complexes and domain-domain interactions.Interacting proteins and protein domains coevolve on multiple scales, from their correlated presence across species, to correlations in amino-acid usage. Genomic databases provide rapidly growing data for variability in genomic protein content and in protein sequences, calling for computational predictions of unknown interactions. We first introduce the concept of  Interactions between proteins and their domains are at the basis of almost all biological processes. To complement labor intensive and error-prone experimental approaches to the genome-scale characterization of such interactions, we propose a computational approach based upon rapidly growing protein-sequence databases. To maintain interaction in the course of evolution, proteins and their domains are required to coevolve: evolutionary changes in the interaction partners appear correlated across several scales, from correlated presence-absence patterns of proteins across species, up to correlations in the amino-acid usage. Our approach combines these different scales within a common mathematical-statistical inference framework, which is inspired by the so-called direct coupling analysis. It is able to predict currently unknown, but biologically sensible interaction, and to identify cases of convergent evolution leading to alternative solutions for a common biological task. Thereby our work illustrates the potential of global statistical inference for the genome-scale coevolutionary analysis of interacting proteins and protein domains. Essential to life at the molecular level is the interplay of molecules and macromolecules. Interactions contribute to diversity and coordination of reactions to accomplish feats that would be impossible if all parts worked fully in isolation. Proteins are no exceptions and many of them undergo concerted interactions to achieve their full potential. Many interactions have been described in detail, including inter- and intra-protein domain-domain interactions, which will be the focus of this work. However, many more meaningful interactions await to be discovered and explored. An issue with the experimental description of such interactions is that many are transient and that high-throughput technologies to identify such interactions are very error prone . Advancecoevolution of interacting protein domains. Coevolution can be detected at very different scales, ranging from the correlated presence or absence of related proteins (or their genes) across genomes, down to the correlated usage of amino-acids in residues, which are located in different proteins but in contact across the interface. Each scale contains valuable information for detecting and understanding interactions between proteins and their domains, and adapted methods have been designed to unveil this information from data. However, none of the scales contains exhaustive information. Therefore, our work proposes a coherent mathematical-algorithmic framework bridging different scales, thereby combining the information content of the different scales.We suggest a statistical approach based on the phylogenetic profiling [The first, largest scale concerns the correlated presence and absence of interacting proteins in genomes. If a biological function depends on two proteins simultaneously , we will either observe both proteins in a genome, i.e. the function is present, or none of them, i.e. the function is absent. More rarely we may observe the presence of only one of the two proteins. This idea is at the basis of a classical method called rofiling , 5, whicPhylogenetic relationships between considered genomes may introduce correlations unrelated to biological function; single evolutionary events may be statistically amplified when closely related species are included in the data. Evolutionary models taking into account the underlying species tree, have been proposed [proposed –13 to prdisentangle direct from indirect correlations.Correlations may result from direct couplings, e.g., when two domains or proteins interact physically, but they may be caused by intermediate partners: If A co-occurs with B, and B with C, also A and C will show correlations. Analyses based on partial correlations  and specdiscriminate physical interaction from more general relationships, like co-occurrence in a biological pathway or genomic co-localization. Here, using full amino-acid sequences instead of presence/absence patterns may help to refine the analysis, e.g. via the comparison of protein-specific phylogenetic trees [Simple presence/absence patterns cannot ic trees .This last point actually suggests to change resolution, and to consider coevolution at the residue scale to refine the analysis of phylogenetic profiles. The last decade has seen important progress in this respect , 17, relBeyond structure prediction, DCA was suggested for the identification of interacting proteins , 26, 27.Our work addresses this issue, together with Points 2 and 3 given above. We propose a common statistical-modeling framework, which is applied successively to the genomic and the residue scale (presence/absence patterns and amino-acid sequences) of coevolution. It is intended to extract information from data, which cannot be extracted at each individual scale. Performing the genome-wide analysis on the coarse scale of presence/absence patterns, we can identify promising protein-domain pairs, which are subsequently analyzed using DCA at the fine residue scale. A direct comparison of our genome-wide results with those obtained using a phylogeny-aware method  unveils Escherichia coli is used as a reference, i.e. only the 2682 domain families existing inside the K12 strain of E. coli are considered (the SupplementM = 1041 rows (species) and N = 2041 columns (domains), with entries 1 if a domain is present at least once in a genome, and zero if it is absent, cf. Methods and The analysis starts with a fairly standard construction of phylogenetic profiles , as outlPhyletic Direct Coupling Analysis (PhyDCA), infers a statistical model P for the phylogenetic profile of an entire species, i.e. for a binary vector  signaling the presence or absence of all N considered domains in the considered species, cf. Methods for details. The PhyDCA model resembles a lattice-gas model in statistical physics, describing N coupled particles that can be present or absent. The phyletic coupling Jij between particles / domains i and j can be positive–i.e. the presence of one domain favors the presence of the other. In this case we expect a positive relationship between the two domains, corresponding to biological processes requiring both domains. The coupling Jij can also be negative–i.e. the presence of one domain favors the absence of the other. We would expect that these domains have overlapping functionalities, and one of the two is sufficient to guarantee this functionality in a species. An important breakthrough in coevolutionary analysis at the residue level was the step from a local correlation analysis to global maximum-entropy models , 32, whiMethods, we have included genomic, functional and structural relationships: domains may coexist inside a single protein, they may be co-localized in an operon, they may be in contact in an experimental crystallographic structure or an interaction might be known according to other experimental techniques, or they may belong to enzymes catalyzing related reactions.The performance of PhyDCA can be assessed by comparing the domain pairs of strongest phyletic couplings to a carefully compiled list of 8,091 known domain-domain relations. As is explained in Jij are ordered by size, and the fraction of positive relations in between the highest-scoring domain-domain pairs is calculated . SupplementMethods) has only a limited effect. As is shown in The PhyDCA couplings Jij-histograms for all domain pairs and those with known relations in From an overlay of the not belonging to our list of positives may actually be considered as predictions for new, currently unknown relations. According to the observations in Jij distribution.Databases of genome-wide protein-protein or domain-domain interactions are currently incomplete. We therefore expect the real PPV to be even higher than the one measured in Phyletic couplings are, like simpler correlation measures, based on counting co-presence and co-absence of proteins or domains. However, due to the uneven phylogenetic distribution of species in our dataset, single evolutionary event may be amplified when appearing in an ancestor of several closely related species. More importantly in the context of this study, phylogeny may introduce spurious correlations in the presence and absence of domains, which are not related to biological function.To remove this bias, several methods have been proposed, cf. , 13, whilog10p < −7.9) contains 3,611 domain pairs, out of which 1,251 (34.6%) are true positives in our list of known domain-domain relationships.To this end, we have used the CoPAP (coevolution of presence-absence patterns) server . It usesSince a further sorting of these pairs using CoPAP results is not possible , we compare them to the first 3,611 domain pairs extracted by PhyDCA, and to the 3,611 domain pairs of highest Pearson correlation. The Venn diagram in While CoPAP and PhyDCA have similar global PPV, with an advantage for CoPAP (34.6%) over PhyDCA (31.2%), Pearson correlation performs substantially worse (PPV 19.7%).Very small fractions of the correlated pairs, which are discarded by PhyDCA or CoPAP, are TP: PhyDCA discards 2,890 pairs of PPV 6%; CoPAP discards only 1,998 pairs, but with even lower PPV (1.2%).74% of the 721 correlated pairs, which are retained by PhyDCA, are TP. Note that almost all of them (719/721) show also a significant CoPAP signal.Only 43% of the correlated pairs, which are retained by CoPAP, are TP. PhyDCA divides them into two groups of comparable size but distinct PPV. For the 719 pairs retained also by PhyDCA, the PPV rises to 74%. The other 894 pairs have weak phyletic couplings, so their significant correlation has to be interpreted as dominated by indirect effects. Actually only 18% are TP.When going to lower Pearson correlations, both CoPAP and PhyDCA decrease their accuracy. However, their intersection shows 613 pairs with a high PPV of 63%.The 2,277 pairs only identified by PhyDCA have a low PPV of only 9%. This is coherent with In principle, CoPAP and PhyDCA treat very different confounding factors of coevolutionary analysis–phylogenetic biases and indirect correlations. So, it might appear astonishing that almost none of the correlated pairs, which are strongly coupled in PhyDCA, are actually discarded by CoPAP. The reason might be given by the spectral properties of the covariance matrices of the input data, and their relation to phylogeny and direct couplings. As shown in , the phySupplementSupplementOn the other hand, we expect non-phylogenetic but indirect correlations to exist, related to the observation that PhyDCA separates the CoPAP output into strongly coupled pairs of high PPV, and weakly coupled pairs of reduced PPV. To further illuminate these indirect effects, we have introduced Fig H into the A smaller tail of negative phyletic couplings can be observed in E. coli as a reference genome, the number of such negative couplings is limited, since only domain pairs co-occurring in E. coli are analyzed. To better understand the meaning of negative couplings, we have therefore extended the original analysis to all 9,358 families containing bacterial protein domains. While results restricted a posteriori to E. coli are very robust  can be considered as a strong indicator for a potential interaction [Relying only on the coarse scale of coupled presence and absence in genomes, does not reveal more detailed information. Since the number of domain-domain pairs under question is limited as compared to all domain pairs existing in é et al. : Two Pfaé et al.  it was seraction , 27. On eraction , a low Dpredictions at the scale of phyletic couplings. The results are presented in Meff above 200, and DCA results can thus be considered reliable. Of those 45 pairs have an inter-domain DCA score above 0.2 (24 out of the first 200 PhyDCA predictions). This number is significantly larger than for randomly selected protein pairs, cf. Meff above 200, mostly related to short amino-acid sequences. This shows that the preselection by high phylogenetic couplings leads to a subsequent enrichment of high DCA scores also at the residue scale. For comparison, we have also applied the matching procedure to the 200 domain-domain pairs, which are known to interact by iPfam [SupplementE. coli. In their case, the DCA score is found to be substantially larger. This is to be expected, since due to the intra-protein co-localization no paralog-matching has to be applied, and therefore the joint MSA of the two domain families are expected to be of higher quality. However, also in this case, some pairs show a low DCA score despite a large sequence number. This is to be expected, since not all domain-domain pairs inside a multi-domain protein have physical interactions, and also small and structurally non-conserved interfaces may lack clear DCA signals, cf. [We have applied the progressive paralog matching procedure of  to the 5by iPfam , and whials, cf. .Domain pairs with both strong PhyDCA and residue-level DCA signals are our strongest candidates for predicted domain-domain interactions. Since they are not co-localized in the same protein, they also provide predictions for new protein-protein interactions. We analyze here in detail the 24 pairs with a DCA score larger than 0.2, which result from the first 200 PhyDCA predictions.+ transporter subunits KdpC (PF02669) and KdpA (PF03814) [Among these 24 pairs we find several examples of known interactions that have not yet been structurally resolved. These include KPF03814) , Sigma54PF03814)  and exonPF03814) .For several additional positively coupled pairs an interaction seems functionally very likely but to our knowledge no interaction studies are available. These are all proteins involved in pilus formation or maturation. Domain PF06750 is a putative methyl transferase domain in the prepilin peptidase PppA, and proposed to interact with methylation motif domain PF07963, found in numerous pilin proteins and with PF05157, a type II secretion system protein , 49. PF0Of interest, there are predicted interactions for several members of biosynthetic pathways catalyzing either consecutive or closely following reactions. These include domains PF02542 and PF13288 of isoprenoid biosynthesis enzymes Dxr and IspF, domains PF00885 and PF00926 of riboflavin biosynthesis enzymes RisB and RibB and domains PF01227 and PF01288 of tetrahydrofolate biosynthesis enzymes Gch1 and HppK. A more complex connection is predicted between multiple domains of molybdenum cofactor biosynthesis enzyme MoaC (PF01967), MoeA (PF03453 and PF03454) and MoaA (PF06463). Similarly, scores suggest a protein-protein interaction between domains of hydrogenase maturation enzymes HypF (PF07503) with HybG (PF01455) and HycI (PF01750).Perhaps most intriguing are the observation of strongly coupled co-occurrence and potential protein-protein interactions of two proteins pairs. Ada (PF02805) and AlkA (PF06029) are two enzymes involved in DNA repair in response to alkylation damage , 52. OneAdditional and perhaps plausible interactions are predicted between domains PF05930 and PF13356 of prophage protein AlpA and several phage integrase proteins as well as between domain PF13518 with PF13817, the former a HTH domain commonly associated with transposase domains and the latter a transposase domain.Insufficient information on the function of two domain pairs and their associated proteins does not allow us to draw any conclusions on the plausibility of interaction. These are for domains PF02021 and PF13335 of proteins YraN and YifB and domains PF01906 with PF02796, the former a metal binding domain and the latter a domain found in site specific recombinases.Lastly, we find three proposed interactions between domains found in ribosomal proteins RL36, RL34 and RL32  and also a protein of unknown function YidD (PF01809). We consider these to be likely false positive predictions since we previously observed spurious results for members of very large macromolecular complexes such as the ribosome . At leasIn summary, we are able to recapitulate several known or plausible but structurally unresolved interactions and find several examples of interaction that should be of interest for future experimental studies.phyletic couplings: by using a global statistical model, we are able to disentangle direct and indirect correlations in the presence and absence of protein domains across more than 1000 fully sequenced representative bacterial species. Couplings substantially increase the capacity to find relations between domains beyond correlations; these relations can be physical interactions, but also genomic co-localization . Standard correlation measures used in phylogenetic profiling only reach 30–50% of true positives between the first 1000 predictions. In contrast the positive predictive value of phylogenetic couplings reaches about 80%. The results are very robust: when applying the same methodology to all 9358 Pfam domains appearing in the bacteria, and selecting only later the couplings between domains present in E. coli, couplings have 96% correlation with the couplings found by the procedure described before.In this work, we propose a coevolutionary analysis connecting signals at the phylogenetic level (correlated presence of domain pairs across genomes) with the residue level (correlated occurrence of amino acids between proteins). At the phylogenetic level, we introduce the concept of SupplementThe high accuracy of phyletic couplings in predicting domain-domain relations, along with the robustness of these couplings when extensively changing the data set, allows us to hypothesize that large couplings not corresponding to known relations predict novel, unknown relations. A list of the 100 first predictions is provided in Tables A and B in As mentioned, a large phyletic coupling does not automatically imply a direct physical interaction. Two proteins may have a strong phyletic coupling because they belong to the same multi-protein complex, without touching each other. They may have a strong phyletic coupling, because they act within the same biological process or pathway, again without any direct interaction. To refine the results and predict physical interactions, we have added a coevolutionary analysis on the scale of residue-residue covariation, as provided by DCA, in the version with paralog matching as recently proposed in . We findThese domain pairs are our strongest candidates for predicted domain-domain interactions. Since they are not co-localized in the same protein, they also provide predictions for new protein-protein interactions. In a detailed discussion, we have shown that most of the 24 pairs with a DCA score larger than 0.2, which result from the first 200 PhyDCA predictions have a sensible biological interpretation and, in principle, could be tested experimentally.Results, we actually find many pairs having the same functionality, including documented pairs of convergent evolution. Some pairs actually are of unknown function, and our method might help to transfer functional annotations from one domain to the other.Similarly, negative phylogenetic couplings appear to be biologically reasonable. They disfavor the joint presence of two domains within the same genome. In our analysis of the pairs of the strongest negative couplings, presented above in An important extension would be the application of our approach beyond the bacteria. Bacteria, due to their compact genomes, are overrepresented in genomic databases, including the Pfam database, which we used for our analysis. To test the applicability to higher organisms, we have repeated the same procedure, concentrating on eukaryotic genomes and taking humans as the reference species. Data get much less abundant; the phylogenetic profile matrix now contains 5343 domains as compared to only 481 eukaryotic species. Still, phyletic couplings, when compared to a positive list extracted from domain architectures of human proteins , from iPfam  and humasamples for the variability in protein content and protein sequences across hundreds and thousands of species; their statistical analysis helps us to detect multiple scales of coevolution between interacting or functionally related proteins.To conclude, our work illustrates the potential of combining rapidly growing genomic databases and statistical modeling: the increasing number of fully sequenced genomes allows for extracting rich genomic scale explores the correlated presence or absence of proteins (in the sense of homologous protein families) across species. This correlation has been used before within phylogenetic profiling to detect functional relations or direct interactions between proteins. Within our work, we propose to infer direct phyletic couplings via global statistical models, and prove that this concept strongly improves our capacity to detect protein relations over local correlation measures.The residue scale of inter-protein coevolution. Interacting proteins show a correlated usage of amino acids across their interface, and again global statistical modeling approaches like DCA have been used to discriminate between interacting and non-interacting protein pairs.However, phylogenetic couplings cannot distinguish between functional relations or direct interactions between proteins. This problem can–at least partially–be resolved at the Since the computational cost of the residue-scale analysis is high, it is possible to analyze all pairings between 10–50 proteins, but not all pairs between thousands of proteins forming a species’ proteome. It is the combination of both scales, which allows us to first explore the genomic scale and then refine promising results at the residue scale. Doing so, we have provided a number of biologically sensible predictions for currently unknown protein-protein interactions. We provide a list of these predictions, which in turn may be tested directly.Last but not least, we want to mention that the analysis of both scales of coevolution is done independently, i.e., in a modular way, even if using a common mathematical-statistical framework. In principle it is therefore possible to improve each single component on its own. We might, e.g., come up with a phylogenetically better-founded version of PhyDCA (i.e. combining the spirit of CoPAP and PhyDCA), to generate better candidates for novel domain-domain interactions. Similarly, improvement in paralog matching and DCA-based interaction prediction might lead to a more sensitive treatment of these candidates.Escherichia coli as reference genome (i.e. counting only domains contained in E. coli) reduces this to 2682 domain families. Since we are interested in the correlated presence / absence of domains across species, we remove all domain families with less than 5% or more than 95%, keeping only domains with at least 53 and at most 988 appearances. This removes in particular omnipresent domains related, e.g., to replication, transcription and translation. The final phylogenetic profile matrix (PPM) is a binary matrix containing M = 1,041 bacteria and N = 2,041 domains. Entries are one if a domain is present in a species (at least once), and zero if it is absent. Note that a zero entry typically indicates a true absence of the domain in a genome, since the profile matrix is entirely built on fully sequenced genomes.Data are extracted from the Pfam 30.0 database . For eacSupplementIn standard phylogenetic profiling , correlan1, …,nN) being a binary vector characterizing the presence (ni = 1) or absence (ni = 0) of domain i in a species, and Z is a normalization constant also known as partition function in statistical physics. The phyletic couplings Jij and biases hi are to be determined such that the model P reproduces the one- and two-column statistics of the PPM (nia)i = 1,…,N; a = 1,…,M:fi being the fraction of genomes in the PPM carrying domain i, and fij the fraction of genomes containing both domains i and j simultaneously. While the exact determination of the marginal distributions of P requires exponential-time computations, we apply the mean-field (MF) and pseudo-likelihood maximization (PLM) approximations successfully used in the context of DCA [SupplementN = 2041−9358), more precise methods based on Boltzmann machine learning, cf. [SupplementSupplementIn analogy to the direct-coupling analysis on the level of amino-acid sequences, we model the phylogenetic profiles via the maximum-entropy principle by a global statistical modelt of DCA , 57; cf.ing, cf. , become SupplementTo assess the coevolution on the finer scale of residue-residue coevolution, we have applied exactly the progressive matching and analysis procedure recently published by part of us in , detailsSupplementTo assess the accuracy of our predictions, we have compiled a number of known relationships Click here for additional data file.


In this paper, the basic components, nutrient composition, and processing characteristics of cassava flour were determined. In addition, the effects of xanthan gum and inulin on the pasting properties, microstructure, and thermal properties of cassava flour were studied. Biscuits were prepared using cassava flour as the main raw material and the optimal technology and formula for the biscuits were determined by single‐factor and orthogonal tests. The effects of xanthan gum and inulin on the quality of cassava flour short biscuits were also investigated, and volatile components in the biscuits were determined using electronic nose technique. The addition of xanthan gum improved the pasting properties and microstructure of cassava flour, and improved the taste and increased hardness and brittleness of the biscuits, making their quality similar to that of commercially available short biscuits. The addition of inulin inhibited the setback of starch and improved starch gelatinization. However, inulin was not suitable for processing of cassava flour biscuits as it decreased their hardness, brittleness, and taste. The optimal formula and baking conditions of cassava flour short biscuits were as follows: cassava flour 100 g, water 24 g, shortening 25 g, sugar 30 g, baking powder 0.6 g, salt 1 g, and egg 25 g; the surface fire and primer fire temperatures were 180°C, and the baking time was 9 min. In addition, although the main aroma volatile components present in cassava flour and low gluten wheat flour short biscuits were similar, the proportions of each component were different. In this study, sweet cassava flour was used as a raw material to prepare gluten‐free (GF) biscuits with addition of xanthan gum and inulin. The effects of the addition of xanthan gum and inulin on the processing characteristics, pasting properties, thermal performance, and microstructure of cassava flour were investigated. Moreover, the effects of xanthan gum and inulin on the quality of cassava flour short biscuits prepared by the best formula were compared with those of commercially available wheat flour short biscuits, to improve the nutritional value and sensory properties of GF biscuits and to provide better GF products for celiac patients. Cassava can be cultivated in both tropical and subtropical regions and has become a staple food in those regions, because of its high starch content , steaming, slicing, drying, and ultra‐fine grinding. It is rich in a variety of nutrients, including fibers, vitamins, and minerals, and is widely used in the feed, food, and chemical industries  to make GF products. Celiac disease is becoming increasingly prevalent internationally, and sufferers tend to have a low dietary fiber intake so there is a need for higher‐fiber GF products . It was prepared by the processes of peeling, shredding , drying, and crushing of the fresh sweet cassava root tubers and was then passed through a 100 mesh screen. Xanthan gum was obtained from Shandong Zhongxuan Co. Ltd., and inulin was purchased from Ruji Biotechnology Development Co. Ltd. Cassava starch and low gluten wheat flour was obtained from Tianzhiyuan Food Co. Ltd. Sugar, salt, and eggs were purchased from a local supermarket. Shortening was procured from Zhirun Oil and Fat Food Industry Co. Ltd. Baking powder and short biscuits were obtained from Angel Yeast Co. Ltd. and Dali Food Group Co. Ltd., respectively.2.2The basic components  and nutrient composition  of cassava flour were determined according to the national standards of China.2.32.3.1g for 20 min. The centrifuge tube was weighed after it was drained upside down, and the percentage of mass increase per gram of sample was calculated. Cassava starch was used as a control.The water‐holding capacity (WHC) of cassava flour was measured following the methods of Du et al.  and Luna2.3.2g for 20 min, and the percentage of mass increase per gram of sample was calculated. Cassava starch was used as a control.The oil absorption capacity (OAC) of cassava flour was determined according to the method of Lin, Li, Xu, Jian, and Zhang . A 5 g s2.3.3g for 20 min, and the quality of the precipitate after leaching was measured. The water evolution rate was calculated using cassava starch as the control, and the formula is as follows:R is water evolution rate, A is the weight of paste, and B is the weight of sediment.Freeze–thaw stability was evaluated based on the modified method of Babu, Mohan, and Parimalavalli . The sam2.4Pasting properties of cassava flour samples were determined using the modified methods of Santos et al.  and Kim,2.5Differential scanning calorimetry (DSC) was performed according to Ye et al. , with so2.6The microstructure characteristics of the samples were evaluated according to Martins, Gutkoski, and Martins . Cassava2.72.7.19(34) orthogonal test was carried out by selecting the amount of water, shortening, sugar, and baking powder as the experimental factors and setting different levels for each factor. In addition, on the basis of previous work, the L4(23) orthogonal test was carried out by selecting three factors, that is, the surface fire, the primer fire, and the baking time. Finally, the optimal formula and the optimal baking conditions were determined. The orthogonal test designs are shown in Tables On the basis of previous experiments, the basic formula for biscuit formation was as follows: cassava flour 100 g, sugar 30 g, salt 1 g, shortening 20 g, water 18 g, baking powder 0.8 g, and egg 25 g. Referring to the basic formula and on the basis of single‐factor analysis, the L2.7.2The hardness of biscuits was measured by the puncture test with a P5 cylindrical probe. The specific test parameters were as follows: test mode, compression; pretest speed, 2 mm/s; test speed, 3 mm/s; post‐test speed, 10 mm/s; straining, 75%; trigger force, 5 g; trigger mode, automatism; and data acquisition rate, 200 pps.The brittleness of biscuits was determined by using HDP/CFS, that is, a P/0.25S spherical probe. The test conditions were as follows: test mode, puncture; pretest speed, 1 mm/s; test speed, 1 mm/s; post‐test speed, 10 mm/s; distance, 3 mm; trigger force, 5 g; trigger mode, automatism; and data acquisition rate, 500 pps.The texture quality of biscuits was measured using a texture analyzer . The hardness and brittleness of the biscuits were determined by puncture and compression modes, respectively.Ten parallel samples were taken for each group, and the average values of the measured data were recorded.Referring to the improved evaluation criteria based on the biscuit standard GB/T 20980‐2007, the sensory evaluation of the products  was examined. The sensory evaluation criteria are shown in Table 2.7.3According to the results of the single‐factor and orthogonal tests, the optimum formula of cassava flour biscuits was obtained. The usage of xanthan gum in food refers to standards for the use of food additives in the national standard for food safety (GB 2760‐2014); that is, the maximum usage of xanthan gum in food was 10.0 g/kg. On the basis of the optimal formula, 1% xanthan gum and inulin were added, respectively, to make the short biscuits, and the effects of xanthan gum and inulin on the sensory and textural quality of the biscuits were observed and contrasted with the short biscuits sold commercially.2.8Referring to the optimal formula and baking conditions obtained from Section 2.8.1The biscuit samples made from low gluten wheat flour and cassava flour were weighed into 20 g, then crushed, and sealed in a 50‐ml small beaker. Thereafter, samples were heated in a 45°C water bath for 30 min and detected by manual headspace injection with an electronic nose .Test conditions were as follows: Carrier gas was clean and dry air; sampling time interval, 1 s; cleaning time, 100 s; zero adjustment time, 10 s; presampling time, 5 s; measuring time, 100 s; sensor chamber flow rate, 400 ml/min; measuring sample flow rate, 400 ml/min; maximum G/G0, 3.0; and sensor, 8.2.9p < .05.The data obtained by DSC were analyzed using the instrument's own software. The rest of the data were analyzed by Origin 8.0 software and tested at a significance level of 33.1The fat content of cassava flour was lower, and the ash and starch contents were higher than those of low gluten wheat flour. The protein content of cassava flour was substantially lower than wheat flour Table . Starch Sweet cassava flour is nutritious and well‐balanced Tables  and 6. I3.2The WHC and OAC characteristics of the raw material flour have significance in guiding food development. In the processing process, good WHC can increase the amount of water appropriately. Good OAC can appropriately increase the amount of oil, and the amount of oil has a great effect on the plasticity and appearance of the dough, especially in baking products. The WHC of cassava flour was better than that of cassava starch Table . This ma3.3p < .05) increased. The results showed that the xanthan gum–cassava flour complex system would be a more suitable thickener in foods than cassava flour alone. The breakdown of the system first increased and then decreased. When the amount added was less than 10%, the shear thinning behavior of the system was more obvious with an increase of xanthan gum; when 10%–20% was added, the shear resistance of the system was enhanced when xanthan gum was increased. This may be because when the amount of xanthan gum added is less, it reacts with starch through hydrogen bonds in the process of heating gelatinization. Then, the starch granules absorb water and expand. When the concentration of xanthan gum is increased further and the viscosity of the system is improved, it results in a larger shear force acting on starch granules and makes them more easily deformed and damaged; that is, the breakdown increases. With the addition of increased amounts of xanthan gum, the trisaccharide side chain and main chain are arranged tightly in a straight line to form a clava with elasticity and stability, which can form a firm three‐dimensional network structure with the starch molecular chain segments of cassava flour, thus reducing the breakdown. When the levels of xanthan gum were increased, the setback decreased at first, then increased, and finally decreased again. This may be due to the fact that xanthan gum can prevent the formation of hydrogen bonds between starches and reduce the setback, but the effects of hydrogen bonds approaching each other between xanthan gum and amylose also increased the setback. The setback decreased again which may be due to the formation of a three‐dimensional network structure between a clava formed by xanthan gum and starch molecules.Table p < .05). Inulin promoted the binding of amylose to amylopectin, and therefore, amylopectin could not fully combine with water; that is, hydration of the starch granules decreased, and therefore, the peak viscosity of the system decreased. In addition, inulin is distributed around the starch granule and encases the starch granules, which limits the release of amylose. Alternatively, inulin may have interfered with the interaction between amylose particles and therefore reduced the viscosity of the system. Thus, it could be assumed that inulin can inhibit in vitro digestion of starch by amylase and thus can inhibit the digestion of starch and delay the blood glucose response. Therefore, inulin could be used for the development of hypoglycemic index products. When inulin levels were increased, the breakdown of the system decreased, indicating that inulin reduced the damaging effects of shear force on starch granules. The setback of starch is affected by the fine structure of amylopectin, the ratio of amylose to amylopectin and the moisture content (Salinas & Puppo, The addition of different proportions of inulin increased the pasting temperature of cassava flour Table . This ma3.4The DSC curves of cassava flour, cassava starch, cassava flour–xanthan gum, and cassava flour–inulin are shown in Figure 3.5Cassava flour Figure a shows aFigure Figure Clearly, the addition of xanthan gum and inulin made the texture of the system more uniform and improved the pasting properties of cassava flour.3.63.6.13B2C1D2. This formula was not one of the orthogonal test combinations, and it was important to confirm its sensory and textural qualities. The sensory score was 87, the hardness was 2,535.892 g, and the brittleness was 1,089.724 g. This confirmed that the optimal ratio of each factor was A3B2C1D2; that is, the optimal formula was 30 g sugar, 0.6 g baking powder, 25 g shortening, and 24 g water.The effects of different amounts of water, shortening, sugar, and baking powder on the brittleness, hardness, and sensory quality of cassava flour biscuits were studied by single‐factor tests (see Appendix 2B2C1; that is, the surface fire and primer fire were 180°C and the baking time was 9 min. These conditions resulted in evenly colored biscuits, with no coke on the surface or bottom, no incomplete cooking, a crisp taste, and the highest quality.The orthogonal test results of the baking conditions are shown in Table 3.6.2The effects of xanthan gum and inulin on the textural qualities of cassava flour short biscuits are shown in Table 3.73.7.1Electronic nose detection and analysis of volatile components in low gluten wheat flour and cassava flour short biscuits was performed. The intensity curves of the electronic nose sensor can be used to characterize the intensity of each volatile component in the sample. The variation trend of the signal values of the 10 sensors was consistent with the intensity difference of the volatile components Figure . The radThe two types of biscuits could be clearly separated, which indicated that the volatile components and their contents in the samples were different, and the electronic nose quickly distinguished between the two types of biscuits by measuring the concentration of each volatile component in the samples Figure . The con4Addition of xanthan gum and inulin to cassava flour significantly modified the pasting properties, thermal properties, and microstructure. Short biscuits were prepared with cassava flour as the main ingredient, and the optimal formula and baking conditions were determined, as well as the effects of xanthan gum and inulin on biscuit quality. Addition of xanthan gum improved both cassava flour properties and biscuit quality. Though inulin could inhibit the setback of starch and improve starch gelatinization, it decreased the hardness, brittleness, and taste of biscuits. The cassava flour short biscuits made by the optimal cassava flour formula were acceptable, and their main volatile components were similar to those of low gluten wheat flour short biscuits. We conclude that it is feasible to make GF biscuits with cassava flour and the biscuit quality can be improved by adding xanthan gum as a gluten substitute. The literature on celiac disease, discussed above, indicates that these new biscuits should be very suitable for celiac sufferers to eat as part of their GF diet; however, our future work will aim to verify that this is correct.There is no conflict of interest.This study does not involve any human or animal testing. Click here for additional data file.


Aeropyrum pernix has non-domain-swapped voltage sensors as well as other unusual features. The new structure, together with previous functional data, suggests that KvAP and the Shaker channel, to which KvAP is most often compared, probably undergo rather different voltage-dependent conformational changes when they open.Conductance in voltage-gated ion channels is regulated by membrane voltage through structural domains known as voltage sensors. A single structural class of voltage sensor domain exists, but two different modes of voltage sensor attachment to the pore occur in nature: domain-swapped and non-domain-swapped. Since the more thoroughly studied Kv1-7, Nav and Cav channels have domain-swapped voltage sensors, much less is known about non-domain-swapped voltage-gated ion channels. In this paper, using cryo-EM, we show that KvAP from  Voltage sensors are integral membrane proteins that ‘sense’ transmembrane voltage differences. They exist as regulatory domains on voltage-gated ion channels and voltage-dependent phosphatase enzymes . All vol+ (Kv) channels demonstrate that nature employs two topologically distinct connections between the voltage sensors and the ion channel pore  and Ca2+ (Cav) channels and other cation channels have domain-swapped voltage sensors  and cholesteryl hemisuccinate (CHS), exchanged into digitonin and then purified as a complex with Fab fragments using size exclusion chromatography  is sometimes replaced by K (lysine) and X stands for a hydrophobic amino acid. More rarely R is replaced by a non-charged polar amino acid, but when this occurs the triplet periodicity almost always persists so that the occurrence of subsequent arginine residues remains in-phase in the sequence. But consider the S4 sequence in KvAP: (RLV)(RLL)(RFL)(RIL)(LII)(SRG)(SKF). Here, we observe a discontinuity between the fourth and fifth arginine residues created by six consecutive uncharged amino acids; this produces a phase shift of the fifth and sixth (lysine) with respect to the first four. The fifth R as well as the following lysine in the sequence are certainly part of the active region of S4, because in the structure the lysine resides inside the gating charge transfer center , but it might not occur in the gap between the fourth and fifth arginine residues. Because triplet periodicity is not maintained over the lower half of S4 in KvAP, it is not entirely clear whether properties learned from other Kv channels apply to KvAP.KvAP is an outlier among voltage-gated ion channels, whether domain-swapped or not, in two respects. First, the distribution of positive-charged amino acids on S4 is unique. In other Kv channels the region of S4 that contains gating charge residues – the active region – consists of the strict triplet (RXX)r center , which ir center . In otheThe second respect in which KvAP is an outlier concerns the gating charge transfer center. KvAP has the typical constellation of negative-charged amino acids to stabilize the positive charge on arginine or lysine. But instead of the phenylalanine found in other Kv channels (357 out of 360 examined), KvAP contains a leucine. The functional significance of this is also unknown.Our original expectation that KvAP and Shaker-like Kv channels probably undergo similar voltage sensor conformational changes may be incorrect. The present study shows that the voltage sensors in KvAP, in contrast to Shaker, are not domain-swapped. In separate studies in our lab, we have captured the HCN channel, another non-domain-swapped voltage-gated channel, in both depolarized and hyperpolarized conformations . When deDNA encoding residues 12–282 of KvAP was subcloned into protein expression vector pET28a (Novagen) between NcoI and BglII restriction endonuclease sites with a thrombin cleavage site between a carboxy-terminal hexahistidine sequence and the channel. Channel protein was expressed in BL21(DE3) cells (Invitrogen). For large scale culture, a single colony was inoculated in LB medium overnight at 37°C and then diluted 1:100 (v:v) into auto-inducing ZYM-5052 medium and further inoculated at 37°C for 24–26 hr .2+ affinity column (Clontech) equilibrated in wash buffer . Nonspecifically bound protein was washed using 15 mM imidazole added to the above buffer. The channel was eluted with 400 mM imidazole, concentrated using an Amicon Ultra centrifugal filter (MWCO 50 kDa), and then injected onto a Superdex-200 (10/30) column  equilibrated with wash buffer. Fractions corresponding to KvAP tetramer  benzenesulfonyl fluoride hydrochloride (AEBSF) and 2 mM phenylmethysulfonyl fluoride (PMSF)) and then passed through a microfluidizer three times before adding lauryl maltose neopentyl glycol/cholesteryl hemisuccinate (LMNG/CHS) to a final concentration of 10 mM/2 mM. After 2 hr of extraction at room temperature, the lysate was clarified by centrifugation at 39,800 x g for 30 min. Supernatant was then applied to a prepacked 4 ml Talon CoMonoclonal antibody 6E1 (mouse immunoglobulin) against KvAP were raised using standard procedures  and puri3.5 μl of purified protein was pipetted onto glow-discharged Quantifoil Au 400 mesh, R 1.2/1.3 holey carbon grids (Quantifoil). Grids were blotted for 4 s with a blotting force of 1 and humidity of 100% and flash frozen in liquid-nitrogen-cooled liquid ethane using a FEI Vitrobot Mark IV (FEI).2 over 50 subframes or approximately 1.5 electrons per Å2 per subframe).Grids were then transferred to a FEI Titan Krios electron microscope operating at an acceleration voltage of 300 keV. Images were recorded in an automated fashion on a Gatan K2 Summit detector (Gatan) set to super-resolution mode using SerialEM . Specifihttps://www.mrc-lmb.cam.ac.uk/kzhang/). The autopicked particles were then subjected to 2D classification in Relion to remove false positives as well as particles belonging to low-abundance classes (Dose-fractionated super-resolution images were 2 × 2 down sampled by Fourier cropping for motion correction with MotionCor2 (5 × 5 patches) . The par classes . Particl classes .2. Symmetry expansion of the above 67 k particles in Relion followed by local refinement in cryoSPARC slightly improved the map quality  and a model of the pore from the crystal structure of the full-length KvAP channel (PDB: 1ORQ) were placed into the density. Small rigid body adjustments of helical segments were made, followed by a round of real-space refinement in Phenix . The onl In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:+ channels. KvAP was the channel for which the first structures were solved and thus has been widely used as a model protein for studying voltage gating, the findings of this study become particularly important and may provide explanations for some discrepant results in the studies of voltage-dependent conformational changes. Overall, this is a straightforward and informative study that will valuable for understanding the fundamental mechanisms by which voltage-activated ion channels open and close in response to changes in membrane voltage. The editors think you have addressed all of the issues raised by reviewers and we are delighted to be able to publish your work in eLife.In this study, Tao and MacKinnon determine the cryo-EM structure of KvAP channel in complex with the Fab fragment of a monoclonal antibody. Despite of low resolution around 6 Å, the previously determined high resolution crystal structure of the isolated VSD of KvAP and the pore region of the full-length KvAP structure conform well with the EM map, allowing them to build an accurate model into the density. The structure reveal some unexpected features that distinguish KvAP from most other canonical Kv channels: (1) the channel adopts a non-domain-swapped topology; (2) S4 does not appear to form a 310 helix and has a distinct arrangement of voltage-sensing arginine residues; (3) KvAP contains a Leu instead of a Phe at its gating charge transfer center; (4) the relative position between VSD and pore in KvAP is different from that in both domain-swapped and non-domain-swapped Kv channels; (5) S1 and S5 form extensive contact in KvAP, which is not observed in other Kv channels. These distinct features suggest that KvAP is an outlier among voltage-gated ion channels and its structural mechanism of voltage gating is likely different from that of most canonical voltage-gated KDecision letter after peer review:eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by Kenton Swartz serving as both Reviewing Editor and Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Gilman Toombes (Reviewer #1); Youxing Jiang (Reviewer #2); Fred Sigworth (Reviewer #3).Thank you for submitting your article "Cryo-EM structure of the KvAP channel reveals a non-domain-swapped voltage sensor topology" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife and reviewer #1 has suggestions for improving the work in revision. Please consider their comments and address their points in preparing a revised manuscript.As you will see from the comments of the reviewers, all three are enthusiastic about publishing your manuscript in Reviewer #1:In 2003 the MacKinnon lab revolutionized our understanding of voltage-gated ion channels with the first x-ray crystallographic structures of KvAP, a voltage-gated potassium channel from a thermophilic bacteria. However, KvAP appeared to be distorted in the full-length crystal structure and despite extensive structural and functional studies, the native structure of KvAP is unknown. In this work, the authors have used modern single-particle cryo-EM techniques to obtain a seemingly native-state structure of KvAP in which the voltage-sensing domain (VSD) and pore domain of each KvAP sub-unit are in direct contact, unlike canonical voltage-gated potassium channels (Kv1-9) in which each VSD nestles against the pore domain of the neighboring sub-unit.This is an important result, and the article is exceptionally clear and well written. However, there are a few aspects of the study that could benefit from further discussion.1) Effect of Resolution on Model:The authors have clearly accounted for the EM map resolution (~6 Å) during modelling, but it would help a general reader to present whatever cross-checks the authors performed. For example, did the authors manually assess map uncertainties by plotting different sigma level surfaces, or use a more automated approach . Thresholding of cryo-EM density maps by false discovery rate control. IUCrJ 6, 18-33.). Is the direct S4-S5 linker clearly visible in the raw unmasked ~8 Å map of Class I, or only after masking and symmetry expansion steps? What is the worst resolution at which the EM map could exclude a domain-swapped linker "smeared out" by C4 averaging of multiple conformations, especially if the inner gate were to be in a mixture of open and inactivated states ? Does the direct linker density still jump out even if one incorrectly uses a domain-swapped KvAP model  for masking and refinement?2) Effects of Detergent and Antibodies on Conformation:The MacKinnon lab has previously shown that the conformation of voltage sensing proteins, and KvAP in particular, can be very sensitive to the composition of the lipid membrane. In addition, although Jiang et al. (2003) elegantly showed that binding of the 6E1 antibody drives the channel into a non-conducting state this may not be the physiological inactivated state. In this KvAP structure the VSD domain seems to be displaced towards the extra-cellular side of the membrane (relative to the pore domain) whether one looks at the position of aromatic residues in the TM helices or PEG-Biotin accessibility. It may seem crazy to think lipid composition or antibody-protein interactions could switch a 6-TM channel between direct and domain-swapped configurations, but the Sobolevsky lab observed exactly such a transition for TRPV6 following a single point mutation in the S4-S5 linker . Swapping Of Transmembrane Domains In The Epithelial Calcium Channel TRPV6. BioRxiv 141523.) Thus, it would be helpful for the authors to discuss whether the conditions used to obtain the structure could influence the domain organization.3) Consistency of Structure with Published Cysteine Cross-Linking Data:The MacKinnon lab has previously reported that KvAP can tetramerize through introduced cysteine residues into the VSD and pore  These residue pairs were quite close in KvAP domain-swapped homology models, but are quite distant in this direct KvAP structure. A membrane-based assay to excluded a domain swapped architecture would be especially valuable to check that these cross-links are indeed non-specific.Reviewer #2:+ channels. As KvAP has been widely used as a model protein for studying voltage gating, the findings of this study become particularly important and may provide explanations for some discrepant results in the studies of voltage-dependent conformational changes. In short, this is a simple but informative study that will bring some clarifications to the channel field.In this study, Tao and MacKinnon determine the cryo-EM structure of KvAP channel in complex with the Fab fragment of a monoclonal antibody. Despite of low resolution around 6 Å, the previously determined high resolution crystal structure of the isolated VSD of KvAP and the pore region of the full-length KvAP structure conform well with the EM map, allowing them to build a pretty accurately model into the density. The structure reveal some unexpected features that distinguish KvAP from most other canonical Kv channels: (1) the channel adopts a non-domain-swapped topology; (2) S4 does not appear to form a 310 helix and has a distinct arrangement of voltage-sensing arginine residues; (3) KvAP contains a Leu instead of a Phe at its gating charge transfer center; (4) the relative position between VSD and pore in KvAP is different from that in both domain-swapped and non-domain-swapped Kv channels; (5) S1 and S5 form extensive contact in KvAP, which is not observed in other Kv channels. These distinct features suggest that KvAP is an outlier among voltage-gated ion channels and its structural mechanism of voltage gating is likely different from that of most canonical voltage-gated KReviewer #3:This is an important study that answers questions that surrounded the first X-ray structure of a voltage-gated channel, namely this one. It is particularly satisfying to see the mapping of accessibility and EPR experiments on the new structure. Reviewer #1:[…]1) Effect of Resolution on Model:The authors have clearly accounted for the EM map resolution (~6 Å) during modelling, but it would help a general reader to present whatever cross-checks the authors performed. For example, did the authors manually assess map uncertainties by plotting different sigma level surfaces, or use a more automated approach . Thresholding of cryo-EM density maps by false discovery rate control. IUCrJ 6, 18-33.). Is the direct S4-S5 linker clearly visible in the raw unmasked ~8 Å map of Class I, or only after masking and symmetry expansion steps? What is the worst resolution at which the EM map could exclude a domain-swapped linker "smeared out" by C4 averaging of multiple conformations, especially if the inner gate were to be in a mixture of open and inactivated states ? Does the direct linker density still jump out even if one incorrectly uses a domain-swapped KvAP model  for masking and refinement?Yes, we studied these maps carefully at different contours and at different stages of map refinement. The non-domain-swapped S4-S5 linker is clearly visible in the raw unmasked ~8 Å map of Class I. A new supplementary figure  is now added to demonstrate this fact.In cryo-EM image analysis, as it was carried out here, information from an atomic model never enters into the map calculation . Thus, neither a correct nor incorrect model is used to modify phases. The maps we show are experimental. We showed the maps for the entire molecule in stereo to answer the questions this reviewer raises. There is no doubt about the connectivity of the non-helical S4-S5 linker and about the absence of a helical, domain-swapping S4-S5 linker. Furthermore, we show in stereo the unmodified models of the pore and voltage sensor from crystal structures to emphasize how well the map conforms to them.2) Effects of Detergent and Antibodies on Conformation:The MacKinnon lab has previously shown that the conformation of voltage sensing proteins, and KvAP in particular, can be very sensitive to the composition of the lipid membrane. In addition, although Jiang et al. (2003) elegantly showed that binding of the 6E1 antibody drives the channel into a non-conducting state this may not be the physiological inactivated state. In this KvAP structure the VSD domain seems to be displaced towards the extra-cellular side of the membrane (relative to the pore domain) whether one looks at the position of aromatic residues in the TM helices or PEG-Biotin accessibility. It may seem crazy to think lipid composition or antibody-protein interactions could switch a 6-TM channel between direct and domain-swapped configurations, but the Sobolevsky lab observed exactly such a transition for TRPV6 following a single point mutation in the S4-S5 linker . Swapping Of Transmembrane Domains In The Epithelial Calcium Channel TRPV6. BioRxiv 141523.) Thus, it would be helpful for the authors to discuss whether the conditions used to obtain the structure could influence the domain organization.We cannot conclude with certainty what the functional state of this channel is. In fact, no one can ever do that with a structure. Based on comparison to other voltage sensors and to the biotin-avidin accessibility data on KvAP, recapitulated in the paper, we conclude the voltage sensors are depolarized. As we stated, the pore could be activated, inactivated  or some combination of these states.We do not think that the voltage sensor is displaced towards the extracellular side abnormally. Please note Figure 5A. Also note that voltage sensor Fabs capture the top of the paddle only upon depolarization, and they associate very rapidly, implying the epitope becomes exposed to solution without hindrance by the membrane . We think the structure is in good agreement with these observations. Also, the cryo-EM structure is consistent with independent EPR studies of KvAP in lipid vesicles, shown in the paper.We think that the interchangeable topologies in mutant TRPV6 channels of a single species is a singular observation and very confusing and the physiological significance unclear. Time will tell what those findings mean. Since such behavior has never been seen anywhere else I do not think our paper is the place to discuss it.Prior to knowing the existence of Kv channels with non-domain-swapped voltage sensor topology, we modelled KvAP as having a domain-swapped voltage sensor . This was difficult because it seemed there were not enough amino acids to make S4 and an S4-S5 linker helix. Consequently, S4 had to be very short and the tip of the voltage sensor paddle had to be embedded in the membrane. From the present work we now understand the problem. KvAP has a non-domain-swapped topology. The structure we present here is the first hard evidence for the real topology .3) Consistency of Structure with Published Cysteine Cross-Linking Data:The MacKinnon lab has previously reported that KvAP can tetramerize through introduced cysteine residues into the VSD and pore  These residue pairs were quite close in KvAP domain-swapped homology models, but are quite distant in this direct KvAP structure. A membrane-based assay to excluded a domain swapped architecture would be especially valuable to check that these cross-links are indeed non-specific.Both studies  were done in lipid vesicles, not detergents. It is worth pointing out that in our non-domain-swapped cryo-EM model, the Cα-Cα distance between residue 117 (S4) and 169 (S5) on the same subunit is ~35 Å, whereas distance between 117 and 169 on neighboring subunits is ~42 Å. For the other pair, i.e. residue 47(S1) and 183 (pore), the Cα-Cα distance on the same subunit (20.5 Å) and neighboring subunits (21.1 Å). For both cysteine pairs in both models the distances are too far. Neither model is consistent with these disulfide bridges. Unfortunately, when given enough time, as was the case in the Lee et al. studies, disulfide bridges can occur between cysteine residues that are very far apart in protein structures . Trapping of distant cysteines is a common occurrence unless the kinetics of bridge formation is monitored and shown to be rapid. We now have a much better data set – a structure that explains that KvAP has non-domain-swapped voltage sensors.


Electric switching of non-polar bulk crystals is shown to occur when domain walls are polar in ferroelastic materials and when rough surfaces with steps on an atomic scale promote domain switching. All domains emerging from surface nuclei possess polar domain walls. The progression of domains is then driven by the interaction of the electric field with the polarity of domain boundaries. In contrast, smooth surfaces with higher activation barriers prohibit effective domain nucleation. We demonstrate the existence of an electrically driven ferroelectric hysteresis loop in a non-ferroelectric, ferroelastic bulk material. Additional ferroic properties often emerge when ferroelastic materials show polarity inside their domain boundaries while no such polarity exists in their bulk14. Non-polar bulk materials like SrTiO3 and LaAlO3 show strong local dipoles inside twin walls20. These polar properties do almost cancel in complex domain patterns21 but slight biases lead to an overall polar behavior of the material even while the bulk of the material remains strictly non-polar22. Electric fields may hence influence such domain structures24 and the question arises whether hysteretic behavior itself can be induced by electric fields. This would mean that ferroelastic materials are indeed also (weakly) ferroelectric due to polar domain boundaries. The same question arises for ferromagnetic domain walls, namely whether magnetic fields can switch ferroelastic materials if domain walls contain ferromagnetic properties? A simple answer is found when we consider simple ferroelastic needle domains. These domains can be propagated and retracted by electric fields, which constitutes ferroelectricity25 although the ferroelectric, switchable polarization is very small. In addition, an electric field can switch the spontaneous strain so that a ferroelastic/ferroelectric hysteresis is observed.Ferroelastic materials are defined by their ferroelastic hysteresis26. We distinguish the current models according to their treatment of free surfaces. We take into account a single ferroelastic domain with two kinds of surfaces, namely (i) a charge-free smooth surface with no excess net charges at surface layers with an equal number of A and B atoms27 and (ii) rough surface with excess net charges at surface steps. Electric fields are then applied to study the evolution of domain patterns and generate a polarization hysteresis loop.A more stringent test is the switchability of uniform samples. In this case, the switching needs an additional, less obvious mechanism: domains with different elements of the strain tensor need to nucleate in order to switch domains. Here we focus on the nucleation and growth of ferroelastic domains under an applied electric field. In order to explore this effect we follow previous work and simulate the switching by molecular dynamics techniquesx-[10] and y-[01], as shown in Fig. We start from a single domain configuration with {11}-type smooth surfaces, which is cut from a bulk containing A and B sublattices with orientation of y direction (Py) under the external field. The change of polarization density along the x axis (Px) behaves similarly to that of Py. The insets show the typical snapshots at different states as marked in e1-e5. As the electric field increases, the new domains nucleate near the corner (e2) and propagate by kinks movement (e3). The domain switching is only limited to a very thin layer (~3 lattice units) near the surface. When the electric field is retraced, the new domain switches back. Similar surface nucleation (e4) and kink movements (e5) are observed near another surface as the field is applied in the opposite way ([-1-1] direction). The polarization hysteresis loop is hardly observable in Fig. We first explore the domain nucleation at smooth surfaces by an electric field, which is applied along the [11] direction (upwards) and retraced back (downwards). Figure 28. Figure x = −0.373 C/m2 and Py = 0.187 C/m2.We now explore the influence of surface roughness on domain nucleation. The rough surface was constructed with randomly distributed steps. These steps have net charge due to the breaking of AB stacking at surfaces. Figure y) as function of the external field. We observe a typical ferroelectric hysteresis loop during field cycling. The insets illustrate the configuration of domain patterns at different states as indicated by e1-e8. Starting from a single domain with a very small net polarization (e1), the new domains initially nucleate at surface steps and corners as the electric field increases (e2). The movement of the twin walls then progresses by nucleation and propagation of kinks (e3). Besides the two-oriented stable domains (red and blue areas), we also find that some areas near the surface stay in the intermediate state after the transition (green areas in e3). Reducing the field leads to domain back-switching. When the field is further applied in the [-1-1] direction (downwards), a similar process of domain nucleation and propagation is observed (e4-e5). The switching is reversible and reproducible during the hysteresis loop (e6-e8). Since the switchable polarity is only generated from free surfaces and domain boundaries /[-1-1] (downwards) cycle. Figure see Fig. , the ferFigure 29. Figure 30. We also calculate the barrier for kink propagation inside twin boundaries. These barriers are very weak (~0.004 eV) and are identical for both systems  methodems Fig. . Thus, o3 is further enhanced by dielectric pressure because the dielectric parameters are highly anisotropic and favor or disfavor specific domain orientations24. In our model, the polarization is entirely localized inside domain walls and near the rough sample surfaces, which agrees with earlier results by phase field modelling31. The applied electric field rotates the polarization and stabilizes (or destabilizes) domain walls because their polarization vector is no longer compatible with the adjacent bulk material. The rough surfaces carry relatively large and randomly distributed polar vectors. The (de-) stabilization effect is much stronger than that for smooth surfaces where all surface dipoles are orientated perpendicular to the surfaces and only the corners act as nucleation sites. The polarization hysteresis is hence a result of the nucleation of interfaces near surfaces and the subsequent evolution of domain pattern. The magnitude of polarization strongly depends on the twin boundary density. In realistic cases, the concentration of interfacial atoms is rather small, namely in the order of 1 ppm (part per million). This explains why the ‘parasitic’ hysteresis is very slim. Nevertheless, we have shown that interfacial ferroelectricity is a physically viable proposition that explains previous observations35.The observation of an electric hysteresis in purely ferroelastic materials is related to the polarization of domain walls and is greatly facilitated by rough sample surfaces. The movement of ferroelastic domains in materials like SrTiO36. The twin structure is constructed by the anion sublattice A whose interaction contain anharmonic elastic interactions (Landau springs) while the interactions between atoms of the B sublattice and between A and B atoms are purely harmonic. Flexoelectricity is caused by strain gradients and is therefore restricted to generate polarity near twin walls and surfaces21. The parameters for A-A and B-B interactions are illustrated in our previous paper21. However, the coupling coefficient between A and B sublattices is modified to be stronger ( = 1.60 × 10−19 C)37. The model parameters and potential form are summarized in Table I in the Supplementary Materials. The dielectric constant is chosen to be isotropic, and set to 1000 in order to avoid the additional effect of dielectric pressure on the domain wall movements. The boundary condition is ‘open’ (free boundary condition) in both two dimensions, i.e. the surface atoms are specified to be stress-free (Neumann condition). We construct two kinds of surfaces. (i) The surface is smooth containing the same number of A and B atoms. Such smooth surface has no excess charge except at four corners. (ii) Roughness is added at the surface. The depth of surface steps is around three atomic distances. These steps contain excess charges because the charge balance between A and B atoms is not conserved locally . Our more extended simulations were conducted at low temperature at T = 0.001 K to avoid thermal effects39. The electric field is applied stepwise along the [11] direction. For each step, the system relaxes for 100 ps and the snapshots within the last 50 ps were averaged to show the microstructure and the polarization. All calculations were carried out by using the LAMMPS code40.The molecular dynamics simulations are implemented based on a two-dimensional toy model consisting of two base charged atoms (A and B)Supplementary information