Most of the loops of the GTPase domain were found to be flexible, revealing why GTP binding is needed for hGBP1 dimerization to occur.Guanylate binding proteins (GBPs) belong to the dynamin-related superfamily and exhibit various functions in the fight against infections. The functions of the human guanylate binding protein 1 (hGBP1) are tightly coupled to GTP hydrolysis and dimerization. Despite known crystal structures of the hGBP1 monomer and GTPase domain dimer, little is known about the dynamics of hGBP1. To gain a mechanistic understanding of hGBP1, we performed sub-millisecond multi-resolution molecular dynamics simulations of both the hGBP1 monomer and dimer. We found that hGBP1 is a highly flexible protein that undergoes a hinge motion similar to the movements observed for other dynamin-like proteins. Another large-scale motion was observed for the C-terminal helix  Guanylate binding proteins are key fighters against microbial and viral pathogens. In the human body there are seven types of such proteins, among which is the guanylate binding protein 1 (hGBP1). This protein is able to perform its function only once it is activated by binding and converting guanosine triphosphat (GTP) to guanosine diphosphat and guanosine monophosphat via hydrolysis. In concert with the conversion of GTP the dimerization of hGBP1 occurs, which can further interact with the lipid membrane of the pathogen and disrupt it. While the crystal structure of the protein is known, the activation and dimerization steps are not well understood at molecular level as studying them experimentally is difficult. An alternative approach is given by molecular simulations, allowing us to elucidate the protein dynamics closely connected to these steps. From our simulations applied to both the hGBP1 monomer and dimer we identified large-scale motions taking place in hGBP1 that had not been reported before. We discuss the relevance of these motions in terms of their biological function, such as possible membrane damage caused by one of the motions or locking the protein in the dimer state. Among these proteins are the guanylate binding proteins (GBPs), which belong to the dynamin-related protein family, even though the GTPase domain is the only conserved sequence. They have various functions in the resistance against intracellular pathogens via GTP binding and hydrolysis –5. Gener viruses . The hum viruses , 8. hGBP viruses .3/4 (PDB 2B8W and 2B92) are available [hGBP1 is a large, multi-domain GTPase with similar, but quite low nucleotide binding affinities for GTP, GDP (guanosine diphosphate) and GMP (guanosine monophosphate) . It can vailable .β-sheet with six parallel and two antiparallel strands, which is surrounded by six main helices. The GTP-binding site contains four conserved sequence elements G1–G4: the canonical G1 motif or phosphate-binding loop , the G2/switch 1 motif (G2-SW1), the phosphate- and Mg2+-binding G3/switch 2 motif (G3-SW2), and the nucleotide-specificity providing G4 motif, which is part of a loop and will be called G4-L2 in the following [α7/8 and α10/11 that are connected by α9 and extend over a length of 90 Å, giving hGBP1 an elongated shape. The helical effector (E) domain (amino acids T481–I591) involves a very long helix, α12, that stretches over a length of 120 Å from the tip of the M domain back to the LG domain, where it forms multiple electrostatic contacts with helix α4’. At the C-terminal end of the E domain, there is a helical turn leading to the short helix α13 and the last seven residues, which are unstructured.The hGBP1 structure is divided into three domains as can be seen in ollowing . This G4ollowing . The cryμs of sampling at the atomistic scale and 1.1 ms at the coarse-grained level. To elucidate the dominant motions from the large amount of simulation data, we applied state-of-the-art techniques, such as principal component analysis and Markov state modeling. The enhanced MD simulations of the monomer revealed that the monomeric apo form is highly flexible and exhibits a hinge motion that is similar to the motions observed for other dynamin-like proteins. This motion is also present in the hGBP1 dimer, for which a structural model is provided in this study. Other large-scale motions were observed for the C-terminal helix α13, which allows us to explain previously reported experimental data, and for most of the LG domain loops, which provides a rationale why GTP is required for hGBP1 dimerization to occur. Our study provides fundamental insights into the dynamics of both the hGBP1 monomer and dimer with consequences for its function.The aim of the current work is to elucidate the intrinsic dynamics of apo-hGBP1 and the hGBP1 dimer. Given the considerable size  and elongated shape of hGBP1, it is to be expected that even without nucleotide binding this protein is flexible. A thorough characterization of the conformational dynamics in the apo state is the prerequisite for understanding the changes in structure and dynamics of hGBP1 following GTP binding, hydrolysis and dimerization. To this end, we applied multi-resolution molecular dynamics (MD) simulations to both the hGBP1 monomer and dimer, involving in total 13 To reveal the conformational dynamics of apo-hGBP1 on the microsecond time scale, we performed an all-atom Hamiltonian replica exchange MD simulation (H-REMD) with 30 replicas of 400 ns length per replica  of the Csee also , which wα7/8. The residues connecting these two helices are the most flexible, resulting in an RMSF peak of ∼7 Å , while residues 101–110 of G3-SW2 are flexible. Loop L1, which is not part of the GTP binding site or dimerization interface, is particularly flexible between residues 156 and 165, while the other residues remain in their position. This is understandable as some of these residues belong to an α-helix  of the H-REMD data  describe best the main conformational motions of the M domain and α12 , we perth hGBP1 .α12 helix. This helix is 81 residues long and contains 21 i, i+ 3 or i, i+ 4 combinations of oppositely charged side chains , while upon turn formation E508 becomes able to interact with R511 instead (minimum distance below 2 Å). Interestingly, the predicted positions of turn formation coincide with the positions with a reduced α-helical coiled-coil probability of the E domain, which was determined by Syguda et al. [α12 helices. An alternative scenario would be that the isolated E domain forms an intraprotein coiled-coil structure with the first helix extending from L482 to A503 and a second helix from L506 to the end of α12. A third helix with a turn around residue 540 might also be possible, which would be in agreement with both our MD simulations and the COILS predictions. It should be further noted that the helical propensity of the isolated E domain is only slightly reduced upon turn formation. It is 79% averaged over the 500 ns MD simulation, which is close to the value of 82% extracted from the circular dichroism (CD) spectrum of the E domain [α12/α13, while it was 84% in the H-REMD simulation of hGBP1. Thus, the presence of the LG and M domain has stabilizing effects on α12, but the overall helicity is not lost in their absence.We aimed at understanding the reasons for turn formation that occurred in the isolated e chains , which ma et al.  using thE domain , especiaα11 of the M domain, as its inclusion in the simulation of the E domain prevented the formation of turns at residues I489 and M505/L506. This can be explained by attractive interactions between α11 and α12, inhibiting structural changes in this part of α12. Especially residues close to the turns that formed in the isolated E domain are involved in interactions with α11: two stable hydrogen bonds are present between L476–K487 and D479–K487, supported by a salt bridge between E458 and K504  and paired with a similar abundance of positively and negatively charged residues in the oppositely located α12 helix.A major stabilizing effect originates from and K504 . Only atand K504 . For insα11 plus E domain revealed that a folding out of the E domain seems to be an unlikely event. First, it would require the simultaneous breaking of several hydrogen bonds and salt bridges that the E domain forms with both the LG domain and the M domain. Second, even if such detachment of the E domain should happen, the missing tertiary contacts would provoke turn formation at specific residues of α12 on the nanosecond time scale, as demonstrated by our MD simulations. It should also be mentioned that this helix is by a factor of at least four longer than the optimal length of an isolated helix  and thuμs to 200 μs using the coarse-grained Martini force field  analysis to amino acids Q541–T581, which contain the last third of α12 and the helix α13  are intermediate states between A and B. We calculated the reactive fluxes between the states, which are shown as gray arrows in μs. The MFPT for the reverse transition from B to A is 5,536 μs, which is consistent with the observation that the complete reverse motion of helix α13 to the conformation associated with the crystal structure has not occurred in our simulations. It should be noted that the MFPTs reported here are obtained from coarse-grained simulations and might not be equivalent to actual MFPTs. Generally, events observed in simulations with the Martini force field are between 3 to 5 time faster than similar events observed in atomistic simulations [Another substantial motion observed in the Martini simulations was a change in orientation of helix ulations . The maiα13 on the surface of the LG domain requires its motion beyond the loop L1 from the LG domain. Our H-REMD simulations had revealed that especially K582 and K587 have a high tendency to form salt bridges with different residues from that loop , and L1 by calculating an average distance map during the simulation interval when this motion occurred. The distance map  and the following, flexible residues R584–A588. The strongest contacts are formed between polar residues: E146 and T149–R151 from the LG domain and Q580–T581 from α13. The second interaction hot spot involves sequence N220–F230, i.e., half of helix α4’ that extends from S213 to F230, which is in contact with residues S569–Q580 from the first part of α13. Here, the strongest contacts are mainly of hydrophobic nature, involving L224 and C225 from the LG domain and M572 and I576 from α13. In addition, helix α13 has residue L579 exposed to the LG domain, constituting another hydrophobic interaction. Thus, we conclude that the initial motion of α13 is driven by the coaction of the electrostatic interactions between α13 and L1 and the flexibility of L1, and once the helix has moved beyond this loop, it is stabilized by hydrophobic and polar interactions with helices α3 and α4’ from the LG domain.The mobility of helix ance map  revealedp L1 see , allowinμs and 150 μs, see β-sheet and the helices of the LG domain to their original positions  has moved beyond the corresponding loop L1 within 2 μs, and after 55 μs it has adopted a position similar to the one in state B identified in the monomer simulation  under study by applying the elastic network ELNEDIN . Howeverolecules  at the aolecules  and simuα13 in one of the hGBP1 molecules  also the M and E domain of both proteins had moved considerably in the prior Martini simulation. They continued to do so during the all-atom simulation as the snapshots sampled at 50 and 100 ns  [2+ revealed that dimer formation and the presence of the nucleotide stabilize the closed loop conformations, including GC, and and also the orientations of R48, S73 and T75 in positions supporting GTP hydrolysis, explaining why the hGBP1 dimer is better able to hydrolyze GTP than the hGBP1 monomer is. However, it should be noted that it is not fully clear yet how much of the loop stabilization derives from dimerization and to what extent it is due to GTP and Mg2+. To this end, the hGBP1 monomer with GTP and Mg2+ needs to be simulated too, which will be addressed in our future studies.We studied the conformational dynamics of the nucleotide-free hGBP1 monomer and the hGBP1 dimer using multi-resolution and enhanced MD simulations on the micro- to millisecond time scale. As expected from its highly conserved sequence in the dynamin family, the LG domain is overall very stable in our atomistic H-REMD simulation. The residues involved in the flexible . The les 470 μM) . The LG α12 from domain E. The region of highest structural flexibility was found at the middle of the M and E domain by PCA , which also revealed reversible turn formation accompanied with local unfolding between residues Q525 and L542, while the contacts to the M domain were found to be vital for overall stability of the long helix α12. Without these tertiary interactions, α12 unfolds on the nanosecond time scale, making it unlikely that the ∼120 Å long E domain folds out as intact helix [One of the main results from our atomistic and coarse-grained simulations is the highly flexible nature of the M domain and the long helix n by PCA , giving n by PCA . During ct helix , which iNostic punctiforme (see min in α7/8 and α10/11 of the M domain. It enables a kinking motion of these two domains and involves a (reversible) unfolding of α12, dividing it into a short helix close to the LG domain, which would correspond to the neck of BDLP, and a longer helix corresponding to the trunk of BDLP. The presence of a hinge 2 in hGBP1 remains to be shown. Another similarity between BDLP and hGBP1 is that the tip of the trunk of BDLP is the region of highest flexibility.An alternative scenario is a motion similar to that of the bacterial dynamin-like protein (BDLP) from It should be noted that there are also certain differences between the two proteins. First, the closed state is the preferred conformation of BDLP in its apo form , while fAnother difference between hGBP1 and BDLP is the presence of a paddle domain in BDLP at the trunk tip via which membrane binding is facilitated, while hGBP1 binds to the membrane following farnesylation at the C-terminus see , leadingα13 of the E domain. Markov state modeling indicated an MFPT for the complete α13 motion of ∼127 μs, while the reverse motion is more than an order of magnitude less likely. The slow time scale of the α13 motion also explains why it was not observed in our atomistic simulation, even though an enhanced sampling scheme was employed. The motion of the helix α13 has clear implications for the dimerization process. A recent study has suggested that, besides the LG domain interface, the dimerization of hGBP1 also involves an interface between the two helices α13 [α13 observed in our simulations leads to an increased proximity of the two helices in the dimer  were both simulated at the all-atom level for 500 ns using Gromacs 2016 [− ions and for α11 plus the E domain one Na+ ion were addded for neutrality of the systems. The energy of both systems was first minimized using a steepest descent algorithm, followed by equilibration of the systems to the desired temperature of 310 K and pressure of 1 atm for mimicking the physiological environment. First, a 0.1 ns NVT equilibration was performed in which the number of atoms (N), the box volume (V) and temperature (T) were kept constant, followed by a 1 ns NpT equilibration to adjust the pressure (p). During equilibration, the protein atoms were restrained with a force constant of 10 kJ mol−1 Å−2 allowing the water molecules to relax around the solute. Finally, the 500 ns MD production runs in the NpT ensemble were performed. As no restraints were applied to the protein during the simulations of the E domain (with and without α11), a large cubic box with an edge length of 180 Å was created, allowing free rotation and translation of the 120-Å long helix α12 in all directions. The resulting system sizes involved about 573,000 atoms. The velocity rescaling thermostat was employed to regulate the temperature in the NVT simulations, while the Nosé-Hoover thermostat [NpT simulations.The isolated E domain (residues 482–591) and the E domain plus helix acs 2016 , 55. Forermostat  were use+ ions resulting in a system with 18,438 particles. In order to avoid overall translation and rotation of the protein in the box, the stable secondary structure elements of the LG domain, which were identified based on the atomistic H-REMD simulation results, were restrained to their original positions. After an initial energy minimization and short equilibration, five MD simulations of the system at a temperature of 310 K were performed for different lengths, ranging from 63 μs to 200 μs  and one simulation at 320 K (270 μs).Similar Martini simulations were conducted for the hGBP1 dimer. The dimer was built by superimposing the LG domain of the apo monomer (PDB 1DG3) to one of the LG domains (residues M1–L309) of the LG dimer in complex with GDP, which was resolved by crystallography (PDB 2B92) . In ordePDB 2B92 . As listβ-sheets of both LG domains. The resulting system size involved 376,899 atoms, including 14 Na+ ions, in a rectangular box with edge lengths of 290, 115 and 115 Å. In this simulation, GTP and the Mg2+ co-factor were included. To this end, the converted dimer structure was further processed with the free version of the Maestro program [2+ and GDP⋅AlF3 obtained from the crystal structure of the GDP⋅AlF3-bound LG domain of hGBP1 (PDB 2B92) [3 was removed, and a phosphate group was attached to the β-phosphate of the existing GDP. The γ-phosphate was added in a way to avoid atom clashes and to be in a reasonable position relative to important protein residues, in particular to K51 and the Mg2+ co-factor as described by Kravets [2+ in both LG domains was then used for initiating the MD simulations, including energy minimization and equilibration. During the production MD run, no restraints were applied to GTP and Mg2+. As there are no parameters available for GTP in Amber99SB*-ILDNP, we used the GTP model provided by Meagher et al. [To test the stability of the hGBP1 dimer conformation obtained at 320 K, we converted the final snapshot from this Martini simulation to the atomistic level using the backmapping Martini tool  and simu program . It was DB 2B92) , AlF3 wa Kravets . The GTP Kravets ) giving  Kravets . The resr et al. . The stir et al.  at the Hr et al. .α around their average positions was calculated. The RMSF calculated for different time intervals of the H-REMD simulation was used to demonstrate that this simulation had converged within 400 ns per replica  software  and PyMO replica . To deteα12 during that analyis as the RMSF analysis had revealed that these regions exhibit the largest flexibility. Helix α13 of the E domain was not included as it was very stable throughout the H-REMD simulation. We projected the conformations from the H-REMD target replica onto the first two principal components (PCs), calculated two-dimensional histograms and then the 2D free energy surface along the two PCs.The main structural changes of the M and E domain sampled by the target replica of the H-REMD simulation were identified based on a principal component analysis (PCA) . Given tα13. The MSM was then built by clustering the trajectories projected onto the first two TICs using the uniform time clustering algorithm with 300 microstates. We estimated the implied time scales from the MSM for 10 different lag times, based on which we selected a lag time of 450 ns for calculating the MSM of our system. For the identification of metastable Markov states we applied the fuzzy spectral clustering method PCCA+ [For the analysis of the coarse-grained simulations of the hGBP1 monomer, we applied the Markov state model (MSM) approach using the PyEMMA software . First, od PCCA+ , 80 and od PCCA+ –83, whicS1 Fig2+ (carmine) are also shown, allowing to distinguish open and closed loop conformations from each other. However, it is emphasized that GTP and Mg2+ were not present in the H-REMD simulation of the hGBP1 monomer.(A) Initial conformation of the LG domain from which the H-REMD simulation of the hGBP1 monomer was started. The central conformations for all clusters obtained for (B) G1-P, (C) G2-SW1, (D) G3-SW2, (E) G4-L2, and (F) GC are shown. The number of clusters for each loop are given, too. The coloring is the same as in (PDF)Click here for additional data file.S2 Fig(A) Amplitude of the first ten eigenvalues obtained from principal component analysis (PCA) of the all-atom H-REMD simulation of the hGBP1 monomer. (B) Projection of the unbiased H-REMD replica onto the first five PCA eigenvectors.(PDF)Click here for additional data file.S3 Figα11 plus the E domain (bottom). The cluster structures were aligned to the crystal structure of full-length hGBP1 (shown as transparent cartoon) using residues 482–484 for the alignment of the isolated E domain and α11 for alignment of α11 plus the E domain. (B) Evolution of the secondary structure shown for each residue as a function of time for the isolated E domain (top) and for helix α11 plus E domain (bottom).(A) Representative conformations for clusters 3–5 with their occurrence (in %) obtained from the MD simulations of the isolated E domain (top) and of helix (PDF)Click here for additional data file.S4 FigkBT as the Boltzmann constant and T = 310 K). (B) Fuzzy PCCA+ clustering of the microstates resulted in six macrostates. The microstates are projected onto the first two TICs and their membership to one of the six macrostates is identified by different colors.(A) Free energy surface plotted along the first two eigenvectors obtained from TICA. The free energy values correspond to the color scale on the right (in (PDF)Click here for additional data file.S5 Fig(A) Convergences of the implied timescales derived from MSMs for different lag times. One step corresponds to 1.5 ns. (B) Relative implied time scales for a lag time of 450 ns.(PDF)Click here for additional data file.S6 Figα atoms of the C-terminal region, including the helix α13, and the loop formed by residues R151–F174 during transition from the Markov state A to other states in the MSM. The residue index on the x and y axes is accompanied by the corresponding amino acid sequence on the respective opposite side of the plot. (B) Cα-distance map between the C-terminal region as in (A) and relevant residues of the LG-domain calculated for conformations belonging to the Markov state 4. The sequences of the LG domain stretches that form strong contacts with the C-terminal region are displayed on the right, together with structural snapshots where the same residues are highlighted in red, while the helices α12 and α13 are shown on blue and cyan, respectively.(A) Distance map between the C(PDF)Click here for additional data file.S7 Figα4’ from the hGBP1 monomer  with that in the LG domain dimer . This is the major conformational difference between the LG domain monomer and dimer. (B) Comparison of the position of helix α3 in the LG domain dimer  with that from the hGBP1 monomer . In order to avoid atom clashes when building the full-length hGBP1 dimer, the yellow helix had to be replaced with the red one.(A) Comparison of the position of helix (PDF)Click here for additional data file.S8 Figμs simulation), (red) and (blue) the hGBP1 dimer at 320 K (simulated for 270 μs). In one of the monomers composing the dimer the helix α13 moves (blue) while it does not in the other monomer (red). This motion correlates to the presence of the salt bridge between E217 and K567. In the monomer this salt bridge is never formed (black) as these two residues are too far away from each other. In the dimer, helix α4’ adopts a slightly different position than in the monomer  while it remains intact in the other one (red). At 50 μs the salt bridge in the monomer with the flexible α13 is completely broken, which corresponds to the time when this helix has adopted the 90° rotated position (see Distance between the side chains of E217 and K567 for representative Martini simulations of (black) the hGBP1 monomer at 310 K Click here for additional data file.S9 Fig2+, which were present in this simulation, are shown in magenta and carmine, respectively.The final snapshot of the dimer obtained from the coarse-grained MD simulation at 320 K was backmapped to the all-atom level. Hence this dimer is called CG-to-AA dimer in the following. The initial CG-to-AA dimer structure is shown in (A) and compared to the dimer model produced for starting the Martini simulations (gray) in (B). Snapshots of the CG-to-AA dimer obtained at 50 and 100 ns of the all-atom simulation are displayed in (C) and (D). The RMSF calculated from that trajectory is projected onto the dimer structure in (E) according to the color scale given in that panel. The evolution of the Q577–Q577 distance during the simulation is plotted in (F). In (G) the conformational clusters of the loops of both LG domains are displayed. The same coloring scheme for hGBP1 as used in the other figures is employed. GTP and Mg(PDF)Click here for additional data file.S10 FigThe fluctuations of hGBP1 during the first half of the H-REMD simulation (red), the second half (blue) and the full simulation (black) are almost identical, showing that no new conformations are sampled in the second half of the simulation. The results are shown as RMSF values calculated for the target replica .(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.


The G′ of ECF gel  was higher than that of DCF gel and WCF gel. Dynamic viscoelastic measurement indicated that the tortillas made of ECF had lower G′ and G″ over the frequency range (0.1–100 rad/s) after adding xanthan gum and cassava starch. The gel structure of tortillas made of ECF was homogeneous in distribution of pores. The gelatinization, thermal and rheological properties of corn flour were improved by addition of neutral protease. The addition of xanthan gum and cassava starch helped to make the tortilla with porous structure and good sensory quality.Corn flour was prepared by wet-milling with the treatment of neutral protease and the gelatinization, thermal and rheological properties were analyzed. Tortilla was prepared with enzyme treated corn flour (ECF) and additives (xanthan gum and cassava starch) and the properties were analyzed. Compared with dry-milling corn flour (DCF) and wet-milling corn flour (WCF), the ECF had less average particle size (16.74 μm), higher peak viscosity and higher final viscosity of 2997 cP and 3300 cP, respectively. The thermal properties showed that ECF had higher ∆H and lower T Corn, as a staple food in Mexico and Central America, is one of the three great grain crops of the world with a global production of 1.045 billion tons in 2017/2018. Corn is used for production of an array of traditional and industrialized human food such as tortillas, corn chips, nachos, popped corn, porridges and corn grits [2 per kilogram of corn [2 during nixtamalization could effectively reduce the content of acrylamide in tortilla chips. It is widely believed that the changes occurring in starch during nixtamalization are responsible for the textural and sensory properties of masa and its products [Traditional nixtamalization processes require about 12–16 h soaking time, 3 L of water and 1–2% of Ca(OH)products ,5. Mondrproducts , ultrasoproducts  and ohmi2 to release the starch granules from the corn endosperm. Ramírez [2 to reduce to levels sufficient to inhibit microbial activity and decreased the steeping time from 36 to 6 h. Alvoila [Enzymatic corn wet-milling is a process derived and different from conventional wet-milling. More starch granules were exposed by hydrolyzing proteins in the endosperm after grinding ,10,11. E Ramírez  reported Alvoila  studied max and G″max, whereas methylcellulose increased G′max but decreased G″max. A higher temperature and shorter time were required to gelatinize starch when the hydrocolloids were added to batter systems.In recent years, the texture and structure of foods are improved by combining cereal flour with hydrophilic colloid. Hydrocolloids, such as methylcellulose, xanthan gum, guar gum, Arabic gum are frequently applied in food as fat replacers, water binders, texturizers and adhesives ,16. RomáThe purpose of this study is to develop a new method to produce masa flour and evaluate the masa flour by making tortillas. This method is different from lime-cooking or nixtamalization in procedures, commercial wet-milling in products, and dry-milling in properties of corn flour. Moreover, the aim to use the enzyme in this study is different from the isolation of starch or modification of starch reported in existing literature. Therefore, the enzymatic wet-milling corn flour was prepared and the physicochemical properties were analyzed. Enzymatic wet-milling corn flour and additives (xanthan gum and cassava starch) were used to make tortillas, and the analysis on the properties of tortillas was used to evaluate the procedures of enzymatic wet-milling.p < 0.05) higher than that of dry-milling corn flour (DCF) (75.04%), while the protein content of WCF (4.79%) and ECF (4.29%) was significantly (p < 0.05) lower than that of DCF (6.93%). It revealed that the enzymatic wet-milling and the wet-milling caused protein loss and more starch granules exposed [p < 0.05) in three corn flours, which was highest for DCF (3.11%) and lowest for ECF (2.04%). The moisture content of DCF (11.22%) was significantly higher than that of WCF (7.85%) and ECF (8.05%). The variation in chemical composition between flours in the present study was due to different preparation methods. Both the content of protein and fat of three corn flours in this study were lower than that of the nixtamalization corn flour according to the report of Salazar [Proximate composition varied significantly among three flours . The sta exposed . The fatf DCF 6.9%. It rev4, ×2000). However, smooth starch granule surface and small amount of protein adherence could be observed in 4 (×2000). It revealed that the enzymatic wet-milling was an effective method using neutral protease to release the starch granules from the corn endosperm. This result was consistent with previous reports [Polarized light micrographs and SEM image of corn flours are presented in  reports .p < 0.01) in the granule size distribution of the three corn flours  (data not shown). The crystallinity of ECF, WCF and DCF were 32.43%, 30.40%, 27.18%, respectively . In geneGelatinization is a phase transition of starch granules from an ordered state to a disordered state during heating with excess water. Disordered states include melting of ordered regions at the level of crystallite (inner and surface) and amylopectin double-helical order . Figure temp of the three corn flours varied from 78.23 to 80.45 °C. The Ptemp of DCF was the highest, indicating that starch granules in DCF were not easy to swell and rupture [The gelatinization characteristic values of corn flour were summarized in  rupture . The PV, rupture . During  rupture . It indi rupture ,32,33,34o, Tp, Tc and ∆H of corn flour (p < 0.05). The To (69.99 °C), Tp (75.36 °C) and Tc (79.32 °C) of DCF was higher than that of WCF and ECF. The difference in gelatinization temperature between corn flour may be attributed to the granule size of starch and distribution of starch [p < 0.05). This result could be explained by degree of crystallinity. The crystallinity of ECF, WCF and DCF was 32.43%, 30.40% and 27.18%, respectively. There were significant differences in Tf starch . ∆H, reff starch . The ∆H The samples were recycled after DSC-scanned and observed under a light microscope. There were aggregation structure in DCF and WCF a,b. The During the gelatinization process, the starch granules in the corn flour swelled, accompanied by the dissolution of the particulate components (mainly amylose) and the formation of a three-dimensional network structure. The rheological properties of corn flour depend on the shape of the particles, the particle size distribution, the interaction between the particles and the viscosity of the continuous phase . The pas−1 (n < 1). The relationship between shear stress and shear rate of the three corn pastes was fitted by the Herrschel-Bulkey model, and the parameters were summarized in 2 = 0.97–0.99). The apparent viscosity of the ECF and WCF paste was higher than that of the DCF paste  between the hardness of the four tortillas (p < 0.05) after the addition of xanthan gum and cassava starch. Luis [p < 0.05) after the addition of xanthan gum and cassava starch, which was consistent with some previous studies [There was a significant difference ; the content of starch, protein and fat was 71.52%, 8.50% and 3.88%, respectively; the bulk density and relative density was 778.71% and 1.29%, respectively) and cassava starch  were obtained from a local supermarket. Neutral protease (1.27 × 105 U/mL) was obtained from Novozymes . Xanthan gum and guar gum was obtained from Danisco .Corn flour was prepared from enzymatic wet-milling by the following procedures: 50 g of corn was soaking in 150 mL of water in a thermostat water bath  at 50 °C for 6 h and coarsely ground in a knife blender  for 1 min. Then the slurry was placed in a water bath at 50 °C and adjusted to pH 6.6. Neutral protease (0.35 mL) was mixed into the slurry for enzyme treatments for 7 h. The sample was ground in a colloid mill  at rotor speed of 4500 r/min after enzyme inactivation and passed through a 100 mesh sieve. The wet cake of corn flour was obtained by vacuum filtration . The corn flour were dried in an oven at 40 °C for 12 h after washing 3 times with 500 mL deionized water.Wet-milling experiments were done followed the conditions described above without the addition of enzyme.Dry-milling process was done by a cyclone mill  and the flour were passed through a 100 mesh sieve.Xanthan gum, guar gum and cassava starch mixed with ECF were used for the orthogonal experiment (data not shown). The optimum additives  obtained by orthogonal experiments were applied to prepare tortilla. The formula flour was hydrated with distilled water (formula flour: water was 1:3) and blended manually for 2 min, and then the mixture was molded into thin circles (30 g). The thickness and the diameter of the corn dough was about 3 mm and 80 mm, respectively. The corn dough were baked at 200 °C for 6 min and packed into polyethylene bags after cooling to avoid moisture loss and stored at room temperature for evaluation.Determination of starch content in corn flour by 1%-hydrochloric acid optical rotation method. Protein content of the corn flour was measured using AACC Approved method 46-13A . Moisture and fat content were determined according to Approved method 44-15.02  and GB/T 5512-2008, respectively.Corn flour was first sprinkled on double-sides adhesive tape, which was attached to aluminum stubs, and the sample were then plated with a thin layer of gold. By using a high vacuum bench top scanning electron microscope , the sample were observed and photographed.w/v).The size distribution of corn flour was determined by laser diffraction particle size analyzer . Prior to analysis, the samples were dispersed in distilled water to form a suspension  were placed on the microscope slide with a glass coverslip. Then the samples was observed under the polarized light microscope.The morphology and birefringence of corn flour were observed in a digital camera belonged to the optical microscope . Corn flour suspension , which was operated at 30 mA and 40 KV. The diffraction angle (2θ) scanned was from 5° to 45° with a scanning speed of 4°/min. The crystallinity of corn flour was obtained by integrating the diffraction pattern with software MDI JADE 6.0.The thermal properties of the corn flour were determined using a differential scanning calorimeter (DSC) , and nitrogen purge gas was used in the experimental work. Corn flour samples  were wetted with distilled water (7 μL) in an aluminum pan and immediately sealed. The sealed pans were allowed to stand for 24 h at room temperature before the measurements. The sample pans were scanned from 30 to 100 °C with a heating rate of 10 °C/min and an empty pan was used as a reference . The DSCPasting properties of corn flour  were determined using a Rapid Visco-Analyser (RVA) . The heating and cooling cycles were programmed in the following manner. The slurry was held at 50 °C for 30 s, heated to 95 °C within 2.5 min and then held for 20 min. It was subsequently cooled to 50 °C within 3 min and then held for 9 min . Peak viRheological experiments were carried out using a DHR-1 rheometer . A 40 mm diameter parallel plate was used and the gap between the plates was set to 1 mm. The free surface of the sample edges was covered with silicone oil to prevent evaporation of the sample during the measurements . The tem−1 and apparent viscosity was recorded as a function of shear rate. The relationship of shear stress and shear rate were fitted to the Herschel-Bulkley model [τ is shear stress (Pa), τ0 is yield stress (Pa), K is the consistency coefficient (Pa·sn), γ is shear rate (1/s), and n is the flow behavior index (dimensionless).Flour suspensions  was prepared in deionized water and gelatinized by a Rapid Visco-Analyser in order to obtain starch paste and the experimented method was described in ey model .(1)τ = For frequency sweep experiments, the samples  were prepared according to the method depicted in The tortillas were dried in a freeze dryer for 24 h to observe the micro-structure of the cross section. The method was showed in The hardness of the tortilla was determined by puncture test . A probe (HDP/TPB) at a constant rate of 0.5 mm/s and the compressive stress of 30% was used. Maximum breaking force was the hardness of the tortilla .A piece of tortilla with 40 mm diameter was cut from the central part of tortilla for measurement. The frequency sweep was examined at 25 °C from 0.1 to 100 rad/s at 0.5% strain (within the linear viscoelastic region) . The visA panel of 12 trained persons carried out the tortilla sensory evaluation. The panel evaluated five attributes  using a hedonic scale, where number 10 to 1 corresponded to the decrease in the level of preference .n = 4). Duncan’s Pairwise Comparisons was used to analyze the difference of mean values, and p < 0.05 was considered to be statistically significant during operating the analysis of variance (ANOVA) test.All measurements were done in triplicate. Data in all the tables were reported as mean ± standard deviation  revealed that neutral protease could effectively hydrolyze protein in corn endosperm and weaken the combination of starch and protein, improving the gelatinization, thermal and rheological properties of corn flour. The prepared tortilla possessed porous structure and good sensory quality after the addition of xanthan gum and cassava starch. Therefore, application of neutral protease could be favorable to corn enzymatic processing and the quality of tortilla could be significantly improved by a combination of hydrophilic colloid and ECF.


Difference in life expectancy between males and females has been suggested to rest on sex difference in physiological dysregulation. But allostatic load, a physiological index, has not been carefully examined for an extended period beyond middle age. We aim to draw longitudinal trajectories of allostatic load in a national sample of older Americans and Britons; also to examine sex-based trajectories and factors behind their differences.N = 15,583 person-years) and the English Longitudinal Study of Ageing Waves 2, 4 and 6, 2004–2012 . Because of the difference in provenance, we included different number of biomarkers to calculate allostatic load in HRS and ELSA. In HRS we used 8 biomarkers , while ELSA allostatic load was constructed from 10 biomarkers . A growth curve model was fitted to repeated observations of allostatic load, demographic characteristics, socioeconomic position, comorbidities and health behaviours . To account for attrition, a joint model was applied.We studied men and women aged ≥50 years participating in the Health and Retirement Study Waves 8–11, 2006–2012 (The analysis showed that allostatic load increases linearly with age in the U.S. However, there are different levels for males and females. In England allostatic load follows such different paths that their trajectories cross in later life.Sex-based trajectories of allostatic load showed distinct female advantage and are mostly consistent with female advantage in life expectancy. The increasing proportion of older people in the population and their increasing life expectancy give larger number of older people longer lives . This prDespite the female advantage in life expectancy, they have higher morbidity rates and a declined quality of life than males in later life . LiteratAllostatic load captures the notion that the human physiological system is not a fixed and stable system but a dynamically stable one, especially in the face of insults, hazards and injuries to various organ systems –21. AlloFew studies have been devoted to uncovering the longitudinal pattern of allostatic load based on repeated measurements. As to socioeconomic gradient in allostatic load, what role the gradient plays in longitudinal change remains relatively uncharted. When examining longitudinal patterns of allostatic load in later life, a study must also contend with the problem of attrition: i.e. when participants are lost to follow up for various reasons, some to do with health and death. This attrition problem is pervasive in ageing studies , and to To focus our efforts four questions will be answered. First, is there a sex difference in longitudinal trajectories of allostatic load? Second, given the female advantage in life expectancy, do these trajectories show a female advantage, i.e. a lower allostatic load trajectory for females? Third, given that psychosocial factors get under the skin and affect physiological regulation , 30, 31,This work makes three contributions. It is the first to draw longitudinal trajectories of allostatic load for males and females based on the major study of ageing in America and Britain. Second, in drawing these trajectories, it has uncovered unique patterns that are consistent with the female advantage in life expectancy. Lastly, it showed perceptibly advantageous trajectories of allostatic load in older Americans consistent with emerging comparative research –34.We used two nationally longitudinal studies of aging: the U.S. Health and Retirement Study (HRS)  and The Due to the availability of the data, we included different numbers of biomarkers in HRS and ELSA. Eight biomarkers  in the HRS and ten biomarkers  in the ELSA were included in the analysis. Following Stephan et al. (2016) , we calcDemographic covariates include sex and age. Since age is capped at 90 in ELSA, information from respondents aged 50 to 89 in both surveys was used. Like other health functions, allostatic load is also shaped by social determinants of health. These include wealth tertiles , education (some college and high school or less as reference), and marital status (married/cohabiting and other as reference) , 38, 39.Based on positive medical history , a sum of indicators about comorbidities were included, covering diabetes, cancer, hypertension, lung disease, heart condition, and stroke. Behavioural risk factors known to be important in other studies include smoking (current smoker versus not current smoker as reference), drinking (days in a week having a drink) and physical activity  .N = 15,583 observations) and ELSA  in the analysis. Differences between those included and left out were tested using t-test (continuous variables) or χ2 test . At baseline, compared to those left out the analytic sample is younger , has fewer women , and has lower levels of allostatic load .We included only observation of respondents with complete information on the allostatic load components and the covariates in HRS  below) in the joint model influence both allostatic load, y, and attrition, t, given these, allostatic load and attrition are independent. A joint model has two parts: the growth curve model (f(.) below) and the survival model (g(.) below)with gender, age polynomial of degree three and the random intercepts from the first partt [To model how allostatic load changes over time using repeated observations, a growth curve model with random intercepts is applied. This model gives consistent estimates of the effects of risk factors when some participants are lost in subsequent visits, assuming attrition at random. We fit two models to address the questions above. First the main  is used , 27, 41. is used , 26. We st partt , 26.\docWe compare the results of growth curve models with those of joint or attrition-corrected models. The analyses were done in Stata 14  and Latent Gold Syntax 5.1.We carried out three supplementary analyses to check the robustness of our results. In the first supplementary analysis, we followed Read and Grundy (2014) by computing allostatic load score in ELSA using 13 indicators of whether biomarker values fell into their high risk quartiles . The indIn the second supplementary analyses, we analysed only the post-war cohort (those who were born on 1946 onwards) of ELSA to examine the effect of cohort on the allostatic load. Our previous work found that the trajectories of depressive symptoms between cohorts  in the US and England took different shapes . The traThe sample for analysis consists of 56% and 54% females in HRS and ELSA data, respectively Table . The tabMarried, better-educated and wealthy participants have lower allostatic load compared to reference categories in both data. Lastly (bottom block), to pick one behavioural factor, more physical activity inversely associates with allostatic load.Main, showed that allostatic load increases with age in HRS , and ELSA . To check for different trajectories for females and males, a sex-age interaction term was added in the Interaction model. This model gave the interaction terms positive and significant coefficients in both data, suggesting the trajectories are significantly different for females and for males.Table To ease interpretation and comparison, the coefficients for the two joint models are plotted together in a figure of two panes corresponding to the two models Fig.  for ELSAp < 0.001) (see p < 0.001) in which the allostatic load of the males is higher than that of females afterwards  see , we findards see . The effards see . Supportards see . This seWe set out to draw the sex-based trajectories of allostatic load in samples representing the American and British older populations. Upward trajectories of allostatic load for both males and females are found, but the rates of change differ for the two sexes. This led to allostatic load incline among older Americans and additionally a cross-over in older British allostatic load trajectories at the mid-60s. Allostatic load trajectory as a physiological signature of ageing unfolds differently for males and females. This is a novel finding that deserves replication and reflection, some of which are offered later.The patterns in America, i.e. linear accumulation and female advantage, are consistent with the patterns of life expectancy worldwide. These patterns also showed a gentler incline of allostatic load overtime, compared to those in Britain. The somewhat advantageous patterns revealed in America is consistent with recent evidence uncovered using the HRS and ELSA. Tampubolon and Maharani  showed tp values are statistically significant. Although there is a statistically significant change, the change is not strong.This study has a number of limitations. Direct measures of the stress response system such as urinary norepinephrine, epinephrine and salivary cortisol are not available. Including these is wholly desirable  but woulAn advantage of this study is the nationally representative nature of both HRS and ELSA, ensuring that the accumulation of physiological wear and tear in these populations can be uncovered without being unduly influenced by specific patterns in some clinical or patient samples. Another strength arises from its longitudinal design, with repeated measures not only of allostatic load but also of an extensive set of covariates.The similar increase of allostatic load found among older Americans and older Britons stands in contrast with the trajectories found in American adults . There tThe socio-economic gradient in trajectories of allostatic load found here is consistent with results from an earlier study using a shorter period of the ELSA sample . SimilarThe trajectories of allostatic load in later life show distinct signatures for males and females. To uncover them not only is it necessary to use longitudinal data but also to account for attrition. With these done, both trajectories show clear social gradients in physiological wear and tear for an extended period in later life. Efforts to reduce these gradients earlier may yield fruit in later life by lightening the allostatic load, thus giving a healthier ageing experience to an increasingly large proportion of the population.


The hot deformation behavior and microstructure of the alloy were significantly affected by the deformation temperatures and strain rates. The peak flow stress decreased with increasing deformation temperatures and decreasing strain rates. According to the hot deformation behavior, the constitutive equation was established to describe the steady flow stress, and a hot processing map at 0.4 strain was obtained based on the dynamic material model and the Prasad instability standard, which can be used to evaluate the hot workability of the alloy. The developed hot processing diagram showed that the instability was more likely to occur in the higher Zener–Hollomon parameter region, and the optimal processing range was determined as 420–475 °C and 0.01–0.022 s−1, in which a stable flow and a higher power dissipation were achieved.The hot deformation behavior of a new Al–Mn–Sc alloy was investigated by hot compression conducted at temperatures from 330 to 490 °C and strain rates from 0.01 to 10 s Such mechanical properties are attractive for aerospace industries. However, this Al–Mn–Sc alloy has only been studied in the additive manufactured condition, but has not been investigated in other forms like casting and wrought products.Casting and wrought aluminum (Al) alloys have been widely used as structural materials in aerospace industries owing to their high specific strength (strength to weight ratio), excellent fatigue resistance, and good formability ,2,3. TheAfter direct chill casting, Al ingots generally need various thermo-mechanical processing steps to obtain different types of semi-finished products. The microstructure of the material depends on the thermo-mechanical processing parameters, which also determine the quality of the formed part. Thus, it is necessary to understand the influence of deformation parameters on hot deformation behavior and microstructure in this Al–Mn–Sc alloy. In general, the isothermal compression test is an appropriate method to study the hot deformation characteristics of materials ,9. The rThe present work aims to investigate the hot deformation behavior, to reveal the microstructure change, and to obtain the hot processing map of the casting Al–Mn–Sc alloy over wide temperature and strain rate ranges. The results will provide guides on its hot deformation processing and industrial applications.−1. Graphite sheet was used as a lubricant between the compression plate and the sample to reduce friction. Before the compression test, the samples were solution treated at 500 °C for 5 min followed by gas quench in the Gleeble chamber. These samples were heated again to the testing target temperature at a ramping rate of 10 °C/s and held for 5 min to eliminate the thermal gradients before compression. Isothermal hot deformation experiments were conducted afterwards at various temperatures and strain rates. After a 60% deformation, the samples were gas-quenched to room temperature to freeze the microstructure after the hot deformation. The gas-quenched deformed samples were sectioned by EDM wire-cut along the axial direction, which is parallel to the compression direction. A standard metallographic sample preparation and etching by Keller’s solution (1 mL HF + 1.5 mL HCl + 2.5 mL HNO3 + 95 mL H2O) were carried out, and the microstructure characterization was conducted by using a Leica DMi8A light microscope (LM) and a FEI QUANTA 450 scanning electron microscope (SEM) equipped with an energy dispersive X-ray spectrometer (EDS) detector. The grain size was measured by ImageJ software.The raw material  used in 6Mn phase can hinder the growth of recrystallized grains, refine the grains, and improve the strength of the alloy. For Scandium (Sc) addition, the Al3Sc phase with an L12 structure can prevent recrystallization and promote fine grain strengthening and fine precipitation strengthening. Sc is considered to be the most effective alloying element for aluminum alloys. Al3Sc precipitates show small lattice mismatches and low interfacial energy in aluminum matrix, and the low diffusivity of Sc also helps to improve thermal stability , and Q/Rn is the average slope of the linear relationship between ln[sinh(ασ)] and (1/T) in Equation (11). As shown in the n2 and R, the hot deformation activation energy Q of the Al–Mn–Sc alloy is determined at 200.581 kJ·mol−1. According to Zener and Hollomon , which leads to the structural factor A at 8.171 × 1013. In summary, all constant values are determined above. The constitutive equation for the hot compressed Al–Mn–Sc alloy can then be presented in Equation (14).By substituting the values of Q is closely related to the thermodynamic mechanism of dislocation movement and can reflect the processability of the material. It is thus meaningful to understand the effect of processing parameters on activation energy.In general, the activation energy According to Equations (9) and (11), the activation energies obtained under various deformation conditions are shown in As shown in First, the activation energy decreases with increasing strain rates. This phenomenon should be related to dislocation movement. The external stress increases with increasing strain rates, and the shear stress applied in the dislocation sliding direction also increases . TherefoSecond, the activation energy decreases with increasing deformation temperatures owing to the effect of dislocation density. At higher temperatures, the rearrangement of dislocations during DRV and the formation and growth of recrystallized grains during DRX will be promoted, leading to a reduction in dislocation density. Therefore, as the deformation temperature increases, the resistance to dislocation movement decreases.The thermo-mechanical processing maps (PMs) were extensively used to describe the microstructure evolution and establish the processing window, which provide guides for the industrial manufacturing such as rolling, extrusion, and forging ,14,32,33P obtained by the material per unit volume within a certain time can be divided into two parts according to the report by Prasad et al. [G. Most of G is converted into heat, and a small portion is stored as crystal defect energy. (2) The energy consumed by the microstructure evolution during hot deformation is J, which represents the evolution of the microstructure during the deformation process, such as DRV, DRX, internal cracks (voids formation and wedge cracks), dislocations, growth of grains and precipitates under dynamic conditions, spheroidization of needle-like structures, phase transitions [P can be expressed as follows:During thermo-mechanical processing, the energy d et al. : (1) Thensitions , and so G and J satisfies Equation (16) when the temperature and strain rate are constant:m = 1), J has the maximum value at maxJ = P/2. The ratio between J and maxJ was determined by the dissipation efficiency factor (ƞ) [ƞ represents a dimensionless parameter that describes the ratio between the energy consumed by the microstructure evolution and the total energy consumed by a linear hot deformation. ƞ is also termed as the microstructure trace. Domains with high dissipation efficiency factors in PMs indicate the formation of special structures or occurrence of softening behaviors, such as DRX and DRV, and possible local deformation instability. Therefore, the unstable region of the material in hot deformation must be identified in order to determine a suitable processing window [For most pure metal or low alloy materials, the energy distribution relationship between ctor (ƞ) :(17)η=JJg window ,35. On tm,η, and ξ were calculated by cubic spline interpolation. The equivalent maps of the dissipated power η and the instability factor ξ are plotted in The values of η at ɛ = 0.4. With the temperatures at 420–475 °C and the strain rate at 0.01–0.022 s−1, the dissipated power increases to the maximum value of 26%. ɛ = 0.4, and the shaded domains (the negative instability factor) represents the unstable windows. When the deformation temperature is low and the strain rate is large , the instable deformation is more likely to occur. Hot processing in these parameters is prone to introduce defects and is not effective in facilitating the recrystallization. A suitable processing window should be the rest region outside the regions mentioned above.A domain with a higher dissipation efficiency factor indicates that more energy is dissipated in microstructure evolution, which is preferred as the processing window for hot deformation. −1 (stable region) and 490 °C/10 s−1 (unstable region) in −1.−1 is the most suitable processing window. The strain rate is low, but the deformation temperature is high, and there should be enough driving force to promote the dynamic recovery or dynamic recrystallization to optimize the microstructure. Although the dissipated power value is low at 370 °C, regions A and B in −1.The peak domain (26%) with deformation temperatures and strain rates at 420–475 °C and 0.01–0.022 sZ) parameter [Z parameter was used to evaluate the hot deformation behaviours, and a larger lnZ corresponds to a higher strain rate or a lower deformation temperature. In order to further verify the feasibility of the hot processing map and understand the deformation mechanism, the deformed microstructures processed at the five regions (A) to (E) in Z are listed in Z value decreases with reducing deformation rates when the deformation temperature is 370 °C. Most of the grain boundaries remain straight, and DRV is the main softening mechanism in −1, and the value of lnZ decreases with an increasing deformation temperature, as shown in Z is the smallest with the deformation temperature at 490 °C and strain rate at 0.01 s−1, which leads to a substantially increased DRX and is consistent with the findings in previous studies [−1 for the new Al–Mn–Sc alloy.Many studies have shown that the microstructure after hot deformation is closely related to the Zener–Hollomon Q at 200.581 kJ·mol−1 and a structural factor A at 8.171 × 1013.The dynamic softening is sufficient at higher temperatures and lower strain rates. In general, the flow stress decreases with increasing deformation temperatures and decreasing strain rates. The deformation behavior satisfies the hyperbolic sinusoidal constitutive law with an activation energy (2)−1, in which a stable flow and a high power dissipation are achieved.On the basis of DMM, a hot processing map is established at a 0.4 strain. The optimal processing range is between 420–475 °C and 0.01–0.022 s(3)Z (low deformation temperatures and high strain rates) suggests a dynamic recovery dominated mechanism and a low lnZ indicates a dynamic recrystallization dominated mechanism.In hot deformation, a high lnHot compression tests were conducted at 330–490 °C and 0.01–10 s


These results show that the role of the N-terminal α-lobe is to provide a suitable configuration (i) of the α2–α3 helices to feature the α3-motif that has a role in NAD+ substrate binding and possibly in the interaction with the protein target; (ii) the α3–α4 helices to provide the α3/4-loop with protein-protein interaction capability; and (iii) the α1-Ntail that features specialized motif(s) according to the toxin type (A-only or A-B toxins) exhibiting an effect on the catalytic activity via the ARTT-loop, with a role in the inter-domain stability, and with a function in the binding and/or translocation steps during the internalization process.The C3-like toxins are single-domain proteins that represent a minimal mono-ADP-ribosyl transferase (mART) enzyme with a simple model scaffold for the entire cholera toxin (CT)-group. These proteins possess a single (A-domain) that modifies Rho proteins. In contrast, C2-like toxins require a binding/translocation partner (B-component) for intoxication. These are A-only toxins that contain the E-x-E motif, modify G-actin, but are two-domains with a C-domain possessing enzymatic activity. The N-domain of the C2-like toxins is unstructured, and its function is currently unknown. A sequence-structure-function comparison was performed on the N-terminal region of the mART domain of the enzymatic component of the CT toxin group in the CATCH fold (3.90.210.10). Special consideration was given to the N-domain distal segment, the α-lobe (α In addition, the 3rd position of the I-motif, (N/D)p3, makes effective contacts with the α3/4-loop in the C2N-domains (α3/4N-loop) to stabilize both domains (see the next section).(4) The “I-motif”, D–K(N/D), has residues in the T domains . In agreTN–(E/D)E–K contains residues that are unique in the TC3-segments and are not found in the previous motifs (gray residues in (5) The “T-motif” E–3 and α4, the “α3/4-loop”, is either short and unstructured  or contains a small 310-helix configuring a loop-helix-loop motif , called the LHL-motif that protrudes from the α-lobe [C-domains, the α3/4C-loop is also short and unstructured  or harbors a small α-helix . In most of these toxins/domains, there is an abundance of Asn, Gly, and Pro residues, which imply a structural role in allowing the αα-corner that connects α3 with α4 . However, the presence of both charged and hydrophobic residues in the turn of the α3/4-loops of some C3-like toxins , along with their solvent exposure, makes the α3/4-loop a probable interacting motif in these toxins. Indeed, even the short LHL-motif of C3bot1 mediates the non-enzymatic interaction of the toxin with the RalA GTPase [In the single-domain Plx2A, and C3-like toxins, the segment that connects helices αe α-lobe . In the A GTPase . 3/4N-loop is longer than its counterpart in C3-like toxins and C2C-domains and harbors an invariant Phe as well as other polar and hydrophobic residues. Interestingly, in Ia, CdtA, and SA toxins, the α3/4N-loop and α4N are highly conserved with an average identity of 80.3% (90.2% similarity), and the Ia α3N−α4N superstructure may be involved in the interaction with Ib (iota B-component) [iota toxin is not located at the N-terminus (TC2N-segment), likewise in C2I toxin (see previous section). Alternatively, in Ia toxin, this segment is more centrally located (residues 62–257), arising from α3/4N (in the αN-lobe) to α3C (in the αC-lobe) including both elements [3/4N-loop can be considered part of the C2C-domain according to distance and packing criteria  and 222–257 (α1C−α2C of the αC-lobe) may control binding to the Ib protein with a C3-like toxin chimera [p3 residue of the I-motif interacts with the enlarged α3/4N-loop to stabilize the inter-domain architecture (see the previous section) with an invariant Ile residue in α4aN (in α4N for Vip2N).On the other hand, the αmponent) . In effeelements . This pocriteria , such th chimera . In addiB. cereus (PDB: 4GF1) is a bi-domain A-component toxin with the catalytically C-domain homologous to the C3-like toxin or the C2C-domain, but with the N-domain homologous to the PA-binding domain of anthrax lethal factor from B. anthracis [1 configuration and catalytic ability (N-like binding/translocation) motifs; it exhibits inter-domain interactions via a modified I-motif. Thus, Certhrax clusters with the C2C-subgroup based on T-segment similarity (not shown), which is compatible with its catalytic non-terminal T-segment.Certhrax toxin from nthracis . Accordi ability . IncidenSalmonella spp, lacks atomic coordinates of the region corresponding to the T-segment , and lacks the S- and C-motifs. Accordingly, an estimate of the secondary structure of this segment by the PSIPRED server predicted a coiled structure for this region, and several in-house homology models of the full-length SpvB from S. typhimurium report an extended coil for the T-segment.The X-ray structure of the mART domain of SpvB (PDB: 2GWM), a single-component actin-ADP-ribosylating toxin from A. hydrophila [2). The sequence of the VahC T-segment is highly like the corresponding segment in SpvB, and both segments harbor a poly-proline sequence. Likewise, Photox toxin from P. luminescens [β5, which is part of the canonical α1 configuration with catalytic ability . The phenol side chain of Tyr β5 is the H-acceptor from the conserved Gly (backbone) in the α1-motif  is a special case. The elongated α1 of Vis toxin follows a V-shaped configuration of the α1−α2 superstructure like C2/C3-toxins, although in a slightly ‘altered’ orientation . Although it possesses some aromatic residues in the T-segment, Vis does not possess a C-motif and is unique in having the small, polar Ser residue replacing the conserved Gly. Notably, this Ser residue interacts in a similar manner with a residue  proximal to the ARTT-loop as observed for Gly in the C-motif.Vis toxin from entation . AccordiPseudomonas syringae (PDB: 3UOJ) has α1 with an orientation diametrically opposed to the canonical conformation: an α1−α2 forming an αα-corner rather than the V-shaped configuration  [1 to the βI strand and lacks the catalytic C-motif. Nevertheless, in HopU1 the N-end of the ARTT-loop (the end linked to strand β5) is stabilized by the αα-corner configuration, while the C-end of the ARTT-loop (the end linked to strand β6) is left partially exposed.In contrast, the HopU1 toxin from guration . The αα-rmed L1)  and is s2–α3 helices to feature the α3-motif that has a role in NAD+ substrate binding and possibly in the interaction with the protein target; (ii) the α3–α4 helices to provide the α3/4-loop with protein–protein interaction capability; and (iii) the α1-Ntail, defined in the T-segment, that features specialized motif(s) according to the toxin type (A-only or A-B toxins) exhibiting an effect on the catalytic activity via the ARTT-loop, with a role in the inter-domain stability, and with a role in the binding and/or translocation steps during the internalization process. In summary, the role of the α-lobe is to provide a suitable configuration (location and orientation) of (i) the α1 helix forming a V-shape with α2 that surrounds the ARTT-loop. This configuration is stabilized by the ubiquitous S-motif in the T-segment of C3-like, C2-like (N- and C-domains), Plx2A and Certhrax toxin. Accordingly, other non-PT-like toxins  with known structures show the canonical α2–α4 configuration. However, the α1 helix in an alternative configuration consistent with the lack of the S-motif in its T-segment. The canonical (C3-like) α-lobe configuration has the α1–α2 helices, and specifically by the C-motif of the T-segments. In particular, the Gα1−Tyrβ5 pair is involved in the mutual stability of α1 and the ARTT-loop. Incidentally, C3larvin, which has a truncated α1, still has the C-motif and Tyrβ5 and exhibits GH activity [1 configuration, Tyrβ5, and C-motif and shows GH activity [1 conformation of Vis toxin encloses the ARTT-loop and shows GH activity [β5 and the C-motif (their T-segments are likely unstructured) and do not show GH activity [1 conformation and shows GH activity (unpublished data).The presence of the catalytic signature on the ARTT-loop is not enough to guarantee GH- activity—the contrary is true—no catalytic residues in the ARTT-loop means no GH-activity. In non-PT-like toxins, GH-activity requires a stabilized ARTT-loop conformation, and this is achieved in toxins with the canonical α-lobe in the V-shape configuration of the αactivity ; also, Cactivity . Accordiactivity ,39,40,43C2N- and TC2C-segments evolved to harbor residues appropriate for their location and roles. The TC2N-segment features the S- and B motifs. In effect, the inability of the C2N domain to bind NAD+ must have forced the atrophy of the C-motif and the ARTT-loop , and instead, the TC2N-segment evolved to interact with the B-component. On the other hand, the TC2C-segment features the S-, C-, and I- motifs. In effect, the TC2C-segment might have evolved according to (a) a higher specialization of its ancestral kinetic role. Accordingly, the C-motif is better defined in the C2C-domains  than in the single-domain toxins with the general definition; and (b) possesses an emerging structural role associated with the bi-domain topology—the I-motif. The I-motif, along with specific substitutions in the β3N and β4C strand, and with the α3/4N-loop, participates in inter-domain stabilization. The specialization of the A-component of binary toxin classes  likely involves selective forces related to the intoxication mechanism that may dictate the composition of the T-segment. The bi-domain constitution might have arisen by gene duplication of an ancestral ADP-ribosyl transferase ; consequN domain for C2 toxin and the RxG motif  for C3bot1 may be the minimum sub-structure/motif necessary to stably bind to the membrane component—C2II and vimentin, respectively. In addition, the N-terminal T-segment may also participate in the process of internalization of both toxin groups into the host cell; it be involved in the translocation step from the early endosome to the cytoplasm. Accordingly, C3-like toxins feature the T-motif with acidic residues that might trigger the conformational changes required for membrane translocation; while C2-like toxins feature the B- motif. In this sense, the B-motif may bind to the B-component and/or to mediate the translocation into the cytoplasm. Intoxication experiments that monitor phenotypic alterations of the host cell do not distinguish which event is abolished when working with a toxin. Thus, in agreement with the translocation role of the B-motif, a construct of C2IN without the first 29 residues and the C3bot1 toxin, C2INΔ29NC3bot1, failed to be transported into HeLa cells, although binding of the construct to C2II on cell membranes was still observed [3/4N-loop is the sub-structure needed for binding (as has been observed in Ia toxin), while the B-motif is needed for the translocation step. It has been reported that the α-lobe of the C2Iobserved . Also, iapo forms. However, in some cases proteins in different liganded states and diverse catalytically altered variants were also included for comparative purposes. When multiple molecules were presented in the asymmetric unit of some crystal forms, the most complete molecule was selected. X-ray structures entries were downloaded from the Protein Data Bank (PDB). The datasets include high resolution (1.57 to 2.70 A) X-ray structures mainly of WT proteins in + molecule and co-solvents. For the implicit solvent model, the Generalized Born-Volume Integral (GB/VI) formalism was employed, with dielectrics Protein preparation and molecular mechanics (MM) calculations were performed using the computational suite Molecular Operative Environment (MOE) release 2018.10 . The force field employed was the MOE Amber12: EHT, with AMBER12 parameters set (ff12) for protein, and parameters calculated from the Extended Hückey Theory for the NAD2. When short sections of X-ray structures were missing in the PDB data files, the peptide segments were crafted by using built-in homology model procedures in MOE. Then, the full X-ray structures were protonated using the MOE Protonate3D module to assign the ionization states and tautomers of protein side-chains and to orient crystallographic water molecules (CWMs) at T = 300 K, pH 7.4 and 0.1 M of ionic strength, along with the GB-VI  solvation model and MMFF94 partial charges. The protonated structures were initially geometry-optimized by keeping backbone coordinates fixed, tethering all other heavy atoms with a 100 kcal/mol force constant (0.25 Å buffer), and then energy-minimized until an RMS gradient ≤0.001 kcal/mol/ÅThe multiple sequence alignments (MSA) were also performed using MOE 2018 software and based on the overall matching of secondary and tertiary structural elements. For a higher resolution alignment of short segments, the procedure was enhanced from the pattern of common molecular interactions. All protein structures were rendered in MOE 2018 software.