Salasmaviridae and Guelinviridae families. These phages have relatively small ~20 kb linear protein-capped genomes and their compact organisation, relatively few structural elements, and broad host range, are appealing from a phage-engineering standpoint. In this study, we focus on portal proteins, which are core elements for the assembly of such tailed phages. The structures of dodecameric portal complexes from Salasmaviridae phage GA1, which targets Bacillus pumilus, and Guelinviridae phage phiCPV4 that infects Clostridium perfringens, were determined at resolutions of 3.3 Å and 2.9 Å, respectively. Both are found to closely resemble the related phi29 portal protein fold. However, the portal protein of phiCPV4 exhibits interesting differences in the clip domain. These structures provide new insights on structural diversity in Caudovirales portal proteins and will be essential for considerations in phage structural engineering.Widespread antibiotic resistance has returned attention to bacteriophages as a means of managing bacterial pathogenesis. Synthetic biology approaches to engineer phages have demonstrated genomic editing to broaden natural host ranges, or to optimise microbicidal action. Gram positive pathogens cause serious pastoral animal and human infections that are especially lethal in newborns. Such pathogens are targeted by the obligate lytic phages of the  Salasmaviridae, Guelinviridae and Rountreeviridae, and represent families of bacterial viruses in the order Caudovirales (tailed dsDNA phages) characterised by their short linear DNA genomes (the observed genome length range at the time of writing is c. 16.5–27.5 kb). To target a bacterial cell, these phages bind to host-specific types of teichoic acids by specialised appendages and then infect by hydrolysing the peptidoglycan crosslinks via the tail tip rather than using a specific bacterial receptor to inject the viral genome . A. A12]. Ag motors .Bacillus pumilus, and phiCPV4, targeting Clostridium perfringens, using cryoEM with computational methods to provide insights that could support engineering designed tail switching aims. The structures of the GA1 and phiCPV4 PPs were obtained at near atomic resolution of 3.3 Å and 2.9 Å, respectively. Comparison of these structures to the portal proteins of phi29 and other phages demonstrates that the GA1 and phiCPV4 PPs closely resemble the fold of the phi29 PP. An interesting discovery is that the phiCPV4 PP exhibits a significant structural distinction within the clip domain compared to the phi29 and GA1 PPs.In the present study, we analysed the portal protein structures of two phages: GA1, targeting E. coli strains were sourced from New England BioLabs (NEB), unless otherwise stated. Bacteriophage GA1 (DSM-5548) was propagated on Bacillus pumilus (DSM-5549). The GA1 portal protein was amplified to encode a Strep-tag II/TEV cleavage sequence at the N-terminus with primers AAACCTGTACTTCCAGTCCGTGTTAGAAGATGGTTTTTCTTATAAGACC and GAATGGGCAACACAGTTTGATGATCCGGCTGCTAACAAAG and joined via Gibson Assembly to a pRSET-A (Invitrogen) backbone. A Genestring (IDT) encoding the phiCPV4 portal protein sequence with overlapping overhangs to the vector was sub-cloned into a NheI/HindIII (NEB) linearised pRSET-A (Invitrogen) backbone via Gibson Assembly to encode a hexahistidine tag at the N-terminus.Synthetic DNA was sourced from Integrated DNA Technologies (IDT), and DNA modifying enzymes and E. coli strain NEB-5α was used for cloning while the E. coli strain T7 Express qlysY/I was used for protein expression. Recombinant expression was performed overnight at 37 °C in LB-Miller media in 12 × 500 mL Erlenmeyer flasks. Pure GA1 gp10 was obtained via a 2-step purification using a StrepTrap™ HP 1 mL column and 120 mL Superdex HiLoad 200 pg® column . The Strep-tag II was cleaved overnight using TEV protease (3:1 ratio). Pure phiCPV4 gp17 was obtained similarly, with the same buffer but using a HisTrap HP 1 mL column and eluted with a gradient of imidazole (50 mM to 500 mM).The Clustal Omega was used with default settings . All resPurified naïve complexes of GA1 gp10 and phiCPV4 gp17 (at ~0.05 mg/mL each) were deposited to lacey carbon grids . Grids with sample were vitrified using a Vitrobot Mark IV  at 100% humidity and 8 °C. Purified sample aliquots (3 μL) were applied to grids, incubated for 30 s, blotted for 3.5 s (blot force 6) and then plunge frozen in [liquid nitrogen cooled] liquid ethane. Grids were clipped and transferred to the microscope for data collection. Individual datasets were collected using EPU software  on a Titan Krios  electron microscope, equipped with a K2  direct electron detector. -/Å2  and 0.985 e−/Å2 per frame  for GA1 and PhiCPV4 respectively. All data was collected with a pixel size of 1.05 Å/pixel  and 2903 movies (phiCPV4) were recorded, each movie containing 50 frames, with a dose of 0.98 e Å/pixel .Movies were aligned using MotionCor2 . AlignedParticles were initially picked manually in RELION to generate 3D templates for subsequent auto-picking using Gautomatch . Fifty rExtracted particle images were subjected to 2D classification to clean the dataset such that featureless images were discarded. To assess symmetry of portal complexes, 2D classes that showed end views of portal complexes were used as the basis for symmetry analysis in the data set. First, particles in the end-view 2D classes were sub-classified to assess variations in rotational symmetry, a common occurrence in naïve preparation for portal proteins ,28. ThisBoth datasets were subsequently subjected to initial asymmetrical 3D reconstruction using reconstructions with symmetry C1 generated from particle images comprising classes corresponding to side-views and particle images composing a few 2D classes showing the 12-fold symmetry end views; (in RELION), That was followed by 3D classification . Some clThe resulting 3D refined maps were then subjected to 3D classification in two ways: For GA1, 3D classification was run with alignments with the refined map as the initial map. For phiCPV4, 3D classification was run with no alignment, with the intention of separating out the high-resolution contributing particle images. Five K seeds were used as the basis for running 3D classification in RELION. 3D maps that corresponded to 12-mer complex with sufficient domain-level details were selected and used for further 3D refinement, reducing the angular search to 1.8 degrees and translation shift search to 0.5 pixels. The overall outline of processing is shown in For fitting the cryo-EM density maps, individual monomers were segmented from the sharpened maps using Chimera tools ,31, and For the GA1 model, the phi29 gp10 portal protein structure (PDB: 1FOU) was used as the initial model to fit and refine the cryoEM map. First, the phi29 model was rigid body fitted into the cryoEM map of the GA1 portal complex. A single monomer was segmented out of the 12-mer cryoEM, together with the pdb model (phi29 gp10 PDB: 1FOU) and manually refined using Coot . The resde-novo and completed by adding residues across the monomer chain in Coot, based on the cryoEM map. The resulting model was refined against the EM map for model parameters using the real space refinement option in Phenix [The phiCPV4 gp17 3D map had better resolution, and the model was built using a homology model generated by the I-TASSER server . The besn Phenix . To imprn Phenix . Based on Phenix ,31. Bothn Phenix  and Phenn Phenix ,31.E. coli hosts and purified by column chromatography as described in the methods. Whilst PPs are dodecameric within capsids of fully assembled phages, PPs expressed in the absence of other viral proteins (assembly-naïve conditions) produce C11, C13 and C14 oligomers [PPs GA1 gp10 and phiCPV4 gp17 were recombinantly expressed in ligomers ,28. Bothligomers . For GA1Salasmaviridae and Guelinviridae phages are slightly bigger compared to the PPs of larger phages of Caudovirales described to date [Both data sets (GA1 gp10 and phiCPV4 gp17) were subjected, in the first steps of processing, to asymmetrical 3D analysis followed by the first round of 3D classification. Particle images that comprised 3D classes with clear C12 symmetry were re-extracted from low-dose frames (See methods). Subsequent 3D refinement of the classes having C12 symmetry in both PPs were performed with imposed C12 symmetry A,B. The  to date ,27,38  and refinements of atomic models for both proteins  respectively . The subproteins .In both structures, the α3 and α5 helices of the stem domain have the highest resolution at ~2.5 Å, whilst the clip domain and outer area of the wing domains are at a lower resolution indicating their flexibility . The N-tThe crown domain is formed by the C-terminus of the PP, as in all known Caudovirales phages, but appears highly flexible in many phages as exemplified by phi29 ,8 and P640, Glu53 and Arg263 in GA1 gp10, and Trp40, Glu53 and Arg261 in phiCPV4 gp17 correspond to residues Trp41 of β1, Glu54 of α2 and Arg261 of α6 in the PP of phi29 gp10  although β7 is not clearly defined in the GA1 wing. Helix α2 is located nearly in the middle of the wing domain and tilted 50–60° with respect to the vertical axis of dodecamer. Helix α6 (residues 256–274 in GA gp10 and in phiCPV4 252–271) spans the subunit from the inner rim of the central channel to the outer edge of the wing along the radial direction and forms the upper rim of the wing domain A. AssessIn the PP structure of phiCPV4 strands β1 and β11 are readily identified, while the β2–β7 area appears to be less well defined; however, they are similar to the phi29 beta-cluster in the final model . The β11233 to Val239 in phi29 gp10 being semi-conserved in both Salasmaviridae and Guelinviridae. Residues Lys234, Lys235 and Arg237 in phi29 gp10 have been shown by molecular dynamics modelling in-silico to closely interact with DNA [235Arg in phiCPV4 gp17), supporting the evidence that these residues are essential to the portal protein during DNA packaging [The tunnel loop is the region in the portal protein located between the wing and stem domains (α6 and α5 22 AA) that points to the inner part of the central tunnel. It has been suggested that these loops make contacts with the DNA and together with helices α3 and α5 act as a pumping motor [with DNA ; these rackaging .Caudovirales and Herpesvirus PPs, indicating an ancestral fold. These two helices α3 and α5 are found in all PPs described to date [139A, Thr149A, Gln154B, Lys147B  and α5 (~20 residues), and the N-terminus (~40aa)  and β9  of the adjacent subunit. Within the dodecamer, β9 of phiCPV4 is homologous to β9 in phi29 gp10. Interactions within the parallel β-strands in phiCPV4 gp17 are intra-monomeric  and possibly with the terminal proteins in phi29 . The ideSalasmaviridae and Guelinviridae families respectively. Both GA1 gp10 and phiCPV4 gp17 PPs have the canonical fold and domain organisation found in the portal protein phi29 gp10 [α). The most interesting differences are observed in the clip domain, which is involved in interactions with tail proteins.Using cryoEM, we determined the structures of two portal proteins: GA1 gp10, and phiCPV4 gp17, which are phages of the i29 gp10 ,43. GA1 Salasmaviridae  and Guelinviridae phages (phiCPV4 gp17) contacts inner capsid proteins and the packaged DNA genome. There is no clearly definable secondary structure, unlike SPP1 (Siphoviridae), P22 (Podoviridae), T4  and other phages with genomes larger than 40kB, whose crown domains have α-helical folds in the vicinity of the wing domain. Nevertheless, it should be noted that in the PP of the genome emptied phi29 phage, the crown domain (the C-terminus) of the phi29 PP has one α-helix located close to the upper surface of the wing domain [Salasmaviridae and Guelinviridae phages appear to be much smaller compared to the crown domain sequence of T7 that forms a helix [The crown domain of PPs of g domain . Sizes o a helix . The PPs a helix  and SPP1 a helix  have lonCaudovirales phages. However, in all phages, it serves as a linker for interactions of the portal protein with the adjacent capsid proteins within the pentameric vertex. Their conformations within the capsid vary due to differences in the interactions between protein components that form 12-fold symmetry in the PP and 5-fold symmetry in the capsid. The wing domain organisation in GA1 gp10 and phiCPV4 gp17 has the classical feature of PPs comprising two helices, α2 and α6. The major spine α6 helix located on the top of the wing is present in all PPs described to date. The main difference between Salasmaviridae and Guelinviridae phages is that the α6 helix (21 ± 5 aa) is on average shorter by 11 aa compared to large phages, where α6 helix comprises ~32 aa on average. The β1–β7, and a β11 strands of GA1 gp10 and phiCPV4 gp17 form three anti-parallel β-sheets and a β-hairpin formed by two antiparallel beta strands (β1 and β11) on the periphery of the wing . The stem domain has the most conserved fold between the PPs of different phages. In the PPs of GA1 gp10, phi29 gp10, and phiCPV4 gp17, the stem domain comprises two α-helices that form the central channel within the portal dodecamers. Helices α3 and α5 have conserved sequences of similar length, implying that the same interactions between these helices provide inter-subunit interactions that hold the dodecamer together. It is highly likely that these conserved elements of the stem domain indicate an ancestral origin and its role as a mainstay or ‘spine’ for other domain-level variations related to specific interactions and functions such as with the clip domain.Caudovirales PPs described to date have the β-sheet of the clip domain formed by strands from adjacent subunits. This variation highlights the subtle ways in which portal architectures can be fine-tuned by nature to perform their functions, relating to specificity. The fact that the clip domain architecture can vary across related portal proteins means that the end of the portal that interacts with the tail apparatus can perform some form of regulatory role, either during tail assembly or during genome gatekeeping (entry or exit). Further structural and functional studies, such as in the context of entire phage, are now required to validate the importance of the new observation observed in the clip domain of phiCPV4 phage PP. The overall fold of the clip domains in GA1 gp10 and phiCPV4 gp17 is quite similar to the phi29 clip domain: They are composed of helix α4 and two antiparallel β-strands (three in the case of phiCPV4 gp17), located at the bottom of the PP establishing the tail-interacting interface. The inter-monomeric interactions are located between β9 and α4 in phiCPV4 gp17, which is different from all other PPs; while in phi29 and GA1 gp10 it is the β8 and β9 from adjacent subunits that make the more typical inter-monomeric interactions. This is an unexpected observation, since all other Salasmaviridae may be necessary to fit part of the terminal proteins which are covalently attached to the 5′-ends of the dsDNA in phi29 and GA1 [Guelinviridae phage phi24R is purported to have a terminal protein [Salasmaviridae and Guelinviridae families [The larger size (~34Å) of the narrowest diameter in  and GA1 ,46. Once and GA1 . Similar protein , it is lfamilies .and to retain its dynamic properties in morphological complex as a genomic gatekeeper. Our new PP structural insights expand the available knowledge of Caudovirales portal proteins and will support future studies in PP engineering. The structures reported in this study and their comparative analysis enable us to consider ways in which chimeric phages can be engineered via modifications to the portal proteins. As stated earlier, a chimeric portal could in principle attach the capsid of one phage to the tail of another. Such chimeragenesis has been demonstrated previously to: 1) generate phage bodies with broader host ranges , and (2) generateGuelinviridae and Salasmaviridae phage chimeras can be designed to infect specific bacteria. Due to their compact nature, small genome size, and their morphological relationship to the archetypal phi29, these phages offer an excellent opportunity for targeted genetic manipulation. By identifying specific areas of interaction with capsid or tail proteins, a phi29-phiCPV4 chimera of the portal protein could be built with a modified wing, stem, or clip domain. This would enable the chimeric phages to deliver modified genetic programs to desired gram positive bacterial species in the available host range. Given that a self-replicating synthetic episome based on the phi29 genome and terminal proteins has already been published [C. perfringens [These new structures of portal proteins further offer a platform on which ublished , it is efringens . Salasmaviridae phage but targets the pathogen Bacillus pumilus, relevant to serious infant infections. The phiCPV4 phage is part of the Guelinviridae family and infects Clostridium perfringens, a problematic industrial livestock pathogen, which causes foodborne illnesses in humans. Our results indicate the level of structural insight is now sufficiently detailed to inspire engineering designs aimed at creating functional portal protein chimeras. The challenges to design and engineering of a new biologically active complex are not inconsiderable even now, however, yet such efforts are directly relevant to microbial infections of global importance. GA1 is, like the archetypal phi29, a


Full-wave electromagnetic (EM) simulation tools have become ubiquitous in antenna design, especially final tuning of geometry parameters. From the reliability standpoint, the recommended realization of EM-driven design is through rigorous numerical optimization. It is a challenging endeavor with the major issues related to the high computational cost of the process, but also the necessity of handling several objectives and constraints over often highly-dimensional parameter spaces. From the numerical perspective, making decisions about the formulation of the optimization problem, the approach to handling the design constraints, but also parameterization of the antenna geometry, are all non-trivial. At the same time, these issues are interleaved, and may play an important role in the performance and reliability of the simulation-based design closure process. This paper demonstrates that the approach to arranging the structure parameterization  may have a major effect of the optimization outcome. Our investigations are carried out using three broadband monopole antennas optimized under different scenarios and using different parameterizations. In particular, the results indicate that relative parameterization is preferred for optimization of input characteristics, whereas absolute parameterization is more suitable for size reduction. This can be mainly attributed to the emergence of new technologies , but also trends towards miniaturization and portability . On the one hand, to fulfil the performance requirements imposed by the needs pertinent to specific application areas, antenna structures have to implement additional functionalities , but also offer improved characteristics . This requires incorporation of a variety of geometrical alterations , which makes the designs increasingly sophisticated and described by large numbers of parameters. Also, the use of full-wave electromagnetic (EM) simulation tools is instrumental in ensuring evaluation reliability. On the other hand, miniaturization trends lead to additional challenges as size reduction is detrimental to antenna electrical properties24. In any case, EM-driven design becomes a matter of practical necessity in general, whereas simulation-based parameter tuning belongs to the most important stages of the antenna design cycle.The last decade or so has witnessed a significant increase in the complexity of antenna geometries25, sparse Jacobian updates26, surrogate-assisted methods, involving both data-driven , and physics-based models , or machine learning techniques38. Handling of multiple objectives is often realized using surrogate-enhanced population-based methods41 or penalty function approaches , whereas dimensionality issues are often addressed using high-dimensional model representation (HDMR)43, principal component analysis (PCA)44, model order reduction methods45, or—in the context of response surface approximation—techniques such as orthogonal matching pursuit (OMP)46, or least angle regression (LAR)47. Surrogate-based methods are also popular for aiding global optimization49. The improvement of local search reliability  can be achieved using feature-based optimization (FBO)-type of approaches51. Accelerating specific simulation-driven design tasks can be realized using particular classes of replacement models .EM-simulation-based parameter tuning of antenna systems is imperative, yet it is a daunting task. Its fundamental bottlenecks include high computational cost, a typically large number of parameters, but also the necessity of handling several objectives and constraints. Over the years, the literature offered a variety of algorithmic solutions that aim at alleviating these difficulties. Some of available options include methods for expediting the EM-driven optimization procedures, e.g., adjoint sensitivitiesOne of the important aspects of antenna optimization in general, and EM-driven design in particular, is appropriate geometry parameterization of the structure under design. As a matter of fact, this aspect is almost never explicitly discussed in the literature, although parameterization may have some serious implications for the design process reliability. In the vast majority of cases, a natural (or absolute) parameterization is employed, where specific dimensions of the antenna components  are described using respective variables, typically sized in millimeters. From the perspective of numerical optimization, the fundamental problem incurred by such an approach are design constraints that have to be imposed in order to ensure geometrical consistency of the structure, e.g., to have the radiator allocated within the dielectric substrate outline, the ground-plane slots contained within the ground plane rather than cutting it into disjoint patches, etc. Most of these constraints are linear, although nonlinear ones may also be necessary, e.g., to ensure that certain components are allocated in the interior of circular-shaped slots, etc. The presence of constraints makes the optimization problem more challenging, which is of particular importance when handling EM-simulated antenna responses. The latter is due to the numerical noise inherent to simulated characteristics of the device. A less popular alternative is relative parameterization, where the antenna components are sized in relation to the dimensions of the components encapsulating them . A clear advantage is that the number of geometrical constraints can be reduced to a minimum (often to zero), which leads to an interval-based parameter space, significantly easier to handle.This paper investigates the importance of geometry parameterization from the perspective of EM-driven tuning of antenna structures. Two types of parameterization are juxtaposed, referred to the absolute and the relative ones (as outlined in the previous paragraph), along with their potential advantages and drawbacks from the point of view of solving antenna optimization tasks. Using several exemplary structures of broadband antennas, it is demonstrated that relative parameterization is more suitable for improving electrical properties . On the other hand, the absolute parameterization is more beneficial for explicit size reduction, which is a constrained task by itself due to the necessity of maintaining specific acceptance levels for input matching, and, perhaps, other antenna responses. Consistency of the results throughout the benchmark set as well as considerable differences in the optimization process performance, provide conclusive evidence about the importance of the appropriate choice of antenna parameterization within a given EM-based design context.The originality and the technical contributions of this work can be summarized as follows: (1) formal introduction of absolute and relative antenna parameterizations, (2) qualitative comparison of the benefits and limitations of the absolute and relative parameterizations in the context of different EM-driven design scenarios, (3) comprehensive (based on three antenna structures and multiple optimization runs) and conclusive assessment of the advantages of relative parameterization for matching improvement tasks, and absolute one for size reduction purposes. According to the authors’ knowledge, this is the first treatment of this subject  in the antenna design optimization literature.This section formally introduces the two types of antenna geometry parameterizations considered in this paper, and discusses their qualitative advantages and disadvantages from the point of view of solving EM-driven design tasks. Quantitative evaluation, based on three antenna structures and two specific design scenarios (size reduction and matching improvement), will be provided in “Results”.R, where x = [x1 … xn]T is the vector of (independent) adjustable parameters. R is assumed to be evaluated using full-wave EM analysis. R(x) will represent all relevant antenna characteristics at the design x, in particular, the reflection response S11, gain G, axial ratio AR, etc., for frequencies f within the simulation range of interest. Furthermore, we will denote the antenna size as A(x) .The computational model of the antenna structure under design will be denoted as X is the problem domain, discussed at length in “Geometry parameterization: Absolute vs. relative”. In some cases, additional constraints are imposed, which are typically of an inequality type, i.e., gk(x) ≤ 0, k = 1, …, ng.The parameter tuning task is formulated as a minimization problemF: U(x) = max{f ∈ F : |S11|};Matching improvement in a specified frequency range F: U(x) = max{f ∈ F : AR};Axial ratio improvement in a specified frequency range U(x) = A(x)Size reduction of a planar antenna: The objective function is a metric of the design quality, and, therefore, it is problem dependent. Consider the following examples:F: S(x) ≤ –10 dB, where S(x) = max{f ∈ F : |S11|};Ensuring that antenna reflection does not exceed –10 dB within the frequency range of interest F: AR(x) ≤ 3 dB, where AR(x) = max{f ∈ F : AR}.Ensuring that axial ratio does not exceed 3 dB within the frequency range of interest Design constraints are often related to antenna geometry itself (cf. “Geometry parameterization: Absolute vs. relative”) but also its electrical and field performance figures. Some common examples include:54. This leads to a problem reformulation, so that we haveUP is defined asck(x) quantifying violations of the respective constraints; βk are the proportionality  coefficients. In order to make the objective function setup less dependent on a constraint type and typical tolerance levels, penalty functions may be defined based on relative violations, e.g., for the constraint S(x) ≤ –10 dB, one may define c(x) = [(S(x) + 10)/10]2.As the constraints are often expensive to evaluate , their handling is more convenient when using a penalty function approachThe problem of antenna parameterization is rarely elaborated on in the literature. As a matter of fact, it is one of those aspects of the design process that are usually considered unimportant or straightforward to handle. In practice, it is normally reduced to selecting the crucial dimensions , and labelling them accordingly for further processing. The purpose of this work is to indicate the relevance of appropriate handling of antenna parameterization as well as far reaching consequences of inappropriate parameter space definition.We start by describing the two types of antenna parameterization, referred to as absolute and relative. The absolute parameterization corresponds to what is normally considered in practical antenna design, and, therefore, can also be named a natural one. According to this method, all relevant antenna parameters are simply assigned the labels, and processed  using absolute dimensions expressed in appropriate units .X. However, in order to maintain the geometrical consistency of the structure one has to impose additional constraints. For example, we have to introduce Ld < Lp , Lr < Lg (the ground plane slot does not split the ground place into disjoint parts), or L0 + Lp ≤ Ls (the radiator is contained within the substrate). The full list of the necessary constraints can be found in Table Figure Relative parameterization can be introduced to reduce the number of additional geometry constraints. According to it, most of the parameters are used to dimension particular antenna components in relation to the containing elements . Only the outer dimensions of the antenna as well as parameters that are inherently small  are treated as absolute. Table 58).In other words, the parameter space is an interval, which is much easier to handle by local search procedures, let alone nature-inspired algorithms  objective and geometry-only constraints. As demonstrated below, antenna parameterization is of paramount importance from the point of view of the optimization process performance.60 and II61 are implemented on RF-35 substrate , whereas Antenna III55 is implemented on FR4 substrate . The feed lines widths (parameter w0) are dimensioned to ensure 50-Ω input impedance: we have w0 = 1.7 mm for Antenna I and II, and w0 = 3.0 mm for Antenna III. The EM models are evaluated using time-domain solver of CST Microwave Studio. The models include the SMA connectors. In all cases, metallization if represented as perfect electrical conductor (PEC), whereas dielectric losses are taken into account.The numerical studies are carried out using three broadband monopole antennas shown in Fig. For each antenna, we consider two parameterizations, the absolute and the relative one (cf. “Geometry parameterization: Absolute vs. relative”). The details concerning both parameterizations for Antenna I, II, and III are provided in Table 61, but also in our previous works where their numerical optimization has been performed63.The work focuses on establishing antenna parameterization for the purpose of design optimization. Thus, experimental validation of the designs of the considered antenna structures has not been provided as it is immaterial to the topic of the paper. Moreover, all of the considered structures have been already validated in the respective source papersU(x) = max{3.1 GHz ≤ f ≤ 10.6 GHz : |S11|};Optimization for matching improvement in the UWB frequency range, in which case, the objective function is of the form U(x) = A(x). Furthermore, the condition S(x) ≤ –10 dB is imposed on the reflection response, where S(x) = max{3.1 GHz ≤ f ≤ 10.6 GHz : |S11|}. The constraint is handled implicitly as in (c(x) = [(S(x) + 10)/10]2. For all considered structures, the size is understood as the area of the entire substrate containing the antenna.Optimization for size reduction; the objective function is of the form In order to evaluate the merits of the considered antenna parameterizations, we carry out optimization of the structures of Fig. 62, which produces a series of approximations to the optimal design x* using an auxiliary linear expansion model of antenna responses established at the iteration point. More details about the procedure can be found in the literature64.The optimization algorithm of choice is trust-region gradient search with numerical derivativesFor each design scenario, the optimization process is executed ten times from random initial designs (the same starting points are used in both scenarios). This is to account for the fact that the optimization problem might be multimodal due to a relatively large number of parameters as well as the presence of numerical noise. For both reasons, the final design generally depends on the starting point; consequently, evaluating performance of the algorithm based on a single run is not representative.Optimization for matching improvement: maximum in-band reflection, the value averaged over ten algorithm runs as well as the standard deviation;S(x) ≤ –10 dB ;Optimization for size reduction: obtained footprint area (average and standard deviation), as well as violation of the reflection constraint The following metrics are used:Comparison of the aforementioned factors for both antenna parameterizations will provide meaningful assessment of the optimization process performance and reliability.Table When optimizing for improvement of the in-band matching, relative parameterization is noticeably better (or at least comparable with the absolute one in the case of Antenna I). For Antennas II and III, relative parameterization yields the designs that are better by about 1.5 dB on the average;At the same time, repeatability of results is improved for relative parameterization as compared to the absolute one, which is indicated by a generally smaller values of the standard deviation of the objective function;2 on the average, which is about ten percent in relative terms;When optimizing for minimum size, absolute parameterization is consistently better than the relative one. The improvement is as high as 25 mmRepeatability of solutions when optimizing for size reduction is similar for both parameterizations although slightly in favor of the absolute one on the average;β (cf. (3)), which was set to 103 in all cases. Increasing this value would reduce the average violations while being detrimental to the obtained size reduction65.The average reflection constraint violation (when optimizing for size reduction) is similar for both parameterizations. It should be noted at this point that the level of violation is controlled by the penalty coefficient The computational cost of the optimization process is consistent for both parameterizations, the differences are statistically insignificant. It can also be observed that the expenses associated with size reduction are higher than those required for matching improvement, which is due to the fact that the former task is a constrained one with nonlinear inequality constraint; thus the problem is numerically more demanding. Also, it can be observed that the computational complexity is more or less proportional to the dimensionality of the parameter space, which is expected for gradient-based trust region procedures with numerical derivatives.The results allow us to draw several conclusions concerning the performance of the optimization process, depending on the parameterization used. These can be summarized as follows:The overall conclusion is that the performance of the optimization process demonstrably depends on the parameterization. Based on the numerical experiments presented in this section, the absolute parameterization is shown to be more suitable for solving size reduction tasks, whereas relative parameterization is more beneficial when optimizing electrical characteristics of the antenna. Here, additional studies will be necessary to investigate the properties of both parameterizations, especially when handling other types of antenna responses , or carrying out size reduction under multiple constraints. Notwithstanding, the results obtained in this work provide a clear indication of the importance of geometry parameterization in the optimization context. Furthermore, although our studies were conducted for local parameter tuning , selecting parameterization has further reaching consequences when considering other optimization frameworks. For example, eliminating geometry constraints (both linear and nonlinear) as in relative parameterization facilitates utilization of nature-inspired algorithms for which the ‘natural’ environment is a box-constrained domain.The reasons for superiority of absolute parameterization over the relative one for matching improvement, and quite the opposite performance for size reduction are not clear at this point. It seems that one of the factors is that the primary objective in size reduction task is a smooth function of geometry parameters, whereas the boundary of the feasible region is determined by the EM-evaluated condition . In this setup, the additional constraints pertinent to absolute parameterization do not aggravate the problem because of being smooth as well. On the other hand, absolute parameters give a more direct account for antenna size, which is not the case for relative parameterization. When optimizing for best matching, the latter factor is not as important, whereas the necessity of handling constraints might be detrimental to the quality of the optimization outcome and, therefore, give an edge to the relative parameterization.U(x) = max{3.1 GHz ≤ f ≤ 10.6 GHz : |G|} – min{3.1 GHz ≤ f ≤ 10.6 GHz : |G|}, where G is a broadside realized gain at the design x and frequency f. The task is subject to constraint S(x) ≤ –10 dB, S(x) = max{3.1 GHz ≤ f ≤ 10.6 GHz : |S11|}. Similarly, as for the size reduction task, it is handled implicitly (cf. (3)) with the penalty function of the form c(x) = [(S(x) + 10)/10]2; in this case, we use small value of the penalty coefficient to foster the improvement of the primary objective at the expense of tolerating certain violation of the reflection constraint. The reason for considering this particular problem is that stable gain is important for broadband antennas, yet it is difficult to achieve. Numerical optimization turns instrumental here.Although in this work we focused on two specific optimization tasks: matching improvement and size reduction, a similar analysis can be made for other types of problems. This will be the subject of the future work; however, to emphasize the general importance of parameterization, an additional set of experiments has been performed for Antenna I, which is reduction of in-band realized gain variability. Here, the objective function to be minimized is defined as x*1 = [21.21 14.97 6.26 11.04 11.12 4.87 3.73 0.40]T (absolute parameterization) and x*2 = [7.33 11.06 10.50 1.71 6.70 0.37 0.20 2.40]T (relative parameterization). It can be observed that absolute parameterization is more advantageous for handling gain characteristics. The obtained gain variability lower by over 0.7 dB on the average than for relative parameterization; the standard deviation is noticeably smaller as well. At the same time, the computational costs of the optimization process are comparable for both parameterizations, which is consistent with the results contained in Table Table This paper investigated the issue of geometry parameterization in the context of EM-driven parameter tuning of antenna structures. The two types thereof were considered, the absolute , and the relative one with the sizes of the structure components expresses in relation the size of encapsulating elements. The qualitative merits of both parameterizations were discussed, followed by comprehensive numerical experiments carried out using three broadband antennas. At the level of the optimization problem formulation, the advantage of absolute parameterization is its simplicity, which comes at the expense of being accompanied by a number of constraints necessary to ensure the geometrical consistency of the structure. On the other hand, relative parameterization eliminates the need for additional constraints making the problem domain as simple as an interval .2 (or about ten percent) as compared to the absolute one .The numerical results obtained for two design scenarios (optimization for minimum size and best matching) with the optimization executed multiple times from random initial designs, indicate that the performance of the EM-driven design process is affected by the choice of parameterization to a great extent. In particular, absolute parameterization turns out to be more suitable for solving size reduction tasks, whereas relative parameterization is favoured for handling electrical characteristics . The performance differences are quite significant: the average maximum in-band matching is better by 1.5 dB when using absolute versus relative sizing, whereas relative parameterization leads to size reduction better by 25 mmThe reasons for these differences seem to be related to whether a particular task is formulated as a constrained or unconstrained one, but also to whether the primary objective is computationally cheap (as in size reduction tasks) or expensive . An additional reason might be related to the overall objective function landscape, which favours absolute parameterization when the goal is related to the physical size of the antenna, yet fosters relative dimension sizing in other cases. More experiments are needed to determine a suitability of particular ways of parameterizing antenna geometry across a broader range of EM-driven design tasks, which will be the subject of the future work.


After decades-long investigations of their biological functions, including potential as drug targets, only very recently has atomic-resolution information of NOX enzymes been made available. In this graphical review, we summarize the present structural biology understanding of the NOX enzymes afforded by X-ray crystallography and cryo-electron microscopy. Combined molecular-level insights predominantly informed by DUOX1 full-length Cryo-EM structures suggest a general structural basis for the control of their catalytic activity by intracellular domain-domain stabilization.The NADPH Oxidases (NOX) catalyze the deliberate production of reactive oxygen species (ROS) and are established regulators of redox-dependent processes across diverse biological settings. Proper management of their activity is controlled through a conserved electron transfer (ET) cascade from cytosolic NADPH substrate through the plasma membrane to extracellular O With re density ,22. Furtnumbers) , these c4csNOX5 TM domain was solved with X-ray crystallography defining the fold of the transmembrane helices and the Fe heme cofactors [2 [All NOX enzymes comprise a six-pass transmembrane ɑ-helix domain that complex two b-type hemes responsible for transferring electrons from NADPH through the cell membrane ,22. The ofactors  , and pleckstrin homology-like domain (PHLD). Purification of mDUOX1-DUOXA1 complexes yields either a 1:1 heterodimer ”.


A new tool based on 3D Zernike polynomials is presented that allows the study of the continuous heterogeneity of biological macromolecules, revealing the structural relationships present among different states by the approximation of deformation fields. Structural biology has evolved greatly due to the advances introduced in fields like electron microscopy. This image-capturing technique, combined with improved algorithms and current data processing software, allows the recovery of different conformational states of a macromolecule, opening new possibilities for the study of its flexibility and dynamic events. However, the ensemble analysis of these different conformations, and in particular their placement into a common variable space in which the differences and similarities can be easily recognized, is not an easy matter. To simplify the analysis of continuous heterogeneity data, this work proposes a new automatic algorithm that relies on a mathematical basis defined over the sphere to estimate the deformation fields describing conformational transitions among different structures. Thanks to the approximation of these deformation fields, it is possible to describe the forces acting on the molecules due to the presence of different motions. It is also possible to represent and compare several structures in a low-dimensional mapping, which summarizes the structural characteristics of different states. All these analyses are integrated into a common framework, providing the user with the ability to combine them seamlessly. In addition, this new approach is a significant step forward compared with principal component analysis and normal mode analysis of cryo-electron microscopy maps, avoiding the need to select components or modes and producing localized analysis. The key development in this work is the successful expression of the maps in terms of a mathematical basis on which the displacements are calculated. Although full details are provided in Appendices V1 and V2 represent two conformations of a given molecule. Here it is important to note that we are measuring the distance between the target and the distorted volumes in terms of the L1 norm. Although it would also have been possible to use the L2 norm, we have chosen this definition as it is more robust to outliers (i.e. it is more robust to those cases where the maps do not match completely or have missing regions). The displacement to be applied to the coordinates of V1 is defined by the deformation field g(r) parameterized through the expansion in Zernike polynomials Zl, n, m(r) αl, n, m = 0 for all indices l, n, m and directions x, y, z . This initialization of the minimization method assumes that the identity/equilibrium solution  is close enough to the real solution defining the structural transition represented by the cryo-EM maps. Since in most of the cases this assumption is fulfilled, this initial guess allows the minimization method to find the set of coefficients that appropriately describes the motion between the two maps. However, it is important to note that there are many local minima where the minimization process could be trapped. In this respect, and although in our experience the initialization conditions proposed in this work provide results close enough to the ideal solution, there could be cases in which other ways to initialize the algorithm could be more beneficial in terms of minima search.The amount of displacement at every point is controlled by the deformation coefficients et al. . However, by imposing these kinds of restrictions the algorithm might include artefacts in the surface of the sphere as the molecular regions outside of it will remain untouched. Depending on the molecule and motions to be analysed, the researcher can decide which analysis will be more appropriate for a specific case.It is important to mention that only in a very few cases did we need to increase the degree of the basis to analyse a localized motion that we were interested in, or have to play with the regularization parameter to get a better approximation of the deformation fields, since the default values were good enough for most of the experiments we have performed so far.V1 due to the deformation. Regularization terms λ1 and λ2 are usually given low values to prevent large deviations from the ideal solution. Nevertheless, both can be set by the user to any value they consider appropriate for their specific analysis. The guideline for their selection should be that the three terms in the goal function should have values of the same order of magnitude. In our implementation, we report the three contributions helping the user to choose these multipliers.To reduce the possibility of overfitting as much as possible, we regularize the cost function by adding two penalty terms, 2.2.et al., 2015Probably the two most widely continuous deformation models used by the structural biology community in mapping the conformational space of biomolecules  are principal component analysis (PCA) (Tagare N3 voxels as a vector in N3 × N3), then the principal components form a basis (if the covariance matrix is not degenerate) in which the set of maps can be linearly expressed. The PCA approach approximates the deformed volume by a linear combination of volumes , Vn are the eigenvolumes of the PCA decomposition. The undeformed model is then obtained by subtracting the appropriate amount of each of the eigenvolumes, PCA considers a volume of size V2 can be undeformed by applying gL,gL(r), not the volumes. Our model revolves around the location of the voxel (which implies a nonlinear relationship between V1 and V2), providing an intrinsically better handling of the local characteristics of the map, while in PCA there is a linear model at the level of the volumes themselves .In our model, we assume that any deformed volume V1. For any given atom in the atomic structure at a position r1, that is defined in the same coordinate system as V1, we simply have to move it to the location r1 + gL(r1).Our approach has another potential advantage over the PCA model: it can easily be applied to atomic structures fitted into P pseudoatoms with weights cp and basis function b(r) located at the locations rp  and a basis of the kth normal mode, and pth pseudoatom. To deform V2 to make it similar to V1 we consider the first K normal modes with different weights αk, V2). However, an advantage of our new method with respect to NMA is that the NMA deformation is only known at the location of the pseudoatoms, while our new method is fully defined within the sphere containing the macromolecule. In this way, the NMA would be a discretized version of the underlying continuous deformation field, while 3D Zernike polynomials would be an estimate of that continuous field.In NMA, volumes are approximated by a set of Summarizing, each of the methods described so far  has a basis in different mathematical entities  provides a mechanism for calculating such a distance. For instance, we may define the distance between two cryo-EM maps V1 and V2 as the sum of the lengths of the deformations at each point, V1 and V2 once V2 is undeformed to resemble V1, By estimating the Zernike polynomial deformation for all possible pair combinations in a set of By comparing all cryo-EM maps, we would construct a matrix of the distances of all versus all maps.It is worth mentioning here that, in order to get accurate comparison measurements, it is desirable to have a set of cryo-EM maps with similar characteristics. In particular, it is important to filter the maps in the set so that all their resolutions match the lowest value present in the data set. In this way, the structure mappings and distance matrices will not be affected by resolution changes, leading to a more meaningful projection of the different maps in the low-dimensional space resulting from the application of this method.2.4.p  such that the distances between points in the low-dimensional space represent in some form the distances between the cryo-EM maps in the full dimensionality space  into two groups of conformers, each representative of a different functional state.We first tested our approach using a small data set covering a range of conformational states of a human mitochondrial ribosome    with the deformation vector between the current conformation and the target structure up until the sum of the squared overlaps exceeded a threshold of 0.4. The contribution of each mode to the deformation was chosen so as to take 20% of the maximum provided by the unnormalized dot products  so as to avoid unphysical deformations while maintaining efficiency. The normal modes were recalculated until the root-mean-square deviation (r.m.s.d.) from the target structure fell below 1 Å, resulting in a total of 30 steps. Each step recruited a larger number of modes and had a smaller total size as the required deformation became less cooperative and more local (see Video 2). We focus our discussion on the ring that goes from open to intermediate–closed for simplicity.Our next experiment is aimed at characterizing the ability of the method to recover the sequence of events present in a set of conformations defining a certain trajectory in conformational space. Such conformations can be created computationally by taking advantage of biophysical methods such as molecular dynamics simulations (MD)  as described in previous work  are high-frequency movements  that cannot be fully captured by the Zernike 3D basis with N = 3 and L = 2 . Fortunately, the consensus mapping [Fig. 3d2 mapping (correlation distance) automatically, resulting in an almost exact recovery of the volume sequence along the trajectory.With this example, we additionally illustrate the distinct MDS mappings obtained when the distances d1 is very good for describing the low-frequency movements (e.g. C23–C30), while d2 is very good for characterizing the high-frequency differences (ca C1–C13), and both perform well in the intermediate-frequency regime. Depending on whether our set of input maps are related by large or small movements, one distance or the other will be better suited to capturing the overall set of relationships. The consensus mapping will thus analyse both mappings and automatically determine the optimal weight that results in a low-dimensional mapping that can be readily interpreted.At least in this case we can conclude that 3.3.5tam, 5tau, 5taz, 5tb4 and 5t9n).In the following example, we explored the possibility of matching (pseudo/simulated) cryo-EM maps derived from atomic models with experimental electron microscopy maps in the same low-dimensional space. For this purpose we selected five experimental cryo-EM maps deposited for the ryanodine receptor 1 (RyR1) from rabbit  and their respective atomic models in the PDB . Note that without applying this low-pass filter the minimization process of equation (1)d1 (the distance based on the amount of deformation) illustrates that many pairs are correctly placed together, the structure map based on d2 (the distance based on the similarity after undeforming) discriminated between maps derived from atomic models and maps coming from cryo-EM experiments. However, the point of this example was to intermix maps from different origins, so discrimination by origin was to be minimized, requiring a further adjustment to our approach. To tackle this problem, we extended our methodology by analysing separately the sub-blocks of the distance matrix including only atomic or only cryo-EM maps [see Fig. 5XAA and XCC . These two low-dimensional mappings were the input into the consensus procedure described in Section 2.4XAA and XCC is combined into two different trajectories corresponding to each dimension in the distance matrices  that show a similar distance relationship among their points, illustrating that both trajectories correspond to the same states of RyR1. Therefore, the counterpart of each other, and their relative distances/positions, are retained [Fig. 5Our results, shown in Fig. 43.4.gL as a function that deforms V1 to let it become similar to V2, that is, as we have done in previous cases, acting only on two cryo-EM maps. However, since the deformation field is defined in the coordinate system of V1, it can also be applied to atomic models defined in the same coordinate system and not only to maps. In this way, we can also deform an atomic model defined for V1 and use it as starting point for a model of V2. Obviously, since the new atomic model defined in the coordinate system of V2 has been constructed purely based on geometrical considerations, all the stereochemical constraints have to be further imposed.We described our deformation field ChimeraX) between these two models was 5.29 Å, certainly high, but substantially reduced compared with that between the open–closed models without applying any deformation, which was 7.90 Å. This r.m.s.d. reduction suggests that the deformation applied is appropriately reproducing a conformational change in the right direction, from open towards the closed state.An example of an atomic model deformed following the previous procedure is presented in Fig. 6Phenix software  for the analysis of continuous heterogeneity.The way this new approach works is by defining a new 3D basis where all deformation occurs. It is conceptually similar to the Fourier transform. The movements defining a transition between two different conformational states are decomposed into different components . Those components will depend on the degree of the basis used in the calculations. The displacements needed along each different component to minimize the distances between two electron-density maps are stored in a series of deformation coefficients Apart from the information extracted from the deformation coefficients, our method allows for the definition of a distance measure based on the deformed electron-density maps, which is useful for building distance matrices. These distance matrices can be used afterwards to recover structure mappings that show the structural relationships existing among the diverse conformational states. Different definitions of the distance measures may focus on different aspects of the comparison. For this reason, we have devised a new procedure to combine several low-dimensional mappings into a single consensus mapping based on a minimum entropy criterion that tends to produce well ordered low-dimensional mappings and outperforms the results obtained by individual distance metrics.The possibility of converting atomic models back to electron densities opens the possibility of a combined analysis on maps and models in the same conformational space. An illustrative example has been provided in Experiment 4, where cryo-EM maps, together with their respective structural models between two end points, have been represented in the same space as a set of experimental cryo-EM maps.In the future, it may be interesting to explore alternative bases for the deformation field and the distance between volumes (like the Wasserstein distance).Click here for additional data file.10.1107/S2052252521008903/eh5012sup1.mp4Video 1. DOI: Click here for additional data file.10.1107/S2052252521008903/eh5012sup2.aviVideo 2. DOI:


Fibrillar adhesins are long multidomain proteins that form filamentous structures at the cell surface of bacteria. They are an important yet understudied class of proteins composed of adhesive and stalk domains that mediate interactions of bacteria with their environment. This study aims to characterize fibrillar adhesins in a wide range of bacterial phyla and to identify new fibrillar adhesin-like proteins to improve our understanding of host-bacteria interactions.Through careful literature and computational searches, we identified 82 stalk and 27 adhesive domain families in fibrillar adhesins. Based on the presence of these domains in the UniProt Reference Proteomes database, we identified and analysed 3,542 fibrillar adhesin-like proteins across species of the most common bacterial phyla. We further enumerate the adhesive and stalk domain combinations found in nature and demonstrate that fibrillar adhesins have complex and variable domain architectures, which differ across species. By analysing the domain architecture of fibrillar adhesins, we show that in Gram positive bacteria, adhesive domains are mostly positioned at the N-terminus and cell surface anchors at the C-terminus of the protein, while their positions are more variable in Gram negative bacteria. We provide an open repository of fibrillar adhesin-like proteins and domains to enable further studies of this class of bacterial surface proteins.This study provides a domain-based characterization of fibrillar adhesins and demonstrates that they are widely found in species across the main bacterial phyla. We have discovered numerous novel fibrillar adhesins and improved our understanding of pathogenic adhesion and invasion mechanisms.The online version contains supplementary material available at (10.1186/s12864-021-07586-2). Studying how bacteria interact with their host is essential for understanding bacterial infection processes and characterizing interactions with commensal bacteria. Adhesion to the host cell is the first step of a bacterial infection. Interfering in the adhesion process by Anti-adhesion therapies, primarily by stimulating a humoral immune response, is an effective strategy to prevent infections and has been widely used for the development of vaccines targeting host binding proteins . For exaFibrillar adhesins are long filamentous surface proteins that share similar functions and are expressed by a variety of different bacteria . BesidesFA-like proteins are extracellular.FA-like proteins possess an adhesive region usually at the protein terminus.FA-like proteins possess a stalk-like region that forms a rigid rod-like structure.FA-like proteins usually possess a surface anchoring motif or domain.FA-like proteins are composed of one or a small number of identical fibrillar chains, where each monomer extends through the whole length of the fibril.Few fibrillar adhesins have been studied so far and these come from a limited range of organisms. To our knowledge, no comprehensive review or study of fibrillar adhesins across bacterial species has been performed before. It would be infeasible to experimentally characterise these proteins at large scale and determine their fibrillar nature. In this work, we aim to computationally identify fibrillar adhesin proteins using a sequence-based perspective. Thus, we define a new class of proteins that we call fibrillar adhesin-like (FA-like) proteins based on the common features of known fibrillar adhesins. These proteins have the following properties: Fibrillar adhesins appear to be composed of a relatively limited number of domain families, which facilitates their computational identification. We search for FA-like proteins based on a well studied collection of adhesive and stalk domains and we use Pfam domain definitions to identify and characterize FA-like proteins. This study aims to create a comprehensive survey of fibrillar adhesins across bacterial phyla to gain understanding of their prevalence, distribution and composition. We hope that this study will guide researchers to investigate interesting new fibrillar adhesins, leading to an improvement in our understanding of microbial interactions in health and disease.Fibrillar adhesins are composed of multiple stalk domains, commonly repeated in tandem, and at least one adhesive domain. We gathered a set of 27 adhesive Pfam domain families from known fibrillar adhesins in the literature and 82 stalk domain families from proteins with tandem sequence repeats, as described in the “The majority (19 out of 27) of the adhesive domains in our set bind to protein ligands, while 7 out of 27 bind to carbohydrates (supplementary table S2). The Ice_binding domain Pfam: PF11999) differs from the other adhesive domains in that it doesn’t bind to host cells or to other bacteria, but to ice crystals, allowing organisms to survive in extremely cold environments [999 diffeListeria monocytogenes. Different combinations of adhesive and stalk domains are found across FA-like proteins. The total number of stalk domains in FA-like proteins varies from 1 to 73. Figure We identified a total of 3,542 FA-like proteins composed of at least one adhesive and at least one stalk domain from our sets. These represent 0.013% of all proteins in the UniProt bacterial reference proteomes. Species-wise, FA-like proteins are found in 26% of the bacterial reference proteomes . The larDespite some of the FA-like proteins identified having a low number of annotated stalk domains, the actual number might be higher, since some stalk domains are not yet known and therefore not annotated. In addition, the expression of FA-like proteins with a variety of stalk domain numbers leads to length variation, which is suggested to play a role in the regulation of the bacterial interactions with its binding partner . NeverthBacteria use a wide range of different protein domains to mediate adherence to their host or other organisms. To date, it is not known which of these domains are the most common within fibrillar adhesins. Our domain based screen for FA-like proteins Fig. b shows tWe were further interested to investigate if there is a common domain grammar in FA-like proteins. In the detected FA-like proteins, we found combinations of 76 different stalk domains with 23 different adhesive domains. SlpA (Pfam: PF03217), I-set (Pfam: PF07679) Fil_haemagg (Pfam: PF05594), Fil_haemagg_2 (Pfam: PF13332), DUF5649 (Pfam: PF18886) and Beta_helix_3 (Pfam: PF18889) were the only stalk domains not found in combination with a known adhesive domain and the adhesive domains SabA_adhesion (Pfam: PF18304), SSURE (Pfam: PF11966), FadA (Pfam: PF09403) and FimH_man-bind (Pfam: PF09160) were not found with any known stalk domains.Streptococcus mutans adhesin P1 protein [We plotted the combinations of adhesive and stalk domains as a heatmap in Fig.  protein .Fig. 3SThe pair YadA_head and YadA_stalk (Pfam: PF05662) are the most prevalent domain combination, found in 770 FA-like proteins. YadA_stalk is by far the most commonly occurring stalk domain with annotations in 772 of the 3,542 FA-like proteins, followed by SpaA (Pfam: PF17802), with 668 FA-like protein hits (supplementary figure S1). Collagen_bind (Pfam: PF05737) and SpaA are the second most prevalent domain combination found in 303 FA-like proteins.S. aureus (UniProt: D2JAN8) with AlphaC_N and AlphaC_N2 adhesive domains in combination with Big_6 stalk domains. SAP077A_019 is not part of the UniProt Reference Proteomes and its adhesive domains are not captured by the current Pfam models.There are likely more adhesive and stalk domains not yet known and therefore not included in this study. Furthermore, we only used proteins from the UniProt Reference Proteomes, so potential FA-like proteins and novel domain combinations might be missing in this study. Moreover, imperfect annotation of stalk or adhesive domains by the Pfam hidden Markov models (HMMs) may lead to further missing FA-like proteins. One example is the SAP077A_019 fibrillar adhesin of Besides combinations of adhesive and stalk domains, which occur together in the same protein, there are also stalk domains that are commonly found together (supplementary figure S2). SpaA and Cna_B (Pfam: PF05738) are the stalk domains most often found together, in a total of 99 FA-like proteins. Both domain families belong to the Transthyretin clan (Pfam: CL0287), indicating that they are homologous and possess the same fold. SpaA is found together with a wide range of stalk domains, several of which belong to the Transthyretin clan and others belong to different clans such as the Big_9 domain of the E-set clan (Pfam: CL0159). Out of the 76 stalk domains which are found in combination with an adhesive domain, there are six domains which are not found with another known stalk domain on the same protein (in the Reference Proteomes). These five domains are Rib (Pfam: PF08428), QPE (Pfam: PF18874), Big_3 (Pfam: PF07523), Big_3_2 (Pfam: PF12245), DUF5977 (Pfam: PF19404) and AIDA (Pfam: PF16168).Domain pairs that are annotated together in the same protein and that function together are called supra-domains . In 222 Another example of a supra-domain, present on the heatmap in Fig. Fibrillar adhesins are surface proteins that are attached to the bacterial cell surface. The attachment mechanism can take many forms, such as non-covalent attachment by S-layer homology domains , or covaTo better characterise FA-like proteins we created a list of known anchoring motifs, signals and domains (supplementary table S3). The frequency of each of these different types of anchoring methods is shown in Fig. The Gram positive anchor motif is the most prevalent one in the detected FA-like proteins with 668 hits in total, of which 489 FA-like proteins belong to Firmicutes and 164 belong to the Actinobacteria phylum. In addition to the LPxTG motif, the alternative Gram positive anchor motifs LPxTA, NPxTG, LPxGA, LAxTGand NPQTN were included .The S-layer homology domain was found in 72 detected FA-like proteins and again mostly in the generally Gram positive Firmicutes. The anchor domain CHU_C is found in 134 FA-like proteins, all of which belong to the Bacteroidetes phylum. Por_Secre_tail is found in 124 FA-like proteins and mostly in Bacteroidetes. Four proteins with LysM domains (Pfam: PF01476) were detected, two proteins with Choline_bind_1 (Pfam: PF01473), two proteins with Choline_bind_3 (Pfam: PF19127), two with IAT_beta and one with GW domain. In Proteobacteria the YadA anchor (Pfam: PF03895) domain, Autotransporter domain (Pfam: PF03797) and the LapG + HemolysinCabind anchor were the most prevalent. The LapG motif is the protease cleavage site at the N-terminus of RTX adhesins, where the proteins are cleaved by the LapG protease and subsequently inserted into a beta barrel of an outer membrane pore . To be cWe ran the protein subcellular localization predictor PSORTb to identify that 871 of the 3,542 FA-like proteins were predicted to be localised at the cell wall, 547 to be at the outer membrane, 451 to be extracellular and 1,428 of these proteins had no localisation predicted . PSORTb S. aureus [It is generally assumed that, in fibrillar adhesins, adhesive domains are projected away from the bacterial cell surface by the elongated structure composed of stalk domains, promoting its interaction with the host cell. This architecture is similar to multiprotein pili adhesins, where the protein with the binding function is the membrane distal protein of all pili subunits . In fibr. aureus , 27. In In the Gram negative phyla Bacteroidetes and Proteobacteria, the distribution of domains is more variable. For Proteobacteria, the adhesive as well as the stalk domains show a relatively even distribution along the length of the FA-like proteins with a weak preference for adhesive domains at the termini Fig. c. In BacS. mutans , where the adhesive domain Adhesin_P1_N is annotated at the N-terminus and a second adhesive domain GbpC is annotated in the middle of the protein sequence. Adhesin_P1_N folds then to interact with the stalk domains Antigen_C and AgI_II_C2 at the C-terminus, which results in having GbpC at the top of the protein [There are known examples of centrally located adhesive domains. One example is adhesin P1 protein in  protein . Adhesin protein .Listeria monocytogenes. The number of FA-like proteins per bacterial reference proteome varies: proteomes with the highest prevalence commonly contain in the order of 10 FA-like proteins, reaching in the case of Streptobacillus moniliformis up to 21 FA-like proteins (1.47%).To get a deeper understanding on the origins and evolution of FA-like proteins, we looked at the distribution of adhesive and stalk domains across the bacterial phyla see Fig. . FA-likeSucciniclasticum, Acidaminococcus, Dialister, Veillonella and Megasphaera, whose domain composition are more similar to the Proteobacteria. This underlines the importance of the cell surface composition for the architecture of FA-like proteins. In Proteobacteria the FA-like proteins are mainly built out of YadA_head and YadA_stalk domains. For nearly all of the Firmicutes genera and many Actinobacteria FA-like proteins composed of Collagen_bind, VWA or TED (Pfam: PF08341) in combination with the SpaA stalk domain could be detected. The Gram negative Bacteroidetes use a wider range of domains compared to the Proteobacteria, of which many adhesive domains are the same as in Gram positive bacteria.The adhesive and stalk domains in FA-like proteins mapped to the genera in Fig. There are adhesive and stalk domains found in a wide range of genera, as well as adhesive and stalk domains limited to a few specific genera. VWA, for example, is an adhesive domain widely distributed and found in combination with different stalk domains in Firmicutes, Proteobacteria, Bacteroidetes, Planctomycetes and Actinobacteria. On the other hand, Intimin_C (Pfam: PF07979), seems to be adapted to Proteobacteria as it was only detected in this phylum in combination with the Big_1 (Pfam: PF02369) and Big_2 (Pfam: PF02368) stalk domains.We observed that some of the domains classified as adhesive in our list can also be found in repeats, leading us to question the limits between adhesive and stalk domain functions, and whether they can be interchangeable. Multiple adhesive domains repeated in tandem might form stalks, and stalk domains might evolve adhesion function over time to increase the binding avidity to host cells.Haemophilus influenzae Hap adhesin [S. aureus [E. coli [In Fig.  adhesin , but for. aureus . Similar[E. coli , suggestListeria monocytogenes, but also in poorly characterised pathogens, like Sneathia amnii.In this paper we aimed to comprehensively identify FA-like proteins and describe their characteristic domain composition, architecture and taxonomic distribution. Our identification approach is based on Pfam family HMMs of known adhesive and stalk domains, and has resulted in the discovery of many novel FA-like proteins from a wide range of bacterial genera. These proteins include new putative binding proteins in well studied pathogens like Our results show the various different adhesive and stalk domain combinations that exist in nature and also how frequently they are found. The information can facilitate the identification of new fibrillar adhesins. For instance, when analysing a new FA-like protein, a potential annotated adhesive domain can infer the existence of a specific stalk domain and vice versa. Domain grammar approaches have shown that a knowledge of the domain grammar of proteins can enhance sequence similarity searching , 33. ForOne of the more striking results of our domain grammar analysis was the presence of adhesive domains at the N-terminus of Firmicutes FA-like proteins. This preference is largely due to the presence of a C-terminal sortase motif in these proteins. For many of the analysed adhesive domains their relative position in the FA-like proteins stays constant (supplementary figure S4). This observation can be partly explained by the low number of FA-like proteins in which adhesive domains are found, for example in the case of AlphaC_N. There might be further limitations of adhesive domains to be found at either side of the stalk in FA-like proteins, such as effective binding orientation. The Bact_lectin domain is mostly found in the N-terminal third of FA-like proteins (supplementary figure S4) and binds sucrose at the N-terminal end of the adhesive domain in the SraP adhesin . It is qS. aureus [The investigation of the taxonomic distribution of adhesive and stalk domains show a wide range of different domains in Gram positive bacteria. One reason for this variety might be that the majority of fibrillar adhesins already studied belong to Firmicutes genera, as for example . aureus , and sub. aureus , but the. aureus  for the In our results, we describe that the adhesive domain SSURE is not found in combination with other known stalk domains, and that it is found on average in 3 copies per protein. We additionally know that SSURE is tandemly repeated. This leads to the hypothesis that some adhesive domains might be able to function as the stalk itself. Several adhesive domains are indeed found repeated. A reason for repeating adhesive domains could be to increase the avidity of binding to the host cells. Thus, duplications of these domains may lead to both extending the stalk length and increasing binding efficiency. In particular, when the host ligand is a collagen, the adhesive domain might bind to different positions simultaneously on the host ligand. The adhesive domain Collagen_bind binds collagen with the very specific collagen hug mechanism which requires the supra-domain of Big_8 domain N-terminal to the Collagen_bind domain. Therefore, it would be interesting to find out why Collagen_bind occurs in repeats and whether all repeats contribute to the adhesion process.The greatest limitation of our FA-like protein discovery process is that it requires a protein to contain a combination of known stalk and adhesive domains. There are undoubtedly numerous fibrillar adhesive proteins missed by our approach. There are several reasons for these missed proteins. Firstly, there may be FA-like proteins that have missing domain annotations for either the stalk or adhesive domain or both. This could be because there is no domain family represented within Pfam currently. A second possibility is that the current Pfam model misses true examples of the stalk or adhesive domain. And thirdly, simply that there are adhesive and stalk domains we did not yet identify. An estimation of the completeness of the stalk domain list for known adhesive domains is given in supplementary table S4. Nevertheless, the information about fibrillar adhesins gained in this study will be important for further improvements in future identification of these proteins.This study presents to our knowledge the most comprehensive characterisation of fibrillar adhesins to date. We have used a protein domain based screen to identify over 3,000 fibrillar adhesin-like proteins and characterised their domain content, architecture and taxonomic distribution. The results underline the complexity of fibrillar adhesins, with highly variable domain organisations as well as with various different domain combinations found in nature. The detected FA-like proteins are widespread across the bacterial taxonomic tree. The set of FA-like proteins and associated stalk and adhesive domains identified in this study set a foundation stone to discover further fibrillar adhesins.We searched in the literature for experimentally confirmed bacterial adhesins, for example by a keyword search for adhesins in PubMed. We analysed the domain architecture of the relevant proteins and identified the adhesive domains by searching for the binding regions. We also identified numerous adhesive domains through investigation of structures in the Protein Data Bank. In each case we identified the relevant entries in the Pfam database and recorded the relevant identifiers.Stalk domains were detected by searching for tandem repeat domains in the Pfam database, whereby repeats are counted as tandem repeats when the sequence separation between two domains from the same Pfam family is shorter than 35 residues. The further putative stalk domain families were checked manually in Pfam to ensure their description, species distribution and domain architecture matched the stalk domain definition. Domain families found only in eukaryotes, and non-surface and enzymatic proteins, were excluded.Tandem sequence repeats were identified in proteins without known Pfam annotations with the T-REKS tool using 70% sequence identity and length between 50-200 residues as parameters . Six newIn this study all bacterial proteins from the UniProt Reference Proteomes (release 2020_03) were used . The 7,5The number of stalk domains per protein in combination with one adhesive domain were counted, regardless of whether the stalk domains belonged to the same Pfam family or not. The frequency of adhesive and stalk domains as well as the combinations of adhesive and stalk domains and different stalk domains found on the same FA-like proteins were counted, whereby each domain was only counted once even if multiple domains of the same family were annotated on the protein.https://www.uniprot.org/uploadlists/).For the generation of the density plots representing the domain positions within the proteins, the protein length and the envelope domain location information were used from the HMMER search output file. The fractional start and end positions for the domains were calculated. Furthermore, sortase motifs were identified by regular expression in the C-terminal 50 amino acids of the FA-like protein sequences. For the analysis of the domain architecture between the different phyla, the taxonomic phyla were mapped to the UniProt protein identifier by using Retrieve/ID mapping from the UniProt website (Marinobacter hydrocarbonoclasticus (UniProt: H8W6K8), the described secondary structure motifs were not found so we only searched for the protease cleavage motif AAG within the first 150 amino acids in the FA-like protein sequences. To increase the probability that the found hits are indeed RTX adhesins, we additionally searched for the HemolysinCabind domain, which is characteristic for the RTX adhesins. In total 62 LapG cleavage motifs and HemolysinCabind domains could be found together in a single protein. In 31 of these 62 FA-like protein hits the short coil motif GxDPT could be found, whereas the alpha-helical element motif xxIQxAIAA was only found in two of these proteins. For comparison, the PSORTb webserver (version 3.0.2) was used to predict the localization of the 3,542 FA-like proteins [To find out how FA-like proteins are attached to the bacterial cell, HMMs of known anchor domains were downloaded from Pfam. We used the following models: Gram_pos_anchor (Pfam: PF00746), SLH (Pfam: PF00395), CHU_C (Pfam: PF13585), Por_Secre_Tail (Pfam: PF18962), LysM (Pfam: PF01476), Choline_bind_1 (Pfam: PF01473), Choline_bind_2 (Pfam: PF19085), Choline_bind_3 (Pfam: PF19127), YadA_anchor (Pfam: PF03895) IAT_beta (Pfam: PF11924), GW (Pfam: PF13457), PG_binding_1 (Pfam: PF01471), PG_binding_2 (Pfam: PF08823) and PG_binding_3 (Pfam: PF09374) . By HMMEFor taxonomic analysis, additional to the phyla, the genera were also mapped by using Retrieve/ID mapping from the UniProt website. The 16s rRNA sequences of representative bacterial genera and of genera with a minimum of 5 detected FA-like proteins were aligned in JalView using Muscle with the default settings , 44. AftProtein sequences containing known adhesive domains were downloaded from Pfam version 33.1 . ProteinThe HMMER search results of the long bacterial proteins with known adhesive domains against the Pfam database from the “For each adhesive domain found in the FA-like proteins we calculated if its mid point is within the N-terminal, middle or C-terminal third of the FA-like protein. We then calculated the percentage of occurrence in each of the three relative positions for each adhesive domain. The results are presented in the heatmap in supplementary figure S4.Additional file 1 Supplementary figures and tables.


The Toll-interleukin-1 Receptor (TIR) domain-containing adaptor protein (TIRAP) represents a key intracellular signalling molecule regulating diverse immune responses. Its capacity to function as an adaptor molecule has been widely investigated in relation to Toll-like Receptor (TLR)-mediated innate immune signalling. Since the discovery of TIRAP in 2001, initial studies were mainly focused on its role as an adaptor protein that couples Myeloid differentiation factor 88 (MyD88) with TLRs, to activate MyD88-dependent TLRs signalling. Subsequent studies delineated TIRAP’s role as a transducer of signalling events through its interaction with non-TLR signalling mediators. Indeed, the ability of TIRAP to interact with an array of intracellular signalling mediators suggests its central role in various immune responses. Therefore, continued studies that elucidate the molecular basis of various TIRAP-protein interactions and how they affect the signalling magnitude, should provide key information on the inflammatory disease mechanisms. This review summarizes the TIRAP recruitment to activated receptors and discusses the mechanism of interactions in relation to the signalling that precede acute and chronic inflammatory diseases. Furthermore, we highlighted the significance of TIRAP-TIR domain containing binding sites for several intracellular inflammatory signalling molecules. Collectively, we discuss the importance of the TIR domain in TIRAP as a key interface involved in protein interactions which could hence serve as a therapeutic target to dampen the extent of acute and chronic inflammatory conditions. This event nucleates the formation of a large signaling complex called the “Myddosome” , is an adapter molecule associated with receptor-mediated activation of host immune signaling , 2. The ddosome” –7. The rddosome” , 8–10. T2) binding domain (PBD), which is responsible for targeting TIRAP to discrete regions of the plasma membrane upon Phosphatidylinositol 4-Phosphate 5-Kinase (PIP5Kα)-mediated production of PIP2  is a multi-ligand cell membrane receptor implicated in diverse chronic inflammatory states such as cardiovascular disease, cancer, neurodegeneration, and diabetes –52. RAGEhlighted , 56. It hlighted .3 [phosphatidylinositol P3] generation, and polar shape changes of the macrophage  with TIRAP, is a MyD88-independent response of the TLR2 receptor upon stimulation with bacterial lipoproteins , 21. Thicrophage . Signalicrophage . Ligand crophage , 60. Upocrophage . Upon stcrophage . Whereascrophage . This suvia Akt phosphorylation and Nrf2 activation  tivation . Similarin vitro, an event critical for signal transduction. Such a filament could be disrupted by dimeric, and not monomeric, BCAP. Another angle suggest BCAP phosphoinositide metabolism, which cleaves PIP2 to DAG and IP3 and hence deprives TIRAP for its membrane anchor required for TLR signaling  (BCAP) has been reported to negatively regulate the TLR4/2-PI3K signalling, suggesting its association with TLR and downstream TIR domain of TIRAP, leading to its recruitment to TLR signalasome by TIR-TIR interactions –38. BCAPignaling . OverallAs mentioned above, PKCδ represents an interacting partner of TIRAP that is required for TLR2- and TLR4-induced activation of p38 MAPK, NF-κB and proinflammatory cytokine expression , 30. MicBrucella species viz. Brucella melitensis, Brucella abortus, and Brucella ovis inhibit TIRAP activity by encoding a TIR domain-containing effector protein TcpB, also called Btp1 (Cytoplasmic linker protein 170 (CLIP170), containing two conserved cytoskeleton-associated protein Glycine rich (CAP-Gly) domains and two tandem repeats of zinc knuckle motifs , is a multifunctional protein that binds to and regulates the dynamics of the growing plus end of microtubules –64. In aled Btp1 . TcpB, lled Btp1 , 65. Mecled Btp1 . CLIP170led Btp1 .Delivery of bacterial-derived pathogenic LPS to TLR4 has been shown to activate AP-1 through a series of phosphorylation events on serine/threonine residues mediated by upstream MAPKs  . For exaAs mentioned above, p38 MAPK is one of three members of the MAPK protein family, that in response to LPS, induces the expression of proinflammatory cytokines  through activation of the downstream transcription factor AP-1 . In addiTwo reports on the interaction of TIRAP with Caspase-1 have highlighted the importance of this interaction to macrophage signaling events , 34. CasA novel role for TIRAP in NF-κB p65 trans-activation was described after the identification that TIRAP interacts with TRAF-6 . The intvia different E3 ligases (CLIP170 and Triad3A). Future work should determine the relative contributions and functions to proinflammatory immune signaling.Triad3A is a RING finger-type E3 ubiquitin-protein ligase that recognizes and interacts with the TIR domain of TLRs, promoting their proteolytic degradation , 42. SpeThe interleukin-1 receptor-associated kinase (IRAK) family protein kinase consists of four members, IRAK-1, IRAK-2, IRAK-3/M, and IRAK-4 . The seqInterestingly, the interaction of TIRAP with IRAK-4 and IRAK-1 can result in TIRAP degradation following its phosphorylation and ubiquitination, and therefore inhibition of signaling. In this way, IRAK-1/-4 negatively regulates TLR-4/2 signaling by expressing an auto-active IRAK4 inducing TIRAP degradation . The int3Cys induced TLR4 and TLR2 signaling, but not following TLR7 and TLR9. Unlike TIRAP, MyD88 does not contain a PEST domain. This degradation appears dependent on SOCS1; a mutant SOCS1 (SH2 and SOCS Box) variant, as well as SOCS1 deficient macrophages, are unable to target and degrade TIRAP in response to LPS ligation, whereas wild type SOCS1 interacted and degraded TIRAP as early as 15 to 30 minutes post LPS stimulation  serves as a key physiological regulator of both innate and adaptive immunity acting through macrophages, dendritic cells, B-cells, and T-cells . Structumulation . As tyromulation , 24. Furmulation . In addimulation , 92. In mulation . Therefoy events Figure 2st to 83rd amino acid containing the PBD  Binding Domain) from the 15th to 35th amino acid sequence that is rich in lysine residues. A C-terminal region mainly encodes the TIR Domain, spanning for 130 amino acid long from 84th to 213th (www.genome.jp (https://www.genome.jp/tools/motif/) to obtain the domain details from different databases as produced by the tool search results.Studies have described the role of TIRAP in modifying the innate immune response elicited largely by endotoxin , 17, 93.to 213th , 13, 94.In all the interactions discussed above Figure 3https://www.phosphosite.org/homeAction) and the same sites were retrieved for human TIRAP TIR domain. Further, the predictions of possible phosphorylation sites in TIR domain were obtained from kinase-based phosphorylation prediction tools; GPS-phosphorylation (http://gps.biocuckoo.cn/) (http://www.cbs.dtu.dk/services/NetPhos/) (http://sno.biocuckoo.org) (www.deepnitro.renlab.org) (http://csb.cse.yzu.edu.tw/SNOSite/predict.php)] (http://dog.biocuckoo.org/). In support of these predictions, the tyrosine residues Y86, Y106, Y159, and Y187 have been reported to be the experimentally confirmed phosphorylation sites which modulate the interaction of TIRAP with BTK protein and downstream transcription factors activation  was used for the prediction and study. We searched the protein phosphorylation database PhosphoSitePlus server (koo.cn/)  and NetPetPhos/) . Includikoo.org) , DeepNitlab.org)  and SNOSct.php)] . Interestivation , 26, 27.Helicobacter pylori released stimuli  receptor in response to a  stimuli . Though The report that TIRAP acts as a second adaptor protein after MyD88 in the year 2001 was marked as a major discovery in the mechanism of TLR4-dependent inflammatory signaling. Subsequently, two more adaptor proteins, TRIF and TRAM, mostly responsible for IRF3 activation, were added to the family of TIR domain-containing adaptor proteins. More recent studies show that TIRAP not only acts as a bridging protein between TLR4/2 and MyD88, but also propagates transduction of downstream signaling events, sometimes in a MyD88-independent manner. Clearly, the ability of TIRAP to interact and collaborate with several signaling molecules in a context-dependent manner means this protein is a major regulator of cell signaling.Conceptualization: MB. Investigation: MB. Writing : MB and SR. Reviewing and editing TT, RI, KW, US, and DL. Supervision: TT. All authors contributed to the article and approved the submitted version.A Biotechnology and Biological Sciences Research Council (BBSRC) David Phillips Fellowship (BB/R011834/1) funds TLMT.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.