PhyloPro is a database and accompanying web-based application for the construction and exploration of phylogenetic profiles across the Eukarya. In this update article, we present six major new developments in PhyloPro: (i) integration of Pfam-A domain predictions for all proteins; (ii) new summary heatmaps and detailed level views of domain conservation; (iii) an interactive, network-based visualization tool for exploration of domain architectures and their conservation; (iv) ability to browse based on protein functional categories (GOSlim); (v) improvements to the web interface to enhance drill down capability from the heatmap view; and (vi) improved coverage including 164 eukaryotes and 12 reference species. In addition, we provide improved support for downloading data and images in a variety of formats. Among the existing tools available for phylogenetic profiles, PhyloPro provides several innovative domain-based features including a novel domain adjacency visualization tool. These are designed to allow the user to identify and compare proteins with similar domain architectures across species and thus develop hypotheses about the evolution of lineage-specific trajectories.Database URL: http://www.compsysbio.org/phylopro/ Phylogenetic profiling has been widely adopted as a method to visualize evolutionary conservation of genes/proteins. This approach has been facilitated by improvements in sequencing technology resulting in an ever increasing number of fully sequenced, eukaryotic genomes. Aside from using phylogenetic profiles to predict gene function , a numbePhylogeny based methods, which are computationally intensive, provide robust support for orthology focused at the level of individual genes. PhylomeDB , for insUnderscoring their importance, ortholog databases are beginning to include domain level information, for example, the latest release of PhylomeDB  includesAddressing this gap, we present PhyloPro2.0, a database integrating pre-calculated, Inparanoid-based orthologs. Here, we rely on Inparanoid orthology assignments as it is a well-established, BLAST-based method which has been shown to perform as well as or better than other methods across a wide range of eukaryotic genomes with both sensitivity and specificity >80% . Due to Since its original publication  PhyloProFocusing on 12 model organisms , the Inphttp://pfam.xfam.org/help). For our analysis, we only included Pfam-A definitions for entries labelled as either ‘Domains’  or ‘Families’ (defined by Pfam as ‘a collection of related protein regions’), as these best fit our criteria as independent folding units. Domains found in each of the proteins in the 12 reference sequences were compared with domains representing the full proteome of every other species. Domain architectures of target and reference orthologs were compared and classified parsimoniously as having gained or lost domains, having the same (conserved) domain architecture, or having rearrangements. Where more than one sequence of gains, losses or rearrangements were equally parsimonious, this resulted in a classification of ‘complex’ type. For purposes of the comparisons, domain order was taken into account. Adjacent domains were defined in the N-terminal to C-terminal orientation. Reverse orientations were considered to be unique (i.e. A − B ≠ B − A).Domain predictions, based on Pfam-A definitions, were performed on a parallel computing platform using HMMER 3.0 with default parameters as implemented in PfamScan . Data flFunctional annotations (GOSlim) for human proteins were acquired from BioMart . We usedPhyloPro features several ways to launch a search. A quick search using default options can be performed by entering a space separated list of gene or protein identifiers for a select reference species of interest into the search box, selecting the type of information to return  and clicking on the ‘Go’ button. For quick searches, the default reference organism for comparison corresponds to the type of identifier first identified among the first 10 listed genes. For example, the use of a mouse gene identifier (e.g. ENSMUSG00000034205) would result in an analysis with mouse as the reference species. Identifier types are not limited to Ensembl but reflect a variety of identifiers in use for various species, depending on cross-referencing available at the time the species was loaded. Alternatively, users have the option of choosing a functional category from a list of Gene Ontology (GO) terms to automatically populate the search list with proteins annotated to the selected term. For performance considerations, available terms are based on GOSlim annotations with a frequency cutoff of 1000 proteins.Beyond the quick search and GO browsing capabilities, PhyloPro also offers a search option based on sequence similarities, using the well-established BLAST algorithm. It is recognized that as genomes and gene models become updated, gene and protein identifiers may become obsolete. The inclusion of the sequence similarity search option is introduced to guards against such possibilities. After selecting this option from the home page, the user is presented with a sequence similarity search page with options to run a nucleotide-based (BLASTx) or protein-based (BLASTp) search against a reference proteome of their choice. The user pastes in a set of sequences in fasta format and after clicking the ‘Go’ button, PhyloPro retrieves the top BLAST hit associated with each query sequence. The resulting page  is then constructed from these hits. Mappings of the user sequences to the identified hits are also presented.http://www.postgresql.org/) database to speed the retrieval of large amounts of pre-calculated orthology and domain predictions. After a few moments the user will be taken to one of three views depending on their initial choice.Finally, PhyloPro also offers an advanced search option that allows users to specify a number of parameters for the analysis including: (i) choice of reference species, (ii) limit the range of target species, (iii) choose the similarity metric and clustering method used for clustering the resulting heatmap (if applicable), (iv) choose the type of view (as above) and (v) upload a text file corresponding to the proteins to be searched. Users can review the selected parameters before clicking ‘Go’ to start the search. It is worth mentioning that there is a slight difference in the search depending on whether the user chooses to use a gene identifier vs. a protein identifier in the search box. The use of gene identifiers will result in PhyloPro finding the longest peptide of those which map to the selected gene identifier as the basis for orthology prediction, whereas the use of a protein identifier will result in a protein conservation profile including the exact protein specified as the reference. By design, all domain-based views use the longest peptide for the corresponding gene as the basis for domain comparisons. PhyloPro uses a PostgreSQL  have resulted in some progress ; the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-06664). N.L. received a postdoctoral fellowship, in part, through the Hospital for Sick Children Research Training Centre. High performance computing resources were provided by the SciNet HPC Consortium at the University of Toronto.Conflict of interest. None declared.


This study determines dam deformation similarity indexes based on an analysis of deformation zoning features and panel data clustering theory, with comprehensive consideration to the actual deformation law of super-high arch dams and the spatial–temporal features of dam deformation. Measurement methods of these indexes are studied. Based on the established deformation similarity criteria, the principle used to determine the number of dam deformation zones is constructed through entropy weight method. This study proposes the deformation zoning method for super-high arch dams and the implementation steps, analyzes the effect of special influencing factors of different dam zones on the deformation, introduces dummy variables that represent the special effect of dam deformation, and establishes a variable-intercept panel model for deformation zoning of super-high arch dams. Based on different patterns of the special effect in the variable-intercept panel model, two panel analysis models were established to monitor fixed and random effects of dam deformation. Hausman test method of model selection and model effectiveness assessment method are discussed. Finally, the effectiveness of established models is verified through a case study. The entire service period of a super-high arch dam can be divided into several stages, and each stage presents a different deformation behavior pattern. Although plenty of analysis models have been developed for the complex influencing factors and deformation law of super-high arch dams, the majority of these models are just extension of the traditional safety monitoring model of dam deformation. Analysis models are based on one-dimensional time series of single measuring point. For this reason, the effects of measurement error, missing data, and collinearity on model precision are difficult to avoid. Moreover, spatial–temporal monitoring information on super-high arch dam deformation cannot simply be randomly obtained. The spatial–temporal deformation features of the entire dam structure throughout its service period, as well as their correlations, are difficult to comprehend. Therefore, the traditional deformation analysis model fails to completely reflect the deformation behavior of a super-high arch dam. In addition, influencing factors  of the deformation behavior differ significantly at different regions of a super-high arch dam. However, the traditional method does not consider such difference and still analyzes all measuring points with reservoir water level, temperature, and aging. The actual deformation laws of different regions of a super-high arch dam are difficult to depict. Therefore, an analysis model that can reflect the different influencing factors of deformation should be established.Panel data contain deformation information of two dimensions—time and cross section—which adequately reflect the spatial–temporal features of arc deformation. A deformation panel sequence is superior to pure time series and cross section sequence for rich information, high degree of freedom (DOF), and effective collinearity reduction. Hence, panel data can be used for modeling an analysis of the deformation behavior of a super-high arch dam. Before establishing the panel model, all monitoring points on the super-high arch dam were zoned according to deformation similarity with consideration for regional characteristics of overall deformation and correlation of deformation at all measuring points, which can eliminate the influence of different deformation laws at different monitoring points and measuring error on the model. Furthermore, because common influencing factors  cannot easily depict the different deformation laws of different regions, this study introduced dummy variables that can represent the effect of special influencing factors of different regions, called special deformation effect variables. On this basis, an analysis model that considers both common and special influencing factors of deformation is established, which has higher explanatory power and estimation precision. Meanwhile, studying special deformation effect variables is beneficial in comprehensively analyzing the deformation behavior of the super-high arch dams and is expected to offset some shortcomings of the traditional analysis model.Dam deformation zoning analysis has to address two key problems: (1) determining the statistical magnitude that can be used to represent the deformation similarity between measuring points and (2) determining the specific systematic zoning method to be used, that is, the criteria to be used to determine deformation similarity between different dam regions. The criteria that would determine the deformation similarity of different dam regions were constructed based on the panel features of super-high arch dam deformation combined with spatial–temporal information of deformation. The criteria created the deformation zoning method for the super-high arch dam by studying structural deformation behavior in time dimension and cross-section dimension.To solve the first key problem, this study constructed deformation similarity indexes based on the idea of panel data clustering.The deformation zoning of a super-high arch dam makes the deformation law within the same region as similar as possible and makes the deformation laws of different regions as different as possible; such consideration is an important standard in measure the dam deformation zoning effect. Traditional deformation zoning applies the mean time series of deformation of all measuring points. In other words, it degenerates deformation sequence into a cross-section sequence. This method can express only the mean variation of dam deformation and loses the temporal information of deformation. In addition, it makes an invisible assumption that temporal deformation at different measuring points changes toward the same direction, which cannot easily reflect the variation law of deformation behavior with time. This assumption is unreasonable. As shown in Fig. The deformation data of super-high arch dams reveal at least three aspects: (1) the absolute deformation of the arch dam; (2) the dynamic level of time series of deformation, that is, the deformation growth with time; and (3) the fluctuation of deformation development, that is, the degree of variation or fluctuation. The focus of this chapter is dam deformation zoning based on the similarity of deformation sequence when the dam structure has not obviously changed or the time series of the deformation that has smoothly change. The similarity of the deformation sequence is reflected by combining the “absolute deformation” and “deformation growth” of the dam.When analyzing deformation monitoring results, “absolute deformation” and “deformation growth” can be depicted by some distances Fig. . Common δit is often used to express the deformation dataset of the super-high arch dam, where N is the total measuring points on the dam, and T is the total monitoring periods, that is, monitoring time series. With respect to the deformation dataset, δit, St is defined as the standard deviation (SD) of deformation during t, and dij is the direct distance between measuring points i and j. dij meets the following basic axiom :δit is the deformation of measuring point i during t, δjt is the deformation of measuring point j during t, and dij(AQED) is the distance between measuring points i and j during the entire T.The “absolute” distance between measuring points i and j is recorded as dij(ISED):δit = δit − δit−1 and Δδjt = δjt − δit−1. Δδit and Δδjt are the difference of the absolute deformation between two adjacent periods. dij(ISED) describes the difference of the deformation growth between measuring points i and j with time. If deformations of measuring points i and j change toward the same direction, the higher harmony of such change will lead to a higher deformation similarity between these two measuring points and smaller dij(ISED). If deformations of measuring points i and j change toward the opposite directions, the change will cause a poorer deformation similarity and a larger dij(ISED). These conditions conform to the basic principle of similarity measurement.The “growing” distance between measuring points i and j is introduced, which is recorded as dij(CED):ω1 and ω2 represent weights of two distances, which meet ω1 + ω2 = 1.To accurately describe the deformation characteristics of different measuring points, establishing a comprehensive criterion that can depict deformation similarity is necessary. As a result, the “comprehensive” distance between measuring points dij(CED) is the weighted array of the “absolute” distance and the “growing” distance. The weight coefficient can be determined subjectively or objectively according to practical situations of the problem being studied. To comprehensively reflect the total information of spatial deformation data of the super-high arch dam, the weight coefficient that conforms to practical engineering significance was given. In this study, the weight coefficient of the “comprehensive” distance was calculated through an entropy weight method. Based on the idea of information entropy and comprehensive considerations to the evaluation system of distance indexes, the method assumed that m evaluation objects and n evaluation indexes exist. Then, the original data can be expressed by matrix R = (rij)m*n asrij is the evaluation value of the jth evaluation object under the ith evaluation index. Combined with abovementioned deformation similarity indexes of ultrahigh arc dam, the “absolute” distance and the “growing” distance were used as the evaluation indexes of deformation, and the distance between two measuring points is viewed as the evaluation object.ith evaluation index to the jth evaluation object aspij ϵ  and the original proportional relationship between deformation monitoring sequences remains undamaged, that is, Calculate the characteristic proportion of the ith evaluation index values is given asCalculate the entropy. The entropy of the ith evaluation index isDetermine the entropy weight. The entropy weight of the wi is the weight coefficient of the ith evaluation index. Substituting into Eq. (dij(CED) between the different measuring points can be used as the criterion of the deformation similarity. In this way, the first key problem, that is, determining the statistical magnitude that can be used to represent deformation similarity between measuring points, is solved. Similar statistical magnitudes can be acquired according to other common distance forms  or correlation coefficient and included angle cosine. These other distance forms, coefficient, and angle cosine are not introduced in this paper  in Gl. For the deformation data of N measuring points during T, the sum of squares of deviations of sequence at different measuring points in Gl isTo address the second key problem, this study assumes that k regions isThe total sum of squares of deviations of Wl is the total sum of squares of the deviations of Nl measured values, Xit is the deformation of measuring point i during t, i in Gl during t, ΔXit = Xit − Xit−1 is the difference of absolute deformation of measuring point i in Gl between t and In Eq. , Wl is tk, the deformation zoning with minimum W is the optimum. In practical engineering, because of the complexity of the super-high arch dam structural system, predetermining k is actually an artificial interference to dam deformation zoning, which is likely to cause subjective errors. However, increase in k will cause continuous changes of similarity distance in the same region and between different regions. Therefore, obtaining absolute optimum k is impossible. A method that determines the threshold of dam deformation zoning is proposed in this paper based on a thresholding method  is calculated asAccording to the above clustering method, given fixed Sl differs slightly with Sl+1, but significantly differs with Sl−1, corresponding regional distance Dl can be used as the threshold of deformation zoning. Based on this threshold, the number of regions can be further calculated.If k.The implementation method and steps to the second key problem are interpreted based on Ward clustering. A complete dam deformation zoning process is proposed in next according to deformation similarity criteria and determination method of N deformation measuring points during T. On the basis of the basic idea of the proposed dam deformation zoning, the deformation distance between different measuring points and distance between deformed dam regions are calculated first. During the initialization, these N deformation monitoring points are classified into the same class. The regional distance and the distance between deformation sequences of different measuring points are equal. Next, two measuring points with the closest distance are combined into a new region, of which distance with other regions will be calculated again. Later, the dam is rezoned according to the principle of minimum sum of squares of deviations (W). The process is repeated until all measuring points are combined into one region. The specific steps are as follows:Step 1 Calculate the “absolute” and “growing” deformation distances and regional distances through Eqs. .Step 2 Calculate the entropy weights above two distances through Eqs. , 6), an, an6), adij(CED) between two measuring points and matrix of regional distance D(0).Step 3 Substitute the calculated weight coefficients into Eq. (i = 1) all measuring points into one region, that is, k = N. Let D(1) = D(0) and the ith region be Gi = {Xi).Step 4 Initialize ) between the new region with other regions and obtain new distance matrix Di. Repeat Steps 5 and 6 until all measuring points are divided into one region.Step 6 Calculate comprehensive distance  according to method to determine the dam deformation zoning threshold and practical situations. Draw the dam deformation zoning distribution map.Step 8 Derive the optimum zoning scheme and the optimum number of zoning  that can be decomposed into radial horizontal deformation (δx), tangential horizontal deformation (δy), and vertical deformation (δz) , temperature component (δT), and aging component (δθ):Except for hydraulic pressure (water level) and temperature changes, aging is another influencing factor of super-high arch dam deformation. In recent years, P Lin, among others, studied how deformation is influenced by arch dam seepage, crack, etc.  Fig. . Every vPc) makes nonlinear changes because of the combined action of the horizontal arc and cantilever beam :For the ultrahigh arc dam, the hydraulic pressure load distributed on the girder  are related with H, H2, H3, and H4 (or H5) because and have quadratic or cubic curve relations. δH3 is similar with δH1 and δH2. Therefore, hydraulic pressure component of the super-high arch dam can be expressed asδT refers to the deformation caused by temperature changes of concrete and basement of the arc dam. In terms of mechanics, δT shall choose temperatures of dam concrete and basement reading by the thermometer as factors. With respect to arch dam under normal operation, when hydration heat of concrete has been dissipated and a quasi-equilibrium temperature field is developed inside the dam body, dam temperature is determined by boundary temperature changes. At this moment, the periodic term combined by multiple harmonic waves can be used as the factor of δT:Temperature component δθ) has complex causes. It comprehensively reflects the creep of dam concrete, the creep deformation of dam basement, and compressive deformation of the basement tectonics. The variation law of δθ of common dams under normal operation is shown in Fig. Aging-induced dam deformation . However, the deformation at C and D, which are close to the dam foundation and bank slope, differs significantly from that at A and B, which are close to the dam top. This finding is closely related with the synergistic effect of constraints, material properties, and surrounding environments at different dam regions. As a result, different parts produce different special deformation effects (α). Parameter heterogeneity of the deformation analysis model will occur once the explained variables fail to capture these complex factors that can not be monitored and quantized. Although the traditional deformation analysis model can depict the main influencing factors of the deformation using the independent variables of the model, the model often neglects deformation specificity at different measuring points caused by these complex factors. In particular, different deformation effects at different measuring points have to be taken into account for a super-high arch dam structure with large span. Based on the panel data theory, αi that is used to represent special deformation effect is introduced to establish a variable-intercept panel model for deformation zoning of the super-high arch dam.Various function forms are options in establishing the mathematical model of ing Fig. , most deYit is the deformation panel sequence of i measuring points; i = 1,2, … N; t = 1,2, … T), yit is the monitored deformation of the ith measuring points during t, Xit is an independent variable , and α is a scalar constant that represents the special deformation effect at different dam regions caused by special influencing factors. Explicitly including these special influencing factors  into independent variables of the model is difficult. Therefore, α is employed to absorb individual special deformation effects caused by these factors. uit is an independent identically distributed random error component with a mean value of 0 and a variance of σ2.The general form of variable-intercept panel model isαi that varies at different measuring points is introduced into the model. Then, the panel model of fixed deformation effect of super-high arch dam isH is the depth of water, T is the thermometer read value, θ is an influencing factor that is related with time, αi is the fixed deformation effect at different measuring points, and ε is the random error term.On the basis of the general expression of the panel model [Eq. ], αi thaYit represents all the monitoring sequences of i measuring points, Xit is an independent variable that reflects the main influencing factors of deformation, parameter β is independent from measuring points i and time t, αi is the unique fixed deformation effect of different measuring points, which depicts the special deformation effect at different dam positions and deformation differences between different dam positions. At this moment, β are two fixed parameters that have to be estimated. Then, the panel model matrix of fixed deformation of the super-high arch dam can be expressed asThe panel model of fixed deformation effect can be expressed as a matrix, that is,If the special deformation effect of different measuring points is viewed as a random variable and the random effect model is used to describe the actual state of dam deformation so that the model parameters focus on the main components of monitored deformation, the random effect will reflect the special deformation component of different measuring points. In other words, individual effect in the variable-intercept panel model 17), the Y) exist: independent variables and special effect. The independent variables In summary, two kinds of influencing factors of super-high arch dam deformation  and F. It is defined asESS is the regression sum of squares, TSS is the total sum of squares of deviations, n is the sample size, and k is the number of explanatory variables, including the intercept.The overall goodness of fit is measured with corrected sample determination coefficient ,,2), whicTo choose the appropriate regression model to establish the panel model for deformation zoning of the super-high arch dam, Hausman test was implemented to deformation time series of all measuring points based on the dam deformation zoning. In other words, the appropriate panel model (fixed-effect model or random effect model) shall be chosen according to the correlation test between random error term and variables. In this study, Hausman test was implemented to the deformation time series of all six deformation zones. The test results of two typical zones are listed in Tables According to the Hausman test results, Wald test statistics Prob (hereinafter referred as P), Eq. , and theBased on the dam deformation zoning and Hausman test results, a modeling analysis was performed using the corresponding variable-intercept panel model for deformation zoning, thus obtaining coefficients and test statistics of the influencing factors. The results of zones I and III are shown in Tables αi) of every measuring point is given explicitly. Deformation difference within the same zone (αi) conforms to the known random distribution. The analysis on six deformation panel models reveals that: (1) the model is established with full consideration to the difference of different measuring points. An appropriate panel model is established for different deformation zones according to the method for solving fixed-effect and random-effect models. Hydrographs of the measured value and the fitting value of panel models are presented in Figs. αi) = 10.331 mm, which reflects that the structural deformations of six divided zones make the same responses to external complex factors and can be viewed as the structural deformation of the same nature in the analysis and computation.According to the comparison of the modeling results of six deformation zones, no significant individual difference is observed among measuring points of the same zone. For measuring points of the same zone, max(ΔAccording to the regional difference of dam deformation, in this study, all measuring points of a super-high arch dam are divided into six zones on the basis of deformation similarity. Modeling analysis is implemented to every zone on the basis of the monitored deformation, thereby effectively eliminating the interference of the deformation difference among different measuring points and measurement errors in the model.The effects of the special influencing factors of different dam zones on dam deformation was analyzed based on the basis of the studied deformation features of the super-high arch dam. Dummy variables that represent the special effects of dam deformation were introduced to establish the variable-intercept panel model for the deformation zoning of the super-high arch dam. The variable-intercept panel models, except those for zones I and II, can adequately explain the actual deformation behavior of the dam, as manifested by the fitting effect. Furthermore, the inherent differences in the deformation laws among different dam zones can be discovered by analyzing the special deformation effect.By considering the different patterns of the special effect in the variable-intercept panel model, two panel models consistent with practical engineering situations were established based on the Hausman test: the fixed-effect model the and random-effect model. The evaluation method of model effectiveness was explored.Although the random-effect model can adequately explain the overall dam deformation behavior, an accurate fitting is difficult to achieve if only the special deformation effect is involved. For example.in this study, the deformation laws in zones I and II, which are related to various factors, such as constraints and regional difference of foundation properties, are very complex because these two zones are close to the dam foundation. Therefore, dynamic adjustment of the model coefficient is needed during modeling analysis, which requires further research.


The combination of domains in multidomain proteins enhances their function and structure but lengthens the molecules and increases their cost at cellular level.The dependence of domain length on the number of domains a protein holds was surveyed for a set of 60 proteomes representing free-living organisms from all kingdoms of life. Distributions were fitted using non-linear functions and fitted parameters interpreted with a formulation of decreasing returns.P is a function of domain accretion. P holds two terms, one reflecting the matter-energy cost of adding domains and extending their length, the other reflecting how domain length and number impinges on information and biophysics. The pattern of diminishing returns can therefore be explained as a frustrated interplay between the strategies of economy, flexibility and robustness, matching previously observed trade-offs in the domain makeup of proteomes. Proteomes of Archaea, Fungi and to a lesser degree Plants show the largest push towards molecular economy, each at their own economic stratum. Fungi increase domain size in single domain proteins while reinforcing the pattern of diminishing returns. In contrast, Metazoa, and to lesser degrees Protista and Bacteria, relax economy. Metazoa achieves maximum flexibility and robustness by harboring compact molecules and complex domain organization, offering a new functional vocabulary for molecular biology.We find that domain length decreases with increasing number of domains in proteins, following the Menzerath-Altmann (MA) law of language. Highly significant negative correlations exist for the set of proteomes examined. Mathematically, the MA law expresses as a power law relationship that unfolds when molecular persistence The tendency of parts to decrease their size when systems enlarge is universal for language and music, and now for parts of macromolecules, extending the MA law to natural systems. He foun Teile”) . These q Teile”)  and supp Teile”)  follows y(x) being the length of the parts, x representing the length of the system (or constructs of parts), and A, b and c fitting parameters. x can also represent a discrete variable describing the number of parts that make up the system. A more general formulation adds dependences on additional variables [y(x) is generally measured by counting parts defined at a deeper level of the system’s organization . This general formulation of the law accommodates the effects of multi-level structure that is typical of language. Two special cases of the equation occur when b = 0 or c = 0. The first mathematical formulation describes how the length or size of parts y(x) decreases monotonically with the length or size of systems. However, the second formulation, eq. (with ariables . y(x) ision, eq. 2\documenis the most commonly used equation of the MA law, since it enables computation of fitting parameters in log-log plots. This equation delimits a curve of a general two-parameter power law form.Language-like behavior has been extended to music  and receProtein molecules are eminently modular . RecurreMultidomain proteins, which globally make a significant minority (26–32 %) of proteins in proteomes (they are highly represented in eukaryotes), have on average substantially smaller domains than single domain proteins . This trzk) of a protein on the number of protein domains it holds (k) for a set of 60 proteomes representing organisms in superkingdoms Archaea and Bacteria and kingdoms Metazoa, Fungi, Plants and Protista of superkingdom Eukarya. Each and every one of the 60 proteomes examined showed a significant negative correlation between average domain lengths and numbers of domains in proteins, both in logarithmic scale, when using a weighted nonlinear least-squares curve fitting approach (Table Homo sapiens. The linear regression lines in the log-log plots showed high coefficients of determination (R2) with values ranging 0.85–1.00 and significant F test-derived correlations   in the log-log plots ranged −0.113 to −0.404 (Table We studied the dependence of the average domain length (ch Table . To avoii and j of a system to be ‘MA-related’ when (i) the system is hierarchically structured with n + 1 levels of organization and i > j > n, (ii) a significant fit of the relation between the length x of a higher level i sub-system and the average length y(x) of the parts of a lower level j sub-system exists, and (iii) immediate parts and subsystems (level i parts and level i + 1 subsystems) are stochastically independent. Specifically, length x of subsystem i (proteins in proteomes) can be measured by counting terminal (lowest) level n parts (amino acids) or by counting the number of level-j subsystems (domains). Table Following elaborations by Meyer , we conssomehow connected with the principle of least effort or with some not yet known principle of balance recompensating lengthening on one hand with shortening on the other” [Altmann suspected that the MA law was “e other” . Here weP) is related to two terms, a cost describing the energy-matter investment in the molecule (PC) that depends both on k, the number of domains in a protein, and zk, the average length of a domain [corresponding to x and y of eq. (PFR) that depends on L1, the length of single domain proteins [i.e., the intercept, which corresponds to A of eq.  and k. Persistence follows eq.  plane. However, eq. [P between this curve and the k and zk axes. Thus eq. ,,5), the wing eq. 6\documenb, −1 < b < 0) give bigger R ratios, which suggest increased trade-offs benefitting flexibility and robustness over economy in the frustrated landscape of molecular persistence. As we will now elaborate, this agrees with b representing a measure of structural and functional cooperativity among domains as these accrete in proteins and extend their length.Steeper slopes  versus intercept (L1) for each proteome that we studied with the goal of dissecting the contributions of economy and length of domains in single domain proteins that are characteristics of organismal groups  to −0.404 for Brachiostoma floridedae (Metazoa). Intercepts ranged from 183 for Medicago truncatula to 247 for Aspergillum nidulans (Fungi). Most fungi exhibited the largest intercepts and a substantial number of plants and metazoans showed the smallest. Higher intercepts should be interpreted as larger ‘starting’ domain sizes fostering opportunities for flexibility and robustness but counteracted by increased burdens of cost. Most metazoans showed the steepest slopes and substantial number of plants and protists the shallowest. Steepest slopes should be interpreted as stronger ‘push’ towards flexibility and robustness and corresponding ‘counter-push’ towards economy in domain organization. Proteomes distributed in the plot following a fan-like pattern, with the top segment of the semi-circle occupied by Fungi, Protista-Bacteria-Plants, and Archaea, in that order, and the bottom part by Metazoa. Plants and Protista occupied the fan handle.The MA-law imposes patterns of decreasing returns for domain lengths of proteins of a proteome. These patterns relate to protein domain make up, domain function, and evolutionary pressures imposed on the proteome as an interacting body of the cell. Analyses of domain length in proteins sampled from many proteomes  may notups Fig. . The len2 = 0.59-0.87; F = 11.4-54.6; p < 0.0001-0.01), the lowest correlation for Bacteria , and no significant trends for Metazoa and Protista . Since slopes of proteome groups in the slope b versus intercept L1 plots increase with single-domain length (intercept L1) and increasing linear fits, we hypothesize that this increasing trend, which is maximal in Fungi, describes a ‘compressible’ property capable of reducing domain length (Lk) when additional domains are accreted in proteins (k > 1). In other words, proteomes like those of fungi that exhibit on average longer domains in single domain proteins are capable of considerable length reduction as domains accrete in proteins. In turn, those that have shorter average single domain proteins relax the reductive tendency in multidomain proteins. Given the theoretical link that exists between b and both domain cooperativity and stability elaborated above, and the high surface area to volume ratio detected in new emergent proteins [We find that proteomes in the plot showed higher linear correlations for Fungi, Archaea and Plants  to reveal the effect of reductive evolution at proteome level on starting domain size of organismal groups .We also plotted total number of domains in proteomes versus intercept  represents the sum of the length of individual domain constituents of a protein, without considering linkers and terminal non-domain sequences. We calculated Le for each proteome using weights Mk, the number of proteins with k domains, and averaging over all k up to K’, the largest value of k on the linear part of the log-log plot. The plot L1 versus Le  with the exception of Bacteria . All trend lines clustered together quite tightly showing an expected overall increase of L1 with increasing Le. The slopes, which vary from 0.352 to 0.866, represent the fraction of total domain length apportioned to single domain proteins (L1/Le). Slopes show the disproportionate large representation of single domain proteins in microbial proteomes that hold only a limited repertoire of multidomain proteins. Slopes are maximal in Fungi and Archaea (0.866 and 0.742), intermediate in Plants and Bacteria (0.545 and 0.458) and minimal in Protista and Metazoa (0.352 and 0.390). Thus, Fungi and Archaea have significant overrepresentation of the length of single domain proteins, a feature that correlates with the high ‘compressible’ property revealed in Fig. L1 takes more of Le. A similar trend exists in fungi, especially in ascomycetous yeast, which already show significant reductive trends compared to other fungi and other eukaryotes [L1 (236 ± 6) and steeper slopes (−0.259 ± 0.018) than the rest of fungi examined (220 ± 10 and −0.202 ± 0.045), supporting the reductive trend visible in Fig. The effective average protein length  but non-significant fits for the rest  showed maximum slopes in the plot, matching patterns observed in Fig. We also plotted est Fig. . Most coin vitro evolution experiment revealed strong diminishing returns on enzymatic activity [Processes of diminishing returns manifest when systems search for optimality. The closer to the optimum condition, the more difficult the effort invested in attaining it. For example, laboratory optimization of an arylesterase function in an activity . The firactivity ). Here wP as a function of domain accretion, measured as number of domains k in proteins. P holds two terms, one reflecting the matter-energy cost of adding domains and extending their length in proteins, the other reflecting how domain length and number impinges on information and the flexibility and robustness of the molecular system. Thus, our persistence function describes a frustrated landscape in a ‘persistence triangle’ with vertices representing the three main strategies.Menzerath’s insight suggested the existence of a universal tendency of parts to decrease their size when systems enlarge. The MA law appears universal for language and music. Our study extends its validity to biological parts and systems. In language, constituents of language constructs, such as the phonemes of words, are dynamic. They change as language unfolds in human history. Similarly, parts of biological systems, such as the domains of proteins, change in molecular evolution. In the case of domains, they increase or decrease in length and accrete in multidomain proteins by the pervasive effects of mutations and genomic rearrangements. We now find that protein domain length decreases with increasing number of domains in the proteins of proteomes. The existence of an MA law in protein domain organization can be explained as the consequence of the frustrated interaction between the strategies of economy, flexibility and robustness. The MA law represents a power law relationship that manifests when unfolding molecular persistence A previous analysis of proteome makeup revealed that organisms in kingdoms and superkingdoms preferentially use flexibility and robustness properties in trade-off relationships with economy as they face environmental uncertainties and negotiate survival . ArchaeaP < 0.0001) that could affect assignments of sequences to multi-domain protein groups, and the absence of biases imposed on length estimates by superkingdom-specific Markovian models [Ykj) against each domain number (k) for the selected proteins. The following eqs. (zk) and variance (sk2) respectively.We selected 60 proteomes of free-living species from the highly curated dataset of Wang et al. , which hn models . A flat ing eqs.  and 8)  P < 0.00k = mean value of Ykj within a k, Ykj = sum of the value for Mk’s at k point, Mk = number of proteins with k domains, i = number of unique domains starting from 1 to M, k = unique domain number, j = number of Yk points starting from 1, and (sk)2 = variance.where zk versus zk were plotted with both axes on a log10 scale. To avoid biases introduced by a small minority of proteins harboring a large number of domains (outliers with k ≤ K domains), we excluded proteins with more than K’ domains and used the rest to fit the lines. K’ was chosen by eye with the goal of maximizing both R2 and the number of proteins retained. Initial boundaries for the optimization were R2 > 0.8 and > 95 % of protein entries retained. Analysis of several proteomes in preliminary studies showed that the by-eye choice of K’ judged by marked departures from a line gives nearly optimal fit. For example, inclusion of proteins with K’ ≥ 14 domains of H. sapiens in the example of Fig. 2 statistics from 0.91 to 0.7. In turn, selecting K’ ≤ 5 domains decreases the number of proteins retained from 99.7 to 95 %. This brackets the K’ = 13 domain boundary by exactly k = ±7.The graphs of Lines were fitted in log space to eq. 9\documenk (k = 1 to K’), zk, standard errors of the means (Yerr), and weight of kth value (wk) to calculate the slope (b), intercept (L1) and their respective standard errors of the means (SEM). We used the following eqs. (Yerr) and (wk):using the Excel solver for weighted and non-weighted least squares of Harris , which fing eqs.  and 11)k (k = 1 Le) were calculated using the following eq.  to test the linear relationship between k vs. zk, b vs. L1, genome size vs. L1, Le vs. b and Le vs. L1. We report dependencies that are most useful for biological interpretation. In particular, L1 describes the average length of single domain proteins, which serves to define an upper bound for the MA-dependency of a proteome. In turn, Le describes the sum of the length of individual domain constituents of a protein, which is an indicator of mass economy for growth rate optimization. An example of a regression model is given by eq. , i.e., normality, independence and identical data distribution.where Vhttp://dx.doi.org/10.6070/H4513W6X.A file with the proteomic data of Wang et al.  analyzed


Protein domains display a range of structural diversity, with numerous additions and deletions of secondary structural elements between related domains. We have observed a small number of cases of surprising large-scale deletions of core elements of structural domains. We propose a new concept called domain atrophy, where protein domains lose a significant number of core structural elements.Here, we implement a new pipeline to systematically identify new cases of domain atrophy across all known protein sequences. The output of this pipeline was carefully checked by hand, which filtered out partial domain instances that were unlikely to represent true domain atrophy due to misannotations or un-annotated sequence fragments. We identify 75 cases of domain atrophy, of which eight cases are found in a three-dimensional protein structure and 67 cases have been inferred based on mapping to a known homologous structure. Domains with structural variations include ancient folds such as the TIM-barrel and Rossmann folds. Most of these domains are observed to show structural loss that does not affect their functional sites.Our analysis has significantly increased the known cases of domain atrophy. We discuss specific instances of domain atrophy and see that there has often been a compensatory mechanism that helps to maintain the stability of the partial domain. Our study indicates that although domain atrophy is an extremely rare phenomenon, protein domains under certain circumstances can tolerate extreme mutations giving rise to partial, but functional, domains.The online version of this article (doi:10.1186/s13059-015-0655-8) contains supplementary material, which is available to authorized users. Protein domains are independently evolving structural and function units. These units have been recombined in many ways creating proteins that perform diverse functions. Proteins, in the course of evolution, may acquire (domain gain) or lose (domain loss) one or more entire domains by a variety of means ,2. At a Analyses of a large number of domain structures have indicated significant variability in domain lengths . These vSimilar to domain elaborations, one could envisage events leading to loss or degradation of a domain’s secondary structure elements necessitated by similar evolutionary constraints as above. We propose the term ‘domain atrophy’ for events that lead to significant loss of core secondary structure elements Figure . Such loIt seems possible that the process of domain atrophy occurs by a single large-scale mutation, which raises the question of how can a protein tolerate such a large destabilising event. Such events of loss of >10 amino acid residues are more likely to be very destabilising. Losses at protein termini are more likely to be tolerated, particularly if they are of peripheral secondary structures. For example, partial deletion of the first seven residues at the N-terminus of Ig-domain of the human muscle protein titin only marginally decreased its stability by 2.8 kcal/mol , howeverAn alternative possibility is that a globular domain that undergoes an atrophy event then becomes disordered and may adopt some new function. We believe that we are unlikely to be able to observe such events as the pattern of conservation is likely to significantly change leading to very rapid sequence divergence. However, the relationship of domain atrophy to protein disorder may be an interesting avenue for future research.Unlike domain elaboration, which is commonly seen in protein structures, structural data and literature on domain atrophy is very scarce. Using sequence based protein-HMM models of domain families, we have devised an algorithm to identify potential cases of domain atrophy and have identified several new cases in proteins of known structure. Where 3D structures of the atrophied domain were not available, we have used sequence mapping onto reference complete domain structures to infer potential domain atrophy events.A companion paper by Triant and Pearson  describeIn order to quantify the magnitude of structural loss we have defined a metric called the Atrophy Score see , which iA partial match to a Pfam profile-HMM is only weak evidence for domain atrophy. As Triant and Pearson show, even if a match to the whole HMM is not present one can often find that the similarity can be extended to the full domain length with a sequence similarity comparison. Therefore, we look in particular at partial domains that are end bounded by either another domain, or the terminus of the sequence.We have categorised domain atrophy into five different types that are fully described in the Methods section Figure . The fivDomain instances that scored between 0.15 and 1 were manually examined to identify potential false positive cases (see Table Photobacterium phosphoreum LuxF (UniProt: P12745), shows an atrophy score of 0.31, indicating a loss of nearly one-third of the domain’s structure.The bacterial luciferase domain of the non-fluorescent flavoprotein (NFP) from The bacterial luciferase domain, homolog of bacterial luciferase subunits , is presP. phosphoreum (PDB: 1FVP) resembles a partial TIM-barrel-like fold with one β-strand and three α-helices missing [Bacillus cereus (PDB: 2B81) was chosen as the reference domain for comparison. The reference domain has a (β/α)8 TIM-barrel fold , have no corresponding matching structural elements on 1FVP. This suggests that P. phosphoreum bacterial luciferase domain is an example of within-domain atrophy rather than N-terminal end-bounded atrophy as first thought. We also identified a similar case of domain atrophy in the orthologous protein of Photobacterium leiognathi .Although identified as N-terminal end-bounded atrophy, comparison with the reference domain (PDB: 2B81) shows that βA key issue is in determining whether domain atrophy has occurred is whether the ancestral fold of bacterial luciferase domains was indeed a TIM-barrel. Phylogenetic analysis of bacterial luciferase family indicates that the LuxF protein clade is completely enclosed by LuxB Figure  and thatin vivo and forms a homo-dimer with the dimer interface being on the same side of the molecule as observed in the atrophied domain-dimers   binding domain of phenylacetate-coenzymeA ligases from Phenylacetate-coenzymeA ligases are adenylate-forming enzymes, which are involved in the metabolism of phenylacetate . The enzAlcaligenes sp. (PDB: 3CW9), shows a large, globular, distorted α/β structure comprising three distinct β-sheets with a large cleft containing the binding pocket [The AMP-binding domain is present in proteins such as acyl-CoA synthetases , aryl-Cog pocket   [Structural superposition of the ‘N-terminal microdomains’ of B: 2Y27)  onto theB. cenocepacia phenylacetate-coenzymeA ligase results in the formation of an intramolecular β-sheet at the dimer interface [B. cenocepacia is thought to be functional and is involved in recruiting subsequent enzymes of the phenylacetic acid degradation pathway [B. cenocepacia, we find similar domain atrophies in the phenylacetate-coenzymeA ligase orthologue from Bacteroides thetaiotaomicron  and the phenazine antibiotic biosynthesis protein from Enterobacter agglomerans .It can be seen from the reference structure that the first sub-domain has minimal interactions with the substrates during adenylation and the subsequent thioester formation processes . The homnterface . Interesnterface   domain of the rat Ral-GTPase-activating subunit α-1 (UniProt: O55007) has a C-terminal atrophy score of 0.46.The 747 amino acid residue isoform 1 has an atrophied RapGAP domain at the C-terminus with no other detectable domains at the N-terminus. The RapGAP catalytic domain of the human Rap1-GAP protein (PDB: 3BRW) was chosen as reference to infer atrophy. The atrophied rat RapGAP domain and the reference human RapGAP catalytic domain show 30% sequence identity in the aligned region.GTPase-activating proteins (GAPs) terminate G-protein signalling by inducing hydrolysis of bound GTP to GDP . The humRat RapGAP protein isoform 2 (906 amino acids) and isoform 3 , however, show no signs of atrophy, suggesting exon loss mediated by alternative splicing as a probable mechanism for the observed atrophy in isoform 1.Of the 125 cases of upstream domain-bounded atrophy identified by our pipeline none were confirmed as true atrophy events.Staphylococcus aureus PurK (UniProt: A6QFS4)  domain of ) Figure A shows a2-Hacid_dh_C domains are found in dehydrogenases and oxidoreductases among prokaryotes and eukaryotes. Bacterial PurK and PurE are involved in the two-step conversion of 5-aminoimidazole ribonucleotide to 4-carboxy-5-aminoimidazole ribonucleotide .Lactobacillus jensenii D-lactate dehydrogenase (PDB: 4PRL) adopts a Rossmann fold with six parallel β-strands bound to NAD [S. aureus (PDB: 3ORQ) 2-Hacid_dh_C domain adopts a partial Rossmann fold and have an additional alpha helix (α2) and β-strand (β3) insertion [The reference 2-Hacid_dh_C domain from d to NAD  .Structural superposition on 4PRL indicates a partial domain at the C-terminus of 3ORQ Figure C. The at1 with α1. This binding motif and other interaction sites are conserved and are at structurally equivalent locations  compared to the NAD bound 2-Hacid_dh_C of L. jensenii  and might bind to the adenine moiety, however other residues interacting with the nicotinamide moiety at the C-terminus (4PRL: Asp259 and His295) are absent. His295 located near the nicotinamide ring is thought to be catalytically important [Bacillus anthracis  also shows similar atrophy.It could also be seen that despite atrophy at the C-terminus, the domain retains the ‘reverse’ Rossmann fold motif (GXXGXG) on the loop, at the N-terminus, connecting βmportant ,31 but iPyrococcus furiosus RNA-binding protein AU-1 (UniProt: Q8U4Q7) shows atrophy at the C-terminus with a score of 0.19.The RNase E domain of P. furiosus RNA-binding protein is a large oligomeric complex that binds specifically to AU-rich RNA sequences and may be involved in RNA metabolic processes [E. coli RNase E [P. furiosus RNase E domain and therefore the region of atrophy was inferred through sequence mapping onto the reference structure.The rocesses . The RNA RNase E , and a CE. coli RNase E large domain (PDB: 2C0B) is a large multi-domain structure comprising four domains: S1, 5’ sensing region, RNase H and DNase I [P. furiosus RNase E large domain sequences on to the E. coli RNase E reference domain shows the region of atrophy corresponds to the C-terminus of the DNase I domain (residues 339-393)  Figure .Figure 81–2–3–4–6–5. The DNase I domain is the catalytic centre of the complex with active sites Asp303 and Asp346 present on β3 and β4, respectively [E. coli and P. furiosus RNase E large domains share 14% sequence identity and atrophy at the C-terminus of the DNase I domain results in the loss of one of the active site residues, Asp346. However, a more accurate multiple sequence alignment of the domain family indicates that both catalytic residues may be present resulting in a potentially functional domain.The DNase I domain has an α/β arrangement of two α-helices and an antiparallel β-sheet with six antiparallel β-strands in the order βectively . Asp303 ectively . PairwisE. coli RNase E catalytic domain is a tetrameric complex formed by a dimer-of-dimers, where the dimer interface of this complex is largely mediated by the interaction of DNase I domains [P. furiosus RNA-binding protein AU-1 forms a homo-oligomeric complex that is mainly trimeric [E. coli RNase E small domain, is thought to cause the difference in the oligomerisation of the complex.The  domains , howevertrimeric . The atrCryptococcus albidus Endo-1,4-beta-xylanase (UniProt: P07529) is found with a within-domain atrophy score of 0.24.The Glycosyl hydrolase family 10 (Glyco_hydro_10) domain of C. albidus endo-1,4-beta-xylanase is an inducible extracellular enzyme with xylobiose as its natural inducer [Xylanase are found in bacteria, fungi and other microbes ,36, whic inducer .Thermotoga petrophila endo-1,4-beta-xylanase reference protein (PDB: 3NJ3) folds into a TIM-barrel structure [4 and two core alpha helices, α3 and α4 (residues 110-176)  Figure . AtrophyC. albidus endo-1,4-beta-xylanase is inefficient in degrading xylobiose and hydrolyses xylotriose at very slow rates although longer substrates such as xylotetraose are hydrolysed [Pyrococcus furiosus Nicotinate-nucleotide pyrophosphorylase (PDB: 2I14). The hydrophobic cores of these partial TIM-barrels are buried by interactions with their N-terminal domains. It would be very interesting to see the structure of C. albidus endo-1,4-beta-xylanase to better understand how the atrophy event is compensated for.It has been shown that drolysed ,39. It iProteins evolve by a number of different mechanisms that lead to changes in sequence, structure and function. Events such as domain gain or loss have been extensively studied in terms of evolution of prokaryotic and eukaryotic genomes ,41, otheDomain atrophy events were classified into five groups based on the location of the domain within the protein and the region of atrophy in the domain. Domain atrophy events are found to be rare accounting for only a small fraction (0.06%) of the total domain instances analysed. One of the reasons for such rare occurrence is that significant loss of structural elements would usually be detrimental for domain stability. A large number of factors such as hydrophobic  and elecP. leiognathi bacterial luciferase domain and the AMP-binding domain of B. cenocepacia.An intriguing aspect of atrophied domains is the energetics of their folds. Deletions or insertions of individual residues can significantly influence the overall energetics of the fold . Deletio4 half-barrel [8 TIM-barrel may have evolved from duplication and gene fusion of an ancestral (β/α)4 half-barrel [Atrophied domains should have incomplete/partial folds, but the converse may not be true. For example, the histidine biosynthesis pathway enzymes HisA and HisF consist of two copies of a (β/α)f-barrel . It has f-barrel .An important issue is to understand how domain atrophy events arise. Mutational events such as a mutation to a premature stop codon could lead to domains with atrophy at the C-terminus. While the creation of a new downstream start codon could lead to domains with atrophy at the N-terminus. Other mechanisms such as exon loss or deletion could additionally lead to atrophy within the domain. We observe a large number of domain atrophy cases at the termini of proteins: N-termini (n = 40) and C-termini (n = 26), compared to atrophy cases within a protein. We see just four cases of atrophies within a domain, and we only see five cases of atrophy events that are bounded by other domains, and hence also occur within the protein. It is unclear whether this is a reflection of the true proportions or simply due to our methodology. Alternative splicing provides one possible mechanism that enables an organism to explore atrophied domain sequence space while still retaining a functional copy . AnalysiUnfolded proteins or misfolded proteins are targeted by protein degradation machinery by recognising their solvent exposed hydrophobic residues. Although no clear experimental data are available from known cases of atrophied domains on their possible mechanisms to evade recognition and degradation by the protein degradation machinery, we speculate atrophied domains escape unfolding and degradation by burying their exposed solvent-accessible areas through dimerisation or complex formations.Disordered or transiently unfolded regions are known to fold cooperatively upon contact with their binding partners  and evadWe observe that domain atrophy is a very rare event compared to domain loss or elaborations. Our analyses of a subset of protein sequences, with protein existence value of 1, defined by UniProt, indicates only a minute fraction of domains exhibit atrophy. An independent analysis  of a broTo identify potential cases of domain atrophy we use matches of the UniProt sequence database (release 2012_06) against the profile-HMM models from the Pfam database release 27.0 . This seN) and C-terminal (ASC) boundaries of each domain instance. Figure To determine which domains may be cases of atrophy we calculate a measure called the Atrophy Score (AS) at both the N-  is similar to ASN.Instances of within-domain atrophy can be identified in cases where the profile-HMM matches to a single domain have been split into two profile-HMM matches, with the first corresponding to the N-terminal part of the domain and the second corresponding to the C-terminal part of the domain. We can distinguish these cases from tandem repeats by considering the HMM match states of each domain. The start HMM-match state of the downstream domain is greater than the end HMM-match state of the upstream domain in cases of split domains. The computation of within-domain atrophy score  in the protein and the region of atrophy in the domain: (1) N-terminal end-bounded atrophy: structural loss at the N-terminal region of the N-terminal domain; (2) C-terminal end-bounded atrophy: structural loss at the C-terminal region of the C-terminal domain; (3) Upstream domain-bounded atrophy: structural loss at the N-terminal region of an inner domain, also including the N-terminal region of the C-terminal domain; (4) Downstream domain-bounded atrophy: structural loss at the C-terminal region of an inner domain, also including the C-terminal region of the N-terminal domain; and (5) Within-domain atrophy: structural loss within the domain.Initial results from applying the atrophy score to all UniProt proteins showed that there were numerous common failure modes Figure  that wouOf a total of 23,193,494 sequences in the database, 18,523,877 sequences, which have at least one Pfam-A domain instance, were used in the analysis. Initial filtering was applied to exclude domain models from sequences with protein existence (PE) levels of 2 to 5. These are enriched in gene prediction errors and fragment proteins. This reduced the number of sequence considered from 18,523,877 to 77,305. Proteins with a protein existence level of 1 have clear experimental evidence for the existence of the protein from Edman sequencing, mass spectrometry, X-ray, NMR or other experimental evidence. Although not strictly a measure of protein sequence quality these proteins usually have highly accurate protein sequences. We also removed sequences annotated as fragments in UniProt, which further reduced the set of sequences considered from 77,305 to 75,435.Adjacent domains that are of the same clan, similar to figure Perl scripts were run to calculate atrophy scores across the set of domains. Domain instances with an atrophy score of 0.15 or more were further investigated.Domain instances that were obtained after applying the above filters were then selected for manual inspection. Only those domains that had an atrophy score of 0.15 or above were checked manually for identification of false positives. We checked each potential domain atrophy case for evidence of any of the following failure modes  of our method is 0.055.Structures were visualised with Chimera . Where eEvolutionary information was inferred from phylogenetic trees constructed from multiple sequence alignment of domain family seed sequences and homologous sequences from a JackHMMER search . A non-r


The per-ARNT-sim (PAS) domain represents an ancient protein module that can be found across all kingdoms of life. The domain functions as a sensing unit for a diverse array of signals, including molecular oxygen, small metabolites, and light. In plants, several PAS domain-containing proteins form an integral part of the circadian clock and regulate responses to environmental change. Moreover, these proteins function in pathways that control development and plant stress adaptation responses. Here, we discuss the role of PAS domain-containing proteins in anticipation, and adaptation to environmental changes in plants. The PASper) and developmental regulator Sim (single-minded) of Drosophila and the vertebrate aryl hydrocarbon receptor nuclear transporter (ARNT), which comprise two PAS motifs each  proteins  receptor protein kinase Phot1 was initially identified by a forward genetic screen for plants showing defects in the phototrophic response . MoreoveThe structure of the two phototropins have been extensively studied  and helpArabidopsis , ELF4, and LUX ARRHYTHMO (LUX) during hypocotyl elongation , FLAVIN-KELCH-FBOX-1 (FKF1), and LOV-Kelch-Protein-2 (LKP2), share a high degree of amino acid identity of about 70–80% . PLP harbors a PAS domain at either end of the protein, the C-terminal domain showing homology to FMN-binding LOV domains. Having no predicted effector domain, however, it is likely that they act in trans on other proteins , PHABULOSA (PHB), PHAVOLUTA (PHV), INCURATA4/CORONA (ICU4/CAN), and ATHB8 (ARABIDOPSIS THALIANA HOMEOBOX PROTEIN 8) constitute the plant-specific Class III of homeodomain leucine zipper (HD-ZIP III) transcription factors. They are well studied major regulators of developmental processes and involved in establishing adaxial-abaxial polarity in lateral organs, meristem regulation and formation, vascular development, as well as embryonic patterning . As a rePRR5 is a target gene, representing a link with the morning-loop of the circadian clock  genes. The subgroup B2 of Raf-like kinases consists of six members and comprises an N-terminal PAS domain (Mitogen-activated protein kinase (MAPK) cascades are a common way of signal transduction and they are associated with integrating environmental cues into adequate responses . ArabidoS domain . To dateMAP3Kδ4 was reported to be an auxin- and abscisic acid (ABA)-responsive kinase affecting plant growth, mediating tolerance to salt stress, and it is likely to be involved in other abiotic stress responses as well . RecentlLight is the major clock input signal, which is used by plants to set the pace and phase of the oscillator, adjusts growth and development to changing environmental conditions. Most of the here described PAS domain-containing proteins sense light, and have been shown previously to be involved in entraining the circadian clock.per se . CIB1 regulates the floral integrator gene FLOWERING LOCUS T (FT), which in turn controls floral initiation (The LOV domain-containing ZTL was shown to interact with PhyB as well as with CRY1, linking both phytochrome and cryptochrome with the circadian clock . The ztlt manner . Therefoitiation . In blueitiation  emphasizitiation . In lineitiation .prr5 prr7 prr9 mutant displays enhanced cold, drought and salinity tolerance, placing the clock PRR proteins in a central position mediating adaptation to environmental cues. Interestingly, overexpression of LKP2 improves plant drought tolerance by positively regulating dehydration-inducible genes such as DREB1A-C, RD29A, as well as DEHYDRIN (prr5 prr7 prr9 mutant (In addition, the ZTL family target PRR5 was shown to repress the expression of genes involved in abiotic stress , 2012. FDEHYDRIN . Moreove9 mutant , in agre9 mutant , and theArabidopsis (The HD-ZIPIII transcription factor REV was shown to control oxidative stress tolerance in bidopsis , aside fThe PAS domain fold is a common protein module found throughout the kingdoms of life. Although the domain has been implicated in sensing a multitude of signals, in plants only evidence for a role in light signaling has been obtained so far. Many of the PAS domain-containing proteins are intimately connected to the circadian clock, suggesting that they might contribute to linking environmental conditions with clock entrainment.Here, we presented the emerging links between PAS domain proteins, time keeping and the response of the plant to the environment. So far, a clear implication for light signaling and plant adaptation through PAS domain proteins has been established by predominantly altering protein–protein interaction properties of their proteins. Still, plenty of effort is needed to dissect the biochemical and mechanistic functions of most PAS domain proteins in plants. The role of most of these proteins in response to the environment is unexplored. In addition, the stimulus upon which they exert their function remains elusive. An appealing aim would be to characterize the family of MAPKKK proteins further, which seem to be linked with abiotic stress tolerances. It would be intriguing to reveal whether the PAS domain controls their signaling activity.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Biochemical Journal, Lolli et al. presented evidence that the C-terminal STAS (sulfate transporter and anti-sigma factor antagonist) domain of the motor protein prestin possesses an anion-binding site. This discovery might shed light on an aspect of the function of this mysterious and fascinating protein that is crucial for the human hearing system.In a recent paper published in the  Biochemical Journal that suggest the mammalian prestin STAS domain possesses an anion-binding site, to which the physiologically relevant Cl− ion binds. The idea that intracellular Cl− ions might act as an extrinsic voltage sensor for prestin makes this discovery all the more exciting [Prestin [SLC26A5 (solute carrier 26A5)] belongs to the ubiquitous SLC26/SulP (sulfate permease) family of anion exchangers and is one of ten SLC26A transporters that have been identified in animals, most of which are known to transport anions across membranes either in an electroneutral or electrogenic manner . In humapresto=fast).Electromotility of cochlear OHCs, which is their ability to elongate or contract actively and quickly (>20 kHz in humans) in response to changes in membrane potential, was first observed in 1985 . Over thPrestin is the fifth mammalian member of the SLC26A proteins, which in turn belong to the large family of SLC26/SulP anion-transporter-related proteins ,12,13. HDespite belonging to a family of anion-transporter-related proteins, mammalian prestin is not an anion transporter as such. Instead, it is the protein driving electromotiliy of OHCs by undergoing incredibly rapid conformational changes. These generate a force great enough to result in alteration of OHC length. The resulting mechanical amplification of sound waves is a mechanism unique to mammals. OHCs and IHCs (inner hair cells) are two distinct types of mechanosensory cells found in the organ of Corti, where IHCs function as primary auditory signal receivers. Sound amplification by OHCs occurs in a cycle-by-cycle manner and relies on the ability of prestin to sense changes in membrane potential, either depolarization or hyperpolarization. In response to that, it must be able to change its conformation very quickly, causing the cell to either contract or elongate. Thus, presumably, prestin has at least two functional domains, namely the voltage-sensor and the actuator.− from the cytoplasmic side may act as an extrinsic voltage sensor since the removal of intracellular Cl− resulted in the absence of electromotility of rat OHCs [As mentioned above, prestin in mammals and non-mammalian animals does not have the same function despite amino acid similarity. Although only prestin in mammals acts as a molecular motor that drives electromotility in OHCs, the non-mammalian orthologue functions as a divalent/chloride anion exchanger . Interesrat OHCs .− anions potentially playing a part in voltage sensing was confirmed by structural analysis of the rat prestin (rPres) STAS domain. Lolli et al. [−, in order to assess conformational changes that may occur within the STAS domains. To obtain crystal structures of the STAS domain, it was necessary to delete a region of the domain called the variable loop, which has a length of 74 amino acids in rPres STAS and 83 amino acids in the chicken prestin (cPres) STAS domain. By comparing the structures of rPres STAS in the presence of Cl− in the alleged anion-binding site and in the absence thereof, it does not appear that the crystallized part of the protein undergoes any major conformational rearrangements as a result of Cl− binding. This indicates that the anion-binding site maintains its conformation throughout the process of anions binding and dissociating, which underpins the concept of the binding site to be pre-formed and ready to accommodate anions that could actuate prestin function. In fact, all other anions, besides Cl−, that were also shown to bind to the crystallized rPres STAS domain trigger prestin activity, which indicates a specificity for these anions. In addition to solving the structure of the rPres STAS domain, Lolli et al. [− binding. However, this may be a crystallization artefact or the result of the missing variable loop, as well as the absence of the rest of the prestin protein [It was not until 2016, when Lolli et al.  publishei et al.  examinedi et al.  obtainedi et al. . Clearly protein .− is clearly a key component of prestin's voltage-sensing ability, it poses a chicken-and-egg problem. Does the binding of Cl− anions to prestin act as an extrinsic voltage sensor or is it the consequence of a preliminary intrinsic voltage-sensing mechanism? Although anions might be binding to the site in the STAS domain, the mystery of its actual function and how it fits in with the rest of the prestin protein has not yet been solved. Recently, the crystal structure of a SLC26 protein from Deinococcus geothermalis, SLC26Dg, was obtained and published as the first ever complete structure of a SLC26 protein [presto?Despite the progress brought about by revelations such as the identification of the C-terminal anion-binding site within the STAS domain, unanswered questions remain. Although Cl protein . Unfortu protein . Besides


The analysis shows that conserved structural surfaces distinguish which PH domains associate with membrane from those that do not. Moreover, the results indicate that lipid-binding PH domains can be classified into different functional subgroups based on the type of membrane insertion elements they project towards the bilayer.The human genome encodes about 285 proteins that contain at least one annotated pleckstrin homology (PH) domain. As the first phosphoinositide binding module domain to be discovered, the PH domain recruits diverse protein architectures to cellular membranes. PH domains constitute one of the largest protein superfamilies, and have diverged to regulate many different signaling proteins and modules such as Dbl homology (DH) and Tec homology (TH) domains. The ligands of approximately 70 PH domains have been validated by binding assays and complexed structures, allowing meaningful extrapolation across the entire superfamily. Here the Membrane Optimal Docking Area (MODA) program is used at a genome-wide level to identify all membrane docking PH structures and map their lipid-binding determinants. In addition to the linear sequence motifs which are employed for phosphoinositide recognition, the three dimensional structural features that allow peripheral membrane domains to approach and insert into the bilayer are pinpointed and can be predicted  The pleckstrin homology (PH) domain was discovered 22 years ago in various proteins including pleckstrin which are involved in signaling, cytoskeletal organization, membrane trafficking and phospholipid processing ,2. The eIn order to predict which members of the PH domain superfamily bind membranes, we applied the MODA program that has been trained to identify membrane binding surfaces in any high resolution protein structure . We focuProtein sequences that contain at least one PH domain were selected from the curated SwissProt database. Only one isoform per set of entries was chosen, and the PH domains of the selected proteins were extracted for alignment. In total, the September 2015 release of the Uniprot database contained annotated PH domains in approximately 285 distinct human proteins. The alignment was solely based on the sequences of the PH domains, and the phylogenetic tree was constructed accordingly. The sequences were aligned using Multiple Alignment using Fast Fourier Transform (MATFF) and subsequently used to build a phylogengetic tree within phyML . The conin vitro. This was also intended to minimize the potential for indirect interactions that could arise from analyses of cellular data or complex systems. Predicted membrane binding was considered to be validated when experiments were carried out with membrane mimics such as phosphatidylinositolphosphate (PIP) ligands, micelles, bicelles, or liposomes using methods including NMR, X-ray crystallography, fluorescence, or dot-blot assays.The coordinates of the PH domains were obtained from the Protein Data Bank and submitted to the MODA algorithm . When prAll members of the PH domain superfamily contain a conserved fold based on seven antiparallel β-strands arranged in a sandwich which is capped at its splayed corner by a C-terminal α-helix. These elements form a central hydrophobic core that stabilizes the consensus structure A–C. The To inspect the PH domain superfamily as a whole and understand how its domains are involved in diverse biological functions, we gathered the amino acid sequences of 334 human PH domains. They are present in 285 distinct proteins some of which contain up to 5 PH domains, such as ARAP members. Only 13% of these multifaceted proteins contained more than one PH domain, and most of these have two (9%). Based on multiple-sequence alignments, a phylogenetic tree was built by maximum likelihood for the PH domain superfamily . SeveralComparison of protein domain architectures alongside the phylogenetic tree  also higThe membrane interactions of 69 human PH domains and 19 mammalian orthologues were analyzed using available structural and experimental data. This yields a total of 88 cases that together represent 26% of the PH superfamily .in silico and in vitro binding assays, while 21 PH domain structures lack obvious membrane binding features or abilities.In each case, the membrane interactions sites were calculated using MODA , a molecIn particular, MODA predictions of membrane binding PH domains were confirmed experimentally for 36 out of the 53 positive cases ,67,68,69Detailed structural analysis of the false negatives indicates that in most cases MODA failed to predict a known membrane binding site due to structural factors. For example, some structures lack density for exposed hydrophobic residues in known binding loops, or contain an element that is too constrained to allow a predictable membrane interaction such as in the ADAP1-2, IRS1, GRB14, and RAPH1 proteins. For instance, the membrane binding site of ADAP1-2 was co-crystallized with a phosphoinositide headgroup  but was The false positives generated by MODA include PRKD2 or PRKD3 where the β3-β4 loop or the β5 strand were identified as possible membrane interacting sites. However, these sites actually interact directly with the kinase domain that they are known to regulate, although we cannot formally exclude the possibility that lipids may also bind transiently here within the cell.1H, 15N resolved NMR spectroscopy showing a lack of membrane interactivity (data not shown).Definition of which PH domains do not associate even transiently with any membrane is challenging. There is a scarcity of studies reporting the lack of interaction between PH domains and membranes for obvious reasons. The experiments needed to disprove membrane binding are demanding, in particular when the affinity is typically in the μM-mM range and there are many potential lipid ligands and membranes. Moreover there is generally little appetite for publishing negative results. Nonetheless, the absence of membrane interactions is experimentally established for four proteins: AKAP13, ARHGAP21, PRKD2, and PRKD3, with the predicted lack of membrane binding by AKAP13 being in agreement with recent results . Given tInterestingly, the RhoGEF subfamily members generally exhibit a PH domain that have no membrane binding signatures and are positioned immediately after the DH domain. Such cases include AKAP13, ARHGEF3, ARHGEF11, ARHGEF12, FGD3, FGD5, ITSN1, MCF2L, NET1, and VAV. No accurate prediction could be achieved by MODA with the related PH domains of FARP1-2 or FARP2-2 due to the lack of sufficient resolution, although a structured interaction of the C-terminal PH domain of FARPs with the DH domain is unlikely . In contIn total 88 PH structures were analyzed, although poor resolution thwarted prediction from 11 structures. Out of the remaining 77 structures, membrane interaction sites were predicted for five domains, 38 of which contained a binding patch at the β1-β2 loop A. ComparIn summary, our genome-wide analysis indicates that the exposed β1-β2 loop represents the primary bilayer interacting element in 70% of those PH domains which associate with membranes. Our previous studies of the FAPP1 protein ,72 reveaThe results of MODA predictions can be translated into the discovery of novel membrane interactions sites, and can drive the design of mutations or experiments to gain insight into protein functions. Through the following examples, we inspect the consistency of predictions of membrane binding across the family.The PH domain of FAPP1 illustrates a case where the prediction is fully consistent with the binding site determined experimentally. Extensive biophysical, structural and computational data ,72 suppoBesides the PIP canonical binding site, a sub-family represented by β-spectrin-like proteins hosts a PIP binding site at the “open side” of the β1-β2 loop . This noThe regulation of the ARHGEF9 protein, which is also known as Collibistyn I, is dominated by the relative position of its domains. The activity of its DH-PH tandem domain is largely controlled by the position of its SH3 domain, which binds the DH domain and prevents the PH from interacting with membranes . Once thAKAP13 remains one of the few examples of PH domains for which the absence of membrane interaction has been determined experimentally . The absThe interactions of peripheral proteins with membranes range over a wide spectrum of affinities ranging from nM to mM. This is consistent with the biological roles of initial sampling of surfaces, specific recognition of lipid ligands, tight anchoring of multi-subunit assemblies on membrane, and dynamic transfer of cargo. While a tight binding event is easily confirmed with most experiments, the discovery of weak interactions proves more challenging and requires more sensitive approaches.Moreover, it is common to observe that the lipid specificity contains a method-dependent component which complicates the analysis and interpretation of the binding. For instance, the use of dot-blots or PIP strips on complex surfaces generates results which do not necessarily agree with solution data from NMR, fluorescence data with modified proteins, or surface plasmon resonance (SPR) data from sensor chips. Similarly, excessive addition of anionic lipids can systematically increase the affinity of some proteins for membrane. Here, MODA focuses on the inherent membrane binding properties, regardless the lipid specificity. We show that it provides a useful tool for discovering membrane interaction sites and allows discrimination of binding modes within a family for validation by virtually any experimental tool.This study sheds a new light onto the PH domain family, comparing the sequences, structures and membrane binding capabilities of a large number of PH domains. First, it showed the striking number of GEF or GAP domains associated with PH domains which are often situated proximally and regulate their activities.The second observation derived from this genome-wide analysis of PH domains is that the number of PH domains that bind membranes is far greater than those that do not. Current estimates places the fraction of PH domains that bind membranes to only about 20%, while our analysis of a well-characterized quarter of the PH proteome shows that at least 61% of the members bind membranes. Moreover, in light of their mechanisms, it is now feasible to test how PH domain interactions contribute to, for example, membrane limited GTPase reactions, as well as assembly of and signaling from membrane-associated complexes. Importantly, such sites can be exploited for therapeutic intervention, as demonstrated by the identification of inhibitors directed at the membrane binding PH domain of oncogenic Akt ,77,78,79This analysis resolves whether protein superfamilies possess conserved features that can be used to distinguish cytosolic and peripheral membrane states, thus addressing a long-standing debate. In particular, previous postulations that primary sequence motifs can predict lipid binding have proven tenuous, and here we show that 3D signatures are genuinely predictive. While some 3-phosphoinositide binding PH domains appear to exhibit a common motif , the seqA note of caution is warranted. The definition of a membrane binding domain may vary with the case under investigation. Weak binding may not be detected by most techniques and therefore, when the analysis is carried out, a genuine transient or non-specific membrane binding domain may be regarded as a non-binder, introducing a bias towards PH domains that are tight binders or required a specific lipid ligand. Nonetheless, weak binding events predominate in nature, and often contribute to multivalent interactions and multi-step assembly processes. Sensitive tools to detect and allow the characterization of even weakly binding domains are needed to understand the execution and regulation of cellular events. Here this capability has been demonstrated with a large superfamily for the first time, and should allow discovery of many more biological and regulatory functions in other proteins.


GmrSD is a modification-dependent restriction endonuclease that specifically targets and cleaves glucosylated hydroxymethylcytosine (glc-HMC) modified DNA. It is encoded either as two separate single-domain GmrS and GmrD proteins or as a single protein carrying both domains. Previous studies suggested that GmrS acts as endonuclease and NTPase whereas GmrD binds DNA.In this work we applied homology detection, sequence conservation analysis, fold recognition and homology modeling methods to study sequence-structure-function relationships in the GmrSD restriction endonucleases family. We also analyzed the phylogeny and genomic context of the family members.Results of our comparative genomics study show that GmrS exhibits similarity to proteins from the ParB/Srx fold which can have both NTPase and nuclease activity. In contrast to the previous studies though, we attribute the nuclease activity also to GmrD as we found it to contain the HNH endonuclease motif. We revealed residues potentially important for structure and function in both domains. Moreover, we found that GmrSD systems exist predominantly as a fused, double-domain form rather than as a heterodimer and that their homologs are often encoded in regions enriched in defense and gene mobility-related elements. Finally, phylogenetic reconstructions of GmrS and GmrD domains revealed that they coevolved and only few GmrSD systems appear to be assembled from distantly related GmrS and GmrD components.Our study provides insight into sequence-structure-function relationships in the yet poorly characterized family of Type IV restriction enzymes. Comparative genomics allowed to propose possible role of GmrD domain in the function of the GmrSD enzyme and possible active sites of both GmrS and GmrD domains. Presented results can guide further experimental characterization of these enzymes.The online version of this article (doi:10.1186/s12859-015-0773-z) contains supplementary material, which is available to authorized users. Restriction systems are protein complexes able to recognize and destroy foreign DNA based on sequence and modification patterns. They are found in Archaea, Bacteria, Eukaryota, bacteriophages, and some viruses infecting algae –3. Restrin vivo .Altogether, we gathered 1119 GmrSD homologs (both single- and double-domain proteins) from 769 organisms, 613 of which have fully sequenced genomes (according to the GOLD database v.4 ). Among gmrSD system genes in different phyla. Representatives of this family were found across most of the major bacterial lineages as well as in several Archaea and Eukarya (representatives of Bacillariophyta and Chlorophyta phyla and Heterolobosea class)  Fig. . The higWe constructed a multiple sequence alignment that contains sequenecs of both single- and double-domain GmrSD family members. The alignment contained 862 sequences of double-domain GmrSD proteins, 25 predicted pairs of single-domain GmrS and GmrD proteins and 207 single-domain GmrS and GmrD homologs not organized into pairs (I/V)DGQQRLTT(I/L/V)xLL, in which the underlined DGQQR sub-motif is conserved in nearly 100 % of sequences. It resembles the DGQHR signature motif of the so-called DGQHR domains (TIGR03187/PF14072). However, none of the identified conserved regions contains previously suggested LAGLIDADG and HNH endonuclease motifs  -D-G-Q-Q-R motif of GmrS proteins was consistently aligned to the Srx signature sequence F-(G/S)-G-C-H-R Fig.  52]. As. As52]. E. coli and ParB-like nuclease from P. furiosus were long known to possess a nuclease activity  and [PDB:4OO8] are structures of the HNH endonuclease domain from the Cas9 protein of CRISPR-Cas system.  and [PDB:2VND] are classified as members of Endonuclease I family, also within the His-Me finger endonucleases superfamily (SCOP d.4.1.6). These structures were identified as best possible templates for a part of the target sequence corresponding to residues 75–226 of the GmrD domain from E. coli CT596 GmrSD protein. The lengths of aligned fragments were however not always the same and depended mainly on the fold recognition server. Secondary structure prediction for this region revealed an α-β-α-α-β-α pattern.The 3D structure of the GmrD domain was predicted to be similar to HNH endonucleases from the His-Me finger superfamily. Best matches (mainly from FFAS and HHSEARCH) referred to  and [PDB:2VND] structures and were reported by FFAS with scores ranging from -8.5 to -13.7 and HHSEARCH with probabilities ranging from 64.2 to 85.9 %. [PDB:4H9D] is a HNH endonuclease from deltaproteobacterium We modeled the GmrD HNH catalytic core (residues 75–196) using the [PDB:4H9D] structure as a template  acts as the general base what suggests that H114 in GmrD may play the same role. N169, which is homologous to N68 from [PDB:4H9D], is then most probably involved in maintaining the proper structure of the protein. Overall our model supports the prediction that the GmrD domain possesses an HNH endonuclease active site but it is not sufficient to predict how it interacts with the substrate.Based on the structural and biochemical data available for other HNH endonucleases, residues D113, H114, N169 and N178 were predicted to be crucial for GmrD structure and function. Biochemical data regarding the HNH endonuclease from etal ion . The equ ion  are present most frequently. We also noticed that some of GmrSD homologs found by our sequence searches are annotated as “RloF” or “RloF-like”. Indeed, DUF262 (GmrS domain) has been previously identified within the Type I RM system locus in r et al. , who havType I RMs elements, integrases, helicases and transcription regulators, which we found to be among most important contributors to the GmrSD genomic neighborhood, were reported among protein families overrepresented in the genomic neighborhoods of DUF262 , 67, whiFinally, we found tetratricopeptide repeat (TPR_15) domains and DUF262 to be enriched in the GmrSD neighborhood. Proteins containing the TPR_15 domain were found to be overrepresented in the genomic neighborhoods of the COG1479 family proteins in defense islands [To examine coevolution of GmrS and GmrD domains we have built two phylogenetic trees comprising GmrS and GmrD domain sequences from both double-domain and single-domain proteins, which we predict to form functional pairs see .Fig. 7PhMycoplasma mobile (a fish pathogen), is most closely related to a double-domain GmrSD system from Streptococcus parauberis . This indicates a horizontal transfer of the complete GmrSD enzyme between these two distantly related species dwelling in the same environment.The tanglegram reveals very similar topologies of GmrS and GmrD phylogenetic trees, which suggests a coevolution of these two domains. In order to further support this observation we calculated a single phylogenetic tree for double-domain GmrSD proteins and merged GmrS-GmrD protein pairs . Among other results it shows that the conserved D-H-N motif located at the C-terminus of the Eco94GmrSD protein is the potential endonuclease catalytic site . These results partially confirm our predictions, since D507 and H508 of Eco94GmrSD are homologous to D113 and H144 of GmrD CT, which were predicted by us (in this work) to be involved in the catalysis. The influence of residues homologous to GmrD CT N169 and N178 was not studied. The authors did not propose a possible active site responsible for the NTPase activity and reported that they could not detect similarity to any known ATPase or GTPase. Hence, our predictions for GmrS provide additional information to guide further experimental studies. In particular our functional annotation of the DGQQR motif in GmrS remains to be experimentally verified.After the submission of this manuscript a study of another member of the GmrSD family was published , which d