RCKW. We find that the binding of MLi-2 (a type I kinase inhibitor) stabilizesa closed kinase conformation and reduces the global dynamics of LRRK2RCKW, leading to a more compact LRRK2RCKW structure.In contrast, the binding of Rebastinib (a type II kinase inhibitor)stabilizes an open kinase conformation, which promotes a more extendedLRRK2RCKW structure. By probing the distinct effects ofthe type I and type II inhibitors, key interdomain interactions arefound to regulate the communication between the kinase domain andthe GTPase domain. The intermediate states revealed in our simulationsfacilitate the efforts toward in silico design ofallosteric modulators that control LRRK2 conformations and potentiallymediate the oligomeric states of LRRK2 and its interactions with otherproteins.Mutations in the human leucine rich repeat protein kinase-2(LRRK2)create risk factors for Parkinson’s disease, and pathologicalfunctions of LRRK2 are often correlated with aberrant kinase activity.Past research has focused on developing selective LRRK2 kinase inhibitors.In this study, we combined enhanced sampling simulations with HDX-MSto characterize the inhibitor-induced dynamic changes and the allostericcommunications within the C-terminal domains of LRRK2, LRRK2 Mutat11 To date, most drug research focuses on type I inhibitorsbecause of their better selectivity,13 and many experimentshave been conducted to increase their efficacy. Structural studiesbased on homology models or a kinase domain surrogate were used fordesigning optimal inhibitors with better potency and exquisite selectivity.15 However, some potent type I kinase inhibitors, such as MLi-2 (MerckLRRK2 Inhibitor 2), appear to stabilize a disease-like cellular phenotypewhere LRRK2 accumulates on microtubules (MT).16 In contrast, some high affinity type II kinase inhibitors, suchas Rebastinib, Ponatinib, and GZD-824, appear to lock LRRK2 into aconformation that is unable to bind to MT.18 Both type I and type II LRRK2 inhibitors inhibit LRRK2-mediatedphosphorylation of Rab proteins, and both can also stimulate mitophagy,which is negatively regulated by LRRK2.19 Only type I inhibitors, however, reduce the phosphorylation of well-studiedLRRK2 biomarker sites at the N-terminal region of LRRK2 by inducingdephosphorylation while type II inhibitors do not.20 This suggests that, in addition to the intrinsic kinaseactivity toward Rab substrates, the conformation of the LRRK2 protein,likely regulated by the opening and closing of the kinase domain andmodulated by binding of 14–3–3 proteins,22 plays an important role in mediating the steady state phosphorylationof the biomarker sites, which regulate LRRK2 function.20There are two widely studied classes of kinaseinhibitors, ATP-competitivetype-I inhibitors that bind to the kinase domain and lock it intoa closed and active-like conformation and type-II inhibitors thatcan be either ATP-competitive or noncompetitive with ATP and typicallymaintain the kinase in an open inactive conformation.RCKW), and these providean incredibly valuable resource for delving more deeply into the mechanisticfeatures that regulate LRRK2 structure and function.23 The kinase domain is surrounded by the CORA, CORB, and WD40 domains function; the relationshipbetween the kinase activity and its MAP function is not well understood.26 A key question is what roles the conformation of the kinase domainplays in the intrinsic regulatory processes that mediate subcellularlocation and activation of LRRK2.27To date, several high-resolution LRRK2structures are availableof both full length LRRK2 and a truncated version of LRRK2 that containsonly the C-terminal domains , to show how LRRK2 dynamics is affected differently by bindingto type I vs type II kinase inhibitors. The results show how the Nand C lobes function as independent rigid bodies that are stabilizedby MLi-2 in a closed and active-like conformation but stabilized byRebastinib in an open conformation where the two domains are uncoupled.The critical regulatory triad  formed in the MLi-2 structure allowsthe regulatory spine (R-spine) to assemble in an active-like conformation.In contrast, the triad is broken in the Rebastinib-bound LRRK2RCKW structure where D2017 is far from the K1906-E1920 ionpair, and in this structure the R spine is broken. Our studies alsovalidate the importance of the dynamic features of the Dk helix, whichplays a crucial role in bridging the two catalytic domains, the kinaseand GTPase domains.28In this study, using Gaussian accelerated molecular dynamics MD(GaMD) simulations coupled with hydrogen–deuterium exchangemass spectrometry (HDX-MS), we studied the C-terminal domains of LRRK2,which include the ROC, COR, kinase, and WD40 domains .We then mapped the HDX-MS data onto the LRRK2RCKW model,which is based on the LRRK2RCKW structure (PDB: 6vno),17 and focused on the kinase domain first (Table S1). We found that while MLi-2 reduces the deuterium uptakeof the kinase domain, Rebastinib shows a different effect on the Nlobe and C lobe of the kinase domain. For peptides that cover theCORB–kinase linker (aa 1876–1883), β3−αClinker (aa 1905–1916), and N-Lobe–C-Lobe linker (aa1948–1958), binding of Rebastinib reduces the H–D exchangebut not as effectively as MLi-2. For example, in the linker peptide(aa 1948–1958), Rebastinib reduces the deuterium uptake at2 min from 50% to 35% of its maximum uptake (5 to 3.5 Da out of maximum10 Da), while MLi-2 reduces the uptake to 27% (2.7 Da). In contrast,for the glycine-rich loop (aa 1884–1893), which is highly protectedby MLi-2, the deuterium uptake was unchanged when binding to Rebastinib.For the αC−β4 loop, the low deuterium uptake indicatedthat it is almost completely shielded from the solvent in the conditionwithout an inhibitor, and both MLi-2 and Rebastinib reduce their deuteriumuptake even more.To gain insight into how typeI and type II inhibitors affect the solvent accessibility of the catalyticdomains of LRRK2, we used HDX-MS to measure the deuterium uptake changesof LRRK2 protein S1.We thInterestingly, binding of Rebastinib does not reducethe deuteriumuptake in the C lobe of the kinase domain. Instead, the deuteriumuptake is actually increased in several regions in the C lobe 2. The acRCKW (RCKW), LRRK2RCKW in complex with MLi-2 (RCKW/MLi-2), and LRRK2RCKW with Rebastinib (RCKW/Rebastinib). The RCKW structure was builtbased on the reported cryo-EM structure (PDB: 6VNO), and the inhibitorcoordinates were obtained by superimposing the reported respectiveinhibitor-bound kinase structures through the conserved αE andαF helices (PDB: 5OPB for MLi-2 and PDB: 6MWE for Rebastinib).29 The RCKW/inhibitor models were relaxed using energy minimization,applied first to the side chain and then to the overall structure.Ten replicated simulations were carried out for each of the models. Both inhibitors stayed inthe binding pocket throughout the simulation, and the RMSD value ofthe inhibitors indicated that both inhibitors bound to the LRRK2 proteinstably. To show the detailed binding modes of the inhibitors, therepresentative structure of RCKW/inhibitor is shown in 30 The LRRK2 kinase was ina “DYG-In” conformation for 90% of the simulation time,which indicates that binding of MLi-2 stabilizes the LRRK2 kinasein an active-like state .To explore the allosteric impact of inhibitor binding, we carriedout three GaMD simulations: LRRK231 The salt bridges between two conserved residuesin the N lobe, E1920 in the αC helix and K1906 in β3,and D2017 in the DYG motif in the C lobe are essential for activeforms of kinases. The free energy profiles along the distances ofthe two salt bridges are shown in Movie 1).32 Consistent with HDX-MDdata, the YRD motif, where Y1990 is an R-spine residue, is also stabilizedby MLi-2, shown by the smaller RMSD values of residues in the regionof the YRD motif . In addition,MLi-2 stabilized residues in the hinge region. Another hallmark signatureof an active kinase that is in a fully closed conformation is theposition of the hydrophobic residue in the glycine-rich loop, whichis F1890 in LRRK2. As seen in The binding of MLi-2 also promoted the assemblyof the highly conservedregulatory triad, the salt bridges that define every active kinase3A.31 TheRD motif S3. In ad30 The DYG motif was unable to flip back into theDYG-In orientation for the entire simulation time . Rebastinib breaks the hydrophobic bridge betweenthe N and C lobes, which defines the active kinase; it makes the closingof the active site cleft impossible. When they are uncoupled fromthe N lobe, the dynamics of the DYG motif and the YRD motif were bothalso increased compared to the LRRK2RCKW alone, as indicatedby the higher RMSD values of residues in both motifs . The isoquinoline of the Rebastinibis positioned against the αC helix and inhibits the movementof the αC helix toward the C lobe, preventing the kinase domainfrom adopting a closed, active-like conformation. The E1920 on theαC helix is bound to Rebastinib through H-bonding with the twonitrogens on Rebastinib, leaving it far away from D2017 in the DYGmotif of the C lobe. Although the salt bridge between K1906 and E1920was stably present throughout the MD simulations, the regulatory triadnever assembled (Movie 2).In contrast to MLi-2, Rebastinib occupies the ATPbinding siteand the adjacent hydrophobic pocket, replacing the space that wasoccupied by Y2018 of the DYG motif in the MLi-2 structure 3D, E. Thion time S2. RebasRCKW, we performed clusteringanalysis to extract representative structures from our simulationdata. The first class of each condition, RCKW, RCKW/MLi-2, and RCKW/Rebastinib,is aligned by the stable helices, helixes E and F, in the C lobe ofthe kinase domain , while the WD40 domain remains stablyanchored to the C lobe in all of the structures solved so far. TheGly-rich loop and the αC helix are closer to the C lobe whenLRRK2RCKW binds to MLi-2 compared to the RCKW or RCKW/Rebastinibsystems, showing a closed conformation.To further deduce how binding to the kinase inhibitors changesthe conformation of LRRK2e domain 5A. While33 where the Gly-loop folds over the nucleotideand the αC helix is closer to the β-sheet core of theN lobe . The glycine loop, includingthe Phe residue at the tip of the G loop, folds over the MLi-2 whilethe Phe in the ATP:PKI bound structure of PKA (Phe54) folds over ontothe DFG+1 residue and reinforces the hydrophobic latch between theN and C lobes. The tip of the αC helix, however, remains anchoredto the Dk helix, although it is closer to the activation segment ofthe kinase domain. When binding to Rebastinib, the N lobe is furtheraway from the C lobe compared to the MLi-2 bound conformation. Byoccupying the space that was filled by Y2018 in the MLi2-bound structure,Rebastinib essentially breaks the R-spine, which severs the N andC lobes and forces the N-terminus of the αC helix away fromthe C lobe . This inhibited structure, however,is distinct from the RCKW/Rebastinib structure; it does not correspondto the stable inhibited conformation that is found in the full lengthLRRK2.We note that the RCKW/MLi-2 structure is stilldistinctly differentfrom the ATP-bound, fully closed, active conformation, based on PKA,eN lobe S5. The gFigure S7A). In agreement, the COR-B loop alsoshows a smaller deuterium uptake when LRRK2RCKW binds toMLi-2 . This orientation resemblesthe active LRRK2 structure recently solved by Sun’s group.24 In contrast to MLi-2, binding of the Rebastinibdid not alter the N lobe orientation, and the HDX-MS result also showsno protection effect on the COR-B loop. We also analyzed the reported“seesaw-like” motion of the ROC αC helix relativeto the COR-B domain, which was shown to be related to LRRK2 activation.24 In the inactive conformation, the C-terminalend of the ROC αC helix stays closer to the C-terminal end ofthe Dk helix. R1441 at the C-terminal end of the ROC αC helix,in particular, can interact with W1791 at the C terminus of the Dkhelix, which anchors the ROC αC helix to the surface of theCOR-B domain in the inactive state. Rebastinib binding stabilizesthis inactive conformation and tilts the ROC αC helix in thedirection toward the Dk helix .We then sought to understand how interdomaininteractions between the COR-B domain and the kinase domain are involvedin regulating the activation of LRRK2. When the kinase domain is stabilizedin the closed conformation by MLi-2, the N lobe of RCKW/MLi-2 rotatesalong the Dk helix of the COR-B domain and moves closer to the COR-Bdomain 5B andC.Table S1). The peptide thatcovers the C-terminus of the ROC αC helix (residues 1436–1449)shows increased deuterium uptake in the presence of MLi-2. This isconsistent with the MD results showing that the interaction betweenW1791 and R1441 was disrupted by the binding of MLi-2, releasing theROC αC helix and making it more dynamic and solvent accessible.Interestingly, two PD mutations are included in this important peptide,R1441 and N1437. In addition, the decreased deuterium uptake of theC terminus of the Dk helix (residues 1788–1795) can be attributedto its increased interactions with the activation segment observedin the simulation that shielded these residues from the solvent . Binding of the Rebastinib does notshow any effect on the deuterium uptake of either the ROC αChelix or the Dk helix. However, peptides that cover the residues atthe interface of ROC–CORA (residues 1391–1401), CORA–CORB(residues 1666–1673), and ROC–CORB (residues 1467–1484)show decreased deuterium uptake in the presence of Rebastinib, whilebinding to MLi-2 has no protection effect. This suggests that theanchoring of the ROC αC helix to the Dk helix not only influencesthe communication that takes place between the kinase domain and theCORB domain but also alters the cross-talk within the ROC, CORA, andCORB domains.The dynamicchanges related to the domains that flank the kinase domain were alsocaptured by HDX-MS . To explore how binding of MLi-2 and Rebastinib affects the breathingdynamics of LRRK2RCKW as well as the kinase domain equilibrium,we computed a two-dimensional energy landscape for each simulationcondition  and Rebastinib-boundLRRK2 RCKW (29.8 Å).Using GaMD simulations,we had previously captured the breathing dynamics of LRRK2bodies28 5 and S4.ondition 7A–C. Thetogether 7D. For aA2054 and CαW1791 in RCKW were sampled accordingto the distributions along the x axis of x axis for Rebastinib-bound LRRK2 shifts to a more extended configurationand has a minimum at 25 Å. We also calculated the 2D energy profilesusing the C lobe–CORA distance and the kinase conformationalcoordinate in Figure S9. Similarly, onlyMLi-2-bound LRRK2RCKW has a population representing a verycompact LRRK2, while the Rebastinib-bound LRRK2RCKW showsminimal population for the compact configuration.The distance between the C lobe and the CORB domain.Based on the three energy profiles, the closedor open conformationof the kinase domain is in concert with the compact or extended conformationof LRRK2d states 7B. MLi-2 sampled 7C and S936 Previous studies showed that different distributionsof LRRK2 in the cell and different phenotypes are captured in thepresence of type I or type II kinase inhibitors.18 The study of Rab protein phosphorylation with type I and type IIinhibitors also indicates that the function of LRRK2 is more thanthe activity of the kinase domain. In this study, we used GaMD simulationscoupled with HDX-MS to analyze the dynamic changes of LRRK2 and itsinterdomain allosteric communications. We have shown that the kinasedomain serves as a central hub for interdomain communication and isthe major driver for LRRK2’s conformational transitions.37 We also identified the key interactions within and between domains,which connect different effects of kinase inhibitors to the overallconformational states of LRRK2.The function of LRRK2 is mediated by twofinely tuned regulatoryswitches, a kinase domain and a GTPase (ROC) domain, which regulatehow LRRK2 toggles between its active and inactive states.Here, we use MD simulationsto show how different inhibitors capturedifferent conformational ensembles and then validated these conformationaldifferences using HDX-MS. Even though MLi-2 (type I) and Rebastinib(type II) both inhibit the kinase activity with high affinity, theydrive LRRK2 into different conformational states. Binding of MLi-2reduced the solvent accessibility of the kinase domain not only inthe active site cleft where the MLi-2 is directly docked but alsoin regions in the C lobe that lie far from the active site cleft butnevertheless interact with other domains. The HDX-MS data indicatethat binding of Rebastinib traps the kinase domain in an open conformation,which separates the C lobe of the kinase domain from the N lobe, makingit more solvent exposed and more dynamic. The MD simulation resultsshow that Rebastinib-bound LRRK2 results in a more dynamic kinasedomain that favors an open, inactive conformation where the N andC lobes are uncoupled. In contrast, the MLi-2-bound LRRK2 simulationsshow that the closed, active-like conformations are sampled more frequently.RCKW. In this study, we showed the distinct roles of the Dkhelix in the CORB domain, which is anchored to the αC helixin the N lobe of the kinase domain. This Dk helix is part of the allostericcross-talk that takes place between the kinase and ROC domains. Ourresults are in agreement with the recent report showing that bridgingof the ROC domain and the activation segment in the kinase domainis different in the active and inactive structures.24 When the kinase is locked in an open conformation by Rebastinib,the communication between the N and C lobes of the kinase domain isdisrupted, which is consistent with the N lobe functioning as an independentrigid body.23 The C lobe of the kinasebecomes more dynamic and uncoupled from the CORB domain.The changes in conformation and dynamics that are caused by bindingof kinase inhibitors also extend beyond the kinase domain in LRRK2RCKW is stabilized in an extended state by Rebastinib,whereas binding of MLi-2 stabilizes a closed, active-like conformationof the kinase domain  was expressedand purified from Sf9 cells as described previously.18 The protein used for hydrogen/deuterium exchange mass spectrometry(HDX-MS) was analyzed by SDS-PAGE . HDX-MS was performed using a Waters Synapt G2Si equipped witha nanoACQUITY UPLC system with H/DX technology and a LEAP autosampler.The LRRK2RCKW concentration was 5 μM in LRRK2 buffercontaining 20 mM HEPES/NaOH pH 7.4, 800 mM NaCl, 0.5 mM TCEP, 5% glycerol,2.5 mM MgCl2, and 20 μM GDP. MLi-2 and Rebastinibwere dissolved in DMSO to make a 10 mM stock solution. Inhibitorswere added into LRRK2RCKW protein in LRRK2 buffer to makethe LRRK2/inhibitor complex samples. The deuterium uptake was measuredin LRRK2 buffer in the presence and absence of the kinase inhibitorMLi-2 (50 μM) or Rebastinib (50 μM). For each deuterationtime, 4 μL of the complex was equilibrated to 25 °C for5 min and then mixed with 56 μL of D2O LRRK2 bufferfor 0, 0.5, 1, or 2 min. The exchange was quenched with an equal volumeof quench solution . Thequenched sample (50 μL) was injected into the sample loop, followedby digestion on an in-line pepsin column  at 15 °C. The resulting peptides were captured on a BEHC18 Vanguard precolumn, separated by analytical chromatography using a 7–85% acetonitrile gradient in 0.1% formic acid over7.5 min and electrosprayed into the Waters SYNAPT G2Si quadrupoletime-of-flight mass spectrometer. The mass spectrometer was set tocollect data in the Mobility, ESI+ mode; mass acquisition range of200–2000 (m/z); scan time,0.4 s. Continuous lock mass correction was accomplished with infusionof leu-enkephalin (m/z = 556.277)every 30 s .For peptide identification, the mass spectrometer was set to collectdata in MSE, ESI+ mode instead.LRRK2E analyses of 10 μMLRRK2RCKW, and data were analyzed using PLGS 3.0 (WatersCorporation). Peptide masses were identified using a minimum numberof 250 ion counts for low energy peptides and 50 ion counts for theirfragment ions. The peptides identified in PLGS were then analyzedin DynamX 3.0 (Waters Corporation) using a cutoff score of 6.5 anderror tolerance of 5 ppm and requiring that the peptide be presentin at least two of the three identification runs. The peptides reportedon the coverage maps are those from which data were obtained. Therelative deuterium uptake for each peptide was calculated by comparingthe centroids of the mass envelopes of the deuterated samples vs theundeuterated controls.43 For all HDX-MSdata, at least two replicates of protein ID runs were applied to checkthe quality between protein batches. Each time point was analyzedwith three replicates. Data are represented as mean values ±SEM of three replicates. The deuterium uptake was corrected for back-exchangeusing a global back exchange correction factor  determinedfrom the average percent exchange measured in disordered termini ofvarious proteins.44 Deuterium uptake plotswere generated in DECA (github.com/komiveslab/DECA), and the data are fitted with anexponential curve for ease of viewing.45The peptides wereidentified from triplicate MSRCKW model for simulations was prepared basedon the reported LRRK2RCKW structure (PDB: 6VP6). The inhibitorbound structures were modeled based on PDB: 5OPB (MLi-2) and PDB: 6MWE (Rebastinib). Modellerwas used to model the missing loops.46 TheProtein Preparation Wizard was used to build missing side chains andmodel charge states of ionizable residues at neutral pH. Hydrogensand counterions were added, and the models were solvated in a cubicbox of TIP4P-EW water molecules47 and 150mM KCl with a 10 Å buffer in AMBER tools. AMBER16 was used forenergy minimization, heating, and equilibration steps, using the CPUcode for minimization and heating and GPU code for equilibration.Parameters from the Bryce AMBER parameter database were used for phosphoserineand phosphothreonine.48 Systems were minimizedby 1000 steps of hydrogen-only minimization, 2000 steps of solventminimization, 2000 steps of ligand minimization, 2000 steps of side-chainminimization, and 5000 steps of all-atom minimization. Systems wereheated from 0 to 300 K linearly over 200 ps with 2 fs time-steps and10.0 kcal/mol/Å position restraints on protein. Temperature wasmaintained with a Langevin thermostat. Constant pressure equilibrationwith an 8 Å nonbonded cutoff and particle mesh Ewald was performedfor 300 ps with protein and peptide restraints, followed by 900 psof unrestrained equilibration. Gaussian accelerated MD (GaMD) wasused on GPU-enabled AMBER16 for enhanced conformational sampling.49 GaMD applies a Gaussian distributed boost energyto the potential energy surface to accelerate transitions betweenmetastable states while allowing accurate reweighting. Both dihedraland total potential boosts were used simultaneously. Potential statisticswere collected for 2 ns standard MD followed by 2 ns of GaMD, duringwhich boost parameters were updated. Each GaMD simulation was equilibratedfor 10 ns. For each construct, 10 independent replicates of 210 nsproduction GaMD were run in the NVT ensemble, for an aggregate of2.1 μs of accelerated MD.The LRRK2M bins. The weighted histogram at bin m can be determined byVi is the boost potential at the ith frame, N is the total number of frames, and δm,i is an indicator functionthat determines if frame i falls into bin m. The Maclaurin series expansion methodwas used to approximatethe exponential term.50 The free energyprofile can be determined byFree energy landscapes wereprojected along selected conformational coordinates e.g., 7A–C. For


In this study, we aimed to untangle the effects of domain–domain correlations on Kratky’s q3 peak based on the computed SAXS of the crystal structure of IgG1. The q3 peak appeared in the static structure and was more prominent in the Fc region than in the Fab or isolated domains. Further brute-force analysis indicated that longer domain–domain correlations, including the inter-region, also positively contribute to Kratky’s q3 peak. Thus, the distortion of the Fc region and a longer inter-region correlation initiate acid denaturation and aggregation.Antibody aggregation, followed by acid denaturation and neutralization of pH, is one of the reasons why the production of therapeutic monoclonal antibodies (mAbs) is expensive. Determining the structural details of acid-denatured antibodies is important for understanding their aggregation mechanism and for antibody engineering. Recent research has shown that monoclonal antibodies of human/humanized immunoglobulin G1 (IgG1) become smaller globules at pH 2 compared to their native structure at pH 7. This acid-denatured species is unstable at pH 7 and prone to aggregation by neutralization of pH. Small-angle X-ray scattering (SAXS) data have revealed an acid-induced reduction in the subpeaks in Kratky plot, indicating conformational changes that can lead to aggregation. The subpeaks are well resolved at pH > 3 but less pronounced at pH ≤ 2. One of the weakened subpeaks indicates loosely organized inter-region (Fab-Fab and Fab-Fc) correlations due to acid denaturation. However, the structural origin of the other subpeak (called  Protein denaturation and aggregation are inherent and universal phenomena in vivo and ex vivo, and both are often undesirable factors for health. The subject of this study is the latter, ex vivo denaturation and aggregation of therapeutic monoclonal antibodies (mAbs), which are biopharmaceuticals. Because the aggregates of mAbs lead to reduced drug efficacy and potential side effects ,2, they mAbs are purified by affinity chromatography, where an acid solution is used as an eluent . An acidTo address the acid-denatured structure, we have collected small-angle X-ray scattering (SAXS) data of mAbs of human/humanized immunoglobulin G1 (IgG1) in an acid solution . IgG1 isRg). The SAXS signals of the domain–domain correlations often appear at the q-region higher than the Guinier region  yields P(r)IgG-Fc_3c and their corresponding Kratky plots indicate the q3 peak is attenuated and conserved. Subtracting the P(r) component of 10c [P(r)IgG_10c] from P(r)IgG, which is P(r)IgG-IgG_10c, reduces the q3 peak intensity and shifts it to a higher q. We found that subtracting both [P(r)Fc_3c and P(r)IgG_10c] effectively diminished the q3 peak. Other domain–domain correlations and their combinations also contributed to the q3 peak.In q3 = ~0.16 Å−1, was investigated based on the crystal structure. In addition to the shorter CH2-CH3 correlations in Fc, the longer correlations between the domains with distances of ~80–90 and ~125 Å also strongly contributed to this q3 peak. For example, CH3(H) in Fc and CL(L) in Fab1 are separated at an averaged distance of 85.4 Å and this combination is the positive contributor. The contributions of the domain–domain correlations are shown in In summary, the origin of the subpeaks in the Kratky plot of IgG1, especially q3 peak are sensitive to conformational fluctuations  in the Fc region. A specific combination positively (or negatively) contributing to the q3 peak in this analysis  analysis at lower q to higher q and P(r) analysis at smaller r to longer r to be self-consistent. Such an analysis would be more feasible for a monodispersed sample (impurity-free or aggregate-free) and for SAXS data with a higher S/N ratio. Even if the mixture contains unavoidable large impurities, such as aggregates, the SAXS of the large particles decays steeply and is less prominent in the higher q region, in which Kratky analysis is preferred, and extracts information from such limited data. Even if the preliminary data are noisy, the Kratky peak is useful for deducing structural changes, which can serve as a working hypothesis for further research.The subpeaks in the Kratky plots involve many correlations. Assigning them should be in conjunction with q3 peak is shared by IgG1, IgG2, and IgG4 and is less pronounced upon heat-induced aggregation (IgG1) [q3 peak in the Kratky plot can be used as a good indicator of the native-like, long-range-ordered structure or the dynamics of mAbs.Interestingly, this n (IgG1)  or acid-n (IgG1) . The lonα-based” computation in this manuscript. The measure of the distance between selected pairs of IgG1 Cα coordinates (PDB ID:1HZH) provides the number of pairs of points separated by a distance, r, termed n(r). n(r) is proportional to the distance distribution function P(r) of the SAS object and is therefore regarded as P(r). Scattering I(q) was calculated using the Fourier transform of P(r), as shown in Equation (2):q is defined as q = |q| = 4πsinθ/λ, q is the scattering vector, 2θ is the scattering angle, and λ is the wavelength of the X-ray or neutron. n(r), P(r), and I(q) were processed using an in-house program [The following SAXS calculation is called “C program  in IGOR https://www.uniprot.org (accessed on 25 July 2023)) after sequence alignments. The UniProt IDs used were P01857 (CH1), P01857 (CH2), P01857 (CH3), P0CG04 (CL), P01825 (VH), and P01703 (VL). Linker regions were not included in the I(q) calculation.Each domain of IgG1 was assigned according to “Family & Domains” information in UniProt  and (2) is straightforward. The CRYSOL program [I(q), which involves both the intradomain and the inter-domain correlations, and thus does not intend to address the decomposition of these contributions.This study focused on extracting the inter-domain correlations, for which “C program  outputs H and VL sequences.Experimental SAXS data for humanized immunoglobulin G1 (IgG1) (148 kDa), termed mAb-A, were collected using the BL-10C beamline at the Photon Factory of the High Energy Accelerator Research Organization (KEK) in Tsukuba, Japan . The dat


Escherichia coli and Bacillus subtilis, it contains five single-copy domains. Deviations from this canonical domain architecture have been reported, however, a broad genome-wide analysis of CheA diversity is lacking. Here, we present results of a genomic survey of CheA domain composition carried out using an unbiased set of thousands of CheA sequences from bacteria and archaea. We found that four out of five canonical CheA domains comprise a minimal functional unit (core domains), as they are present in all surveyed CheA homologs. The most common deviations from a classical five-domain CheA architecture are the lack of a P2/CheY-binding domain, which is missing from more than a half of CheA homologs and the acquisition of a response regulator receiver (CheY-like) domain, which is present in ~35% of CheA homologs. We also document other deviations from classical CheA architecture, including bipartite CheA proteins, domain duplications and fusions, and reveal that phylogenetically defined CheA classes have pre-dominant domain architectures. This study lays a foundation for a better classification of CheA homologs and identifies targets for experimental investigations.Chemosensory systems in bacteria and archaea are complex, multi-protein pathways that enable rapid cellular responses to environmental changes. The CheA histidine kinase is a central component of chemosensory systems. In contrast to other histidine kinases, it lacks a sensor (input) domain and utilizes dedicated chemoreceptors for sensing. CheA is a multi-domain protein; in model organisms as diverse as  Escherichia coli is the best studied chemosensory system  are multi-protein pathways that enable fast “decision making” in bacteria and archaea in response to rapid changes in their microenvironment, such as increasing or decreasing concentrations of nutrients and toxins , 2. A pay system , 4. The y system  is the cy system . Chemorey system , 7. An iy system . Phosphoy system . The E. mination .E. coli pathway were identified and experimentally studied in other bacterial and archaeal species, where they were shown to control not only flagellar motility, but also type IV pili (Tfp) based motility, biofilm formation, cell-cell interaction, biosynthesis, development, and other cellular functions  itunctions , and ; it is responsible for the movement of the phosphate group from its substrate histidine residue to CheY and CheB response regulators (dimerization domain (H-kinase_dim) mediates CheA dimer formation  binds ATP  and other  cellular functions (termed ACF class)  , whereasmaritima . This fienterica , Bacillusubtilis  and an aalinarum . Howeverterminus . Azospirr domain , 33. Cheterminus . Althoughttps://github.com/mberry14/Diversity_of_CheA_domain_architectures).The number of microbial genomes is rapidly increasing, and currently reaches almost 1.4 million of bacterial genomes in the NCBI database . HoweverOur analysis revealed substantial variations in CheA domain composition: most frequently found domain combinations are shown in As expected, this domain is present in every CheA , as it dThe dimerization domain (H-kinase_dim) is a CheA-specific version of the histidine kinase dimerization domain. We found that it is always occurs in combination with the histidine kinase and CheW domains, as was reported previously for a much smaller dataset . It is formed by two helices (~ 26–28 a.a. each) connected by a short loop. A recent study revealed an extended dimerization domain in Spirochetes . It was suggested that the extended H-kinase_dim domain might enable stronger dimerization of CheA molecules  H-kinase_dim domain, many sequences contained its extended versions (>70 a.a.). We found the large range of length variation of the extended dimerization domain, with the longest being up to 3 times of the classical one (170 a.a.). These longest dimerization domains were found in CheAs from Chloroflexota and Cyanobacteria belonging to Tfp system. Thus, it appears that this region of CheA is subject to various duplications resulting in substantial elongation of dimerization helices.This domain is found exclusively in chemosensory systems . CheW isDesulfobacteraceae family), three CheW domains were identified by HMMER. But upon closer inspection, we concluded that only two CheW domains are present in these proteins. A 50 a.a. insertion in the middle of the N-terminal CheW domain splits CheW into two parts thus “misleading” HMMER into identifying two CheW domains. On the other hand, in two organisms from Chloroflexota (e.g. Thermomicrobium roseum) CheA proteins of the F1 class contain three true CheW domains, with one of them separated from another two CheW domains by a CheY-like domain  showed that the strain where CheW2 was deleted together with the C-terminal response regulator receiver domain, was just as defective in their chemotactic ability as the ΔcheA mutant, suggesting that both CheW domains are involved in controlling chemotaxis  sphaeroides, which has two of its four cheA genes, namely cheA3 and cheA4 located in the same operon (E. coli CheA) and was experimentally shown to be phosphorylated in vitro (Rhodobacteraceae family have CheA3 and CheA4 orthologs in similar gene neighborhoods indicating the timing of emergence of this unique CheA system.A unique case of partitioning CheA functions between two proteins is seen in e operon  46). Ch. ChCereie operon . Both pre operon . We weree operon  model alin vitro , the secSynechocystis PCC6803  from  of them . The phoablished . Our anaablished . Examinaablished . AdditioE. coli has a P2 domain , whereas the CheY-binding model was developed using CheA from E. coli, where its role was studied experimentally, and related organisms. However, structurally, these versions of the P2 domain match closely . The existence of two models for this domain is likely due to substantial sequence variability: this domain is much less conserved than any of the CheA core domains . The P2  closely , thus we closely . The lac to CheY . Deletio to CheY , 52; how to CheY . Thus, tBorreliales (Spirochaetota phylum), as all members of this order have CheA proteins with a duplicated P2 domain. Phyletic distribution of other CheA proteins with a duplicated P2/CheY-binding domain suggests several independent duplication events.The vast majority of CheA proteins contain only one copy of the P2/CheY-binding domain, although we detected some sequences with duplications of this domain . NotablyE. coli and B. subtilis, this domain is present in two response regulators – CheY and CheB. However, in other homologous systems, for example in C. jejuni and H. pylori, it was also found as a component of CheA. The function of the CheY-like domain in CheA homologs has been studied experimentally in several organisms, and its common role appears to be that of the phosphate sink  caused the lack of chemotaxis and phototaxis  but it is also found in ACF class. Second, the majority of CheA homologs that contain the CheY-like domain do not have the P2/CheY-binding domain. Finally, domain architectures are generally class-specific . For example, all CheA sequences from F4 class contain three Hpt domains and 99% of CheA sequences from F5 class have a duplicated CheW domain.CheA homologs have four core domains: (i) the phosphotransfer domain, which can be present in multiple copies and, occasionally, reside as a separate gene in the same gene neighborhood, (ii) the dimerization domain of a variable length, (iii) the histidine kinase domain, which is always present in a single copy, and (iv) the CheW domain, which is duplicated in some homologs. The P2/CheY-binding domain, which enhances phosphotransfer, and the CheY-like domain, which likely serves as a phosphate sink, are the most common auxiliary domains found in CheA homologs. CheA homologs from different classes have a predominant domain composition.In spite of their high specificity, current models (profile HMMs) for the Hpt, P2/ CheY-binding, and dimerization domains models have low sensitivity and perform poorly in automated sequence similarity searches, often resulting in missing domains. However, models for the histidine kinase (HATPase_c) and CheW domains are both highly sensitive and specific. Thus, because the presence of these two core domains uniquely distinguishes CheA homologs from other proteins, CheA sequences can be easily identified by automated searches and then further explored for the presence of other domains using more sensitive approaches, such as. HHpred  and AlphThe following databases were used in this study: GTDB release R95 , MiST v3The domain architectures for all collected CheA sequences were identified using two approaches. First, domains were predicted using HMMER . Then, iMultiple sequence alignments were built using MAFFT  with aut


Subsequently, processing maps at a strain from 0.4 to 1.39 were established according to the stress–strain data obtained from various deformation parameters. The microstructural evolution of the target alloy was observed with an optical microscope and transmission electron microscope. The results reveal the unstable regions are located at  and . Precipitation particles, pinned dislocations, and highly dislocated areas can be observed in the microstructure of the alloy in the unstable regions. This is a potential crack and defect formation point. The identified optimum processing parameters are located at , with a maximum dissipation efficiency of 0.6.The investigation of thermal deformation behavior plays a significant role in guaranteeing the overall performance of alloy materials. In this manuscript, a series of isothermal compression tests at different temperatures  and strain rates (0.001, 0.01, 0.1, and 1 s Because of their excellent mechanical properties ,2, Al-Zn−1). To improve the thermal workability of the 2014 forging aluminum alloy, Li et al. [−1. The thermal deformation behavior of 6000 series aluminum alloy was reported in references [Founded on the dynamic material model , Prasad and his colleagues proposed the processing map theory ,9. This i et al.  conductei et al. . Via proferences ,18, and ferences . By taki−1). The deformation behavior and hot workability of 7075 aluminum alloy (abbreviated as AA7075 thereinafter) were investigated via isothermal compression tests under different deformation parameters [−1). After the isothermal compression test, the authors of reference [−1). By considering the strain effect, Luo et al. [−1).The processing map, as an effective tool, has been widely adopted in 7000 series aluminum alloy for investigating its hot workability and obtaining the optimum processing parameters. Jin et al.  conducterameters . The reseference  establiso et al.  construcDuring the hot working process, the formation of a microstructure is sensitive to the deformation temperature, strain rate, and strain level. Additionally, the macroscopic properties of the materials are decided according to the microstructure of the materials. Thus, the selection deformation parameter is a key procedure in the material working process ,25. AccoIn the present manuscript, the stress and strain characteristics of AA7075 under isothermal compression tests with different parameters are studied. In order to construct the processing map, the strain rate sensitivity, power dissipation efficiency, and instability parameters are evaluated. Subsequently, using the DMM, processing maps are established. Optical microscope (OM) and transmission electron microscope (TEM) observations are employed to analyze the microstructure evolution and determine the hot working parameters of the target alloy.In this manuscript, a typical Al–Zn–Mg–Cu alloy, namely AA7075 , was selected as the research object. The chemical composition of the aluminum alloy is given in −1) and temperatures . The specimens were heated at a 10 °C/s rate to the predetermined deformation temperature before the test started. They were then held at the tested temperature for 180 s to remove the thermal gradient. The sample was compressed to a 75% reduction for all the deformation conditions (the corresponding true strain was approximately 1.39), as shown in The cylindrical sample was obtained for isothermal compression tests. Its dimensions are displayed in 4 + 96% H2O solution . The observation was conducted using an Axio Vert A1 CarlZeiss optical microscope, which was produced by GmbH in Oberkochen, Germany. The samples intended for TEM observation were mechanically reduced to a thickness of 80 μm, and subsequently, using Tenupol-5 electro-polishing apparatus , they were thinned with a twin jet in 30% HNO3 and 70% CH3OH solutions  at a temperature of −25 °C. The microstructures were observed under a Tecnai G2 20 TEM. This was provided by FEI Company from the Hillsboro, OR, USA.In order to explore the effect of the deformation parameters on microstructure evolution, OM and TEM observation methods were used. After the thermal compression test, all the samples were cut along the compressed direction containing the axis surface, and the center area of the cutting surface was selected as the microstructure observation region, as depicted in P is composed of two parts, which can be expressed as:σ represents the flow stress, and G denotes the power dissipated by deformation heating and plastic deformation; J signifies the power consumed by microstructural evolution, such as dynamic recovery, super-plastic flow, dynamic recrystallization, and so on.In order to improve the hot workability of alloy materials, a processing map was developed, which was founded on the DMM theory ,27. The m) is first introduced to characterize the hot working process. m can be obtained by differentiating the power between the deformation heat and microstructural evolution:It is commonly accepted that, during the hot working process, the strain rate has a great effect on the flow stress level. In order to explore the energy dissipated status, the strain rate sensitivity coefficient (K is a constant. Substituting Equation (3) into Equation (1), the value of J can be obtained as:Utilizing the strain rate sensitivity coefficient, the flow stress during the hot working process can be expressed as:m is equal to 1. In this situation, the value of J is the max.At the condition of linear dissipation, η is defined as the ratio of J to Jmax:η signifies the power dissipation efficiency of the alloy materials during the thermal deformation process. The dissipation efficiency at various deformation conditions can be calculated by utilizing Equations (2) and (6), so as to construct a visualized power dissipation map. There exists a certain relationship between η and deformation mechanisms that can reflect the workability of materials. Thus, during the construction of the processing map, η occupies an important position.However, during the hot working process, it cannot be regarded as a linear dissipater. The power dissipation efficiency Moreover, to avoid the occurrence of cracks and flow localization, the calculation method of the instability parameter was also presented by Prasad and Seshacharyulu , which cξ alters with the deformation parameters. By calculating ξ under different temperatures and strain rates, a visualized instability map can be established. On the instability map, the domain with negative ξ values signifies flow instability. The thermal workability of a material will be significantly deteriorated when processing in the flow instability region. Working in the instability regions, the microstructure of alloys tends to show localized flow, cracks, and other deformed defects. Therefore, during the structural component manufacturing process, the hot working parameter should not be located in those zones.From Equation (7), it can be seen that the value of For better guidance in the selection of hot processing parameters, the power dissipation map is combined with the instability map to obtain the processing map. Abundant documents have proved that the processing map is capable of finding the optimum processing parameters. Additionally, it can establish a certain relationship between the power dissipation efficiency and the deformation mechanisms. Therefore, the processing map can be a useful tool for guiding the metalworking and controlling of alloy microstructures.The initial microstructure of AA7075 before the isothermal compression test is displayed in After the processing of the engineering stress–strain data, the true stress–strain curves of AA7075 from the compression tests can be obtained. The stress–strain curves under different parameters are presented in In addition, it can be discovered from η and instability parameter η and m) under different conditions should be calculated in advance. The factor m plays a vital role in the interrelation between the flow stress and the processing map. According to Equation (2), m can be computed via the curving fitting of a, b, c, and d can be estimated via curving fitting. The processing map is very useful for guiding the hot working process. It shows both the power dissipation situation and the distribution of unstable areas. Thus, the power dissipation efficiency m:According to Equations (8) and (2), the following expression can be utilized to compute the factor m values under four different temperatures and strain rates can be stored in a 4 × 4 matrix. In order to visualize the relationship between the deformation parameters and m value, the m value was interpolated, and then a 100 × 100 matrix was derived. m value, in which the x-axis represents the temperature and y-axis represents the strain rate. Clearly, the m values alter irregularly with the deformation temperature and strain rate. However, the shapes of the surfaces at different strains are similar. The statistical results of the maximum and minimum m values are shown in m value alters irregularly with the increase in strain.The calculated η can be calculated once the m values have been obtained. η and the strain. It can be clearly observed that the effects of the strain on η are different under various strain rates. When the strain rates are 0.001 and 1 s−1, η is highly dependent on the strain. During deformation at the strain rates 0.1 and 0.01 s−1, η is relatively unaffected by the strain. Also, it can be seen that the η value decreases with the increase in the strain rate at a constant deformation temperature.According to Equation (6), Based on the above dissipation efficiency, a 2D dissipation map can be established. ξ can be obtained: Utilizing Equations (7) and (9), a calculated formula for the instability parameter ξ value can be estimated. Subsequently, the instability maps were constructed, as illustrated in −1) and . For different strains, the shapes of the instability domains are similar. However, the areas of the instability region are different. When the strain increases from 0.4 to 1.39, the areas of the instability regions increase to 6.86%, 7.03%, 7.67%, 12.26%, 17.58%, and 20.54%, respectively.Based on Equation (10), the A power dissipation map was obtained, which can contribute to identifying the high-dissipation-level zones. On the other hand, an instability map was also acquired, which is capable of avoiding the flow instability regions. Taking the comprehensive influence of these two kinds of maps, a processing map can be established . Figure −1) and . In domain 1, the dissipation efficiency changes from 0.15 to 0.2, and it is a stable region. In domain 3, the dissipation efficiency increases to 0.3. This is identified as a stable region. Domain 4 occurs at , which owns the highest η value, ranging from 0.3 to 0.6. It is considered the optimum processing domain.According to −1 (domain 1), the slenderness ratio of the fibrous grains decreases compared to that of the initial grain, as depicted in −1 (domain 3), more dynamic recovery and a slight dynamic recrystallization structure can be found, as shown in −1 (domain 4), the fibrous grains become shorter and finer compared to those in the other deformation conditions. Additionally, many dynamic recovery and recrystallization structures can be observed. Thus, it can be regarded as the optimum processing region in this study. However, there exists a possibility that if the compression temperature is higher or/and the strain rate is lower, a better grain structure can be obtained [obtained .−1 (domain 1), grains of the compressed sample contain a high density of dislocation pile-up and entanglement, as depicted in When the deformation temperature increases to 450 °C (domain 2), the dislocations pile-up and entanglement decrease dramatically. This can be seen by comparing −1 (domain 3), the density of dislocation shows a decreasing trend, as depicted in When the strain rate decreases from 0.001 s−1 (domain 4), as shown in When the sample is compressed at 450 °C/0.001 s−1), in which the corresponding peak efficiency is 0.6.Though only one hot working parameter was selected in each domain, the microstructure analysis matches the processing map results very well. This validates the effectiveness of the processing map. Thus, it can be concluded that, under a strain of 1.39, the optimum hot working domain is identified at  were performed using a Gleeble-3500 isothermal simulator. The hot workability of 7075 aluminum alloy was investigated using a processing map and microstructural observation. The main conclusions are as follows:The flow stress level of 7075 aluminum alloy under isothermal compression increased significantly with an increasing strain rate or decreasing deformation temperature.In the construction of the processing map, the determination of the instability region should consider both the negative dissipation efficiency and instability parameters.−1), in which the maximum efficiency reaches 0.6. The most unfavorable areas are located at  and , which should be avoided in the hot working process.The microstructure analysis matches the processing map results very well. Based on the processing maps, the optimum hot processing domain of 7075 aluminum alloy at a strain of 1.39 is located at  and four strain rates (0.001, 0.01, 0.1, and 1 s


The use of alternative vegetal sources is a proposed strategy to improve the diversity and quality of plant-based products on the market, currently led by soy and pea. This study compares the techno-functional properties of seven vegetable flours  and the rheological properties of their flour pastes and gels. All techno-functional properties significantly (α = 0.05) varied depending on the type of flour. Among the flours studied, the highest swelling capacity was for white bean and the lowest for chickpea and red lentil. Water holding capacity was high for white bean and oat flours and low for red lentil. Oat and quinoa flours had the highest oil-holding capacity. Emulsifying and foaming capacities were high for all pulse flours but poor for amaranth and oat flours. However, amaranth and oat provided a much higher viscosity during heating than the rest of the flours. The viscoelastic properties of the flour pastes indicated that they all had a gel structure with storage modulus (G′) values over loss modulus (G″) values. From the viscoelastic properties, amaranth and quinoa showed a weak gel structure with low G′ and G″ values, and the chickpea, lentil, and red lentil formed pastes with a high elastic contribution . In agreement, these three pulse flours were the only ones able to form hard, self-standing gels. These results show the potential of vegetal flours from alternative sources in the development of new plant-based products. There is increasing interest in plant-based food products as consumers become more aware of the need to change to more sustainable and healthy food consumption . ReducinProteins are interesting not only because of their nutritional value but also because of their techno-functional properties, which usually play a relevant role in the elaboration process and are responsible for the final structure of the product. For years, both research and industry have focused mainly on two high-protein vegetable sources: soy and pea ,3. ReseaIn addition, to protein, flour also includes carbohydrates (starch and fibre) and lipids that may also influence its techno-functional properties . Starch Recent papers have studied the different properties of pulse flours ,11, amarTherefore, the objective of this research is to determine and compare the techno-functional properties of seven plant-based flours  and the rheological properties of their pastes and gels.Chickpea, lentil, red lentil, white bean, and quinoa flours were supplied by the Dacsa Group ; amaranth and oat flours were purchased from Tentorium Energy S.L. . The flours were all wholegrain, except for red lentils, which came from dehulled seeds. The granulation (particle size distribution) and the moisture content of each flour are presented in Total dietary fibre content was determined according to the AOAC method 991.43  with a tThe fat content was determined according to the AACC method 30.10 . The extProtein content determination was performed using the Dumas combustion procedure according to ISO /TS  16634-2 . The totThe carbohydrate content was calculated by subtracting the moisture (%), fat (%), protein (%), and total dietary fibre (%) content from 100.All measurements were performed in duplicate.Swelling capacity of flours determination was based on the method proposed by Robert son et al. (2000) . The flog for 10 min , and the supernatant was carefully removed and the resulting pellet was weighed. The results of water and oil holding capacity were calculated by deducting the weight of the dry sample from the weight of the resulting pellet and dividing the result by the weight of the dry sample. The final results were expressed as the g of water (water holding capacity) or g of oil (oil holding capacity) retained per g of flour. For each flour, measurements of water holding capacity and oil holding capacity were conducted in triplicate.For determining the water holding capacity and oil holding capacity, the method proposed by Azia and Komathi (2009)  was follg for 30 min . A second centrifugation was conducted for 5 min at 3000× g . The volume of the emulsified layer was measured, and the emulsifying capacity (%) was expressed as the volume of the emulsified layer/total volume. For each flour, the measurements of the emulsifying capacity were conducted in triplicate.The emulsifying capacity of flour was determined based on the methodology described by Yasumatsu et al., 2014 [The methodology for determining the foaming capacity and foam stability of flours was based on the protocols described by Bencini et al. (1986) . One graw/w, 25 g total weight). Viscosity values were registered while shearing the mixture at constant shear (160 rpm) and applying the following temperature programme: held at 50 °C for 1 min, heated to 90 °C over 3 min, held at 90 °C for 5 min, and cooled to 25 °C over 5 min. The values of the pasting temperature , maximum viscosity (Pa·s) during heating to 90 °C, and final viscosity (Pa·s) after cooling to 25 °C were obtained from the curves. Each flour was measured in triplicate. The flour pastes obtained were stored at 4 °C for 24 h to evaluate the viscoelastic properties and freeze-thaw stability.The viscosity profile was determined during the thermo-mechanical processing of the flours using a starch paste cell adapted to a controlled stress rheometer  with a Peltier concentric cylinder system to control the temperature. The flour was mixed with water at a concentration of 10% , using a 60 mm parallel plate system with a 1 mm gap. Stress sweeps were performed at 1 Hz from 0.001 to 10 Pa to determine the linear viscoelastic region of the samples; then, frequency sweeps were run from 10 Hz to 0.01 Hz. The values of the storage modulus (G′), the loss modulus (G″) and the loss tangent (tan δ) were recorded as a function of frequency. The tests were performed in triplicate.g for 15 min , and the supernatant was weighed. The percentage of syneresis after freeze-thawing (%) was calculated as g of supernatant/g of flour paste. For each flour paste, a syneresis of freeze-thaw stability measurements was conducted in triplicate.Freeze-thaw stability of flour pastes from pasting properties analysis was determined using the methodology previously described by Wu et al. (2020) with modifications . The synGels were prepared by mixing flour and water  with a Heidolph stirrer  at 130 rpm while heating for 25 min in a water bath at 90 °C. After heating, the amount of evaporated water was added to the mixture while gently stirring for another 5 min at 60 rpm and decreasing the temperature of the sample to 50 °C. Finally, the sample was poured into two glass containers , covered with plastic film, and stored at 4 °C for 24 h. Two batches were prepared per sample. The gel cylinders were cut into cylinder-shaped probes: height = 17 mm; diameter = 17 mm.Instrumental texture measurements were performed at 20 °C with a TA-XT2 plus Texture Analyser . For each test, four cylinders from two batches were analysed.F, N/mm2) and the true rupture strain  (Equation (1)):The gel cylinder was compressed at a speed of 1 mm/s up to 50% of its original height to ensure rupture with a 35 mm diameter cylindrical aluminium plunger. The Young’s modulus was calculated as the result of the division between the true rupture stress (σF) and the true rupture strain (εF) were calculated using Equations (2) and (3) [F is the maximum rupture force and ∆h is the height variation of the sample height during compression (both obtained from the force-time curve); h0 is the initial height area of the sample, and A0 is the cross-sectional area of the sample.The true rupture stress (σ and (3) :(2)σF=F×The gel cylinder was compressed twice, up to 10% of its original height, at a speed of 1 mm/s with a 35 mm diameter cylindrical aluminium plunger. From the force-time curves , the valA one-way analysis of variance (ANOVA) was used to study the effect of the type of flour on each technological and rheological parameter. The significance of the differences between the mean values was determined by post-hoc Fisher tests (α = 0.05).To compare flours, principal component analysis (PCA) with varimax rotation was applied to the correlation matrix of the techno-functional properties of flours  and separately to the correlation matrix of the rheological properties of flour pastes , final viscosity after cooling (25 °C); G′, G″, and tan δ, and finally syneresis after freeze-thawing). The proximate composition data was included in the supplementary variables table. All calculations were performed with XLSTAT version 2020.4.1 software .p < 0.01) depending on the source of origin. For red lentil and lentil flour, the protein content was significantly higher than for the rest of the flours, and for oat flour, the protein content was lower than for the rest of the flours. Although the percentage of fat was low for all the studied flours (from 1.4% to 6.3%), it varied significantly depending on the source (p < 0.01). The fat content was significantly higher for chickpea flour and significantly lower for lentil flour than from other sources. Dietary fibre content varied greatly among flours depending on the source (p < 0.01). The fibre content was much higher (more than twice as high) for white bean flour (33.5%) than for the other six flours. It was intermediate for lentil and oat flour (16.4% and 15.3%) and low for quinoa and red lentil flour . The carbohydrate content ranged from 31.3% to 57.4% and significantly depended on the type of flour (p < 0.01). Quinoa, oats, and amaranth were the three flours with a higher carbohydrate content (>55%), whereas white bean flour had only 31.3% of carbohydrates.The proximate composition of the seven flours  is shown in The pulse flours had the highest protein content compared to other flours. The fat content was low for the vegetal flours, with chickpea flour being the highest. The white bean flour had the highest amount of fibre compared to the other flours, which had less than half of the amount in white bean flour. Quinoa, oat, and amaranth flours had the highest content of carbohydrates compared with the other pulse flours.The mean values of the techno-functional properties of the flours  are shown in p < 0.01). White bean flour had higher swelling capacity values than the rest of the samples. Chickpea and red lentil were the flours with the lowest swelling capacity values.Swelling capacity values, corresponding to the volume occupied by fully hydrated flour, ranged between 2.49 and 3.99 mL/g and varied significantly with the type of flour (p < 0.01). White bean and oat flours retained more water, and the red lentil flour retained less water than the rest of the flours.Water holding capacity values, corresponding to the grammes of water held per gramme of flour, ranged from 0.72 to 1.64 g/g and varied significantly with the type of flour (p < 0.01). The oat and quinoa flours had higher oil-holding values than the rest of the flours.Oil holding capacity, which corresponds to the grammes of oil held per gramme of flour, ranged from 0.69 to 0.97 g/g and significantly varied with the type of flour (p < 0.01). Pulse flours  showed a high emulsifying capacity, as they formed an emulsion layer that occupied 50% of the volume of the water and oil mixture. In contrast, the quinoa, amaranth, and oat flours formed only a thin emulsion layer that occupied less than 14% of the volume.The emulsifying capacity values (volume of the emulsion layer) varied highly (0.5–51.4%) depending on the type of flour (p < 0.01). The volume of foam that formed was very high for red lentil and lentil flours (>55%), high for chickpea and white bean (46–49%), and intermediate for quinoa (34%). For these flours, the foam was stable, as 70% of its volume remained after 30 min, except for the white bean, where more than half of the initial foam disappeared after 30 min. The red lentil and lentil flour produced the highest amount of foam that was also stable. However, amaranth and oat flour produced little foam.The foaming capacity (volume of the foam layer) and foaming stability (volume of the foam layer remaining after 30 min) varied greatly depending on the type of flour  explained differences in foaming, emulsion, and oil holding capacity of flours. On the right, chickpea, lentil, red lentil, and white bean flours are differentiated for their high emulsifying and foaming capacity against their low oil holding capacity. On the left, the oat, amaranth, and quinoa flours had high oil holding capacity but low emulsifying and foaming capacity. Composition variables were also included in the PCA as supplementary variables to explain differences in the functionality of flours. The protein content appeared related to the first factor, explaining the differences in emulsifying, foaming, and oil holding capacity among flours. Proteins are good at emulsifying and foaming because they lower surface tension at the interface (air/water or oil/water) and form a layer that maintains separated air bubbles or oil droplets and avoids coalescence . IncreasThe second component that explains swelling and water holding capacity mainly separates pulse flours: white bean flour (on top) with the highest values of these parameters and chickpea and red lentil (on the bottom) with the lowest swelling and water holding capacity. The variable composition related to this second component is the fibre content. Fibre content explained the large differences in swelling and water holding capacity observed between the white bean and red lentil flours. As observed for other flours, fibre can greatly absorb water due to the abundance of hydroxyl groups in polysaccharides, which allows for more water interaction through hydrogen bonds . Howeverp < 0.01). Quinoa, chickpeas, and lentils had a lower pasting temperature, so they gelatinised sooner, whereas amaranth and oats needed a higher temperature to start the gelatinisation process. Maximum viscosity during heating (90 °C) varied greatly (0.21 and 1.21 Pa·s) with the type of flour (p < 0.01). The dispersions of amaranth and oat flour reached viscosity values much higher (0.84 and 1.21 Pa·s) than the rest of the flours and were three times the viscosity values of white bean, red lentil, and lentil (0.21–0.36 Pa·s).The pasting profiles were different among the vegetable flours, and, for comparison, three parameters were obtained: pasting temperature, maximum viscosity during heating, and final viscosity. The mean values for these parameters are shown in p < 0.01). The oat flour paste showed again the highest viscosity value, which was over three times higher than the other flours. White bean and quinoa flours produced pastes with lower viscosity at the end of the elaboration process.After cooling, viscosity values at 25 °C ranged from 0.59 to 3.58 Pa·s and varied significantly depending on the type of flour . G′ and G″ were higher for chickpea, lentil, and red lentil flour pastes than for quinoa and amaranth, with the lowest values. The loss tangent was much higher for the amaranth flour paste (0.32), indicating a system with a higher relative contribution of viscous behaviour than for the rest. Along with the low values of G′ and G″, the amaranth flour pastes form a weak gel system. However, the red lentil and chickpea flour pastes showed low tan δ values (0.12–0.13), indicating a more structured system with a greater contribution to solid behaviour.The values of G′, G″, and loss tangent (tan δ) were taken at a frequency of 1 Hz to compare flours. The three parameters were shown to vary significantly with the type of flour (p < 0.01). It was high for white bean and quinoa flour pastes , indicating poor stability. However, the syneresis was very low for amaranth and oat, indicating that they were the more stable flour pastes.In p < 0.01), being higher for chickpea and lentil than for red lentil. According to these results, chickpea gels are stiffer, harder, and more elastic, while red lentil gels are malleable, softer, and more pliable.White bean, quinoa, amaranth, and oat flours could not form a self-supporting gel in the range of concentrations assayed (15–30%). Chickpea, lentil, and red lentil flours formed self-supported gels at a concentration of 15%. The texture of these gels was characterised by three parameters: Young’s modulus, hardness, and springiness . For theIn β-glucans, which have shown a high capacity to increase viscosity in starch dispersions. In this study, the protein content shows a correlation with the pasting properties of the flours, as those with higher protein contents, such as pulses and quinoa, had lower viscosity values. The decrease in pasting properties of starch regarding protein contents has been observed [The increase in viscosity in flour dispersions during heating is mainly attributed to starch gelatinisation; the starch granules swell because they absorb water molecules. Furthermore, there is a loss of crystalline regions of amylopectin that continue to incorporate water, and eventually amylose is leached into the continuous phase and the structure of the starch granule may disappear ,37. The observed  and has Differences in the viscoelastic properties of flour pastes and the texture of gels after storage showed a different trend from viscosity, probably due to the increased relevance of the protein gelation phenomenon. During heating, protein molecules unfold above the denaturation temperature and then can aggregate and form a gel that also entraps water . The finThe techno-functional properties of vegetable flours, pastes, and gels highly varied among the seven sources studied . The differences were partly explained by the proximate composition (protein and fibre content), and some could be attributed to the types of protein and starch that vary between sources.Pulse flours provide high emulsifying and foaming capacity, which is explained by the high protein content of these flours. Chickpea, lentil, and red lentil produce flour pastes with a more elastic character and form hard, self-standing gels. White bean flour has a high water holding capacity due to its high fibre content and produces more fluid flour pastes with low freeze-thaw stability that do not form gels.Quinoa, amaranth, and oat flours show a low emulsifying and foaming capacity, explained by the low protein concentration but also by the hydrophobicity and low solubility of the proteins. Oat and amaranth show high viscosity pastes with a low elastic character and are very stable to freeze-thawing. However, the quinoa flour showed flour pastes with low viscosity and low elastic contribution. None of the three flours formed a self-standing gel.This research shows that different sources of vegetal protein offer a wide range of functionalities and gives insight into their suitability as ingredients to develop plant-based products. The mixing of some of these different flours is expected to also expand the opportunities to obtain combined functionalities in the product.Further studies considering the qualitative analysis of components (starch and protein) from different sources would help to explain the different properties of vegetable flours. It should also be considered that, in this study, the described properties correspond to the natural condition of the flour. Additional studies on how pH and ionic strength conditions may also help to understand differences in functionality of different vegetable flours and also provide a broader range of applications for them.


The oddball paradigm is commonly used to investigate human time perception. Trains of identical repeated events (‘standards’) are presented, only to be interrupted by a different ‘oddball’ that seems to have a relatively protracted duration. One theoretical account has been that this effect is driven by repetition suppression for repeated standards. The idea is that repeated events seem shorter as they incur a progressively reduced neural response, which is supported by the finding that oddball perceived duration increases linearly with the number of preceding repeated standards. However, typical oddball paradigms confound the probability of oddball presentations with variable numbers of standard repetitions on each trial, allowing people to increasingly anticipate an oddball presentation as more standards are presented. We eliminated this by making participants aware of what fixed number of standards they would encounter before a final test input and tested different numbers of standards in separate experimental sessions. The final event of sequences, the test event, was equally likely to be an oddball or another repeat. We found a positive linear relationship between the number of preceding repeated standards and the perceived duration of oddball test events. However, we also found this for repeat tests events, which speaks against the repetition suppression account of the temporal oddball effect.The online version contains supplementary material available at 10.3758/s13414-023-02730-4. The oddball paradigm involves presenting a sequence of identical repeated events that are broken by a surprising ‘oddball’. This paradigm has been used to examine the effect of repetition and surprise on perceived duration. It has been shown that oddballs seem longer than repeated ‘standards’—the temporal oddball effect  Birngruber et al. . All participants reported having normal or corrected-to-normal visual acuity. Ages ranged from 18 to 26 . Participants were informed that they could withdraw from the study at any time without penalty. The experiment was approved by the University of Queensland ethics committee and adheres to the Australian Code for the Responsible Conduct of Research.Eighteen volunteer participants were recruited for testing via a research participation scheme at the University of Queensland (in exchange for course credit), as well as two naïve lab members—for a total of 20 participants . For the conceptually central effect of varying the number of events, one of the closest studies to ours had a r et al.  ExperimeStimuli were black  or white  circles presented against a grey background . Circles had a diameter subtending ~10 degrees of visual angle. Stimuli were presented on a calibrated 20-in. CRT SONY Multiscan G420 monitor, driven by a Psychtoolbox-3 . An example of a four-event trial sequence is depicted in Fig. Event timings were matched to Pariyadath and Eagleman . All staOn 50% of trials the test event was a different colour  relative to preceding standards. Event colours (black or white) were counterbalanced within each block of trials and were presented in a randomized order.At the end of each trial, participants were asked if they thought the test had seemed to last longer (right click) or shorter (left click) than standards. There were a total of 180 trials in each block . In total, participants completed 1,080 individual trials, across six blocks of trials completed in a randomized order.“Your task is to report if the LAST flashed circle in each sequence seemed to be presented for shorter or longer than the ones that came before it”.Participants received written and verbal instructions. They completed six trials at the start of the first block, which were set to the easiest difficulty (either 300-ms or 700-ms test events) in order to verify that they had understood task instructions and to check if they could discern a 200-ms duration difference . These practice trials had feedback, whereas nonpractice trials did not. Participants were told that there would always be a difference in physical duration between test and standard event durations, but that on some trials it would be very hard to detect. They were also told that there would not necessarily be an equal number of trials where the test event would be shorter or longer than standards. The precise wording for the test used in the written instructions was: The experiment was therefore a 6 (number of events) × 2 (test event type) repeated-measures design, with the dependent construct being the apparent duration of the test event relative to preceding standards  = 4.36, p < .001. We also found that oddball tests  seemed longer than repeats tests , βTrial Type = 0.037, t(236) = 6.45, p < .001. However, we did not find an interaction between the number of events and the trial type, βNumber of Events × Trial Type = −0.002, t(236) = 1.47, p = .142. So, the more repeated standards, the longer the test event seemed—and while oddball tests generally seemed longer than repeat tests, the pattern of change with event number was not significantly different for test types.We examined the relationship between DDFs and the number of repeated events preceding oddball and repeat tests see Fig.  using a Number of Events = 0.004, t(196) = 2.36, p = .019, that oddball tests seemed longer than repeats tests, βTrial Type = 0.04, t(196) = 6.37, p < .001, and no interaction between the number of events and the trial type, βNumber of Events × Trial Type = −0.002, t(196) = 1.23, p = .221.Inspection of Fig. t = 2.66, p = .02, with moderate evidence for the alternative hypothesis, that there would be a difference, BF10 = 3.55.As an additional point of clarification, we verified that the difference between repeat and oddball tests was present even in the two-event condition, pairwise comparison statistics: t tests. These revealed that DDFs for repeat tests were anecdotally no different from 1, regardless of the number of standards . However, for oddball tests, DDFs were always greater than 1 , except for the two-event condition, where they were no different from 1 (moderate evidence for the null hypothesis).We were motivated to examine if perceived duration was exaggerated relative to the objective duration of standards, separately for each combination of Trial Type × Number of Events  = 3.51, p < .001. However, we did not find an effect of trial type, βTrial Type = 4.56, t(236) = 1.78, p = .076, or an interaction, βNumber of Events × Trial Type = 1.22, t(236) = 1.31, p = .192. So, participants were better able to discriminate test durations after seeing more repeated standards, but this effect was no different for repeat and oddball tests.We also checked participants’ ability to discriminate between test durations  will dilate our experience of it  longer than repeat tests. This was despite oddball and repeat tests being equally likely to occur, and participants knowing when the test event would occur. Again, this could be explained by differences in attention—but this time for the test event itself. Perhaps oddballs capture our attention better due to their e et al. , who shoe et al. . This caodd? Adaptation might seem like an obvious explanation, except that this has been ruled out by Schindel et al.


Previous research found that college students have exhibited a group of concern, and aggressive behavior occurs from time to time in daily life. In order to investigate the effect of relative deprivation on aggressive behavior of college students, this study conducted a moderated mediation model to examine the relationship between relative deprivation, aggressive behavior, belief in a just world, and moral disengagement.SD = 1.3, range = 17-30years) participated in and completed measures of Relative Deprivation Questionnaire, Belief in a Just World Scale, Moral disengagement Scale and Aggression Questionnaire. The data were analyzed by using a moderated mediation model with SPSS and Process 3.1 macro.1169 college students. (2) Belief in a just world played mediating role in relative deprivation and aggressive behavior. (3) The moderated mediator model test showed that moral disengagement regulates the first half of the mediator effect path .The results revealed that: (1) Relative deprivation significantly positively predicted college students’ aggressive behavior controlling for gender, grade, and age (The present study analyzed the moral disengagement mechanisms deeply and shed light on how to decrease the aggressive behavior of college students. In recent years, frequent incidents of campus violence and conflict have aroused widespread concern among researchers about aggressive behavior. Aggressive behavior refers to destructive behavior that intentionally causes physical or psychological harm to others , includiPrevious studies on the mechanisms of college students’ aggressive behavior mainly focused on individual factors such as narcissism and refusal sensitivity , 8; FamiAt present, the relationship between relative deprivation and college students’ aggressive behavior has received some researchers’ attention, but how relative deprivation affects college students’ aggressive behavior remains to be further explored.According to the general aggression model, both individual and environmental factors can have impact on aggressive behavior, including values, attitudes, personality and beliefs . The belIndividual environment interaction model believes that the interaction between individuals and environmental factors jointly affects individual development , 39. BasIn summary, based on the integration of the general aggression model and the individual environment interaction model, this study constructed a moderated mediation model see Fig. . This stUsing a convenient sampling method, 1259 college students in Zhejiang Province were selected for testing, and 1169 (92.9%) valid questionnaires were collected. The age distribution of the subjects was 17–30 years old, with an average age of 19.41 ± 1.3. Among them 330 were male and 839 were female. There are 606 only children and 563 non only children. This study was approved by the local Ethics. All participants provided written informed consent. All procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration.Referring to Ma Ai’s (2012) , we measThe Belief in a Just World Scale , developChinese version of the Moral disengagement Questionnaire revised by Wang Xingchao and Yang Jiping (2010)  was usedAggression questionnaire developed by Buss and Perry (1992)  was usedSPSS 23.0 was used for descriptive statistics, correlation analysis, and regression analysis. Process 3.1 macro was used for mediating and moderated effects analysis.Since data were collected using a self-reported method, the results may be affected by common method bias. In addition to using anonymous testing, reverse scoring, this study also used Harman’s single factor test method to conduct non rotated principal component analysis of the measured items. The data generated a total of 14 common factors with a characteristic value greater than 1, and the interpretation amount of the first factor was only 17.4%, less than 40%. No serious common method bias was observed.r = -0.26, p＜0.001); relative deprivation was significantly positively correlated with moral disengagement  and aggressive behavior ; belief in a just world significantly negatively correlated with moral disengagement .Table According to the suggestions of Wen Zhonglin and Ye Baojuan –52, all B = 0.45, p＜0.001); After incorporating belief in a just world into the regression equation, relative deprivation still had a significant positive predictive effect on aggressive behavior , relative deprivation significantly negatively predicted belief in a just world , belief in a just world significantly negatively predicted aggressive behavior . ab = 0.04, Boot SE = 0.01, 95% confidence interval was , excluding 0. It indicated that belief in a just world played a partial mediating role in the impact of relative deprivation on aggressive behavior. The proportion of intermediary effect to total effect was 9%.The first step was to test the simple mediation model (model 4), the results were shown in Table β=- 0.27, p < 0.001), moral extrapolation negatively predicted the belief in a just world , and the interaction between relative deprivation and belief in a just world had a significant predictive effect on belief in a just world , with a 95% confidence interval of , excluding 0. This result indicated that moral disengagement played a regulatory role in the first half of the intermediary path of “relative deprivation → belief in a just world → aggressive behavior”.The second step was to test the moderated mediation model (model 7). The results were shown in Table SD), the positive predictive effect of relative deprivation on belief in a just world was significant , with a 95% confidence interval of . When the level of individual moral disengagement was high (+ 1SD), the positive predictive effect of relative deprivation on belief in a just world was smaller , with a 95% confidence interval of . In short, as the level of moral disengagement increased, the predictive effect of relative deprivation on college students’ belief in a just world decreased.In order to reveal how moral reasoning moderated the relationship between relative deprivation and belief in a just world, a simple slope test was conducted. The simple effect analysis diagram was drawn Fig.  based onThis study examined the impact of relative deprivation on college students’ aggressive behavior and its underlying mechanisms. The results showed that relative deprivation could positively predict college students’ aggressive behavior, which was consistent with previous research conclusions , 54–56. This study showed that belief in a just world played a mediating role between relative deprivation and college students’ aggressive behavior. That was, the effect of relative deprivation on college students’ aggressive behavior was achieved through direct prediction of their aggressive behavior and the mediating role of belief in a just world. Hypothesis 1 was supported and consistent with previous research . An indiThis study also found that moral disengagement moderated the first half of the path that relative deprivation affects college students’ aggressive behavior through belief in a just world. However, the results showed that rather than increasing, high moral disengagement delayed the negative effect of relative deprivation on college students’ beliefs about a just world, which contradicts hypothesis 2. It was also not conform to the risk enhancement model. The following explanation may help us understand this result: Previous studies have shown that only individuals with low levels of moral reasoning are susceptible to negative situational factors . In otheThis study explored the impact of relative deprivation on aggressive behavior through a moderated mediation model. Theoretically, it helps to understand how relative deprivation affects college students’ aggressive behavior, and when the impact is more significant. At the same time, it provides some empirical evidence for general aggression model, individual environment interaction theory. After reviewing literatures, it is found that most of the previous studies have explored the impact of the interaction of personal traits  and moral disengagement on individual psychology . Their fAt the practical level, the findings of this study remind us: First, we should pay attention to the influence of moral cognitive factors in the prevention and treatment of college students’ aggressive behavior. School psychologists and educators should pay extra attention to students with high levels of moral disengagement, and provide psychological counseling and educational guidance to these teenagers in terms of moral cognition. Second, unlike the absolute deprivation of the objective state, the sense of relative deprivation is a subjective state, can be effectively intervened and improved in a short period of time through cognitive training. Finally, pay attention to the cultivation and guidance of college students’ belief in a just world, establish a reasonable belief in a just world, and use positive encouragement to reduce their aggressive behavior.In addition, this study also has some limitations. Firstly, the cross-sectional design cannot reveal the dynamic changes of relative deprivation in aggressive behavior. Future research can combine longitudinal design to examine the relationship between them. Secondly, the use of self-reporting may increase social expectation effects. In the future, it may be considered to obtain data through multiple channels . Thirdly, due to the large gap in the gender ratio in the sampled schools, the gender ratio in the study sample is not uniform enough, which may limit the popularization of this study. Fourthly, all participants were from one university in a local city. The sampling range is a bit narrow and the representativeness of the sample is not high. Future research is needed with large sample from different cities to test our findings. Finally, whether there are other underlying mechanisms in the effects of relative deprivation on college students’ aggression behavior besides moral disengagement remains to be supplemented by future research.This study showed that relative deprivation is associated with increased aggressive behavior through decreasing belief in a just world, and this effect was particularly pronounced among less moral disengagement groups. The findings highlighted the importance to develop prevention and intervention regarding relative deprivation, as a way to prevent aggression among college students.