PTPN11, plays a fundamental role in the modulation of severalsignaling pathways. Germline and somatic mutations in PTPN11 are associated with different rare diseases and hematologic malignancies,and recent studies have individuated SHP2 as a central node in oncogenesisand cancer drug resistance. The SHP2 structure includes two Src homology2 domains (N-SH2 and C-SH2) followed by a catalytic protein tyrosinephosphatase (PTP) domain. Under basal conditions, the N-SH2 domainblocks the active site, inhibiting phosphatase activity. Associationof the N-SH2 domain with binding partners containing short amino acidmotifs comprising a phosphotyrosine residue (pY) leads to N-SH2/PTPdissociation and SHP2 activation. Considering the relevance of SHP2in signaling and disease and the central role of the N-SH2 domainin its allosteric regulation mechanism, we performed microsecond-longmolecular dynamics (MD) simulations of the N-SH2 domain complexedto 12 different peptides to define the structural and dynamical featuresdetermining the binding affinity and specificity of the domain. Phosphopeptideresidues at position −2 to +5, with respect to pY, have significantinteractions with the SH2 domain. In addition to the strong interactionof the pY residue with its conserved binding pocket, the complex isstabilized hydrophobically by insertion of residues +1, +3, and +5in an apolar groove of the domain and interaction of residue −2with both the pY and a protein surface residue. Additional interactionsare provided by hydrogen bonds formed by the backbone of residues−1, +1, +2, and +4. Finally, negatively charged residues atpositions +2 and +4 are involved in electrostatic interactions withtwo lysines (Lys89 and Lys91) specific for the SHP2 N-SH2 domain.Interestingly, the MD simulations illustrated a previously undescribedconformational flexibility of the domain, involving the core βsheet and the loop that closes the pY binding pocket.SH2domain-containing tyrosine phosphatase 2 (SHP2), encoded by  The presence in their structuresof domains devoted to protein/protein interactions leads to properpositioning of these enzymes close to their substrates.7 In pY signaling, kinases “write”the phosphorylation signal, which can be “erased” byphosphatases. SH2 domains “read” this information, usingit to localize signaling proteins correctly.8 As a general scheme, binding of an extracellular ligand to a receptortyrosine kinase induces activation of the receptor, which phosphorylatesitself and other nearby proteins. These phosphorylated tyrosine residuesthen function as docking sites for the SH2 domains of other proteins,which are thus recruited to the cell membrane or activated, causingpropagation of the signal.9 In addition,SH2 domains enhance tyrosine phosphorylation in vivo by protectingbinding sites in their target proteins from dephosphorylation.10Tyrosine phosphorylation contributes only ∼0.5% ofthe total phosphoproteome, yet it plays critical roles in eukaryoticcell regulation.11 These domainscontain approximately 100 amino acids, with a central β strand,flanked by two α helices. These secondary structure elementsare labeled according to their position along the sequence: βAαA βB βC βD βE βF αB βG; the C-terminal side, flanked by helix αB andthe EF and BG loops, provides a more variable binding surface (specificitydetermining region) that typically engages residues C-terminal tothe pY  must bind only to phosphorylatedproteins and (ii) must associate specifically only with certain sequences.Threehundred 3D structuresof approximately 70 different SH2 domains have been determined. Theyreveal a highly conserved topology.βF αB βG1A. Each othe pY 1B.3,9,1313 consistentwith the observation that SH2 domains are able to associate with theircognate proteins even when these are denatured.14In most structures of SH2-ligand complexes, the phosphopeptidebinds across the surface of the domain, orthogonal to the centralβ sheet, in an extended conformation 1B,13 con15 Associationof the SH2 domains with pY motifs favors N-SH2/PTP dissociation andthereby activation of the phosphatase  SHP1 and SHP2 contain two SH2 domainsthat are N-terminal to the catalytic domain, termed N-SH2 and C-SH21C. In thsphatase 1D.6 The PTPN11 (the gene codingfor SHP2) cause more than 30% of cases of juvenile myelomonocyticleukemia (JMML) and are variably found in other childhood malignancies.19 In addition, SHP2 is required for the survival of receptor tyrosinekinases (RTK)-driven cancer cells,20 playsan important role in resistance to targeted cancer drugs,21 is a mediator of immune checkpoint pathways,22 and is involved in the induction of gastriccarcinoma by Helicobacter pylori.23PTPN11 mutations also causethe Noonan syndrome and Noonan syndrome with multiple lentigines,two disorders belonging to a family of rare diseases collectivelyknown as RASopathies.25 For all these reasons, SHP2 isan important molecular target for therapies against cancer and rarediseases.28 At the molecular level, pathogenic mutations of PTPN11 often cause an increase in the binding affinity ofthe SH2 domains of SHP2, leading to hyperactivated signaling of theRas/MAPK pathway.32The SHP2 protein was the first oncogenic PTPdiscovered. Mutations of 9 The fact that short pY-containing peptides  are sufficient to compete with larger proteinligands for SH2 domain binding has prompted researchers both in academiaand industry to develop inhibitors of clinically relevant SH2 domains.33 However, no molecules targeting the SH2 domainsof SHP2 for therapeutic purposes have been reported. Considering itsrole in the allosteric regulation of SHP2, the N-SH2 domain is particularlyinteresting under this respect.Due to their role in many signaling pathways, SH2 domainshavereceived much attention as potential targets of pharmaceuticals.36 and severaladditional partners have been reported since then.43 A database of the known interactions is available at phospho.elm.eu.org. However,in many of these cases, the sites of interaction, the pY residuesthat bind specifically to the SHP2 N-SH2 domain, and the binding affinitieshave not been determined. Several proteinsinteracting with SHP2 through its SH2 domainshave been identified. Lists of more than 50 known or putative interactingproteins have been compiled in the past,53 analyzed positions from −6 to +6,but distinct preferences were observed only in the −3 to +5sequence stretch. The results of these investigations are summarizedin Thesequence selectivity of the N-SH2 domain of SHP2 has been analyzedalso by utilizing phosphopeptide libraries. Oriented peptide librarystudies have examined positions from −1 to +6 with respectto pY. More recently, high-throughput studies with surface-immobilizedpeptide arraysDistinct selectivityfeatures emerge from these data. Definingthe determinants of N-SH2 selectivity is essential to allow the designof new peptides, peptidomimetics, and small molecules targeted tothis domain. To this end, we analyzed collectively the available X-raystructures and performed several molecular dynamics (MD) simulationsof N-SH2/phosphopeptide complexes.55 while in 1AYC theN-SH2 domain is complexed with a nonspecific peptide.56 The phosphopeptides present in the remaining structuresare listed in Seven experimental structuresof N-SH2/phosphopeptide complexes,obtained by X-ray crystallography, are available . In this work, 3TKZ and 1AYC were excluded fromfurther analysis as, in 3TKZ, a non-canonical 1:2 protein/peptidecomplex is formed,Whilethese structures provide insights into the determinants ofN-SH2 selectivity, characterization of the dynamics of domain/peptidecomplexes is essential to evaluate (i) the stability of the interactionsobserved in the crystallographic data and (ii) possible conformationaltransitions of the peptide or of the domain. In addition, no structuresare available for the IRS-1 pY1179 peptide (which has one of the highestaffinities among known sequences) or for high-affinity artificialpeptides that were isolated in library screening studies. To addressthese issues, we performed 12 (microsecond-long) MD simulations ofcomplexes of the N-SH2 domain with Gab1, IRS-1 pY1172, pY895, pY546, PDGFR pY1009, and three artificial peptides isolated in refs  and 36)36). MoreTable S1, for five of the simulatedsequences , X-raystructures were available, but some residues had to be removed oradded. In the other cases ,the sequence to be simulated was obtained by substituting (and addingor removing) some residues, starting from the crystallographic structureslisted in Table S1. The termini of thepeptides were capped by acetyl and amide groups. These modificationsin the peptide molecules were performed by means of Sequence Editorand Protein Builder functionalities in Molecular Operative Environment(MOE) . The backbone of the addedresidues (at the termini) was initially modeled in an extended conformation.The side chains of the substituted residues were modeled by meansof conformational search using a rotamer library as starting guessand allowing repacking. The structures were minimized, with the AMBER12:EHTforce field57 in generalized Born implicitwater,58 first on substituted side chains,constraining the backbone, and then on all substituted/added aminoacids and on adjacent residues, without restraints, yielding a reasonablebinding pose for all peptides. In all cases, the N-SH2 domain comprisedresidues 3 to 103. Each protein molecule was put at the center ofa dodecahedron box, large enough to contain the domain and at least0.9 nm of solvent on all sides. The protein was solvated with explicitTIP3P water molecules.59 All MD simulationswere performed with the GROMACS 4.6.5 software package60 using the AMBER99SB force field61 augmented with the parm99 data set for phosphotyrosine.62 Long-range electrostatic interactions were calculatedwith the particle-mesh Ewald (PME) approach.63 A cutoff of 1.2 nm was applied to the direct-space Coulomb and Lennard-Jonesinteractions. Bond lengths and angles of water molecules were constrainedwith the SETTLE algorithm,64 and all otherbonds were constrained with LINCS.65 Thepressure was set to 1 bar using the weak-coupling barostat.66 Temperature was fixed at 300 K using velocityrescaling with a stochastic term.67 Forall systems, the solvent was relaxed by energy minimization followedby 100 ps of MD at 300 K while restraining protein and peptide atomicpositions with a harmonic potential. The systems were then minimizedwithout restraints and slowly equilibrated to remove any possiblestrains in the starting structures. Their temperature was increasedin steps of 50 K from 50 to 300 K. Each step from 50 to 200 K compriseda first stage of 0.5 ns at fixed temperature and a linear temperatureramp of 50 K, lasting 0.5 ns; for the steps from 200 K to 300 K, theduration of these two stages was increased to 1 ns, and then 3 nswere performed at 300 K, for equilibration. Finally, productive runsof 1 μs were performed.Initial atomic coordinateswere taken from crystallographic structures.As shown in 68 The orderparameter Θχ for the side-chain dihedral angleχ was calculated asN frames in the MD trajectory and i.69 Θχ = 1 and Θχ = 0 correspond to a fixed dihedraland free rotation, respectively. In the present work, we limited ouranalysis only to the order parameter for side-chain dihedral angleχ1.Analysis of structural propertieswas performed using the GROMACS2016 analysis tools, on the last 500 ns of the simulations, whereconvergence of the structural properties was confirmed by block averaging.For crystallographic structures, hydrogen bonds were detected followingthe usual geometric criteria.www.cgl.ucsf.edu).Molecular graphics were prepared with UCSFChimera ,K91 (BG7), and K89 (BG5), as illustrated in The peptide position inthe N-SH2 domain is strongly stabilized also by the interactions ofthe pY residue with its binding pocket. Several pY interactions arewidely conserved in SH2 domains.4 which forms a salt bridge with the phosphate.72 This is by far the most stabilizing interaction73 and is responsible for the specificity for bindingpY (as opposed to other phosphoamino acids): only the lengthy tyrosineside chain allows the phosphate to interact productively with thisarginine, whereas serine and threonine are too short.13The most conserved residueis R βB5 (present in 98% of SH2 domains),4 interacts with the phosphate group and makes an amino-aromaticinteractionwith the phenol ring of the pY.9R αA2 (present in 82% of SH2 domains)9K βD6 is located on the other side of the pY phenol ringfrom R αA2 so that the two residues together form a clamp aroundthe pY.72 In particular, S βB7 (presentin 88% of SH2 domains) and T/S BC2 form direct hydrogen bonds withthe phosphate. The BC loop backbone also contributes to H-bonding.9The pY recognition sitealso contains an extensive network of hydrogenbonds.73 it has a G in place of R αA2, and in the crystallographicstructures, K βD6 contacts the phenol ring solely with its hydrocarbonchain and not with the amine.With respect to these general featuresof SH2 domains, the N-SH2domain of SHP2 presents several peculiarities:In the MD simulations, the R βB5 (R32)-pY ionpair is stablymaintained, as well as the H-bond formed by T βC3 (T42) (peculiarof SHP2 N-SH2). The other H-bonds are less stable, indicating a significantmobility of the SH2 BC loop.56 does form often during the simulations.In addition, while the N-SH2 domain lacks the conserved R αA2,it has a K residue in position BC1 (K35), adjacent to the phosphate-bindingsite. In the crystallographic structures, its side chains point towardthe solvent, but in some of the simulations, conformational fluctuationsof the BC loop allow the formation of this additional ion pair.The distances between the pY phosphateand the charged side chainsof R32, K35, and K55 are reported in Overall, the MD data suggest that a significant mobility of thepY pocket might be possible while maintaining a strong and stableinteraction of the pY residue with the protein domain; when H-bondsare lost, ion pairs can form and vice versa.74 The N-SH2 domain of SHP2 belongs to type II, called “opengroove”, or “PLC-γ1-like”, in which residuesC-terminal to the pY bind in a long hydrophobic groove, delimitedby EF and BG loops. This is illustrated in Selectivity of SH2domains is commonly considered to be determined mainly by residuesC-terminal to the pY. Based on the interactions in this selectivity-determiningregion, the domains have been classified in three classes.Residues +1, +3, and +5 of the peptide form several interactions with hydrophobic amino acids that line thegroove, remaining in contact with them for the whole length of theMD trajectory. In particular, residue +1 interacts with I54 (βD5),I96 (BG12), and methyl groups in the side chains of T52 (βD3)and E90 (BG6); residue +3 makes stable interactions with I54 (βD5),L65 (βE4), L88 (BG4), and I96 (BG12), and residue +5 interactswith L65 (βE4), Y81 (αB9), and L88 (BG4). Interestingly,the +1 pocket is the only one where polar residues are present inaddition to hydrophobic amino acids. This might explain why peptidelibrary studies and the sequences of known binding partners indicatethat T can be present at position +1 of the peptide. As shown in Table S2. For all the simulated sequences, residues +1 and +3 remained stablyembedded in the domain groove. Residue +5 was also buried in all caseswhere a hydrophobic side chain was present at that position , with the single exception of the IRS1-1172_11 trajectory.To quantify the stability of thehydrophobic interactions betweeneach peptide residue and the N-SH2 domain during all simulations, Overall, the hydrophobic interactions involving residues+1, +3,and +5 of the peptide, which characterize type II SH2 domains, remainedstable in most of our simulations, corroborating their importancein determining the affinity and selectivity of the N-SH2 domain ofSHP2.3 In the N-SH2 domain, the lack of side chain atposition 13 (G αA2) favors the accessibility of an exposed V14at position αA3. This peculiarity has been previously described73 and explains why the N-SH2 ofSHP2 is one of the few SH2 domains in which residues N-terminal tothe pY contribute to the binding specificity. Indeed, in several simulations,we observed that hydrophobic residues in −2 inserted betweenthe pY ring and V14, interacting hydrophobically with both  pattern in the positions correspondingto K89 and K91, which face toward the peptide binding groove, is sharedonly by the SHP2 N-SH2 domain and by the C-terminal SH2 domains ofPLC-γ1 and 2. In principle, these side chains could form electrostaticinteractions with acidic residues present in +2 and +4 of the peptide,which are shown to be favorable at those positions by peptide arraystudies and by the sequences of high-affinity natural partners . The alignment of the human SH2domainspartners 1 and 2. partners 6.Among thesimulated sequences, a D/E residue is present at position+2 or +4 (or both) in eight of the twelve simulations. Different fromthe X-ray conformations, our simulations show that a stable salt bridgeforms between the +2 residue and K91 in all cases where this is possible.An ion pair between residues +4 and K89 forms, too, although onlyfor a fraction of the simulation time 8. IntereA third characteristic featureof the N-SH2 domain of SHP2 is theK residue at position BC1, which is present only in the C-terminaldomain of ZAP70, while an R is present at that position in the N-SH2domain of SHP1. As discussed above, in the crystallographic structures,K35 points toward the solvent. However, in the simulations, when Ewas present at position −1 (with the single exception of IRS-895),it interacted electrostatically with K35 (BC1). Interestingly, thetrajectories in which this happened (GAB1 and IRS1-546) were the samein which the K35-pY ion pair was observed, as discussed above 5. ProbabFinally, our simulations showed that some interactionsare alsopossible for negatively charged or polar residues in +6. An aspartatein that position can interact electrostatically with K91 (BG7) . By contrast, in the crystallographic structure4QSV, D +6 and K91 are very distant. In addition, the side chain ofresidue +6 can also form a H-bond with the EF or BG loops.The data reportedabove on interactions in the pY binding pocketin the MD simulations indirectly suggested a significant conformationalvariability of this region. This is clearly shown by an overall analysisof the domain mobility in the 12 trajectories. As shown in 76 In our simulations, we find that the loops can attain a significantlyclosed conformation even when a phosphopeptide is present in the bindingcleft. In some trajectories ,they stably embraced the peptide, clasping it tightly and gettingin contact. The high sequence variability of the BG loop does notallow a quantitative comparison with the structures of other SH2 domainsin this case. However, while such closed conformations have neverbeen observed in X-ray structures of SHP2, for other SH2 domains,the EF and BG loops have been described as a ″set of jaws″that clamp down on the peptide.72The EF and BG loops,which regulate the accessibility of the specificityregion, are distant in all structures of phosphopeptide/N-SH2 complexesand more closed in the structure of the autoinhibited state of SHP2.Indeed, based on structural data, this transition has been hypothesizedto be part of the allosteric switch controlling SHP2 activity andbinding affinity.Another elementof structural flexibility that we observed in oursimulations is a variable length for the central β sheet. Asshown in As shown in Thiswork analyzed the structural determinants of the binding affinityand selectivity of the N-SH2 domain of SHP2. Some of the featuresresponsible for the sequence preferences of this domain were alreadyvisible in the previously published crystallographic structures. Thesimulations confirmed that, even in solution and notwithstanding thesignificant motions of the domain and of the bound peptide, theseinteractions are conserved. In particular, residues −2 to +5are stably interacting with the domain, and this region of the peptideadopts an extended conformation (particularly from 0 to +3). The pYis stabilized in its pocket by multiple electrostatic and H-bondinginteractions, while hydrophobic residues are needed at positions +1,+3, and +5, where they interact with apolar side chains of the domainbinding groove.These properties are common to type II SH2 domains.However, thesimulations confirmed some peculiarities of the N-SH2 domain of SHP2,which differentiate it from other SH2 domains and might contributeto its selectivity. Specifically, in place of the commonly conservedR αA2, the N-SH2 domain of SHP2 has G13. As a consequence, ahydrophobic peptide residue at position −2 can insert in thespace left free by the missing side chain and interact with the accessibleside chain of V14 αA3, as well as with the phenol ring of pY,stabilizing its orientation and the overall complex. Indeed, selectivityfor residues N-terminal to the pY is peculiar to the N-SH2 domain.Another characteristic property of the N-SH2 domain of SHP2 is thenonconserved T42 in βC3, which forms a stable H-bond with thepY phosphate.More importantly, the simulations highlightedsome features thatwere not visible in the crystal structures, thus providing novel insightsinto the binding preferences of the N-SH2 domain. A peculiarity ofthis domain is the K-X-K motif in the region of the BG loop facingtoward the peptide binding groove. Anionic residues at positions +2and +4 strongly interact with the two K side chains. Even polar aminoacids at these positions in the peptide sequence can interact withthem through H-bonds. These observations are supported by the frequentpresence of acidic residues at these positions in the sequences ofnatural binding partners, while a similar sequence selectivity didnot emerge clearly from peptide library studies.Another featurecharacterizing the N-SH2 domain is that, in somecases, interactions extended up to residue +6 through H-bond or ion-pairformation with the EF or BG loops. This previously unexplored possibilitywarrants further investigation.Polar amino acids at +1 canform H-bonds with residues in the correspondingdomain pocket. This finding explains why a T residue was shown tobe permitted at that position by library studies (in addition to hydrophobicamino acids).Surprisingly, the conserved K βD6 does notform an ion pairwith the pY phosphate in crystallographic structures. MD simulationsindicated that, in solution, a slight rearrangement of the pY bindingpocket might allow salt-bridge formation.Another cationic residueis present in the pY pocket ,but in the crystallographic structures, it points toward the solvent,without interacting with the pY. Our simulations showed that the presenceof an acidic residue at position −1 of the phosphopeptide canfavor a conformational transition that brings K35 toward the domain.In this new orientation, it interacts both with pY and with the residuein −1.Finally, we observed in our simulations a significantconformationalflexibility of the domain. These conformational transitions were associatedwith the BC loop (which forms the pY pocket), with the DE and BG loopscontrolling access to the peptide binding groove and with the centralβC and βB strands, and were broader than those previouslyhypothesized based on the different crystallographic structures ofthe domain. Investigation of the possible role of these motions inthe function of SHP2 will require a more extensive exploration ofthe conformational properties of the N-SH2 domain.


Foldable and deployable flexible composite thin-walled structures have the characteristics of light weight, excellent mechanical properties and large deformation ability, which means they have good application prospects in the aerospace field. In this paper, a simplified theoretical model for predicting the position of the neutral section of a lenticular deployable composite boom (DCB) in tensile deformation is proposed. The three-dimensional lenticular DCB is simplified as a two-dimensional spring system and a rigid rod, distributed in parallel along the length direction. The position of the neutral cross-section can be determined by solving the balance equations and geometric relations. In order to verify the validity of the theoretical model, a finite element model of the tensile deformation of a lenticular DCB was established. The theoretical prediction results were compared with the finite element calculation results, and the two results were in good agreement. The limitation of vehicle storage space and the demand for the construction of large space structures  has promoted research and applications in relation to foldable (deployable) structures. A foldable structure can be stored in a relatively small-volume space before launch, and can be deployed into a large structure its working state in space ,2,3,4. SSikinger et al. ,10,11 stIn summary, many investigations have been conducted on lenticular DCBs, mainly focuses on two aspects—their large deformation abilities and fully deployed performance. According to the actual needs of engineering, there are still many problems to be further studied. There have been some relatively mature investigations on the large deformation of lenticular DCBs in terms of analytical models, finite element and test methods, but the analysis of the neutral section position of the tensile deformation of a lenticular DCB has not been reported, and the neutral cross-section position is very important for the design of the folding mechanism and the accurate control of folding and deploying. Therefore, it is necessary to carry out the relevant research.The lenticular DCB can realize the folding and deploying function through its own elastic strain, which is stored in the deformation process. In the first step, one end is stretched and deformed. In the second step, the folding or deploying process is completed through the roller. A schematic diagram of the entire deformation process is shown in F1  and tension (OA part) of the “spring”, which can be written ask is the stiffness per unit length of the “spring” part of the simplified model. For the calculation of this stiffness k, please refer to . The mechanical properties of the composite ply are listed in The position of the neutral cross-section predicted by the theoretical model is given in the previous section, and the theoretical model is verified via finite element (FE) calculation in this section. The FE model of the lenticular DCB was established using commercial software ABAQUS. In the example of the finite element calculation, the cross-section geometric parameters of the lenticular DCB are shown in Using the established finite element model of the lenticular DCB during tensile deformation, the cross-section change along the length direction of the flattening deformation process can be calculated. The change can be quantitatively described by transverse displacement . When the displacement in the X direction is greater than 0, it indicates that the cross-section at the corresponding position is stretched and widened. When the displacement in the X direction is less than 0, it indicates that the cross-section at the corresponding position is compressed and narrowed. In order to obtain the neutral cross-section position of the lenticular DCB during tensile deformation, the transverse displacement of each position corresponding to the length direction (z direction) of the bonding edge is the output, and the obtained curve is shown in The neutral cross-section position predicted by the theoretical model is at the position 2/3 of the normalized axial length. Therefore, according to the above quantitative analysis, the relative error range between the theoretical predictions and the finite element calculations were between 6.45% and 1.80%. The theoretical predictions were in good agreement with the finite element calculations. With the increase in the tensile deformation, the prediction accuracy of the theoretical model decreased. This is because in order to facilitate the calculation and simplified analysis, a simplified model—“spring system” was proposed in this paper. The one-dimensional spring response was used to approximate the elastic response of the circular arc segment, and the bonding edge was simplified into a rigid rod. When the tensile deformation increases, the elastic deformation of the bonding edge can also affect the position of the neutral section. With an increase in the elastic deformation of the bonding edge, the position of the neutral section will move forward, which is reflected in the finite element calculation results. If the elastic deformation of the bonding edge can be considered in the theoretical model, the prediction accuracy can be improved, but the complexity of the model will be significantly increased. More detailed large deformation calculations can be analyzed with the help of finite models, referring to the methods described in the relevant literature ,22.(1)A simplified theoretical model for predicting the position of the neutral cross-section of a lenticular DCB during tensile deformation was established. The position point of the neutral cross-section predicted by this theory was at the position 2/3 of the normalized axial length, and the shape of the neutral cross-section remained unchanged during tensile deformation. The width direction of the area near the applied tensile load was obviously stretched and widened, and the height direction became flat, whereas the width direction of the area near the free end was obviously compressed and narrowed, and the size in the height direction became larger.(2)The tensile deformation of the lenticular DCB was calculated using the finite element method. It was found that under different tensile deformation conditions, there was a 0-point displacement in the X direction, which was between 0.624 and 0.679 in the normalized length direction. The normalized axial length of the neutral cross-section point decreases with an increase in tensile deformation, but the floating range is small.(3)The relative error range between the neutral cross-section position predicted by the simplified analytical model and the finite element calculation results was between 6.45% and 1.80%, which shows good agreement, verifying the effectiveness of the theoretical model. However, the simplified theoretical model has not been verified by experiments.(4)The finite element calculation showed that the elastic strain energy stored in the lenticular DCB increases with the increase in tensile deformation, and this is a nonlinear response relationship.


In this paper, a neural adaptive fault-tolerant control scheme is proposed for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law are designed by adopting the neural networks (NNs) to approximate the upper bound of the lumped unknowns. Benefiting from the indirect neural approximation, the proposed controller does not need any model information for feedback. In addition, only two adaptive parameters are required for the indirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Lyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, the effectiveness and superior performance of the proposed control scheme are confirmed by a simulated example. Nowadays, with the rapid development of sensing and control technologies, space missions have become increasingly complicated. The spacecraft proximity operation plays an important role in various space missions, such as rendezvous and docking, active debris removal, and on-orbit servicing. The relative attitude and position control is a critical technique for spacecraft proximity operations. During the proximity operations, the chaser and target are inevitably affected by uncertain parameters and disturbances. Even worse, the parameters of the target may be fully unknown for noncooperative proximity operations. In addition, the chaser also frequently suffers from actuator faults due to the harsh space environment. The presence of unknown parameters, disturbances, and actuator faults bring great difficulty to the relative attitude and position control of spacecraft proximity operations. Traditionally, the spacecraft relative attitude and position control systems are often designed independently. However, the inherent couplings between the relative attitude and position are neglected in this way and these controllers cannot be directly applied to the spacecraft proximity operations, especially when high control accuracy is required. The integrated attitude and position control based on the six-degree-of-freedom (6-DOF) dynamic model of spacecraft proximity operations is an effective solution to this problem.Until recently, many relevant results have been reported for the integrated attitude and position control of spacecraft proximity operations. Singla et al.  designedIt should be noted that most of the above controllers require prior knowledge of nominal model information for feedback. Nevertheless, the physical parameters of the chaser and the target may be fully unknown in some extreme cases. The intelligent approximation is an efficient tool to construct the model-free controllers, owing to the powerful learning capability of the neural network (NN) and fuzzy logic system. By adopting the NNs or fuzzy logic systems to approximate the lumped unknowns, the intelligent control does not need any model information for feedback. In ,29, robuBoth the relative attitude control law and relative position control law are designed by integrating with the neural approximation. Benefiting from this design, the proposed controller is model-free and strongly robust against the lumped unknowns in 6-DOF dynamics;Rather than the conventional intelligent approximation ,29,30, iLyapunov analysis shows that the overall closed-loop system is ultimately uniformly bounded. The proposed controller can ensure that the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin.Motivated by the above discussions, this paper proposes a neural adaptive fault-tolerant control scheme for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. In comparison with most of the existing investigations, the main contributions of this research are summarized as follows:The remainder of this paper is arranged as follows: Consider the spacecraft proximity operation system depicted in The modified Rodrigues parameters (MRPs) are utilized to represent the attitude orientation of the chaser. Then, the attitude and position dynamics of the chaser can be expressed in frame Similarly, the attitude and position dynamics of the target can be expressed in frame According to the geometric relationship in Note that Remark 1.From the 6-DOF dynamic model of spacecraft proximity operations (8) and (9), the relative translational motion of the target with respect to the chaser is heavily affected by the relative rotational motion due to the inherent coupling between the relative attitude and position.The purpose of this research is to design a controller for the spacecraft proximity operation system such that relative attitude Lemma 1.For any continuous nonlinear function,, it can be approximated by a radial basis function NN (RBFNN) asRef.  For any whereis the ideal RBFNN weight,is the basis function vector,is the identification error satisfying,is a positive constant, andis the number of RBFNN nodes. Moreover,is commonly chosen as the Gaussian functionwhere, andare the center and width of the Gaussian function, respectively.The structure of the proposed neural adaptive fault-tolerant control scheme is shown in First, consider the relative attitude subsystem described as (6) and (7). Introduce the following filtered error:Theorem 1.When the relative attitude control law (18) and the adaptive updating law (19) are employed to the relative attitude subsystem described as (6) and (7), the overall closed-loop system is ultimately uniformly bounded and the relative attitude can stabilize into the small neighborhoods around the origin.Proof. Introduce the following Lyapunov function:Substituting the relative attitude control law (18) and the adaptive updating law (19), we haveConsider the following inequalities:Then, consider the relative position subsystem described as (8) and (9). Introduce the following filtered error:Theorem 2.When the relative position control law (31) and the adaptive updating law (32) are employed to the relative position subsystem described as (8) and (9), the overall closed-loop system is ultimately uniformly bounded and the relative positioncan stabilize into the small neighborhoods around the origin.Proof. Introduce the following Lyapunov function:Remark 2.In the conventional intelligent approximation ,29,30, t Alternatively, the indirect neural approximation is exploited in this paper by adopting NNs to approximate the upper bound of the lumped unknowns. In this way, only two adaptive parameters,, are required for theindirect neural approximation, and the online calculation burden of the proposed controller is therefore significantly reduced. Actually, theindirect neural approximation makes the proposed controller more suitable for practical engineering, especially considering the onboard computer has limited online calculation capability.Remark 3.According to Theorems 1 and 2, the proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. From (26) and (39), it follows that the small neighborhoods around the origin are adjustable. If we set the parameters as large as required, the small neighborhoods can be made sufficiently small.Remark 4.It is noteworthy that the RBFNN utilized for intelligent control in this paper can also be replaced by some other approximation tools, such as recurrent NNs, wavelet NNs, and fuzzy logic systems. Moreover,adaptive dynamic programming is an effective methodology for the optimal control of unknown nonlinear systems with the help of critic NNs ,33,34,35. Future investigation building on this research will focus on extending the present results by embedding them with an adaptive dynamic programming approach.A simulated example is performed to illustrate the proposed control scheme. The sampling frequency for feedback is set as The masses of the chaser and the target are chosen as Due to the actuator saturation, the acceptable maximum control torques and forces are set asIn addition, the actuator faults are also considered. The actuator health factor matrices are given asThe commonly used proportional-derivative (PD) controller is employed for performance comparisons. The compared PD controller is also made up of a relative attitude control law and a relative position control law. The relative attitude control law is designed asThe parameters of the proposed neural adaptive fault-tolerant controller are given as The translational motion of the chaser and the target for proximity operation is provided in From Furthermore, some crucial indexes are introduced to quantitatively compare the performance between the proposed controller and the PD controller. Specifically, the integrated absolute errors (IAEs) are defined as In summary, the simulation results indicate that the proposed neural adaptive fault-tolerant controller can realize superior performance and good uncertainty rejection capability, which guarantees the successful implementation of the spacecraft proximity operation.This paper aimed to propose a neural, adaptive, fault-tolerant control scheme for the integrated attitude and position control of spacecraft proximity operations in the presence of unknown parameters, disturbances, and actuator faults. The proposed controller is made up of a relative attitude control law and a relative position control law. Both the relative attitude control law and relative position control law were designed by adopting the NNs to approximate the upper bound of the lumped unknowns. By introducing the indirect neural approximation, the proposed controller is more suitable for practical engineering, especially considering the onboard computer has limited online calculation capability. The ultimate uniform boundedness of the overall closed-loop system can be achieved through Lyapunov analysis. The proposed controller can ensure the relative attitude, angular velocity, position, and velocity stabilize into the small neighborhoods around the origin. Lastly, simulation results indicate the effectiveness and superior performance of the proposed control scheme.


A. Soto et al. Nature Communications 10.1038/ncomms10870 (2016)Nature Communications1 the use of multidimensional signal processing, especially image denoising techniques, to improve the signal-to-noise ratio (SNR) of distributed optical fibre sensors. The benefits of the method were demonstrated for distributed Raman and Brillouin sensors, both proving a significant performance improvement. Here we show that, while the SNR enhancement for the case of Brillouin distributed sensing was correctly estimated in our publication1, an overestimation of the Brillouin frequency uncertainty reduction was reported as a result of the inadvertent use of a conventional methodology for performance evaluation. Based on a better understanding of the impact of image denoising applied to Brillouin distributed sensors, here we report a revised estimation of the Brillouin-frequency shift (BFS) uncertainty obtained after image denoising, verifying that although 2D image denoising can significantly improve the measurement SNR, this cannot be fully transferred to the overall Brillouin sensing performance.In 2016, our research team proposed in an issue of 2, and for the sake of clarity, we start by clearly stating the fundamental limitations and real benefits of image denoising applied to Brillouin distributed sensing, by partially reusing the analysis and conclusions drawn in ref. 2. In the following, the effects of 2D filtering in the 2 dimensions of the matrix containing the measured data  are separately addressed.Based on novel findings and a deeper understanding of the methodTraditional noise reduction filters correspond to lowpass filters that can efficiently improve the quality of a signal by eliminating noise out of the signal spectral band. When applied to time-domain traces of a distributed fibre sensor, the filter has a longitudinal smoothing effect that results from the convolution between the filter impulse response and the noisy input time-domain trace. At the filter output, adjacent signal samples increase their degree of mutual correlation, which in the context of distributed sensing could lead to a reduction of the spatial resolution of the sensor. The proper design of a denoising strategy requires that the filter reduces the noise bandwidth without impacting the useful signal bandwidth, so as to avoid degrading the spatial resolution of the sensor.1, i.e., nonlocal means (NLM)3 and wavelet denoising (WD)4, requiring to carefully define their filter parameters to match the noise bandwidth to the relevant Brillouin signal bandwidth, which is determined by the spatial resolution of the sensor, even though the adaptive feature of these techniques looks appealing at first glance. This implies that the spatial-domain filtering contribution of 2D denoising is only beneficial when the noise bandwidth in the electrical domain is larger than the useful signal bandwidth. In other words, if the electrical bandwidth has been optimised to match the signal bandwidth, there will be no real benefit provided by the spatial-domain filtering component of the 2D denoising2. In addition, the sampling rate of the Brillouin-spatial-domain traces must fulfil the Nyquist criterion for the noise bandwidth to avoid aliasing of the noise2.Such a universal strategy also applies to 2D denoising methods presented in ref. 2. The reason lies in the following feature: the noise removal contribution from the 2D filter in the spectral domain turns out to be redundant with the spectral fitting used to determine the Brillouin gain peak frequency2; and therefore, the filtering in the spectral domain should have no impact on improving the BFS uncertainty, even if a large SNR enhancement can be verified on BOTDA traces.Image denoising can also correlate adjacent data points describing the Brillouin gain spectrum at each fibre position. This effect can also contribute to the SNR improvement of the measurements. Recent findings have however demonstrated that the attained SNR improvement cannot be straightforwardly translated into a BFS uncertainty improvement1.In awareness of these fundamental limitations, the methodology to evaluate the sensor performance is revisited hereafter, aiming at correcting the overestimation of the BFS accuracy in our previous publicationdistance-domain approach, a spatial moving window is used to calculate the mean value and standard deviation over the temporal trace acquired at maximum Brillouin gain condition (for SNR estimation), as well as to calculate the standard deviation of the BFS distribution (for the frequency error estimation). The statistical values are obtained along the fibre from a single measurement using a proper spatial window length. To avoid including any longitudinal BFS variation that can bias the frequency error estimation, a reference BFS trace  must be subtracted from the actual measured BFS distribution before calculating the longitudinal BFS standard deviation.In the sequential-domain approach, several consecutive measurements are used to calculate the statistical values at each fibre position. The sequence length must be chosen to minimise any temporal drifts of the environmental quantities, such as temperature and strain.In the The estimation of the SNR and BFS uncertainty of a standard Brillouin sensor normally assumes that calculations of the statistical values in the sequential domain (i.e. using successive measurements) and in the distance domain  are equivalent in the context of a purely additive white Gaussian noise. This feature has been verified several times by the scientific community and its validity is commonly applicable to many practical scenarios. This assumption is however only valid if specific conditions are satisfied in both sequential and distance domain. Under such conditions, the following two approaches can be followed:5. Under such a situation it turns out to be equivalent to estimate the measurement SNR and BFS uncertainty in either sequential or distance domains . Based on this property, the data processing used to obtain results in Fig. 3 of ref. 1, showing the SNR and frequency uncertainties of a Brillouin optical time-domain analyser (BOTDA), followed the distance-domain approach. In the case of estimating the frequency uncertainty of the raw and denoised data, a reference BFS trace was subtracted from the actual BFS trace to get rid of longitudinal Brillouin-frequency variations before calculating longitudinal standard deviations.In a well-designed conventional Brillouin sensor, noise affecting the Brillouin measurements can be safely considered as additive white Gaussian noise, especially over long sensing ranges1 (obtained with the distance-domain approach) resulted to be unwittingly overestimated when compared to the sequential-domain approach. To perform this comparison, the following analysis shows some results obtained after processing a series of BOTDA measurements acquired under similar conditions than the ones reported in ref. 1. 2D filtering using nonlocal means (NLM)3 and wavelet denoising (WD)4 has been applied to measured data using the same algorithms and parameters previously used1. The SNR and frequency uncertainty are then calculated in both sequential and distance domains for comparison.In the following, we show that the BFS uncertainty reported in ref. 1. The sequential-domain approach is also consistent with previous results, although showing slightly lower SNR values after 2D filtering, thus resulting in 13.8 and 13.5 dB SNR at a 50 km distance for each of the respective cases.Figures 1, showing uncertainties of 0.22 and 0.23 MHz at 50 km distance for the NLM and WD cases, respectively. However, significant differences can be observed when the sequential-domain approach is applied. In this case, the BFS uncertainty is improved from 4.5 MHz (raw data) down to 1.2 and 1.3 MHz, for the NLM and WD methods respectively; thus, representing a much smaller BFS uncertainty improvement when compared to the estimations obtained in the distance domain.Interesting results are shown in Figs. 6. This kind of result led us to assume that the BFS estimations obtained following the distance domain approach in ref. 1 were correct and closely matching the expected theoretical BFS enhancement. Based on a better understanding of the impact of image denoising on the Brillouin sensing data, now it becomes clear that the actual BFS improvement attained by the method is only provided by the filtering effect in distance domain of BOTDA traces and not by the contribution of the filter in the Brillouin-frequency domain. The correct estimation of the BFS uncertainty is given by the sequential-domain approach and the additional BFS enhancement obtained in the distance-domain approach is only fictitious and does not represent a correct evaluation of the uncertainty because of the added correlation between adjacent data points caused by the photodetection bandwidth and the 2D image denoising filters. These points turn out to be no longer fully statistically independent6.Note that the improvement from 4.5 MHz down to 0.2 MHz obtained in the distance-domain approach closely matches  the expected BFS enhancement that would have been obtained from the entire SNR enhancement1, has been here presented, which agrees well with the theory and results reported in ref. 2.In conclusion, special attention must be paid when calculating the BFS uncertainty of Brillouin distributed sensors when using image denoising. The correlation induced on closely located data points makes the distance-domain approach an unreliable approach to estimate the performance of the sensor when using image denoising. In contrast, the sequential-domain approach turns out to be reliable to estimate the measurement uncertainty and secures a full statistical independence. Based on the understanding of this specific behaviour occurring only when image denoising is applied to Brillouin distributed sensors, a corrected evaluation of the BFS uncertainty for the data reported in ref.


The crystal structure of the TLDc domain of the human NCOA7-AS protein, which acts as an interferon-induced antiviral inhibitor, is reported. NCOA7 gene encodes several mRNA isoforms; among these, isoform 4, named NCOA7-AS, is up-regulated by type 1 interferon in response to viral infection. NCOA7 and NCOA7-AS both interact with several subunits of the vacuolar proton pump V-ATPase, which leads to increased acidification of the endolysosomal system and consequently impairs infection by viruses that enter their host cells through the endosomal pathway, such as influenza A virus and hepatitis C virus. Similarly to full-length NCOA7, NCOA7-AS possesses a TLDc domain in its C-terminus. Structures of TLDc domains have been reported from zebrafish and fly but not from humans. Here, the expression, purification and crystallization of the TLDc domain from NCOA7 and NCOA7-AS is reported. The crystal structure solved at 1.8 Å resolution is compared with previously solved three-dimensional structures of TLDc domains.The TLDc  domain is associated with oxidation-resistance related functions and is well conserved among eukaryotes. Seven proteins possess a TLDc domain in humans, notably proteins belonging to the oxidation resistance protein (OXR), nuclear receptor coactivator 7 (NCOA7) and TBC1 domain family member 24 (TBC1D24) families. Although the mechanism is unknown, a protective role of TLDc proteins against oxidative stress, notably in the brain, has been demonstrated. Neurobiological disorders caused by mutations in the TLDc domain have also been reported. The human  In human and mouse, seven TLDc domain-containing proteins have been reported, among which are oxidation resistance (OXR) proteins 1 and 2 and NCOA7 short and long  isoforms . NCOA7-AS notably impairs IAV replication in pulmonary cells through the regulation of V-ATPase activity  was amplified from pRRL.sin.cPPT.SFFV/IRES-puro.WPRE.NCOA7 variant 6  strain resistant to phage T1  carrying pRARE2. One colony was used to inoculate an overnight culture of 500 ml LB medium supplemented with kanamycin (50 µg ml−1) and chloramphenicol (34 µg ml−1). This culture was diluted in 10 l LB medium supplemented with the two antibiotics. The cells were grown at 289 K to an optical density at 600 nm of 0.8, and protein expression was then induced with 1 mM isopropyl β-d-thiogalactopyranoside (IPTG). The culture was grown overnight at 289 K. The cells were harvested by centrifugation at 8200g for 20 min and were resuspended in 100 ml buffer A . The cells were disrupted by sonication and cell debris was removed by centrifugation at 28 000g for 60 min. The supernatant was loaded at 277 K onto Ni–NTA agarose beads previously equilibrated with buffer A. The beads were washed twice with buffer B  and elution was performed with buffer E . The eluted protein was incubated with His-tagged TEV protease purified in our laboratory in a 1:100(w:w) ratio; the cleavage reaction was performed during dialysis  against 1 l dialysis buffer D  overnight at 277 K. After dialysis, the proteins were centrifuged for 20 min at 28 000g and the supernatant was again loaded at 277 K onto Ni–NTA agarose beads equilibrated with buffer D. The TLDc domain without the tag was collected in the flowthrough, concentrated to 5 mg ml−1 using a Vivaspin column (10 kDa cutoff), loaded onto a size-exclusion chromatography column  and eluted with buffer D. Following this protocol, 1 mg highly pure protein as judged by a Coomassie Blue-stained denaturing gel was obtained from 1 l culture. Macromolecule-production information is summarized in Table 1The recombinant plasmid pET-30 Ek/LIC::2.3.−1. The vapor-diffusion method was performed in sitting drops by mixing 0.6 µl protein solution with 0.6 µl reservoir solution using 96-well Swissci MRC plates  at 291 K. The commercial Structure 1 + 2 (Molecular Dimensions), Index, SaltRx and PEGRx screens (Hampton Research) were assessed. Several hits were obtained and crystal optimization was performed in Swissci 48-Well MRC Maxi Optimization Plates. The best diffraction crystals derived from these optimizations were obtained by mixing 1.5 µl protein solution at 10.9 mg ml−1 with 1.5 µl reservoir solution consisting of 0.1 M sodium acetate pH 4.5, 32% PEG 300 , Grenoble, France. The data set was recorded on a PILATUS3 2M detector (Dectris) at a wavelength of 0.965 Å (12.842 keV) and a crystal-to-detector distance of 190.5 mm. A total of 934 images were collected with an exposure time of 0.097 s, a rotation range of 0.15° and full beam transmission. Data were processed, scaled and merged with 2.5.Phaser .The structure was solved by molecular replacement performed with 3.E. coli to homogeneity. NCOA7-AS has a tendency to aggregate and to form large oligomers as judged by size-exclusion chromatography (SEC). We therefore alternatively expressed and purified a truncated form of NCOA7-AS corresponding to the TLDc domain, hereafter referred to as TLDcHs, using E. coli as an expression host .As NCOA7-AS plays an important function, notably as an interferon-induced antiviral inhibitor, we aimed to solve its crystal structure. The 219-amino-acid-long NCOA7-AS can be divided into two parts: an N-terminal domain (residues 1–53) followed by the TLDc domain (residues 54–219). We have performed bioinformatic analyses that predicted the first 53 amino acids to be mainly unfolded, with only the presence of two β-strands formed by residues 10–15 and 24–28. Despite numerous efforts, we have so far been unable to purify full-length NCOA7-AS expressed in t Table 2. TLDcHs c), which were obtained in 0.1 M sodium acetate pH 4.5, 32% PEG 300  of 2.2 Å3 Da−1 assumes the presence of 44.5% solvent and six molecules of TLDc in the asymmetric unit .We could crystallize the domain under several conditions using commercial screens. Crystallization-condition optimization led to rod-shaped crystals with a length of about 100–150 µm Fig. 1c, which0 Table 2. A full it Fig. 1d.Hs structure was solved by molecular replacement using the TLDc domain (PDB entry 4acj) from D. rerio (TLDcDr) as a search model, which shares 62% sequence identity with its human homologue. Six molecules of TLDcHs were found in the asymmetric unit, as expected from the VM. The structure was manually rebuilt and refined to Rwork and Rfree values of 0.176 and 0.221, respectively, with rather good geometry, as indicated in Table 4A and the first three residues at the N-terminus for chains B, C, D, F and E. Gly175 was not modeled in chain A as well as the glycine stretch ranging from 174 to 176 in chain F. Residues 146–148 were also disordered in chain F but were well ordered in the other chains. Analysis of the crystal packing with the PISA server . Nonetheless, our SEC analysis demonstrated that the protein behaves only as a monomer in solution , as also described for the TLDc domain from zebrafish  and D. melanogaster TBC1D24 (TLDcDm). TLDcHs shares 62% sequence identity with TLDcDr  and the two structures display an r.m.s.d. of 0.7 Å when superposed over 164 Cα atoms. Few structural differences could be observed. TLDcHs does not possess an α-helix after strand β1 as seen in TLDcDr . TLDcHs and TLDcDm are more distant as they present only 38% sequence identity , and superimposition of the two structures leads to an r.m.s.d. of 1.1 Å over 144 residues. The N-terminal sequences are not well conserved in the three proteins compared with the rest of the sequence, which is reflected by a few differences at the three-dimensional level. TLDcDm possesses one extra helix (α2) in the N-terminus that is not seen in TLDcHs or TLDcDr. Finally, noticeable differences are found as two extended loops between β3 and β4 and β5 and β6 in TLDcDm.The overall structure of TLDcDr Fig. 2a and thDr Fig. 2b. TLDcHty Fig. 2a, and set al., 2010et al., 2016et al., 2019et al., 2019et al., 2015et al., 2018Hs therefore offers the possibility to map these mutations onto the domain. We performed mapping of these mutations onto TLDcHs , and mutation(s) breaking this interaction might therefore destabilize the long loop spanning between strands β4 and β5  in a structured region such as strand β6 will probably affect the folding of this strand. The Gly511 residue mutated to Arg matches Gly174 in TLDcHs and is situated in a loop formed by a five-glycine stretch between strands β7 and β8. Finally, Ala515, which has been reported to be mutated to Val in several studies, is not conserved in TLDcHs as Gly179 is instead found. Nonetheless, both residues are at the beginning of strand β8, and these mutations could destabilize the integrity of this strand. The missense mutations found in the TLDc domain of human TBC1D24 which are linked to neurological disorders are all found at structurally conserved positions. It is indeed clear that the Gly511Arg and Gly501Arg mutations as well as the Arg360His/Leu, Ala500Val and Ala515Val mutations could disturb the folding of the TLDc domain and potentially destabilize the protein because of the nature of the amino-acid substitution and/or its position in the structurally conserved regions. This analysis is therefore in strong agreement with the previously proposed effect on protein stability of the TLDc domain induced by pathological mutations found in TBC1D24 (Lüthy et al., 2019Several missense mutations have been reported in the TLDc domain from TBC1D24 Table 5 that are al. 2019. The patTo conclude, this structural work strongly attests to the high protein similarity between TLDc domains from vertebrates as well as, albeit slightly more distantly, that from an arthropod. Although this was expected from the high sequence identity that is shared between these proteins, reporting the first crystal structure of a human TLDc domain is notably of interest because of the versatile roles of TLDc proteins and also because pathological mutations affect this domain.human TLDc domain, 7obpPDB reference:


D‐optimal mixture design looked to be a priceless tool for optimizing the influences of semolina flour (SF), defatted soy flour (DSF), whole quinoa flour (WQF), whole rye flour (WRF), whole oat flour (WOF), whole barley flour (WBF), and rice flour (RF) on the quality attributes of multigrain pasta (MP). Multigrain flours were considered as the independent variables evaluated with respect to three response variables containing hardness and the amount of protein and fiber. Quadratic, linear, and linear models were chosen to explain the hardness and the amount of protein and fiber of the MPs, respectively. In optimal formulation of MP, that is, SF , DSF (14%), WQF (11%), WRF (7.54%), WOF (5.61%), WBF (2.51%), and RF (2%), the content of fiber and protein enhanced more than 4.12 and 1.34 times compared with SP, respectively. Therefore, according to the European Union law, it can be claimed that this pasta is a source of fiber. As the amount of protein and fiber increased, the hardness and optimal cooking time decreased, while the cooking loss increased. After cooking, MP was murkier and less yellow in color. The 2, 2‐ diphenyl‐ 1‐ picrylhydrazyl (DPPH) inhibition activity of the MP was about 2.5 times higher than the SP. Analysis of the antioxidant properties of the samples after cooking showed that the DPPH inhibition activity of the SP and MP reduced. The results indicated that the overall acceptability of MP was higher than SP. Based on our findings, these multigrain flours are probable to be applied as nutritious complements in the pasta industry to improve the functional characteristics. The fiber and protein content of multigrain pasta was higher than regular pasta. From a sensory point of view, multigrain pasta was better than semolina pasta although it was dark in color. Each of these compounds has its own functional properties. For example, oat and barley flours are great sources of dietary fiber, especially beta‐glucan that saves people from diabetes, cardiovascular disease, blood cholesterol, and excessive weight gain. Oat contains more protein than other grains do. Additionally, it is an excellent source of vitamins, minerals, and natural antioxidants  to investigate the possibility of producing new functional pasta using a combination of different cereal flour, (b) to use the D‐optimal mixture design to obtain optimal formulation based on nutritional and rheological properties of multigrain pasta (MP), and (c) to study the physicochemical, nutritional, and sensory attributes of optimal formulation of MP. To the best of our knowledge, our experiment is the pioneer study in the literature concentrated on the production of MP by the mixture of semolina flour (SF), DSF, whole quinoa flour (WQF), whole rye flour (WRF), whole oat flour (WOF), whole barley flour (WBF), and rice flour (RF).22.1Semolina was obtained from Zar Semolina Co. , rye, rice, and soy flour were purchased from local market . Oat and quinoa flour (Organic Bolivian quinoa) were purchased from Iranian Health‐Based Biotechnology Co. . Flours were sieved to pass through an 840 µm mesh screen. All the used chemicals were provided from Sigma Aldrich .2.2The Design‐Expert software version 9.0.4  was implemented to design the multigrain formulation. In this research D‐optimal design was applied with seven ingredients: semolina flour (SF), defatted soy flour (DSF), whole quinoa flour (WQF), whole rye flour (WRF), whole oat flour (WOF), whole barley flour (WBF), and rice flour (RF). Table 2.3The pasta was prepared according to the formula of Jalgaonkar et al., . For thi2.4To determine moisture content, crude protein, fat, fiber, and ash of flours and pasta products (MP and SP), the AACC methods  and cooking loss were determined based on the procedure described by Tudorica et al., .2.6The color values of products were evaluated implementing a Hunter Color flex colorimeter  by determining L* (black (0) to white (100)), a* , and b*  values  equipped with a steel cylindrical probe (p/75 mm) was used to determine the textural properties of cooked pasta (in OCT). The textural parameters were adjusted according to the method described by Kamali Rousta et al. . During 2.8• assay according to the protocol of Gull et al., (AControl) is the absorbance of the control reaction and (ASample) is the absorbance of the test samples. The absorbance was observed at 517 nm.Free radical scavenging activity for samples was conducted using 2, 2‐ diphenyl‐ 1‐ picrylhydrazyl (DPPH) et al., . The per2.9Sensory evaluation of the cooked samples was carried out by thirty trained panelists  that were selected from Zar Co. employees. The samples (50 g of MP and SP) were cooked at the optimum time in 250 ml boiling water. Panelists were asked to present their liking scores from 1 to 9  on texture, flavor, color, and overall quality  with the independent variables were fitted by these two models.Linear and quadratic models were investigated (eq.  and 3)))3)) and All experiments were done in triplicate. Fisher's least significant differences test was applied to estimate the significant differences at 95% confidence level. Statistical analysis was done by SAS 9  software.33.1Table 3.2R‐squared, the best model was chosen  greater fiber content in comparison with the control. The highest amount of fiber was observed with the combination of 52% SF, 4% DSF, 12% WQF, 8% WOF, 8% WRF, 8% WBF, and 8% RF flours (run 26). Combination of these compounds increased the amount of fiber by 4.89‐fold compared to the control sample. Increasing the fiber content of MP formulated with different compounds compared to semolina pasta (control) has been reported by other authors  increased hardness, while WBF, WOF, and WQF with their negative coefficients reduced this factor in the pasta. However, WRF, DSF, and RF had the highest effect on the texture of MP, respectively. As shown in Table p < .0001). The hardness of run 38 was similar to the control, while the hardness of run 2 was 3.28 times lower than the control sample. In general, hardness is reduced by replacing semolina with other compounds. Contrary to these results, many researchers indicated that by increasing the protein content, the value of hardness of this product increased . The optimal content of SF, DSF, WQF, WRF, WOF, WBF, and RF were 57.34%, 14%, 11%, 7.54%, 5.61%, 2.51%, and 2%, respectively. MP in this condition had protein content of 16.77%, a fiber content of 4.17, and a hardness of 8.2 N. The amount of protein and fiber in the optimal sample were 1.34 and 4.12 times higher than the control sample. The desirability score of chosen mixture was .92. The function of desirability transforms an estimated response to a scale‐free value  was higher than SP (14.23 ± 0.03). These results are consistent with the findings of Kamble et al., , who rep3.9Color occupies an effective role in the appearance of the pasta and actively influences the consumer's decision to buy the product ; Investigation ; Methodology ; Resources ; Software ; Writing‐original draft . Amir Pouya Ghandehari Yazdi: Conceptualization ; Project administration ; Resources ; Supervision ; Validation ; Visualization ; Writing‐review & editing . Sepideh Khorasany: Data curation ; Methodology ; Resources ; Writing‐original draft . Mohammad Tavakoli: Data curation ; Formal analysis ; Investigation ; Methodology ; Writing‐original draft . Zahra Ahmadi: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Writing‐original draft . Mehdi Amini: Data curation ; Formal analysis ; Investigation ; Methodology ; Software ; Writing‐original draft .


Denitrification plays a central role in the global nitrogen cycle, reducing and removing nitrogen from marine and terrestrial ecosystems. The flux of nitrogen species through this pathway has a widespread impact, affecting ecological carrying capacity, agriculture, and climate. Nitrite reductase (Nir) and nitric oxide reductase (NOR) are the two central enzymes in this pathway. Here we present a previously unreported Nir domain architecture in members of phylum Chloroflexi. Phylogenetic analyses of protein domains within Nir indicate that an ancestral horizontal transfer and fusion event produced this chimeric domain architecture. We also identify an expanded genomic diversity of a rarely reported NOR subtype, eNOR. Together, these results suggest a greater diversity of denitrification enzyme arrangements exist than have been previously reported. Nitrite reductase (Nir) and nitric oxide reductase (NOR) are the two central enzymes in denitrification, a key process in the global nitrogen cycle. This study identifies a novel Nir domain architecture and expanded diversity in a rarely reported nitric oxide reductase variant (eNOR) in members of the bacterial phylum Chloroflexi. Most bacterial NORs are homologous and closely related to one another, and to oxygen reductases in the heme–copper oxygen reductase superfamily  to avoid injury, using membrane‐bound NOR enzymes  MAG 42 , hits were subsampled to the genus level, except for members of the Chloroflexi (to fully capture the taxonomic distribution of the novel gene variant). One additional, divergent multispecies hit was allowed per genus. The genus‐level filter was also removed for C1, where non‐Chloroflexi hits were severely limited (see below). Duplicate sequences (from strains with multiple genome entries or in multiple databases surveyed) were removed. Expanded database hits and filtering data are available as Supporting Information Data Files at https://doi.org/10.6084/m9.figshare.14515554.v2.Additionally, putative environmental homologs were evaluated using protein sequence data from SURF MAG 42 to query NCBI's nonredundant environmental metagenomic sequence database ; to improve accuracy and avoid misalignment, this region was manually removed, and the remaining sequences were realigned before tree construction. Two sequences  with missing N‐terminal regions and three sequences  with missing C‐terminal regions were included in the final alignment; the placement of these sequences is therefore based upon fewer alignment sites than other taxa. All retain key active site residues and show no clear evidence of long‐branch attraction artifacts in the tree.A preliminary alignment for the 2.5An initial alignment for the C1 domain showed a poorly conserved N‐terminal region. To improve accuracy, this region was manually removed, and the remaining sequences realigned before tree construction.2.6Because the NirS domain had a C‐terminal placement in the ORF across hits, C‐terminal sites extending beyond the identified NirS domain were included in the trimmed alignment.2.72.7.1eNOR subunit I sequences were identified by the presence of a conserved Gln residue in alignment position 323. This site distinguishes eNOR not only from other NORs but also from members of the oxygen reductase superfamily, which have a conserved Tyr in this site that plays a role in cofactor crosslinking  rooting , the genus‐level filter was removed for all phyla to increase the resolution of the domain phylogeny. A preliminary tree Figure  was expa2.7.3Sequences were rooted using MAD rooting  and by hmmscan  hit (e = 1.6 × 10−5). C1 did not show a strong pfam match in hmmscan; the closest match was PF13442.8 (independent e = 0.18). C2 from SURF MAG 42 was classified by CDD as COG2010 (e = 5.31 × 10−9), and included an annotated region classified as pfam 13442 (e = 4.06 × 10−7); C2 was identified in hmmscan as a cytochrome c superfamily (accession 46626) hit (e = 1.2 × 10−17), with a pfam match to PF13442.8 (independent e = 2.8 × 10−10). While clearly homologous, the C1 and C2 families appear distantly related and do not appear within the other's data set of closely related sequences (see above). Gene neighborhoods were visualized using Gene Graphics  in the Anaerolineales‐type ORF was identified by NCBI's CDD .Domain phylogenies indicate similar taxonomic distributions for the C2 and NirS domains Figure . The relC2 and NirS domain trees reconstructed exclusively from ORFs containing both domains produce similar topologies, albeit with slightly different placement of these major groups of taxa Figures  and A5. The inferred phylogeny for C1 shows a much different evolutionary history than the other two domains Figure . In contPseudomonas norC genes.The majority of ORFs represented in the C1 domain tree contain the C1 domain homolog either as a free cytochrome or as one of multiple cytochrome‐type or cytochrome superfamily domains. In rare or isolated cases, C1 homologs co‐occur in ORFs with membrane or structural protein domains Table . The ORFnir genes have the cytochrome‐type NirS domain; however, a small number of Chloroflexi MAGs contain an ORF pairing the C1 cytochrome with the copper‐type NirK domain instead. This NirK ORF also included an N‐terminal cupredoxin/plastocyanin domain. As this fusion is only apparent within a small number of MAGs, which are identical across the length of the analyzed ORF, this may represent an assembly artifact. However, several of the nir genes that contained a C1 homolog and a cytochrome‐type NirS (not copper‐type NirK) also contained cupredoxins or other copper‐containing domains . The full C1‐C2‐NirS architecture appears unique to Chloroflexi and is not observed in other groups. Querying NCBI's nonredundant environmental database (env‐nr) with the full ORF from SURF MAG 42 did not identify additional examples of the full gene construct. While several hits were identified that reflected putative homology to the joint C2‐NirS domains, none of these included the C1 domain as well. An independent search of the env‐nr database using the C1 domain as a query returned few overall hits. While some of these putative C1 homologs were identified in ORFs containing additional cytochrome‐type enzyme superfamily domains or subunits, none co‐occurred with NirS or NirK domains. These data suggest that there is little to no missing diversity of the Chloroflexi‐type chimeric nitrite reductase in existing metagenomes.nirS gene sequence does retain several conserved residues present in the crystal structure of P. aeruginosa NirS. In P. aeruginosa NirS, His51, and Met88 coordinate heme c; His182 coordinates heme d1; and His327 and His369 are believed to stabilize the active site nitrite anion . Though the absence of the nor gene in genomes with nirS is not unprecedented, previous genomic surveys suggest it is relatively uncommon, and the toxicity of the product of Nir (NO) makes this absence counterintuitive  in Chloroflexi, as well as a broad distribution of Proteobacteria  has a proton channel, and therefore the capacity for proton pumping  , Data curation (lead), Formal analysis (lead), Funding acquisition , Investigation (lead), Methodology (lead), Project administration (lead), Validation , Visualization (lead), Writing – original draft (lead), Writing – review & editing‐Lead. Lily M. Momper: Conceptualization (lead); Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – original draft (supporting). L. Thiberio Rangel: Data curation (supporting), Investigation (supporting), Methodology , Writing – review & editing (supporting). Cara Magnabosco: Data curation (supporting), Investigation (supporting), Methodology (supporting). Jan P. Amend: Conceptualization (supporting), Data curation (supporting), Project administration (supporting), Resources (supporting), Writing – review & editing (supporting). Gregory P. Fournier: Conceptualization (supporting), Formal analysis (supporting), Funding acquisition , Investigation (supporting), Methodology (supporting), Project administration (lead), Resources (lead), Supervision (lead), Writing – review & editing (lead).


A new generation of SBA-15, plugged SBA-15, was initially synthesized in 2002 using extra silica precursors (Si/organic template molar ratios ≈ 80–140) in the gel mixture. The plugged SBA-15 materials possess short cylinders (length ≈ 20–100 nm), which are connected to neighbors by constricted entrances (windows) through the central axis. The gas adsorption–desorption isotherms of plugged SBA-15 materials present unique hysteresis loop Type H5 classification identified by IUPAC in 2015, which is related to certain pore structures containing open and plugged mesopores. The plugged SBA-15 has been used to support various types of catalysts, including metal complexes, metal nanocatalysts, and active metals by the incorporation in their framework demonstrating excellent (enantio)selectivity, stability against coke, and thermal stability. The plugged SBA-15 materials bear the other unique properties of the ship-in-the-bottle synthesis of, e.g., metal complexes that confine homogeneous catalysts, which is not possible by conventional SBA-15 due to leaching. In this mini-review, the challenges and progress of the synthesis in controlling the plugging and incorporation of metals and organic moiety in their framework, characterizing the short mesochannel dimensions (window and length sizes) by several advanced techniques and applying plugged SBA-15 materials in heterogeneous catalysis for challenging reactions, has been discussed. The silica-based ordered mesoporous Santa Barbara Amorphous (SBA) materials have been widely used as catalysts, catalyst supports, and for the fundamental study of designing efficient catalysts . SBA-15,2 adsorption isotherms of plugged SBA-15 materials are similar to those of conventional SBA-15 . T. T2 isotpossible . Indeed,possible . This ispossible . The respossible . The micsotherms . The cha2 sorption isotherms, transmission electron microscopy (TEM), atomic layer deposition (ALD), and surface modification by alkoxysilanes are the most used techniques to estimate the window sizes of ‘‘ink-bottle’’ materials. The sizing of plugs (plug size = mesopore size—window size) via TEM is very challenging, because there is almost no difference between the diffraction contrast of plugs and that of the walls of mesochannels. The use of ALD technique requires extensive cycles of oxide deposition, making this technique practically very difficult ”, where n is the number of carbon atoms of the alkyl group [Understanding the window sizes of plugged mesochannels of SBA-15 is extremely important for their various applications . The N2 ifficult . This is and TEM . Once th and TEM . Therefo and TEM . The pluyl group ,52. The yl group . The mesyl group . De Jongyl group . HRTEM iyl group . The HRTyl group . These cThe plugs of SBA-15 materials have been investigated by the low-voltage high resolution scanning electron microscopy (LV-HRSEM) technique , presentx and VOx synthesis in the mesochannels of plugged and conventional SBA-15 materials were reported by the adsorption of metal complexes of TiO(acac)2 and VO(acac)2 followed by thermolysis [x and VOx species on plugged SBA-15 was lower than that on conventional SBA-15, the difference resulting from a limited diffusion of corresponding metal complexes into constricted windows of the plugged mesochannels [The performances of plugged SBA-15 as the catalyst, catalyst support, and adsorbent have been investigated. The plugged and conventional SBA-15 materials were used for the adsorption of different alkanes . The adsrmolysis . The amochannels . The pluchannels . The PdOchannels . The suschannels , which pm-cresol [m-cresol were 78.4% and 59.2% by plugged AlSBA-15 and conventional AlSBA-15, respectively, and the higher activity by the former originated from the higher number of medium strength acid sites by the plugging of silica [In another study, Oliveira et al. investigated Pd nanoparticles on thiol-functionalized plugged SBA-15, comparing them with aerosol-380, m-MCF, and SBA-16 for liquid-phase Heck and Suzuki reactions . The Pd m-cresol . The catf silica . Recentlf silica . The . These catalysts showed excellent catalytic performances by benefiting from controlled physical properties and sizing their dimensions. The plugged AlSBA-15 showed stronger acidity compared to the conventional AlSBA-15, suggesting the attractiveness of the exploration of further heteroatom-substituted plugged SBA-15 materials for efficient catalytic applications. The supported catalysts in plugged SBA-15 showed higher stability than those in conventional SBA-15. However, the limited thermal/hydrothermal stability, lower mechanical stability, and weaker acidity of these materials in comparison to zeolites set still hurdles for their application in challenging reactions, such as biomass conversion. In addition, the use of expensive structure directing agents in the synthesis has made the preparation being less economically viable. Therefore, the development of synthesis and post-synthesis procedures and incorporation of tri-and tetra-valent heteroatoms  in the silica wall of plugged SBA-15 are essential to overcome their weaknesses. In addition to that, the degradation of mesoporous materials by abrasion in slurry liquid phase reactions is another challenge for their applications. Work should be carried out on the shaping of these materials to enhance their mechanical and thermal/hydrothermal stability.