Apis mellifera) further indicate that the vitellogenin (vg) gene undergoes selection driven by local pathogen pressures. Determining the complete 3D structure of full‐length Vg (flVg) protein will provide insights regarding the structure–function relationships underlying allelic variation. Honey bee Vg has been described in terms of function, and two subdomains have been structurally described, while information about the other domains is lacking. Here, we present a structure prediction, restrained by experimental data, of flVg from honey bees. To achieve this, we performed homology modeling and used AlphaFold before using a negative‐stain electron microscopy map to restrict, orient, and validate our 3D model. Our approach identified a highly conserved Ca2+‐ion‐binding site in a von Willebrand factor domain that might be central to Vg function. Thereafter, we used rigid‐body fitting to predict the relative position of high‐resolution domains in a flVg model. This mapping represents the first experimentally validated full‐length protein model of a Vg protein and is thus relevant for understanding Vg in numerous species. Our results are also specifically relevant to honey bee health, which is a topic of global concern due to rapidly declining pollinator numbers.Vitellogenin (Vg) has been implicated as a central protein in the immunity of egg‐laying animals. Studies on a diverse set of species suggest that Vg supports health and longevity through binding to pathogens. Specific studies of honey bees  using homology modeling, AlphaFold, and a negative stain electron microscopy map. Revealing the oligomeric arrangement and composition of the functional domains around the binding cavities, brings us closer to understanding the molecular mechanisms for Vg's functional role in insect immunity. BN‐PAGEblue native polyacrylamide gel electrophoresisCCScross‐correlation scoreDAMPsdamage‐associated molecular patternsDUF1943/1944domain of unknown function 1943/1944EMelectron microscopyfbVgfat body VgflVgfull‐length VgLClower cavityMSAmultiple sequence alignmentMTPmicrosomal triglyceride transfer proteinNDN‐terminal domainPAMPspathogen‐associated molecular patternsQMEANQualitative Model Energy AnalysisSECsize exclusion chromatographyspdbvSwiss‐PdbViewerUCupper cavityVADARvolume, area, dihedral, angle reporterVgvitellogeninvWFvon Willebrand factorΩgap regionvg genes are commercially important, and this has incentivized analyses of reproductive and immune traits in which Vg is likely to play a role. Effects of Vg on host immunity have been studied in animals as diverse as bees and fishes [Staphylococcus aureus, Micrococcus luteus, and Bacillus subtilis) and gram‐negative bacteria  in nonbilaterian coral (Euphyllia ancora) and zebrafish (Danio rerio) [Apis mellifera) where Vg appears to have similar immunological binding properties [Vitellogenin (Vg) belongs to an ancient and phylogenetically broad protein family called large lipid transfer proteins . In mostd fishes , 4. For o rerio) , 6. Thesoperties . In addioperties  and dispoperties , 9, 10.The honey bee is one of the best studied species in terms of the diverse roles of Vg , 11, 12.In most egg‐laying animals, Vg consists of three conserved domains: The N‐terminal domain (ND), a domain of unknown function 1943 (DUF1943) and the von Willebrand factor (vWF) type D domain Fig. . In honevg gene, while the presumed lipid‐binding region  undergoes positive selection [Ichthyomyzon unicuspis) Vg , which ID: 1LSH  with lamSolving the structure of Vg in more species can increase our understanding of ligand interactions and provide important insights into structure–function relationships. However, even in otherwise well‐studied species like honey bees, this centrally important information on the DUF1943 and vWF domain is lacking.Fortunately, the number of experimentally solved protein structures is growing, and the computational modeling software is becoming more powerful. For example, a crystallographic protein structure of the D’D3 assembly in human vWF protein was resolved in 2019 , and theIn this study, we make progress in describing the structure and interpreting the function of the vWF domain in honey bees. In addition, we compile results from template‐based, deep learning modeling methods, and the ground‐breaking neural network‐based algorithm, AlphaFold , to presQ868N5) was inputted to the HHpred [blast of honey bee Vg (UniProt ID: Q868N5) against the UniProtKB. The target database was restricted to only include UniProt sequences having a PDB ID. The query was run with default settings . This blast returned 26 hits, and hits from regions already satisfactorily modeled in earlier work were ignored. The remaining hits included the VWF_HUMAN . Residues 1453–1612 of the vWF domain in Vg were aligned to residues 864–1013 of vWF, Homo sapiens. These residues correspond to the WD3 domain in the D’D3 assembly in the human vWF protein.The full‐length honey bee Vg sequence  was obtained by aligning residues 1440–1634 (target) with residues 836–1031 (template) using the Emboss Needle pairwise alignment tool , 30, wit% was obtspdbv; v. 4.1.0), a recommended approach when building target models with low sequence identity to the template [Q868N5) was fitted onto the 3D coordinates of the template (PDB ID: 6N29). Backbone building was performed automatically after editing the alignment as described above. Ab initio loop building was performed to ligate breaks in the backbone caused by gaps in the alignment (insertions/deletions). The loop option with the lowest clash and energy scores was chosen in all cases. In this way, nine loops were inserted into the model  [https://modelarchive.org/doi/10.5452/ma‐sfueo (access code: okHs98Pcl2).Quality control was performed on the model to determine whether the structural features are consistent with the physiochemical rules. Stereochemical consistency was evaluated residue‐by‐residue using procheck . Global procheck , powered(v. 1.8)  assesses2+‐ion was copied from the template to the target model, and the contacts to the binding residues were verified to be reasonable in pymol (v. 2.2.2) [pymol.The Ca. 2.2.2) . All illmodeller [raptorx web server [Q868N5) as input, which generated a structure consisting of six domains, each built using one to five templates or template‐free modeling  was followed for setting up and running AlphaFold using Docker. Dependencies that were not included in the AMI were installed manually using the apt package manager. The input sequence was UniProt ID: Q868N5, and AlphaFold was run with the full_dbs preset. Model parameters, downloaded databases, and the output files were stored on the 4TB EBS volume. The run resulted in five models, ranked by average plDDT  [chimera [ucsf chimera (v. 1.14) [The high‐resolution full‐length model and separate chains, in addition to two previously published homology models , 25 and 1LSH) , were fi1LSH) , 41 and [chimera . In bothv. 1.14)  to optimchimera and pymol were also used to generate the figures of the fits and apply a hydrophobicity scale [pymol, where it was aligned to lamprey Vg (PDB ID: 1LSH). The generate symmetry function in pymol was used to produce the dimer formation presented by Anderson et al. [modeller (not shown). The distances of polar contacts were measured in pymol.ty scale . The finn et al.  of lamprm Tris/HCl pH 7.6, using BD needles (30 G) as described earlier [m Tris/HCl as the sample buffer and 0.5 m Tris/HCl with 0.45 m NaCl as the elution buffer. 400–450 µL diluted hemolymph was manually injected and Vg eluted at a conductivity of 15–22 mS·cm−1. All fractions from this peak were collected, pooled and concentrated using an Amicon® Ultracel 100 kDa membrane centrifuge filter . The fraction purity was verified by running SDS/PAGE, which contained only one band of the correct size (~ 180 kDa). The protein concentration was measured with Qubit.To obtain purified Vg, we collected 1–10 µL honey bee hemolymph in a 1 : 10 dilution in 0.5  earlier . The dilm Tris pH 7.6 and 225 mm NaCl. The SEC was run on an ÄKTA Pure 25 system  in micro configuration that allows the use of very small sample volumes. This modification prevents dilution of the sample by effectively reducing the internal volume since it bypasses the multicolumn valve and the pH flow cell and has a shorter path length between the injection valve and the UV monitor. We injected 50 µL of sample (0.26 mg·mL−1) and manually collected fractions directly from the outlet of the UV monitor.Blue native polyacrylamide gel electrophoresis (BN‐PAGE) was performed at 4 °C in precast 3–12% acrylamide gels  for 2 h at a constant voltage of 150 V. The NativePAGE Novex Bis‐Tris Gel System  protocol was used both for sample and buffer preparation, and Native‐PAGE Running Buffer (1×) and the Dark Blue Cathode Buffer (0.4% Coomassie G‐250) were used. Size exclusion chromatography (SEC) was performed of Vg in a Superose 6 Increase 3.2/300 column  at 4 °C equilibrated with a buffer containing 50 m6N29_A), the sequence identities fall below 20%. By dividing the query sequence into known subdomains and domain boundaries and repeating the search, we generated more specific alignments. The top two ND subdomain templates increased their sequence identities to 19%. In contrast, the DUF1943 was demonstrated to be more distinct compared to human microsomal triglyceride transfer protein (MTP) and lamprey Vg, having sequence identities of only 13% and 11%, respectively.Increased insight into the tertiary structure of Vg's domains is beneficial to our understanding of how Vg contributes to honey bee immunity. To build a full‐length structure prediction of honey bee Vg, we first identified potential templates using HHpred  Fig. 1A1A with tblast [H. sapiens has a sequence identity of the pairwise alignment of 24.1%, which is slightly below the suggested threshold (25%) for creating a reliable homology model [Among the three highly conserved domains, the vWF is a major unknown piece in the structural puzzle of Vg. Our initial search discovered a recently published and promising template for this domain, which we confirmed using blast . The WD3gy model . In othe2+‐binding site experimentally known from the structural template with key residues also present in the target sequence [2+‐binding residues, are conserved and correctly aligned. We also conclude that the MSA was able to identify robustly conserved features of Vg, and we therefore proceeded with interactive homology modeling using the structural alignment provided by the MSA  have been introduced (Ω4–7). Ω4 is also missing in all species containing the vWF protein based on the MSA, while downstream Ω5 and Ω7 are conserved in most of the species containing the Vg protein Fig. A. Ω6 seesequence . We recosequence  , but we suggest that binding of a Ca2+‐ion might confer stability to this region. The Ca2+‐binding segment of the domain exhibits higher quality than the antiparallel β‐sandwich. Despite the lower quality, the residues in the secondary structure elements exhibit a higher local quality score compared to the residues in the loop regions  and lower cavity (LC) in reference to this specific orientation  to the ely Fig. . The besely Fig. . The verely Fig. . In addiely Fig. . The modely Fig. B2, chainely Fig. C, PF1, βely Fig. B, ADP2, 2+‐ion into close proximity to the cavity , which increases the margin of error. To limit the number of possible orientations, we fitted the homology models according to size, beginning with the largest. We also fitted the previously predicted domains, the crystal structure of lamprey Vg and the AlphaFold prediction to validate our modeled fold and its placement in the EM map. We evaluated each fit based on the scoring, protruding atoms and overlapping fits of separate domains. We concluded that the AlphaFold PowerFit orientation, with the DUF1943 domain, the two downstream β‐sheets and vWF domain oriented around the LC and the β‐barrel and α‐helical subdomain toward the UC . On the contrary, we demonstrated that the seven residues of each monomer that are creating polar contacts during dimerization in lamprey Vg are conserved in honey bee Vg, making it plausible that Vg dimers can form in honey bees in certain cases. We note that no reducing agent was present in the loading buffer or gel, making it possible that dimers are stabilized by disulfide bonds. Additionally, we cannot rule out that high salt concentration in the SEC prevented the formation of the Vg dimer. Taken together, it is difficult to determine whether dimerization occurs in vivo or is an artifact of the in vitro conditions, as dimerization occurs frequently in a high concentration sample containing just one type of protein [−1, illustrating that the protein is highly soluble [Our results suggest that honey bee Vg is predominantly monomeric  protein . We spec soluble . More ef soluble , 67.To summarize, our study presents new evidence of the full‐length protein and domain assembly for honey bee Vg. We are thus able to identify properties and describe the structural landscape of the large and versatile protein. Our results verify a second cavity of honey bee Vg in addition to the well described lipid‐binding cavity and describe the structural units potentially forming this cavity. As a result, we are able to suggest the possibility that the vWF domain contributes to the immune system of honey bees, which is currently of global concern due to declining pollinator numbers. Efforts are being made to generate a higher resolution and up‐to‐date EM map, which could be used to preform molecular dynamic flexible fitting and enable studies of Vg protein–protein interactions and ligand binding. Our findings encourage future initiatives in investigating this domain together with the full‐length protein to unravel some of the questions asked here.The authors declare no conflict of interest.VL executed homology modeling, structure predictions, rigid‐body fitting, and purification of honey bee Vg and ØH and GVA supervised the research. EH‐G done the negative staining and generation of the EM map. MM‐C and ESC performed native gel and SEC and HL supervised the research. VL wrote the manuscript with assistance from ØH and GVA. All authors contributed to the manuscript.Fig. S1. Domain architecture of honey bee and lamprey vitellogenin. The N‐term (green), DUF1943 (pink) and vWF (blue) domains are conserved in both species, as well as the two structural subdomains, β‐barrel (red arrow) and α‐helical domain (dark green curved line). A) Honey bee Vg contains a proteolytic cleavage site, polyserine region (yellow S) linking the two subdomains. The five residue‐positions  identified to be candidates of functional polymorphisms are marked (brown stars). B) Lamprey Vg contains an addition domain, DUF1943 (purple). The yolk protein organization of IuVg is shown as gray boxes; lipovitellin heavy chain (LvH), Phosvitin (Pv), lipovitellin light chain (LvL), β‐Component (β‐C) and C‐terminal coding region (CT). The dotted lines indicate that these regions  are missing from the crystallographic structure (PDB ID: 1LSH).Fig. S2. Multiple sequence and structural alignment. The coloring for the conserved residues/regions, gaps and secondary structure annotations are explained in the green box. The conserved Ca2+‐binding region are colored in two shades of pink, dark pink is more conserved compared to the lighter pink. A) Extraction of the MSA. The original residue numbering for honey bee Vg is included on top. B) The final structural alignment with the original residue numbering included above each sequence. The annotations are retrieved from the template (PDB ID: 6N29). Both figures are created in Geneious Prime (v. 2019.0.3) and Adobe illustrator (v. 24.0.02).Fig. S3. ProCheck summary, local quality estimate and Ramachandran plots. A) The ProCheck quality evaluations summarized and categorized by calculation results. The ideal residue values and standard deviation for any given model are derived from Morris et al. 1992.1 The max deviation, in residues properties, is calculated from the mean value of the residue‐by‐residue listing values  of the full‐length structure. The number of bad contacts is defined as the non‐bonded atoms at a distance of <= 2.6 Å. The bond length and angles are calculated in similar manner as the max deviation, but the ideal values are based on Engh and Huber 1991.2 The Morris et al. (1992) class summarizes the three above stereochemical parameters by assigning a number between 1 (best) to 4 (worst), indicating the overall quality of the model. B) Local QMEAN results are presented. The first plot is analysis of the template (green), while the second is analysis of the target modeled interactively (cyan) and automatically (red). The Ca2+‐binding region (magenta Ca), the Cys residues forming the intra‐chain disulfide bridges  are in the higher quality region, while Ω5‐7 (black) are in the lower quality region. The local score is calculated for each residue in the model and the average local score for the template is 0.93 ± 0.07, while the target average score is 0.40 ± 0.07 (cyan) and 0.44 ± 0.06 (red). C) The Ramachandran plot produced by ProCheck. The plot on the left is the template (PDB ID: 6N29), while the target (honey bee vWF domain) is on the right. Below each plot, the statistic is presented.Fig. S4. Global quality estimate, VADAR plots and ProCheck residue listing. A‐C) The plots of the global QMEAN have the QMEAN4 scores for a set PDB structures plotted (gray dots) with the QMEAN4 score along the x‐axis and the number of residues in the structures as long the y‐axis. The global scores value QMEAN4 range from 0 to 1, where 1 is good. A) Analysis of the template (red star) and the QMEAN4 value is written on the plot. B) Analysis of the interactively homology modeled (red star) structure and C) The automatically homology modeled (red star) structure from MODELLER. D) Four different analyses were performed by VADAR, presented in one plot each, with the template (gray) compared to the target (green). Plot 1: a low fractional ASA score indicates a buried residue, while a score above 0.5 (dotted black line) indicates an exposed residue. A score above 1.0 (red line) indicates a problem in the structure. Plot 2: When a protein structure is efficiently packed the score should be around 1.0 ± 0.1. A score above 1.2 (blue line) or below 0.8 (red line) could indicate a poor refinement or identify cavities. Plot 3: Each residue is assigned a score between 0‐3  for three different measurements . The total quality score for each residue can be from 0‐9 and the threshold for a good quality is set to 6 (red line). Plot 4: Calculates the 3D quality of each residue based on its environment and gives a score between 0‐9 , and the threshold for a good quality is set to 4 (red line). E) The Residue‐by‐Residue listing for ProCheck lists all residues in a structure and present all calculations for each. A short example is shown here for the first six residues in the target structure. Each value is compared to the ideal values which is noted on top. The deviating values are marked with * (one standard deviation) and +  sign. For example, the omega dihedral angle of residue S1443 is 16.9 standard deviation away from the ideal value, which is a result from the loop building of Ω1.Fig. S5. Comparison of vWF homology models. A) The sort region around the Ca2+‐binding site  is shown from the interactively modeled (cyan) structure and the automatically modeled (gray) structure. The Cys‐residues  and Ca2+‐binding residues are shown as yellow/cyan (interactively) and orange/magenta  sticks. The missing C1634 and β‐strands in the automatically modeled structure are shown (gray arrows). B) All the Ca2+‐binding residues are in the same orientation in both models , except N1607. The interactions to the Ca2+‐ion is shown as yellow dotted lines and measured (Å) for N1607.Fig. S6. Comparison of homology models from MODELLER and RaptorX. A) The N‐terminal domain: Model 1 (green) aligned with Model 4 (red), 5 (yellow) and 6 (forest green). B) The DUF1943 domain: Model 1 (magenta) aligned with Model 8 (cyan), Model 7 (orange) and Model 9 (blue). The identified curve in the longer β‐sheet in Model 1, 8 and 9 and the missing curve in Model 7 is marked with arrows. C) The DUF1943 domain Model 1 (magenta), the downstream region residue 1060 to 1140 of Model 9 (hot pink) and the loop region (gray). D) The undetermined domain: Model 2 (purple) aligned with Model 9 (blue), with the long loop region (gray). E) The interactively homology model of vWF domain (cyan) with the C‐terminal region from Model 9 (orange).Fig. S7. RaptorX structural prediction of full‐length honey bee vitellogenin. A) The β‐barrel subdomain (red), the polyserine linker (yellow), the α‐helical subdomain (forest green), the DUF1943 domain (magenta), elongation of the DUF1943 domain (hot pink arrow), the undetermined structural region (purple), the vWF domain (cyan) and the C‐terminal region (orange) are generated as one full‐length model. The two loop regions (gray arrows) are also predicted. B) Domain 1 to 6 from Table S7 are colored red, cyan, purple, blue, green and orange, respectively, and if templates was used, the PDB ID is written in parenthesis.Fig. S8. AlphaFold output. A) The number of sequence hits in the MSA produced by AlphaFold, is plotted per residue. The average number of hits per residue (gray dotted line), and the threshold at 100 sequence per residue (red dotted line) is marked. B) The plDDT score for the five outputted models by AlphaFold is plotted per residue, and the average plDDT score per model is listed to the right, which produces the rank from 0 (best) to 4 (worst). C) The ranked models are aligned, colored by the same coloring scheme in panel B, and the consistently folded domains (β‐barrel (β), α‐helical (α), DUF1943 (DUF), β‐sheet 1 (β1), β‐sheet 2 (β2) and vWF domain (vWF)) are labeled in bold letters, while the more variable domains  and C‐terminal) are labeled in grey letters. D) The final homology model domains (β‐barrel (red), polyserine linker (yellow), α‐helical (green), DUF1943 (magenta), β‐sheet 1 (hotpink), β‐sheet 2 (purple), vWF  and C‐terminal domain (orange) is aligned to their respective domains in the top ranked AlphaFold prediction (grey). The grey brackets to the lower right indicate the region where AlphaFold have predicted a fold for the main missing atoms in the homology model.Fig. S9. EM map validation. A) Map visualization to allow visual inspection of the internal detail of the map and identification of artifacts. The primary map, central slices of the map and largest variance of the map is shown in three orthogonal directions. The 3D surface view of the primary map at recommended contour level 0.07. B) Statistical analysis of the map. In the first graph the map‐value distributions is plotted in 128 intervals along the x‐axis, and the y‐axis is logarithmic. The spike around 0 indicate that the volume has been masked. The second graph shows how the enclosed volume varies with the contour level. The volume at the recommended contour (red line) is 289 nm3; this corresponds to an approximate mass of 261 kDa. C) The provided Fourier‐Shell Correlation (blue) is plotted together with the reported resolution, . A curve is displayed for the half‐bit criterion (dashed red), in addition to lines showing the 0.143 gold standard cut‐off (dashed orange line) and 0.5 cut‐off (green dotted line). All the graphs are assembled from the EmDataBank map validation report (copy included).Fig. S10. Rigid‐body fitting for honey bee vitellogenin homology models. A) The EM map is shown as a gray surface. The distinct cavity creases are marked with stars and arrows, upper cavity (blue) and lower cavity (yellow). The four curves in the surface are labeled . B) The correlation score and precent of atoms outside the contour calculated by Chimera was plotted for each fit from PowerFit  and ADP_EM , and ranked according to the correlation score . C‐E) The fits from the full‐length homology model, RaptorX and chain A is presented inside the EM map, with the same coloring scheme as in panel B. The β‐barrel (β), α‐helical (α), DUF1943 (DUF), vWF and C‐terminal (C‐t) domains are labeled. If the domain is outside of the contour it is noted by a “*”‐mark. F) The fits of chain B to E separately with the same coloring scheme as in panel B, but they are labeled according to chains and not domains.Fig. S11. Rigid‐body fitting of chain A to C and D. A) The correlation score and precent of atoms outside the contour calculated by Chimera was plotted for each fit from PowerFit  and ADP_EM , and ranked according to the correlation score . B) The EM map are shown as a transparent surface, and the fits of chain A to C from PF and ADP are shown as cartoons and colored by method and scores . The β‐barrel (β), α‐helical (α) and DUF1943 (DUF) domains are labeled. C) The EM map and the fits of chain D is shown in same coloring scheme as in panel B. The label is marked with “*” if the fit is outside the contour of the EM map. D) The EM map are shown as a surface, less transparent than in panel B, with the fits of chain A to C  in the same coloring scheme as in panel B. The EM map is shown at four different angels, and arrows points to secondary structure elements from β, α or DUF domain which are outside the contour of the EM map.Fig. S12. Rigid‐body fitting for previously published homology models and a distant homologue. A) The same plot as in Fig. S10 for the β‐barrel and α‐helical subdomains, and the crystal structure of lamprey Vg (1LSH). B‐D) Same presentation and coloring scheme as in Fig. S10C‐S10F.Table S1. Alignment parameters.Table S2. List of species used in the multiple sequence alignment.Table S3. Loop building based on gaps in the structural alignment.Table S4. Edited residues during quality control.Table S5. Rigid‐body fitting scores from PowerFit and Chimera.Table S6. Rigid‐body fitting scores from ADP_EM and Chimera.Table S7. RaptorX structure prediction.Click here for additional data file.Appendix S1. wwPDB EM Validation Summary Report.Click here for additional data file.Appendix S2. Top ranked Vitellogenin model by AlphaFold.Click here for additional data file.


Despite the importance of PAS/CNBH domain interactions, little is known about their affinity. Here, we used the surface plasmon resonance (SPR) technique to investigate interactions between isolated PAS and CNBH domains and the effects of LQTS-causing mutations R20G, N33T, and E58D, located at the PAS/CNBH domain interface, on these interactions. We determined that the affinity of the PAS/CNBH domain interactions was ∼1.4 μM. R20G and E58D mutations had little effect on the domain interaction affinity, while N33T abolished the domain interactions. Interestingly, mutations in the intrinsic ligand, a conserved stretch of amino acids occupying the beta-roll cavity in the CNBH domain, had little effect on the affinity of PAS/CNBH domain interactions. Additionally, we determined that the isolated PAS domains formed oligomers with an interaction affinity of ∼1.6 μM. Coexpression of the isolated PAS domains with the full-length hERG channels or addition of the purified PAS protein inhibited hERG currents. These PAS/PAS interactions can have important implications for hERG function in normal and pathological conditions associated with increased surface density of channels or interaction with other PAS-domain-containing proteins. Taken together, our study provides the first account of the binding affinities for wild-type and mutant hERG PAS and CNBH domains and highlights the potential functional significance of PAS/PAS domain interactions.Human  The C-linker/CNBH domains ring the intracellular pore entrance with the PAS domains located at the periphery of the tetrameric ring assembly (B). Despite the structural homology to the cyclic nucleotide-binding domains in hyperpolarization-activated cyclic nucleotide-gated (HCN) channels, CNBH domains in KCNH channels do not function as cyclic nucleotide-binding domains (B)  . Transmeassembly B. Despit domains , 17. Ins domains B  .Figure 1via several important interactions. Firstly, functional and structural studies showed that PAS domains interact with C-linker/CNBH domains from adjacent subunits, and this interaction confers the hallmark slow deactivation of hERG channels that is essential for the repolarization of cardiac action potential (B). The interaction interface is largely formed by the C-linker resides, similar to the “elbow-on-shoulder” interactions initially observed in the cyclic nucleotide-binding domains of HCN channels (B), potential interactions between the PAS domains of neighboring hERG channels or between hERG PAS and PAS-domain-containing non-KCNH proteins might be physiologically relevant.The intracellular domains of hERG channels exert functional effect otential , 28, 29.otential , 24, 30.otential , 24, 31.otential B. The inchannels , 33. Finchannels . While Pchannels B, potentXenopus laevis oocytes or injection of the purified PAS domain protein into hERG channel expressing oocytes resulted in a dramatic decrease in hERG currents, suggesting that the PAS/PAS domain interactions can affect hERG channel function. To the best of our knowledge, our findings provide first look at the affinity of the interdomain interactions in hERG channels, the effect of the mutations in the residues at the PAS/CNBH domain interface on the interdomain interactions and highlight the potential importance of PAS domain oligomerization for hERG-channel-dependent cellular signaling.Although the structural and functional studies highlight the importance of PAS and CNBH domains and interactions between them for hERG channel gating, the affinity of these interactions is not known. Here, we used surface plasmon resonance (SPR) technique to directly investigate interactions between the isolated PAS and CNBH domains of hERG channels. Using this approach, we determined the affinity of PAS/CNBH and CNBH/CNBH domain interactions. We found that the double mutation F860G/L862G in the intrinsic ligand and LQTS-causing mutations R20G and E58D located at the interdomain interface had no effect on the PAS/CNBH domain binding affinity, while N33T mutation, also located at the interdomain interface, and high ionic strength abolished PAS/CNBH domain interactions. We also determined the affinity of PAS/PAS domain interactions with SPR and using electrophysiology showed that coexpression of hERG channels with the isolated PAS domain in C and D). Due to the limitations with purifying concentrated PAS and CNBH domains in volumes necessary for the SPR experiments, the maximal concentration for free domains used in our study was 10 μM. The CNBH domain used in our study contained the same stretch of amino acid as the protein used to determine the recently published high-resolution crystal structure of the isolated CNBH domain of hERG channels . The SPR response increased with the increase in the PAS domain concentration. To determine the interaction affinity between the PAS and CNBH domains, the SPR response profiles were fitted with the two-state reaction model, using Biaevaluation software version 1.0, as predicted using procedures described in a previous publication (We applied free PAS domains over the range of concentrations to the immobilized CNBH domains C. The SPlication  and desclication .Table 1SD). Fits of the SPR response indicated that CNBH domains interacted with an averaged affinity of 2.7 ± 1.3 μM. Since the CNBH construct used in our study lacked most of the C-linker responsible for the formation of the “elbow-on-shoulder” interface in the tetrameric C-linker/CNBH domain ring assembly, we expect the affinity of the C-linker/CNBH interdomain interactions in the intact channels to be even higher. Taken together, our results provide the first measurements for the affinities of the PAS/CNBH and CNBH/CNBH domain interactions in the tetrameric PAS/C-linker-CNBH ring assembly in hERG channels.We also determined the affinity of CNBH/CNBH domain interaction by applying free CNBH domains over the range of concentrations to the immobilized CNBH domains. The SPR response increased with the increase in the free CNBH domain concentration D. Fits oB) and applied free PAS domains over the range of concentrations to the immobilized mutant CNBH-F860G/L862G domains (A). The SPR response increased with the increase in the PAS domain concentration. Fits of the SPR response indicated the averaged interaction affinity of 1. 3 ± 0.4 μM for the mutant CNBH and PAS domains. Therefore, our results indicate that at the level of the isolated PAS and CNBH domains, the double mutation in the intrinsic ligand appears to have no effect on the affinity of the interaction between the PAS and CNBH domains.Two recent FRET-based studies suggest that mutations in the intrinsic ligand diminish interactions between PAS and CNBH domains in the full-length channels , 38. EspFig. 1 domains A. The SPB). To test the effect of the LQTS-causing mutations on the interaction between the PAS and CNBH domains, we immobilized CNBH domains and applied free PAS domains with the LQTS-causing mutations to the immobilized CNBH domains (B–D).Analysis of the interaction interface between the PAS and CNBH domains indicates that the interface harbors several LQTS-causing genetically occurring mutations , 24. SomFig. 1 domains , B–D.B) and E58D  mutations have no statistically significant effect on the binding affinity of the isolated PAS and CNBH domains, as summarized in p > 0.3 for the unpaired Student t test). Importantly, we found that N33T mutation completely abolished PAS/CNBH domain interactions . It has been shown that the N33T mutation causes substantial shift in the voltage dependence of hERG channel activation and acceleration of the deactivation kinetics . Similarly, application of mEAG PAS domains to the immobilized hERG CNBH domains increased SPR response in a concentration-dependent manner with the affinity of binding of ∼1.7 μM (B). These results indicate that PAS and CNBH domains of hERG channels can interact not only with PAS domains of hERG channels but also PAS domains from homologous mEAG channels. Although not tested here, this opens a possibility of hERG PAS domain interactions with non-KCNH PAS domains. The promiscuity of these interactions may have far-reaching implications, as discussed in the Interestingly, application of mEAG PAS domains to the immobilized hERG PAS domains also increased SPR response in a concentration-dependent manner with the affinity of binding of ∼5.6 μM A. Simila ∼1.7 μM B. These Xenopus laevis oocytes and HEK cells had no effect on hERG currents , currents recorded from oocytes in the second group coexpressing hERG and isolated PAS domains had much smaller steady-state activation currents and had no tail currents (B). Noteworthy, the steady-state activation currents recorded from oocytes coexpressing full-length hERG channels and isolated PAS domains were substantially larger than the currents detected in oocytes injected with only PAS domain mRNA , while currents recorded from hERG channels in the presence of the isolated PAS domain protein were substantially reduced and lacked tail currents (D). These results were consistent with the decrease of currents from hERG channels coexpressed with the isolated PAS domains. Taken together, these results suggest that the isolated PAS domains are decreasing currents most likely by interacting with hERG channels.Our results indicate that coexpression of the isolated PAS domains with full-length hERG channels decreased hERG currents. However, this decrease could be due to the suppression of hERG channel expression by the coinjection of the mRNA encoding the isolated PAS domains by possibly overtaking the protein expression machinery of oocytes, rather than the direct interaction with hERG channels. To test this possibility, we first expressed hERG channels in oocytes and then divided the oocytes into two groups, the control group expressing only hERG channels and the test group where the oocytes expressing hERG channels were injected with ∼36 nls of the purified PAS domain protein at 40 μM concentration. We then compared currents recorded from the two groups of oocytes 3 h after the PAS protein injection. Currents recorded from oocytes in the control group expressing hERG channels in the absence of the isolated PAS protein had typical characteristics of currents recorded from hERG channels C, while currents D. These A). We also tested the contribution of electrostatic interactions to the PAS domain oligomerization. For these experiments PAS domains were immobilized on CM5 sensor chip and free PAS domains at 3 μM concentration in solutions containing 150, 300, or 600 mM KCl were applied to the immobilized PAS domains. Similar to the effect observed for PAS/CNBH interactions, increasing ionic strength of the solution gradually decreased the SPR response, with no binding detected in 600 mM KCl . These results indicate that electrostatic interactions play a major role in the PAS/CNBH and PAS/PAS domain binding.PAS and CNBH domains form an extensive interaction network in hERG channels. Many of the interactions at the interface of the PAS and CNBH domain are electrostatic in mechanism. For instance, it was shown that E56 on the PAS domain forms electrostatic interactions with D803 on the CNBH domain of hERG channels . To testFig. 5Here, we determined the binding affinities for the isolated hERG PAS and CNBH domain interactions using SPR. We found that the binding affinity for the isolated PAS and immobilized CNBH domains was 1.4 ± 0.6 μM and for the isolated CNBH and immobilized CNBH domains was 2.7 ± 1.3 μM. We found that the R20G and E58D LQTS-causing mutations in the PAS domain had no statistically significant effect on the binding affinity to the immobilized CNBH domains, while N33T LQTS-causing mutation completely abolished the binding between the isolated PAS domains and immobilized CNBH domains. Double mutation F860G/L862G in the intrinsic ligand of the isolated CNBH domain had no statistically significant effect on the binding affinity of the isolated PAS and immobilized CNBH domains. We also found that the isolated PAS domains bound to the immobilized PAS domains with an affinity of 1.6 ± 0.8 μM. Importantly, coexpression of the isolated PAS or injection of the purified PAS domain protein decreased currents recorded from hERG channels.B and D). This is in agreement with the previous report that R20G and E58D mutations do not affect the kinetics of hERG channel deactivation, which is a functional indicator of PAS and CNBH domain interactions in the intact channels (C). This is in agreement with the previous report indicating that N33T mutation accelerates deactivation of hERG currents, suggesting that the mutation decreases interaction between the PAS and CNBH domains in the intact hERG channels. Finally, we also observed that the increase in the ionic strength abolished interactions between the free PAS domains and immobilized CNBH domains (A). This finding is consistent with previous reports on the importance of electrostatic interactions for PAS and CNBH domain binding in hERG channels . This is contrary to the findings of two recent studies. One of the studies found that F860G/L862G double mutation in the intrinsic ligand almost completely abolished the FRET signal between the CFP-tagged PAS and Citrine-tagged CNBH domains in the intact hERG channels and, also, drastically accelerated the deactivation kinetics of currents recorded from hERG channels  and was nonspecific for hERG PAS domains, as we also detected binding between the isolated mEAG and hERG PAS domains (A). These results indicate that the isolated PAS domain of hERG channels can form homomers and heteromers with PAS domains of other proteins. PAS domains of KCNH channels share structural homology to a vast family of PAS proteins that function as ligand-binding and protein–protein interaction domains and frequently oligomerize  (A). However, there are several physiological and pathological situations when the PAS/PAS interactions could become important. It has been well documented that hERG channels are overexpressed in cancer (B). It has been shown that some LQTS mutations introduce premature stop codon in the N-terminal region of hERG channels (via the nonsense-mediated mRNA decay mechanism (C). Finally, our results suggest that the PAS/PAS interactions are not specific. Therefore, PAS domains of hERG channels could interact with PAS domains of other proteins (D), forming heteromers as observed for many structurally similar PAS proteins , t, tB) (14B) A. Howeven cancer , 48. Then cancer B. It haschannels , 51, 52.echanism , it is pechanism C. Finallproteins D, forminproteins . Future Escherichia coli cells as previously described  and eluted on a linear gradient to 500 mM imidazole. The 6-His tag was cleaved with TEV protease. The protein was further purified on a Superdex 200 Increase 10/300 column  equilibrated with 150 mM KCl, 1 mM TCEP, 10% glycerol, and 30 mM HEPES, pH 7.5. The protein concentration was determined with Bradford Protein Assay Kit (Pierce).DNA encoding wild-type and mutant PAS (residues 2–134) and CNBH (residues 734–867) domains of hERG channels (GI # Q12809) and PAS domains (residues 7–136) of mEAG channels (GI # Q60603) was synthesized by BioBasic and subcloned into pETM11 bacterial expression vector containing an N-terminal 6-His affinity tag followed by a tobacco etch virus (TEV) protease cleavage site. The DNA sequences were verified by sequencing (Genewiz). The PAS and CNBH domains were expressed in BL21 (DE3) escribed , 55. TheThe purified protein was stored at −80 °C in aliquots and thawed immediately before the experiments. The molecular weight of the PAS and CNBH domains used in the study was verified on Coomassie-Blue-stained gels and with mass spectrometry at Proteomics and Metabolomics Core Facility at Georgetown University Medical Center.2). For all experiments, a reference flow cell (FC) was activated and deactivated, using the same standard amine coupling chemistry, as in active FC with immobilized proteins, but without protein. This reference FC was used as a reference surface to account for a potential nonspecific binding to the chip surface. The SPR signals corresponding to the reference FC were subtracted from the ones corresponding to the surfaces with immobilized proteins (active FCs). In addition, the binding corresponding to blank injections (buffer only) was subtracted from the reference subtracted SPR data.All SPR binding experiments were performed on a CM5 chip  at 25 °C using a Biacore T200 Instrument . The purified PAS and CNBH domains were immobilized on the CM5 chip  using a standard amine coupling chemistry in the presence of 10 mM sodium acetate buffer at pH 5.5 as the immobilization buffer (buffer used to directly dissolve ligands). HBS-P buffer  surfactant P20, pH 7.4) was used as the immobilization running buffer (buffer that runs in the background during immobilization). Proteins were then injected at 200 nM concentrations and immobilized at ∼2000 to 3000 RU (1 RU = 1 pg of protein per mmd), the SPR sensorgrams were first fitted with a simple 1:1 binding model using Biaevaluation software version 1.0. However, the 1:1 model failed to predict the data (A), indicating that the binding mechanism is more complex. We then followed the procedures outlined in the previous publication (B).A running buffer containing 150 mM KCl, 1 mM TCEP, 10% Glycerol, 0.05% Tween 20, 30 mM Hepes, pH 7.5 was used in all SPR direct binding experiments between analytes (free PAS or CNBH domains) in solution and immobilized proteins, unless specified. Analytes were injected over the range of concentrations over the chip surface in triplicates for 60 s at a flow rate of 50 μl/min (association phase), followed by buffer only injections for 150 s (dissociation phase). Injection of Glycine (pH 2.0) for 15 s was used to regenerate the chip surface between the analyte injections. To determine the binding affinity . Each batch of oocytes was harvested from one frog. The experiments were repeated in oocytes harvested from three different frogs.The cDNA encoding hERG1-S631A in pGH19 vector was kindly provided by G. Robertson . For the isolated PAS domain expression, cDNA of the isolated PAS domain was subcloned into pGH19 vector. The mRNA was transcribed using the T7 mMessage mMachine kit (Thermo Fisher Scientific). Defolliculated For current recordings, oocytes were placed into a RC-3Z chamber (Warner Instruments). The currents were recorded using Two-Electrode Voltage Clamp (TEVC) technique with OC-725C amplifier (Warner Instruments) and pClamp11 software (Molecular Devices). The signals were digitized using Digidata 1550 (Molecular Devices). Microelectrodes were pulled from borosilicate glass and had resistances of 0.7 to 1.5 M when filled with 3 M KCl. The recording (bath) solution contained 96 mM NaCl, 4 mM KCl, 0.1 mM CaCl2, 1.8 mM MgCl2, and 5 mM HEPES, pH 7.5. The hERG currents were elicited by applying a series of 0.1-s voltage pulses ranging from −100 to +70 mV in 10 mV increments from a holding potential of −80 mV, followed by a 0.15-s voltage pulse to −100 mV. The currents were not leak-subtracted.V represents the test voltage (mV), V1⁄2 is the midpoint activation voltage (mV), and s is the slope of the relation (mV).To analyze voltage dependence of the tail currents, peak tail-current amplitudes were normalized to the largest peak tail-current amplitude (Gmax). These normalized data were then plotted against the test voltage and were fitted with a Boltzmann equation,tib5@georgetown.edu.All data are contained with the article or available on request by contacting the corresponding author: This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Recent advances in protein structure prediction using machine learning such as AlphaFold2 and RosettaFold presage a revolution in structural biology. Genome-wide predictions of protein structures are providing unprecedented insights into their architecture and intradomain interactions, and applications have already progressed towards assessing protein complex formation. Here we present detailed analyses of the sorting nexin proteins that contain regulator of G-protein signalling domains (SNX-RGS proteins), providing a key example of the ability of AlphaFold2 to reveal novel structures with previously unsuspected biological functions. These large proteins are conserved in most eukaryotes and are known to associate with lipid droplets (LDs) and sites of LD-membrane contacts, with key roles in regulating lipid metabolism. They possess five domains, including an N-terminal transmembrane domain that anchors them to the endoplasmic reticulum, an RGS domain, a lipid interacting phox homology (PX) domain and two additional domains named the PXA and PXC domains of unknown structure and function. Here we report the crystal structure of the RGS domain of sorting nexin 25 (SNX25) and show that the AlphaFold2 prediction closely matches the experimental structure. Analysing the full-length SNX-RGS proteins across multiple homologues and species we find that the distant PXA and PXC domains in fact fold into a single unique structure that notably features a large and conserved hydrophobic pocket. The nature of this pocket strongly suggests a role in lipid or fatty acid binding, and we propose that these molecules represent a new class of conserved lipid transfer proteins. The sorting nexins (SNXs) are a large family of proteins with diverse structures and functions. They are found in all eukaryotes and their defining feature is the presence of a phox homology (PX) domain, which most commonly associates with phosphatidylinositol phospholipids (PtdInsPs) to mediate interactions with membranes of the endolysosomal system . SNX proSaccharomyces cerevisiae and Snazarus (Snz) from Drosophila melanogaster. These proteins have relatively low sequence similarity across homologues and across species but share a conserved architecture with an N-terminal hydrophobic membrane anchor, and a central RGS domain and PX domain (except for SNX19 orthologues which lack the RGS domain). In addition, the RGS and PX domains are flanked by two sequences referred to as the PX-associated domains PXA and PXC at the N- and C-terminus respectively  domains or SNX-RGS proteins . In humaectively . The N-tectively , and thein yeast , the plaosophila  and endoosophila .s subunit of trimeric G-proteins  of the SNX-RGS proteins requires further knowledge of their structures and molecular interactions. Here we have solved the crystal structure of the human SNX25 RGS domain, revealing a typical α-helical RGS fold. Building on this we took advantage of the recently developed AlphaFold2 machine learning (ML)-based structural prediction algorithm  and the Escherichia coli BL21-CodonPlus (DE3)-RIL cells and plated on lysogeny-broth (LB) agar plates supplemented with ampicillin (0.1 mg/ml). Single colonies were then used to inoculate 50 ml of LB medium containing ampicillin and the culture was grown overnight at 37°C with shaking at 180 rpm. The following day 1 L of LB medium containing ampicillin (0.1 mg/ml) was inoculated using 10 ml of the overnight culture. Cells were then grown at 37°C with shaking at 200 rpm to an optical density of 0.8–0.9 at 600 nm and the protein expression was induced by adding 0.5 mM IPTG . Expression cultures were incubated at 20°C overnight with shaking and the cells were harvested the next day by centrifugation at 4000 rpm for 15 min using a Beckman rotor JLA 8.100. Cell pellets were resuspended in 20 ml (for cell pellet from 1 L culture) of lysis buffer , 500 mM NaCl, 5% glycerol, Benzamidine (0.1 mg/ml), and DNAse (0.1 mg/ml)). Resuspended cells were lysed by using the cell disrupter  and the soluble fraction containing the protein was separated from cell debris by centrifugation at 18,000 rpm for 30 min at 4°C. The soluble fraction was first purified by affinity chromatography using Glutathione Sepharose 4B resin  and the GST tag was cleaved on column by incubating the protein with Thrombin (Sigma Aldrich) overnight at 4°C. The next day the protein was eluted using 50 mM HEPES (pH 7.5), 200 mM NaCl. The eluted protein was then concentrated and further purified by gel filtration chromatography (Superdex 75 (16/600), GE Healthcare) using 50 mM HEPES, pH 7.5, 200 mM NaCl, 0.5 mM TCEP (tri(2-carboxyethyl)phosphine) and the fractions corresponding to SNX25 RGS domains were analysed by SDS PAGE.The genes encoding the human SNX25 RGS domain  and the SNX25 RGS(C526A) mutant were synthesised by Gene Universal (USA) and cloned into the expression plasmid of pGEX-4T-2 for bacterial expression with an N-terminal GST tag and thrombin cleavage site was construct was transformed into 28) in PHASER . The optimised diffraction-quality crystals were obtained in 24-well plates using hanging drop method. The SNX25 RGS domain was crystallised in 30% PEG 4000, 0.2 M Sodium acetate, 0.1 M Tris pH 8.5, while the SNX25 RGS(C526A) domain was crystallised in 18% PEG 8000, 0.1 M calcium acetate, 0.1 M sodium cacodylate, pH 6.5. Diffraction data was collected at the Australian Synchrotron MX2 beamline , integrated with XDS  and scaln PHASER  and the n PHASER  and PHENn PHASER  respectiS. cerevisiae, D. melanogaster, C. elegans, P. falciparum, D. discoideum, A. thaliana, S. pombe, M. janaschii, D. rerio and H. sapiens analysed in this study were obtained from the AlphaFold2 database .The structural predictions of the full-length proteins from different species including i.ac.uk; ). To geni.ac.uk;  and the -network  implemen-network . For eac-network  and stru-network  to gener-network . Interio-network , with de5) were plated in six well culture dishes  and grown to 50% confluency before transfection of SNX25-FLAG construct using lipofectamine 3000 (Invitrogen) as per manufacturer’s protocol. Cells were shifted to DMEM media containing oleic acid (50 µg/ml) 4 h post transfection and incubated for another 20 h. DMEM with oleic acid was prepared by mixing oleic acid  in DMEM medium containing bovine serum albumin (fatty acid free) to final oleic acid concentration (50 µg/ml) with rigorous shaking at 37°C to avoid precipitation. Cells were fixed 24 h post-transfection with 4% paraformaldehyde in phosphate buffered saline (PBS) at 4°C and subsequently permeabilised with 0.1% Triton X-100 in PBS for 7 min. Cells were probed with FLAG antibody  and anti-Rabbit secondary antibody Alexa Fluor®488 conjugate (4 µg/ml) to visualise SNX 25 protein. Cells were finally labelled with nile red solution (100 nM) to visualise lipid droplets. Confocal images (1024 × 1024) were acquired on a Zeiss inverted LSM 880 microscope coupled with fast airyscan detector  equipped with ×63 oil immersion objective, NA 1.4.The human SNX25 open reading frame (ORF) cloned into pcDNA3.1+-C DYK was obtained from Genscript (NM_031953). This expresses a C-terminally tagged SNX25 (SNX25-FLAG) that has the same sequence as the AlphaFold2 model shown in The SNX-RGS family members are typically represented as modular proteins with four to five distinct conserved domains  Amatya . Our preα atoms  of the AlphaFold2 models provide important insights into the organisation of the full-length proteins . The PAEThe AlphaFold2 predicted atomic structures of the human SNX-RGS proteins are shown in For a closer examination of the PXA-PXC structure we focus on these domains from the SNX13 model . Both th3 compared with typical sized lipid binding pockets in other LTPs of ∼400–2000 Å3  in the nolamine .Several previous studies have shown that the SNX-RGS proteins are rapidly recruited to the surface of newly formed lipid droplets. This has been observed for Mdm1 in yeast , Snz in D. dictyostelium or P. falciparum and they therefore do not seem to be present in all eukaryotes. Secondly, we identified three proteins in the fission yeast S. pombe with this fold. Meiotically up-regulated gene 122 protein (Mug122) (O74444) lacks the RGS domain but has both transmembrane and central PX domains, while snx12 (Q9USN1) has each of the domains common to the SNX-RGS family. The protein annotated as Pxa1 (O14200) appears to be most similar to Nvj3, as it lacks transmembrane, PX and RGS domains. We also performed a BLAST sequence search of the Chaetomium thermophilum genome, a thermophilic filamentous yeast popular with structural biologists because of the generally higher stability of the encoded proteins. The C. thermophilum genome encodes three proteins with putative PXA-PXC structures  also possessed a superficially similar architecture. Lec1/Ypr097w is 1073 residues in length and is annotated to have a central PX domain between an N-terminal PXB domain and C-terminal domain of unknown function (DUF3818) . AnalogoSchizosaccharomyces pombe (Q9Y7N9), and Candida albicans (A0A1D8PQ52) (3) and could potentially accommodate multiple phospholipid or neutral lipid molecules. In each of the predicted PXY protein structures from different species the PX domain forms a loose plug over the large hydrophobic cavity suggesting a potential regulatory role for this domain in lipid binding  . Althoug binding . This ma binding .S. pombe, as well as a protein in D. dictyostelium. S. pombe PX domain-containing protein C1450.12 (Q9Y7N9) is highly akin to Lec1/Ypr097w from S. cerevisiae, while uncharacterized protein C663.15c (O74521) appears to lack a central PX domain between the two halves of the PXYn-PXYc structure (D. discoideum DUF3818 domain-containing protein (Q54JB8) also lacks a PX domain, and while its C-terminal PXYc domain is like Lec1/Ypr197w, its N-terminal region is structurally divergent from the PXYn domain (C. thermophilum protein sequences using both S. pombe sequences as inputs and found one protein homologous to the Lec1/Ypr097w-like Q9Y7N9 (G0SCP5) and a protein lacking the PX domain like O74521 (G0S997). These discoveries further demonstrate the utility of AlphaFold2 for predicting novel structures, and its power in aiding evolutionary analyses of protein homology.The PXYn-PXYc structure appears to be relatively restricted throughout evolution, as we only found examples in yeast, including two proteins in tructure . The D. n domain . DespiteWith the recent advances made by the AlphaFold2 machine learning algorithm and public release of the AlphaFold2 database , it is nBroader analysis of other PX domain-containing protein family members indicate that the yeast protein Lec1/Ypr097w and related molecules share a superficially similar LTP structure and function. Here the central PX domain is found between N-terminal PXYn and C-terminal PXYc domains that also become conjoined to form an all α-helical barrel-like structure with a large hydrophobic cavity, although their sequences are not related and their structural details are entirely different. As we were performing these studies, a preprint was published by the Schuldiner lab that identified novel proteins that reside at or regulate formation of membrane contact sites in yeast . In thisvia their N-terminal membrane anchor, and subsequently colocalise with LDs particularly under conditions of new LD synthesis. They are typically found to be enriched in regions of the ER and ER-associated LDs that are in contact with other membranes where they can play a tethering role via their phosphoinositide-binding PX domain. These membrane contacts include the cell surface in the fat body cells of fruit flies  of the SNX-RGS proteins? The hydrophobic nature and conserved surface of the large interior cavity found in the PXA-PXC domain structure indicates they are very likely to mediate the binding of lipids, fatty acids or other lipid-derived metabolites. A general finding is that the SNX-RGS proteins reside in the ER at steady state it flies , the nucit flies , and witit flies . This loit flies ), and thvia β-oxidation  and accumulation of cholesterol in late endosomal compartments . Yeast Mxidation . Drosophxidation . Knockouxidation . Niemannxidation , which ixidation . In screxidation . AlthougThe PXA-PXC domains were previously considered as conserved but essentially independent structures in the SNX-RGS family of proteins. From the analyses presented here it is clear that these two domains are intimately associated in a single structural unit and that neither the PXA or PXC sequences are likely to fold correctly in the absence of the other. Previously, several studies have examined the impact of deleting these regions on SNX-RGS protein function. While deletion of either domain generally renders the proteins non-functional as expected, there is evidence that the two sequences contain regions that are important for LD targeting and potentially protein interactions. In yeast, it was found that the PXA domain of Mdm1 could define sites of LD formation, and could recruit the Faa1 fatty-acid—CoA ligase . In contIn conclusion, although further experiments are required to experimentally validate these models and define the functional lipid interactions of the SNX-RGS proteins and their distantly related Lec1/Ypr097w cousins, these structural studies provide strong evidence for a novel lipid transport activity mediated by these conserved molecules. As a final general observation, this work further confirms the ability of AlphaFold2 to predict not only already known folds, but to define completely new structures without previously recognised topologies and thus provide new insights into biological function.


Thermal compensation control can correct errors caused by the thermal deformation of phased array antenna (PAA) panels. Thermal deformation of the panel is needed to calculate the compensation value. While the PAA is working, thermal deformation is unconditional to measure, but predicting it by temperature is feasible. However, thermal deformation is also affected by other factors, such as the structural shape, assembly method, and material parameters, and it is difficult to measure these parameters of PAA because of the complex structure. In contrast, the measurement method of the temperature and thermal deformation of the PAA in the laboratory is much easier. Therefore, a comprehensive influence parameters (CIPs)-finite element method (FEM) method was proposed in this study, it can extract the influence of above parameters on thermal deformation from temperature and thermal deformation measurement data and build a thermal deformation prediction model. Experiments have verified that the CIPs-FEM can greatly reduce the difficulty of thermal deformation modeling and have a high prediction accuracy. The phased array antenna (PAA) is an array of multiple T/R modules. The emission direction of the electromagnetic wave depends on the phase differences of all T/R modules . The powThe electrical performance decay of the PAA caused by the phase changes can be corrected by the compensation control method ,11,12. Ts−∆φs) ,14. In ts−∆φs) ,16. Lebrs−∆φs) . HoweverCurrently, the temperature is used to predict the thermal deformation of mechanical equipment in many studies . Thermal deformation is also affected by other factors such as the structural shape, assembly method, and material parameters  . For exaTheoretically, the influence of the structural shape, assembly method, and material parameters on thermal deformation is implicit in the real thermal deformation, and it is easy to directly measure the thermal deformation and temperature of the PAA panel in the laboratory ,16,17. TThe constitutive equations between the temperature and the thermal stress are shown as Equations (1)–(3):Due to the influence of the thermal deformation influence parameters, the coefficient of thermal expansion of the panel was not uniform. Therefore, CIPs are introduced in Equation (1), as shown in Equation (4):Then, the panel is divided into elements. The CIPs in Equation (4) are independently introduced into each element, and the element stiffness equation is established by the principle of the minimum potential energy , as showThe overall stiffness equation is synthesized, as the displacements of the coincident nodes of adjacent elements are equal, as shown in Equation (9):The solution method of CIPs is as follows.The IFME algorithm was proposed to obtain the CIPs according to the measurement data of temperature and thermal deformation in this study. The CIPs are set to the unknown in Equation (9). After introducing the thermal deformation and temperature measurement data, the The specific calculation method is as follows:The elements in Symbolic variables are used to calculate The function of derivation twice (Matlab code: diff) is used to calculate any value in For example, After finding all the coefficients in an equation, the constant term can be found. For example, After the calculation is completed, the symbolic variable can be converted into an ordinary variable .The values of A simulation panel that can simulate the heating process of the PAA is made. Based on the temperature and thermal deformation measurement data of the panel, the thermal deformation modeling effects of the CIPs-FEM and the traditional thermal deformation calculation method (FEM) were compared.As shown in All the temperature sensors were placed on the panel, and the initial temperature was recorded. Then, all temperature sensors were removed, and the initial positions of all thermal deformation measurement points in the X, Y, and Z axes were measured;All the temperature sensors were placed on the panel, and the heaters were turned on. After it was stabilized, the temperature was recorded. Then, all temperature sensors were removed, and the positions of all thermal deformation measurement points in the X, Y, and Z axes were measured. The temperature rise and the thermal deformation were calculated as: the measured value in step (2) minus the initial value in step (1).A total of 9 batches of experiments were performed in this study, referred to as M1 and P1–P8. M1 was used for modeling the deformation, and P1–P8 were used to test the prediction effect of the model. The processes of all experiments were the same, but the voltages of the heaters were different shown in . The detAs shown in E: 206 GPa;Elastic modulus v: 0.3;Poisson’s ratio Thermal expansion coefficient α:The data of M1 were used for modeling. Because the node was the deformation measurement point, the thermal deformation data were directly brought into the divided elements. For the temperature data, according to RSD) was used to measure the prediction accuracy, in Equation (16) [RSD, the higher the prediction accuracy.The temperature data of P1–P8 were taken into the CIPs-FEM and traditional FEM separately to predict thermal deformation. Then, the residual standard deviation (ion (16) : (16)RSDRSDs of P1–P8 are shown in RSD of the CIPs-FEM was smaller than that of the FEM. The mean RSDs of P1–P8 in the X, Y, and Z axes were 0.8, 0.9, and 2.0 μm, respectively, for the CIPs-FEM and 4.0, 6.0, and 26.4 μm, respectively, for the FEM. The increases in the accuracy were up to 80%, 85%, and 92%, 80%, with an average value of 85.6%.In Based on the thermal deformation prediction data, the performance compensation control effect of the PAA was tested by simulation. M and N are the numbers of rows and columns of the array; m, n), respectively.The phase of each T/R module includes the in-array phase and space phase. The in-array phase is controlled by a phase shifter. The space phase depends on the position of T/R modules on the panel. Then, if the space phase is changed by thermal deformation, the total phase can be corrected by adjusting the in-array phase, as shown in φ=0°,θ=−60°to60°were calculated as follows:The thermal deformation measurement points were considered as T/R modules. Then, a Ideal pattern: the T/R modules were in the ideal position;Pattern after thermal deformation: the positions of the T/R modules were the ideal position + the thermal deformation measurement;Pattern after compensation control: the positions of the T/R modules were the ideal position + the thermal deformation measurement. The in-array phase was adjusted for compensation control.The patterns of P1 and P8 are shown in According to the simulation test results, the compensation control can correct the decay of the PAA performance caused by thermal deformation. However, it needs to accurately predict the thermal deformation of the panel of the PAA. However, factors such as the structural shape, assembly method, and material parameters will affect thermal deformation, and these parameters are difficult to measure because of the complex structure. Therefore, some assumptions and simplifications have to be made when using the traditional FEM to simulate thermal deformation, which leads to a decrease in the simulation accuracy. The CIPs-FEM algorithm can reproduce the thermal deformation law from the thermal deformation measurement data. The above factors and the effect of simplifications and assumptions on thermal deformation can be fitted by CIPs. Therefore, the thermal deformation model can be established on a simplified structure. In addition, the thermal deformation can be predicted on the same simplified structure. The experiment results showed the CIPs-FEM has a high accuracy of predicting thermal deformation. The model problem: the thermal deformation characteristics extracted by the modeling algorithm are not real, that is, overfitting or underfitting.The structural problem: the thermal deformation characteristics of the structure are not stable.The most concerning issues for readers should be the robustness of the accuracy of the CIPs-FEM, which was analyzed as follows. The CIPs-FEM relies on data. It is like a memory algorithm that can extract and remember the thermal deformation characteristics of the structure from the temperature and thermal deformation data. Then, there are two reasons affecting the accuracy of the model:For the model problem, the nonuniform thermal deformation characteristics make the thermal deformation complicated. That is, after the same temperature change, the thermal deformations at different positions are different. In this study, the CIPs were introduced into each element separately for the FEM (as shown in Equation (4)). Then, each element could produce an independent anisotropic thermal normal strain and thermal shear strain. As long as the CIPs were appropriately selected, this modeling algorithm could fit complex thermal deformations. Therefore, the CIPs-FEM was not under-fitting. The experimental results can also prove this. For over-fitting, the experimental results showed that the CIPs-FEM model built by one batch of data had a good prediction effect for eight different temperature environments. Therefore, there was no obvious over-fitting. However, there is a potential collinearity problem that may lead to overfitting . The colFor the structural problem, the CIPs-FEM is a combination of the FEM and a data-driven model. It can only fit the thermal deformation characteristics in the measurement data. If the change in thermal deformation characteristics exceeds the measurement data, the prediction accuracy of the model will decrease. Obviously, the data-driven model cannot solve this problem. In this regard, the model can be rebuilt with new data. Alternatively, the thermal stability of the structure can be improved through a structural design. For example, the OKUMA company proposed a THERMO-FRIENDLY CONCEPT, to make the thermal deformation more stable when heated . In addiTo solve the problem of thermal deformation modeling in the PAA thermal compensation control, a new thermal deformation modeling method combined with the FEM and a data-driven model was proposed in this study, referred to as the CIPs-FEM. Based on the FEM, new parameters called CIPs were introduced. CIPs can fit the influence of other factors on thermal deformation, such as the structural shape, assembly method, and material parameters. The CIPs can be obtained using the proposed IFEM algorithm with the measurement data of temperature and thermal deformation. Then, an accurate thermal deformation model can be built with simplified material parameters and structure shape, which significantly reduces the difficulty of the thermal deformation modeling. The experimental results showed that under different temperature environments, based on the same simplified conditions, the CIPs-FEM can increase the thermal deformation prediction accuracy of the PAA panel by 85.6% compared to the traditional FEM. The simulation results showed that the compensation control can effectively correct the decay of the PAA electrical performance caused by thermal deformation.In addition to PAA thermal compensation control, the CIPs-FEM can also solve thermal deformation modeling problems in other fields, as long as there is a way to measure thermal deformation and temperature.


The effects of room temperature water, hot water, and steam tempering methods were investigated on sorghum kernel physical properties, milling, flour, and bread-making properties. Overall tempering condition and tempering moisture content were found to have a significant effect on the physical properties. Milling properties were evaluated using a laboratory-scale roller milling flowsheet consisting of four break rolls and eight reduction rolls. Room temperature tempering (18% moisture for 24 h) led to better separation of bran and endosperm without negatively impacting flour quality characteristics i.e., particle size distribution, flour yield, protein, ash, damaged starch, and moisture content. Bread produced from the flour obtained from milling sorghum kernels tempered with room temperature water (18% m.c for 24 h) and hot water (16% m.c at 60 °C for 18 h) displayed better bread-making properties i.e., high firmness, resilience, volume index, higher number of cells, and thinner cell walls when compared to other tempering conditions. Room temperature water tempering treatment (18% m.c for 24 h) could be a better pretreatment process for milling white sorghum kernels without negatively impacting the flour and bread-making quality characteristics. Although sorghum has advantages from some nutrition aspects and a crop production standpoint, the commercialization of sorghum-baked products is limited due to the lack of standard milling processes. The technology of milling sorghum is not as developed as that of wheat, rice or maize ,2. TradiThe similarity in kernel size of sorghum with that of wheat has led to the study of roller milling on sorghum . KebakilIn order to overcome bran contamination, the effect of tempering on sorghum kernels has been studied. Tempering, conditioning with water, is known to toughen the bran and soften the endosperm of grains to facilitate easy scratching of endosperm . AdditioSorghum can be a good gluten-free replacement for those suffering from celiac disease, a disorder that induces gluten intolerance. Currently, the only cure for this autoimmune digestive disease is to consume a gluten-free diet throughout their life . Hence, Due to the interconnection between tempering, grain properties, milling, and flour properties, the goal of this project was to have a complete assessment of the impact of tempering on the roller milling, physical properties of grains, flour properties, and bread properties using a commercially available white sorghum grain. While some past research has also been conducted in this area, the current study provides additional information to the important area of milling, flour quality, and food production, and will serve to identify areas where improvements can be made to sorghum milling and flour quality.Sorghum bicolor, were procured from Nu Life Market, Scott City, KS, USA for this study. The grains were of food grade and are free from foreign matter such as dust, dirt, stones, and chaff with an initial moisture content of 13.46% wet basis (w.b.). The initial moisture content (m.c) of sample was measured using ASABE Standard S352.2 method [The white sorghum grains (45.4 kg) belonging to the species 2 method .Samples were tempered with a predetermined amount of distilled water (water available at room temperature) . The temThe samples were treated with the pre-calculated amount of room temperature distilled water in the rotating tempering drums to bring them to a final m.c of 16 and 18% (w.b.). The treated samples were then placed into glass beakers and sealed with aluminum foil to prevent water evaporation. The beakers were next placed in a hot water bath at 60 °C for 12, 18, and 24 h . The glaThe samples were tempered with steam at 20 psi for 5, 10, and 15 s in a hollow container with a screw passing horizontally through the inside of the container. The horizontal screw was used to mix the sorghum samples as steam passed through the inlet, which allowed the uniform distribution of steam throughout the sample. After the samples were steam treated, the samples were spread on flat trays for about 8 h to evenly dry the samples. The samples were then transferred to zip lock bags and stored at 5 °C until needed.−4 m3) from a funnel set at a height of 10 cm from the mouth of the cup. The cup filled until excess sample began to overflow, and excess grain was removed by passing a scrapper in a zigzag motion over the cup. The bulk density of the samples was then calculated from the weight of the sample and volume of samples in the cup of known volume. Tapped density of both tempered and untempered sorghum samples was measured using Autotap Density Analyzer . A total of 100 ± 1 g of grain sample was filled into the cylinder of known volume, and the cylinder was then tapped using Autotap Density Analyzer for 750 times. The tapped density was calculated as the ratio of the mass of sample taken to the final volume after tapping.The bulk density of the tempered and untempered white sorghum samples was measured using Winchester cup arrangement . The samples were allowed to fall freely into a cup of volume 1 pint  was used to measure the true density of both tempered and untempered white sorghum samples. Helium gas was diffused into the chamber to fill the entire volume of the chamber. The volume occupied by the sample and the helium gas in the closed chamber was measured by the pycnometer. The true density of the sample was then determined as the ratio of the mass of the sample taken to the volume of sample occupied by the solid particles in the chamber.Physical properties  of untreated and tempered grain were measured by both the single kernel characterization system (SKCS) and abrasive hardness index (AHI) using a tangential abrasive dehulling device (TADD) .d) and height (h) of the formed grain pile was measured and the angle of repose (ϴ) was calculated using Equation (1).For the angle of response measurements, 450 g of tempered and untempered white sorghum sample was allowed to freely fall onto a flat surface from a funnel held at a height of 10 cm above the horizontal flat surface. The diameter  at which the sample begins to slide down the plate was measured to calculate the coefficient of rolling friction (r) using Equation (2).Approximately 150 g of sample was placed in a two-sided open cylinder resting on a horizontal galvanized steel plate fitted on a wooden board. The cylinder was removed to allow the sample to form a stationary cone on the plate. A magnetic compass was placed on the horizontal plate and calibrated to 0⁰ or no inclination. The plate attached to the board was tilted on one side by rotating the screw on the opposite side of the board. The angle , which was followed to estimate the damaged starch in flour. Total starch (%) of flour samples was analyzed using Megazyme Total Starch Assay Procedure  (AACCI Method 76-13.01). Protein (%), crude fat (%), crude fiber (%), and ash (%) content were analyzed using AACC Method 46-30.01, AACC Method 30-25.01, AACC Method 32-10.01, and AACC Method 08-01.01, respectively [The proximate composition of the sorghum flours was determined following the standard procedures AACC 76.33  using SDectively .The moisture content of the flour was measured in wet basis using standard method, AACC 44-15.02 . Bulk, tThree loaves of bread were made from each tempering treatment. The following formulation was used to make the sorghum bread with all ingredients in flour weight basis (fwb) (%): milled sorghum (white and waxy white sorghum) flour (90%), white rice flour  (10%), refined white sugar (6%), emulsified shortening (5%) , xanthan gum (3%) , double acting baking powder  (8%), common salt (1.5%), and distilled water (110%) .3) and baked in a rotary baking oven  at 204 °C for 30 min. Loaves were withdrawn immediately from the pans after removal from the oven and cooled for 2 h on wire racks to bring to room temperature. After cooling the breads, they were weighed to evaluate the loaf weight and bagged individually in polyethylene bags. The packed loaves were stored at room temperature overnight.The batter was made in KitchenAid 6 Quart Professional 600 mixer with a coated flat beater . The shortening and dry ingredients were weighed and first added to the mixing bowl after which the required amount of distilled water was added to the same. All ingredients were mixed with the flat beater paddle on speed 1 for 30 s. The batter was scraped down with a rubber spatula and mixed on speed 2 for 1.5 min. The final batter was weighed and transferred into greased metal pup loaf baking pans .Plus Texture Analyzer  in accordance with AACCI Method 74-10.02 [Volume index was determined using a cake template as described in AACC International Approved Method 10-91.01  using a 74-10.02 .p ≤ 0.05.The study was a completely randomized design with 11 treatments as follows: room temperature water ; hot water ; and steam for 5, 10, and 15 s. The results were analyzed for statistical significance using the PROC GLM  procedure in SAS  . Based on ANOVA, significant differences (α = 0.05) among the treatments were reported as significant at To evaluate how tempering may impact the physical properties of sorghum and subsequent milling quality and flour and bread quality, a commercial sample of white sorghum grain was tempered using different conditions and results were evaluated. Sorghum is known to be a diverse crop that can have a wide range of physical and chemical properties, which could impact tempering, milling, and flour quality. Compared to other crops used for flour production, there has been relatively little research conducted to standardize sorghum flour milling procedures. Thus, from a practical standpoint, a commercially available white sorghum sample was selected for this research to identify tempering methods that can be used in future research with additional samples.The bulk density for white sorghum kernels decreased when tempered . This waTapped density decreased for both room temperature water and hot water tempering as tempering moisture content increased . Likewis3 to 1325.13 kg/m3. The results obtained from this density study agreed with results obtained for coriander seeds [Steam-treated sorghum exhibited the lowest true density values respectively when compared to kernels tempered with room temperature and hot water . The sorer seeds  and soyber seeds .The dehulling characteristics of grains can be predicted using the abrasive hardness index, AHI ,23. TherTempering conditions had various effects on grain SKCS hardness index values. There was no significant difference in the HI values upon cold water tempering. Tempering with room temperature water had a significant effect on SKCS-HI values . LikewisMean kernel diameter and mean kernel weight of steam-tempered white sorghum were greater than untempered kernels , again lIn addition to density measurements and grain hardness and physical size measurements, tempering also influenced frictional properties of the grain. Duration of tempering or heat treatment did not significantly influence the coefficient of static friction . The greSteam-tempered white sorghum displayed the highest rolling coefficient of friction when compared to other treatments. The rolling coefficient of friction increased with increment in the moisture content when treated with room temperature or hot water . These rNo significant trend was observed in the rolling coefficient of friction with increasing heat treatment time. However, a distinct increase was observed in this frictional property when white sorghum was steam tempered at 20 psi from 5 to 15 s. The hollow cylinder containing the grains was removed for the sample to form a cone on the flat galvanized steel plate before the plate was inclined to calculate the rolling coefficient of friction. From the angle of repose results , steam-tp < 0.05) difference in flour yield with increasing tempering moisture content or type of tempering. Bran extraction from the grain during milling increased with increasing moisture content when tempered with room temperature, hot water or steam , tapped (581.40 kg/m3), and true (1478.83 kg/m3) density when compared to the other treatments.Bulk density of flour plays an important role in developing packaging material. Lower density flours occupy greater space, consequently increasing packaging material per unit weight. Tapped density is expressed as compacted bulk density. True density is the density of the material with respect to the actual volume occupied by it excluding closed and open pores. The bulk, tapped, and true density of the white sorghum flour produced in this research increased with increasing tempering moisture content when tempered with room temperature and hot water . SimilarThe lightness value of flour can be related to the bran contamination in flour. Along with the ash content, Kim and Flores  used colAnalysis of flour color indicated a slant towards the red side due to all positive a-values . The redThe positive b-values indicated that the flour obtained from all tempering treatments fell in the yellow range . The yelThe type of tempering, increasing moisture content, or tempering time, did not affect bake loss for white sorghum breads . Steam-tr = −0.84) indicated that the slice brightness of the bread crumb was significantly influenced by the ash content in the flour. Thus, white sorghum tempered with steam for 15 s produced flour with lowest bran contamination and highest lightness value. However, tempering time and tempering moisture content did not affect the slice brightness of the sorghum bread . No relationship was observed between slice resilience and type of tempering, tempering time, or tempering moisture content (The firmness of white sorghum bread decreased with the tempering condition changing from room temperature water (1359.83 g) to hot water (1229.30 g) to steam (1015.92 g). Crumb firmness of gluten-free bread was reported to increase with increasing amount of protein in the flour . Flour f content . The study revealed that steam tempering has the greatest impact on grains physical properties of all the different tempering conditions tested. Steam tempering of sorghum grain at 20 psi for 15 s produced the desired physical kernel properties such as lower bulk density; higher angle of repose and coefficients of friction; and lower hardness index for efficient handling, transportation, and processing, however, the high moisture content resulting from this treatment is unsafe and risky for storage. Though the steam-tempered kernels required the least time to temper, proper drying techniques and storage conditions would be essential to maintain the quality of the grain.The developed flowsheet using laboratory-scale roller milling produced an average of 60.24% flour from the white sorghum used to evaluate the tempering treatments. Steam tempering for 15 s produced white sorghum flour with greater total starch, least bran contamination, brightest color, and low ash content. However, this tempering method produced the lowest flour yield, protein content, and high damaged starch. Tempering white sorghum with room temperature water for 24 h to a final moisture content of 18% (w.b.) produced better flour yield without compromising the protein content of flour and with the lowest ash content and damaged starch. More than 98% of the total milled sorghum flour produced from all tempering methods was less than 212 μm and as such could be claimed to be flour under the current CFR. Tempering white sorghum with room temperature water for 24 h could be a suitable tempering method to obtain good flour yield and flour characteristics. However, the scaling up, cost estimation, and energy consumption assessment of the developed technique need to be evaluated.Tempering was also found to influence flour characteristics and, subsequently, sorghum bread characteristics. For example, sorghum bread made from flour using steam-tempered grains produced bread with higher brightness, which was attributed the lowest bran and ash content. Both room temperature-tempered grain (18% moisture) and grain tempered with hot water for 18 h (16% moisture) produced bread with a greater number of crumb cells and high firmness and resilience. As tempering conditions influenced flour properties such as starch damage, color, etc., tempering may be one avenue to impact sorghum food product quality, especially when considering interactions with ingredients such as hydrocolloids. Manipulating starch damage to alter water absorption and batter viscosity for example, may help reduce the use of expensive ingredients such as hydrocolloids or other additives in gluten-free breads. Further research on the interactions between tempering, milling, and flour functionality would benefit the gluten-free flour-baking industry.


IXA has overlapping mechanisms of action with BTZ and showed anti-leukemic activity in primary leukemic cells, encouraging further pre-clinical in vivo evaluation.At present, 20–30% of children with acute leukemia still relapse from current chemotherapy protocols, underscoring the unmet need for new treatment options, such as proteasome inhibition. Ixazomib (IXA) is an orally available proteasome inhibitor, with an improved safety profile compared to Bortezomib (BTZ). The mechanism of action  and growth inhibitory potential of IXA vs. BTZ were tested in vitro in human (BTZ-resistant) leukemia cell lines. Ex vivo activity of IXA vs. BTZ was analyzed in 15 acute lymphoblastic leukemia (ALL) and 9 acute myeloid leukemia (AML) primary pediatric patient samples. BTZ demonstrated more potent inhibitory effects on constitutive β5 and immunoproteasome β5i proteasome subunit activity; however, IXA more potently inhibited β1i subunit than BTZ (70% vs. 29% at 2.5 nM). In ALL/AML cell lines, IXA conveyed 50% growth inhibition at low nanomolar concentrations, but was ~10-fold less potent than BTZ. BTZ-resistant cells (150–160 fold) displayed similar (100-fold) cross-resistance to IXA. Finally, IXA and BTZ exhibited anti-leukemic effects for primary ex vivo ALL and AML cells; mean LC Acute leukemia is the most common type of pediatric malignancy, accounting for almost 30% of all cancer cases in this age group . While tBuilding upon the successful application of the proteasome inhibitor (PI) Bortezomib (BTZ) in the treatment of multiple myeloma (MM) ,8,9,10 aIxazomib (IXA) is an orally bioavailable drug with an improved pharmacokinetic profile  and safety profile (reduction in PN) ,26,27,28IXA and BTZ were kindly provided by Millennium Pharmaceuticals Inc. , a wholly owned subsidiary of Takeda Pharmaceutical Company Limited. Dexamethasone (DEX) was purchased from Sigma-Aldrich  and cytarabine (Ara-C) from Hospira Benelux BVBA . Proteasome subunit-specific fluorogenic substrates of β5, β5i, and β1i, AC-WLA-AMC, Ac-ANW-AMC, and Ac-PAL-AMC, respectively, were kindly provided by Millennium Pharmaceuticals Inc. , and the β1 substrate, Z-LLE-AMC, was purchased from BostonBiochem .2 humidified atmosphere. Cell cultures were refreshed twice weekly and seeded at a density of 3 × 105 cells/mL. BTZ-resistant cell lines with a low level of resistance  and a high level of resistance : CEM/BTZ7, CEM/BTZ200, THP-1/BTZ-7, THP-1/BTZ200, 8226/BTZ7, and 8226/BTZ100, were developed and cultured as described previously -2,5-diphenyl tetrazolium bromide (MTT) cytotoxicity assay (in triplicate) was used to determine in vitro drug sensitivity, essentially as previously described in detail ,42. Prio6 cells/mL) or AML (1 × 106 cells/mL) cells were incubated with different concentrations of IXA and BTZ in 100 µL cell culture medium in a 96-well round-bottom plate. Cryopreserved primary cells were carefully thawed and counted for viability by trypan blue staining before adding to the plate (only cell samples with >70% viability were included). After 96 h, before adding MTT, one control well per primary sample was used to examine the quality of the samples with trypan blue count. Patient samples were included when the optical density (OD) values from the MTT-assay were above 0.220. For samples with OD values between 0.120 and 0.220, the following additional criteria were applied: OD values of blank control wells must be at least lower than half of the OD values of the control wells and a regular dose–response curve was obtained. Samples with OD values below 0.120 were excluded. Since these primary patient samples do not proliferate and spontaneous cell death can occur, rather than IC50 values, LC50 values are presented, referring to the lethal concentrations that result in 50% more cell death compared to untreated control cells [To determine ex vivo drug sensitivity in primary leukemia cells, ALL  for 24 h. IXA-induced apoptosis was determined by measuring 7-amino-actinomycin D (7-AAD)  and Annexin V-FTIC staining  according to manufacturer’s instructions. Cells were first incubated for 15 min with 7-AAD and then washed with phosphate buffered saline (PBS) containing 0.1% human serum albumin before Annexin-V staining. Cells were measured using a Fortessa flow cytometer (BD Bioscience) and analyzed with FACSDiva software (BD Bioscience).Cells were seeded at 5 × 10Cells seeded for apoptosis assay were also used for cell cycle analysis. Cells were washed with PBS, fixed with 70% ice-cold ethanol, and placed at 4 °C overnight. After washing the cells twice with PBS, cells were incubated with RNAse (100 µg/mL) for 30 min at 37 °C. Then, propidium iodide was added at a concentration of 50 µg/mL, followed by analysis  with a Fortessa flow cytometer.p < 0.05. All statistical analyses were performed using GraphPad Prism Version 8.2 software .For the proteasome activity assay, slopes were calculated using a linear regression analysis. Paired t-testing was used to determine the statistical significance of the differences between IXA and BTZ. Statistical significance between AML and ALL was determined using the Mann–Whitney U test. Statistical significance was achieved when p = 0.01). For β5i, β1, and β1i, these inhibition profiles were comparable in BTZ7 and BTZ200 cells, but for β5, increasingly higher concentrations of both BTZ and IXA were required for (50%) inhibition. This is explained by the upregulation of β5 subunit expression in BTZ-resistant cells [The inhibition profile by IXA vs. BTZ of two constitutive proteasome (cP) subunits β5 and β1 and their immunoproteasome (iP) counterparts β5i and β1i was examined in CEM (ALL) and THP-1 (AML) WT cells and their BTZ7 (low level of BTZ resistance) and BTZ200 (high level of BTZ resistance) sublines . For WT nt cells , which i50 values are presented in 50 values for IXA were still in the low nanomolar range. All six BTZ-resistant cell lines displayed cross-resistance to IXA with a factor of 5–11 in the /BTZ7 cells (being 5–27-fold resistant to BTZ) and a factor of 103–111 in CEM/BTZ200 and THP-1/BTZ200 cells and 8226/BTZ100 cells (being 40–170-fold resistant to BTZ).The in vitro anti-leukemic activity of IXA was determined in CEM and THP-1 cells and, for comparison, 8226 (MM) cells and their BTZ-resistant sublines with a low level of BTZ resistance (/BTZ7) and a high level of BTZ resistance . The IXA dose–response curves for 4-day cell growth inhibition assays are depicted in Exposure of CEM/WT cells to 20 nM IXA for 24 h resulted in considerable apoptosis induction A. Quanti50 values of the single drugs are depicted in Since (pediatric) acute leukemia patients will not be treated with IXA as monotherapy, IXA was also tested in combination with chemotherapeutics commonly used for the treatment of ALL and AML . For ALL50 values for the individual samples are presented in 50 values of 4.5 ± 1 nM for BTZ and 24 ± 11 nM for IXA (p = <0.0001), and for AML, mean LC50 values of 11 ± 4 nM for BTZ and 30 ± 8 nM for IXA (p = 0.0001). In line with our previous data [p = <0.0001). However, no significant difference was found between ALL and AML cells for IXA (p = 0.0813). The combined dose–response curves for IXA vs. BTZ sensitivity in ALL and AML samples are shown in 50 values for IXA and BTZ were correlated . Interestingly, in AML samples, LC50 values for BTZ and IXA were not correlated  and cells with lower sensitivities for BTZ remained sensitive to IXA.Beyond analysis of IXA sensitivity in leukemia cell lines , we alsoous data , ALL celrrelated C. In ALLThe pivotal role of the ubiquitin–proteasome system for protein degradation and processing in immune cells has been successfully exploited for the treatment of hematological malignancies such as MM by a PI, initially BTZ ,47,48,49IXA showed overlapping activities with BTZ, although equipotency in T-ALL and AML cell lines was reached at approximately 10-fold higher concentrations than BTZ in the BTZ-sensitive and -resistant cell lines. Moreover, IXA was a more potent inhibitor of β1i immunoproteasome subunits than BTZ. However, this property did not overcome cross-resistance to IXA in BTZ-resistant cell lines.NPM1 mutation status in AML cells was associated with IXA sensitivity [To anticipate the potential efficacy of PIs in leukemia cells, understanding of the proteasome subunit composition is of relevance, as unlike solid tumors, the dominant fraction (>70%) is composed of iP subunits rather than cP subunits ,11,41,50sitivity ,54, indiPSMB5 gene, encoding the β5 subunit, introducing amino acid substitutions  in the conserved BTZ binding pocket of the β5 subunit protein [PSMB5 mutations have been commonly found in hematological and solid tumor cell line models [PSMB5 mutation in MM cell line models with acquired resistance to IXA, which were cross-resistant to BTZ. Interestingly, these PSMB5 mutations in IXA-resistant MM cells introduced an additional amino acid substitution , indicating another critical amino acid in the β5 binding pocket for IXA. This may not be surprising given the two bulky chlorine moieties in the chemical structure of IXA [PSMB5 mutation as a common molecular mechanism of (cross-)resistance, it is important to acknowledge that several other mechanisms of resistance have been reported [Direct comparison of the growth inhibitory effectiveness of IXA vs. BTZ in leukemic cells lines showed that BTZ was ~10-fold more potent than IXA, being consistent with data for MM cell line studies . Conceiv protein ,40. Exone models ,58,59,60e models . Anothere models  also idee of IXA . Althougreported ,62 whichreported ,65,66,67reported ,68.Even though primary ALL and AML cells differ from leukemia cell lines in the constitutive and immunoproteasome composition profile ,41,51, tThe lower (in vitro) potency of IXA compared to BTZ may, in vivo, be compensated by a better pharmacokinetic profile with higher plasma levels and longer plasma half-life of IXA ,26,69. OC+ AML cells [Considering that IXA will not be used as a single agent in leukemia treatment, it is important to identify potential candidates among commonly used anti-leukemic drugs for combination therapy regimens in pediatric acute leukemia treatment. In this respect, a study in relapsed/refractory AML revealed that IXA did not appear to increase the toxicity of the standard MEC regimen with mitoxantrone, etoposide, and Ara-C, although effectivity was limited . In MM, ML cells . More reML cells . Our obsIn conclusion, IXA has overlapping mechanisms of action with BTZ and showed anti-leukemic activity (in the low nanomolar range) in (CEM and THP-1) leukemia cell lines in vitro and in primary leukemic cells, including AML and relapsed ALL patient samples. However, for equi-effective activity, higher concentrations of IXA were needed than for BTZ in ALL. This apparent shortcoming may be compensated by IXA’s more favorable pharmacokinetic and safety profile over BTZ, as demonstrated in clinical studies in MM and adult acute leukemia. Therefore, optimal IXA dose scheduling deserves further exploration in future pre-clinical leukemia models.