Using a 1TEL–CMG2-vWa construct, evidence is provided to support limiting the flexibility of linkers between TELSAM and proteins of interest and considering retaining polyhistidine purification tags in TELSAM-fusion constructs. The phenomenon of TELSAM-polymer flipping is also identified and a correction strategy is developed. et al. (2022Open Biol.12, 210271]. Here, requirements for the linker composition between 1TEL and a fused CMG2 vWa domain were investigated. Ala-Ala, Ala-Val, Thr-Val and Thr-Thr linkers were evaluated, comparing metrics for crystallization propensity and crystal order. The effect on crystallization of removing or retaining the purification tag was then tested. It was discovered that increasing the linker bulk and retaining the 10×His purification tag improved the diffraction resolution, likely by decreasing the number of possible vWa-domain orientations in the crystal. Additionally, it was discovered that some vWa-domain binding modes are correlated with scrambling of the 1TEL polymer orientation in crystals and an effective mitigation strategy for this pathology is presented.TELSAM-fusion crystallization has the potential to become a revolutionary tool for the facile crystallization of proteins. TELSAM fusion can increase the crystallization rate and enable crystallization at low protein concentrations, in some cases with minimal crystal contacts [Nawarathnage  al. 2022, Open Bi However, when the pH decreases below 7, TELSAM forms a six-subunit-per-turn helical polymer that can enable fused target proteins to co-crystallize  that allows different possible orientations of the fused protein in the final crystal lattice, which may facilitate crystallization. The higher flexibility may alternatively lead to a greater degree of disorder in the crystal, leading to poorer diffraction resolution and higher a). This construct also featured an N-terminal 10×His-SUMO domain that was proteolytically removed during protein purification. We discovered that TELSAM fusion could increase the rate of crystallization, form crystals without direct contact between TELSAM polymers and enable crystallization at low protein concentrations, in some cases using only very minimal crystal contacts overall  domain] followed by an alanine residue and then residues 40–217 of human capillary morphogenesis gene 2 (CMG2)/anthrax toxin receptor 2 (ANTXR2) [the von Willebrand factor type A (vWa) domain] Fig. 1a. This (i) The linker length and sequence between TELSAM and the fusion protein dictates the available motion of the fused protein in the crystal. In the previous study, the 1TEL-flex-vWa linker consisted of a single alanine. In the resulting crystal lattice this alanine adopted an α-helical conformation, extending the C-terminal α-helix of 1TEL. The second and third amino acids of the vWa domain, Ala-Ala in this construct, preceded the first β-sheet of the vWa domain and adopted a more flexible conformation. No optimization of the 1TEL–vWa linker was carried out in this initial study.(ii) A cleavable 10×His-SUMO tag was included in the construct to increase protein solubility and to allow His-tag removal before crystallization. The effects on crystallization propensity or crystal quality of SUMO-tag cleavage, SUMO protease enzyme activity or retaining the 10×His tag were not tested in the previous study.(iii) In the previous study, we discovered that TELSAM can increase the crystallization rate but did not produce crystals that diffracted to better than 2.8 Å resolution. Optimization of the crystallization or cooling conditions was not investigated.−1, we did not determine the lower concentration limit for crystallization of this construct. Low-concentration protein crystallization using TELSAM is a promising avenue of investigation because it can potentially decrease batch sizes and the cost of protein crystallization, as well as enable the crystallization of poorly soluble and difficult-to-produce proteins.(iv) Although we determined that 1TEL-flex-vWa could be crystallized at concentrations as low as 1 mg mla) and address the issues identified.As reproducibility in protein crystallization is desirable for drug screening and structure-based drug-design applications, we sought to explore these variables Fig. 1a and ad2.2.1.et al.  and Geneious . Gene fragments were synthesized by Twist Bioscience (https://www.twistbioscience.com) and were assembled into the pET42_SUMO vector  cells and sequence-verified by Sanger sequencing in both directions by Eton Bioscience (https://www.etonbio.com).The 1TEL-AA-vWa (formerly 1TEL-flex-vWa) construct from Nawarathnage  al. 2022 was used2.2.−1 kanamycin was inoculated with a stab of frozen cell stock and shaken at 37°C and 250 rev min−1 overnight. The following day, 15 ml of the overnight culture was added to 1 l LB supplemented with 0.05% glucose and 100 µg ml−1 kanamycin and incubated at 37°C and 250 rev min−1 until the optical density (OD) reached 0.6. At an OD of 0.6, isopropyl β-d-1-thiogalactopyranoside (IPTG) was added to a final concentration of 100 µM. The culture was incubated at 18°C and 200 rev min−1 overnight. The following day, the cells were collected by centrifugation, snap-frozen in liquid nitrogen and stored at −80°C.20 ml lysogeny broth (LB) medium with 100 µg ml2.3.M Tris, 200 mM KCl, 50 mM imidazole pH 8.8) containing 1 mM phenylmethylsulfonyl fluoride (PMSF), 100 µM dithiothreitol (DTT), 0.5 mg ml−1 lysozyme and 800 nM deoxyribonuclease I. The suspended cells were then sonicated at 60% power at 12 s on/59 s off for 25 cycles (Qsonica Q500) in a spinning ice bath. The resulting lysate was centrifuged at 40 000g and the supernatant was loaded onto 6 ml HisPure Ni–NTA resin (Thermo Scientific). The column was then washed with 7 column bed volumes (CV) of wash buffer . The protein was then eluted with about 7 CV of elution buffer  until protein stopped appearing in subsequent fractions as detected using Bradford reagent . The diluted protein was loaded onto 5 ml Source 15Q anion-exchange resin (Cytiva) and eluted with 50 mM Tris pH 8.8, 1 M KCl using gradient elution. Fractions containing the pure protein of interest were concentrated to 2 ml and loaded onto a 100 ml Superdex 200 prep-grade size-exclusion column (Cytiva). The proteins were judged to be greater than 95% pure by SDS–PAGE.All purification steps were performed on ice or in a refrigerator at 4°C. Cell pellets (20 g) were resuspended in a fivefold excess of wash buffer  and custom screens (PEG-custom and ammonium sulfate) were screened at the above protein concentrations. 1.2 µl of the indicated protein solutions was combined with 1.2 µl reservoir solution (SPT Labtech Mosquito) and equilibrated against 50 µl reservoir solution in a sitting-drop vapour-diffusion format. Crystals appeared after two days for His-1TEL-TV-vWa, 3–7 days for 1TEL-AA-vWa (SUMO), 1TEL-AV-vWa (SUMO), 1TEL-TV-vWa (SUMO) and His-1TEL-AA-vWa, and 35 days for vWa alone. No crystals appeared for 1TEL-TT-vWa (SUMO).The concentration of the purified protein was adjusted to 1 and 20 mg mlM sodium acetate pH 4.6, 2.0 M sodium formate. Other conditions giving 1TEL-AA-vWa (SUMO) crystals included (i) 100 mM bis-Tris pH 5.7, 3.0 M NaCl  polyethylene glycol 3350; (iii) 100 mM Tris pH 8.5, 200 mM ammonium acetate, 25%(w/v) polyethylene glycol 3350; (iv) 100 mM Tris pH 8.5, 200 mM ammonium sulfate, 25%(w/v) polyethylene glycol 3350; (v) 100 mM bis-Tris pH 5.5, 200 mM ammonium acetate, 25%(w/v) polyethylene glycol 3350; (vi) 1.8 M ammonium citrate pH 7.0; (vii) 200 mM ammonium formate pH 6.6, 20%(w/v) polyethylene glycol 3350; (viii) 200 mM ammonium acetate pH 7.1, 20%(w/v) polyethylene glycol 3350; (ix) 100 mM bis-Tris pH 6.25, 130 mM magnesium formate and (x) 40 mM sodium phosphate, 960 mM potassium phosphate pH 8.2. Crystals also appeared in many conditions containing just polyethylene glycol 3350 at concentrations between 16% and 24% at pH values of 6.5, 8.5 and 9.0 and with MES, Tris and glycine buffers.The largest 1TEL-AA-vWa (SUMO) crystals appeared in 100 mM bis-Tris pH 5.7, 3.0 M NaCl and in 0.07 M bis-Tris propane pH 7.8, 0.03 M citric acid, 20% PEG 3350. The largest 1TEL-AV-vWa (SUMO), 1TEL-TV-vWa (SUMO) and His-1TEL-TV-vWa crystals appeared in 1.3–2.0 M ammonium sulfate, 0.1 M HEPES pH 5–8.5. Crystals were cryoprotected by briefly passing them through a solution of 20% glycerol in reservoir solution prior to cooling in liquid nitrogen.The largest His-1TEL-AA-vWa crystals appeared in 100 m2.6.X-ray diffraction data were collected remotely on SSRL beamlines 9-2, 12-1 and 12-2. Diffraction data for most crystals were obtained at an energy of 12 658 eV. The data for 1TEL-AV-vWa (SUMO) and selected 1TEL-TV-vWa (SUMO) crystals were obtained at an energy of 12 398 eV. Diffraction data for iodine-treated crystals were obtained at an energy of 6690 eV. Typically, 120–360° of data were collected in 0.1° or 0.2° increments with 0.2 s exposures.autoPROC pipeline . The I-to-F conversion was completed separately for the two partial data sets using CTRUNCATE from the CCP4 suite ], B11 [5.18 (3) Å2] and B33 [2.13 (4) Å2] were calculated from the respective absolute values. This translates into a relative scaling coefficient of about 7 in terms of intensities.The diffraction images of the 1TEL-TV-vWa (SUMO) crystal were processed with 3.3.1.M bis-Tris pH 5.7, 3.0 M NaCl) with an average diffraction limit of 2.9 Å (and a range of 2.8–3.0 Å), as reported previously ] generated large hexagonal crystals in three days in a single condition  and greater propensity  for 16 h prior to mounting and cooling. Only three of these soaked crystals diffracted to better than 4 Å resolution. A portion of the protein from each batch was instead co-crystallized with PGM. This proved challenging because PGM rapidly crystallized on its own and often prevented protein crystal formation. Eight crystals obtained from co-crystallization experiments were screened for X-ray diffraction, but none diffracted X-rays.Both batches 2 and 3 of 1TEL-AA-vWa (SUMO) yielded crystals in around ten days, but with a different crystal habit  distinct from those identified previously. These crystals were not soaked or co-crystallized with PGM and did not diffract X-rays. A fifth batch did not form crystals.To determine the robustness and reproducibility of TELSAM-mediated crystallization, a third team of students prepared two additional batches of 1TEL-AA-vWa (SUMO). A fourth batch of protein produced crystals in a single condition  and 1sht (vWa) as search models, yielding a solution with 1TEL polymers along the 65 axes and two additional 1TEL polymers along the two 32 axes of the P65 unit cell . The asymmetric unit thus contains one 1TEL domain (chain B) from a polymer lying on a 65 axis and two 1TEL domains (chains A and C) from a polymer lying along a 32 axis. The vWa domains corresponding to the chain A and B 1TEL domains were also located, but the density for the 65 axis vWa domain (chain B) was incomplete, suggesting that this chain B vWa domain may exist in at least two orientations relative to its host 1TEL domain . The vWa domain corresponding to the chain C 1TEL domain (32 axis) in the asymmetric unit was not located, although there is ample space in the unit cell for it and ample unassigned electron density in the area where it is expected to be found. We began by modelling extended polyalanine peptides into this unassigned electron density and later replacing these with UNX atoms modelled at the coordinates of the peptide Cα atoms to approximate the area occupied by the various putative chain C vWa orientations .Batch 2 1TEL-AA-vWa crystals featured target proteins in multiple orientations relative to their host 1TEL domains across chains in the asymmetric unit, in multiple orientations within a single chain or that were not located. The batch 2 crystals that diffracted X-rays were obtained in 100 mn Table 1. Moleculll Fig. 1c. The ain Fig. 1d. The vns Fig. 1e.C vWa domain may exist in a variety of orientations relative to its host 1TEL polymer. Taken together, these data suggest that the vWa domains in this crystal may adopt at least six different orientations . An alternative possibility is that interaction with the PGM drug molecule may have partially misfolded the chain B and C vWa domains or disordered their positions in the crystal. In this scenario, the chain B vWa domain may still be partially resolved because it interacts with two copies of chain A vWa domains from two distinct 32-axis polymers. This second possibility is supported by the fact that PGM soaking invariably worsened the diffraction quality of 1TEL–vWa crystals relative to crystals that had not been soaked with PGM. However, no clear sign of PGM was detected in the electron density.These observations suggest that the chain Rwork and Rfree values of 0.28 and 0.31, respectively, which diminishes confidence in the space-group assignment and molecular-replacement solution. Fortunately, TELSAM-fusion crystal structures have two built-in sources of validation which bolster confidence in this space-group assignment and solution: (i) the 1TEL monomers form the expected monomer–monomer interfaces and sixfold left-handed helical polymer and (ii) the N-termini of the vWa domains are within the expected distance of the C-termini of their host 1TEL monomers and continuous electron density can be seen for the 1TEL–vWa linkers . This is notable because molecular replacement placed the 1TEL and vWa domain search models as independent monomers and included no constraints to enforce the observed symmetry or contacts. No twinning or pseudosymmetry was detected in this crystal.We were able to refine this model to only relatively poor rs Fig. 1f. This A vWa domain (hosted by a 1TEL domain in the 32-axis polymer) adopts a novel binding mode to its host 1TEL polymer. Considering its evidently critical role in maintaining the integrity of the crystal, the chain A vWa domain makes surprisingly minimal contact with its host 1TEL domain, burying only 270 Å2 of solvent-accessible surface area (average of both sides of the interface) in a fairly solvated interface. Specifically, Arg119, Leu177 and Lys178 of the vWa domain make van der Waals contacts with Arg10 and Leu11 of 1TEL .We note that the well resolved chain EL Fig. 1g.A vWa domain also makes direct contacts to the chain B 1TEL and vWa domains (in the 65-axes polymer), allowing the structural integrity of this crystal to be maintained . These interactions are distinct from those observed in the previous batch 1 structure of 1TEL-AA-vWa (SUMO) . The interface of the chain A vWa domain with the chain B vWa domain buries 99 Å2 of solvent-accessible surface area and is more polar. Specifically, the side and main chains of Leu219 and Asp220 of the chain A vWa domain make hydrophobic and van der Waals interactions with Arg139, Gly140 and Ser143 of the chain B vWa domain . This chain A vWa domain also forms contacts with a second chain B vWa domain from the same polymer as the first, burying 164 Å2 of solvent-accessible surface area. Specifically, Asn97 and Ile100 of the chain A vWa domain make van der Waals interactions with Lys240 from this second chain B vWa domain .The chain ed Fig. 1c. TheseEL Fig. 1h. The iin Fig. 1i. This in Fig. 1i.B vWa domain reveal that it adopts a binding mode to its host 1TEL polymer that is distinct from that of the chain A vWa domain but is likely to be similar to that of the unresolved chain C vWa domain . The chain B vWa domain makes only a glancing contact with its host 1TEL polymer, involving van der Waals contacts between Glu111 of the vWa domain and Arg10 of the 1TEL domain and burying 120 Å2 of solvent-accessible surface area . This minimal contact may explain why the chain B vWa domain may exist in more than one orientation relative to its host 1TEL domain.The resolvable portions of the chain in Fig. 1j. The cea Fig. 1k. This i), this batch 2 1TEL-AA-vWa (SUMO) structure suggests that fused target proteins may be able to choose their binding modes to their host TELSAM polymers at the time of polymer–polymer association. Further evidence comes from the extremely weak interactions that the chain A and B vWa domains make with their host 1TEL polymers. It is unlikely that these weak interactions would be made maintained prior to or in the absence of polymer–polymer association. This conclusion should be treated with caution since PGM treatment offers a possible alternative explanation for the disordered vWa domains in this crystal. Nevertheless, the multiple observed binding modes in this crystal establish that the vWa domain can stably adopt multiple binding modes and that doing so does not abolish useful X-ray diffraction.As the three vWa domains in the asymmetric unit appear to adopt at least six binding modes  crystal described in the previous sections, we discovered the vWA domains in at least six different orientations among the three chains of the asymmetric unit, suggesting that the vWa domain may not readily find a single lowest-energy binding mode against its host 1TEL polymer during polymer–polymer association and that while failure to do so complicates structure solution, it apparently does not necessarily abrogate the formation of diffracting crystals. We hypothesized that for TELSAM–target-protein fusions to form well diffracting crystals with well resolved target proteins, all of the fused target proteins must find the same binding mode to their host polymers so as to allow the TELSAM–target-protein polymers to reproducibly find a single binding mode to one another. We further hypothesized that modification of the 1TEL–vWa linker could limit the orientational flexibility of the vWa domain such that all of the vWa domains in a crystal would more easily adopt the same binding mode against their host 1TEL polymers, thus being resolved in the resulting electron density and potentially increasing the diffraction resolution of the crystal.7n1o) became part of the rigid C-terminal α-helix of the 1TEL domain. The second and third amino acids of the vWa domain (Ala-Ala in this construct) were seen in an extended conformation and did not pack well against the vWa-domain amino acids around them, suggesting that they are the most flexible part of the 1TEL–vWa connection in this crystal, despite lying within the vWa domain rather than in the designed linker  structure (PDB entry b–2d). We cloned the three different linker variants to create 1TEL-AV-vWa (SUMO), 1TEL-TV-vWa (SUMO) and 1TEL-TT-vWa (SUMO) constructs. We expressed, purified and crystallized each construct at both 1 and 20 mg ml−1 with a range of commercially available and custom-made crystallization screens. We executed a single round of optimization of the crystallization conditions, but no optimization of cryoprotection or cooling conditions.We substituted the Ala-Ala linker with either Ala-Val (AV), Thr-Val (TV) or Thr-Thr (TT). These substitutions packed better with the amino acids around them and were intended to confer rigidity to the 1TEL–vWa connection Figs. 2b–2 ▸d.e and 2f). We identified initial hits in commercial crystallization screens, and from these hits we developed an ammonium sulfate optimization screen. 1TEL-AV-vWa (SUMO) crystals diffracted to an average resolution of 2.9 Å [I/σ(I) ≥ 2.0 at 2.5–3.5 Å across eight crystals]. For these crystals we were able to index an average of 35% of non-ice reflections (range 12–73%), with an average mosaicity of 0.59° (range 0.23–1.1°) and an average ISa of 20 (range 6–36)  ≥ 2.0 at 2.3–3.0 Å across six crystals]. For these crystals, we were able to index and average 49% of non-ice reflections (range 15–88%), with an average mosaicity of 0.55° (range 0.18–1.1°) and an average ISa of 19 (range 7–34). ISa [I/σ(I)asymptotic] is a measure of the intrinsic error in diffraction data collection, which is dependent on both crystal quality and the experimental setup. Excellent crystals and setups have ISa values greater than 40, while data with ISa values of at least 15–20 are still useful. Data with ISa values below 10 are considered poor  and 1TEL-TV-vWa (SUMO) each crystallized in four different conditions in 2–3 days, while 1TEL-TT-vWa (SUMO) did not crystallize  crystals gave an average resolution of 2.9 Å , indexed 57% of non-ice reflections (range 43–67%), exhibited an average mosaicity of 0.47° (range 0.38–0.60°) and had an average ISa of 16 (range 15–17)  than other 1TEL-TV-vWa (SUMO) and 1TEL-AV-vWa (SUMO) crystals, similar to the unit-cell parameters of the batch 2 1TEL-AA-vWa (SUMO) crystal . This data set also exhibited significant translational noncrystallographic symmetry . Molecular replacement with this data set was carried out as before, once again yielding a solution with 1TEL polymers along the 65 axes and two additional 1TEL polymers along the two 32 axes of the unit cell. In this case we did not see multiple orientations of the vWa domains relative to their host 1TEL polymers . For ease of explanation, we term these three chains chain A [1TEL polymers on the 32 axes of the unit cell whose vWa domains interact with vWa domains from 1TEL polymers (chain B) on the 65 axes], chain B [1TEL polymers on the 65 axes of the unit cell whose vWa domains interact with vWa domains from 1TEL polymers (chain A) on the 32 axes] and chain C . Our initial molecular-replacement solution featured parallel polymers that all ran in the same N→C direction, but we were not able to refine it to acceptable R factors . We next tried to re-index this data set into a smaller unit cell with 1TEL polymers only on the 65 axes. This likewise gave only poor R factors . We attempted twin refinement in phenix.refine using the twin operator h, −h − k, −l  for each unit-cell size, but this likewise yielded poor R factors.Some 1TEL–vWa crystals feature flipped TELSAM–target polymers. The 1TEL-TV-vWa (SUMO) crystal and data set selected for structure solution had a larger c–3e). We hypothesized that this crystal is formed by small domains with P65 symmetry packed with an offset of (a − b)/3 relative to their neighbours.We returned to the 1TEL-TV-vWa (SUMO) diffraction data set to identify the cause of our poor refinement statistics and of the signs of extreme tNCS (non-origin Patterson peak with a height 74.52% of the origin peak). The diffraction data for this 1TEL-TV-vWa (SUMO) crystal featured diffuse scattering between two out of every three reflections Figs. 3c–3 ▸e.c–3e) to broken global periodicity in the crystal. In published structures with monoclinic ordered domains streaky diffraction spots were observed, with streaks extending in the direction of broken crystal periodicity and with rows of streaky reflections alternating with rows of well defined reflections in one of two other directions. Accordingly, the integrated intensities of reflections were modulated in the latter direction, with the period of modulation being non-commensurate with the reciprocal lattice. The diffraction images of the 1TEL-TV-vWa (SUMO) crystal show two-dimensional diffuse patterns in the planes perpendicular to c* . The streaks connect weak reflections h − k = 3n ± 1, forming hexagonal shapes around well defined strong reflections h − k = 3n. Here, the modulation of intensities is consistent with a hexagonal lattice and the modulation coefficients take only two values: one applies to reflections h − k = 3n and the other to the reflections h − k = 3n ± 1. The high ratio of the two coefficients of about 7  molecules, with the 65-axes polymers coinciding with the crystallographic unit-cell axes. TELSAM helical polymers are present in two orientations: one is adopted by polymers lying on the 32 crystallographic axes (further referred to as 32-axes polymers) and the other by polymers lying on the 65 axes (65-axes polymers) . This explains the high R factors from refinement in the small cell, wherein the model corresponds to a putative crystal packing with all polymer helices in the same orientation.The structure and s) Fig. 3f. This 2-axes polymers form a continuous interconnected framework, whereas the 65 helices make no contact with each other. In the model in Fig. 3g) these requirements are satisfied in each of the ordered domains and in the interfaces between them. Were it not for this constraint, there would be nothing to prevent some of the adjacent domains from twinning, However, the H-test for twofold axes perpendicular to c indicates the opposite . The fact that our initial twin refinement failed to improve the fit to the data also suggests that polymers with different N→C orientations are uniformly distributed over the crystal rather than aggregated into separate twin domains. High values of Rmerge for these symmetry operations, above 0.55 for resolution cutoffs of 2.2 and 2.9 Å, are in agreement with the H-test. We thus favour the model in Fig. 3g) with direct contacts between 65-axes polymers prohibited.The 3a − b)/3 is a tNCS operation in the 1TEL-TV-vWa (SUMO) structure because it relates some 32-axes polymers to some other 32-axes polymers, but it is not a pseudosymmetry operation because it places 65-axes polymers in the positions of 32-axes polymers in an incorrect orientation. Therefore, the structure cannot be approximated by a structure with a three times smaller unit cell, and the correction of intensities was critical for structure determination. However, it is easy to imagine a structure with both pseudosymmetry (a − b)/3 and ordered domains offset by this operation. The structure solution in the small cell would then be possible and the electron-density maps would represent an average density from the three molecules related by pseudosymmetry. The weak reflections could still be observed, but refinement of a structure with correct unit-cell parameters to reasonable R factors would be impossible (without correction of intensities). It is likely that the difficulties with refinement in, for example, Ostrowski et al.  of the unit cell ran in the opposite N→C direction from the polymers on the 32 axes, consistent with our model of the overall crystal architecture . The vWa domains in this model were all fully resolved [in contrast to the batch 2 1TEL-AA-vWa (SUMO) structure described above] and all adopted the same binding mode to their host 1TEL polymers, regardless of the polymer orientation . This binding mode was distinct from any of the vWa binding modes seen in either the batch 1 (PDB entry 7n1o) or batch 2 (PDB entry 8fzv) 1TEL-AA-vWa (SUMO) structures .We executed molecular replacement using both a version of the data set that included only the strongest 1/3 of the reflections and a version that included all reflections, with the strongest 1/3 of reflections having a different scale factor to the remaining 2/3 of reflections, both cut back to 1.9 Å resolution. Molecular replacement was executed as before for both versions of the data and produced essentially the same solution in each case, placing 1TEL polymers on both the 6on Fig. 3b. This Rwork and Rfree of 0.23 and 0.28, respectively, and against the complete data set to an Rwork and Rfree of 0.24 and 0.27, respectively (Table 2A as an example) adopt a binding mode to their host 1TEL domains that buries 326 Å2 of solvent-accessible surface area (average of both sides of the interface). Specifically, the side chains of Leu177, Gly176, Ala174 and Thr179 of the vWa domain interact with the side chains of Gln78, Thr81 and Val82 of the 1TEL domain . The chain A vWa domain makes glancing contacts with the chain C vWa domains on either side of it within its host polymer, as well as to the chain A 1TEL domain one turn of its host polymer above it. This last interface buries 188 Å2 of solvent-accessible surface area and is mostly polar and highly solvated. Specifically, this interface involves polar and van der Waals contacts between Asp148 and Arg151 of vWa and Arg18, Asp19, Ala22 and Asn40 of 1TEL . The chain A vWa domain makes no contacts to the 1TEL domains of adjacent polymers, similar to the batch 1 1TEL-AA-vWa (SUMO) structure (PDB entry 7n1o), but in contrast to the batch 2 1TEL-AA-vWa (SUMO) structure.We were able to refine the model against the 1/3 data set to an y Table 2. The vWaEL Fig. 4c. The c1TEL-TV-vWa (SUMO) provides an example in which all target proteins adopt the same binding mode to their host polymers, but adjacent polymers adopt one of two binding modes to one another. This observation suggests that while the binding mode of vWa to its host 1TEL polymer can be selected at the time of polymer–polymer association, at least in the case of 1TEL-TV-vWa (SUMO) there exists a sufficiently low-energy binding mode such that all of the vWa domains reliably select the same binding mode, possibly even some time prior to polymer–polymer association.2-axis chain A vWa domains make C2-symmetric ‘head-to-head’ interactions with 65-axis chain B vWa domains, burying 341 Å2 of solvent-accessible surface area. This interface comprises a mixture of hydrogen bonds, hydrophobic interactions and van der Waals interactions. Specifically, Ser94, Asn97, Asn98, Glu101, Leu219, Asp220 and Phe221 in the chain A vWa domain contact the very same set of residues in the chain B vWa domain. Numerous well ordered water molecules are also seen both within and around the periphery of this interface .To investigate the cause of TELSAM-polymer flipping in this crystal, we analysed the contacts made between parallel polymers and those made between antiparallel polymers. The 3ce Fig. 4d.2-axis chain C  vWa domains adopt a zipper-like arrangement, with each vWa domain contacting two other chain C vWa domains from an adjacent 32-axis polymer. Each vWa domain–vWa domain interaction buries 209 Å2 of solvent-accessible surface area and consists mostly of hydrophobic and van der Waals contacts between the side chains and main chains of Gln244, Gly248, Asn251, Ser252 and Ala255 of one vWa domain and of Asn97, Asn98 and Leu219 of the second vWa domain. Numerous well ordered water molecules and a sulfate ion are also seen around the periphery of this interface . When the solvent content of the antiparallel vWa chain A to vWa chain B interface is considered, we propose that this interface has a similar binding energy to the more hydrophobic parallel chain C to chain C interface, enabling the observed polymer-flipping phenomenon . Similar pathologies were also seen in other 1TEL-TV-vWa (SUMO) and 1TEL-AV-vWa (SUMO) crystals .−1 with different commercially available and custom-made crystallization screens. His-1TEL-TV-vWa formed crystals in 2–4 days, while His-1TEL-AA-vWa took seven days to crystallize. Some His-1TEL-AA-vWa crystals were soaked with iodine before harvesting . The average resolution of these His-1TEL-AA-vWa crystals was 2.6 Å , which was somewhat better than that of 1TEL-AA-vWa (SUMO), which was 2.9 Å  . The vWa domains of His-1TEL-AA-vWa adopt a binding mode against their host 1TEL domains that buries 269 Å2 of solvent-accessible surface area. Specifically, the side chains of Arg122, Phe123, Ser125, Met128, Ile260 and Leu264 of the vWa domain interact with the side chains of Tyr25 and Ile24 of the 1TEL domain . The interface of vWa with the vWa domain of an adjacent polymer buries 188 Å2 of solvent-accessible surface area. Specifically, the side chain of Gln138 in the vWa domain interacts with the side chain of Asn114 in the adjacent vWa domain. Additionally, the side chains of Glu111 and Asn107 in the vWa domain interact with the side chains of Pro144 and Thr140 in the adjacent vWa domain . The vWa domain also contacts the 1TEL domain of an adjacent polymer, burying 270 Å2 of solvent-accessible surface area. Specifically, the side chains and main chains of Tyr208, Val205, Asp202, Leu204 and Tyr169 in the vWa domain contact the side chains and main chains of Leu21, His18 and Gln33 in the 1TEL domain . The vWa domain also contacts a second vWa domain, burying 144 Å2 of solvent-accessible surface area. Specifically, the side chain of Ser207 in the vWa domain makes weak contacts with the side chain of Ala265 in this second vWa domain .His-1TEL-AA-vWa formed around 50 crystals averaging 300 µm in length Fig. 5a. The a Å Fig. 5d. The vin Fig. 5d. The iin Fig. 5e. The vin Fig. 5f. The vin Fig. 5f.et al., 2006ɛ atom of Tyr69 of the 1TEL domain, which faces into the solvent void at the centre of the 1TEL polymer . We also observed 64% iodine labelling of only one of the Cɛ atoms of Tyr208 of the vWa domain, which makes direct contact with the 1TEL domain of an adjacent 1TEL polymer .Prior to cooling, this crystal was briefly (∼10 s) exposed to iodine vapour  structure, thus potentially forcing the vWa domain of 1TEL-AA-vWa into an alternative position relative to its host 1TEL domain . In the light of our prior hypothesis that the vWa binding mode of the 1TEL-TV-vWa (SUMO) construct may be the cause of the polymer-flipping pathology, this provides a possible mechanism by which omission of the cleavable SUMO tag from the 1TEL–vWa constructs prevents these pathologies: the presence of the noncleavable 10×His tag blocks access to the vWa binding mode which allows equally energetic parallel and antiparallel polymer–polymer association (and thus polymer flipping).We were also able to resolve the main-chain atoms of two of the histidine residues in the 10×His tag. These histidine residues pack loosely against the main chains of Arg149 and Ser153 of the vWa domain from an adjacent 1TEL polymer. Together with the fact that inclusion of the 10×His tag did not appear to negatively impact crystal nucleation or growth, this suggests that the His tag did not interfere with 1TEL polymerization or target proteins docking to their host polymers. Notably, the vWa domains adopt a binding mode to their 1TEL polymers that is distinct from that observed for any previous 1TEL–vWa construct, possibly due to the His tag favouring this binding mode or disfavouring the previously observed binding modes. To test this, we superimposed our previous 1TEL-TV-vWA (SUMO) chain in Fig. 5h. In th3.4.a), which is equivalent to the number of crystals formed by 1TEL-AV-vWa (SUMO) and 1TEL-TV-vWa (SUMO) and greater than that formed by 1TEL-AA-vWa (SUMO) (PDB entry 7n1o) and His-1TEL-AA-vWa. His-1TEL-TV-vWa formed crystals that diffracted to an average resolution of 2.0 Å , significantly higher than that of 1TEL-TV-vWa (SUMO), which was 2.7 Å (with a range of 2.3–3.0 Å), and the highest of all 1TEL–vWa fusions tested in this study. The crystals of His-1TEL-TV-vWa indexed an average of 77% of non-ice reflections (range 31–97%), exhibited an average mosaicity of 0.25° (range 0.12–0.40°) and had an average ISa of 25 (range 11–42) . The binding modes of vWa to the host 1TEL polymers and the overall crystal packing were very similar to those observed for the His-1TEL-AA-vWa construct . For example, the vWa domains of His-1TEL-TV-vWa adopt a binding mode to their host 1TEL domains that buries 470 Å2 of solvent-accessible surface area. Specifically, the side chains of Arg122 and Ser125 of the vWa domain make polar interactions with the side chain of Tyr25 of the 1TEL domain. Arg90 in the vWa domain also makes polar interactions with Arg20 and Ile85 in the 1TEL domain. Unlike His-1TEL-AA-vWa, His-1TEL-TV-vWa additionally makes many polar interactions between the vWa domain and its own 1TEL domain, which presumably make the orientation of the vWa domain more stable, potentially leading to an improved diffraction resolution . The vWa domain makes glancing contacts with a second vWa domain, burying 240 Å2 of solvent-accessible surface area. Specifically, the side chain of Gln138 in the vWa domain forms van der Waals contact with the side chains of Ile110 and Asn114 in the adjacent vWa domain. Pro144 in the vWa domain also interacts weakly with Asn107 in the adjacent vWa domain .Molecular replacement of His-1TEL-TV-vWa was carried out as above, yielding a solution with 1TEL polymers along the sixfold axes of the  Å Fig. 6c. The bct Fig. 6d. For eon Fig. 6e. The vin Fig. 6f.a). The decrease in helical rise also correlates with an improvement in diffraction resolution, which is to be expected. Increased unit-cell compactness and lower solvent content would be expected to correlate with increased intermolecular contacts and better resolution , 1TEL-AV-vWa (SUMO), 1TEL-TV-vWa (SUMO), His-1TEL-AA-vWa and His-1TEL-TV-vWa crystals to identify relationships between helical rise, linker composition, SUMO inclusion and diffraction resolution. We observed that the helical rise decreased with an increasing bulk of the amino acids forming the 1TEL–vWa connection (postulated to correlate with the rigidity of the linker) in the cases of 1TEL-AA-vWa (SUMO), 1TEL-AV-vWa (SUMO) and 1TEL-TV-vWa (SUMO). The same trend was also seen in the cases of His-1TEL-AA-vWa and His-1TEL-TV-vWa. We observed that constructs utilizing a SUMO tag had higher helical rises than constructs without a SUMO-tag fusion Fig. 7a. The d1shu (1.5 Å). This is particularly notable because in our hands crystals of vWa alone diffracted to an average resolution of 2.4 Å , index an average of 56% of non-ice reflections (range 19–95%) and exhibit an average mosaicity of 0.30° (range 0.20–0.45°) and an average ISa of 38 (range 23–53). For comparison, we solved and refined the structure of one of these vWa alone constructs at 2.19 Å resolution (PDB entry 8fz4). Data-processing and refinement statistics for this structure are given in Table 2et al., 20221TEL-TV-vWa has a comparable diffraction resolution (1.6 Å) to that of the previous highest resolution structure of the vWa domain, PDB entry B factors between this structure and the previous best-resolution structure. PDB entry 1shu  across just the protein atoms. The vWa domain in the 1TEL-TV-vWa construct had an average B factor of 46.6 Å2 (with a range of 22.8–97.2 Å2), while the 1TEL domain had an average B factor of 35.1 Å2 (with a range of 18.4–96.3 Å2) and the linker between them had an average B factor of 60.4 Å2 (with a range of 35.3–99.6 Å2) . This result confirms our earlier hypothesis , while the solvent content of His-1TEL-TV-vWa crystals was 49.46% (PDB entry 8ft8). Of note, the crystals of the vWa domain crystallized on its own in our hands had a solvent content of 40.76% (PDB entry 8fz4), while the batch 1 1TEL-AA-vWa (SUMO) crystals had a solvent content of 59.59% (PDB entry 7n1o). We anticipate that the increased solvent content of TELSAM-fusion crystals may facilitate drug-soaking experiments. We demonstrated this as described above by soaking diatomic iodine into crystals of His-1TEL-AA-vWa. When soaking 1TEL-AA-vWa (SUMO) and later His-1TEL-TV-vWa crystals with the drug candidate PGM, we observed that PGM becomes yellow upon air exposure and that 1TEL–vWa crystals also became yellow after an overnight soak with PGM, suggesting that the molecule had entered the solvent channels of the crystal. In contrast, crystals of the vWa domain crystallized on its own did not take up the yellow colour after an overnight soak with PGM, suggesting that these crystals do not take up the PGM molecule as readily as the 1TEL–vWA crystals.Having a 1.6 Å resolution 1TEL–vWa fusion structure allowed us to directly compare the refined 2) Fig. 7c. This 7n1o; Nawarathnage et al., 2022A . This observation may suggest that a fully rigid connection between 1TEL and a target protein is not favourable for crystal formation, as we had suggested previously. This is a property that we are testing in more depth in a follow-up study.We sought a structural explanation of how increasing the side-chain bulk of linker amino acids might improve the resolution of 1TEL–vWa constructs. Increasing the side-chain bulk of linker amino acids from Ala-Ala to Thr-Val correlated with a modest improvement in the resolution of 1TEL–vWa (SUMO) and His-1TEL–vWa constructs by 0.3 and 0.6 Å, respectively. Our structures reveal that substitutions of the amino acids in the linker do not make it fully rigid, but rather appear to reduce the number of highly flexible residues that are present. We note that in all 1TEL–vWa structures solved to date there is always at least one amino acid in the linker that is not part of the 1TEL C-terminal α-helix or the vWa N-terminal β-sheet, such as in batch 1 . For example, His-1TEL-TV-vWa had four such amino acids and yielded a 1.6 Å resolution structure, while His-1TEL-AA-vWa had five such amino acids and yielded a 2.6 Å resolution structure. Likewise, in the batch 2 1TEL-AA-vWa (SUMO) structure chain A had one linker amino acid that was not in a regular secondary structure and had a resolution of <3.3 Å, while chain B had five such linker amino acids and a resolution of ∼3.3 Å and chain C had six such linker amino acids and a resolution of ≫3.3 Å. We further note that in structures of 1TEL-TV-vWa constructs with or without SUMO, the valine side chain of the Thr-Val linker segment is nearly always well resolved in the electron density, while the threonine never is, suggesting that it adopts multiple side-chain conformational states. This fits well with the 1TEL-AV-vWa (SUMO) linker variant crystallizing nearly as well as the 1TEL-TV-vWa (SUMO) variant, while the 1TEL-TT-vWa (SUMO) variant did not form crystals.Alignment of the 1TEL domains of all 1TEL–vWa structures solved to date reveals that there is some correlation between the number of linker residues that are not part of the 1TEL C-terminal α-helix or the vWa N-terminal β-sheet and the observed or extrapolated  order of the fused vWa domain Fig. 7d. For ee). Interestingly, the choice of vWa binding mode showed no correlation with the type of linker used , but was strongly correlated with the presence or absence of the SUMO tag and/or the batch of protein. Curiously, 1TEL-AV-vWa (SUMO) (to be described elsewhere) and 1TEL-TV-vWa (SUMO) were prepared in separate batches by the same research team but exhibited the same vWa binding mode. Likewise, His-1TEL-AA-vWa and His-1TEL-TV-vWa were prepared in separate batches by the same research team but also exhibited the same vWa binding mode, which was distinct from that of 1TEL-AV-vWa (SUMO) and 1TEL-TV-vWa (SUMO). Taken together, these observations highlight the still very significant factor of batch-to-batch variation and researcher skill level on crystallization dynamics and thus on crystallization propensity and quality, regardless of whether TELSAM fusion is employed.The 1TEL–vWa constructs characterized to date reveal that the vWa domains appear to be able to adopt at least nine distinct binding modes to their host polymers, four of which were clearly observed and five of which were extrapolated from partial electron density Fig. 7e. Intered Fig. 7d, but w4.et al., 2022B factors. These higher B factors hint that TELSAM-mediated crystals may capture proteins of interest in subtly more physiological conformations, with less interference from crystal-packing artefacts. If this is true, then it may open the door to higher accuracy studies of protein structure and dynamics, potentially benefitting fields such as protein and enzyme engineering and the study of intrinsically disordered and partially folded proteins. Due to built-in validations of TELSAM , TELSAM-fusion crystals are a promising system to enable phase solution and structure determination of such difficult proteins.Fusion to TELSAM has now been demonstrated in multiple cases to allow the rapid crystallization of even difficult target proteins  structure of a protein of interest. This parallels the experience of Kottur and coworkers, where three different linkers were tested in an attempt to crystallize the recalcitrant SARS CoV2 nsp14 N7-methyltransferase. In this case, only one of the three linkers resulted in crystals, but with optimization of the crystallization conditions the authors were able to reliably achieve a diffraction resolution of 1.4–1.6 Å , 8fzuPDB reference: 1TEL-AA-vWa (SUMO), 8fzvPDB reference: vWa alone, 8fz4PDB reference:


The diagnosis and management of sleep problems depend heavily on sleep staging. For autonomous sleep staging, many data-driven deep learning models have been presented by trying to construct a large-labeled auxiliary sleep dataset and test it by electroencephalograms on different subjects. These approaches suffer a significant setback cause it assumes the training and test data come from the same or similar distribution. However, this is almost impossible in scenario cross-dataset due to inherent domain shift between domains. Unsupervised domain adaption was recently created to address the domain shift issue. However, only a few customized UDA solutions for sleep staging due to two limitations in previous UDA methods. First, the domain classifier does not consider boundaries between classes. Second, they depend on a shared model to align the domain that could miss the information of domains when extracting features. Given those restrictions, we present a novel UDA approach that combines category decision boundaries and domain discriminator to align the distributions of source and target domains. Also, to keep the domain-specific features, we create an unshared attention method. In addition, we investigated effective data augmentation in cross-dataset sleep scenarios. The experimental results on three datasets validate the efficacy of our approach and show that the proposed method is superior to state-of-the-art UDA methods on accuracy and MF1-Score. Sleep appears indispensable in all mammals, and many studies try to unravel the regularity of sleep   and a feature generator are used as two players to align allocations in an adversarial way. However, applying these methods to automatic sleep staging continues to have the following drawbacks. First, they disregard the connection between the decision border and the target samples when aligning distributions. In other words, the main task of the generator is to match the distribution between the source and the target. This theory assumes that the classifier can correctly classify these target features because they are consistent with the source samples. So, they do not consider the relationship between the target sample and the decision boundary for distribution alignment. As shown on the right in To overcome the challenges above, this article presents a novel framework named Task-Domain Specific Adversarial Network (TDSAN), composed of a feature generator, a domain discriminator, and a dual classifier. It aims to align feature distributions from the source and target domains by combining the classifier's output for the target data and the domain discriminator's output for the domain identifier. Second, we create a domain-specific attention module to maintain source and target-specific features.Specifically, we train a domain discriminator to predict the input domain and a dual classifier to predict task-specific class labels. We use domain pseudo-labels  as input. The domain discriminator is trained until it cannot distinguish between the distributions of its training and test domain examples. At the same time, the dual classifier is used to correctly classify the source samples while being trained to find target samples that are located far from the source of support. Because they are not grouped into any classes, samples far from the support do not have traits that can be used to differentiate between them. That is to say. While considering the classifier's output to the target samples, it is instructed to produce desired features close to the support points simultaneously. Therefore, our approach uses a domain discriminator to distinguish the features between samples drawn from the source domain and drawn from the target domain by predicting the domain label and a dual classifier to generate the discriminative features of the target sample because it considers how the decision boundary and the target data relate, and training is adversarial. Additionally, we use domain-specific attention to clean up the extracted features so that each domain keeps its essential characteristics.The contributions of our paper are summarized as follows: a novel cross-dataset sleep classification framework is proposed that simultaneously changes the categorization boundaries between classes and the conditional distribution between domains. The algorithm adopts a non-shared attention module to keep critical features during adaptation, thereby improving adversarial performance on the target domain. Aiming at the data imbalance in sleep staging, we applied data augment to effectively improve the impact of sample skew on the classification network. Numerous tests show that our TDSAN delivers more excellent cross-domain sleep stage classification performance compared to cutting-edge UDA techniques.The rest of this paper is organized as follows. Section 2 introduces related work of EEG sleep classification on domain adaptation and describes the proposed model. In Section 3, the experimental results are presented and debated. Section 4 concludes this study.The use of single-channel EEG for automated sleep staging has received much attention in the literature. Specifically, deep learning-based techniques   =  ∈ Rl1· where l is the length of the feature.The feature extractor consists of three CNN-based convolution blocks, each in 1 dimension. To speed up training and keep CNN Network stable, we use a batch normalization layer. It can reduce internal covariate shift . Particularly, x and y, which are transferred into Qsx = M1(psx) and Qsy = M2(psy). Note that the score is expressed as:Inspired by Zhang et al. , as seenxy represents the degree to which the y-th location pays attention to the x-th position. The output iswhere 𝒲 A(·); therefore, Os = As(F(xs)). The same procedure applies to the target domain data stream to train At.Equations 1 and 2 refer to the attention process as The dual classifier consists of three fully connected layers. A dual classifier has two tasks: acting as a classifier for the source data and detecting the target samples outside the source support when we combine two category classifiers. The dual classifiers C1 and C2 first try to maximize the difference for a given target feature to find the target samples away from classification boundaries. Then it minimizes the discrepancy to make them close to the classification boundary. By iterating this process repeatedly, we train a generator that has rare differences with the classification Boundary of the source domain.It is widely used in UDA , sleep-EDF-ST(ST), and self-collection datasets. Before downsampling, a summary of the three datasets above is displayed in The sleep-EDFx dataset .Cutoff frequency design: based on high-pass and low-pass filters (0.3-35 Hz) to reduce the noise.We used the macro-averaged F1-score and the classification accuracy (ACC) to assess the proposed performance. The metrics are denoted as follows:M, and the total class number is K. The experiment was model initialized from various random seeds and repeated five times. The average final result (ACC and MF1) was then presented with the standard deviation.where We divide the experimental data into 80% and 20% for training and testing. We do not disrupt the order of epochs of subjects so that domain-attention-specific can capture the relationship between different sleep stages. We employ the Adam optimizer with a batch size of 128 and a learning rate of 1e−3. We did not fine-tune these hyperparameters for a fair comparison. Another hyperparameter is n, which represents the number of times this operation is repeated for the same mini-batch. This value represents the trade-off between the generator and the classifier. All experiments are done by pytorch1.12 on an NVIDIA GeForce RTX 2080 Ti GPU.We analyze our suggested TDSAN by contrasting it with different baselines. We started by including the Direct Transfer (DT) findings from DeepSleepNet's (Supratak et al., DANN (Eldele et al., ADDA (Ganin et al., CDAN (Tzeng et al., ADAST (Yoo et al., MF1-Score.It is important to emphasize that we use the proposed backbone feature extractor on four baseline methods except DeepSleepNet to ensure a fair comparison. In this setting, we note that methods that consider class-conditional distributions: such as CDAN and ADAST, outperform the globally aligned source and target domains, namely DANN and ADDA. This shows that taking class distribution into account is crucial to improving classification performance on the target domain, mainly when dealing with imbalanced sleep data. Our proposed TDSAN outperforms all baselines in accuracy in four of the six cross-domain scenarios and achieves the second-best average score among all baselines on We consider possible reasons: First, by iteratively training the generator and classifier, we obtain a feature extractor that can extract the smallest difference between the source and target domains. At the same time, the task-specific classifier fully considers the relationship between the target task and the decision boundary. Second, performance is enhanced by TDSAN because it uses a non-shared attention module to preserve domain-specific features.In Each stage's length varies for a sleep recording. Mainly, stage N2 makes up between 45% and 55% of the entire sleep time and contributes to the majority class. N1, on the other hand, only makes up roughly 2%–5% [36]. Every sleep dataset that is accessible has this problem, including public datasets we can retrieve and our self-collection dataset. As mentioned in many studies (Tsinalis et al., The result suggested sample skew affects the performance of the classification network. In domain adaption, data augmentation is still optional when we can not get enough EEG data.Further, we use UMAP to visualize the feature representations learned to make the Comparison more intuitive.Initially, we investigate the alignment quality. Additionally, after the alignment in This paper proposes a novel UDA method, TDSAN, to address the sleep EEG staging scores on unlabeled data. TDSAN is an adversarial learning method that uses a specific classifier as a discriminator whose target samples are remote from the source support detected. To trick the classifier, the feature generator masters to produce target features close to supports. The generator will prevent creating target features close to class boundaries since it incorporates feedback from task-specific classifiers. Meanwhile, a non-shared attention mechanism preserves domain-specific features, which can capture the relationship between different sleep stages. Experiments show that we can achieve the same accuracy on unlabeled sleep data as on labeled data.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.D-RG and JL: conceptualization and writing—review and editing. JL: methodology, investigation, data curation, and writing—original draft preparation. D-RG, JL, and M-QW: visualization and validation. L-TW: formal analysis and resources. Y-QZ: supervision and writing—review and editing. All authors have read and approved the final draft of the manuscript.


Covalent organic frameworks (COFs) are a class of crystalline porous organic polymers with periodic networks that are constructed from small molecular units via covalent bonds, which have low densities, high porosity, large specific surface area, and ease of functionalization. The one-dimension nanochannels in COFs offer an effective means of transporting lithium ions while maintaining a stable structure over a wide range of temperatures. As a new category of ionic conductors, COFs exhibit unparalleled application potential in solid-state electrolytes. Here, we provide a comprehensive summary of recent applications and research progress for COFs in solid-state electrolytes of lithium metal batteries and discuss the possible development directions in the future. This review is expected to provide theoretical guidance for the design of high-performance solid-state electrolytes. T. T53]. Tat 0.2 C b. Furtheat 0.2 C c 54]. C. C−4 S c was 1:1 d. The Lictrolyte e 55]. T. T−4 S cn effect f.+ and its counter anions are mobile, leading toissues such as unfavorable Li+ transference number, concentration polarization and lithium dendrite . T. T3Li) wg of Li+ d. The DL contact e. The Ling tests f. This sA great number of studies have demonstrated that the construction of anionic COFs is an effective strategy for the preparation of high-performance ionic conductors. However, the effects of the type, density and spatial arrangement of anionic groups on ion conduction need to be further discussed, and more attention should be paid to molecular computational simulation and ion transport mechanism to gain a more comprehensive understanding in the future studies.The properties of various COF-based SSEs are summarized in It must not be overlooked that the use of COFs as electrolyte matrices presents several challenges. One of the main issues is the laborious preparation process. The synthesis based on dynamic covalent chemicals is heavily reliant on specific reaction conditions, such as temperature, time, and catalysts, and even slight variations can have a significant impact on the synthesis results. Additionally, the growth and crystallization of COFs occur simultaneously during the synthesis process, making it difficult to produce large quantities of highly crystalline and structurally stable COFs. Typically, the synthesis of COFs requires a minimum of 72 h at a temperature of 120 °C and the raw materials are also costly, resulting in high manufacturing costs. Another obstacle is the insolubility of COFs, which complicates their processing. Currently, COF-based electrolyte films are mostly constructed by powder pressing, which requires high pressure and sometimes heat treatment, further impeding the scale production of COF-based electrolytes. Furthermore, as shown in In summary, the current research on COF-based SSEs is continually advancing, with particularly focus on developing solvent-free solid electrolytes. The distinctive features of COFs can be utilized as a filler in the substrate to overcome some of the inherent limitations. The quantity of filler can be regulated, and even a small amount can yield satisfactory outcomes. Thus, research on utilizing COFs as a filler has become a promising avenue to explore.The poor processing performance of COFs hinders their further development, incorporating COFs into polymer matrices or ionic liquids to create composite electrolytes by casting and tape-casting offer a potential solution. Moreover, the nanopores and ordered channels of COFs can effectively enhance the efficiency of ion transport. and the rigid skeleton also provides certain mechanical properties. Therefore, COFs also demonstrate great potential as fillers.+ transference number of electrolytes due to their unique structure and functional groups. In 2019, Chen’s team reported the successful addition of boron-containing COFs (H-COF-1) into PVDF-based electrolyte to achieve significantly enhanced Li+ transference number [+ are generated, and the high porosity and surface area of COFs provide abundant anion adsorption sites. As a consequence, the addition of COFs resulted in a significant increase in Li+ transference number compared to that of pure PVDF electrolyte (0.36). This increase was related to the specific surface area of COFs, and H-COF-1@10 with the largest specific surface area made the Li+ transference number ascend to 0.71 , which is two orders of magnitude higher than that of pure PVEC, the ion transference number reached 0.6, and the electrochemical window was up to 4.6 V. In terms of mechanical properties, Young’s modulus of the composite electrolyte was as high as 150 MPa, much higher than the 27 MPa of pure PVEC, which could effectively inhibit the lithium dendrites, resulting in the plating/stripping stability of Li symmetrical cell for 600 h at 0.1 mA cm−2. The LiFePO4|COF-PVEC|Li full batteries exhibited excellent rate capability  by room temperature synthesis, and grafted ether-based segments (PEGDA and PEGMA) onto COFs via in situ polymerization a for thepure PDM c. MoreovmAh cm−2 d. The Li0 cycles e. In add0 cycles f. When 2pability g and cyc+ and modulate the composition of SEI , ionic gel electrolytes (IGEs) comprising polymeric ionic liquids and ionic liquids can serve as matrix materials for COFs. The unique nanostructure of COFs enables effective accommodation of ionic liquids, allowing for high loadings of ionic liquids, while also providing fast ion channels that enhance ionic conductivity. In 2021, Sun’s team studied the influence of adding 2D COFs as fillers to ionic liquids and constructed a high-performance ionogel electrolytes (IGEs)  Figure d. They pnd 60 °C e. As a cThe major performance of SSEs with different COF fillers are summarized in As described above, COFs have great merits as fillers for SPEs or IGEs. The crystalline porous structure of COFs and the stable ordered channels in a wide temperature range significantly contribute to promoting ion transport. Moreover, the ease of functionalization of COFs allows for integration of various functional groups to achieve specific goals, achieving such as attaining high-voltage-tolerance, guiding uniform Li deposition. or regulating solvent structure. Generally, the introduction of COFs can enhance the mechanical properties of the polymer matrix and effectively inhibit the growth of lithium dendrites. However, it is a remarkable fact that there commonly exists a threshold effect when COFs are used as fillers. The excessive addition of COFs can negatively impact dispersion, leading to a decrease in ionic conductivity. The morphology and size of COFs will also affect the dispersion. Therefore, it is crucial to explore the appropriate addition of COFs with various morphology and size to achieve optimal electrochemical performance. In addition, the combination of the COF filler and the matrix, as well as the synergistic effect between the matrix and the filler on ion transport, should be thoroughly investigated in the subsequent research to prepare high-performance composite electrolytes.In this review, we summarized the advances of COFs in SSEs for lithium metal batteries over the past decade in order to analyze the relationship between the structure of COFs and the ion conduction mechanism. We also comprehensively sorted out the strategies utilized to achieve superior electrochemical performance of COFs in solid electrolytes, including their applications as host and as fillers. COFs have exhibited great application potential in SSEs due to their unique structural characteristics.Despite the significant progress made in researching COFs as SSEs there remain a number of important scientific issues that require further investigation. Structural design, theoretical study, and practical applications are among the foremost areas of concern. Specifically, the long-range ordered nanochannels of COFs play a significant role in ionic conduction, but the relationships between structure and performance remains unclear. The ionic transport behavior in different types of COFs is also complicated, and host-guest interaction must be considered. To design high-performance SSEs, the ion transport mechanism must be elucidated. In addition, the concentration of mobile ions in the nanochannels of COFs is still low, which limits ionic conductivity and hinders practical application. Finally, the complicated preparation steps and immature processing technology of COFs must be overcome to realize their potential as SSEs in practical applications.In light of the aforementioned issues, in order to comprehensively improve the electrochemical performance and practicality of COFs, future research should be directed to following areas:(1) It is essential to systematically analyze the ion transport mechanisms in COFs in combination with advanced characterization techniques, such as 2D exchange NMR, NMR relaxometry, variable-temperature NMR, pulsed field gradient NMR and time of flight secondary ion mass spectrometry (TOF-SIMS), and computational simulation, such as molecular dynamics simulation and first-principles calculation). Furthermore, it is also crucial to establish the structure–property relationships of COFs, including the specific relationship between the size, morphology, pore size, interplanar spacing and specific surface area on ionic conduction. For functionalized COFs, the relationships between the type, density, spatial arrangement of functional groups and ion transport also need to be systematically studied, so as to achieve purposeful structural design.+ transference number, but also brings high flammability, which violates the original intention of solid-state electrolytes to pursue safety. Therefore, future research could explore the preparation of solvent-free COFs derived solid electrolytes. In fact, different strategies for different types of COFs should be adopted according to their ionic conduction mechanism. For electroneutral COFs, the synergistic effects of different lithium salts, solvents and polymer chains should be considered. For cationic COFs, the influence of different functional groups should be analyzed. As for anionic COFs, it is vital to introduce groups with moderate delocalization ability, and it is also important to optimize the spatial distribution and density of hopping sites. When COFs are used as fillers, the pore wall decoration and the host-guest interaction should be considered adequately. In order to increase the concentration of charge-carriers in COFs, adding extra weakly coordinating lithium salts and decoupling Li+ cations by different methods can by also effective strategies. In addition, existing studies suggest that exfoliation into nanosheets, mechanical stress pressing, and heat treatment can affect orientation, and constructing anisotropic COFs is more beneficial to ionic conduction.(2) Solvents and plasticizers are commonly introduced into COFs for further enhanced ionic conductivity. Yet the addition of solvents not only reduces the Li(3) To increase the industrial applicability of COFs, it is important to develop synthesis strategies that are more rapid, simple and low-cost COFs. The harsh sealed condition, high reaction temperature and relatively long reaction time of traditional solvothermal synthesis of COFs limit their practical use. Instead, various novel synthesis methods have emerged in recent years, such as hydrothermal synthesis , mechanoIn conclusion, the construction of high-performance ion-conducting COFs is still confronted with great challenges. The strategies discussed in this review provide potential solutions to overcome these obstacles. The continued study on ion-conducting COFs can potentially pave the way for the next generation high-performance all-solid-state batteries.


The cut regimen prevails under the interaction between coherent precipitates and dislocations. In the case of a large lattice misfit of 19.3%, the dislocations tend to move toward the incoherent phase interface and are absorbed. The deformation behavior of the precipitate-matrix phase interface was also investigated. Collaborative deformation is observed in coherent and semi-coherent interfaces, while incoherent precipitate deforms independently of the matrix grains. The faster deformations (strain rate is 10−2) with different lattice misfits all are characterized by the generation of a large number of dislocations and vacancies. The results contribute to important insights into the fundamental issue about how the microstructures of precipitation-strengthening alloys deform collaboratively or independently under different lattice misfits and deformation rates.Clarifying the deformation behaviors of microstructures could greatly help us understand the precipitation-strengthening mechanism in alloys. However, it is still a formidable challenge to study the slow plastic deformation of alloys at the atomic scale. In this work, the phase-field crystal method was used to investigate the interactions between precipitates, grain boundary, and dislocation during the deformation processes at different degrees of lattice misfits and strain rates. The results demonstrate that the pinning effect of precipitates becomes increasingly strong with the increase of lattice misfit at relatively slow deformation with a strain rate of 10 It is well known that precipitation-strengthening is a ubiquitous strengthening mechanism for a wide range of materials ,2,3. TheEssentially, the dispersed nano-precipitates strengthen alloys by pinning GBs and dislocations ,20,21. Pf is calculated by The precipitate-matrix interface plays a significant role in coordinating deformation of the precipitates and matrix. The fundamental problems on clarifying how the interface coordinates deformations have attracted lots of interest from scientists. The structure of the precipitate-matrix interface is determined by the lattice misfits between the precipitate and matrix phases, which strongly affects the deformation behavior of materials. Precipitation-strengthening alloys, such as aluminum alloys, are often characterized by a rich variety of precipitates with lattice misfits ranging from less than 1% to even 20% . The latd alloys ,28,29,308/s. In recent years, the phase-field crystal (PFC) model has naturally coupled all the physical properties generated by periodic structures and simulated the diffusive evolution of microstructures on the atomic spatial scale [However, it is still a formidable challenge to study the slow deformation process in real time, e.g., the slow stretching of nano-precipitates and grains of alloys, with the resolution up to the atomic scale in both experiments and simulations ,32. Phasal scale ,38 durinal scale ,40.−4 to 10−2. Then the deformation mechanisms of precipitate-matrix phase interface with different lattice misfits were also characterized. The results provide new insights at the atomic scale into the deformation mechanisms of alloys with precipitate-matrix structures.In this work, the deformation behavior of precipitates in precipitation-strengthening alloys were investigated by using the PFC model ,42. We fThe PFC methods simulate the physical phenomena of atomic length scale and diffusion time scale by introducing periodic atomic density states to minimize the free energy. The PFC methods also capture the salient physics of diffusional phase transitions involving atomic scale elastic and plastic interactions. The free energy function of PFC model comes from the classical density functional theory . The totperature . The idel energy . The excα is a coefficient that sets the energy of the compositional interface [The simplified dimensionless free energy functional reads as ,(4)ΔFkTnterface . The mixeviously .Greenwood et al.  introducj has the general formii = AA and BB, To be specific, a reciprocal space peak of icients. .The dynamic equations of the density and concentration fields can be written asnstant 1 ,49. As fle state ,45. The f mixing ,49. Accoiffusion ,49. The ficients ,45.Stefanovic et al.  introducs of ref .ap are 0.92 (lattice misfit f = 4.3%), 0.88 (f = 9.2%), and 0.8 (f = 19.3%), which, respectively, gives rise to coherent, semi-coherent, and incoherent interfaces. The size of the simulation box is 2400 × 2400 grid spacing . Initially, four grains of the matrix phase are set in each of the X and Y directions; the grains are separated by a very thin liquid film. Two steps are conducted in our simulations. The first step is the precipitation simulation to produce the nano-precipitates from the initial polycrystalline matrix. After some iterations, the four initial separated grains grow together soon; then, the initial liquid films are replaced by GBs, and the precipitated phases nucleate and grow. Finally, the microstructures required for deformation are obtained as shown in  as 0.05 .5 Δt in −4, 10−3 and 10−2. Therefore, the strain is The second step is the deformation simulation. Dynamic Equation (11) is employed. The key is applying the load. The constant volume deformation model and the periodic boundary condition are adopted in our deformation simulations . We cond−4. As we know, the second phases usually prefer to precipitate at GBs in alloys, which may profoundly affect the deformation behavior of GBs. In the case of a small lattice misfit of 4.3% , as the deformation of grains proceeds through elongation along the tensile direction or grain rotation, GBs undergo steep changes, such as the straightening along the tensile direction, break up GBs into small segments, and change GB structure. These changes lead to the generation of a great number of dislocations. Thus, dislocation number density increases steeply at the early stage of deformation for all the cases of lattice misfits of 4.3%, 9.2%, and 19.3%. However, after part of the original GBs has been broken, the increased rates of new dislocation slow down accordingly. Then dislocations may be absorbed into the precipitate-matrix interfaces and adjacent GBs, undergo dislocation reactions, or rearranged into new GBs. These lead to a steep decrease in dislocation dynamics in the later stage of deformation (deformation strain > 25%). The trend of dislocation density-strain curves in Among the three lattice misfits, the maximum dislocation density of 4.3% lattice misfit is the largest as shown in −4, the original GBs are undermined and broken into small segments, with dislocations and vacancies at a strain rate of 10−2. The pinned GBs are decomposed soon, leaving abundant dislocations and vacancies around the phase interface. Even for the case of a small lattice misfit of 4.3%, large lattice distortions, abundant dislocations and vacancies, and even micro-cracks could be observed in the severely deformed precipitate-matrix interface. Because of the decomposition of GBs and high dislocation density in the deformed precipitate-matrix phase interface, there is a limited space to accommodate such newly generated dislocations. Consequently, as quantified in During the fast deformation process with a strain rate of 0.01, the bulk of phases, GBs, and precipitate-matrix phase interface change abruptly to release the deformation energy. In the bulk of precipitate and the grain of the matrix phase, severe lattice distortion has arisen soon at the onset of deformation. However, lattice distortion is far enough to accommodate the fast accumulated elastic energy. Then a large number of dislocations and vacancies are generated in the bulk in precipitate and grain of the matrix as shown in 15 m−2 after tensile deformation in aluminum alloys [Then, the excessive dislocations would change to decrease the free energy of systems. In the slow deformations, the decrease of dislocation number could be realized by absorption into GBs and precipitate-matrix interface, or by dislocation reaction (the merge of dislocations). In the fast deformation, because of the annihilation of GBs and dislocation saturation of the precipitate-matrix interface, such as a great number of excessive dislocations have to rearrange into dislocation walls and even GBs. As shown in m alloys ,67. The m alloys ,45. The Finally, we observed that the three kinds of precipitate-matrix phase interfaces, including coherent, semi-coherent, and incoherent interfaces, show very different deformation behaviors. As shown in The semi-coherent precipitate-matrix interface behaves different from the coherent interface. As shown in In this study, the PFC simulations were used to investigate in detail the precipitate-GB interactions and dislocation-GB/precipitate interactions of deforming precipitation-strengthening alloys. The main conclusions are as follows:−4, the pinning effect of precipitate can actually hinder the motion of deforming GBs, and becomes increasingly strong with the increase of lattice misfit. In the faster deformation with a strain rate of 10−2, the original GBs are undermined and broken into small segments, dislocations, and vacancies. Therefore, the dislocation density of fast deformation is higher.In slow deformation with a strain rate of 10Lattice misfit between the precipitate and matrix phase could also strongly affect the deformation behaviors of alloys in conventional slow deformations. The cut regimen prevails under the interaction between coherent precipitate and dislocations; the dislocations move toward the GBs and are finally absorbed. In the case of lattice misfit of 19.3%, the dislocations tend to move to the incoherent phase boundary and be absorbed. Collaborative deformation is observed in coherent and semi-coherent interfaces in the slow deformation. Interestingly, semi-coherent interface has to emit pairs of dislocations continuously to coordinate the deformation of the abutting precipitate and matrix grain. Incoherent precipitate deforms independently from the matrix grain.Taken as a whole, the results contribute to important insights into the understanding on the fundamental questions that how the microstructure of precipitation strengthening alloys, including nano-precipitates, matrix grains, and precipitate-matrix phase interface deform collaboratively or independently under different lattice misfits and deformation rates.


In this study, the microstructure evolution and deformation behavior of the extruded Mg-5Al-0.6Sc (AS51) alloy during tensile testing at room temperature (RT) and 250 °C were investigated by electron backscattered diffraction (EBSD) characterization and Visco Plastic Self Consistent (VPSC) simulation. The results showed that a continuous hardening behavior of the alloy occurred during the deformation at RT, and a certain softening was caused by the occurrence of dynamic recovery (DRV) and dynamic recrystallization (DRX) in the late stage of deformation at 250 °C. The primary deformation mechanism at both RT and 250 °C was dislocation slip, with prismatic <a> slip being the dominant deformation mode, and no significant changes in grain size or texture type occurred. By identifying the activated twin variants, the results indicated that the selection of twin variants was closely related to the local stress concentration. The relatively low activation frequency of extension twinning at 250 °C is partly attributed to the fact that the consumption of dislocations by DRV and DRX can effectively relax the local stress concentration. Meanwhile, the DRX mechanism during the deformation of the alloy at 250 °C was mainly discontinuous dynamic recrystallization (DDRX), with a low recrystallization fraction. Magnesium (Mg) alloys have the competitiveness of high specific strength and specific stiffness, excellent damping properties, low density, and outstanding biocompatibility, so they are widely used in defense and military, biomedical, and aerospace fields. Unfortunately, the poor plastic deformability and low absolute strength of Mg alloys at RT greatly hinder the further application of Mg alloys in the field of structural materials ,2. To fa17Al12, has a low melting point. When the service temperature is high, the strength of Mg-Al alloys decreases dramatically, which greatly limits the application of Mg-Al alloys. The main mechanism of dislocation slip in Mg alloys at RT is basal <a> slip, and Mg17Al12, as a disc-shaped precipitated phase parallel to the (0001) of α-Mg cannot effectively pin the motion of basal dislocations [17Al12 at RT is also limited. Thus, the introduction of other high-melting point and high-modulus second phases by alloying is an effective strategy to further improve the mechanical properties of Mg-Al alloys. It was found that the addition of rare earth  to Mg-Al alloys could not only produce strong solid solution strengthening but also form high-strength Al-RE intermetallic compounds [Presently, the Mg alloys that have achieved some scales of commercial application are predominantly Mg-Al alloys. The second phase is an effective means of strengthening Mg alloys ,7. Howevocations , so the ompounds ,9, whichThe deficiency of plastic deformability of Mg alloys at RT originates from the hexagonal close-packed (HCP) structure of Mg with a c/a value of 1.624, and this low symmetry structure leads to a much higher critical resolved shear stress (CRSS) in the non-basal slip system than in the basal slip system at RT . TherefoThe CRSS of non-basal slip is significantly reduced during the ET deformation of Mg alloys, allowing a large activation of non-basal slip to yield a uniform plastic deformation . TherefoTherefore, in this study, the extruded AS51 alloy was used as the object of study to characterize the microstructure at different strains at RT and 250 °C. The microstructure evolution of the alloy during deformation at RT and ET was revealed. The deformation mechanisms of the alloy at RT and ET were examined in detail by combining VPSC simulations and the obtained EBSD data. The results contribute to further understanding the RT and ET deformation behavior and microstructure evolution of Mg alloys.For the preparation of as-cast AS51 alloys, one can refer to our previous report . The all3Sc. Twinning, as a deformation mechanism that can coordinate c-axis strain during the RT deformation of Mg alloys, has an important role in coordinating the plastic deformation of Mg alloys . Among tBy analyzing the twin variant, it can be determined whether the activation of the twin follows Schmid law or results from stress concentration ,31. WhenTo further investigate the mechanism of slip during the tensile deformation of the extruded AS51 alloy, the deformation of the alloy at RT and ET is simulated by VPSC. The VPSC framework is first reported by Lebensohn and Tomé, who use a self-consistent approach to describe the interactions between each grain and its local environment ,42. In tpolation . It is wMg alloys usually exhibit better plastic deformability under ET deformation conditions than at RT. One of the reasons is that DRV and DRX can consume the accumulated dislocations during ET deformation, which can effectively relax the stress concentration near the grain boundaries, the second phase , and thu(1)The TYS, UTS, and EL of the extruded AS51 alloy at RT are 122.7 MPa, 219.3 MPa, and 19.7%, respectively. The TYS, UTS, and EL at 250 °C are 89.2 MPa, 110.3 MPa, and 33.2%, respectively. Compared to RT deformation, TYS and UTS decreased by 27.3% and 49.7%, respectively, while EL improved by 68.5%.(2)The extruded AS51 alloy displays continuous hardening behavior during the RT deformation of the alloy, and the late deformation at 250 °C leads to some softening due to the DRV and DRX, with the DRX mechanism being mainly DDRX and a low recrystallization fraction.(3)The main deformation mechanism of the extruded AS51 alloy at RT and 250 °C is dislocation slip, with prismatic <a> slip as the dominant deformation mode, and no significant changes in grain size and texture type occur.(4)The selection of twin variants is closely related to the local stress concentration. The relatively low activation of extension twins at 250 °C is partly attributed to the fact that the consumption of dislocations by DRV and DRX during deformation can effectively relax local stress concentrations.In this study, the tensile deformation process of the extruded AS51 alloy is investigated at RT and 250 °C. The major conclusions drawn are as follows:


Bacterial RosB-like transporters and K+ efflux antiporters (KEA) of plants are homologs of Kef but fulfill different functions. In summary, Kef provides an interesting and well-studied example of a highly regulated bacterial transport system.Kef couples the potassium efflux with proton influx in gram-negative bacteria. The resulting acidification of the cytosol efficiently prevents the killing of the bacteria by reactive electrophilic compounds. While other degradation pathways for electrophiles exist, Kef is a short-term response that is crucial for survival. It requires tight regulation since its activation comes with the burden of disturbed homeostasis. Electrophiles, entering the cell, react spontaneously or catalytically with glutathione, which is present at high concentrations in the cytosol. The resulting glutathione conjugates bind to the cytosolic regulatory domain of Kef and trigger activation while the binding of glutathione keeps the system closed. Furthermore, nucleotides can bind to this domain for stabilization or inhibition. The binding of an additional ancillary subunit, called KefF or KefG, to the cytosolic domain is required for full activation. The regulatory domain is termed K Escherichia coli as an example  were reported to produce electrophilic quinones, which alter amino acids and defend themselves against insects indirectly via an antinutritive effect [Plants use the toxicity of electrophilic compounds as secondary metabolites to defend themselves against microbes and insects ,15. To gbacteria . Boraginications . Tomato e effect . Althouge effect . For exae effect . Electroe effect ,21.In addition to the production of natural products, electrophiles occur as reaction products of oxidative stress. They are formed as second-generation reactive intermediates by the reaction of reactive oxygen species with amino acids, lipids, and carbohydrates . For exaFinally, electrophiles sometimes occur as side products or intermediates of metabolism. For example, in the catabolism of tyrosine, the electrophile fumarylacetoacetate is formed as an intermediate, which may cause disease if it is not quickly metabolized ,25. ReleEscherichia coli releases potassium upon the addition of the electrophiles N-ethylmaleimide (NEM) or iodoacetate [trk loci (for transport of K) were identified in E. coli, which required a higher potassium concentration for growth if mutated in combination with a mutation of the potassium uptake system kdp [TrkB and trkC mutants were related to defects in the retention of potassium. These genes, which were renamed kefB and kefC, were associated with the electrophile-elicited potassium efflux [In 1980, it was observed that oacetate . Soon afoacetate ,34. Fivestem kdp . TrkB anm efflux ,37. Glutm efflux . These mm efflux . It becam efflux . Smallerm efflux . It was m efflux . Its affm efflux . E. coli as a metabolite under phosphate-limiting growth conditions and is as glutathione conjugate activating KefB but only slightly KefC [E. coli cells leads to an acidification of the cytosol, as earlier observed for NEM. This proton influx coupled with the potassium efflux through Kef protects the cells from the detrimental effects of the electrophile because independent acidification of the cytosol with weak acids in strains without Kef has the same protective effect as afforded by Kef [The small electrophile methylglyoxal (MG) occurs in tly KefC . MG is stly KefC . Modulattly KefC ,43. The d by Kef ,45. Surpd by Kef ,45. Somed by Kef . Thus, tE. coli, while menadione activates less and duroquinone not at all [Shewanella denitrificans (SdKef), while smaller and more flexible electrophiles, such as 1-octen-3-one, cause only weak activation, although their GSX binds equally well to Kef [A number of other electrophiles were shown to activate Kef. Benzoquinone is a strong activator of KefC from t at all . Howeverl to Kef   with a hydrophobic N-terminal and hydrophilic C-terminal domain [259ALESDIE267 that is important for regulation in addition to the peripheral C-terminal domain [Cloning of the l domain  connectel domain ,55. The l domain . KefB all domain . The expl domain ,58,59.Structural information was initially only obtained for soluble constructs of the cytosolic domain but these crystal structures already provided deep insights into the regulation of Kef ,58,59. TE. coli cells [The binding site for GSH/GSX does not overlap with the nucleotide-binding site but it is close by . The GSHli cells . Thus, tShewanella has, for example, representatives that have ancillary factors and others that miss them. Kef from Shewanella denitrificans was taken as the model system for ligand binding to the Kef system because the missing ancillary factor simplified the behavior [Regulation of the Kef system is further complicated by ancillary soluble subunits of about 20 kDa, which are required to obtain full activity . They arbehavior . Even wiStructural studies on EcKefF were performed on a fusion construct of the cytosolic KefC domain C-terminally linked to KefF ,58. ThesE. coli [Following up on these similarities, the enzymatic activity of KefF was tested. KefF did not turn over the model compounds NEM and MG or their GSH conjugates. For these electrophiles, other detoxification systems are known in E. coli ,64,65,66E. coli . Thus, tE. coli .124SST…V151xxxQD…K307  of Kef-like CPA2 transporters [Staphylococcus aureus, but the latter does not have a kef gene [E. coli, a third paralog YbaL  exists in addition to KefB and KefC . There is no experimental data that would suggest that YbaL provides protection against any electrophiles. While no function of YbaL in E. coli has been established, a homolog from Yersinia enterocolitica (YeRosB) together with a multidrug efflux pump (RosA) provides protection against cationic antimicrobial peptides (CAMPs) [ros (regulation of O-antigen synthesis) [KefC and KefB from sporters , and thekef gene . Most grkef gene ,73, but kef gene ,75. Whil (CAMPs) . These tnthesis) ,78. To dnthesis) . Similarnthesis) .YbaL showed the highest transcriptional upregulation of all genes in E. coli during an attack by the predatory bacterium Bdellovibrio bacteriovorus [E. coli upon attack. Interestingly, ybaL was the second most mutated gene when the evolutionary response of E. coli to a higher temperature of 42.2 °C was investigated [E. coli were observed after 2000 generations, which accumulated 65 presumably inactivating mutations of ybaL. Independent studies showed similar mutational adaptations of E. coli in the ybaL gene under varying temperatures [rosAB from Y. enterocolitica, it was demonstrated that these genes are temperature-dependent and expressed at higher temperatures (RT vs. 37 °C) [riovorus . Howeverstigated . In thiseratures ,83. For . 37 °C) . E. coli (PDB: 3FWZ) and RosB from Vibrio parahaemolyticus (PDB: 3C85), which were solved in structural genomics projects. Both have a dimeric structure with AMP bound with a stretched AMP conformation in YbaL, while it is bent in VpRosB. The cleft between the KTN subunits is in these RosB-like transporters more open than in the GSH/GSX binding pocket in EcKefC because two helices from the C-terminal extension in Kef are missing. These are framing partly this pocket and also providing with N551 a residue involved in coordinating GSH/GSX in EcKefC [Experimental structural data are available for two cytosolic domains of RosB-like transporters: YbaL from n EcKefC . Other rArabidopsis thaliana owns six homologs where KEA1-3 belongs to the same sub-clade I as EcKefC, while the others (KEA4-6) form a distinct own sub-clade [Arabidopsis [Homologs of Kef in plants, designated as K+ efflux antiporters (KEAs), attracted a lot of attention in recent years and are reviewed in more detail elsewhere ,85. Arabub-clade ,86. Potabidopsis ,88,89. Kbidopsis ,91,92. Tbidopsis ,93. KEA1bidopsis ,95. Furtbidopsis . KEA3 isbidopsis ,98,99. Tbidopsis . The disbidopsis ,102. Kef represents a bacterial transporter where the complex regulation has been well-characterized. Tight regulation is important because activation of Kef leads to an intentional disturbance of potassium and pH homeostasis to protect against the toxic effects of electrophiles. With permanent activators of Kef, the bacterial homeostasis can be so strongly disturbed that one could harm pathogenic gram-negative bacteria. Thus, they are potential new leads for antibacterial drugs . In contWhile the regulation of Kef has been intensely studied, until very recently we knew surprisingly little about the transport itself. Despite its designation as a member of the CPA2 family, it was speculated that Kef could be a channel because of its high transport activity ,79. Acti