Escherichia coli, PBP1b, using cryogenic electron microscopy and a styrene maleic acid anhydride membrane mimetic. PBP1b, in its apo form, is seen to exhibit a distinct conformation in comparison to Moenomycin-bound crystal structures. The work herein paves the way for the use of cryoEM in structure-guided antibiotic development for this notoriously difficult to crystalize class of proteins and their complex substrates.The pathway for the biosynthesis of the bacterial cell wall is one of the most prolific antibiotic targets, exemplified by the widespread use of β-lactam antibiotics. Despite this, our structural understanding of class A penicillin binding proteins, which perform the last two steps in this pathway, is incomplete due to the inherent difficulty in their crystallization and the complexity of their substrates. Here, we determine the near atomic resolution structure of the 83 kDa class A PBP from  Escherichia coli, PBP1b, using cryogenic electron microscopy and a styrene maleic acid anhydride membrane mimetic.Our structural understanding of class A penicillin binding proteins is incomplete due to the difficulty in their crystallization and the complexity of their substrates. Here, authors determine the structure of the 83 kDa class A PBP from  The popularity of cell wall biosynthesis as an attractive set of antibiotic targets is largely a factor of the unique to bacteria nature of several of the enzymes involved and the overall essentiality of the also unique to bacteria cell wall ultrastructure in viability, infection and pathogenesis. This cell wall is composed of an extensive crosslinked mesh composed of peptidoglycan (PG)—polymerized strands of alternating β-1,4-linked N-acetyl-glucosamine-N-acetyl-muramic acid covalently linked via short peptide cross bridges attached at the C3-OH of the muramic acid1.The bacterial cell wall and the enzymes that provide for its biosynthesis have long been targeted clinically, as exemplified by the widespread use of β-lactam antibiotics, which inhibit final crosslinking steps in this pathway2. PBPs are split into two major classes, class A and B PBPs. Class A PBPs perform both a glycosyltransferase activity (GTase), which polymerizes the lipid activated precursor molecules into a growing PG strand  residue on the acceptor (in Gram negatives such as Escherichia coli), resulting in a d-Ala4donor-DAP3acceptor crosslink3. Monofunctional class B PBPs perform only the latter activity3. Inhibition of either of these crucial, PBP-mediated, final steps in PG biosynthesis results in destabilization of the cell wall and ultimately cell death4. The d,d-transpeptidase activity of both classes can be irreversibly acylated and inhibited by β-lactam antibiotics, which act as a substrate mimetic for the donor strand peptide3. The GTase domain can be inhibited by moenomycin, a natural product antibiotic produced by Streptomyces soil bacteria; commonly used as a growth promoter in animal feed, the C24 lipidated moenomycin is untenable as an antibiotic in humans due to poor pharmacokinetic properties3. The polymerizing GTase domain of class A PBPs consists of a membrane inserted variation on a lysozyme-like donor site, putatively accommodating four sugars and the pyrophosphate lipid of the growing PG strand3. A lipid II disaccharide unit binds at the acceptor site, with an intervening strictly conserved Glu providing the catalytic base for the β-1–4 covalent attachment of anomeric C1 of donor and 3′-OH of acceptor3 with concomitant loss of C55 pyrophosphate leaving group from the donor. The processive polymerization of the growing donor strand relies on a dynamic membrane-embedded “jaw” domain that putatively changes conformation to allow new product in each round to slide into the donor site5. The dynamic motion of the GT domain has indeed also likely been a contributing cause of thwarted high-resolution crystallographic efforts on these bifunctional PBP enzymes despite significant efforts globally. Structures to date have relied on the presence of the moenomycin A (MoeA)8, defining the latter as a donor site substrate analogue that likely helps promote stability of the dynamic GT domain, although with typically still large areas of disorder and thus continuing questions about critical active site features. The ability to capture more complex and typically heterogeneous physiological PG strand substrates for either the TPase or GTase domains has also been a thus far intractable hurdle for crystallographic analysis and again leaving many unanswered questions including acceptor strand binding in the TPase domain, donor binding in the GTase domain, and the presumed intervening path for growing substrate between the two distinct catalytic centres lying >20 Å apart.PG is synthesized using a multistage pathway involving: 1) production of soluble precursors in the bacterial cytosol; (2) precursor assembly onto lipid carriers at the inner leaflet of the cytoplasmic membrane; (3) flipping of the lipid-linked precursors to the outer leaflet of the cytoplasmic membrane; (4) polymerization of the lipid-linked precursors into PG strands; (5) crosslinking of these polymerized PG strands into the existing PG sacculus. The last two steps in this pathway occur through the activity of penicillin-binding proteins (PBPs) productiE. coli, PBP1b, using cryogenic electron microscopy (cryoEM) and a membrane mimetic. PBP1b, in its apo form, is seen to exhibit a distinct conformation in comparison to moenomycin-bound crystal structures. The work herein paves way for the use of cryoEM in structure-guided antibiotic development for this notoriously difficult to crystalize class of proteins and their complex substrates.Here, we determine the near-atomic resolution structure of the 83 kDa class A PBP from E. coli PBP1b 3 and PBP1a were reconstituted into polymeric styrene maleic anhydride (SMA), a detergent-free system for imaging via cryoEM9. Both PBP1a and PBP1b . For the purposes of this manuscript, we focus on the atomic details of the unexplored apo state of E. coli PBP1b, although we include initial E. coli PBP1a data to show that this methodology is likely applicable to a range of bifunctional PBPs. Initially, the SMA system was used in an attempt to capture the LpoB and LpoA activators, which, in our hands, did not seem to stably bind PBP1b and PBP1a in detergents. In addition, optimization of PBP1b and PBP1a vitrification with sufficient particle density was achieved more readily and at much lower concentrations (0.1–0.15 mg/mL for both PBP1b and PBP1a) in the SMA, as vitrification in detergents can often be challenging and require significantly more protein. Interestingly, the most well-behaved samples post vitrification contained the addition of the known 13 kDa protein activator of PBP1b transpeptidase activity, LpoB , although we believe this protocol should work sufficiently in the absence of lipoprotein activators. However, no evidence of LpoB was subsequently observed in any particle classes during downstream cryoEM data processing, suggesting that the protein had been lost from the complex during cryogenesis. Following complete processing of the PBP1b data, including subtraction by masking of clear features corresponding to the SMA membrane mimetic in initial maps, a cryoEM reconstruction of the apo state of E. coli PBP1b was produced  domain, central putative activator binding domain (UB2H domain), and N-terminal membrane-anchored glycosyltransferase (GTase) domain all able to be modelled into the cryoEM reconstruction as a complete structure of the apo state of E. coli PBP1b.Detergent-extracted ced Fig.  and an o8, significant domain rearrangements are observed between the two states. The TPase domain and the UB2H remain as a near-fixed domain dyad, but with an ~40° relative rotation of the GTase domain with respect to the fixed TPase/UB2H pair . As these modest displacements are near to the fulcrum of the pivot between the GTase and TPase domains, they radiate outward into larger displacements, such as a 5.4 Å displacement of the loop  between the GTase and TPase domains and further outward from the fulcrum, displacements of 6.8 Å on the GTase loop residues 489–499 (measured from the Cα of A496). Overall, this results in the relatively significant rotational shift seen for the GTase domain between the two states. Due to the similarity of MoeA to the natural ligands of PBP1b, it is possible that these domain rearrangements may occur naturally between stages of the PBP1b activity cycle, with LpoB additionally inducing arrangements that would prime or favour active conformations. The interface between the UB2H domain and the TPase and GTase domains has been explored previously using point mutations of hydrogen bonding networks observed in the MoeA-liganded structures of PBP1b12. Here, we analyse the differential hydrogen bonding of the apo form in the context of these known amino acid substitution effects.Despite the expected broad structural conservation of the apo PBP1b in comparison to previously solved MoeA-liganded structures3. Each of the states has one additional hydrogen bond interaction unique to that state; in the apo PBP1b structure formed between the carboxylic acid group of E123 and the hydroxyl of T751 (E123-T751) and in the MoeA-liganded structure3, between the ε-amino group of K126 and the backbone carbonyl oxygen of N749 (K126-N749). These non-conserved interactions are localized in close proximity due to the minimal domain rearrangement between the UB2H and TPase domains.Between the apo and MoeA-liganded structures of PBP1b, we see a number of conserved residues playing analogous roles in both Fig. . Most no3, the guanidino group of R134 is rotated inward, toward the UB2H domain’s centre of mass, forming hydrogen bonding and electrostatic interactions with the carboxylic acid group of E341 (R134-E341). The latter also interacts with the guanidino group of the adjacent R157 (R157-E341). Two additional interactions observed exclusively in the apo PBP1b structure include a hydrogen bond between the hydroxyl group of T140 with the guanidino group of R397 (T140-R397) and a second ion pair interaction from the latter moiety with the carboxylic acid side chain of E144 (E144-R397).The interactions between the UB2H and GTase domains, on the other hand, are more differentiated, not surprising given the larger observed interdomain movements between apo and MoeA-liganded structures of PBP1b Fig. . Only R1We observe that the interfaces between the TPase, UB2H and GTase domains are complex and dynamic amongst known conformations of PBP1b. We have now resolved the structure of PBP1b in one additional state, the apo form, the starting point of the catalytic cycle of processive polymerization and transpeptidation, with likely additional states to be observed in order to fully unravel the conformational plasticity of PBP1b needed for its multivalent catalytic activities.Staphylococcus aureus PBP26, E. coli PBP1b8, Acinetobacter baumannii PBP1a13, Listeria monocytogenes PBP1a14 and Haemophilus influenzae PBP1a—Supplementary Table The TPase domains of both class A and B PBPs are longstanding drug targets, through their inhibition by β-lactam antibiotics. Despite the relative ease with which one can crystallize monofunctional class B PBPs (as demarcated by the abundance of class B PBP structures from a variety of species deposited in the PDB), class A PBPs have been rather recalcitrant to crystallization, with class A PBPs (or TPase containing portions of class A PBPs) only having been solved from five distinct species , the TPase domain is as modelled in crystal structures . We note that this close overlap also supports that the SMA lipid particle (SMALP) detergent mimetic here has no conformational influence when compared to the liganded structure determined by X-ray crystallography in a detergent micelle. The local resolution for the TPase domain is seen to be higher than the overall resolution of 3.28 Å, with local resolution at ~3.1–3.3 Å  motifs , with the exception of the lone structure of MoeA-liganded PBP1b where the α4 helix loop is stabilized and modelled (PDB 5HLB3). If we use the more complete 5HLB as a model for comparison between our apo and liganded PBP1b GTase domains , of which MoeA is an analogue  jaw regions of the GTase domain  at 2.52 Å resolution, and two structures of PBP2 at 2.90 and 3.10 Å resolution, the class A PBP , all from S. aureus. Similar to what was observed amongst E. coli PBP1b GTase structures, we see high structural conservation on the level of secondary structure when comparing apo PBP1b GTase domain to the apo GTase domains of MGT and PBP2  and MGT (MoeA liganded—6FTB18). Despite the potential artifactual positioning/ordering by crystal contacts of this otherwise dynamic helix, these structures have allowed for insight into the potential interfaces between ligands and the α4 helix loop. In the case of some of the MoeA-liganded structures, such as the E. coli PBP1b structure 5HLB, we see stabilization that could be more indicative of a natural state, as the stabilization of the α4 helix loop is in part driven by interactions with the natural substrate analogue MoeA. From our cryoEM and detergent-free structure of E. coli PBP1b in an apo state, we propose that this loop is forming a spectrum of conformations in the absence of substrate or inhibitor, as density for this region is not observed. Indeed, this ability to rapidly unfold in the absence of donor substrate is a major premise of earlier models of processive polymerization, providing a path for new product to slot into the donor site18.Amongst the observed structures of GTase domains, the main structural differences come down to the position and stabilization of the α4 helix loop. In the case of most structures of GTase domains where the entirety of the α4 helix loop could be modelled, there are contacts with adjacent molecules in the asymmetric unit. Two such examples are the structure of E. coli. This was solved in a detergent-free system, using cryoEM. In comparison to MoeA-liganded structures of PBP1b, the apo form is seen to exhibit a distinct conformation. We observe the dynamic domain interface rearrangements between the TPase, UB2H, and GTase domains of PBP1b. On a domain level, we note that there is clear density for the TPase domain, and we propose that our methodology could be repurposed to play a key role in the structure-guided drug design for this notoriously difficult to crystalize class of proteins. In addition, from the apo structure of E. coli PBP1b, we are able to compare and contrast with both liganded forms of E. coli PBP1b, as well as apo forms of PG GTases from other species. We hope that the techniques described in this work can stimulate the pursuit of further cryoEM structures of class A PBPs from additional species, such as those from ESKAPE pathogens19 and WHO priority pathogens20 with no current structural information and little sequence identity, typically in the range 30–40%. Furthermore, we hope that this cryoEM methodology will allow for the capture of structural information regarding the native acceptor and donor substrates, which have largely eluded crystallization in both class A and class B PBPs. Structural insight into these acceptor and donor substrates will allow for further appreciation of recent work to unravel these mechanisms through the use of continuous assays21 and allow for the development of novel antibacterial compounds that target GTase activity. We believe that the work presented in this manuscript will revitalize the study of class A PBPs and that this and subsequent work will be of great assistance as we attempt to ward off a post-antibiotic era.In this work, we have shown the structure of the 83 kDa class A PBP, PBP1b, from Escherichia coli PBP1b (residues 58–804) and LpoB (resides 78–213) were cloned into the expression vector pET41b with a thrombin-cleavable, C-terminal His8-tag8. Expression constructs were transformed into E. coli C41 (DE3) for expression. Cells were cultured in ZYP-5052 autoinduction media for 4 h at 37 °C followed by overnight protein expression at 25 °C. Cells were pelleted and stored at −80 °C until required.E. coli PBP1b22, cell pellets were resuspended in lysis buffer  and lysed by processing twice with a homogenizer . Cellular debris was pelleted by centrifugation at 10,000 × g for 30 min. The resultant supernatant was centrifuged at 125,000 × g for 1 h to pellet membranes. The membranes were solubilised in Buffer A  with 1% (w/v) DDM for 1 h at 4 °C and loaded onto 5 mL Ni-NTA Superflow resin (Qiagen), washed with 75 mM imidazole in Buffer A with 0.016% DDM, and the protein was eluted with 300 mM imidazole. One unit of thrombin was added per mg of protein to remove the N-terminal His-tag overnight at 4 °C. Samples were exchanged into SMA  by SEC with a Superdex 200 column (GE Lifesciences) equilibrated in Buffer A with no DDM. Fractions containing pure PBP1b were pooled. LpoB was purified as described previously23. To assay lipoprotein activator binding, lipoprotein activator was added to the sample via glycerol gradient centrifugation on a 5–25% gradient overnight at ~30,000 × g.For purification of 2 over an exposure of 50 frames. Automated data collection was carried out using SerialEM with a nominal defocus range set from −0.8 to −2.0 μM. A total of 12,788 movies were collected over 72 h.Aliquots of 3 μL of SMA-PBP1b and a 4-fold molar excess of LpoB were applied to glow-discharged Quantifoil® (1.2/1.3) grids. The grids were blotted for 3 s at 100% humidity with an offset of 3 and plunge frozen into liquid ethane using a Vitrobot Mark IV (Thermo Fisher). Grids were imaged on a 300 keV Titan Krios cryo-electron microscope (Thermo Fisher) equipped with a K3 camera (Gatan). Movies were collected at a calibrated magnification of ×105,000, corresponding to a 0.844 Å per physical pixel. The dose was set to a total of 60 electrons/Å24 and micrographs were binned to 0.844 Å/pixel. The contrast transfer functions (CTFs) of the flattened micrographs were determined using CTFFIND4.125. The micrographs were denoised using JANNI27, and subsequently, 5,279,209 particles were picked using crYOLO26. Particles were additionally binned to 3.376 Å/pixel and reference-free 2D classification was performed in cryoSPARC28, leaving 4,554,764 particles after obvious junk and ice was removed. A subset consisting of the first 4886 micrographs of collection was processed in a similar way. Four ab initio models were generated and subsequently heterogeneously refined in cryoSPARC28. The most reasonable class was subjected to another round of ab initio modelling and heterogeneous refinement. The most reasonable class and a junk class were used in three rounds of iterative heterogenous refinement to reduce the particle stack to 1,929,725 particles, which were limited by the binned pixel size. The particles were unbinned to a pixel size of 1.688 Å and the iterative refinement was continued for six more iterations, resulting in a stack of 1,043,248 particles. At this point, additional 8-class ab initio modelling was performed, followed by an 8-class heterogenous refinement. The best class resulted in 462,997 particles, which were subsequently unbinned to 0.844 Å/pixel. These particles were refined using cryoSPARC local refinement and non-uniform refinement29, with the fulcrum set at the centre of mass. This volume was then reconstructed in RELION 3.130 and Bayesian polishing was performed. The polished particle stack was then locally refined again in cryoSPARC28, and again reconstructed in RELION 3.130. Particle subtraction was performed to remove SMA, lipid, and dimeric PBP1b density. The subtracted particles were then once again locally refined in cryoSPARC28, and globally and locally CTF refined in RELION 3.130. Final refinement was performed in RELION 3.130 to a resolution of 3.28 Å. The resolution was determined at 0.143 criterion using the Fourier shell correlation gold-standard refinement procedure. The final map was sharpened using LocScale31. Throughout the processing pipeline, no density for LpoB was observed in any classes.The 12,788 movies were motion-corrected using MotionCor25HLD) was split into TPase-UB2H and GTase parts and docked into the map using UCSF Chimera11. The resultant model was then refined using Phenix real-space refine32, ISOLDE15 and manual building in Coot33. The final model has a good fit to the map (EMRinger34 score 3.35) and good statistics .Our previous structure of liganded PBP1b (PDB Further information on research design is available in the Supplementary InformationReporting Summary


Effector proteins are bacterial virulence factors secreted directly into host cells and, through extensive interactions with host proteins, rewire host signaling pathways to the advantage of the pathogen. Despite the crucial role of globular domains as mediators of protein-protein interactions (PPIs), previous structural studies of bacterial effectors are primarily focused on individual domains, rather than domain-mediated PPIs, which limits their ability to uncover systems-level molecular recognition principles governing host-bacteria interactions. Here, we took an interaction-centric approach and systematically examined the potential of structural components within bacterial proteins to engage in or target eukaryote-specific domain-domain interactions (DDIs). Our results indicate that: 1) effectors are about six times as likely as non-effectors to contain host-like domains that mediate DDIs exclusively in eukaryotes; 2) the average domain in effectors is about seven times as likely as that in non-effectors to co-occur with DDI partners in eukaryotes rather than in bacteria; and 3) effectors are about nine times as likely as non-effectors to contain bacteria-exclusive domains that target host domains mediating DDIs exclusively in eukaryotes. Moreover, in the absence of host-like domains or among pathogen proteins without domain assignment, effectors harbor a higher variety and density of short linear motifs targeting host domains that mediate DDIs exclusively in eukaryotes. Our study lends novel quantitative insight into the structural basis of effector-induced perturbation of host-endogenous PPIs and may aid in the design of selective inhibitors of host-pathogen interactions. An important goal of systems microbiology is to understand how host-pathogen protein-protein interactions (PPIs) impact host-endogenous signaling networks. Effector proteins are virulence factors secreted by pathogenic bacteria and injected directly into the host cytoplasm via specialized secretion systems . EffectoPast studies on host-bacteria protein-protein interactions (PPIs) have examined either individual interactions at the domain level , or intePrevious studies have established binding site mimicry via convergent evolution as a key feature of human-virus PPIs where, rather than securing new binding sites, viruses have evolved unique, non-host-like structural domains and short linear motifs to compete with host proteins for the same binding sites on a common host target . As bactp < 2.2 * 10−16). vs. host-bacteria PPI network.We found that of the 103 host-targeting bacterial proteins, 95 (92%) bind to the same domains on their host target that are otherwise bound by host-endogenous proteins, suggesting that like viruses, bacteria also tend to recruit domains involved in host-endogenous PPIs for host-pathogen PPIs . We thenHaving examined the mechanism of binding site mimicry in the host-bacteria PPI network, we then asked whether bacterial effectors tend to mimic or target host domains that mediate DDIs predominantly in eukaryotes, as opposed to domains that mediate DDIs in eukaryotes and bacteria with similar likelihood ‐ the rationale being that the former are involved in eukaryote-specific processes such as protein ubiquitination , which ap = 2 * 10−4)   and 55 non-effectors (4%) contain domains that mediate PPIs exclusively in eukaryotes, suggesting that effectors are six times as likely as non-effectors to repurpose eukaryote-specific processes via eukaryotic-like domains  . Table 1ds ratio . We foun * 10−7) . Table 3p = 4 * 10−5)  . Supplemp = 3 * 10−3), but similar density of ELMs  compared to non-effectors. When comparing 521 effectors and 794 non-effectors with unique ELM compositions and not containing eukaryotic-like domains or any Pfam domains, however, we found that effectors and non-effectors encode 12 and 8 ELM classes per protein (p < 2.2 * 10−16) as well as higher density of ELMs  compared to non-effectors.In addition to encoding globular domains that either mimic or target host domains, effectors also encode short linear motifs that bind to host domains with similar specificities as host-endogenous proteins, albeit sharing little homology with the latter . These s protein , Right,  protein , Right, Ralstonia solanacearum have acquired a host-like F-box domain (PF00646) that competes with host-endogenous F-box protein for binding to SKP1, thus hijacking the ubiquitin-proteasome pathway in Arabidopsis thaliana (Shigella flexneri have convergently evolved a GEF domain (PF03278) that competes with host Rho GEF, thus activating the Rho GTPase signaling pathway in humans (Z-score ≥ 4) in the genomes of pathogenic vs. non-pathogenic bacteria , based on which we studied the mechanism of host binding site mimicry by bacterial proteins, and systematically probed the proteomes of pathogenic bacteria for domains that mimic or target host domains engaging in domain-domain interactions (DDIs) that are specific to eukaryotes, as opposed to DDIs that are conserved between eukaryotes and bacteria. Our comprehensive and quantitative profiling of bacterial proteomes reveals statistically significant enrichment of domains and short linear motifs in bacterial effectors that interact with host domains engaged in eukaryote-specific DDIs, which allows host-bacteria PPIs to mimic host-endogenous PPIs on an interactome scale . We founLegionella , Pseudomonas (PF03496), Salmonella , Shigella (PF03278) and Yersinia  effectors. On the other hand, effector domains targeting multiple host domains and thus potentially perturbing multiple host pathways represent targets for multipronged therapeutic intervention. Of the 103 host-targeting bacterial proteins in our PPI dataset, 71 interact with a single host protein, while 32 interact with multiple host proteins. For instance, the Pseudomonas effector ExoS contains the ADP ribosyltransferase domain (PF03496), which it uses to target host proteins containing either a 14-3-3 domain (PF00244) or Ras small GTPase domain (PF00071). These host domains participate in a wide array of signaling pathways , and likely acts as a linker in the multi-domain chitinase (To identify domains in bacteria that are most likely involved in mimicking host-endogenous protein-protein interactions (PPIs), we excluded eukaryotic-like domains which engage in either interchain or intrachain domain-domain interactions (DDIs) in bacteria. Previous studies suggest that intrachain DDIs often occur between adjacent domains within the same protein ; howeverhitinase , rather In conclusion, our demonstration of binding site mimicry and its mechanisms at the domain level in the host-bacteria PPI network provides novel insight into the evolution of host-interacting domains in bacterial effectors. In particular, we showed that convergent evolution (or extreme divergent evolution) appears to be the more dominant mechanism behind binding site mimicry in host-bacteria interactions. To date, similar analysis has only been done for viral proteins. In addition, our estimation of domain’s relevance to eukaryote-specific DDIs provides quantitative, interaction-based criteria for identifying novel effectors, based on: 1) domains that exclusively or primarily mediate DDIs in eukaryotes; and 2) variety and density of short linear motifs targeting host domains that exclusively mediate DDIs in eukaryotes. Although predicting new effector-host interactions is beyond the scope of this paper, our study presents a first step toward resolving PPI interfaces in effector-host interactions: once the domains involved in effector-host PPIs are identified by our method, interface residues inside such domains can be predicted using machine learning methods . By mappEukaryote-endogenous, bacteria-endogenous, and host-bacteria protein-protein interaction (PPI) data were obtained from IntAct and HPIDB 3.0 . Domain-To resolve protein-protein interactions into domain-domain interactions, we first predicted the occurrence of domains in proteins with InterProScan, using Pfam’s gathering threshold . We thenBy gene name: taxonomy:“Bacteria [2]” name:effector (name:“type 1” OR name:“type 2” OR name:“type 3” OR name:“type 4” OR name:“type 5” OR name:“type 6” OR name:“type 7” OR name:“type 8” OR name:“type 9” OR name:t*ss OR name:“secretion system”) By cellular location: taxonomy:“Bacteria [2]” (annotation(type:function effector) OR locations: (note:“type 1”) OR locations(note:“type 2”) OR locations: (note:“type 3”) OR locations: (note:“type 4”) OR locations: (note:“type 5”) OR locations(note:“type 6”) OR locations: (note:“type 7”) OR locations: (note:“type 8”) OR locations: (note:“type 9”) OR locations: (note:t*ss) OR locations: (note:“secretion system”)) (locations: (location:“Secreted [SL-0243]”) OR locations: (location:“Host [SL-0431]”)) Non-effectors consist of cytoplasmic, membrane as well as other secreted proteins encoded by the same pathogen species considered for effector proteins. Cytoplasmic: taxonomy:“Bacteria [2]” locations: (location:“Cytoplasm [SL-0086]”) Membrane: taxonomy:“Bacteria [2]” (locations: (location:“Cell envelope [SL-0036]”) OR locations: (location:“Membrane [SL-0162]”)) Secreted: taxonomy:“Bacteria [2]” (locations: (location:“Secreted [SL-0243]”) OR locations: (location:“Host [SL-0431]”))We included proteins encoded by pathogenic bacterial species catalogued in PHI-base . PHI-base.g. orthologs or fragments of the same protein) while also maintaining sufficient resolution, sequences belonging to the same UniRef50 cluster were ranked based on whether they are: 1) representative for the cluster, as assigned by UniRef50; 2) manually reviewed; 3) assigned high annotation score by UniProt; 4) from UniProt reference proteomes; and 5) longest. Only the top-ranking sequence was retained for each UniRef50 cluster. For domain compositions that are common to effectors and non-effectors, we assessed their relative frequency in effectors vs. non-effectors. Domain compositions that are significantly enriched  in effectors were assigned to effectors, and domain compositions that are significantly depleted  in effectors were assigned to non-effectors. Our final dataset thus contains 238 effectors and 3,921 non-effectors with unique domain signatures.Taxonomy and Pfam domain annotations of proteins were obtained from UniProt and InterPro . For eacFisher’s exact test was used for analyses based on odds ratios, and Wilcoxon test was used for analyses based on difference in means. To control the false discovery rate in multiple hypothesis testing, we calculated the positive false discovery rate, or q-values . All sta


Ex, a new homogenization index for characterizing the overburden deformation, is proposed to describe the broken rules of the HTC. The numerical simulation proves the feasibility of the DOFS monitoring method and the correctness of the deformation pattern and movement law. This study provides efficient methods for DOFS monitoring utilization to investigate mining engineering problems and could be beneficial for unearthing the mechanisms of deep ground rock deformation.Mining activities under the circumstances of huge-thick stratum occurrence commonly result in dynamic response of the working face. It is crucial to understand the rock failure and movement of the huge-thick stratum in order to prevent dynamic hazards. This paper introduces distributed optical fiber sensor (DOFS) monitoring into a large-scale model test to investigate the deformation pattern and movement law of the huge-thick conglomerate (HTC); the monitoring results are verified by numerical simulation. The results indicate that DOFS monitoring captures the spatiotemporal evolution of zoning development in the overburden deformation. The deformation field of HTC is illustrated, and there exists a strain basin that can be used to estimate the movement law of HTC. The average strain variability  An extremely thick and hard rock stratum above the minable coal seam is a special structure of the coal measure stratum and is often encountered in coal mining engineering. It is widely distributed in many mining areas in China, such as the huge-thick conglomerate (HTC) occurrence in the Qianqiu, Changcun, Jiaoping, and Huafeng coal mines ,2,3,4,5.In recent years, many studies have been performed to investigate problems such as strong coal mine ground pressure, uncommon surface subsidence, and frequent rock bursts caused by the breaking of the thick rock stratum ,19,20,21Owing to its traits of distributed measurement and suitability to extreme environments, DOFS technology has attracted increasing attention from researchers in the field of mining engineering. Currently, DOFS for mining rock mechanics has been increasingly used in laboratory studies and field applications . For insConsidering the in-depth application of DOFS technology in mining rock mechanics, the DOFS monitoring method was introduced to study the deformation of HTCs, and some useful results were obtained in a series of studies by the authors’ research team. For example, a displacement measurement device was designed to monitor the internal deformation of the HTC ,28. The Previous studies have made important progress with respect to the application of DOFS monitoring to geotechnical or geologic engineering, and they have provided an understanding of the ground movement and dynamic hazard prevention under the occurrence of the HTC. Nevertheless, there remain two key scientific issues: (1) engineering limitations determine that conventional methods cannot deeply capture the internal deformation pattern of the HTC, which cannot fully reflect the influence of the HTC deformation that acts on the whole overburden; (2) the DOFS monitoring results provide a better understanding of the broken rules under the impact of HTC deformation; however, owing to the lack of further reprocessing of the data distribution characteristics of the DOFS monitoring, the movement law of the HTC has not been well demonstrated.Hence, based on previous laboratory experiments and theoretical modeling using Brillouin optical time-domain analysis (BOTDA) technology, and in order to obtain an effective solution for the abovementioned problems, this study carried out a large-scale 3D model test experiment to thoroughly analyze the deformation pattern and movement law of the HTC using the DOFS monitoring method, which has great significance for the popularization and application of DOFS technology for the traditional engineering discipline of mining engineering, and more efficient methods of DOFS system utilization were derived.To study the movement of HTC in physical modeling, BOTDA technology was chosen owing to its high precision and high spatial resolution in indoor experiment measurements. The monitoring principle of the BOTDA-based DOFS system is summarized in The research object of this study was the Qianqiu coal mine in Henan Province, China, where a HTC is located. The engineering geological analysis indicates that the average thickness of the coal seam and the overlying stratum in the Qianqiu coal mine are approximately 15 m and 806 m, respectively. The conglomerate appears from 210 m to 620 m above the coal seam, which is approximately 410 m thick, and was classified into two groups according to the engineering borehole investigation: an upper group and a lower group with thicknesses of 250 m and 160 m, respectively, separated by a weak intercalated layer with a thickness of 1 m. Preliminary field tests show that the conglomerate rock is a typical hard rock, with a maximum elasticity modulus of 32 × 103 MPa and a maximum compressive strength of 75 MPa.An analogue model test was carried out according to the physical and mechanical properties of the overburden structure in the Qianqiu coal mine. Furthermore, direct observation of the overburden deformation was realized by setting a high-strength transparent acrylic board on the front side of the model, which greatly improved the recording of the experimental phenomena. The excavation range of the coal seam was 240 cm in the strike direction and 160 cm in the dip direction. In addition, two working faces were set along the dip direction, resulting in each working face having a width of 80 cm. Given that the experimental physical model was sealed on all sides, there was no entry to the inside of the model for excavation actions; thus, the pull-out simulation method was applied to simulate the coal seam excavation. That is, 60 metal tubes with rectangular cross sections were deployed in the model during the model construction process, each of which had a cross-sectional length of 6 cm and width of 4 cm. This meant that the coal seam had a height of 6 cm and an excavation pace of 4 cm. One metal tube was pulled out from the model when there was one excavation, and 60 excavations occurred in one working face.To acquire the omnidirectional monitoring of the HTC rock stratum deformation characteristics, a DOFS network was set up by arranging three groups of optical fibers in three directions of the physical model. One group of optical fibers was installed vertically, namely the optical fiber of group Fv, and the other two groups were installed horizontally. One of the horizontally installed groups was arranged in the strike direction of the model coal seam, and the other was arranged in the dip direction, that is, the optical fiber of groups Fs and Fd. The layout of the optical fiber sensors is shown in As shown in The installation method of the optical fiber sensor can be summarized as follows: the vertically installed optical fibers were fixed on the custom-made device before building the physical model, and they were surrounded by anomalous materials during the construction of the model building. Moreover, each vertical optical fiber was stretched in a straight direction and pre-stressed using a force of 25 N; the horizontally installed optical fibers were embedded while the model was constructed, which means that when the model was built at the specifically designed height, the optical fibers were first inserted into the model, and then the model was built to the next height, until the installation of the five layers of optical fibers was completed.Finally, it can be concluded that the optical fiber sensors of groups Fv, Fs3, and Fs4, and Fd3 and Fd4 were combined into a network for HTC deformation monitoring. In particular, Fv detected the entire overburden structure, including the HTC, Fs3 and Fd3 were responsible for monitoring the lower group, and Fs4 and Fd4 were responsible for monitoring the upper group.The simulation excavation was performed for more than a month, with the aim of achieving full monitoring of overburden deformation by the DOFS system. Massive monitoring data in the model test were obtained and provided a thorough explanation of the evolutionary rock failure in the HTC. The DOFS monitoring results analysis of HTC deformation was discussed. More importantly, all the strain data had been already treated with a temperature compensation.According to a previous study  of overbConsidering the boundary effect and mining influence, the position in which optical fiber Fv1 was embedded should be under full mining effect; hence, the strain distribution of that part of the rock mass would be a better representation of the overburden deformation. As a result, the strain distribution of Fv1 in First, the strain curve changed from zero to a negative distribution, as the working face distance increased from 0–56 cm, which indicates that the measured rock mass is situated in the leading abutment pressure influence area where the increasing compressive stress is concentrated.Second, the strain curve emerged as a positive distribution that contains multiple stages of development, for a mining distance of 56–84 cm, which indicates that the overburden rock mass has undergone significant deformation and presents varying degrees of movement, resulting in different changes in the distribution at different overburden heights, i.e., the measured rock mass is located in the fractured rock and separation area. However, the strain value is reduced with increasing height, so in the lower stage it is larger than in the upper stage and at least three stages in the strain distribution curve. For instance, taking as an example the strain distribution at a mining distance of 72 cm, the first stage has a height of 25 cm and the second and third stages have heights of 64 cm and 85 cm, respectively, which demonstrates the developed height of the mining-induced overburden vertical three zones based on the characterization pattern mentioned in the previous study; the caving zone increases to 25 cm, and the fractured zone and bending zone are 64 and 85 cm, respectively. Consequently, this illustration laid the groundwork for the regional rock failure analysis and the vertical three-zone identification.Finally, the measured rock mass will be in the recompacting area when the working face advances over 84 cm, according to the characterization pattern, and the rock mass is subjected to compressive stress again under the influence of overburden deformation so that the strain distribution would restore and maintain a negative value. However, from On the other hand, from the perspective of space development , the strain distribution can be presented as follows: for a model height ranging from 0–50 cm  must induce a large Ex variation based on the mine ground pressure control theory. Consequently, Ex can be used to determine the broken rule of the HTC, which can be summarized as follows: a step offset of Ex represents a larger deformation or large breakage of the HTC, and the Ex value indicates the degree of that deformation. Ex variation calculated by the monitoring results of the optical fiber sensors embedded in both groups of HTC. Ex results calculated by the strain distribution of Fd3 and Fs3, which also represents the broken rules of the lower group of HTC, and Ex results calculated by the strain distribution of Fd4 and Fs4, which also represents the broken rules of the upper group of HTC.The physical significance of Ex curve appears to have a sawtooth multi-peak shape, and the average sawtooth peak value is about 1800 με, which indicates that the lower group of HTC is in the stage of periodic large deformation. That is, the rock strata break periodically with the advance of the working face, resulting in the periodic weighting of the working face. The third stage is when the mining distance is between 184 and 240 cm; the Ex curve still shows the sawtooth shape, but the value of the average sawtooth peak decreases to 800 με, which indicates that the deformation state of the lower group of HTC tends to be eased. However, it still maintains the motion state of periodic small deformation.As shown in Ex curve also appears to have a sawtooth shape with a tooth peak of 600 με, which indicates that the upper group of HTC is in a periodic small deformation; the third stage is the mining distance of 184–240 cm, where the sawtooth shape remains and there is a tooth peak of 1200 με, which indicates that the stratum has a large periodic deformation.The broken rules of the upper group of the HTC also have three stages, as shown in Ex curve, it can be found that the Ex curve rises as the mining distance increases, and it remains stable at a mining distance of 184 cm, which means that the lower group of HTC still exhibits a trend of continuous deformation when the excavation is completed. However, the upper group of HTC maintains a small deformation until the excavation of 184 cm, and it then undergoes a large deformation until the end.Based on the ground pressure control theory, the first break of the lower group of HTC is located at a mining distance of 84 cm, after which it will experience 13 periodic breaks; the upper group of HTC has the first break at the mining distance of 96 cm, and has 12 periodic breaks. Furthermore, from the change tendency of the Ex variation of the lower group is larger than that of the upper group, which proves that the overburden deformation will decrease with increasing stratum height. Meanwhile, there is a transition of the deformation status in the Ex variation of both groups of strata, such as the lower group first presenting periodic large deformation, after which it changes to a periodic small deformation, and the upper group presents the inverse situation. This transition means that the deformation of the lower group is relatively severe in the early stage (84–168 cm) of excavation, and the deformation has been gradually limited with the continuous accumulation of broken rock mass below the lower group of the HTC. This results in the stratum presenting only a periodic small deformation in the later stage of excavation (184–240 cm). However, the upper group of the HTC did not produce a large deformation at the initial stage of the excavation. The upper group began to undergo large deformation only when the lower group was broken on a large scale.Both analyses of broken rules of the two groups of HTC show that the Ex variation, the broken rule can be drawn as follows: the lower group of HTC will undergo a slight deformation for a mining distance of 0–84 cm. Then it will be broken to collapse at a mining distance of 84 cm and maintain a large periodic deformation until the mining distance is 184 cm; finally, it will transition into a small periodic deformation because of the mining influence.From the results, it can be concluded that the broken distance of the lower group is about 84 cm of excavation, and it is 184 cm for the upper group; in other words, the lower group of HTC will collapse at a mining distance of 84 cm, and the upper group will collapse at a mining distance of 184 cm. More importantly, from the However, the deformation of the upper HTC group depends on the deformation law of the lower group. The deformation of the upper group will undergo a slight deformation at a mining distance of 0–68 cm, then the small deformation will accumulate at a mining distance of 68–92 cm, and it will be broken to collapse at a mining distance of 96 cm. After that, there will be a small periodic deformation for a mining distance of 96–184 cm, which changes into a large deformation at a mining distance of 184 cm, until the excavation is accomplished.Considering that the research object of this paper has the obvious attribute of a “black box” problem, that is, it is usually impossible to explore and examine the results intuitively, the numerical simulation could be another key approach to study this kind of issue. In fact, numerical simulation for underground mining has been widely utilized in the last decade. In the study of rock fracture evolution, three basic experiments involving rock uniaxial compression, uniaxial tension, and shear fracture using numerical simulation  were conTherefore, the deformation of the HTC has been investigated by numerical simulation. The feasibility and accuracy of the DOFS monitoring method is discussed by comparing the vertical displacement field of both groups of HTC acquired by numerical simulation and the strain field, which is obtained by DOFS monitoring.The deformation law of the HTC under a mining influence was simulated using FLAC 3D. The geological conditions of the Qianqiu coal mine are shown in The numerical simulation procedure complied with the standard manual ,62,63 anFrom From According to the analysis above, the results of the numerical simulation and DOFS monitoring were compared and are shown in Ex is 84 cm and 184 cm, respectively, which means that the broken distance is nearly the same. Consequently, the broken rule obtained by numerical simulation is consistent with the calculation results of the averaged strain variability Ex, which further proves the correctness of the DOFS monitoring results.In conclusion, the range of the deformation field and the change in displacement of the HTC in the upper and lower groups show the same change in the numerical simulation and DOFS monitoring results, which proves that the DOFS monitoring results are reliable. Moreover, by comparing the results of both methods, it can be found that the large deformation of the upper group of the HTC occurs after an excavation of 720 m, and that of the lower group occurs after an excavation of 240 m. If the field data of the numerical simulation are converted to model test data using the physical modeling principle (a geometric similarity constant of 1:400), the mining distance to the collapse of the lower and upper groups of the HTC is after 60 cm and 180 cm, and the same determination distance calculated by This paper introduced the DOFS monitoring method in mining engineering to reveal the deformation pattern and movement law of the HTC by implementing a large-scale 3D model test, and the feasibility of the DOFS monitoring method was verified by numerical simulations. The results not only demonstrate the comprehensive spatiotemporal evolution process of the fracture and breakage of the HTC, but they also provide an effective solution for probing the inner mechanism of rock mass failure, which cannot be obtained specifically by the conventional detection method. It would be beneficial to study the field engineering roadblocks and basic scientific issues related to deep ground engineering. The detailed conclusions are as follows.(1)The strain distribution acquired by the vertical optical fiber sensors shows that the tensile stress will remain in the HTC after the excavation is complete, which is unlike the common soft sedimentary rock, where the compressive stress occurs when the mining is over. The vertically embedded sensors proved that microcracks were generated in the HTC, and the stratum moved downward by the fractures in a rotary motion into the mining gob.(2)The dynamic development of the three vertical zones demonstrates the broken area of the HTC; the caving zone is much larger than the usual zone. The hard HTC forms the main key stratum, which causes the fracture zone to spread over the conglomerate area, and the stratum above that will bend over.(3)The deformation field of the HTC was deduced by the surface fitting of the strain distribution of the DOFS monitoring, which provided an intuitive way to detect the internal deformation and movement law of the rock stratum. Specifically, the strain basin shape of the deformation field was identified to describe the broken status so that the broken law can be distinguished through the analysis of the strain basin characteristics.(4)Ex variation indicates that the broken distance of the lower and upper groups is 84 and 184 cm of the excavation, respectively, and the deformation of the upper group depends on that of the lower group. The broken rules based on the Ex variation can be summarized as follows: the lower group of HTC will first have a slight deformation, and it will then collapse and maintain a large periodic deformation and finally transition into a periodic small deformation. The upper group will have a slight deformation and maintain a small periodic deformation at first, after which it will collapse and transition into a large periodic deformation.The concept of average strain variability was proposed to determine the broken rules of the HTC. The calculation of the (5)The numerical simulation results show a nearly identical variation law of the deformation morphology and the curve trend compared with DOFS monitoring. The broken rules obtained using both research methods confirm the feasibility of DOFS monitoring. Moreover, by making the conversion using the geometric similarity constant, the collapse distances of both groups of conglomerates obtained by both methods are shown to be nearly the same, which further verifies the accuracy of the deformation pattern and movement law.


Appreciation was brought to the appearance, crust, aroma, and taste in the optimized bread. This study suggests that chickpea flour can be suitably incorporated into bread wheat flour up to a percentage of 24% with 16% milk powder to produce bread with optimal nutritional quality while improving its sensory attributes and consumer acceptability.Flour quality is influenced by the nature of the gluten and its various components. Gluten free flour made of pulses is known to enhance the nutritional quality of wheat flour. However, its addition can compromise the rheological and sensorial attributes of the bread. We used mixture design to optimize nutritional and technological qualities of a wheat–chickpea flour blend by adding milk powder as a natural organoleptic improver. A total of thirteen flour blends were prepared by incorporating 10 to 30% chickpea flour and 10 to 20% milk powder to wheat flour. Our results showed that the optimal flour blend consisted of 60% wheat, 24% chickpea, and 16% milk powder. Farinographic parameters of the optimal dough blend remained on par with those of the control dough (100% wheat flour), thereby preserving its bread-making quality. Sensory analysis of breads made from the optimal flour blend revealed no significant difference ( Pulses are increasingly being recognized worldwide as healthy foods . They arCicer arietinum L.) is an important grain legume with a multitude of nutritional and health benefits [Chickpea  of the ‘Khadija’ variety and chickpea (Cicer arietinum) of the ‘Farihan’ variety that is widely grown in Morocco were used for flour blend preparation. The milk powder that was used was of a known international trademark. After cleaning and the removal of foreign impurities, wheat grains were thoroughly milled in a laboratory pilot mill (BUHLER-Switzerland) with a sieve of 250 μm that was set at a 64.5% extraction rate. Chickpea grains were ground by a hammer mill  with a sieve of 1 mm. All of the ingredients were stored properly until needed.In this study, bread wheat (In order to optimize the flour mixture of bread wheat (A), chickpea (B), and milk powder (C) from nutritional and technological points of view, the experimental design approach that was adopted was mixture design. This approach has the advantage of rationalizing analysis cost, time, and efficiency by reducing the number of experiments and giving maximum information on the constituents, their influences taken separately, and on their eventual interactions . The mixPreliminary tests defined the study area by setting the lower and upper limits as 60–80% for wheat flour, 10–30% for chickpea flour, and 10–20% for milk powder. The experimental matrix (scheffe matrix) obtained was composed of 13 blend combinations in which the ingredient quantities were within the above set limits. Responses to parameters concerning the nutritional composition were proteins, crude fat, carbohydrates, total polyphenol compounds (TPC), antioxidant activity and mineral content , and the technological parameters were the zeleny volume and the yellow index (b*). Trace plot curves and 2D and 3D response surface presentations allowed for the visualization of the effect of variability of one ingredient on the responses while the other two ingredients were fixed.Chickpea flour, bread wheat flour, and their blends with milk powder were subjected to protein content analysis using the Kjeldahl method and crude fat analysis by diethyl ether extraction using a soxhlet apparatus. The available total carbohydrates were determined by difference calculations. All of these analyses were performed according to the official methods of analysis of the Association of Official Analytical Chemists (AOAC) .Total Fe, Zn, and P concentrations in different flour blends were determined using the nitric acid–hydrogen peroxide digestion method ,30. MineThe total phenolic content of flour blends was determined following the method of Singleton and Rossi  using thFor each flour blend extract, DPPH scavenging activity was evaluated according to the method described by Brand-Williams et al. . A totalThe yellow index (b*) of flour blend samples was measured using a Chroma Meter CR-400 . The instrument was calibrated against a white calibration plate. In addition, gluten strength was determined using the sedimentation volume–Zeleny volume according to the international standard ISO 5529 . The resThe optimal combination of the studied responses was obtained by ‘the desirability function’ as outlined by Derringer and Suich . This puThe optimal flour blend was analyzed by a farinograph  and was subjected to bread making against a control flour (100% bread wheat). Absorption, stability, and other characteristics of the dough were determined by farinograph curves according to the AACC method 54-21 .Flat breads obtained with the optimized blend and control flours were prepared and baked using the straight dough method. A total of 1 kg of flour from each sample was mixed with 2% lyophilized yeast and 1% salt. The added water volume corresponded to the maximum absorption given by the farinograph, and the kneading was realized by a spiral mixer (100–200 RPM). The dough was then fermented for 90 min at 30 °C, divided to 150 g round pieces, and was allowed to rest at 30 °C for 11 min. Each piece was manually sheeted to a round shape of a 1 to 2 cm thickness, panned, and proved for 45 min at 30 °C. The bread was baked at 240 °C for 20 min .Sensory evaluation of baked breads was conducted for six defined quality attributes: appearance, chewiness, aroma, crust, crumb, and overall taste by a panel of 30 semi-trained members  on a 9-point hedonic scale .f and p value (p ≤ 0.05), the smallest standard error, the smallest mean, and the sum of the predicted deviations, and maximizing the R2 and the adjusted R2 [p ≤ 0.05) using SPSS statistics 17.A statistical test of the mixture design was conducted using Design Expert Software v11. The model response fitting was verified by the ANOVA test, analyzing the usted R2 . The higp value analysis, and all followed a linear model . Coefficient analysis demonstrated a significant enrichment effect of chickpea flour and milk powder on phosphorus in the blend flours . Analysis of the linear terms of the Fe and Zn regression equations revealed that the iron content of a blend was more influenced by the rate of incorporated chickpea flour, while the Zn content was more influenced by the milk powder rate, demonstrating the highest concentration of 48 ppm Zn and 57.60 ppm Fe in the blend of 60% bread wheat, 20% chickpea, and 20% milk powder. The p value and F test of the quadratic terms of the two equations confirmed significant positive values of the partial factor (BC), which demonstrates the synergistic effect of the interaction between chickpea flour and milk powder for the maximum Fe enrichment of the mixture. Fe ranged from 33.60 to 57.60 ppm, and the maximum value was reported in the mixture of 60% bread wheat, 30% chickpea, and 10% milk powder. This interaction was insignificant for Zn, which can be visualized in the trace plot curves and response surface presentations   . By anal 0.0045) . Signifi 0.0045) .p = 0.0057), and the scavenging activity ranged from 12.73 to 27.72%. Milk powder and chickpea flour positively influenced this response in bread wheat flour through significant terms . This might be due to the chickpea content in terms of the polyphenols. Earlier studies undertaken on dough and pasta revealed that products made with wheat flour fortified with chickpea exhibited a higher polyphenol content and antioxidant activity, eventually doubling the control content [Concerning the scavenging activity, the validated model was special quartic ( content . The pos content .p = 0.002) and was positively influenced by the addition of wheat flour. This makes sense given the nature of the added ingredients. The addition of chickpea flour and milk powder caused the dilution of the gluten provided by wheat flour (The regression equation of the zeleny volume was of a linear order (at flour . Bojnansp = 0.0002)  , where tThe optimization of the flour mixtures was based on the mathematical equation of Derringer and Suich . This opThe optimization of the nutritional quality of the dough by replacing part of the bread wheat flour with chickpea flour and milk powder brought slight modifications in the farinograph results . Water ap < 0.01) higher than that of the control  lower softening degree in comparison to the control dough. When chickpea was used alone, its proportion linearly increased the softening degree [The optimal blend showed a significantly , the bread made with the optimal blend gained the confidence and acceptability of the interviewed panel. This improvement in sensory quality may be due to the conjugated action of the milk powder and chickpea flour. The milk powder enhanced the crust and crumb softness  and thusDue to its functional properties, milk powder upgraded dough viscoelastic development and thus improved the softness of the crumb, confirming the results obtained by Sanchez et al. . Indeed,The aroma and taste of the optimal blend bread were better than those of the control bread. This may be due to the action of the chickpea flour, which, as reported earlier by Miñarro et al. , indicatCrust improvement was expressed by the specific color development in the optimal blend bread. The darkness and yellowness of bread increased, and this might be attributed to the milk solids, which are useful color contributors due to the Maillard reaction, the products of which positively influence the flavor of bread . This reThe present study, conducted through the mixture design methodology, obtained an optimal nutritional mixture based on 60% bread wheat, 24% chickpea flour, and 16% milk powder. The milk powder not only boosted the nutritional potential of the mixture, but it also improved the overall bread making quality of the chickpea–bread wheat mixture in comparison to that of wheat bread by enhancing its rheological and sensorial qualities in regard to chewiness and crumb, which remained unchanged and thus overcame the technological inconvenience of the addition of chickpea flour. This opens the way for future in-depth research on the effect of milk fractions  on the nutritional and rheological quality of cereal–legume composite flours as well as the behavior of milk proteins in this matrix.


The overall acceptability based on physicochemical and sensory attributes of wheat flour were significantly (p < 0.05) higher than tapioca starch and modified starch. The results showed that the relatively low content of wheat flour at 0.625% (w/w) was of sufficient proportion to provide proper physicochemical properties and texture attributes to beef snacks. In addition, the results also indicated that the desirable properties of the obtained meat-based snacks were influenced by the type and content of starch and/or flour used. This study reveals the benefits of meat scraps as a potential protein-rich source and further applications in other meat-based snacks.The global demand for healthy snacks with high protein content is growing annually. Meat scraps generated after meat cutting in the slaughtering process are considered a valuable protein product. The aim of this research was to formulate the meat-based snacks obtained from beef scraps by baking at 150 °C for 20 min. The physicochemical properties, texture and sensory profiles of the beef snacks were investigated. Among tapioca starch, modified starch and wheat flour, the texture profiles and scanning electron microscopy (SEM) revealed that wheat flour contributed to a firm texture of the products, resulting in significantly ( Snacks have typically been associated with the consumption of products with low nutritional value. New dietary trends, on the other hand, have switched toward the consumption of protein-rich foods ,2. Meat Beef is a rich source of macronutrients as well as micronutrients. The macronutrients of meat include high-value protein and fat, containing saturated and unsaturated fatty acids. The micronutrients in beef and edible offal include heme iron, zinc, selenium, and vitamins D, B1 (thiamine), B2 (riboflavin), B3 (niacin), B5 (pantothenic acid), B6 (pyridoxin), and B12 . All of these nutrients are important for human physiological functions . The higA plant-based diet is specified as one that emphasizes foods derived from plants. Fruits, vegetables, grains, legumes, nuts, and meat substitutes, including soybean products, are all examples of this. The consumption of plant-based proteins has attracted attention in recent years . AlthougNon-meat components, including starches and flour, can greatly increase the production of better-quality meat products . StarcheScraps of meat are generally obtained from slaughtering after the manual removal of small pieces of meat left on the bone. An estimation of the market beef carcass generates 25% bone-in trimmed steaks, 25% bone-in roasts, 25% stew beef and ground beef, both boneless, and 25% wastage. The whole carcass accounts for 63% of the market live weight of cattle . The effMany studies in the literature have focused on the use of starch and flour as the components, in high amounts (30–70%), for meat-based snacks ,14,15,16In this study, beef snacks were prepared according to the modified formula of our laboratory. The formula is presented in The experiments were divided into 2 parts: (i) the impact of the type of starch/flour incorporated at a concentration of 2.5%, and (ii) the impact of wheat flour concentration (between 0 and 5%) on product properties.®, Siam Modified Starch Co., Ltd., Pathum Thani, Thailand), or a mixture of tapioca starch and wheat flour (1:1).For the influence of starch/flour type, the following ingredients of flour or starch were added to the recipe at 2.5% compared to 0% (control): tapioca starch , wheat flour , pregelatinized tapioca starch  until a uniform batter was obtained. The batter was then spread on a plastic sheet and cured at 4 °C for 12 h. The sheet of batter was dried at 55 °C for 90 min in a tray drier  and cut into square-shaped pieces (2.5 × 2.5 cm) with a thickness of 0.2 cm by using cooking scissors. The dry samples were baked at 150 °C for 20 min in a Sharp EO-42K Economic Deck Oven . The finished products were sealed in polyethylene zip bags and stored at ambient temperature (approximately 28 °C to 32 °C) for subsequent measurement of physicochemical properties and sensory evaluation. The production of the beef snacks was conducted in triplicate (three independent experiments).Samples from each treatment were selected and cut into small pieces with sharp scissors, and 1 g of each sample was placed in water activity cups. Water activity, represented as the free water level of the sample, was determined with a water activity meter . Water activity was measured in triplicate.Five grams of sample were weighted and dried at 105 °C until the sample reached equilibrium in a hot air oven . Afterward, the samples were weighed, and the percentage of moisture content was calculated. The moisture content was determined in triplicate on each snack product.L* (lightness), a*  and b* . All samples were read at two different positions and measured in triplicate.The color of the samples was analyzed using a colorimeter Hunter LAB . The CIELab color values were used with Textural attributes were analyzed by a TA-XT plus Texture Analyzer  with a crisp fracture support rig, force-versus-distance compression program. The texture profile analysis settings were as follows: Pre-Test Speed: 1.0 mm/s; Test Speed: 1.0 mm/s; Post-Test Speed: 10.0 mm/s; Strain: 70%; Time: 5.00 s; Trigger Force (auto): 0.05 N. Hardness and fracturability values were measured in each test. The hardness was determined from the maximum peak force in newtons (N), fracturability was determined from the distance (mm) at which the sample breaks. Five texture measurements were conducted for each snack product and were performed in triplicate.Sensory evaluation of samples was carried out by 30 untrained panelists , undergraduate and graduate students of the School of Agriculture and Natural Resources, University of Phayao, Thailand. Before starting the analysis, all participants (volunteers) in this experiment were informed of the aim of the sensory evaluation, and gave their informed consent to perform the evaluation. The panelists evaluated the samples using a 7-point hedonic scale ranging from 1 (dislike extremely) to 7 (like extremely), indicating very low to very high desirability for color, flavor, texture and overall acceptability. A glass of water is also provided to cleanse the palate.Beef snacks  were freeze dried. Double sticky tape was used to mount the beef snacks on aluminum stubs. The samples were sputter-coated with platinum to render them thermoelectrically conductive by using a JEC-3000 FC auto fine coater  and then scanned using a JSM-IT200 scanning electron microscope . The study was carried out with a 20 kV acceleration voltage to avoid damage to a sample caused by power beams. The micrographs were taken at a magnification of 85×.p < 0.05.The experiments were conducted using a completely randomized design (CRD). Statistical analysis of the recorded data was performed using SPSS v.18.0 . One-way ANOVA and Duncan’s multiple range tests were carried out to determine the variance between the treatments. The results were presented as the mean ± standard deviation (SD), and statistical significance was expressed at a level of The influence of the type of starch/flour on the desirable properties of the obtained meat-based snacks from beef scraps was investigated, according to the formulation indicated in L*) than the control and tapioca starch (p < 0.05). The results of L* also showed the same tendency as the visual appearance. All beef snacks formulated with starch or flour showed a higher redness (a*) than control. The yellowness (b*) of beef snacks incorporated with wheat flour was significantly increased, followed by tapioca + wheat and modified starch, respectively (p < 0.05). There were no significant differences between tapioca starch and control on L* and b*.As shown in w/w) exhibited no significant difference with the control.The physicochemical and textural qualities of beef snacks are shown in p < 0.05), followed by the control, tapioca starch, wheat flour and tapioca + wheat, respectively. The products obtained from the combination of tapioca and wheat flour provided light and slightly yellow products, with a relatively low value of hardness , with overall acceptability scores of 5.83 and 5.80, respectively (p < 0.05) higher crispness than other formulations.The color of beef snacks formulated with modified starch, wheat flour and tapioca + wheat was more appreciated by consumers than that formulated with tapioca starch and control . The resectively . Additiow/w).It was found that the addition of wheat flour to beef snacks provided the most preferences. In the following part, wheat flour was selected to formulate and produce beef snacks, using the following amounts: 0.625, 1.25, 2.5, and 5.00% (L*), redness (a*) and yellowness (b*) than the control (p < 0.05). There were no significant differences in the color of the beef snacks formulated with wheat flour in the range 0.625 to 5.00 (w/w).The influence of the amount of wheat flour on the desirable properties of the obtained meat-based snack chips was further investigated. As shown in w/w) had an effect on moisture content and water activity of beef snack samples ( samples a. The mo samples b.w/w). SEM observation revealed that increasing wheat flour content led to an increase in the visible holes and pits, which corresponded to the decrease in hardness (hardness b.w/w) exhibited more appreciated color and overall acceptability (p < 0.05) than that of higher proportion (5% w/w). As for the obtained results on the color and overall acceptability, beef snacks incorporated with 0.625% (w/w) wheat flour were the preferred samples. Overall acceptability of the beef snacks formulated with 0.625% (w/w) wheat flour was the highest amongst the obtained samples.The average score of overall acceptability ranged from 3.87 to 5.63 on the 7-point hedonic scale . Formulaa* value) than the control. This occurrence may be related to the moderate production of browning compounds formed in the Maillard reactions due to interactions between protein and carbohydrate at high temperatures [w/w)  .Wheat flour is the only cereal flour that can form cohesive dough upon hydration, hence, it is commonly used to make baking products, including bread . Microstw/w) led to a homogenous matrix between the meat protein and starch granules (p < 0.05) may be due to the rearrangement and association of the dispersed amylose short chains through hydrogen bonding, so called starch retrogradation [For the results of beef snacks formulated using tapioca starch, an amount of 2.5%  of wheat flour was within the appropriate range and can be considered as a preference by sensory evaluation.Textural properties are of high interest in terms of consumer acceptance. Regarding snacks, hardness and fracturability are considered important parameters . Hardnesw/w) was considerable enough to be sufficient to provide crispiness and proper texture to beef snacks with the lower water activity and moisture content. Among the starch and flour used in this study, wheat flour containing a higher protein content (10.5–11%) imparted products with better physicochemical properties and sensory attributes, while the incorporation with a higher amount of wheat flour content attributed low hardness to texture. We have demonstrated that the incorporation of the most commonly used non-meat ingredients, including wheat flour, into beef snacks increased the positive impact on organoleptic attributes of this meat-based product, which can be considered as the greatest choice, in terms of increasing the utilization of meat scraps for the snack food industry.The results obtained from this study indicated that wheat flour, when compared to tapioca starch and modified tapioca starch, was the most potent composition for beef snack processing. The texture profiles also indicated that products made with wheat flour showed a relatively firmer texture. The sensory panelists also preferred products made with wheat flour when compared to products made with tapioca and modified starches, on the appearance, flavor, texture, and overall acceptability. The relatively low content of wheat flour at 0.625% (


Fungi are prolific producers of secondary metabolites (SMs), which are bioactive small molecules with important applications in medicine, agriculture and other industries. The backbones of a large proportion of fungal SMs are generated through the action of large, multi-domain megasynth(et)ases such as polyketide synthases (PKSs) and nonribosomal peptide synthetases (NRPSs). The structure of these backbones is determined by the domain architecture of the corresponding megasynth(et)ase, and thus accurate annotation and classification of these architectures is an important step in linking SMs to their biosynthetic origins in the genome.http://gamcil.github.io/synthaser). We show that synthaser provides more accurate domain architecture predictions than comparable tools which rely on curated profile hidden Markov model (pHMM)-based approaches; the utilisation of the NCBI conserved domain database also allows for significantly greater flexibility compared to pHMM approaches. In addition, we demonstrate how synthaser can be applied to large scale genome mining pipelines through the construction of an Aspergillus PKS similarity network.Here we report synthaser, a Python package leveraging the NCBI’s conserved domain search tool for remote prediction and classification of fungal megasynth(et)ase domain architectures. Synthaser is capable of batch sequence analysis, and produces rich textual output and interactive visualisations which allow for quick assessment of the megasynth(et)ase diversity of a fungal genome. Synthaser uses a hierarchical rule-based classification system, which can be extensively customised by the user through a web application (https://pypi.org/project/synthaser) and GitHub (https://github.com/gamcil/synthaser).Synthaser is an easy to use tool that represents a significant upgrade to previous domain architecture analysis tools. It is freely available under a MIT license from PyPI (The online version contains supplementary material available at 10.1186/s40694-021-00120-9. Domains are distinct functional and structural units that serve as the evolutionary building blocks of proteins. The majority of proteins found across all kingdoms of life consist of multiple functional domains, with the growth in number of multi-domain protein families far outpacing that of single domain protein families , 2. MultA good case study for the evolution of multi-domain proteins with domain architectures and functions can be found in the biosynthesis of secondary metabolites, which are bioactive small molecules with important applications in medicine, agriculture and other industries , 11. SecMegasynthases can be easily identified by the presence of key functional domains. For example, PKSs typically contain a ic acids . There aic acids .This link between domain architecture and compound has several useful applications. Firstly, given some isolated metabolite, one can narrow down to the synthases likely responsible for its production by looking for a domain architecture that matches the structure of that metabolite. This is one of the first steps when taking a ‘retro-biosynthetic’ approach to identifying a BGC . Indeed,There are currently many databases dedicated to the analysis and functional classification of domains. Pfam , SMART  and PROSHowever, there are comparatively few resources dedicated to the analysis of domain architecture. The NCBI offers several tools built on the CDD, most notably CD-Search , which sSeveral tools have been developed specifically for the analysis of secondary metabolite megasynthases. One of the original tools built for this purpose was SEARCHPKS , which wAspergillus genomes, and link it to synthaser domain architecture predictions to demonstrate how synthaser can be used to quickly identify interesting sequence groups for further investigation. We show synthaser to be a useful addition to the genome mining toolbox, particularly within the context of natural products research; however, given the programmable nature of synthaser, we can foresee much broader applications of the software.Here we describe synthaser, a Python based software package leveraging the NCBI’s CD-Search API which can automatically annotate and classify the domain architectures of multi-domain proteins based on a flexible, user-definable ruleset system. Synthaser produces interactive visualisations of proteins grouped by their classification, making proteins with interesting architectures immediately apparent. Below, we extensively detail the synthaser search workflow and other functionality in the package, including modules for downloading search databases and extracting domain sequences, as well as a web application for easily building rule sets. As a proof of concept, we detail the process of building a synthaser ruleset using the web application for the classification of fungal secondary metabolite megasynthases, specifically polyketide synthases and nonribosomal peptide synthases. To evaluate this rule set, we analyse all available PKS and NRPS sequences deposited in the MIBiG repository  and comprequests library to perform remote searches. Synthaser is open source and is made freely available on GitHub (https://github.com/gamcil/synthaser) and PyPI (https://pypi.org/project/synthaser) under a MIT license. To perform local searches, synthaser requires that both Reverse Position Specific BLAST (RPS-BLAST) as well as rpsbproc, the command line utility that formats local RPS-BLAST results to resemble those returned by the CD-Search web service, are installed and accessable on the system  After sequences have been classified, domain architectures of each query sequence is reported, and an interactive visualisation is generated.The final element of the classification rule system is the hierarchy. This takes the form of a tree structure, with each node in the tree containing the name of a classification rule as well as a list of any child rules Fig. . SynthasThe generic nature of this rule system means that although synthaser was written primarily to analyse secondary metabolite megasynthases, it can be readily repurposed for the analysis of any multi-domain protein family.Given how cumbersome it is to manually assemble the synthaser rule file, we provide a web application which can generate rule files in three easy steps Fig. .KS) and domain families (PKS and PKS_KS). The Families selection box is linked to a file containing information about every family in the CDD, so families can be found simply by searching their names or accessions in the box.In the leftmost pane, the collection of domain types are built by specifying names .The web application is implemented using the React framework and is hosted on GitHub pages  image file can be generated by clicking the Synthaser can also generate a static HTML document containing all data and code required to display the visualisation when a file name is provided to the –plot argument. This enables synthaser results to easily be shared between individual computers.https://www.github.com/gamcil/synthaser.js).The visualisation is implemented using the D3 JavaScript library , and is https://github.com/gamcil/synthaser_scripts) and then added to the dataset. Sequences were then analysed using synthaser with default settings , and compared to the antiSMASH domain architecture predictions shown by MIBiG. Sequences were compared for overall domain architecture matches between synthaser and antiSMASH predictions stored in MIBiG. Discrepencies were sorted into groups based on the specific type of mismatch between each prediction: domains identified in synthaser but not in MIBiG; equivalent domains found but mis-named in MIBiG; and mis-named in synthaser predictions. Count data was analysed and visualised using R.Characterised PKS clusters of fungal origin were obtained from MIBiG 2.0 . The corAspergillus species. All remaining sequences were retrieved, and were analysed for PKS domains with the built-in ruleset using default settings with the command ‘synthaser search -qf sequences.fa’. Regions corresponding to KS domains in the identified sequences were extracted using the extract module in synthaser. The extracted sequences were formatted as a DIAMOND database and aligned against themselves using DIAMOND 0.9.17 with the ‘more-sensitive’ flag [https://github.com/gamcil/synthaser_scripts).Sequences containing ketosynthase (KS) domains were identified by querying the NCBI Protein database for entries linked to the ‘cond_en-zymes’ (CDD UID: 238201) superfamily using Entrez Direct . The resve’ flag . An edgeTwo of the major classes of natural products are polyketides and nonribosomal peptides, synthesized by polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS), respectively . There iPKSs, similar to fatty acid synthases (FAS), build the carbon backbone of polyketides through repetitive condensation of short-chain carboxylic acids, catalyzed by a ) domain . KS doma) domain . The decAspergillus terreus [Alternaria solani [Penicillium patulum [Parastaganospora nodorum [pksA, involved in the biosynthesis of aflatoxin in Aspergillus parasiticus is an NR-PKS [PKSs are generally classified as types I, II or III Table , though  terreus  and the a solani . PR-PKS a solani . For ins patulum  and mell nodorum  possess  nodorum . NR-PKSsn NR-PKS . Such clType III PKS are distinguished by their lack of ACP domain and are related to the chalcone and stilbene synthases found in plants . They arorcinols , 56.NRPSs typically consist of multiple modules, each possessing a binding specificity to a specific amino acid, which can be proteinogenic or non-proteinogenic. A nonribosomal peptide is synthesized through the formation of peptide bonds between amino acids attached to adjacent modules . A minimAspergillus burnettii [Aspergillus clavatus [Parastaganospora nodorum [Finally, it is possible to have hybrid enzymes that contain both PKS and NRPS modules  which in turn produce polyketide-peptide metabolites, or pathways of a mixture of PKS types . Notableurnettii , the cytclavatus , and pho nodorum .Multidomain protein families can often be divided into subgroups based on the absence or presence of certain domains, which can facilitate further functional predictions. Likewise, fungal type I PKSs have been subdivided into HR, PR and NR-PKS based on the absence or presence of reductive mentclass2pt{minimOnce domain classes had been established, functional classification rules could be created. Following the framework outlined in Table Within these top-level rules, there are further child rules. For example, FAS sequences are further classified as alpha or beta subunit. Similarly, PKS sequences can be classified as multi-modular (containing multiple KS domains), Type I or Type III; Type I sequences can be further classified into highly-, partially- or non-reducing PKS.Finally, a rule evaluation hierarchy was created Fig. . SynthasThe final rule file is distributed alongside the source code and is freely available from the GitHub repository.fas2 from the ustilagic acid BGC in Ustilago maydis, being a complete single-chain FAS [In order to verify the accuracy of our fungal secondary metabolite megasynthase rule set, we decided to test it against previously characterised megasynthases deposited in the MIBiG database . BGCs ofhain FAS , common hain FAS .All sequences were correctly classified. Domain architectures predicted by synthaser either matched completely or identified more domains than the antiSMASH predictions reported on MIBiG; different naming schemes for equivalent domains, for example ACP, PCP and T domains, were not considered mismatches in this comparison. In total, 182 (64.08%) antiSMASH-generated domain architecture predictions exactly matched those from synthaser. Of the remaining 102 predictions (35.92%), 101 were mismatched due to domains being present in the synthaser predictions but not in the MIBiG records; synthaser reported one extra domain in 75 (26.41%) cases, two in 38 (13.38%) cases, and three in 24 (8.45%) cases. The most frequent of these extra domains were ACP/T domains, in PKS and NRPS, respectively, which were absent in 32 (11.27%) of the antiSMASH-generated predictions, as well as TE domains , KR domains  and SAT domains .Sordaria araneosa [Alternaria alternata [Trichoderma virens [Notably, synthaser architecture predictions for the sordarin HR-PKS from araneosa , and thelternata , both coa virens . While tPenicillium brasilianum [prhL, though correctly classified as non-reducing, lacks an SAT domain in both the synthaser and antiSMASH predictions. This is also observed in trt4, involved in the biosynthesis of another meroterpenoid compound, terretonin, in Aspergillus terreus [Penicillium roqueforti [Aspergillus novofumigatus [Another notable case study is the starter unit:acyl carrier protein (ACP) transacylase (SAT) domain, a characteristic feature of NR-PKSs that is sometimes missed in both the synthaser and antiSMASH-generated domain architecture predictions. For instance, the PKS involved in the biosynthesis of the meroterpenoid paraherquonin in silianum , prhL, t terreus . As thesqueforti  and Aspeumigatus , respectumigatus  in all sA. burnettii, which facilitated the linkage of expressed metabolites to their corresponding synthases [Synthaser can rapidly extract PKS and NRPS genes and generate domain annotations from genome files, making it extremely useful in providing an overview of the diversity of PKS/NRPS domain architectures encoded in an organism. For instance, we recently used synthaser to analyse synthases found in the genome of ynthases . HoweverAspergillus genomes . This superfamily contains a variety of enzymes catalyzing decarboxylating and non-decarboxylating Claisen-like condensation reactions, covering the spectrum of FASs and PKSs. In total, 2923 sequences were retrieved. Using synthaser, we predicted and classified the domain architectures of each retrieved sequence, then extracted the sequence of each KS domain. This consisted of 95 FAS , 292 hybrid PKS-NRPS, 1991 PKS  and 545 thiolases. This dataset was then extended with the PKS, PKS-NRPS and FAS sequences from the MIBiG database analysed above .To test this hypothesis, we constructed a similarity network of ketoacyl synthase (KS) domains in PKS sequences from mes Fig. . While tAll versus all sequence comparisons of the extracted KS domains were performed using DIAMOND , which wFour distinct subnetworks were formed within the KS domain network, corresponding to the four broad classification groups of KS domain-containing sequences. One subnetwork contained mostly highly-reducing and partially-reducing PKS Fig.  top-leftMagnaporthe oryzae, TAS1, which has an NRPS module before a PKS module containing only a KS domain [Aspergillus niger. While the cluster contains sequences matching the domain architectures of TAS1, SwnK and AnATPKS, it also includes more variation that could be explored in further studies. Perhaps more interesting was the second cluster, which fell within the non-reducing PKS subnetwork and consisted of non-reducing PKSs with ketoreductase (KR) domains at the N-terminal. As previously outlined, a typical NR-PKS sequence starts with a SAT domain and contains a product template (PT) domain and no reductive  domains [Perhaps the most powerful aspect of the similarity network approach is its ability to reveal outliers; a characteristic we wished to exploit for the purpose of genome mining for biosynthetic novelty. We were immediately drawn to two specific sequence clusters, which were clearly demarcated from the other members of their respective subnetworks thanks to the architecture-mapped colour scheme . This is advantageous to other tools which rely on manually curated local profile HMMs for domain searches, as new domains can be added just by referencing the corresponding CDD identifier. Moreover, as the CDD and its sub-databases are continually curated, any improvements to domain profiles will automatically filter through to predictions generated by synthaser.Synthaser takes a unique approach in that it explicitly searches for specific domain ‘islands’ during domain architecture prediction Fig. . This diAnother advantage of synthaser is its ability to analyse fungal megasynthases at (pan-)genomic scale. There is currently no tool available that can characterise, classify and display the domain architectures of all PKSs and NRPSs either within a genome or across multiple genomes. As we demonstrate above in our similarity network of KS domains, this can form the basis of a genome mining strategy for uncovering unique synthases encoding potentially novel metabolites.https://gamcil.github.io/synthaser) which allows users to easily add, delete or modify domains and classification rules. The default rule file can be loaded for editing by the click of a button, enabling users to tweak it as necessary for their purposes. Moreoever, we can foresee synthaser being completely repurposed, via its rule system, for the analysis of other multi-domain protein families, outside of the scope of secondary metabolites.Domain architecture prediction and classification in synthaser is controlled by an underlying rule file, which can be freely modified by the user. The rule file consists of three components: classes containing CDD domain families which correspond to domain islands, classification rules, and the rule evaluation hierarchy. This allows for a level of flexibility not possible in tools which rely on manually curated profile HMMs. In addition, we provide a web application  domains in FAS and PKS and peptidyl carrier protein (PCP)/thiolation (T) domains in NRPS, which are structurally and functionally related, hit the same CDD profiles in a CD-Search run. Synthaser attempts to alleviate this issue by allowing domains to be renamed based on the classification of the protein; in the previous example, synthaser will keep the ACP name within a PKS or FAS, but change it to a T (thiolation) if found in a NRPS. Secondly, certain domains may fail to be detected if the corresponding domain profiles are weakly defined. In these scenarios, synthaser will also fail to report the missing domains. However, this is made very clear in the synthaser visual output, as large gaps in sequence can be seen where missing domains should be sequences involved in meroterpenoid biosynthesis showing gaps in N-terminal regions of BAV69313.1 andEAU29529.1. Fig. S3. Extract from multiple sequence alignment of N-terminal regions of non-reducing polyketidesynthase (NR-PKS) sequences involved in meroterpenoid biosynthesis showing conservation of the starterunit:ACP transacylase (SAT) domain active site GXCXG motif.Additional file 2: Table S1.  MIBiG sequences. Table S2. NCBI network.