Phosphotyrosine binding (PTB) domains are critically involved in cellular signaling and diseases. PTB domains are categorized into three distinct structural classes namely IRS-like, Shc-like and Dab-like. All PTB domains consist of a core pleckstrin homology (PH) domain with additional structural elements in Shc and Dab groups. The core PH fold of the PTB domain contains a seven stranded β-sheet and a long C-terminal helix.In this work, the PTB domain of Dok1 protein has been characterized, by use of NMR spectroscopy, in solutions containing sub-denaturing and denaturing concentrations of urea. We find that the Dok1 PTB domain displays, at sub-denaturing concentrations of urea, alternate conformational states for residues located in the C-terminal helix and in the β5 strand of the β-sheet region. The β5 strand of PTB domain has been found to be experiencing significant chemical shift perturbations in the presence of urea. Notably, many of these residues in the helix and in the β5 strand are also involved in ligand binding. Structural and dynamical analyses at 7 M urea showed that the PTB domain is unfolded with islands of motionally restricted regions in the polypeptide chain. Further, the C-terminal helix appears to be persisted in the unfolded state of the PTB domain. By contrast, residues encompassing β-sheets, loops, and the short N-terminal helix lack any preferred secondary structures. Moreover, these residues demonstrated an intimate contact with the denaturant.This study implicates existence of alternate conformational states around the ligand binding pocket of the PTB domain either in the native or in the near native conditions. Further, the current study demonstrates that the C-terminal helical region of PTB domain may be considered as a potential site for the initiation of folding. Conversely, other conformational states of protein including partially folded and unfolded states reside at the high energy region of the folding funnel landscape. The folded conformation of a protein, largely populated under physiological solution condition, often undergoes excursion to other low-lying energy states that surrounds the free energy minimumFolding and unfolding studies of protein interaction domains have generated valuable insights toward the protein folding mechanisms 15N-1H HSQC spectrum of the PTB domain is well dispersed, as expected for a folded protein , in comparison to the other  at four different conditions including 0 M urea or native, in the presence of phosphorylated β3 tail and in sub-denaturing concentrations, at 0.5 M and 1 M, of urea , in 7 M urea, encompass all of the major secondary structural elements allowing us to probe the structural and dynamical characterization of the unfolded state of the PTB domain.The 13Cα  shift of αH indicates population of alpha helical conformations while the opposite trend has been observed for the β strand structural propensity 13Cα chemical shift . In a previous study, the secondary shift of 13Cβ has also been seen to be a poor indictor of the secondary structure for the denatured protein Secondary structural propensity of the unfolded states of Dok1 PTB domain has been assessed from secondary chemical shifts and nuclear Overhauser effect (NOE) data. Secondary chemical shifts of 13Cα  and αH , 9 αH/NH  and 6 βH/NH (i to i+2) NOEs were identified for the Dok1 PTB domain in 7 M urea. The C-terminal region of the PTB domain contains several of these medium range and NH/NH NOEs, indicating preference for helical conformations. By contrast, other residues of the PTB domain in the denatured state appear to be sampling the extended region of the φ, ψ conformational space as suggested by the strong sequential αH/NH NOE connectivities. Interestingly, we have observed as many as 26 NOE contacts involving urea and PTB domain  spectra. Analysis of 3D nge NOEs . All togB domain , Fig. S31 (1/T1), R2 (1/T2) and heteronuclear {1H}-15N NOE  and J(H), were determined  and plotted in N) parameter, largely determined by R1, shows similar values across the polypeptide chain, except for the last two residues, Q102 and G103, at the C-terminus (N) for these residues Q102 and G103 are diminished (H), mostly determined by heteronuclear NOE, represent a wider variation across the polypeptide chain whereby higher values are observed for residues, Q102 and G103, at C-terminus along with fewer residues including A12, S39, R70, G74, A91, A95 at the middle and the N-terminus (H) of residues A12, S39, R70, G74, A91, A95 indicate a fast motion without any associated contribution to intermediate time scale motions  J(0) values. There are also a few residues, R70, S73, G74, T77, A83, G85, A91, T94, A95 at the C-terminus with below average J(0) value , J(terminus . J(N) fminished . The valterminus . The hig motions . The J(00) value . The seq0) value .Protein domains essentially act as a molecular interface for their interactions with nucleic acids, proteins and phospholipids. These binding events in turn control different cellular processes from signal transduction to transcription regulation15N-1H HSQC spectra of PTB domain acquired at various concentrations of denaturant revealed detectable binding of the denaturant with the folded protein. Despite a direct binding of urea, the folded conformation of the PTB domain appears to be stable and unfolds only at 7 M urea. The HSQC spectral changes, with low to high concentrations of urea, have indicated an unfolding transition whereby the folded and unfolded states are in slow exchange at the NMR time scale. The PTB domain of Dok1 assumes highly unfolded conformations at 7 M urea without any evidence of long-range interactions. A large number of residues including polar, charged and Gly demonstrated NOE contacts with the denaturant  NOEs and secondary chemical shift parameters indicated that the unfolded PTB domain is in largely extended conformations. The unfolded conformational ensembles of the PTB domain are also dynamically flexible as indicated by low value of hetero-nuclear NOEs. However, non-random conformations of the PTB domain can be seen at the C-terminal region and for a few short segments of the protein. The C-terminus of the PTB domain is sustained by a long helix encompassing residues N86-K100 in its native state. Residues in this segment of the PTB domain demonstrated a consensus pattern of helical conformations in secondary chemical shifts in 7 M urea  values in spectral density mapping were also indicative of fast motion whereas other residues, with higher J(0) and lower J(H), may be experiencing motional restriction at sub-nanosecond timescale. The β-sheet region of the Dok1 PTB domain in 7 M urea did not exhibit any preferred secondary conformations expect for residues at the loops connecting β4, β5 and β6 strands. Most of the residues corresponding to the β-sheet region of the native PTB domain were also found to be motionally flexible, although non-random conformations and motional restriction can be seen for three short regions namely residues A27-V32, L46-L53 and Y56-V61. Above average values of R2/R1 and positive values of heteronuclear NOE have indicated formation of clusters with non-random motions. Further, the above average J(0) and below average J(H) values in spectral density mapping correlate with a relatively slower motion of these clusters in 7 M urea. Such slow motions may also indicate plausible conformational exchange processes contributing to the higher R2 values. Notably, most of the residues in these clusters are non-polar or aromatic and did not exhibit NOEs to urea. This may perhaps explain non-random characteristics of these hydrophobic clusters. A plot of the average area buried upon folding (AABUF) of the domain sequence correlates well with the residues involved in cluster formation . Cells were induced with 1 mM IPTG at an OD600 of 0.6–0.7 and incubated for 6–12 hours at 16°C for protein production. Cells were centrifuged and resuspended in 20 mM Tris-HCl buffer, pH 8.0. Cell solutions were lysed by sonication and the supernatant was applied to Nickel-NTA column for His tag-based affinity purification (Qiagen). The weakly bound proteins were removed from the column by extensive washing with appropriate buffers. Target protein was eluted with buffer containing 500 mM imidazole. Further, purified protein was extensively dialyzed against 50 mM sodium phosphate buffer, containing 100 mM NaCl and 2 mM DTT at pH 6.0. Samples of Dok1 PTB domain were loaded onto a Hiload Superdex 75 16/26 GL preparative column with the AKTA FPLC UPC-900 system . FPLC column was equilibrated with 50 mM sodium phosphate buffer containing 100 mM sodium chloride and 2 mM DTT, pH 6.0. Protein samples were eluted at a flow rate of 0.5 ml/min and detected spectrophotometrically at 276 nm. The amino acid sequence of Dok1 PTB domain was numbered as Q1- G103.His tagged human Dok1 PTB domain  was sub cloned into the pET14b vector and over expressed into 15N-1H HSQC spectra of native and the 7 M urea unfolded state of Dok1 PTB domain were assigned using standard triple resonances NMR methods with a combination of 3-D spectra: HNCA, HN(CO)CA, HNCACB and CBCA(CO)NH αH and side chain protons were assigned by 3D 15N -NOESY-HSQC (mixing time 120 ms) and 3D 15N-TOCSY-HSQC (mixing time 80 ms) experiments in case of urea unfolded sample. NMR spectra were acquired at sample concentrations of 0.6 mM or 1 mM in 50 mM sodium phosphate, 100 mM NaCl, 2 mM DTT and 10% D2O at pH 6. For the unfolded state of the protein, all the conditions were same except 7 M urea had been added from a stock solution of 10 M urea made in same buffer. Temperature for all the experiments was set to 298 K. The proton chemical shift was directly referenced to 1H resonance frequency of 2,2-dimethyl-2-silapentane-5-sulfonic acid (DSS) whereas the 13C and 15N dimensions were referenced indirectly. NMR parameters of the unfolded state of Dok1 PTB domain have been deposited to BMRB with accession number 19672.All the NMR experiments were performed on Bruker DRX 600 MHz or 700 MHz spectrometers equipped with cryoprobe. Data acquisition and processing were performed either with Topspin software (BRUKER) or NMRPipe and NMRDraw suite 15N labelled samples. A series of HSQC spectra were recorded at different urea concentrations ranging from 0.25 M to 7 M. The urea induced chemical shift changes in 15N and HN resonances was determined using the following equation which is designated as chemical shift perturbation (CSP), H and WN are weighting factors for 1H and 15N chemical shifts, respectively H and ΔN are the chemical shift difference for amide proton and 15N respectively.Urea titration experiments were recorded at 298 K for Dok1 PTB domain by adding aliquots of denaturant from a 10 M stock made in 50 mM sodium phosphate, 100 mM NaCl and 2 mM DTT at pH 6 to the NMR tube containing Temperature dependence of amide proton chemical shift for the Dok1 PTB domain was performed by recording a series of HSQC spectra at seven different temperatures, 285 K, 288 K, 291 K, 294 K, 297 K, 300 K and 303 K. These experiments were done for native and two different concentrations of denaturant, 0.5 M and 1 M urea, in 50 mM sodium phosphate buffer, 100 mM NaCl, 2 mM DTT, pH 6.0. Temperature dependent experiments were also performed at the native condition in the presence of pTyr747 phosphorylated integrin β3 tail at a protein to peptide ratio of 1∶0.6. For each amide proton, a straight line was fitted to the chemical shift temperature dependence and the residuals were plotted to observe the curved temperature profiles.1 = 1/T1) and transverse (R2 = 1/T2) relaxation rate constants of 15N labeled Dok1 PTB domain were determined by collecting a series of 1H-15N HSQC spectra at different time interval with sensitivity enhancement in a Bruker DRX 600 MHz spectrometer at 298 K temperature 1 measurement were set to 5, 40, 80, 130, 210, 330, 470, 630, 800, 1000 and 3000 ms with repeating the experiments at the time points of 80 ms and 470 ms for error estimation. For T2 measurement the data were acquired with delays of 14.4, 28.8, 43.2, 57.6, 72.0, 86.4, 100.8, 115.2, 129.6, 144.0 and 158.4 ms with the duplicate points at 72.0 ms in this case. T1 and T2 relaxation experiments were repeated twice for all the experimental conditions and a total of 2048 complex data points with 128 complex increments were collected. Relaxation data were also collected for the natively folded PTB domain in 50 mM sodium phosphate buffer, 100 mM NaCl, 2 mM DTT, pH 6.0. The relaxation rate constants were determined by fitting the cross peak intensities to a mono-exponential function as in I = A*exp(R*t), where I is the signal intensity, A is the signaling constant, R is the relaxation constant and t is the time point 1H-15N hetero-nuclear NOE values were determined by using a pair of spectra recorded with and without proton saturation. The NOE experiment with proton saturation was carried out with a 2 s inter-scan relaxation delay followed by 3 s of proton saturation. Similar delays were used for the heteronuclear NOE experiments of the unfolded proteins in urea1H-15N hetero-nuclear NOE experiments spectrum were recorded with 2048 complex data points with 256 complex t1 points. The NOE values were determined as a ratio of peak intensities with and without proton saturation. A reduced spectral density analysis of the 15N relaxation data was carried out for the 7 M urea unfolded state of the Dok1 PTB domain as described earlierThe longitudinal Click here for additional data file.Figure S2Chemical shift changes of selected amide proton resonances with temperature. Sections of 15N-1H HSQC crosspeaks of residues showing curved  and linear  temperature dependence. 15N-1H HSQC spectra were obtained at temperatures of 285 K (red), 288 K (light red), 291 K (green), 294 K (yellow), 297 K (purple), 300 K (cyan) and 303 K (pink) at 0 M, 0.5 M and 1 M urea and in presence of tyrosine phosphorylated β3 tail peptide.(TIF)Click here for additional data file.Figure S3Urea-protein NOEs. Slices of 3D 15N-edited NOESY-HSQC spectra of the PTB domain showing NOEs between urea protons resonating at 5.9 ppm with the amide protons of representative residues of the PTB domain in 7 M urea.(TIF)Click here for additional data file.Figure S4Relaxation parameters of the native PTB domain. Bar diagram showing R1 (panel A), R2 (panel B) and heteronuclear NOE (panel C) of the native PTB domain as a function of residue.(TIF)Click here for additional data file.


Cassava is regarded as the nutritional base of populations in developing countries, and flour, product made of cassava, is the most consumed in the world. The cassava leaves are very rich in vegetable proteins, but a big amount is lost in processing the crop. The objective of this study was to do a sensory evaluation of cassava flour to which a protein concentrate obtained from cassava leaves (CPML) was added. The CPML was obtained from cassava leaves by isoelectric precipitation and added to cassava paste for preparation of flour in three parts 2.5, 5, and 10%. The acceptance test was done by 93 consumers of flour, using hedonic scale of 7 points to evaluate characteristics like color, scent, flavor, bitterness, texture, and overall score. By the method of quantitative descriptive analysis (QDA), eight trained tasters evaluated the following characteristics: whitish color, greenish color, cassava flavor, bitter flavor, characteristic flavor, lumpiness, raw texture, leaf scent, and cassava scent. The acceptability test indicated that flour cassava with 2.5 was preferred. Whitish color, greenish color, cassava flavor, bitter flavor, salty flavor, characteristic flavor, lumpiness texture, raw texture, and the smell of the leaves and cassava flour were the main descriptors defined for flour cassava with CPML has better characteristics. The cassava is considered an alimentary base for people in Africa, Asia, and Latin America assuming socioeconomical position in the world, due to its high capacity to adapt to climatic conditions. Its roots can be easily cultivated and it is the principal source of carbohydrates for needy people with protein deficiency, which is one of primary factors of human malnutrition that affects a big part of population. This deficiency can be the result of lack of protein containing foods, of both animal and vegetative origin, and its high price.This vegetable is characterized by its high concentration of carbohydrates that is why it is considered a caloric aliment. The cassava roots also contain vitamin C, carotenoids, thiamine, riboflavin, and nicotinic acid. They also represent considerable quantities of calcium and phosphorus  is one of them. During the harvest, it has its aerial part left in the field  to cassava flour by acceptance test to consumer level and by quantitative descriptive analysis (QDA) with trained tasters.0; 2.5% CPML – F1; 5.0% CPML – F2, and 10% CPML – F3.The CPML was obtained from cassava leaves by isolectric precipitation , No. 069/2010.The acceptance test was done with one sample of cassava flour without CPML (control) and three samples of cassava flour with 2.5, 5, and 10% CPML, respectively. There were 93 nontrained tasters in completely randomized groups, 10 g of encoded samples with three digits, accompanied with one glass of water at room temperature to clean the mouth and tongue before each evaluation Dutcosky .To study the acceptance of the cassava flour, a hedonic scale of 7 points was used (7 = liked a lot and 1 = disliked a lot) to evaluate appearance, scent, flavor, texture, and overall score. The hedonic results were expressed in graphics of frequency distribution and variety analysis  and comparative test of averages (Turkey), according to Stone  and StonA QDA test was used to characterize and quantity the sensory attributes of control flour and with 2.5, 5, and 10% CPML, respectively, was determined using the technique of QDA, according to Stone . The tesThere were recruited 16 individuals among students and employees of the Nutrition Institute of Josue Castro of UFRJ. The criteria to form a team were to be a consumer of cassava flour and to be selected in teams by selective tests as its sensorial perceptiveness is normal (recognition of basic flavors and smells).The following products were chosen to establish descriptive terms: distillated water, wheat flour, toast, lemon gelatin powder, cooked cassava, 0.06% solution of caffeine in water, cookies cream cracker, corn flour, and cassava leaf.The products were served in disposable materials, encoded with numbers of three digits, containing 10–20 g of each product. Panelists were asked to describe the characteristics of the samples related to the color, flavor, texture, and smell. Next, a list of all used terms and number of times that they were reported was prepared. This led to discussions in groups with the objective of creating descriptive terms and reference samples of scale extremes.The result of four evaluation sessions of reference samples and discussions in group was the evaluation list of control flour and flour with 2.5, 5, and 10% CPML.The samples were analyzed initially by 16 panelists, in triplicate, in four sessions in subsequent days. Cards for recording descriptions were used by all team members. Each descriptive term was evaluated in an unstructured line scale, anchored at extremes with the following terms of intensity: strong and weak. After the panelists had completed their test, the distance from the left end of the line to the point marked by the panelist was measured.Psample < 0.05, good reproducibility at trials (Prepetition > 0.05), and accordance with other members. So, the team was formed of eight tasters.The flour was served in paper cups, encoded with numbers of three digits, accompanied by a glass of water at room temperature. The tests were done as open trials. ANOVA was done on the results of each taster for every evaluated characteristic, considering variation point samples and repetition. The final descriptive panel was chosen, those panelist that represented discriminative ability The obtained data were submitted to variance analysis (ANOVA) and comparative test of averages (Turkey), estimation error with of 5%, according to Stone  and Ston0 (0% CPML), F1 (2.5% CPML), F2 (5% CPML), and F3 (10% CPML).In Table The evaluations of color ranged from liked to liked a lot for flour F0, F1, and F2, but for the flour F3 they varied from indifferent and liked. It was noticed that there was no difference in the smell of the flour, the data of tasters varied from indifferent and liked. The flavor was pleasant for flour with 2.5% CPML, followed by flour with 0% CPML, 5% CPML, and 10% CPML. While regarding texture, this characteristic represented the impressions varying between indifferent and liked.Nine verbal terms were developed by the team of tasters to describe the similarities and differences among evaluated cassava flour samples. Each one of descriptors used was defined by the team of tasters and qualitative references were associated to each term Table .0 (without addition of CPML), while the most greenish cassava flour was F3 with 10% CPML. Related to flavor, it was noticed that the less intensive flavor was in flour F2 and F3. On the other hand, the bitter flavor was more intensive in the samples with higher concentrations of CPML . The characteristic flavor of cassava flour was more intensive in flour F1, F0, F2, and F3, respectively. For the characteristic of texture, lumpiness was more intensive in the sample F0, while the texture was less intensive in flour F2. The smell of the leaves was stronger in samples with higher concentrations of CPML. It was more noticeable in flour containing 5% CPML, while the smell of cassava was noticed better in samples F1, F0, F3, and F2, respectively , while the most greenish cassava flour was F3 with 10% CPML. Related to flavor, it was noticed that the less intense flavor was in flours F2 and F3. On the other hand, the bitter flavor was stronger in the samples with higher concentrations of CPML . The characteristic flavor of cassava flour was more intense in flour F1, F0, F2, and F3, respectively. For the characteristic of texture, there was more lumpiness in the sample F0, while the row texture was less in flour F2. The scent of a leave was stronger in samples with higher concentrations of CPML. It was detected better in flour with 5% CPML, while the scent of cassava was noticeably better in samples F1, F0, F3, and F2, respectively  and flour with 2.5% CPML and 5% CPML.QDA has shown that higher concentrations of CPML in cassava flour negatively affected the greenish color, scent of the leaves, cassava flavor, and characteristics of cassava flour. However, the characteristics of flour with 2.5% and 5% CPML were close those of the control flour (without CPML addition), which means it is possible that flour prepared with CPML had better sensorial characteristics according to sensorial terms.


Phylogenies of multi-domain proteins have to incorporate macro-evolutionary events, which dramatically increases the complexity of their construction.We present an application to infer ancestral multi-domain proteins given a species tree and domain phylogenies. As the individual domain phylogenies are often incongruent, we provide diagnostics for the identification and reconciliation of implausible topologies. We implement and extend a suggested algorithmic approach by Behzadi and Vingron (2006). In all child species, the domains are already partitioned. Establish correspondence between domain nodes in the child species and those in the current species (relabeling of domain nodes), then find an optimal partition in the current species which is closest to all child partitions in terms of the weighted number of fusions and deletions.The algorithm consists of the following steps:Previous analyses of MDPs have decided against the use of phylogenetic trees for domains We implemented BV, tested it and and identified critical issues that need to be addressed for successful reconstruction of phylogenies of MDPs using BV. Due to the large number of possible domain combinations a good set of partitions cannot be found by brute-force enumeration. We implemented a heuristic called weak edge erosion,  which  yields  close to optimal solutions faster than simulated annealing suggested by In the following, we present our findings in detail, provide an implementation and show how to use it in practice. First, BV and the individual improvements are introduced formally.domain composition family of domain compositions . As these are sets, the ordering of domains within a gene is ignored.Phylogenetic trees are inferred for each domain family individually. Reconciling domain trees for each domain family using the known species tree assigns domain nodes to each species node The algorithm BV uses the information of domain trees and the composition of extant proteins to infer the domain composition of ancestral proteins which can be arbitrarily defined.Behzadi and Vingron suggest an additive measure given by   where The number of merges to have all domains of Assigning costs for union and deletion yields the partitioning scoreThe entire tree is reconstructed in a bottom-up pass including the root.As the number of possible partitions for domains into genes grows rapidly, complete enumeration is only possible for the simplest cases and not suitable for real applications. For BV, it was suggested to use simulated annealing to solve the partitioning problem weak edges. Let each vertex be labeled by the sum of weights of all its incident edges. From the perspective of an edge, high vertex weights mean that the vertices have a strong connection to a set of other vertices, so edges are regarded to become weaker as their vertex weights grow. We thus define a total order of weakness: let Weak edge erosion is a hierarchical graph clustering method based on the idea of attacking a network of affinities between elements at its weakest points and recursively create clusters by separating network components. The affinity graph is defined as follows:Consider an undirected loop-free graph This first condition accounts for that we prefer weaker affinities to be violated. If the edge weights are equal, we want to exploit the weakening effect of high vertex weights, thus the weakness order is determined by the heavier vertices: If these are equal as well, the relation between the lighter vertices decides: The concept of edge weakness is illustrated in Figure 2. As the cost function is additive, we can find an approximate solution to the partitioning problem by splitting Walking through the cut-tree in a bottom-up procedure, we decide whether the block in the parent node or the two set blocks in its children yield a better score The complete procedure is summarized in Algorithm 1:The original authors of BV propose simulated annealing to solve the underlying partitioning problem Due to their short length, the inferred domain phylogenies often disagree with each other and the species tree. The BV algorithm was proposed for ideal data and does not consider errors in the underlying domain topologies. A practical consequence of such errors are that the order of speciation and duplication events between adjacent domains do not agree. These conflicts can lead to duplicate nodes in the reconstructed composition, for example nodes that have successors within the same protein. The BV algorithm will then produce MDPs with a high partition score due to additional copies of the conflicting domains. Our algorithm aims to produce a partition with an improved score by applying nearest neighbor interchanges on the conflicting domain trees. For each modification the ancestral composition is reconstructed and the modification with the lowest score Species trees were generated following the Yule-Harding model. An initial domain composition of three families in a single protein was placed in the root and passed to its children, whose protein domain compositions underwent evolutionary events of genes  and domains . Subtree pruning and regrafting (SPR) operations were applied on the domain trees to create perturbed input data. For an empirical evaluation we ran global models of the PFAM database As we cannot obtain true ancestral multi-domain protein compositions, we relied on simulations to test the validity of the algorithm and to inspect its performance when errors are introduced. The practical performance was evaluated on known instances of MDPs, available on the accompanying website. A brief, non-trivial example is presented in Fig. 1.To assess the performance of the algorithm on controlled input, species and domain phylogenies were simulated. The reconstructed domain compositions were compared to the original compositions using the partition distance measure, which ranges between zero and the size of the compositions Table 1. Average partition distance and standard deviation of reconstructed domain compositions. The simulated trees had 10, 30 and 50 taxa and up to ten SPR operations were applied. The distance was calculated between simulated and reconstructed domain compositions of all ancestral species and normalized by the number of taxa. Each experiment was repeated 50 times.An automated correction of incongruent domain phylogenies is effective for cases with minor errors. Manual curation is advised if domain phylogenies cannot be inferred reliably. To this end, internal nodes with high scores are tagged for assessment.The run times of our implementation are short. We simulated 50 replicates of input data containing 50 taxa and no regrafting operations and ran the implementation under Linux on an AMD 64 X2 3200+ processor. The erosion heuristic inferred a MDP reconstruction in 2.06s±0.25s, while the simulated annealing procedure took 153s±22s to achieve comparable solutions. In practice, the calculation of reliable domain families bounds the performance. JMJ domains are found in proteins involved in chromatin remodeling complexes often together with domain families such as ARID, PHD and PLUN-1 Our solution for the inference of phylogenies of multi-domain proteins provides a simple and easy-to-use interface. The weak edge erosion heuristic provides considerable speed-up over simulated annealing while maintaining comparable solution quality. Beyond the application on MDPs, such methods could be applied to reconstruction of partial homologous units such as bacterial operons or protein complexes. Future work will be directed at improving the quality of the tree reconciliation. http://virulence.molgen.mpg.de/cocos/ and is freely available under a BSD license.    The program was successfully tested under Python 2.6 and 2.7 on Windows, Mac OS X and Linux. It receives input for species and domain trees as well as parameters in Nexus format. The output can be visualized using GraphViz.The source code with additional figures and examples can be found at Implementation: MH and JW. Weak edge erosion: JW. Domain analysis: ST, CS, RK. Example data: IK, CS. Simulations: MH and RK. Wrote the paper: MH, JW and RK. The authors have no support or funding to report.The authors have declared that no competing interests exist.


Imaging processing techniques have been shown to be useful in studying protein domain structures. The idea is to represent the pairwise distances of any two residues of the structure in a 2D distance matrix (DM). Features and/or submatrices are extracted from this DM to represent a domain. Existing approaches, however, may involve a large number of features (100–400) or complicated mathematical operations. Finding fewer but more effective features is always desirable. In this paper, based on some key observations on DMs, we are able to decompose a DM image into four basic binary images, each representing the structural characteristics of a fundamental secondary structure element (SSE) or a motif in the domain. Using the concept of moments in image processing, we further derive 45 structural features based on the four binary images. Together with 4 features extracted from the basic images, we represent the structure of a domain using 49 features. We show that our feature vectors can represent domain structures effectively in terms of the following. (1) We show a higher accuracy for domain classification. (2) We show a clear and consistent distribution of domains using our proposed structural vector space. (3) We are able to cluster the domains according to our moment features and demonstrate a relationship between structural variation and functional diversity. The study of protein structures is no doubt a very important issue as structures are related to functions and can provide insights on the evolution of proteins. As protein structure is more conserved than its sequence throughout evolution, remote homologies should be detected in the universe of protein structures A straightforward approach to do classification is to align and compare the 3D structures of two protein domains e.g. CE . Based oThere has been quite a lot of work following this feature extraction paradigm. Some characterize the domain structure directly as a topological object. Scaled Gauss Metric (SGM) To speed up the feature extraction, as inspired from computer vision and pattern recognition, Choi et al. Motivated by the previous findings that different types of structures show different images of DM and common types of structures show similar images We illustrate the effectiveness of our feature vectors in the following manner. (1) Based on two well-known protein domain classification databases, CATH Given a protein domain, we represent the distances between any two residues , say the onal  in . FurtherBeta sheets:Two strands of a beta sheet are connected by hydrogen bonds. Depending on the orientations of these two strands , the disOther structures:There are other non-hydrogen bondings that occur between part  in . When twEven though the above features are represented by relatively low values (since they are closer) in the DM, simple threshold filtering θ  =  0°,90°,45°,135° were selected, where wavelength t135 = g135° was used for According to We performed the Gabor filtering by the two-dimensional convolution of DM and three templates independently to obtain three groups of patterns of low values. Three binary contact matrix (BCM) were then constructed by assigning 1 to those elements whose convoluted values in the convoluted DM are less than or equal to zero and assigning 0 to the remaining positive values. The binary slant line ectively . The couThese four BCMs can be treated as four binary images containing different information of the SSEs. Each set of the adjacent 1’s (edges or edge patterns) in each BCM represents one type of SSE or some SSEs’ pairwise interactions. To capture the characteristics of these four BCMs, we define the first set of moments as follow and refer to them as Composition Moments:It has been shown that any image, in principle, can be recovered by a sufficient number of image moments Analogous to Fourier transform, the relationship between different orders of moments and BCM is similar to the relationship between different frequencies and the signal, where high frequencies represent higher-order information of the signal while low frequencies give the signal’s outline. The lower-order Legendre moments depict the outlines of slant lines or maze-like patterns, and the higher-order Legendre moments capture their higher-order information. In the context of BCM, for example, the moments of layout of For anti-parallel The Legendre moments In our study, only the first five degrees of the Legendre polynomials are considered,Thus only those moments In this section, we shall demonstrate that moment features are superior in many aspects, such as protein domain classification, domain structure and function representation, when compared with other approaches.We first illustrate the classification power of the image moments. Our composition moments and all additional Legendre moments We compared the classification performance of the image moments with the algebra-topological description, a most recent approach proposed by We also evaluated the moment features using another popular classification database SCOP(v 1.75). Again, the top 500 superfamilies  were selected for evaluation. Following a similar training scheme as above, we found that the results of classification are also consistent with those of SCOP in all levels .We found that Legendre moments are powerful enough to distinguish folds within a class see  and supeIn this section and the next section, we demonstrate another two more useful applications of moment features. One approach to investigate the relationship between protein structure and function is to represent protein structure in a high dimensional space, e.g. the three-dimensional maps generated by dimension reduction, such as multidimensional scaling (MDS) Though MDS, PCA and SVD (related to PCA strongly) give a good 3-D visualization to domain structure maps, it is difficult to visualize the spatial relationship between small-size and middle-size domains as they always cluster closely together in such space. This problem becomes more eminent as most domains (84.51%) belong to these categories of sizes smaller than 300 peptides. The sizes of domains have been shown to follow approximately the power-law distribution . In addiTo illustrate that the moment features can also perform well in this application, we chose 3,000 domains from non-redundant subset of S_H500 (with less than 40% sequence similarity). The feature vector of each domain is represented as a point in the 49-dimensional space. We applied KPCA to map these points non-linearly onto a reproducing kernel Hilbert space in which the original linear operations of PCA were performed To represent the universe, diverse maps of the selected dataset are shown in  domains -B. (2) Tsee also -H. Fig. see also -J. Fig. see also -M. Fig. Overall speaking, the architecture of our domain structure universe is consistent with those of former works A set of domains in a superfamily has a common functional ancestor de novo creation The variations of domain combinations can make significant contribution to the evolution of organismal function http://www.rcsb.org/pdb/workbench/workbench.do). We selected two superfamilies, a.60.9 and a.69.1, from SCOP (V1.75) and investigated the alignment scores between domains in each superfamily respectively. The higher the alignment score is, the less structural variations two domains have. The detailed scores are listed in lambda integrase-like, N-terminal domain) shows 3 clusters which contain 48, 12 and 1 domains and are rendered by red, green and blue respectively  which share conserved DNA binding mechanism in recombination reaction, but also show apparent mechanistic and regulatory differences C-terminal domain of alpha and beta subunits of F1 ATP synthase) also shows similar results of structural comparison. It has 2 spatial clusters in structural space, each of which is composed of 47 domains. One cluster denotes alpha subunit of F1 ATP synthase and another denotes its beta subunit. Three beta subunits in F1 component are the ATP-ADP binding sites whereas alpha subunits are not sites but just support the F1 ATP synthases structure, even though they are placed together with the alternating arrangement in F1 ATP synthaseUsing vely see -A. The rviral protein domain superfamily(b.19.1). In total, 475 superfamilies with both non-zero values of Combination diversity is investigated by SUPERFAMILY which provides a domain-based gene ontology at the superfamily level To investigate the relations between en  and  -B or   and functional diversity (approximated by the number of non-repeated partners and the number of unique arrangements of the superfamily). Moreover, in the structure universe, the partner map of domain combination shows a significant distribution of superfamily combination diversity.Our work can be explored for other applications. A popular utility is to query some newly determined structures with unknown functions. Mapping it into a structure universe and comparing to other domains will give its structural classification. The feature vector with existing retrieval techniques in computer vision provides an efficient search of structural similarity. Furthermore, diverse moments can be expected to contribute to model domain structure mathematically, and can be used for structure prediction. Finally, the binary contact matrix can be used for other applications, for example, automatic detection of the common structural core within a superfamily and automatic multi-domain decomposition.Figure S1Comparison of different classes of domains. The structures, distance matrices and four binary contact matrices including (TIFF)Click here for additional data file.Figure S2The classification of different folds within a class. Three folds, a.51 , a.52  and a.56 are investigated. For two given folds, one of moments of (TIFF)Click here for additional data file.Figure S3The classification of different superfamilies within a fold. Three superfamilies, a.4.7, a.4.8 are investigated. For two given superfamilies, one of moments of (TIFF)Click here for additional data file.Figure S4The distribution of domain sequence length. The fitting function is a power law function and the fitting performance is indicated by R-square = 0.9917 and RMSE = 9.93.(TIFF)Click here for additional data file.Figure S5Map of domain structure universe with more perspectives. Class map is shown in X-Y view (A) and Y-Z view(B). Size map is shown in X-Y view(C), Y-Z view(D), and X-Z view(E). Composition map of (TIFF)Click here for additional data file.Figure S6The maps of domain structure universe of SCOP. Totally, 109,533 domains are drawn in maps. (A) In the map of class, red for all-(TIFF)Click here for additional data file.Figure S7The maps of domain structure universe of CATH. Totally, 98,033 domains are drawn in maps. (A) In the map of class, red for mainly (TIFF)Click here for additional data file.Figure S8The relationship between the count of clusters (#C) and the count of unique arrangements (#A). The logarithmic values of (TIFF)Click here for additional data file.Table S1Comparison of classifications on S_H500 with different feature groups.(PDF)Click here for additional data file.Section S1Mean shift clustering.(PDF)Click here for additional data file.Score S1Alignment Scores of Domains.(XLSX)Click here for additional data file.


Although T46I does not alter overall dynamics, it does trigger increased dynamics of several local regions of the MSP domain which are implicated in binding to EphA4 and Nir2 peptide. Our study provides the structural and dynamic understanding of the T46I-causing ALS; and strongly highlights the possibility that the interplay of two signaling networks mediated by the FFAT-containing proteins and Eph receptors may play a key role in ALS pathogenesis.T46I is the second mutation on the hVAPB MSP domain which was recently identified from non-Brazilian kindred to cause a familial amyotrophic lateral sclerosis (ALS). Here using CD, NMR and molecular dynamics (MD) simulations, we characterized the structure, stability, dynamics and binding capacity of the T46I-MSP domain. The results reveal: 1) unlike P56S which we previously showed to completely eliminate the native MSP structure, T46I leads to no significant disruption of the native secondary and tertiary structures, as evidenced from its far-UV CD spectrum, as well as Cα and Cβ NMR chemical shifts. 2) Nevertheless, T46I does result in a reduced thermodynamic stability and loss of the cooperative urea-unfolding transition. As such, the T46I-MSP domain is more prone to aggregation than WT at high protein concentrations and temperatures  Amyotrophic lateral sclerosis (ALS) is the most prevalent fatal motor neuron disease, which affects people of all race and ethnic background. Approximately 10% of the cases have hereditary background while the rest are sporadic. Seven ALS-causative genes have been identified to date but the most characterized is ALS1 encoding mutants of Cu/Zn-superoxide dismutase (SOD1). So far, despite intense studies, the exact mechanism underlying ALS pathogenesis remains poorly understood and there is no efficient therapy for ALS. As sporadic and familial ALS affects the same neurons with similar pathology, it is believed that knowledge and therapeutic approaches/agents developed on mutant models can be translated to sporadic ALS Aplysia californica (aVAP33) Recently, ALS8 was identified from a large Brazilian family with dominant motor neuron diseases, which encodes a mutated VAPB (vesicle-associated membrane protein-associated protein B). The missense P56S point mutation results in a typical ALS phenotype with rapid progression or late onset spinal muscular atrophy Despite owning very diverse functions, VAPA and VAPB proteins are composed of three conserved domains, namely an N-terminal immunoglobulin-like β sheet domain with 22% sequence identity to the major sperm protein (MSP), a central coiled-coil domain, and a C-terminal transmembrane domain. After the P56S mutation was identified to be associated to ALS, the VAPB-MSP domain received intensive studies regarding its functions and the biological consequence of the mutation Previously, we have determined the crystal structure of the hVAPB MSP domain, which adopts a well-defined MSP fold with a cooperative urea-unfolding transition In the present study, we aimed to characterize the newly-identified T46I mutant. By extensive CD, NMR characterization and molecular dynamics (MD) simulations, we found that the T46I-MSP domain still retains the native MSP structure with overall dynamics largely unaltered. Nevertheless the T46I mutation on the MSP domain does lead to a reduction of its thermodynamic stability and loss of the cooperative urea-unfolding transition. As such, the T46I-MSP domain is much more prone to aggregation at high protein concentrations and temperatures. Noticeably, the T46I mutation results in only a 3-fold reduction of its binding affinity to the Nir2 peptide, but a significant abolishment of the binding ability to EphA4.E. coli BL21 (DE3) cells (Novagen) for protein expression. Most WT and a small portion of T46I proteins were found in supernatant and thus were first purified by Ni2+-affinity chromatography (Qiagen) under the native condition, followed by in-gel cleavage to remove the His-tag. The released WT and T46I proteins were further purified on an AKTA FPLC machine (Amersham Biosciences) using a gel filtration column (HiLoad 16/60 Superdex 200), followed by an anion-exchange column (Mono Q 5/50). The generation and purification of the EphA4 ligand-binding domain (181 residues) followed the protocol we previously established The DNA fragment encoding the wild-type (WT) hVAPB MSP domain (residues 1–125) was cloned into a modified pET32a vector (Novagen) as previously described 15NH4]2SO4 for 15N labeling and [15NH4]2SO4/[13C]-glucose for 15N-/13C double labeling The production of the isotope-labeled MSP and EphA4 proteins for NMR studies followed a similar procedure except that the bacteria were grown in M9 medium with the addition of [All CD experiments were carried out in a Jasco J-810 spectropolarimeter  as previously described 18 column. Titrations were conducted in the buffer  at 25°C. The peptide or EphA4 (300 µM) was loaded into the syringe while the wild-type hVAPB MSP protein (10 µM) was placed in the sample cell. To obtain thermodynamic binding parameters, the titration data after subtracting the blank values were fitted to a single binding site model using the built-in software ORIGIN version 5.0 .Isothermal titration calorimetry (ITC) experiments were performed using a Microcal VP machine as previously described The T46I-MSP sample was prepared in 10 mM phosphate buffer, pH 6.8. All NMR data were collected at 25°C on an 800-MHz Bruker Avance spectrometer equipped with a shielded cryoprobe as described before 15N-labeled WT- or T46I-MSP domains in the 10 mM (pH 6.8) phosphate buffer was lyophilized and then re-dissolved in D2O. Progress of the exchange process between amide protons and deuterium was followed by collecting a series of successive HSQC spectra starting immediately after the sample re-solubilization in D2O. All exchange experiments were conducted on an 800 MHz Bruker Avance spectrometer at 25°C. The first HSQC spectrum was collected after 15 min, and the last spectra were acquired after 2.5 h.To conduct hydrogen-deuterium (H/D) exchange experiments, the 1H-15N HSQC spectra of the 15N-labeled WT-/T46I-MSP domains were acquired at a protein concentration of 100 µM in the absence or presence of the Nir2 peptide, or EphA4 at different molar ratios, including 1∶0.5; 1∶1; 1∶2; 1∶4; 1∶6; and 1∶8. Similarly, the binding interactions were also visualized by collecting HSQC spectra of the 15N-labeled EphA4 ligand-binding domain at a protein concentration of 100 µM in the absence or presence of WT- or T46I-MSP domain at the same molar ratios. By superimposing the HSQC spectra, the shifted or disappeared HSQC peaks could be identified and further assigned to the corresponding residues of the WT-/T46I-MSP domains or EphA4 ligand-binding domain whose assignment was previously achieved For NMR HSQC characterization of the binding interactions of WT-/T46I-MSP to the Nir2 peptide; as well as to the EphA4 ligand-binding domain, two-dimensional The models of the MSP-EphA4 complex were constructed as we previously conducted on other systems www.pymol.org) to mutate the Thr46 residue to Ile with the option of backbone-dependent rotamers. Out of 5 Ile rotamers, we picked the one that has the highest representation of 81.9%, which has the least steric hindrance. This initial T46I structure was subsequently subjected to a 5000-step energy minimization using steepest descent algorithm and position restraint equilibration for 200 ps.To unravel the dynamic behaviors of both WT- and T46I-MSP domains, two independent, 15-ns MD simulations were performed for each of them. The WT-MSP structure used for MD simulations was 3IKK we previously determined −5 bar−1. The time step was set as 2 fs. All bond lengths including hydrogen atoms were constrained by the LINCS algorithm Simulations for both the WT- and T46I-MSP domains were set up as previously described E. coli BL21 cells. Although a large portion of the T46I protein existed in inclusion body, there was a small portion of the soluble T46I form in supernatant. Consequently we purified the soluble T46I protein by Ni2+-affinity column under native condition, followed by FPLC ion-exchange chromatography. The purified T46I domain with His-tag removed has a far-UV CD spectrum highly similar to that of WT  exchange and to assess the backbone dynamics for both WT and T46I on minute-hour time scale. As well-established, in solution labile hydrogens such as amide protons on proteins are continually exchanging with the solvent at different rates, depending on a variety of factors associated with their environment including their exposure to the solvent or their involvement in H-bonds. Consequently, amide H/D exchange experiments offer a sensitive reflection of the exposure degree of amide protons to the solvent The VAPB MSP domain was demonstrated to functionally interact with the FFAT-motif containing Nir2 protein On the other hand, we also used HSQC titrations to visualize the binding of the WT- and T46I-MSP domains to the Nir2 peptide. As shown in Recently, the cleaved MSP domain was identified to serve as a novel ligand for the EphA4 ligand binding domain 15N-labeled WT- and T46-MSP domains upon gradually adding the unlabeled EphA4 ligand binding domain. As shown in Although previously we have successfully crystallized and determined the crystal structures of the WT-MSP domain We further addressed the binding by monitoring the HSQC spectra of the EphA4 ligand-binding domain. As shown in To understand the underlying mechanism for the significant disappearance of both MSP and EphA4 HSQC peaks, we measured the radius of the EphA4 ligand binding domain by dynamic light scattering (DLS) in the presence of both WT- and T46I-MSP domains at different molar ratios but no significant changes were observed (data not shown). This implies that the binding triggers no formation of the tight EphA4-MSP complex and no significant oligmerization. As such, the disappearance of the MSP and EphA4 HSQC peaks is mostly resulting from the increase of conformational exchanges on the µs-ms time scale upon binding.15N-labeled EphA4 with the Nir2 peptide but no change was detected for the EphA4 HSQC spectra even at a ratio of 1∶10 (EphA4/Nir2), thus clearly demonstrating that no direct binding occurred between EphA4 and Nir2 peptide. Strikingly, in the presence of the Nir2 peptide, addition of the unlabeled WT-MSP domain was no longer able to induce shift/disappearance of the EphA4 HSQC peaks even with the ratio increased up to 1∶10 (EphA4/MSP). This observation strongly indicates that the binding of the Nir2 peptide to the MSP domain would inhibit its further binding to EphA4, either by triggering the structural change or occupying the EphA4 binding interface on the MSP domain. If considering the fact that the MSP domain show no significant change upon binding to ORP1, a FFAT-containing peptide We also titrated the Based on the HSQC titration results, we also constructed the model of the EphA4-MSP complex by HADDOCK and Also in the EphA4-MSP model, Thr46 sits in the central part of the EphA4-MSP binding interface , which rOur results reveal that the T46I mutation causes no significant disruption of the native MSP structure, but it does lead to ALS. This implies that the protein dynamics may play a key role beyond the static structure, as we recently demonstrated on the SARS 3C-like protease Snapshots of two independent simulations are presented in Overall, the T46I-MSP domain has fluctuation patterns similar to those of the WT-MSP domain . NeverthLast year, from non-Brazilian kindred the second mutation T46I has been identified on the hVAPB MSP domain which also causes a familial amyotrophic lateral sclerosis in vitro, the T46I-triggered loss of the cooperative unfolding and stability may be sufficient to cause severe aggregation in macromolecular crowded cells, thus rationalizing the previous in vivo observation Nevertheless, the T46I mutation does abolish the cooperative urea-unfolding transition and reduce the thermodynamic stability of the MSP domain. This allows the T46-MSP domain to more easily access the partially- or/and highly-unfolded intermediates which are prone to aggregation in vivo. As a consequence, future studies on how they interplay in vivo under physiological and ALS pathological conditions may shed critical light on ALS pathogenesis.Amyotrophic lateral sclerosis (ALS) was described more than 130 years ago, but it still remains a mystery whether any signaling pathway is involved in ALS development. In this regard, characterization of ALS-caustive mutations on the VAPB MSP domain offers an unprecedented opportunity because two signaling networks have been identified to be associated with the VAPB MSP domain; one linked to lipid trafficking/metabolism via binding to the FFAT-containing proteins; and another connected to Eph-ephrin signaling, by acting as a novel ligand for Eph receptors. Here we show that the T46I mutation results in only a 3-fold binding affinity reduction to the Nir2 peptide but dramatically disruption of the binding capability to EphA4. In particular, we reveal that the Nir2 peptide and EphA4 are most likely to have overlapped binding interfaces on the MSP domain and consequently the binding of the Nir2 peptide prevents the MSP domain from further interacting with EphA4. This thus raises an interesting possibility that the two signaling networks may interplay With significant advances in experimental and computational methodologies including NMR spectroscopy and MD simulations, protein dynamics have been recently demonstrated to play a key role in functions in vivo. Future understanding how the interplay alters under ALS pathological conditions may decipher the mystery of ALS pathogenesis.In conclusion, here we reveal that unlike the P56S mutation which completely eliminates the native MSP structure, the T46I mutation results in no dramatic structural disruption and alteration of overall dynamics. Nevertheless, the T46I mutation does leads to the loss of the cooperative unfolding transition and reduction of thermodynamic stability, which consequently renders the T46I-MSP domain to be prone to aggregation at high protein concentrations and temperatures. Furthermore, the T46I mutation leads to only a 3-fold affinity reduction to the Nir2 peptide but remarkable loss in binding to EphA4. In particular, the overlapped interfaces on the MSP domain for binding to EphA4 and Nir2 peptide strongly implies that two signaling networks may functionally interplay


Mutations in dysferlin, the first protein linked with the cell membrane repair mechanism, causes a group of muscular dystrophies called dysferlinopathies. Dysferlin is a type two-anchored membrane protein, with a single C terminal trans-membrane helix, and most of the protein lying in cytoplasm. Dysferlin contains several C2 domains and two DysF domains which are nested one inside the other. Many pathogenic point mutations fall in the DysF domain region.We describe the crystal structure of the human dysferlin inner DysF domain with a resolution of 1.9 Ångstroms. Most of the pathogenic mutations are part of aromatic/arginine stacks that hold the domain in a folded conformation. The high resolution of the structure show that these interactions are a mixture of parallel ring/guanadinium stacking, perpendicular H bond stacking and aliphatic chain packing.The high resolution structure of the Dysferlin DysF domain gives a template on which to interpret in detail the pathogenic mutations that lead to disease. Dysferlinopathies are a group of autosomal recessive inherited late onset progressive muscular dystrophies caused by malfunction of dysferlin protein. Mutations in the dysferlin protein cause three phenotypes called limb girdle muscular dystrophy type 2B , MiyoshiDysferlin, a member of the ferlin protein family, is a type II anchored membrane protein with a single C terminal helix buried in the membrane. Ferlin proteins are defined as containing four or more C2 domains and a C terminal trans-membrane helix. There are 6 ferlin proteins expressed in human; dysferlin, myoferlin, otoferlin, Fer1L4, Fer1L5, and Fer1L6 . MyoferlThe multiple domain architecture of dysferlin was analysed by a combination of Gene3D , SMART  and PfamDysferlinopathy causing mutations are dispersed throughout the length of the protein, but many fall in the DysF domains ,16. One In this study, we have determined the three dimensional structure of human dysferlin inner DysF domain by X-ray crystallography at 1.9 Å. This is the first DysF domain crystal structure.13 (cubic), with the best resolution dataset diffracting to 1.9 Å. A single dataset was collected in a different space group P212121 (orthorhombic) to 2.2 Å. The crystal packing is almost the same in the two forms, but the distortions of the perfect cube in the orthorhombic structure means the monomers of the crystallographic trimer in the cubic crystals are no longer identical . These β-strands are connected with a long loop (77 residues). The loop caps the edge of the N terminal β-strand with main chain hydrogen bonds from residues 966, 971 and 973. The C terminal β-strand is capped with main chain hydrogen bonds from residues 993, 995, 996, 1000, 1013 and 1015. These give short β strands within the linking loop. There is also a single turn of 313 crystal superimposes onto the trimer in the P212121 asymmetric unit with an RMSD Calpha of 0.89 Å over 326 residues of the trimer. The four individual chains from the two assymetric units superimpose no worse than 0.79 Å Calpha RMSD over 108 residues for any pair. The conservation with the NMR structure of myoferlin is also extremely high with a Calpha RMSD of 1.78 Å over 106 residues being the worst and 1.45 Å over 103 residues being the best of the 20 models  forms 5(pisa) - 7(ccp4mg) hydrogen bonds and 3 salt bridges. Three of these are mainchain-mainchain, with β3 forming intermolecular β strand links to β1 as well as the intramolecular H bonds to β6. 2 H bonds are mainchain-sidechain and the others are sidechain-sidechain. Indeed the formation of β3 is probably driven by the trimerisation. The relatively low B factor in this side of the loop may be partially caused by this packing. We have no evidence from gel filtration or NMR spectroscopy (data not shown) for the existence of a trimer, nor is there any likely way to invoke a trimer in vivo. There are two DysF domains in dysferlin and the reported oligomeric state of the full length protein is a dimer. Therefore two or four DysF domains seems more likely higher order assemblies, although extending the beta sheet either with other DysF domains or other proteins does seem to be a likely method of interaction.The trimer (with 9 phosphates) is predicted to be the stable assembly by PDBePISA . FormatiAs reported for the myoferlin inner DysF domain , the dysThe details of the R/W interactions are laid out in Table There are 15 missense mutations in the inner dysferlin domain reported in the Leiden dysferlin mutation database . These aThe structure of the inner DysF domain of dysferlin confirms the unusual fold of this domain first seen in the myoferlin homologue. The high resolution of the structure allows detailed analysis of the interactions forming the R/W stacking seen in this domain. It also provides a better model for understanding the disease causing point mutations in this domain seen in dysferlin patients. The most common mutations will disrupt the R/W stacking, making the domain more unstable or inherently unfolded, probably enhancing the degradation of the protein. Some of the dysferlin mutants map to the surface, implying that protein-protein interactions may have a role in the function of this domain.E. coli strains Rosetta 2 (DE3) cells. One fresh colony was inoculated into 100 ml of Luria Broth media (LB) and the culture was grown overnight. 8 × 500 ml of LB were inoculated with 1% of overnight culture and incubated at 37°C until the optical density reached 0.5 at a wavelength of 600 nm. Then the cultures were induced with 0.5 mM of IPTG and incubated at 18°C for 20 hours. Cells were collected by centrifuging at 4°C at 4000 rpm for 20 minutes and resuspended in binding buffer . The cells were stored at -20°C. After incubating with EDTA free protease inhibitor cocktail , 0.1% Triton X-100 and DNAseI on ice for 30 minutes, the cells were sonicated at 20 W output for 4 minutes of 4 second on/off pulses for three cycles on ice and then centrifuged at 48,000 g for 1 hour. The protein lysate was applied to a HisTrap  affinity column. The column was washed with binding buffer for 10 column volumes and the protein eluted in 20 mM Tris, pH 7.5, 500 mM NaCl, 500 mM Imidazole, 10 mM 2-mercaptoethanol buffer. The eluents containing the dysF domain were pooled together and treated with TEV protease for 20 h at 4°C to remove the histidine tag. Then the volume was decreased to 4 ml by Vivaspin concentrator  with 5,000 Da molecular weight cutoff. The sample was further purified by size exclusion chromatography using Superdex 200 16/60 column . The protein was purified further by anion exchange chromatography (Resource Q) on a gradient of 0-0.5 M NaCl in 20 mM Tris, pH 7.5, and 5 mM 2-mercaptoethanol. The protein concentration was estimated using NanoDrop spectrophotometer at UV light absorbance at 280 nm (absorbance coefficient = 45950 M-1 cm-1). The protein sample was concentrated to 10 mg/ml (750 μM) in final buffer  and used for crystallisation trials.Human dysferlin cDNA (Jain Foundation) was used to amplify the DNA encoding for the residues 943-1052. The human dysferlin inner DysF domain was cloned into pNic28Bsa4 plasmid , which is a modified pET28a plasmid that allows ligation independent cloning . The vecThe inner DysF domain was screened for crystallisation at 16°C, by the sitting-drop method in 96- well crystallisation plates (Molecular Dimensions), using PACT screen . After 1No human subjects were directly used in this study. Human mutation data was taken from publicly accessible databases.-10 m); CING: Common Interface for NMR structure Generation; EDTA: Ethylenediaminetetraacetic acid; IPTG: Isopropyl β-D-1-thiogalactopyranoside; LB: Luria broth; NMR: Nuclear magnetic resonance; RMSD: Root mean square deviation; R/W: Arginine/Tryptophan stacks; TEV: Tobacco etch virus.Å: Ångstrom (10The authors declare that they have no competing interest.AS prepared the expression construct and protein, grew the crystals and carried out the refinement and drew most of the figures. AC collected the diffraction data and advised on crystallisation. CY and CO defined the domain boundaries and the multiple domain architecture. NK wrote the first draft of the manuscript. CO and NK devised the project and trained AS. All authors have read and agreed the final manuscript.


Bacillus subtilis and Escherichia coli, known to undergo genetically regulated autolysis, lacked metacaspases. Pfam domain architecture analysis combined with operon identification revealed rich and varied configurations among the metacaspase sequences. These imply roles in programmed cell death, but also e.g. in signaling, various enzymatic activities and protein modification. Together our data show a wide and scattered distribution of caspase homologs in prokaryotes with structurally and functionally diverse sub-groups, and with a potentially intriguing evolutionary role. These features will help delineate future characterizations of death pathways in prokaryotes.Caspases accomplish initiation and execution of apoptosis, a programmed cell death process specific to metazoans. The existence of prokaryotic caspase homologs, termed metacaspases, has been known for slightly more than a decade. Despite their potential connection to the evolution of programmed cell death in eukaryotes, the phylogenetic distribution and functions of these prokaryotic metacaspase sequences are largely uncharted, while a few experiments imply involvement in programmed cell death. Aiming at providing a more detailed picture of prokaryotic caspase homologs, we applied a computational approach based on Hidden Markov Model search profiles to identify and functionally characterize putative metacaspases in bacterial and archaeal genomes. Out of the total of 1463 analyzed genomes, merely 267 (18%) were identified to contain putative metacaspases, but their taxonomic distribution included most prokaryotic phyla and a few archaea (Euryarchaeota). Metacaspases were particularly abundant in Alphaproteobacteria, Deltaproteobacteria and Cyanobacteria, which harbor many morphologically and developmentally complex organisms, and a distinct correlation was found between abundance and phenotypic complexity in Cyanobacteria. Notably,  Programmed cell death (PCD) is involved in numerous important stages of metazoan life, including tissue homeostasis, embryo development, defense against viral pathogens and elimination of cancer cells Prokaryotes, largely single-celled organisms, may reap evolutionary benefits from behaviors that directly favor the survival of clones rather than the individual cell performing them, i.e. a form of altruism. For instance, sacrifice of a subset of the cells through genetically controlled death would in certain situations allow the remaining to re-initiate growth and perpetuate the genotype, rather than going extinct. This type of behavior has been observed in bacterial strains in response to stress Bacillus subtilis deploys PCD in conjunction with spore formation, in the form of autolysis of the mother cell leading to release of the endospore Streptococcus pneumoniae VncS protein is part of a pathway that controls antibiotic-induced autolysis, in order to eliminate damaged cells, a response that may be interpreted as PCD Escherichia coli may undergo PCD under control of the toxin-antitoxin system mazEFrelBE and hipBA, in addition to mazEF, in E. colimazEF module seizes, since the toxin will remain active beyond that of the antitoxin, and is a response to stress and a defense against phage P1 Myxococcus xanthus DK 1622. During sporulation, 80% of the population is killed in a tightly regulated PCD process, via inactivation of a protein kinase cascade and an activation of the MazF-mx ribonuclease M. xanthus autolysis process, which is likely to depend on a set of autolytic enzymes, complementing the effects of the MazF-mx ribonuclease, has not been characterized Xanthomonas campestrisTrichodesmium erythraeum IMS101 X. campestris pv. glycines occurs in the post-exponential growth phase and is associated with features characteristic of metazoan apoptosis X. campestris pv. glycines.PCD in T. erythraeum IMS101 was shown to undergo PCD in response to conditions mimicking natural surface “bloom” conditions Microcystis aeruginosa also exhibits caspase activity in response to induced oxidative stress The marine Cyanobacterium Caspases, i.e. cysteine-dependent aspartate-directed proteases, are the central executioners of metazoan PCD Metacaspases have been implicated in PCD in fungi Streptomyces, Pseudomonas, Mesorhizobium, Myxococcus, Xyllella, Anabaena, Synechocystis and TrichodesmiumOver the years, metacaspases have been observed in multiple bacterial genomes, e.g. http://pfam.sanger.ac.uk/), shows that 372 prokaryotic hosts contain 872 caspase homologs . To obtain a deeper appreciation of the functional diversity and taxonomic distribution of caspase homologs and PCD in prokaryotes we set out to explore the 1463 finished prokaryotic genomes . The caspase homologs discovered were further characterized at the protein level by identification of protein domains and transmembrane helices. The data obtained revealed the existence of a wide variety of caspase homologs in prokaryotes, and their significance in the evolution of caspases and programmed cell death in pro- and eukaryotes is discussed.As of today, Pfam, a manually curated database of protein families , it was possible to identify 671 putative metacaspases. These were present in 267 strains, i.e. in 18% of the total query set. Out of these, 157 carried a single metacaspase gene, whereas 110 were equipped with two or more putative metacaspases. Five (4%) out of the 114 archaeal genomes analyzed, all belonging to the Euryarchaeota, contained one single metacaspase each. As seen in Haliscomenobacter hydrossis DSM 1100, belonging to the phylum Bacteroidetes, with 28 genes encoding proteins with a putative metacaspase domain (corresponding to 4.15 metacaspases per 1000 proteins). Other strains with high numbers of metacaspases (≥5) are commonly found among Alphaproteobacteria, Deltaproteobacteria and Cyanobacteria  dyad of caspases is highly conserved in most taxonomic groups examined. Overall, 84% of the putative metacaspases identified in the bacterial genomes examined carry an intact C-H dyad. Furthermore, the dyad is conserved in all identified archaeal metacaspases. Six bacterial groups are distinguished by a cysteine-histidine conservation frequency lower than 90%: Gemmatimonadetes , Nitrospirae (57%), Cyanobacteria (63%), Planctomycetes (64%), Alphaproteobacteria (75%) and Spirochaetes (89%). Substitutions are equally prone to occur in the cysteine or the histidine residues. These residues are most commonly substituted by the polar serine  and hydrophobic tyrosine (63%), respectively.In the 671 putative prokaryotic metacaspases, 71 different Pfam domain types were identified, including the bacterial Peptidase C14  domain.Out of the 398 sequences that possess only the metacaspase domain, and no other Pfam domains, 69 (17%) contain one or more transmembrane helices. The bacterial metacaspase domain was located C-terminally to a single N-terminal transmembrane helix in 46 sequences . In theshttp:/pfam.sanger.ac.uk/). FGE-sulfatase (formylglycine-generating sulfatase enzyme) is the fifth most spread domain, found in 4.9% (33) of the sequences, and catalyzes post-translational modifications of sulfatases. Polysaccharide deacetylase , NACHT  and a putative peptidoglycan binding domain  follow in abundance. The latter may be extracellular, at least in part, and would allow binding to the bacterial cell wall. Less common domain types, although occurring more than once, include ATPases, helicases, various enzymes, signaling and protein-protein interaction domains. Interesting to note are the four sequences that contain an N-acetylmuramoyl-L-alanine amidase activity (Amidase_2) domain, which belongs to a group known as peptidoglycan hydrolases. Such proteins have been implicated in autolysis, fratricide and developmental lysis processes Polaromonas naphthalenivorans CJ2 (Betaproteobacteria), Nitrosococcus oceani ATCC 19707 (Gammaproteobacteria), Chitinophaga pinensis DSM 2588 (Bacteroidetes) and Desulfobacterium autotrophicum HRM2 (Deltaproteobacteria). The Chitinophaga pinensis DSM 2588 sequence also carries a GDSL-like lipase (Lipase_GDSL) domain, turning this particular protein into what seems to be a highly destructive weapon, i.e. it combines lipase, amidase and protease activities. Out of the 71 domain types that are identified together with the putative bacterial metacaspase domain, 33 are particularly rare and were only found once each.Taking transmembrane helices into account but disregarding consecutive repeats of domains, 112 unique domain architecture types could be identified, including single-domain proteins. 93 of the architecture variants are unique to their prokaryotic group , whereas 19 showed a wider distribution and may be found in two or more groups . The fouChloroflexus (phylum Chloroflexi). It should be noted that NACHT and NB-ARC belong to the same Pfam clan , as do a few other NTPase domain types identified in the putative bacterial metacaspases.Two domain types with a link to eukaryotic PCD reside in the prokaryotic dataset: NACHT, an ATPase domain which is found in 15 sequences belonging to the groups Actinobacteria, Bacteroidetes, Chloroflexi and Cyanobacteria, and NB-ARC, a signaling motif found in two sequences from the genus As mentioned above, a majority of the putative bacterial metacaspase architectures are unique to particular prokaryotic groups. Since Cyanobacteria provide an example of prokaryotes with developmental cycles generating phenotypes ranging from simple unicellular (genome sizes up to about 2 Mbp) to complex types with distinct cell differentiation capacities , repeated nine times, followed by the metacaspase domain.Some Cyanobacterial metacaspases have domain types or architectures that are unique to Cyanobacteria . The mosne helix . Three ane helix . The sin helices . The pro helices  has a Cy helices . A varia helices  is appar helices . The Pen helices , is spec helices . FinallyT.erythraeum IMS101 and Anabaena/Nostoc, with the exception of ‘Nostoc azollae’ 0708, the latter holding an eroding genome Acaryochloris marina MBIC11017 and a number of strains within the genus Cyanothece, while the clade comprising 26 small unicellular non-nitrogen-fixing marine strains within Synechococcus, Prochlorococcus and Cyanobium lack metacaspases altogether. However, the “primitive” strain at the root of the Cyanobacterial tree, Gloeobacter violaceus PCC 7421, is equipped with metacaspase genes  is of special interest due to the verified correlation between caspase activity and PCD in the strain X. campestris pv. glycines AM2. Eight out of 10 analyzed Xanthomonas genomes harbour one metacaspase gene each, except for X. campestris pv. campestris str. B100, which has two , its domain repertoire is also highly diverse. It possesses several single-domain metacaspases, as well as more complex variants, such as the metacaspase-NACHT-WD40-repeat  of the 671 putative bacterial metacaspases . Other cCellular localization could be established, as being intracellular, membrane-associated or extracellular, only in a fraction of the metacaspase-linked domain types. Not less than 529 out of 671 sequences completely lacked this kind of information. The remainder may be grouped into four categories: Membrane (98 sequences), of which the majority possess an N-terminal transmembrane helix that might in fact be a signal peptide, Extracellular (30 sequences), Intracellular (7 sequences) and Membrane and extracellular (7 sequences).http://www.microbesonline.org/operons/, Desulfococcus oleovorans Hxd3), were found in 20 operons. Only four bacterial groups had such multi-metacaspase operons, namely Alphaproteobacteria, Bacteroidetes, Cyanobacteria and Deltaproteobacteria.By employing the MicrobesOnline operon database (Roseiflexus castenholzii DSM 13941 (Chloroflexi). Furthermore, an N-acetylmuramoyl-L-alanine amidase domain, which can play a role in bacterial autolysis Geobacter sulfurreducens PCA (Deltaproteobacteria). Despite all this data, the function remains unknown for 70 out of the total of 217 metacaspase-containing operons.Operon neighbors were analyzed using the same HMM-based Pfam domain identification approach as was used for the metacaspases themselves (see methods). Together with the original annotation of the neighboring sequences, this allowed the addition of another layer of functional annotation to the metacaspases. Consequently, a highly diverse assortment of functions where attributed to the operons. Most conspicuously, various enzymes were found in 35 operons. The most common types were different hydrolysases, an enzyme involved in antibiotic resistance (Lactamase B), and a sulfate metabolism enzyme (ATP-sulfurylase) specific to Cyanobacteria in this dataset. Less common enzymes were linked to e.g. carbon and lipoprotein metabolism. Protein-protein interaction domains and signaling, sensor and regulatory domains were found in 30 and 23 operons, respectively, while domains implicated in proteolysis  were found in 23 operons. DNA interaction, as performed by for example DNA binding domains, various nucleases and a recombination domain, was identified in 18 operons. Three of these operons appeared to also be connected to restriction activity. Sixteen metacaspase operons are involved in functions at the cell surface, as indicated by e.g. transporters, flagellar proteins, surface antigens and cell wall binding domains. OmpA, an outer membrane-associated protein domain whose precise function is unclear, occurs in 14 operons.The core processes transcription and translation were linked to 15 and 11 operons, respectively. Domain examples include RNA polymerase subunits, sigma factors, tRNA processing proteins and even ribosomal proteins. Less common functions that were inferred from the domains were e.g. protein modification, chaperone activity and secretion. Another rare occurrence was the finding of the PCD-connected NACHT domain in a non-metacaspase protein in the operon identified in Rhizobium strains  and inner membrane protein translocase component yidC. These genes are found in the same configuration also in the other two genera, but were not predicted to be in the same operon. Another seemingly conserved configuration is found in the four Myxococcus xanthus DK 1622 metacaspase operons, which thereby includes four of the total of five metacaspases in this genome. All of these operons share a common denominator, i.e. sigma factors (sigma-70), placing them in the DNA interaction and transcription categories. This operon structure also appears in genomes of the other Deltaproteobacterial genera Anaeromyxobacter, Haliangium and Sorangium, but also in Ralstonia (Betaproteobacteria) and Fibrobacter (Fibrobacteres).Certain operon configurations appeared to be conserved between strains, e.g. an operon variant with protease and protease inhibition activities found in four  strains . Anotherphomonas , XyllelaAlcanivorax borkumensis SK2 , although at a considerably lower frequency in the latter. Nevertheless, it is also clear that a minority of the sequenced prokaryotes, about one fifth, carry genes encoding these proteases. However, there is a significant phylogenetic distribution bias among sequenced genomes, as also highlighted earlier B. subtilisE. coliThe comparatively low incidence of metacaspase genes (18% of genomes) discovered among the sequenced prokaryotes combined with their heterogeneous taxonomic distribution suggest that only certain prokaryotic life strategies require, or benefit from, the employment of metacaspases and/or PCD. Further, the fact that for instance Bradyrhizobium japonicum USDA 110 and Rhizobium leguminosarum bv. trifolii WSM1325, that enter nitrogen-fixing symbioses with legumes Methylobacterium nodulans ORS 2060 and Methylobacterium sp. 4–46, are likewise symbiotically competent Acaryochloris marina MBIC11017, Nostoc punctiforme PCC73102 and ‘Nostoc azollae’ 0708), and potentially also the ancestor of plant chloroplasts Sinorhizobium meliloti strains are lacking) the latter may reflect a mere coincidence or the sequencing bias. It is however known that the role of metacaspases may go beyond that of PCD processes A few of the more extensively sequenced bacterial groups, namely Alphaproteobacteria, Deltaproteobacteria and Cyanobacteria, display pronounced metacaspase abundance. The metacaspase-endowed Alphaproteobacteria are to a great extent rhizobial species, e.g. Myxococcus species that possess metacaspase genes, i.e. M. fulvus HW-1 and M. xanthus DK 1622  which suggests a link between developmental complexity and the presence of metacaspases. Exceptions are the plant endosymbiont ‘Nostoc azollae’ 0708, however this strain holds a proven eroding genome Cyanothece and Acaryochloris, however both possess comparatively larger genomes reflected in a wider repertoire of capacities  compared to other unicellular representatives Gloeobacter violaceus PCC 7421, a ‘primitive’ Cyanobacterium with rudimentary thylakoids  to genotypic and/or phenotypic complexity. All species that lack metacaspases belong to a monophyletic clade of extremely wide-spread marine planktonic and unicellular Cyanobacteria with small genomes , i.e. Syylakoids . This suXanthomonas provides a remarkable exception showing correlation between PCD and caspase expression and activity X. campestris pv. glycines, whose product cross-reacted with both caspase and PARP (poly ADP-ribose polymerase) antibodies X. campestris pv. glycines gene (GI 88792532) is homologous to genes identified in this study  encoded by the genome of Streptococcus aureusMyxococcus xanthus and Bacillus subtilisWe further discovered a strikingly high number of metacaspases with the conserved catalytic amino acid dyad substituted by other amino acid residues in both Cyanobacteria and Alphaproteobacteria. This could indicate that the metacaspases of these groups have been subject to a longer and more eventful evolutionary history, giving rise to a multitude of paralogs, some of which may be adapted to perform functions that do not require an intact catalytic site. For instance, it is known from mammals that caspase homologs lacking catalytic residues may even function as negative caspase activity regulators In addition, many metacaspase-associated domains seem to have an extracellular localization, as also indicated by the presence of N-terminal transmembrane helices . ExtraceT. erythraeum IMS101 has also been shown to undergo PCD We have demonstrated a limited (18%) but wide-spread taxonomic distribution of prokaryotic caspase homologs (metacaspases) among the prokaryotes examined, including archaea, and that these seem to play a variety of roles, both PCD- and non-PCD-related, based on the abundant protein domain architectures and operon structures discovered. In Cyanobacteria, a correlation was apparent between the presence of metacaspases and complex prokaryotic life styles, and Xanthomonas-type metacaspase outside of the genus Xanthomonas, and even outside the class Gammaproteobacteria, the function of the metacaspase in the cell division/cell shape operon of A. borkumensis SK2 and the significance of the stunning 28 metacaspase genes in H. hydrossis DSM 1100. Yet another interesting aspect to explore is the evolution of metacaspases within bacteria and archaea and especially their relation to the evolution of eukaryotes and the use of PCD in e.g. structuring body plans of metazoans. To achieve this it is now essential to include the metagenomes of the vast and untapped natural microbiome populations, such as those from terrestrial and aquatic habitats and not least the human microbiome.To further our knowledge on the occurrence and role of prokaryotic caspase homologs, including and beyond PCD, some intriguing avenues are worth exploring. These include to what extent the many metacaspase genes found may have any links to symbiotic events, the role of the http://pfam.sanger.ac.uk/) protein family Peptidase_C14 (PF00656) were used to train a bacteria-specific metacaspase Hidden Markov Model (HMM) search profile. The Pfam Peptidase_C14 profile HMM and the HMMER 3.0 (http://hmmer.janelia.org/) program hmmsearch were used to identify the Peptidase_C14 domains in the selected protein sequences. The Peptidase_C14 domains, defined by the HMMER domain envelope, were then aligned using MUSCLE v3.8.31 Bacterial protein sequences belonging to the Pfam-A 25.0  on September 23rd 2011. Metacaspases in these genomes were identified using the bacteria-specific metacaspase profile HMM and HMMER 3.0 hmmsearch at default settings. Sequences with a domain independent E-value ≤0.01 and a score/bias ratio ≥10 were accepted.FASTA files representing 1463 finished prokaryotic genomes were downloaded from NCBI  was analyzed in the same manner as the metacaspases identified in the genomes, but excluding alignment of the metacaspase domain for investigation of the cysteine-histidine dyad conservation.The domain architecture of the http://pfam.sanger.ac.uk/) was consulted, which includes additional information from InterPro (http://www.ebi.ac.uk/interpro/). The Pfam release used for the functional annotation was 26.0. Each domain type found in the metacaspase data set was looked up and assigned a specific function category and cellular localization, if possible. In some cases it was not possible to find a clearly defined function, or no function had been assigned at all. These domains were determined to have an “Unknown function”.In order to get a better grip on the functions of the identified domains, and thereby the functions of the metacaspase proteins, the information available on the Pfam website . Functional classification of each operon was carried out by manual inspection, aided by gene names and information about identified Pfam domains .Data on predicted operons in metacaspase-containing genomes covered by the MicrobesOnline operon database .The molecular weight of proteins carrying a polysaccharide deacetylase domain, such as those found in http://www.ncbi.nlm.nih.gov/taxonomy) were used to determine the distribution of the identified putative metacaspases among the 1463 genomes. Proteobacteria were divided into classes to provide better taxonomic resolution.Taxonomic classifications from NCBI  and the original Pfam caspase (PF00656) profile HMM.(TIFF)Click here for additional data file.Table S1Metacaspase distribution among analyzed genomes.(XLS)Click here for additional data file.Table S2Metacaspases in operons.(XLS)Click here for additional data file.Dataset S1Details of the identified prokaryotic metacaspases.(TXT)Click here for additional data file.


We can describe protein-protein interactions (PPIs) as sets of distinct domain-domain interactions (DDIs) that mediate the physical interactions between proteins. Experimental data confirm that DDIs are more consistent than their corresponding PPIs, lending support to the notion that analyses of DDIs may improve our understanding of PPIs and lead to further insights into cellular function, disease, and evolution. However, currently available experimental DDI data cover only a small fraction of all existing PPIs and, in the absence of structural data, determining which particular DDI mediates any given PPI is a challenge.parameter-dependent DDI selection (PADDS), which, given a set of PPIs, extracts a small set of domain pairs that can reconstruct the original set of protein interactions, while attempting to minimize false positives. Based on a set of PPIs from multiple organisms, our method extracted 27% more experimentally detected DDIs than existing computational approaches.We present two contributions to the field of domain interaction analysis. First, we introduce a novel computational strategy to merge domain annotation data from multiple databases. We show that when we merged yeast domain annotations from six annotation databases we increased the average number of domains per protein from 1.05 to 2.44, bringing it closer to the estimated average value of 3. Second, we introduce a novel computational method, We have provided a method to merge domain annotation data from multiple sources, ensuring large and consistent domain annotation for any given organism. Moreover, we provided a method to extract a small set of DDIs from the underlying set of PPIs and we showed that, in contrast to existing approaches, our method was not biased towards DDIs with low or high occurrence counts. Finally, we used these two methods to highlight the influence of the underlying annotation density on the characteristics of extracted DDIs. Although increased annotations greatly expanded the possible DDIs, the lack of knowledge of the true biological false positive interactions still prevents an unambiguous assignment of domain interactions responsible for all protein network interactions.http://www.bhsai.org/downloads/padds/Executable files and examples are given at:  The living cell is a dynamic, interconnected system where proteins interact with each other to facilitate biological processes. Large protein-protein interaction (PPI) datasets have become available due to advances in experimental biology and the development of high-throughput screening techniques. However, while existing data describe thousands of protein interactions, such interactions still constitute only a fraction of all PPIs for a small number of available organisms -5. Moreoi.e., mediate) a given PPI based on limited structural information remains a challenge.Proteins consist of one or more domains and multiple studies have shown that domain-domain interactions (DDIs) from different experiments are more consistent than their corresponding PPIs, suggesting that domains may be fundamental in mediating physical interactions between proteins -12. Undee.g., Conserved Domain Database (CDD) , where an α of 0.0 favors observed protein interactions and an α of 1.0 maximally penalizes non-observed interactions. We first used PADDS to investigate the choice of selecting different values of the parameter α on retrieved DDIs. Here, the DDIs were constructed from a study containing multiple organisms, but with protein-domain annotations from a single database. The PADDS-extracted DDIs were compared to other methods and validated using the iPFAM  in 0.1 increments, ranked the extracted DDIs based on the corresponding benefit value, and extracted the corresponding ranked data for MSSC and GPE (see Methods). Although, by construction, all obtained DDI sets accounted for all original PPIs, different methods yielded DDI sets of different sizes for each of the six domain annotation schemes, with PADDS consistently extracting the smallest sets of DDIs. Additional file e.g., a specified PPI would occur if two proteins were in close proximity, it is likely that in their native environment they are under additional biological regulation. Thus, one cannot assume that all proteins that contain interacting domains will necessarily interact within the cell, due to the existence of alternative regulatory mechanisms that control these interactions  range that specifies the amount of tolerable non-interacting protein pairs. We evaluated the probability of the occurrence of a domain pair i and j in a set of PPIs as a modified association score Let α = 0.0, i.e., the number of PPIs in which one protein contains domain i and the other contains domain j, whereas for α = 1.0, i and j [defined in Equation (1)] multiplied by the number of domain interaction occurrences. Thus, for α = 0.0, the modified association score corresponds to domain interactions that explain the largest number of PPIs, while for α = 1.0, the score corresponds to domain interactions that do not introduce large number of false positive PPIs. We multiplied the probability of interaction by Oij to differentiate between DDIs that have the same probability by assigning a higher score to those DDIs that account for a larger number of PPIs.For i and j represents the propensity that these two domains mediate protein interactions. We defined the benefit by combining the above modified association score with a term that takes into account the co-occurrence of domains, because domains that appear together within a protein often interact [Cij denote the number of proteins in which domains i and j co-occur, and let maxCij denote the maximum number of co-occurring domains observed in a given set of proteins. We then defined the benefit Bij of the interaction between two domains i and j as:The benefit of interaction between two domains interact ,47. Let α, one can rank the same set of DDIs differently based on their Bij value.Using different values of 1) these DDIs account for a given set of PPIs and 2) the sum of benefits of this set is higher than the sum of benefits of any alternative DDI set of the same size that explains the same PPIs. The optimal solution for this problem would require the exhaustive enumeration of all possible combinations of DDIs that account for the original set of PPIs. Because, in practice, the exhaustive enumeration is computationally unfeasible, PADDS uses a heuristic solution. Given a set of PPIs, a list of protein-domain annotations, and a user-specified parameter α, PADDS calculates Bij for each potential DDI , and s represents a scaling factor used to additionally reduce the benefit value of DDIs. We empirically selected s = 0.01. Out of all evaluated DDIs, PADDS finds a DDI with the highest reassessed benefit and adds it to the main set. In the second phase, the algorithm iterates between the alternative set and the main set until all PPIs that are explained by potential DDIs in one set are also explained by potential DDIs from the other set. In phase three, PADDS calculates the total accumulative benefit where k = 1 for DDIs that were added into a set based on their original benefit value Bmn, and k = 0 for DDIs that were added into a set based on their reassessed benefit value main and alternative sets. If the main set has the greatest final set of DDIs  minimum Nij, 2) maximum Oij, 3) maximum number of times the interaction between domains i and j explains a single PPI multiple times , 4) maximum Cij, 5) maximum number of unique PPIs that a DDI explains, and 6) maximum benefit. In cases where ties are not broken after this procedure, they are broken randomly.Ties between two DDIs are broken in the following order: et al. [GPE-HC”) and the second contained 7,554 DDIs of lower-confidence (denoted “GPE-LC”) that were not necessarily included in the first set. In the comparisons, we used the DDI rank information provided with the published data [For the comparison with the GPE method  on the det al. , we usedhed data .For the yeast high-confidence dataset comparison, we used the MSSC program available from the authors’ Web site to extract and rank DDIs using the association score . We implaThis set of proteins contains the translations of all systematically named ORFs, except ORFs designated as “dubious” or “pseudogenes.”bThe remaining domain annotations did not change, had minor modifications compared to the previous version, had domains of unknown function, had domains assigned to previously unannotated proteins, or had domains assigned to previously unannotated sequence segments.CDD: Conserved Domain Database; DDI: Domain-domain interaction; FN: False negative; FP: False positive; GPE: Generalized parsimonious explanation; IDBOS: Interaction detection based on shuffling; MSSC: Maximum-specificity set cover; PA: Parsimonious approach; PADDS: Parameter-dependent DDI selection; PPI: Protein-protein interaction; ROC: Receiver operating characteristic; SF: Superfamily; SGD: Saccharomyces Genome Database; TN: True negative; TP: True positive.The authors declare that they have no competing interests.VM, AW, and JR conceived of the study and participated in its design and coordination. VM collected the data, developed the algorithms, and performed the calculations. VM and AW drafted the original manuscript, which was edited by JR. All authors read and approved the final manuscript.Merged annotation. This file contains lists of new protein-domain annotations and lists that map new domain labels onto the domain labels used in the original annotation databases.Click here for fileMerged protein-domain annotations that correspond to domain annotations from the new PFAM-A release. This file contains a list of 202 merged protein-domain annotations corresponding to domain annotations from the current PFAM-A release (release 26.0) that did not exist in the previous PFAM-A release (release 25.0), but had a matching domain annotation in our merged set. Additionally, this file contains the merged protein-domain annotations corresponding to the current PFAM-A domain annotations that replaced the annotations from the previous PFAM release.Click here for fileDomain-domain interactions extracted by the parameter-dependent DDI selection (PADDS) method – multiple organisms. This file contains lists of domain-domain interactions extracted by PADDS for the Riley dataset [α analyzed in the main text. dataset  for all Click here for fileSupplementary material. This file provides the supplemental text, supplemental Additional file 4: Figures S1 – S4, and supplemental Additional file 4: Tables S1 – S4.Click here for file