PDZ domain-mediated interactions have greatly expanded during metazoan evolution, becoming important for controlling signal flow via the assembly of multiple signaling components. The evolutionary history of PDZ domain-mediated interactions has never been explored at the molecular level. It is of great interest to understand how PDZ domain-ligand interactions emerged and how they become rewired during evolution. Here, we constructed the first human PDZ domain-ligand interaction network (PDZNet) together with binding motif sequences and interaction strengths of ligands. PDZNet includes 1,213 interactions between 97 human PDZ proteins and 591 ligands that connect most PDZ protein-mediated interactions (98%) in a large single network via shared ligands. We examined the rewiring of PDZ domain-ligand interactions throughout eukaryotic evolution by tracing changes in the C-terminal binding motif sequences of the PDZ ligands. We found that interaction rewiring by sequence mutation frequently occurred throughout evolution, largely contributing to the growth of PDZNet. The rewiring of PDZ domain-ligand interactions provided an effective means of functional innovations in nervous system development. Our findings provide empirical evidence for a network evolution model that highlights the rewiring of interactions as a mechanism for the development of new protein functions. PDZNet will be a valuable resource to further characterize the organization of the PDZ domain-mediated signaling proteome. Rewiring of interactions is a powerful tool for the evolution of organism complexity. Rewiring among preexisting proteins provides a simple mechanism for the development of new signaling circuits by redirecting information flows without a gain or loss of genes. Particularly, interactions mediated by short linear motifs can be easily changed by mutations during evolution, resulting in a rewiring of interactions. However, how interaction rewiring of linear motif interactions facilitates the emergence of new protein function during evolution is poorly understood. Here, we systematically investigated the rewiring of interactions mediated by PDZ domains, which are one of the most commonly found peptide recognition modules. We found that PDZ domain-ligand interactions are frequently rewired by C-terminal sequence mutations in PDZ ligands during evolution. Especially, rewiring of PDZ domain-ligand interactions was involved in neuronal function development, occurring concurrently with the emergence of vertebrates and suggesting that reorganization of signaling pathways by rewiring PDZ domain-ligand interactions significantly contributed to the evolution of nervous systems in vertebrates. Our findings highlight the rewiring of interactions as an effective means for functional innovation, providing new insight into eukaryotic evolution, which has not been fully explained by only the expansion of protein families. PDZ domains are linear motif-mediated protein-protein interaction modules. PDZ domain-ligand interactions have been greatly expanded in metazoans and are widely used to assemble signaling complexes, including those found in neuronal synapses Systematic analysis of interaction rewiring will provide new insights into eukaryotic evolution, which is not fully explained via only the expansion of protein families. Recently, it was suggested that rewiring of interactions is an important mechanism for the evolution of biological systems. Network comparison studies showed that protein interactions frequently change after gene duplication Structural information of interacting cellular components  would provide a more complete picture of a cell and help elucidate the evolutionary principle of the protein interaction network In this work, we attempted the first systematic investigation of interaction rewiring in the PDZ domain-ligand interaction network and its role in eukaryotic evolution. We constructed a comprehensive human PDZ domain-ligand interaction network and traced the changes in interaction rewiring during evolution. We developed position weight matrices (PWMs) of human PDZ domains from the experimental data of PDZ domain-ligand interactions. The binding motif information of PDZNet helped to elucidate the changes in PDZ domain-ligand interactions. We found that PDZ domain-ligand interactions are frequently rewired throughout evolution via mutations of C-terminal PDZ ligand sequences. Particularly, interaction rewiring occurred concurrently with emergence of vertebrates whose rewired interactions were largely involved in neuronal signaling, suggesting that nervous system evolution might be achieved by the interaction rewiring of signaling components, such as PDZ protein-ligand interactions. Furthermore, the broad specificity of PDZ domains contributes to interaction rewiring by increasing the chance of acquiring PDZ binding motifs by sequence mutations. Our findings will prompt a new approach for the study of eukaryotic evolution by considering the rewiring of interactions as a major evolutionary process of domain-ligand interactions.To elucidate how PDZ domain-ligand interactions have evolved, an accurate and detailed understanding of their interactions is essential. Furthermore, a network approach is useful to understand how evolution of PDZ domain-ligand interactions contributed to eukaryotic evolution, because protein functions may not be encoded in an individual protein but rather be encoded in the relationships between proteins in a protein-protein interaction network in vivo PDZ domain-ligand interactions (PDZBase) We developed a quantitative model of PDZ domain binding strengths from the experimental data of PDZ domain-ligand interactions, including interactions between 81 PDZ domains and 217 peptides from a protein array 2 = 0.76) and ERBIN (R2 = 0.85) PDZ domain-peptide interactions. Moreover, in vivo binding affinities of PSD-95_1 (the first PDZ domain of PSD-95) with its ligands correlated well with its binding scores from the PWMs  of the known PDZ ligands were found in the top 10 percentile of binding scores  the introduction of new PDZ ligands by gene duplication of existing partners, or (2) the g motifs . To examNext, we examined the sequence evolution of the binding motifs of human PDZ ligands and discovered that a large portion of PDZ ligands acquired their binding motifs via sequence mutations. We examined the C-terminal sequences of PDZ ligands in each PDZ domain-ligand interaction pair across 16 representative species. We found that nearly one-third of human PDZ ligands gained their PDZ domain interactions by C-terminal mutations during evolution . For exaEscherichia coli to humans and examined the changes in the C-terminal sequences of PDZ ligands and the binding specificities of PDZ domains  changed Ile to Thr and another mutation (C→T) changed the codon for Gln to a stop codon  that describe the number of hydrogen bond donors Our goal was to predict the specificities of a PDZ domain without knowledge of its structure. As such, a method to extract pocket residues from the sequences of PDZ domains was designed. To identify the positions of pocket residues within the PDZ domain sequence, an MSA was constructed, and the known structure of the PSD-95_1 domain was referenced. We performed a multiple alignment of the PDZ domain sequences using a HMM a, i) is the affinity contribution of amino acid a at the ith position, a,if is the frequency of amino acid a at the ith position in the collected set, and a,ip is the background frequency, defined as the probability of observing amino acid a at the ith position in any ligand protein. The constant k was empirically determined to be 1.921, so that PDZNet includes all experimentally confirmed PDZ domain-ligand interactions as positive binding scores.To estimate the PDZ domain-ligand binding affinity, we adopted an information theory-based PWM method that is widely used to estimate protein-DNA binding affinities i,iS) is the affinity contribution of the amino acid iS at the ith position in the matrix and iS is the amino acid at the ith position of the peptide.A PWM was used to calculate the binding score of a potential interaction partner with a given sequence by summing the corresponding amino acids for the affinity contribution of each position. The binding score of each peptide was calculated according to the following formula:Affinity values of the 5,257 peptides against both the SNA1 and ERBIN PDZ domains were obtained from Wiedemann et al. th∼100th percentile of the binding score; To evaluate the performance of our method, we measured the ability to identify the 217 known binding partners of 145 PDZ domains in the PDZBase We compiled human protein interactions from a total of 22 existing protein interaction databases: the Bio-molecular Interaction Network Database (BIND), the Human Protein Reference Database (HPRD), the Molecular Interaction database (MINT), DIP, IntAct, BioGRID, Reactome, the Protein-Protein Interaction Database (PPID), BioVerse, CCS-HI1, the comprehensive resource of mammalian protein complexes (CORUM), IntNetDB, the Mammalian Protein-Protein Interaction Database (MIPS), the Online Predicted Human Interaction Database (OPHID), Ottowa, PC/Ataxia, Sager, Transcriptome, Complexex, Unilever, protein-protein interaction database for PDZ-domains (PDZBase), and a protein interaction dataset from the literature We collected all physical interactions mediated by the PDZ proteins from the integrated PPI network. This PDZ protein-mediated interaction set may have some interactions that are mediated by interaction domains other than PDZ domains, because many PDZ proteins have various domains other than PDZ domains. Therefore, we removed such interactions that were connected by domain-domain interactions rather than PDZ domain-ligand interactions. First, we confirmed that PDZ domain-mediated interactions are rarely augmented by other interaction domains. We found that domain-domain interactions are not present in the experimentally confirmed PDZ protein-ligand interactions from the PDZBase We also removed interactions that could be mediated by other peptide-binding domains, such as SH3 and WW domains, rather than PDZ domains. We searched the known peptide-binding motifs and removed interactions mediated by peptide-binding domains that had low binding scores. The cut-off binding score was set to the lowest binding score of the experimentally confirmed PDZ domain-peptide interactions from the PDZBase i and j, be in a common tree with humans, and species i is more distant from humans. If a human PDZ interaction is absent in species i and present in species j, we define the interaction as rewired. Thus, the rewiring occurred during the time interval between the emergence of species i and j. We also consider that all proteins in species i could be rewired to PDZ proteins in species j. Thus, we define the rewiring rate as the following:j is the number of rewired interaction found in species j; t is the divergence time from human; pall,i is the number of proteins orthologous to human protein in species i; and ppdz,j is the number of proteins orthologous to human PDZ proteins. Divergent time was obtained from the timetree (http://www.timetree.net).Let two species, To analyze the interactions between orthologous PDZ domains, we calculated the binding scores of the C-terminal sequences of orthologous PDZ ligands and the predicted PWMs with orthologous PDZ domain sequences.We examined whether particular protein functions were enriched for protein categories that were defined based on the time of protein emergence and PDZ-binding motif acquisition. We systematically classified PDZ ligands into two categories: (1) proteins arose in invertebrates and acquired PDZ domain interaction sites in vertebrates; (2) proteins arose and acquired PDZ domain interaction sites in invertebrates; we then analyzed the overrepresented functional terms of each group. We used DAVID p values for over- or under-representation of the disease-associated genes in PDZNet were obtained using a hypergeometric distribution. We independently calculated the probability of the disease-associated genes in each class.Mutations of PDZNet proteins were mapped to genetic diseases using disease-gene association databases from OMIM. The OMIM database lists gene-disease associations between 2,929 disease types defined by Morbid Map (MM) and 1,777 genes associated with particular disease types. Disease types were further categorized into 1,340 distinct diseases by joining disease subtypes into a single disease if similar disease names were used. These disease types were further classified into 20 disease classes based on the physiological system affected We created a user-friendly web service that provides a PWM and rank list of interaction candidates of a given PDZ domain sequence . The autFigure S1Comparisons of the quantitative model- and phage display data-derived PWMs of MAGI1_2, DLG1_2, and PTN13_2.(PDF)Click here for additional data file.Figure S2Distribution of the interaction partners of 97 human PDZ proteins. The maximum number of ligands per PDZ protein is 102. The average interaction partner of the human PDZ protein is 12.(PDF)Click here for additional data file.Figure S3Network representation of domain-level interactions in PDZNet (best viewed by magnification in a PDF viewer). Domain numbers are presented on the right side of the PDZ protein names with a delimiter (‘_’). The network is composed of 2,643 interactions between 190 PDZ domains and 593 ligands.(PDF)Click here for additional data file.Figure S4Dendrogram of PDZ domains based on the identity of pocket residues. Domain numbers are presented on the right side of the PDZ protein names with a delimiter (‘_’).(PDF)Click here for additional data file.Figure S5Relationship between specificity determining residue (SDR) identity and PWM similarity. Each point represents an orthologous PDZ domain pair.(PDF)Click here for additional data file.Figure S6Phylogenetic profile of human PDZ proteins and ligands across 13 fully sequenced species. The presence (yellow) and absence (black) of orthologs for the 104 PDZ proteins and 554 PDZ ligands are presented.(PDF)Click here for additional data file.Figure S7Multiple sequence alignment (MSA) of EXOC4 orthologs. The MSA was generated using Muscle with default options. C-terminal PDZ binding motifs are shown in bold.(PDF)Click here for additional data file.Figure S8Amino acid preference patterns of human PDZ domain pockets. (A–D) Clustering of amino acid preference profiles of 241 human PDZ domain pockets is shown.(PDF)Click here for additional data file.Figure S9Alternative expression of SAP97 ligands across three human tissues. The protein expression levels of SAP97 PDZ protein and its 13 ligands were compared across brain, bone, and epidermis. Protein expression was measured by quantitative mass spectrometry (PDF)Click here for additional data file.Figure S10Macaca mulatta PBK protein. The binding motif is highlighted in the PWM of SAP97_1 (right). Mutations in the PDZ-binding motif are shown in the alignment of DNA sequences (bottom). (B) A DNA segment insertion generated a PDZ-binding motif in the C-terminal amino acids of the Oryzias latipes EXOC4 protein. The binding motif is highlighted in the PWM of SAP102_1 (right). The inserted DNA segment is shown in the alignment of DNA sequences (bottom).Types of DNA modifications that gain PDZ-binding motifs. (A) A point mutation generated a PDZ-binding motif in the C-terminal amino acids of the (PDF)Click here for additional data file.Figure S11Mutation effects of the C-terminal GluR7 sequence. (A) C-terminal sequences and binding scores of wild-type and mutation forms of GluR7. (B) The PWM of the PICK1 PDZ domain. Four C-terminal residues of wild-type GluR7 are highlighted.(PDF)Click here for additional data file.Figure S12left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S13left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S14left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of proteins . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S15left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S16left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S17left) and PDZ proteins that share the same PDZ ligands (right).Repeated analysis of PDZNet by randomly removing 20% of interactions . (A) Network representation of PDZNet. (B) Paralog fractions of PDZ ligands that share the same PDZ proteins ((PDF)Click here for additional data file.Figure S18Discriminating power of selectivity axes. Each boxplot shows distributions of binders and non-binders of an amino acid, which are presented at the top of the plot. Binders are PDZ domain pockets that prefer the amino acid, and non-binders are those domain pockets that do not prefer the amino acid. The vertical axis corresponds to an axis of a selectivity space. Fisher's score (FS) is presented at the top of each plot, indicating the discriminating power of the selectivity axes.(PDF)Click here for additional data file.Figure S19Procedure for extracting pocket residues. (A) Schematic drawing of the PSD-95_1 domain structure. (B) Position of each pocket residue on the structure. (C) The MSA of three representative PDZ domains was constructed using the hidden Markov model that was optimized for the PDZ domain. By adjusting the secondary structural profile on the MSA, the positions of pocket residues were identified. Gray boxes indicate the positions of pocket residues.(PDF)Click here for additional data file.Figure S20Fraction of domain-domain interactions according to the binding scores of all PDZ protein-mediated interactions. The PDZ protein-mediated interactions were binned based on binding score. The fraction of domain-domain interactions were measured for each bin.(PDF)Click here for additional data file.Figure S21Flow chart of web server and a sample output. The web server takes a query PDZ domain sequence and a species name. The outputs are pocket residues, a PWM of the query PDZ domain, and a genome-wide rank list of proteins from the species chosen by the user.(PDF)Click here for additional data file.Table S1Position weight matrices (PWMs) for 515 human PDZ domains. For resource purposes, homologous PDZ domains are included in the list.(XLS)Click here for additional data file.Table S2in vivo partners derived from various species.Validation of PWMs on (XLS)Click here for additional data file.Table S3PDZ domain-ligand interactions in PDZNet.(XLS)Click here for additional data file.Table S4C-terminal sequences of human PDZ ligand orthologs.(XLS)Click here for additional data file.Table S5Experimental evidence of human PDZ domain-ligand interactions that emerged via sequence mutations. ‘−’ indicates the absence of an ortholog(XLS)Click here for additional data file.Table S6Over-represented gene ontology (GO) terms of PDZNet proteins based on the time point of acquiring PDZ domain interaction sites.(XLS)Click here for additional data file.Table S7Disease classes associated with mutations of PDZNet components.(XLS)Click here for additional data file.Table S8A morbid map of PDZNet components with the classification of genetic diseases.(XLS)Click here for additional data file.


Shipboard is not an absolute rigid body. Many factors could cause deformations which lead to large errors of mounted devices, especially for the navigation systems. Such errors should be estimated and compensated effectively, or they will severely reduce the navigation accuracy of the ship. In order to estimate the deformation, an unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, a nonlinear shipboard deformation model is built. Simulations demonstrated the accuracy reduction due to deformation. Then an attitude plus angular rate match mode is proposed as a frame to estimate the shipboard deformation using inertial measurement units. In this frame, for the nonlinearity of the system model, an unscented particle filter method is proposed to estimate and compensate the deformation angles. Simulations show that the proposed method gives accurate and rapid deformation estimations, which can increase navigation accuracy after compensation of deformation. The iShipboard is not an absolute rigid body. Many factors could cause deformations which lead to larger misalignment angles between the coordinates of the main INS and slave INSs ,7. GenerWhen deformation occurs, the attitude information provided by the main INS contains not only the attitude angle between navigation frame and the local coordinate frames of every equipment, but also the deformation angle between the installation spot of the main and slave INS . This era posteriori probability density function, and obtain an estimation using these sample points and weights. If the sample points are sufficient, their statistical property approximates that of an a posteriori probability density. Therefore, such estimations only have theoretical significance. The standard particle filter uses a prior probability density as an important sampling function without considering the latest measurement information. Even when the noise in the measurement information is small, on the contrary, filter accuracy decreases or diverges with a heavy calculation burden. However, the UKF focuses on the transformation and improvement of the linear Kalman filter for nonlinear systems. The system should meet Gaussian distribution conditions, otherwise, the simple representation of the state probability distribution by mean and variance will cause poor performance T with °/h units; the constant bias of the accelerator is [100 100 100]T with μg units; the white noise variance of the accelerator is [10 10 10]T with μg units; the initial installation error angles are [0.3° 0.4° −0.2°]. The wave influence on the ship is small near the dock, and will get larger if the ship departs to sea. Therefore, in order to simulate a practical situation, we assume the ship is first in uniform linear motion for 10 s, then in sinusoidal motion because of the wave influence. According to the real ship and sea environment, simulation parameters are set as follows: the sinusoidal motion of the ship is along a rolling axis with amplitude 10° and period 5 s; the correlation time of the deformation process is 60 s; according to different sea conditions , we set two deformation amplitudes, the first is [1′ 31.2′ 5.2′] and the second is [0.69′ 17.4′ 3.1′]. Simulation results are as follows: the motion transformation of the ship leads to the spike at t = 10 s in During sailing, a lot of environmental disturbance will bring different levels of ship deformation and influence the aided alignment accuracy of the shipboard INS. In order to test the influence of deformation on aided alignment, we set the initial conditions of the INS as follows: the constant drift of the gyro is [0.01 0.01 0.01]e 0.3° 0.° −0.2°. Simulation results demonstrate that the deformation angles have a certain influence on aided alignment, increasing with deformation. The installation error angles reflect the shipboard deformation estimated by the IMU. From comparison of 3.A2= −A1, A3, is a unit matrix, A4= −A1, A5, and A6 are unit matrices, A8=diag. w(t) is the unit white noise:G1(t) is the random drift of gyro,This method compares the outputs of gyro and computed attitude angles between the main INS and slave INS to estimate the gyro drift and deformation. The system equation is as follows: first, we choose the state variable We choose the measurement 4.a posteriori probability density function and the estimation because in the standard PF, the importance sampling density function did not include the latest measurement information. There is large error between this sample and the one produced by the true a posteriori probability density function. The UPF method is an expanded PF with the same basic recurrence structure. Because UKF can provide accurate three order a posteriori variance and estimation of true state variance, this method obtains better importance particle density functions and is more suitable for the PF iteration frame. The re-sampling method can avoid sample impoverishment and increase the diversity, which resolves the degeneracy phenomenon as the key problem of PF. Based on the above idea, the UPF method can be described as follows:t=0Initialization: i=1,2,…, N, N samples a priori distribution are generated:For t=1,2,…For In the PF method, we choose a set of discrete random sample points (particles) with weights to approximate the (1)(i)i=1,2,…, N:For Using UKF to update new particles:Compute sigma points:Forward propagation particles (time update):Acquire new measurement (measurement update):Sampling Assuming Sampling Assuming (ii)i=1,2,…, N, computing the importance weight:For (iii)i=1,2,…, N, normalize the importance weight:For Importance sampling procedure:(2)Choosing procedure:N random particlesWe use high/low importance weight (3)Output: As the regular PF:a posteriori probability density function may not follow a Gaussian distribution, we can use a Gaussian distribution to approximate every particle and keep the nonlinearity of system by UKF. Static deformation could be addressed by simply comparing the angular rates of the different IMUs. However, in practical application, static deformation should also be considered during dynamic deformation estimation. Therefore, in the proposed method, we take both static and dynamic deformation parameters into the state equation, and then use the UPF method to estimate them simultaneously. Based on the above UPF method, a flow chart of the proposed deformation estimation method is shown in From the above procedure, we embed the UKF into the PF frame, and can easily introduce new measurements into the state estimation. Although the 5.In order to verify the proposed method, we choose the shipboard deformation nonlinear model from It is demonstrated in Under some severe conditions, large amplitude swings caused by wave and wind disturbances lead to large dynamic deformations. In order to test the effectiveness of UPF in dynamic deformation, we increase the pitch, roll, and azimuth to 5°, 4° and 2°. Choosing 200 particles, we have the estimation results of UPF in From the results we can conclude that under the same conditions, deformation increases will cause longer convergence times. This is because of the large variation of deformation with the same particle number. Increasing the particle number will reduce the filter convergence time, but the computation burden will increase according to the theoretical analysis. For small deformations, UPF will achieve higher accuracy; for large deformations, UPF will also converge in a shorter time. The accuracy of both situations is less than 1′.The constant drift of the gyro will be obtained by initial alignment or calibration methods at the beginning when the ship departs from the dock. Moreover, such biases are not important in shipboard deformation estimation. Therefore, even though the parameter of gyro's constant drift is in the state equation of the proposed method, we only discuss the simulation results of the shipboard deformation parameters.In order to verify the efficiency of the UPF method, we choose the traditional EKF method for comparison. Simulations of static and dynamic deformation are shown in In order to verify the efficiency of the proposed method, we present a simulation of the attitude plus angular rate match mode in aided alignment. The initial conditions are the same as Section 2.2. Assuming three compensated shipboard deformation angles are [0.5′ 9.2′ 3.4′]. Estimations of attitude error angles and installation error angles are simulated in Compared to the simulations in 6.An unscented particle filter method for estimation of shipboard deformation based on an inertial measurement unit is presented. In this method, we first build the nonlinear shipboard deformation model, and produce a simulation of accuracy reduction due to deformation. An attitude plus angular rate match mode is proposed to estimate the shipboard deformation during aided alignment. For the nonlinearity of the system model, a UPF method is proposed to obtain the deformation angles and compensate to aid alignment.We use the UPF method to estimate the nonlinear model of large shipboard deformation. For different deformation angles, estimations can rapidly converge with high accuracy. The amplitude of deformation determines the convergence speed of UPF. Simulations demonstrate the estimation comparison of UPF and EKF. For static and dynamic deformation angles, UPF, which is a more effective estimation method of deformation, has higher estimation accuracy and converges more rapidly than EKF. After shipboard deformation compensation, simulation shows the accuracy of aided alignment is also increased. In future work, multiple IMUs should be considered for the ship deformation measurement. Multiple spots will provide full and effective ship deformation measurements.


Arabidopsis LSH1 and Oryza G1 (ALOG) family of proteins have been shown to function as key developmental regulators in land plants. However, their precise mode of action remains unclear. Using sensitive sequence and structure analysis, we show that the ALOG domains are a distinct version of the N-terminal DNA-binding domain shared by the XerC/D-like, protelomerase, topoisomerase-IA, and Flp tyrosine recombinases. ALOG domains are distinguished by the insertion of an additional zinc ribbon into this DNA-binding domain. In particular, we show that the ALOG domain is derived from the XerC/D-like recombinases of a novel class of DIRS-1-like retroposons. Copies of this element, which have been recently inactivated, are present in several marine metazoan lineages, whereas the stramenopile Ectocarpus, retains an active copy of the same. Thus, we predict that ALOG domains help establish organ identity and differentiation by binding specific DNA sequences and acting as transcription factors or recruiters of repressive chromatin. They are also found in certain plant defense proteins, where they are predicted to function as DNA sensors. The evolutionary history of the ALOG domain represents a unique instance of a domain, otherwise exclusively found in retroelements, being recruited as a specific transcription factor in the streptophyte lineage of plants. Hence, they add to the growing evidence for derivation of DNA-binding domains of eukaryotic specific TFs from mobile and selfish elements.Members of the  Naegleria the C6 finger is the dominant family [Entamoeba have an abundance of specific TFs from the MYB family, which have arisen through independent lineage-specific expansions (LSEs), whereas the bZIP family is only expanded in the former subclade [Specific transcription factors (TFs) bind DNA sequences distinct from the promoter elements recognized by the basal TFs to activate or repress transcription . In contt family . The abusubclade . Indeed,subclade ,5.Naegleria , the MYB family in ciliates, Entamoeba, and Trichomonas and IBD family in Trichomonas[The lineage-specific diversity in eukaryotic specific TFs have posed interesting computational and evolutionary problems in terms of identifying these proteins from the genome sequences of non-model eukaryotes and elucidating the ultimate origins of the DBDs found in them. The principle of LSE as the main evolutionary trend in specific TFs, together with sensitive computational analyses, have aided in unearthing the principal specific TFs of diverse eukaryotes. These include the ApiAP2 family in apicomplexa, the C6 finger family in ichomonas. In termichomonas. 2) DBDsichomonas. 3) DBDsichomonas. The lasichomonas-14. Wherichomonas, while iichomonas,17. Acroichomonas. Likewisichomonas,19.Arabidopsis LSH1 and Oryza G1 (ALOG) family of developmental regulators (corresponding to DUF640 in Pfam) were identified in the context of homeotic and developmental mutants in both eudicots and monocots [long sterile lemma1 or g1 mutants of the cultivated rice a homeotic transformation of the sterile lemma into a regular lemma is observed [Oryza G1 gene is homologous to the Arabidopsis LSH1 gene, which is involved in light-dependent regulation of hypocotyl length. Dominant mutants of the paralogous LSH3 and LSH4 genes suppress differentiation of leaves and disrupt the normal boundary regions between different floral organs [The monocots -22. In gobserved . The prol organs ,22. The l organs ,21. Thisl organs . We alsoArabidopsis LSH1  as a query recovered, in the first iteration, the previously reported ALOG family of proteins in multicellular plants, an AP-ATPase and TIR domain containing protein in Arabidopsis  and proteins from the metazoans Nematostella and Branchiostoma (E between 10-4 and 10-12). Subsequent iterations of the search recovered similar proteins from other plants and converged in 3 iterations. Additionally, searches of the EST database with the TBLASTN program allowed us to recover an ALOG domain protein from Spirogyra which is a representative of the algal clade of Zygnematophyceae. These translating searches also led to the detection of versions of the ALOG domain in several other metazoans, such as the molluscs Lottia and Crassostrea, the starfish Asterina pectinifera (Patiria pectinifera), the coral Acropora digitifera and the stramenopile brown alga Ectocarpus siliculosus. Iterative profile searches with the JACKHMMER program, further consistently recovered the N-terminal regions of XerC/D-like tyrosine recombinases at E-values below the significant threshold .Members of the plant ALOG family are characterized by a single globular region flanked by short N- and C- terminal low-complexity segments . We initOryza. We then ran a profile-profile comparison using the HHPRED program with a HMM derived from the ALOG domain alignment against a panel of HMMs derived using PDB structures as search seeds. This search recovered the N-terminal DBD of several members of the tyrosine recombinase clade prototyped by the XerC/D recombinases with significant scores . The profile-profile alignments completely covered the conserved core of four helices in the N-terminal DBD of the XerC/D-like clade of tyrosine recombinases by precisely skipping the central Zn-ribbon (ZnR) insert in the ALOG domain. A comparison of the conservation profiles of the tyrosine recombinase N-terminal DBD and the ALOG domain revealed a shared pattern of hydrophobic residues in all the four helices, which are critical for the stabilization of the core tetrahelical fold  or in the middle of the protein   Figure . The ALOn Figure  revealedEctocarpus. In metazoans they are found in certain cnidarians, molluscs, echinoderms and the cephalochordate Branchiostoma floridae. However, in most metazoan groups for which genome data exists, these domains are observed as being absent in the sister lineages of those that possess them. Thus, ALOG domains are present in Nematostella and the coral Acropora, but are absent in Hydra. Likewise, while they are present in the starfish Asterina pectinifera, they appear to be absent in the sea urchin Strongylocentrotus. This phyletic pattern pointed to the possibility of active mobility of this domain across phylogenetically distant organisms living in the marine environments. To better understand this mobility, we investigated the domain architectures of these versions and found that they tended to be fused to a distinct, catalytically inactive N6-adenine methylase domain at the N-terminus, and a tyrosine recombinase catalytic domain at the C-terminus that is fragmented in the metazoan versions  [ftp://ftp.ncbi.nih.gov/blast/documents/blastclust.html). The HHpred program [http://www.pymol.org) programs. The in-house TASS package, which comprises a collection of Perl scripts, was used to automate aspects of large-scale analysis of sequences, structures and genome context.Iterative sequence profile searches were performed using the PSI-BLAST  and web ckhmmer)  programsckhmmer)  and MUSCckhmmer)  programs program  was used program . Seconda program  program. program  was used program . StructuThe authors declare that they have no competing interests.LMI and LA made the discovery and prepared the paper. Both authors read and approved the final manuscript.Reviewer 1: Prof. Valerian Dolja This work by Iyer and Aravind presents a remarkable case of the Virus World incursion into evolution of the cellular organisms. Using sensitive bioinformatics approaches, the authors trace origins of the ALOG domain-containing regulators of plant development to a distinct lineage of the DIRS-1-like retrotransposons. Furthermore, investigation of the predicted folding pattern of the ALOG domains suggested that these domains function as the DNA-binding transcription factors thus enabling experimental inquiry into action mechanism of these enigmatic plant proteins. I am particularly impressed with the breadth and depth of this work spanning the areas of molecular and evolutionary biology of the selfish genetic elements and plants, exactly the areas of my own research interest. Is it not ironic that the retroelement-derived domain was recruited by flowering plants to not only modulate their development, but also to fortify their innate immune response via including DNA-sensing ALOG domain into counterdefensive TIR and LRR repeat proteins? I am certain that the students of both plant development and plant-pathogen interactions will be enthusiastic about testing the predictions of this study.The manuscript is very well written and documented and is a pleasure to read. There are only a few stylistic inaccuracies that need to be corrected (one example is fourth sentence of the Abstract starting with Recently).Authors’ response:We thank the reviewer for his positive comments. We have revised the manuscript for stylistic, grammatical and typographic errors.Reviewer 2: Dr. Gaspar Jekely Ectocarpus. The ALOG domain was also identified in several marine invertebrates, and the domain composition of these ALOG domain-containing proteins suggests that they also derive from mobile elements.The paper by Iyer and Aravind convincingly demonstrates that the ALOG domain, first described as being part of certain plant developmental proteins, derives from a DNA-binding domain found in DIRS1-like mobile elements. This discovery suggests that the ALOG domain may also have a DNA-binding function in plants. The ALOG domain thus represents an addition to the growing list of DBDs derived from mobile elements. The careful sequence and structural analyses presented could guide future experiments in plant models. The authors also identify a putatively active, novel type of DIRS1-like retrotransposon from the brown alga Crassostrea gigas (EKC24824) that has recently been added to the database could also be added to the sequences.) If independent origins can be confirmed, this would suggest that with further sampling in marine organisms, an active version of the element may be found (as in Ectocarpus) or at least could be reconstructed from several recently inactivated ones. This could have potential practical uses in emerging marine models like Nematostella.The widespread but patchy occurrence of the ALOG domain together with domains characteristic of mobile elements points to the active mobility of these elements in marine environments. However, given that all the elements seem to be degraded in animals, the alternative possibility is that the patchy distribution is due to a single early origin and linear descent combined with occasional losses (e.g. in Hydra). It would be interesting to see whether or not the animal part of the tree corresponds to the known phylogeny. A correspondence would support linear descent, the lack of it would rather suggest independent multiple origins. (The ALOG domain from the oyster Authors’ response:We thank the reviewer for pointing out to us about the availability of the Crassostrea genome, data from which we have now included in this study. We recovered three, almost identical, copies of the element in its genome, but they all possess several stop codons and frame shifts in the coding sequences of the different ORFs (see Additional file suggesting that, like the versions in other metazoans, they have been recently inactivated. We provide, in Additional file 1, several examples of reconstructed ALOG-containing DIRS1 elements from different metazoans andEctocarpus. Although the metazoan sequences group together in phylogenetic trees, their branching patterns fail to recapitulate even the reliable species relationships. Thus, while gene loss in sister species could have played a role, the available evidence supports the important role of lateral transfer of this element between various marine species.ALOG domains: provenance of plant homeotic and developmental regulators from the DNA-binding domains of a novel DIRS1-class retroposon.Click here for file


Chlamydia trachomatis. Despite structural homology within both receiver and effector domains to prototypical subfamily members, ChxR does not require phosphorylation for dimer formation, DNA binding or transcriptional activation. Thus, we hypothesized that ChxR is in a conformation optimal for DNA binding with limited interdomain interactions. To address this hypothesis, the NMR solution structure of the ChxR effector domain was determined and used in combination with the previously reported ChxR receiver domain structure to generate a full-length dimer model based upon SAXS analysis. Small-angle scattering of ChxR supported a dimer with minimal interdomain interactions and effector domains in a conformation that appears to require only subtle reorientation for optimal major/minor groove DNA interactions. SAXS modeling also supported that the effector domains were in a head-to-tail conformation, consistent with ChxR recognizing tandem DNA repeats. The effector domain structure was leveraged to identify key residues that were critical for maintaining protein - nucleic acid interactions. In combination with prior analysis of the essential location of specific nucleotides for ChxR recognition of DNA, a model of the full-length ChxR dimer bound to its cognate cis-acting element was generated.ChxR is an atypical two-component signal transduction response regulator (RR) of the OmpR/PhoB subfamily encoded by the obligate intracellular bacterial pathogen  Two-component signal transduction systems (TCS) are a fundamental mechanism employed by bacteria for rapid adaptation to environmental changes. TCS typically consist of a membrane-bound sensor histidine kinase (HK) and an associated response regulator (RR). Upon sensing stimuli, the sensor kinase undergoes an autophosphorylation event from which the phosphoryl group is then transferred to the receiver domain of a cognate RR. Phosphorylation of the RR promotes oligomerization, stabilizing the active form of the protein. The majority of response regulators contain a DNA-binding domain that alters gene expression in response to phosphorylation Members of the OmpR/PhoB RR subfamily are composed of a receiver domain that contains the site of phosphorylation and homodimerization, and an effector domain that interacts with DNA through the subfamily-defining winged helix-turn-helix motif and RNA polymerase machinery through a transactivation loop M. tuberculosis and DrrB T. maritima support that the receiver domain forms an extensive intramolecular interface with the effector domain effectively occluding the α4-β5-α5 dimerization interface and resulting in an equilibrium skewed towards an inactive (off) state M. tuberculosis and DrrD T. maritima, have relatively limited interdomain interfaces for which the DNA binding regions are in solvent accessible positions. These structures reflect the ability of unphosphorylated forms to bind DNA, albeit at lower affinity than phosphorylated forms, in a weak on-state that is enhanced by phosphorylation and stabilized by homodimerization. Importantly, these observations are from a collection of full-length structures of unphosphorylated OmpR/PhoB response regulators limiting our understanding of the structural and functional diversity employed by this large subfamily.OmpR/PhoB interdomain interactions and overall conformations are diverse and appear to reflect the relative DNA ‘on-off’ equilibrium for an individual RR H. pylori for which a full-length NMR solution structure has been determined A relatively new subset of response regulators are the atypical RRs which do not require phosphorylation for activity and have been described in a broad range of phylogenetically diverse organisms. The receiver domain active site typically lacks conserved residues involved in phosphorylation, yet maintains structural homology with prototypical OmpR/PhoB RRs ChlamydiaChlamydia gene expression based upon the relatively high number of potential binding sites Chlamydia formation ChxR is an atypical OmpR/PhoB subfamily response regulator encoded by the medically important bacterial pathogen Eff) and full-length ChxR (ChxR) were purified as previously described E. coli BL21(DE3)  and initially purified through metal (Co2+) affinity chromatography. Following their elution from the affinity column, each protein was further purified by size exclusion chromatography and determined to be >95% pure by Coomassie staining after SDS-PAGE.The effector domain of ChxR (ChxREff was overexpressed in E. coli BL21(DE3) cells and 13C/15N labeled using a previously established method Eff was purified as described above. The purified protein was equilibrated in 20 mM Na2HPO4 pH 6.5, 20 mM KH2PO4, 20 mM NaCl, and 1 mM DTT and then concentrated to 1.5 mM using an Amicon  3,000 molecular weight cut-off centrifugal device.ChxREff and 10% D2O. All NMR spectra were recorded on a BRUKER AVANCE 800 MHz NMR instrument equipped with a TCI cryoprobe. All NMR experiments were recorded at 25°C. Sequential assignments of the backbone resonances of ChxREff were achieved by 2D and 3D- hetero nuclear triple resonance NMR experiments, 2D-1H-15N-HSQC, 2D-1H-13C-HSQC, 3D-HNCA, 3D-HNCO, 3D-HNCACB, 3D-CBCA(CO)NH, 3D-HBHA(CBCACO)NH, 3D-HBHANH. Side chain assignments were obtained from 3D-H(CCCO)NH, and 3D-HCCH-TOCSY experiments NMRPipeSPARKYThe sample for NMR spectroscopy experiments was comprised of 90% 1.5 mM ChxRRec-ChxREff chemical shift titration experiment, ChxREff was 15N labeled and purified as described herein and ChxRRec was expressed and purified as described previously 1H-15N-HSQC was taken of 1 mM ChxREff, unlabeled ChxRRec was titrated into the protein sample. 2D-1H-15N-HSQCs were taken at 2∶1 and 1∶1 molar ratios of ChxREff to ChxRRec.For the ChxRTALOS+CNS 1.2 PROCHECK-NMR15N and 13C edited NOESY-HSQC experiments. The mixing time was 100 ms for 15N-edited NOESY and 120 ms for 13C-edited NOESY NMR experiments. The distance restraints were subdivided into four groups on the basis of NOE peak intensities: 1.8–2.8 Å for strong NOEs, 1.8–3.4 Å for medium NOEs, 1.8–5.0 Å for weak NOEs and 1.8–6.0 Å for very weak NOEs. In addition, backbone dihedral angles φ, and ψ determined using TALOS+ were restrained to −60°±35° (φ) and −40°±30°(ψ) for α-helical regions. For β-strands the values were taken as φ = −120°±30° and ψ = 120°±30°. Final statistics are listed in Structures were calculated by restrained simulated annealing using NOE based distance restraints and 15N T1 and T2 relaxation times and values for the 1H-15N NOE were measured on a 600 MHz VARIAN INOVA Spectrometer using standard pulse sequences SPARKYC; ns time scale) of a monomeric protein (<25 kDa) in solution is approximately 0.6 times its molecular weight (kDa). The τC value was calculated from the following equation chxR promoter Eff. The assay was performed in triplicate and the amount of DNA shifted was visualized and quantified using the Odyssey Infrared Imaging System .Electrophoretic mobility shift assays were performed as described for ChxR with DNA corresponding to the high-affinity (DR2) binding site within the Site-directed Mutagenesis - Mutations were introduced into the full-length ChxR plasmid −1 to 0.33 Å−1. The scattering vector is defined as q = 4π sinθ/λ, where 2θ is the scattering angle. All experiments were performed at 20°C and data was processed as previously described SAXS data were collected at the ALS beamline 12.3.1 (SIBYLS) LBNL Berkeley, California g) was derived by the Guinier approximation I(q) = I(0)*exp(-q2Rg2/3) with the limits qRg <1.3. The curves measured for different protein concentrations  displayed a concentration dependence arising from inter-particle interaction (attractions) at q <0.05 Å−1 and interference free scattering profiles were estimated by extrapolating the measured scattering curves to infinity dilution  were merged to define the entire scattering profile. Different protein concentrations were tested for aggregation and examined by Guinier plots. The radius of gyration (R1 symmetry operator unconstrained) and one requiring that a head-to-tail orientation of the effector domains be maintained (constrained). The unconstrained model set allowed for all possible orientations of the linker and effector domain, relative to the receiver domain dimer. All possible orientations were also generated for the constrained model set, with the requirement that a head-to-tail orientation be maintained for the effector domains. A Minimal Ensemble Search (MES) was ultimately used to select two conformers from a pool of all generated constrained conformers that achieved the best fit (χ) to the experimental curve The overall shape was restored from the experimental data using the program DAMMIF with PComparison of the structural properties of the selected conformers allowed us to distinguish the degree of flexibility and heterogeneity of the experimental system C. trachomatis, 15605361; PhoB, E. coli, 213521171; OmpR, E. coli, 242378928; HP1043, H. pylori, 15645657; DrrB, T. maritima, 15642901; RegX3, M. tuberculosis, 15607632; MtrA, M. tuberculosis, 509815; PrrA, M. tuberculosis, 397672721; DrrD, T. maritima, 15643165. Three-dimensional structures were superimposed using the Local-Global Alignment method (LGA) C. trachomatis ChxR sequence. Secondary structure elements are numbered with respect to their position in full-length ChxR.Multiple sequence alignments were carried out using ClustalW http://www.rcsb.org/) as well as Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu/) (code 17014).The atomic coordinates and structure factors (code 2M1B) have been deposited in the Protein Data Bank, Research Collaboratory for Structural Bioinformatics, Rutgers University, New Brunswick, NJ (5-TAGATAA-3′) binding site in the chxR promoter with a dissociation constant (Kd) of approximately 44 nM Eff (residues 115–227) alone can to bind to DNA, an electrophoretic mobility shift assay (EMSA) was performed with the DR2 DNA sequence. Indeed, ChxREff can bind to DNA , albeit at a much weaker affinity than full-length ChxR. Increasing concentrations of ChxREff (50 nM–10 μM) were quantified with respect to DNA interaction and a Kd was calculated (450±75 nM). The calculated Kd assumes that two ChxREff molecules are bound to the DNA as the DR2 sequence contains two binding sites. The approximate 10-fold decrease in DNA affinity for ChxREff relative to ChxR likely results from the lack of receiver domain-mediated dimerization to stabilize the protein-nucleic acid complex. This result is in agreement with previous studies on OmpR/PhoB RRs that demonstrated dimerization promotes DNA interaction We have previously shown that ChxR interacts with DNA corresponding to the DR2 (5′-TCGATCA-NRec), which has many unique features compared to typical OmpR/PhoB subfamily members Eff) through NMR Spectroscopy. Analysis of the structure of ChxREff will help facilitate the identification of residues important to DNA binding.We have previously elucidated the structure of the ChxR receiver domain  spectrum was analyzed with a 1H-15N-labeled sample of ChxREff. Resonance signals for 105/112 residues were detected  displaying good Ramachandran plot statistics and low restraint violations were selected for further analysis. An average of 15 NOEs per residue (1637/112) constrains the ChxREff structure, while 84% (94/112) of the dihedral angles were defined. The RMSD of the backbone atoms of the mean structure was 0.76±0.14 Å, indicating a high degree of structural similarity across the 25 lowest energy structures. All relevant NMR statistics are listed in Eff was deposited in the Protein Data Bank under the identification number 2M1B and the Biological Magnetic Resonance Data Bank under code 17014.To determine whether an NMR approach was suitable for investigating the structure of ChxRdetected Fig. 1. Eff , characterized by a high degree of conformational flexibility . The overall topology of ChxREff is β6–β7–β8–β9–α4–β10–α5–α6–α7–β11–β12.The structure of ChxREff Fig. 2A Eff. were investigated by 15N T1, T2 and Heteronuclear-NOE, with an average T1 value of 0.43±0.06 s and an average T2 value of 0.064±0.018 s. Het-NOEs for well-defined regions in the structure were found to be in the range of 0.75–0.85 indicating that internal motions on the ps (fast motion) time scale are restricted. Het-NOEs for the N- and C-termini as well as loop residues 174–182 were significantly smaller than average. The overall rotational correlation time (τC) was estimated to be 8.66±0.40 ns from T1/T2 ratios of residues selected from well-defined regions of the structure. This value indicates that ChxREff exists in a monomeric state in solution  = 7.6 ns) and is in good agreement with those reported in the literature for proteins of similar size The backbone dynamics of ChxREff . The high structural similarity between OmpR/PhoB subfamily effector domains has been proposed to be a result of the conservation of 13 hydrophobic residues that comprise the hydrophobic core of the effector domain Eff.In contrast to the atypical receiver domain dimerization interface, the structure of ChxREff and structurally characterized OmpR/PhoB effector domains exist within the transactivation loop and the disordered C-terminal extended coil. The transactivation loop is a site of variable function within this subfamily of DNA binding proteins. Not surprisingly, sequence conservation is completely absent within this region for the aligned OmpR/PhoB subfamily members  was used to analyze the solution state of ChxR.Due to the paucity of full-length structures of OmpR/PhoB subfamily RRs in the active state, little is known structurally about receiver-effector domain interactions and effector domain orientation upon phosphorylation. While ChxR is a member of the OmpR/PhoB subfamily, it is an atypical response regulator, meaning that dimerization and function are retained in the absence of phosphorylation SAXS, with recent technical and computational advances, has become a robust and effective technique for analyzing macromolecular structures, including the generation and experimental validation of relatively high-resolution models of proteins in solution , Fig. S4A). Linear dependence between Intensities, I(q), and concentrations indicated a systematic influence of individual scattering factors, S(q), up to q <0.05 Å−1 . As this q value was outside the first Shannon channel  SAXS profile at q >0.05 Å−1. This scattering profile was used for subsequent data analysis. The resulting Guinier plot  was linear, which indicated the sample was relatively free of aggregation and gave a radius of gyration of 30.8±0.3 Å. Estimated molecular mass using the Porod Volume was ∼62 kDa, which is consistent with the dimeric state of ChxR . All further scattering analyses were determined from this interference free (e.g. aggregation) SAXS curve. Analysis of the Kratky and Porod-Debye plots  reveals that ChxR may have partially unfolded or flexible regions . Additional SAXS data collection parameters are available in SAXS data were collected and analyzed on full-length ChxR. Concentration dependent scattering (from interparticle interference or aggregation) can be revealed by superposition of scaled scattering curves at multiple concentrations . The entire ensemble of both ChxR model sets was used to calculate theoretical SAXS profiles with FoXS constrained conformer , indicative of an incorrectly modeled domain conformation constrained versus unconstrained conformers indicated a better relative fit across all constrained models (e.g. constrained vs. unconstrained) analyses provided stronger support for the ChxR DNA binding domain being in a ‘head-to-tail’ orientation, reflective of the direct symmetry required to interact with the DNA repeats found within ChxR promoter sites Reconstitution of the solvated molecular envelope was carried out with a full-length model of ChxR generated by MODELLER onformer Fig. 5 wconstrained dataset, Minimal Ensemble Search (MES) was applied to determine the level of conformational heterogeneity in ChxR and develop refined conformers that better match the experimental scattering (reviewed in constrained dataset (including the single best fit conformer) were together compared to the experimental curve  relative to the remainder of the protein Eff and ChxRRec failed to detect interdomain interactions (unpublished data).Full-length RRs, including ChxR, have proven quite recalcitrant to structural determination, which likely is a reflection of the highly flexible interdomain interfaces formed by the receiver and effector domains. As such we propose that the available structures can be classified into three structural and functional subclasses based upon interdomain interactions and the resulting steric hindrance of the DNA recognition helix within the effector domain. The full-length structures of MtrA et al. demonstrated that interdomain interfaces stabilize the inactive state and inhibit phosphotransfer-mediated activation These structural subclasses are merely snapshots of an equilibrium that exists between inactive and active conformational states for prototypical OmpR/PhoB RRs black star in blue triangles in TXGAXXX) are highly conserved among ChxR binding sites. Additionally, when these conserved nucleotides were mutated and naturally variant, ChxR binding was severely reduced OmpR/PhoB subfamily RRs regulate a diverse collection of biological processes involving signaling, metabolism and development, among others. As such, great diversity in the target DNA sequences of these members is not unexpected. This naturally leads to unique protein-DNA interactions among subfamily members. Residues critical for DNA binding have also been identified in OmpR pho box having been reported 3–activated α4–β5–α5 receiver dimer Fig. S6). Of the 17 amino acid contacts within the PhoB-DNA complex, 9 are conserved within ChxR. Furthermore, the majority of the contrasting side chain interactions can be found within the recognition helix, which is anticipated given the differences in target DNA sites The large majority of OmpR/PhoB subfamily RRs have been found to bind direct DNA repeats, which thus requires these proteins to form functional dimers et al.The proposed model of full-length ChxR has the domains (receiver and effector) in different paired orientations. Specifically, the regulatory domains have been determined to interact with two-fold symmetry, which is strongly supported by crystallographic data for ChxR and almost all other OmpR/PhoB subfamily members. However, the DNA binding domains of ChxR appear to have tandem symmetry (head-to-tail) based upon the SAXS analysis presented herein, as well as their monomeric state and reduced DNA binding affinity in the absence of the receiver domain. This matches the orientation of the DNA sequence motif (direct repeat) recognized by ChxR Support for the ChxR-DNA model is limited by the relative structural absence of effector domains bound to DNA with only two examples of protein bound to neighboring sites. However, the overall expectation of independent domain orientation is further supported as most OmpR/PhoB subfamily response regulators bind to direct repeats of DNA sequences, which would seemingly require two similarly oriented winged helix DNA binding motifs. One of the few OmpR/PhoB subfamily members that bind to inverted repeats is the atypical response regulator HP1043 Chlamydia genetics Chlamydia. Observations described herein are essential in directing those future studies, specifically the possibility of generating dominant negative ChxR variants. Substitutions that rendered ChxR incapable of binding to DR2 still retained the ability to form homodimers. Furthermore, prior studies demonstrated that binding to both direct repeats was essential to stabilizing the protein-nucleic acid complex Chlamydia. This would enable phenotypic and functional studies  to provide a better understanding of the role of ChxR in the chlamydial developmental cycle and pathogenesis.Lastly, the recent advances in Figure S1Eff.DNA-binding analysis of ChxR To determine if ChxREff can interact with DNA in the absence of the receiver domain, EMSAs were performed with IR800-labeled DNA corresponding to the DR2 site (1 nM) from the chxR promoter and increasing concentrations  of recombinant ChxREff. The first lane (left) contains DNA in the absence of ChxREff.(TIF)Click here for additional data file.Figure S2EffSuperposition of 25 lowest energy conformers of ChxR  NMR solution structure.(TIF)Click here for additional data file.Figure S3Structural Superposition of OmpR/PhoB Effector Domains. Stereo view of OmpR/PhoB effector domain structures in ribbon format. Structures correspond to the following proteins/organisms: ChxR, C. trachomatis (gray); OmpR, E. coli (green); PhoB, E. coli (yellow); KdpE, E. coli (blue); PhoP, M. tuberculosis (orange) and HP1043, H. pylori (magenta).(TIF)Click here for additional data file.Figure S4Experimental Scattering Profile, Guinier Plot and P(r) function of ChxR.A, Experimental scattering profile of ChxR for 5 mg/ml (blue), 2.5 mg/ml (magenta), 1.25 mg/ml (green) and extrapolated curve at the infinity dilution (cyan). B, Intensities obtained for scaled SAXS profiles (panel A) at q = 0.02, 0.03 and 0.05 Å−1 indicate effect of the Structure factor at higher protein concentration for q <0.05 Å−1. To eliminate this effect we used infinity dilution for further data analysis C, Guinier plot with Guinier region. A linear dependence of ln(I(q)) vs. q2 indicates the sample is free of aggregation. Radius of gyration (Rg) values as obtained from Guinier plot: Glmn Rg = 30.8±0.3 Å. D, Pair distribution function P(r) calculated from the SAXS curve shown in (TIF)Click here for additional data file.Figure S5Kratky and Porod-Debye Plot of ChxR.A, Experimental SAXS curve shown as a Kratky plot indicate minimal flexibility. B, We performed a Porod–Debye analysis to obtain direct insights into their flexibility. In a plot of the normalized q4•I(q) vs. q4, the positive slope and obtained Porod-Debye coefficient of P = 3.8 is consistent with inter-domain flexibility (27). This observation suggests that the ChxR C-terminus remains flexible, resulting in the upward slope in the Kratky plot at high q values.(TIF)Click here for additional data file.Figure S6Structural Superposition of ChxR and PhoB Receiver and Effector Domains.A, Structural superposition of a full-length ChxR monomer (cyan) model from SAXS analysis with receiver (PDB ID: 1B00) and effector (PDB ID:1GXQ) domain monomers from E. coli PhoB . B, Structural superposition of ChxR receiver domain dimer  and BeF3–activated PhoB receiver domain dimer . C, Structures from panel A and B were superimposed by Local-Global Alignment in order to access structural similarities. Table displays quantitative analysis of all superimpositions.(TIF)Click here for additional data file.


NOS are large modular enzymes present in all kingdoms which through evolution were the result of multiple gene and genome duplication events together with changes in protein architecture  circulating in the troposphere and the earth surface during the last century, correlating with the population increase. As an undesirable consequence, high levels of reactive N are polluting the environment where humans inhabit. Nitric oxide (NO) is one of the reactive N species with both positive and negative impact on life. NO synthases (NOSs) are enzymes that oxidize arginine to citrulline and generate the denitrifying intermediate NO which can be subsequently reduced to N4-Cys motif involved in Zn coordination seems to be conserved in all animal NOS described so far  were the first NOS structures to be biochemically characterized, crystallized and their complete structure deciphered by X-ray diffraction. These NOSs are functional as homodimers, each monomer consisting on an N-terminal oxygenase domain (NOSoxy) containing the binding sites for the cofactors heme, tetrahydrobiopterin (BH4) and for the substrate L-arginine, and a C-terminal reductase domain (NOSred) that binds to NADPH, FAD and FMN Figure . Mammalir Figure . The NOSr Figure . Particur Figure . The isor Figure .Ostreococcus tauri represents the first NOS characterized in photosynthetic organisms (plant kingdom)   required for NOS activity  resulted in a different and novel NOS structure. ScNOS is the only characterized bacterial NOS with a covalently attached reductase domain (NOSred). This reductase module has a 2Fe2S ferredoxin domain, a FAD- binding motif and a NAD-binding motif. Interestingly, scNOS has an inverted structure: the NOSred domain is located at the N-terminal and NOSoxy at the C-terminal , although it still remains to be confirmed the NOS activity of these proteins.Most bacterial NOSs have been described in Gram-positive bacteria and consist of the NOSoxy domain lacking of the C-terminal NOSred domain Figure . NOSoxy The lack of apparent NOS in the plants which are most commonly worked on has led to suggest that either plants have lost this gene in the course of evolution or the gene has strongly diverged to a yet unknown new type of NOS. Since several evidences support an arginine-dependent NO production in higher plants reminiscent of a NOS activity (Cueto et al., Overall, the unexpected diversity of the NOS structures that are currently reported in the literature should allow us to keep optimistic for identifying the NOS gene/s or protein/s complex responsible of NO generation from L-arginine in higher plants.


The kinase-inducible domain interacting (KIX) domain is a highly conserved independently folding three-helix bundle that serves as a docking site for transcription factors, whereupon promoter activation and target specificity are achieved during gene regulation. This docking event is a harbinger of an intricate multi-protein assembly at the transcriptional apparatus and is regulated in a highly precise manner in view of the critical role it plays in multiple cellular processes. KIX domains have been characterized in transcriptional coactivators such as p300/CREB-binding protein and mediator of RNA polymerase II transcription subunit 15, and even recQ protein-like 5 helicases in various organisms. Their targets are often intrinsically disordered regions within the transactivation domains of transcription factors that attain stable secondary structure only upon complexation with KIX. In this article, we review the KIX domain in terms of its sequence and structure and present the various implications of its ability to act as a transcriptional switch, the mechanistic basis of molecular recognition by KIX, its binding specificity, target promiscuity, combinatorial potential and unique mode of regulation via allostery. We also discuss the possible roles of KIX domains in plants and hope that this review will accelerate scientific interest in KIX and pave the way for novel avenues of research on this critical domain. These coactivators thus serve as bridging molecules between transcription factors and the transcription machinery, or as enzymes that introduce chemical modifications on DNA. One such coactivator is the cAMP-response element-binding protein (CREB)-binding protein (CBP), a large multi-domain protein containing stretches of intrinsically disordered linker regions and a number of structured protein-binding domains that interact with a large variety of transcription factors. One of the domains of CBP is a vital, independently folding and highly conserved three-helix bundle region termed as the kinase-inducible domain interacting (KIX) domain. It was discovered by Parker et al. , in 1996et al. . As showet al. ,5. KIX het al. , breast et al. , c-Jun  Tax , human iV-1) Tax  and E2 (V-1) Tax  of virusV-1) Tax B. Directbidopsis . Importabidopsis . KIX dombidopsis D, which bidopsis ,22. In tbidopsis ,24. SoluDespite elucidation of its crucial role in transcription, several gaps remain in our knowledge of the KIX domain, specifically in terms of its ability to recognize and assist in folding of intrinsically disordered proteins, its role in secondary structure determination, its varying binding modes, mechanism of allostery, high structural plasticity and even some of its cellular functions. Furthermore, very little is known about KIX domain proteins in plants, although a recent association analysis of SNP loci in 23 rice genotypes identified three non-synonymous SNP loci in plant KIX domains that showed strong association with important agronomic traits like grain size . Thus, iSer-133 of CREB KID domain, resulting in CREB–CBP complex formation  region of CREB, as shown in 2 and α3 B.FigureTyr-640 and positively charged guanidinium group of a highly conserved Arg-600  linked by a turn, that wrap around α3 of KIX in a shallow hydrophobic pocket on KIX surface B. This p Arg-600 B. It has of CREB . Interes of CREB , disruptApart from the cation–pi interaction, residues that lie beyond the globular KIX core have been shown to influence its secondary structure and stability. In particular, a stretch of six residues (KRRSRL) observed at the C terminus of an extended α3 helix in some KIX domains  of KID that makes direct contacts with the KIX domain. The phosphate group of pSer-133 forms a hydrogen bond with Tyr-658 and a salt bridge with Lys-662 of KIX domain , a subunit in the mediator/ARC complex of humans, was originally identified as a SREBP1a-binding domain . SREBP p-Myb ADs ,45. ThusSaccharomyces cerevisiae and Candida glabrata recognize the presence of xenobiotics by directly binding to them and then binding to KIX domain of Gal11p  protein involved in maintenance of homeobox (Hox) gene expression during early embryogenesis, while c-Jun is a human transcriptional coactivator and a mediator of protein–protein interactions, involved in diverse cellular process including differentiation and apoptosis. The first discovery was the outcome of an investigation into whether MLL and c-Myb proteins were competing for CBP binding or instead interacting cooperatively with CBP, based on the facts that, (i) MLL is a binding partner for both c-Myb and pKID, (ii) both MLL and c-Myb were known to express at the same time during development of hematopoietic precursor cells, (iii) both play a pivotal role in blood cell proliferation, and finally, (iv) both could bind KIX domain  in contrast to a single aromatic side chain Tyr-631 of KIX in the vicinity of the c-Jun-binding site revealing two distinct interaction surfaces . This conserved TAD motif does not provide any binding site specificity to transcription factors as it is observed in the amphipathic α helices of transactivation domains of many transcription factors such as CREB-KID, MLL, c-Jun, c-Myb, p53, E2A and Forkhead box class O 3a (FOXO3a) . This siInterestingly, as mentioned in Arg-61 and Glu-306, subsequently followed by a rapid folding step driven by h-bond interactions at the opposite side of the interface  is due to the fact that they constitute the allosteric regulatory network described above  methylate various proteins including histone and non histones . PRMTs hArg-600 in the KIX domain, a residue discussed earlier in this review as being critical for KIX stabilization. Modification of the Arg-600 was believed to destabilize the KIX domain by disrupting a buried cation–pi interaction between positively charged guanidinium group of this residue and the aromatic ring of Tyr-640, an interaction that is critical for specifying the unique cooperatively folded KIX structure as shown in in vitro and in vivo) carried out by independent groups following the initial observation of KIX methylation at Arg-600, a consensus could not be reached for the exact region of methylation on CBP and it was argued that methylation by CARM1 actually occurs at an unstructured C-terminal extension of the CBP KIX domain (region 568–828), rather than its core region . Initial reports suggested that KIX domain may not be conserved in Arabidopsis thaliana but later studies using profile based search tools established the presence of at least five KIX-like domains in proteins of this plant including three HATs, namely HAT of CBP family 1 (HAC1), HAC5 and HAC12  proteins of rice. Although functional analysis is yet to be done, it seems that KIX domain in such FBX proteins can help them interact with the proteins targeted for proteasomal degradation. We also analyzed these proteins in terms of gene structure, domain architecture, phylogenetics, comparative transcriptomics and SNP based association analysis  grant from the Department of Biotechnology (DBT), Government of India, awarded to [BT/BI/12/045/2008 to J.K.T. and BT/BI/12/040/2005 to G.Y.]; Research grant [BT/PR14519/BRB/10/869/2010 from DBT to J.K.T.]; Recipient of the junior research fellowship (JRF) of the University Grants Commission, Government of India (to A.V.). Funding for open access charge: National Institute of Plant Genome Research.Conflict of interest statement. None declared.


Key steps in a viral life-cycle, such as self-assembly of a protective protein container or in some cases also subsequent maturation events, are governed by the interplay of physico-chemical mechanisms involving various spatial and temporal scales. These salient aspects of a viral life cycle are hence well described and rationalised from a mesoscopic perspective. Accordingly, various experimental and computational efforts have been directed towards identifying the fundamental building blocks that are instrumental for the mechanical response, or constitute the assembly units, of a few specific viral shells. Motivated by these earlier studies we introduce and apply a general and efficient computational scheme for identifying the stable domains of a given viral capsid. The method is based on elastic network models and quasi-rigid domain decomposition. It is first applied to a heterogeneous set of well-characterized viruses  for which the known mechanical or assembly domains are correctly identified. The validated method is next applied to other viral particles such as L-A, Pariacoto and polyoma viruses, whose fundamental functional domains are still unknown or debated and for which we formulate verifiable predictions. The numerical code implementing the domain decomposition strategy is made freely available. The genetic material of viruses is packaged inside capsids constituted from a few tens to thousands of proteins. The latter can organize in multimers that serve as fundamental blocks for the viral shell assembly or that control the capsid conformational transitions and response to mechanical stress. In this work, we introduce and apply a computational scheme that identifies the fundamental protein blocks from the structural fluctuations of the capsids in thermal equilibrium. These can be derived from phenomenological elastic network models with minimal computational expenditure. Accordingly, the basic functional protein units of a capsid can be obtained from the sole input of the capsid crystal structure. The method is applied to a heterogeneous set of viruses of various size and geometries. These include well-characterised instances for validation purposes, as well as debated ones for which predictions are formulated. The genomic material of many viruses is encapsidated inside icosahedral protein shells with diameters in the 20–100 nm range. The number of structurally inequivalent protein units that tessellate these capsids is usually very small Understanding the organization of viral capsids at levels that are intermediate between the single protein units and the fully assembled, infectious particles is crucial to elucidate key aspects of the viral life cycle. These include the molecular basis of capsid conformational changes, such as swelling or maturation events The identification of these units has so far been carried out for few viruses using advanced experimental or numerical techniques for probing and modelling capsids assembly/disassembly kinetics and thermodynamics, internal dynamics and response to mechanical stress These approaches have proved extremely valuable to gain insight into various mechanisms controlling the physico-chemical behaviour of few specific viruses As a step towards developing a general scheme for identifying functional and structural units in viral shells, here we introduce and apply a novel and efficient computational strategy that can single out capsid domains that, according to various criteria, are expected to be mechanically stable. The method consists of a decomposition of the capsid into quasi-rigid units based on a suitable analysis of its internal dynamics. In accord with the mesoscopic spirit of the approach, the sought internal dynamics can be efficiently obtained from elastic network approaches, in place of computationally-demanding molecular dynamics simulations.The variational decomposition strategy is applied to several viruses covering a wide range of sizes and capsid classes, from T = 1 to pT = 7. For validation purposes, the set includes several well-characterised instances: the cowpea chlorotic mottle virus (CCMV), the MS2 virus, the satellite tobacco necrosis virus (SNTV) and satellite tobacco mosaic virus (STMV). The units obtained from the decomposition are in excellent agreement with known basic blocks of the assembly/disassembly process or of the structural transitions.These successful comparisons give confidence in the viability of the strategy for identifying putative functional units of viral capsids. This suggests that the method could be profitably used for interpreting viral assembly, disassembly and genome uncoating experiments or as a predictive tool. Towards this latter goal, we conclude the present study by formulating predictions for a number of viruses whose capsid structure is available but whose functional units are still unknown, or debated. This prediction set includes the L-A (pT = 2), Pariacoto (T = 3) and polyoma viruses (pT = 7).http://people.sissa.it/~michelet/vircapdomains.The decomposition algorithm, which is formulated in a general and hence transferable way, is made freely available for academic use at the link: The main objective of this study is to investigate whether, and if so how, a suitable analysis of quasi-rigid domains of fully-assembled viral shells can identify the functional units of a capsid. With this term we refer to those protein domains that are either: (i) the basic, undeformable building blocks (capsomeres) that can be used to describe the structural transitions of a capsid or (ii) its fundamental assembly/disassembly blocks. Although, for brevity, these two unit types are collectively referred to as “functional units”, their clear distinction must be borne in mind The quasi-rigid decomposition approach is motivated by the observation that the large-scale internal dynamics of proteins, or protein assemblies, is often well-described by the relative rigid-like motion (rotations and translations) of a limited number of subdomains Based on this observation and building on the successful multiscale or coarse-grained simulations of viral shells modeled as assemblies of rigid tiles We accordingly performed quasi-rigid domain decompositions of several viral capsids for which the atomic structural data is publicly available The decomposition algorithm is detailed in the Calculation of structural fluctuations via ENM. The first step consists of characterizing a capsid's internal dynamics using an elastic network model (ENM). As detailed in the in vivoin silico when realistic force fields are used Exploration of capsid subdivisions into putative quasi-rigid domains. Second, the ENM-based structural fluctuations are analysed to identify the putative quasi-rigid domains. Specifically, the capsid is subdivided into non-overlapping groups of amino acids whose internal pairwise distances have negligible fluctuations compared to the overall capsid motion. Because the optimal, “innate” number of quasi-rigid units is not known a priori, we consider all possible capsid subdivisions into a priori to be uninterrupted in sequence or compact in space, nor to coincide with entire proteins.As detailed in the Selection of an optimal subdivision into basic mechanical units. Finally, several order parameters are examined to identify the most plausible subdivision of the capsid into mechanically-stable units. In principle, the optimal subdivision could be identified by examining how the internal strain of the putative quasi-rigid domains decreases with desiderata for the optimal, basic mechanical units is that: (i) they should preserve the structural integrity of proteins (or protein domains), (ii) it should be possible to group them into only few structurally inequivalent types, (iii) they cannot be further partitioned into smaller units that meet the two previous criteria. Accordingly, among the strain-minimizing subdivisions for varying number of domains Arguably, a minimal set of In the next section we present the application and validation of this strategy to four viruses, namely CCMV, MS2, STNV and STMV, whose functional units have been established in previous studies We start by considering the cowpea chlorotic mottle virus, which is well suited for validation purposes because it has been extensively studied both experimentally CCMV is an icosahedral RNA plant virus whose capsid is constituted of 180 chemically identical protein subunits assembled in the shape of a truncated icosahedron with T = 3 geometry. The protein units adopt three different, quasi-equivalent conformations, conventionally denoted as A, B and C According to various experiments these dimers correspond to the capsid assembly blocks for the virion Indeed, for CCMV various studies consensually indicate that these mechanical units correspond to the pentameric and hexameric capsomeres In accord with this result, recent coarse-grained simulations of CCMV nanoindentation have demonstrated that mechanical failure occurs along the seams that bridge hexamers and pentamers, which remain largely undeformed by the application of mechanical stress The above-mentioned phenomenology provides a clear context for benchmarking the proposed strategy for identifying mechanical units in viral capsids. Specifically, for CCMV it ought to return hexamers and pentamers, and not the dimers, as the primary quasi-rigid blocks.desiderata: the preservation of protein structural integrity and the small number of structurally-inequivalent domain types.We started by characterising the internal dynamics of CCMV by computing its collective low-energy modes of structural fluctuations and used the data to partition the capsid into a number of putative quasi-rigid units, To this purpose we computed and analysed the order parameters shown in It is seen from The combined inspection of the integrity score and the domain types therefore provides a clearcut and non-ambiguous indication of the “innate” character of the CCMV capsid subdivision into 32 quasi-rigid domains which in turn can be grouped into only two structurally inequivalent types. The corresponding subdivision is shown in box C of A further relevant parameter to consider for assessing the functional role of the subdivision is the degree of domain interlocking. The corresponding profile is shown in the bottom graph of box B in across different quasi-rigid domains provides valuable clues regarding the relationship between the mechanically stable domains and capsid assembly/disassembly. In particular, the absence of cross-domain interlocking ought to be a good indicator that the mechanical domains are viable assembly/disassembly units too. The opposite should hold in case a significant amount of cross-domain interlocking is observed. It should, nevertheless, be borne in mind that cross-domain interlocking can arise after the assembly process.This parameter is monitored because several viruses, including CCMV, are assembled from protein dimers stabilised by the mutual interlocking of their termini which reach inside the partner protein core. The incidence of such interlockings For the case of CCMV, we observe that the degree of inter-domain interlocking for In conclusion, the emerging quasi-rigid domain subdivision matches correctly the units identified by previous experimental and numerical studies.We next consider the MS2 virus, which is constituted by 180 chemically-identical coat proteins with a total of 23220 amino acids The results of the MS2 quasi-rigid domain subdivisions are illustrated in the upper panel of It is worth recalling that the MS2 capsid is in the same T = 3 class as CCMV. Hence their very different number and types of fundamental quasi-rigid units point to the important role played by specific capsid proteins in shaping the properties and behaviour of viral capsids that are not large enough to be dealt with by continuum approaches a priori conclusion has necessarily a tentative character. In fact, because the method is based on the properties of fully-assembled protein shells, it cannot account for the interaction of coat proteins and genomic material during the assembly process. Such interaction can be crucial to aid the fast and correct assembly in vivoin vitro assembly does occur in the absence of the genome, it appears plausible to consider non-interlocked quasi-rigid units as putative assembly units.We emphasize that this These considerations are fully supported by the successful comparison with experimental data for MS2. In fact, it has been established that the capsid is assembled from the A/B and C/C dimeric units In summary, the MS2 findings reinforce the CCMV indications that the innate functional units identified with the quasi-rigid domain decomposition correspond to those established experimentally.The satellite tobacco necrosis virus has been one of the first to be determined at high resolution The quasi-rigid domain decomposition, whose results are reported in A noteworthy implication is that the fundamental units of the assembly process, in which the RNA is known to play a major role, can be correctly identified through the quasi-rigid domain decomposition of the empty capsid. In this regard, it must be borne in mind that elastic network models guarantee by construction the stability of the model capsid for structural fluctuations of the crystal structure. Therefore, as remarked earlier, ENM approaches can make up for the missing stabilizing interaction of capsid proteins and the packaged nucleic acid. At the same time, the finding indicates that the mechanical stability of the individual (non interlocked) assembly units is still discernible in the internal dynamics of the fully-assembled capsid.a posteriori that even in cases where protein-nucleic acids interplay is important, the quasi-rigid domain analysis of the pure protein shell can still give valuable clues about the assembly process.This remarkable property shows It is interesting to compare the above analysis with the one for another plant virus, the satellite tobacco mosaic virus (STMV), which presents several similarities with the STNV Because of its relatively small size, STMV represents an ideal and natural reference for numerical investigations This study as well as coarse-grained simulations While this represents a further point of contact with STNV, it should be noted that the similarity of their assembly processes is still disputed. In fact, it is not yet understood whether assembly proceeds as a condensation of a protein-RNA complex The results of the quasi-rigid domain decomposition of STMV are provided in the bottom panel of However, at variance with STNV, the analysis of the interlocking profiles shows that, at low values of et al.As a matter of fact, McPherson We now turn to discuss three viruses for which the basic, mechanically stable functional units are not conclusively known. The following viruses are considered, chosen in order of increasing complexity of the capsid type (T-numbers): the L-A (pT = 2), Pariacoto (T = 3) and polyoma (pT = 7) viruses. We recall that the pT = 2 and pT = 7 cases refer to non-standard Caspar-Klug geometries.in vitro and it has been suggested that A/B dimers are the basic assembly building blocks a priori which dimer type could be the basic assembly block. As we discuss hereafter, the quasi-rigid domain analysis can provide valuable insights into this open problem.The L-A virus is a double-stranded RNA (dsRNA) yeast virus whose capsid is composed of 120 chemically-identical coat proteins with a total of 78120 amino acids. The proteins are assigned to two types, A and B, based on their inequivalent positions, see box A in From the analysis of the plots in box B of in This result is therefore fully consistent with the experimental indication of A/B dimers being the basic assembly units.The notable point is that the quasi-rigid domain analysis discriminates very clearly between the two inequivalent asymmetric A/B dimers shown in box A, arguably because of their different networks of intra- and inter-dimer interactions. In fact, upon repeating the quasi-rigid domain partitioning into The Pariacoto insect virus belongs to the nodaviridae family and has a T = 3 capsid While the C-terminal arm of each A protein is located in a channel formed by the A, B, C monomers at the quasi-3-fold axes, the N-terminal arms of the A proteins are involved in an extensive interaction with the encapsidated single-stranded RNA The inspection of the profiles in box B of The identification of a trimer of proteins as the first stage of assembly is also consistent with the theoretical work by Reddy We conclude the analysis with the discussion of the murine polyoma virus. This non-enveloped DNA virus has an icosahedral capsid with a pT = 7 (non Caspar-Klug) geometry The peak structure of the integrity score profile indicates that the optimal subdivision involves This conclusion is reinforced by the analysis of the suboptimal subdivision into Identifying the fundamental, and typically multimeric, protein units that control the mechanical response of viral capsids or its assembly and disassembly is important both for rationalizing and for modeling key steps of viral life cycles Here we introduced and applied a novel computational strategy that, to our knowledge, represents the first attempt to develop a general and efficient method for identifying the basic, mechanically stable protein units starting from the sole input of the fully-assembled protein capsid. The method relies on the characterization of the internal dynamics of the capsid by means of elastic network models and uses it to optimally decompose the protein shell into blocks that have the characteristics expected for genuine capsid functional units, such as mechanical stability (quasi-rigidity), structural integrity of the constitutive proteins, or small numbers of inequivalent block types etc.The viability of the scheme was first assessed and validated by considering a set of four viruses  for which the fundamental functional units are known. In all cases, the results of the optimal decomposition scheme were fully consistent with available experimental or numerical results for the known mechanical and/or assembly protein units. We next turned to a further set of three viruses, namely polyoma, Pariacoto and L-A virus, whose functional units are debated or not known, and for which we formulate verifiable predictions.The positive validation of the method and its affordable computational cost  to larger structures that are well approximated by continuum theory Proteins and protein assemblies in thermal equilibrium can sustain structural fluctuations of appreciable amplitude. A large body of experimental and numerical evidence has indicated that the principal fluctuation modes, those of lowest energy, have a collective character. This means that the structural deformations associated to these modes entail the concerted displacements of groups of several amino-acids.As was first shown by Tirion A commonly used framework for such coarse-grained calculations is provided by elastic network models. The latter rely on a quadratic approximation of the near-native protein free energy,Within the quadratic approximation of In the following we shall indicate by In this study, we shall resort to the beta-Gaussian network model The computational burden associated with the memory storage and diagonalization of the For the polyoma capsid alone which, at The subdivision of viral capsids into quasi-rigid domains is based on the PiSQRD strategy introduced in refs. Accordingly, one can quantify the viability of a tentative capsid subdivision into To turn this observation into a quantitative scheme amenable to numerical implementation, we consider the geometric strain, a priori and needs to be found based on physical considerations, see the next subsection.Accordingly, the internal strain of the For each explored value of To reduce the impact of getting trapped in local minima of a priori information about the capsid's parsing into single proteins is used to identify the domains. Indeed, in principle mechanical domains can cut through proteins, for example when a rather loose loop tightly binds to a different block. The comparison between the mechanical and the proteins boundaries is done a posteriori, providing information on the reliability of the subdivision itself.It is important to note that no The Besides calculating the geometric strain, the genuine quasi-rigid character of a given decomposition into The algorithm for the subdivision into Given a subdivision into domains, an integrity parameter was defined for each protein. For a general subdivision, the amino acids of a protein can be assigned to a number of different domains. However, a good subdivision should preserve the integrity of the protein, i.e. almost the whole protein should belong to a single domain. We thus defined the integrity score for a protein as the largest fraction of its amino acids assigned to a single domain. This quantity was then averaged for all the proteins, providing a score for the capsid subdivision.We also computed the number of similar tiles identified by our subdivision by size inspection. Specifically, we defined the size of the Viable subdivisions into basic mechanical units were identified by maxima in the integrity score corresponding to a small number of tile types.interlocking parameter. Specifically, we considered separately the two termini of each protein in the capsid, namely the first and last twenty amino acids, and counted the number of amino acids assigned to a rigid domain different from the dominant one (i.e. the domain to which most of the protein's amino acids belong). This calculation returned the number of interlocked amino acids for each terminus of each protein in the capsid. The numbers relative to the N and C terminals were averaged separately, and the largest of the two averages was taken as a measure of the interlocking of the quasi-rigid domains. In other words, if a quasi-rigid domain subdivision has interlocking number equal to 10, it means that on average one protein has 10 terminal residues assigned to a different domain than its core. Clearly, this also implies that the other terminus has less than 10 interlocked amino acids.To detect possible intertwinings between quasi-rigid units (e.g. due to swapped tails or subdomains of the parent proteins) we computed the Figure S1Decomposition into basic mechanical units of the HEV virus-like particle. As is shown in box A, each of the 60 coat proteins features three distinct structural subdomains, named S , P1 (which forms a protrusion around the three-fold axis) and P2 (which forms spikes on the two-fold axis). The optimal subdivision, corresponding to (TIF)Click here for additional data file.Figure S2Fraction of overall capsid motion  that can be ascribed to the pure rigid-like movements of the  quasi-rigid domains. For each value of (TIF)Click here for additional data file.Figure S3Suboptimal decompositions of CCMV. Panel A shows a close-up of the CCMV profiles for the integrity score and number of tile types for subdivisions from (TIF)Click here for additional data file.Figure S4Structural coarse-graining and robustness of quasi-rigid domain decompositions. Optimal subdivision of the STMV capsid into 20 quasi-rigid domains obtained by using the coarse-grained ENM where only every other (TIF)Click here for additional data file.Figure S5-dependence of the miminized geometric strain. Panels a-h refer respectively to: CCMV, MS2, STNV, STMV, L-A virus, Pariacoto virus, polyoma virus and HEV. Notice that at the value of (TIF)Click here for additional data file.