Human blood coagulation factor VIII (fVIII) is a large plasma glycoprotein with sequential domain arrangement in the order A1-a1-A2-a2-B-a3-A3-C1-C2. The A1, A2 and A3 domains are interconnected by long linker peptides  that possess the activation sites. Proteolysis of fVIII zymogen by thrombin or factor Xa results in the generation of the activated form (fVIIIa) which serves as a critical co-factor for factor IXa (fIXa) enzyme in the intrinsic coagulation pathway.In our efforts to elucidate the structural differences between fVIII and fVIIIa, we developed the solution structural models of both forms, starting from an incomplete 3.7 Å X-ray crystal structure of fVIII zymogen, using explicit solvent MD simulations. The full assembly of B-domainless single-chain fVIII was built between the A1-A2 (Ala1-Arg740) and A3-C1-C2 (Ser1669-Tyr2332) domains. The structural dynamics of fVIII and fVIIIa, simulated for over 70 ns of time scale, enabled us to evaluate the integral motions of the multi-domain assembly of the co-factor and the possible coordination pattern of the functionally important calcium and copper ion binding in the protein.MD simulations predicted that the acidic linker peptide (a1) between the A1 and A2 domains is largely flexible and appears to mask the exposure of putative fIXa enzyme binding loop (Tyr555-Asp569) region in the A2 domain. The simulation of fVIIIa, generated from the zymogen structure, predicted that the linker peptide (a1) undergoes significant conformational reorganization upon activation by relocating completely to the A1-domain. The conformational transition led to the exposure of the Tyr555-Asp569 loop and the surrounding region in the A2 domain. While the proposed linker peptide conformation is predictive in nature and warrants further experimental validation, the observed conformational differences between the zymogen and activated forms may explain and support the large body of experimental data that implicated the critical importance of the cleavage of the peptide bond between the Arg372 and Ser373 residues for the full co-factor activity of fVIII. Factor VIII is synthesized as an ~300 kDa single chain protein containing 2332 residues and composed of six domains: A1-a1-A2-a2-B-a3-A3-C1-C2 [trate fX . The cli2R7E:3.7 Å and 3CDZ:3.98 Å) X-ray crystal structures of fVIII zymogen were reported [2. While these models provided a great detail about the overall global features of the domain organization within the X-ray crystallographic space, the low resolution of the crystal structures limits their reliability for the atomic-level structural details of the domain-domain interactions. Backbone superimposition of the two crystal structures (PDB: 2R7E and 3CDZ) revealed significant conformational differences in the solvent-exposed loop regions. Also, the reported crystallographic data lacks the structural information for the functionally important linker peptides that interconnect the A-domains. These regions include the acidic linker peptide between Pro333 (A1-domain) and Lys377 (A2 domain); the C-terminal peptide from Lys713 to Arg740 within the A2-domain.The circulating fVIIIa in blood plasma has limited half-life due to rapid dissociation of A2 domain . The losreported ,14. Both2R7E) [In this paper, we developed the solvent-equilibrated B-domainless solution structures of zymogen fVIII and its activated form, fVIIIa, in several stages starting from one of the X-ray crystal structures (PDB ID:2R7E)  by refin2R7E and 3CDZ). The alignment of the backbone atoms of the two structures revealed significant conformational differences in several solvent-exposed loop regions, though the core region of the both structures was similar with an overall RMS difference of 0.98 Å for the backbone atoms. The fIXa binding loop (Tyr555-Asp569) within the A2-domain and the surrounding region was also markedly different in both crystal structures. This region was implicated in several experimental studies as functionally important for fVIIIa:fIXa interactions as well as thrombin interactions with fVIII during the activation. The A1-A2-A3 domains of fVIII share ~35% sequence identity with the structure of human ceruloplasmin, from which the X-ray structural models were built. It is not surprising that such low sequence identity reflected on the poor stereo-chemical quality of the crystal structures. The analysis of the Ramachandran plot showed that the initial X-crystal structure has ~290 residues (out of 1329 residues) as the outliers in the Phi-Psi dihedral space . The domain-wise alignment of the starting X-ray crystal structure with the MD derived model showed overall agreement in the core-region of the domains but significant differences in the domain-domain interface in the A-domains. The superimposed structures are shown in Additional file Human fVIII model in the current study was built using the 3.7 Å resolution X-ray crystal structure of fVIII published by Shen et al . AnotherIn order to understand the overall dynamics of the structures, we monitored the structural changes over the simulation time by computing the RMS deviations with reference to the starting structure Figures . The RMS2 may be considered hyper-variable in the protein structures. In experimental X-ray crystal structures, the B-factors usually provide such information. While it is difficult to quantitatively relate the positional fluctuations from MD simulations with that of experimental B-factors in the X-ray crystal structures, both share a qualitative comparison. Since the published X-ray crystal structure of fVIII was derived by fitting the homology models, the crystallographic B-factors were assigned >190 Å2 of positional uncertainty. Consequently we could not make direct comparison with the computed atomic positional fluctuations. It is evident from Figures 2 of the atomic positional fluctuations. The acidic linker peptide region between the A1 and A2 domains spanning the residues Pro330 and Lys380, the loop regions 21-34, 397-415, 594-600, 1713-1725, 1796-1805 and 1889-1901 exhibited relatively large mobility during the MD simulations.To probe the intra and inter-domain mobility of the solvent-exposed loop regions further, we computed the atomic positional fluctuations in fVIII and fVIIIa models. A comparative plot of the positional fluctuations averaged over the last 2 ns of MD trajectory is shown for the A1-A2 and A3-C1-C2 domains in Figures YsulfDDDLTD from Glu344 to Asp352 and the activation peptide sequence SFIQIR from Ser367 to Arg372 as shown in Figure In the present study, the acidic-rich linker peptide connecting the residues Cys329 of A1-domain and Thr381 of A2-domain was derived based on MD simulations for a total time of 200 ns. Though the starting structure for the loop optimization was based on a linear conformation, over the MD refinement, the linker peptide adopted helical structure for two acidic rich regions. The helices correspond to the sequence EDThe linker peptide connecting Cys710 (A2-domain) and Thr1695 (A3-domain) in the B-domainless fVIII form was modeled based on a truncated model between the A2 and A3 domains as described in the modeling section. The residues at the C-terminus end of A2 domain are predominantly acidic with three tyrosine residues at positions 718, 719 and 723 that were post-translationally sulfated. In addition, the residue 1680 at the junction of A2-A3 domain is also sulfated. The A2-A3 linker peptide was stabilized over the 100 ns of MD simulations as shown in the RMSD plot Figure . The las+) and Type-II (Cu+2) ions covalently bind to factor VIII. These studies suggested that the primary role for copper (Cu+) was to enhance the inter-chain affinity by ~100 fold [+1) bound to the inter-domain interface is not sufficient alone to regenerate the full co-factor activity but might need a calcium ion binding to augment the stability of the structure. However, the precise location of the calcium ion binding, probably a low-affinity binding site as suggested by Wakabayashi et al, is not yet experimentally characterized [d value of 8.9-18.9 μM while a low-affinity site was proposed to bind to an unknown site with a weak Kd value of 4.0 mM [The function and stability of the fVIII depend critically on metal ions, particularly calcium and copper ions, covalently bound to the protein. Studies based on the reconstitution of isolated subunits as well as site-specific mutagenesis studies indicated that Type-I  of the carboxylate groups together with four oxygen atoms of the solvent water molecules Figure . While t+1) copper ions bind to the A1 and A3 domains. The reconstitution of fVIII from isolated heavy and light chains revealed a primary role for copper ion was to enhance the inter-chain affinity by ~100 fold [Factor VIII is structurally homologous to the copper-binding human ceruloplasmin and factor Va ,28. The 100 fold . It is k100 fold -34. In fThe B-domainless FVIII contains five ASN-glycosylation (N-linked) sites. These sites are located in A1 (41 and 239), A2 (582) and A3 (1810) and C1 (2118) domains . All the+ hemophilia A [sulf is located within the α-helix formed by the acidic-rich sequence EDYsulfDDDLTD (from Glu344 to Asp352). One direct impact of Tyr346suf mutation to cysteine could be the reduction of overall acidic nature of the helix and possibly destabilizing the helix. It may also be seen from Figures There are six tyrosine sulfation sites in FVIII. These are located at the positions: 346 (A1 domain), 1664, 1680 (A3 domain) and at 718,719 and 723 in the C-terminus end of the A2 domain ,37. Excephilia A . This de2 respectively. The buried surface area among the A-domains (fVIII/fVIIIa) is as follows: A1:A2 (4022/3354 Å2); A2:A3 (4329/3690 Å2) and A1:A3 (2709/2632 Å2).The structural stability originating from the inter-domain interactions of fVIII/fVIIIa is mainly due to the metal ion dependent association and the inter-chain association due to residue-level interactions. Indeed, 80% of the thermodynamic stability of fVIII is attributed to the latter. Upon the generation of the fVIIIa followed by the cleavages at three proteolytic sites in B-domainless fVIII, the activated form has limited half-life since it goes through the spontaneous inactivation through A2 subunit dissociation from the other domains . While tAnalysis of the interaction data showed significant contribution form several ion-pair interactions that promote stability between A1 and A2 domains as shown in Table The mutations of Arg282, Ser524 and Arg531 were shown to increase the fVIII decay by > 5-fold. As shown in Table Despite the relatively smaller binding surface compared to that of A1:A2 domains, the A1 and A3 domains are largely stabilized by the copper ion coordination at the A1 and A3 domain interface. In addition, four major ion-pair interactions add to the overall binding affinity between the two domains. These are Arg220:Glu1914; Arg221:Arg1719; Arg226:Asp1909 and Glu113:Arg1997. It should be noted that the Glu113 is located in the calcium-binding loop within the A1 domain. The mutagenesis studies suggested that the Glu113Ala mutation within this calcium binding loop showed four-fold increase in co-factor activity. While the mutation of Glu113 to large polar residues was shown to be detrimental to the co-factor activity, Glu113Ala mutation seems to increase the stability. In our structure, we identify that the backbone oxygen atom of Glu113 forms hydrogen-bonding with two hydrogen atoms of the side-chain of Arg1997 within the A3-domain. We observed that this interaction was consistently maintained over the entire stabilized MD trajectory and, indeed, was the only inter-domain interaction that contribute to the association of the calcium binding loop to the A3-domain. Therefore, the mutation of Glu113 into other bulky groups might disrupt this interaction and could be deleterious to the inter-domain affinity between A1 and A3 domains.2 surface area in the activated form when compared with that of the zymogen. The A2-subunit dissociation in the activated form and the reduced half-life in the blood plasma could be attributed, at least in part, to the diminished interactions between the A2 and A3 domains. In a study based on the Glu1829Ala mutation, the A3-domain residue Glu1829 was proposed to contribute to the A2 subunit retention [The interactions between the A2 and A3 domains in fVIII zymogen are governed by a large number of hydrogen bonded interactions. The A2:A3 surface has three ion-pair interactions due to the clustered network of charged residues Arg740:Glu378/Glu1811; Lys1833:Asp666/Tyr664; Glu607:Arg1900/Ala1901. Unlike the A1:A2 and A1:A3 domains that did not show a great loss of inter-domain interactions upon activation, the A2:A3 domain interface appears to loose a large number of hydrogen bonds (9 out of 25) as seen from the Table etention . Howeveretention . As show2 of buried surface with the A1/A2/A3 domains. Despite the large binding surface, the C-domains have limited stabilizing interactions with the A-domains. We noticed that only one clustered ion-pair formation of Glu1751 with Arg2116 and Tyr2105. Other H-bonding interactions include Arg4:Tyr2332; Ala1866:Ser2119; Glu1754:Gln2113; Arg2116:Thr2122 and Tyr2115:Pro2142. Interestingly, the C1-domain shares 670 Å2 of surface area with C2-domain but no hydrophobic or electrostatic interactions were found between the two domains. As discussed in the early part of the text, the large-scale domain motions, that we observed  when all the domains are considered, stems for the lack of stabilizing interactions between the C2 and C1 domains. As the C1-C2 domains seem to bind to the platelet surface together, the overall structure of fVIIIa might adopt a stabilizing orientation, for functional co-factor activity, on the anionic phospholipids surface through the likely hydrophobic interactions with C1-C2 domain residues Phe2093, Leu2251, Leu2252 and Met2199 residues. All these residues are projected towards the possible membrane surface.The platelet binding C-domains together share 16962 ÅThe B-domainless factor VIII zymogen has three proteolytic sites at Arg372 (A1-A2 junction), Arg740 (A2-B junction) and Arg1689 (at the N-terminus of A3) that must be cleaved by the physiological activator, thrombin ,47. WhilIn the present study, we applied a step-wise modeling and MD refinement approach to develop the solution structural models of the zymogenic and activated forms of B-domainless human factor VIII based on the X-ray crystal structure of human fVIII. The models showed significant improvement in the stereochemical quality of the core region of the domains over the X-ray crystal structure. The solvent equilibrated models derived from several hundred nanoseconds of MD simulations in explicit water provided a detailed understanding of the dynamics of the multi-domain assembly. Structural defects in fVIII lead to the hereditary bleeding disorder, Hemophilia-A. More than 200 natural mutations are known that cause mild to severe bleeding in hemophilia patients. While a detailed structure-function correlation of the known genetic and synthetic mutations with fVIII/fVIIIa structures is beyond the scope of the present work, we were able to successfully correlate some of the recently reported experimental mutagenesis data with the MD derived models.In the absence of experimental structural data, we applied MD simulation techniques to predict the likely conformations for the linker peptides between the A1:A2 and A2:A3 domains. Though the models are derived from long time scale MD simulations and appear to be well-stabilized during the dynamics, we must emphasize that the loop conformations are truly predictive in nature and need experimental attestation. However the proposed model for the linker peptide between A1 and A2 domains may shed some light on the current understanding of the zymogenicity of fVIII. The linker peptide between A1 and A2 domains was predicted to block the "steric access" of the putative factor IXa binding region (Tyr555-Asp569) in the A2-domain. The simulation of the activated form predicted that the loop re-organizes upon cleavage and relocates to A1 domain, thereby exposing the Tyr555-Asp569 loop and the surrounding region. Since this loop was proposed by several experimental studies as functionally important for fIXa enzyme binding, the conformational differences between fVIII and fVIIIa in the current study may provide a structural basis for the zymogenecity of fVIII and the importance of the proteolysis of Arg372-Ser373 peptide bond for productive interactions between fIXa and fVIIIa. The extensive MD simulations employed in the current study also characterize the specific residues involved in the co-ordination network of the high-affinity calcium-ion within the A1-domain. The proposed data related to the inter-domain interactions may serve as a reasonable structural basis to design the site-specific mutagenesis studies to further characterize the residues involved in promoting the overall stability of fVIII/fVIIIa and, in turn, the validation of the proposed solution structures.Upon the loss of a 19 residue length signal peptide, the mature fVIII zymogen circulates in blood as a single chain protein with 2332 residues (A1-A2-B-A3-C1-C2) out of which the B-domain consists of 573-residues . Factor et al [The B-domain in fVIII has no known structural homology in the protein database. Consequently, the modeling of B-domain is intractable to modern computational methods. The single-chain fVIII protein without the B-domain (741-1668) appeared to retain the full fVIII functionality as investigated by Donath et al . In thei2R7E) [http://modbase.compbio.ucsf.edu/modloop/modloop.html[The full model of single chain B-domainless fVIII was generated using the 3.7 Å resolution X-ray structure of the fVIII zymogen (PDB:2R7E) . The cryloop.html. The iniloop.html,63-66. TThe 46-residue length linker peptide between Glu332 and Pro379 is predominantly acidic with 18 residues being negatively charged. The sequence from 341 to 349 (EEAEDY*DDD) has large density of acidic residues. The residue Tyr346 (Y*) in the sequence represents sulfated tyrosine. Attempts to generate a possible structure for the linker peptide using homology modeling methods failed due to no corresponding template structure in the PDB. Consequently, the structure of the peptide was modeled using MD simulations for a total time period of more than 200 ns. Initially two beta-sheets that connect the linker peptide from the core residues of A1 (Ala322-Glu332) and A2 domain (Pro379-Ala388) were selected from the solvent equilibrated model (step1) as shown in Figure The structure prediction for the linker peptide between the A2 and A3 domains followed a similar approach adopted in step2. MD simulations on fVIII model from step1 revealed that the C-terminal residues from 712 to 725 within the A2-domain appear to be flexible with no stabilization of the conformation. Consequently, these residues were deleted from the model of fVIII and reconstructed to build the linker peptide between Cys711 and Arg1689. The linker peptide from Cys711 to Arg740 of A2-domain and Ser1669-Arg1689 forms a contiguous sequence in the B-domainless fVIII model considered in the present study. The Cα-Cα distance between Cys711 (A2-domain) and Ser1690 (A3-domain) in the solvent equilibrated model of fVIII (from step1) is ~43 Å. In order to generate a possible structure for the linker peptide using MD simulations, a short model of A2 and A3 domains was selected from the MD equilibrated model of fVIII (from step1). The model comprised of selected residues of the A2 and A3 domains. The residues corresponded to: Val621-Ile639/Ser650-His660/Val678-Asp712 of A2-domain and Ser1690-Arg1705/Gly1760-Pro1809/Glu1811-Lys1827 of A3-domain. We chose the reduced model in place of the full structure of fVIII to increase the computational speed of MD simulations and also to minimize the computational cost. Initially, a random conformation was created for the missing linker peptide corresponding to the residues between Asp712-Arg740 and Ser1669-Ser1690 as a contiguous sequence Figure . The Arghttp://glycam.ccrc.uga.edu/. In addition, tyrosine (TYR) residues at positions 346 (within the A1-A2 linker region) was modified to incorporate the sulfated tyrosines [The solvent-equilibrated models of A1-A2 and A2-A3 linker peptide structures, derived from the steps 2 and 3, were inserted into the equilibrated model from step-1 to generate the full structure of fVIII. Since the non-loop regions of the models used for A1-A2 and A2-A3 linker peptide modeling were constrained during the MD simulations, both models superimposed with the solvent equilibrated model (step1) with no steric clashes. Sequence characterization studies showed that asparagine (ASN) residues at positions 41, 239, 582, 1810 and 2118 were post-translational glycosylation sites . Accordiyrosines . The othThe domain organization of fVIII zymogen and its activated form, fVIIIa, are similar except that the activated form does not possess B-domain. The residues involved in the inter-domain interactions between A1:A2 and A2:A3 domains appeared to be comparable as suggested by recent site-specific mutagenesis studies ,68,69. IIn order to build the activated fVIII model in which the Arg372-Ser373 peptide bond gets cleaved during the proteolysis by thrombin and/or Xa, the solvent-equilibrated fVIII zymogen structure, derived from the 70 ns of MD simulations, was modified to generate the activated form. This was accomplished by explicitly breaking the Arg372 and Ser373 bond and creating a hetero-trimeric complex among the A1 (Ala1-Arg372), A2 (Ser373-Arg740) and A3-C1-C2 (Ser1690-Tyr2332) domains. The full model of fVIIIa, thus obtained, was subjected to further refinement of 75 ns of MD simulations to obtain the solution structure of fVIIIa.The MD simulations on all the structures, considered in the present study, were carried out in explicit water medium using AMBER10 program  package with PARM99/SB force-field ,71. The The long-range interactions were treated using the particle mesh Ewald (PME) method . Constan2. This followed a full minimization of the entire system.1) The water molecules and counter-ions in the periodic box were energy minimized to a root mean square (RMS) gradient of 0.1 kcal/mol/A2) NVT simulation for 5 ps was performed on the entire system.3) NPT simulation for 50 ps was performed on the whole system except the backbone atoms of the protein residues to equilibrate the solvent waters and the protein side-chain atoms.4) The entire system is subjected to slow heat-up from the initial temperature of 150 K to 300 K under NVT conditions.5) Finally the system was subjected to production-run under NPT conditions and a constant temperature of 300 K. The final density of all the simulations in the current study was close to 0.99 g/cc.ptraj module of AMBER10 package. The simulations of full length structures of fVIII and fVIIIa were performed without any geometrical or positional constraints on the atoms. The computations were performed on the multi-core linux clusters using MPI/Infiniband version of PMEMD. The final setup of the zymogen and the activated forms of fVIII for MD simulations is given below.Analysis and clustering of the MD trajectories were performed using the a) Factor VIII: Total system size: 242,051 atoms; 1404 protein residues; 7 calcium ions (6 unbound to protein); 1 chloride ion; 73158 watersb) Factor VIIIa: Total system size: 202,946 atoms; 1383 protein residues; 5 calcium ions (4 unbound to protein); 1 chloride ion; 60235 watershttp://mi.caspur.it/PMDB/main.php. The accession numbers for PDB coordinates is PM0076106 and PM0076119.The atomic coordinates of the solvent-equilibrated structures of fVIII and fVIIIa are deposited at protein model database DV planned and performed all the proposed work, structural analysis and wrote the manuscript.Figure S1. The Phi-Psi Ramachandran plots (only non-glycine residues were considered) of the X-ray crystal (upper left), initial homology model (upper right), the solvent-equilibrated MD models of fVIII zymogen (lower left) and activated form (lower right).Click here for fileFigure S2. Overlay of the MD equilibrated structure of fVIII zymogen (white) and the X-ray crystal structure (PDB:2R7E). The backbone atoms of the individual domains of A1, A2, A3, C1 and C2 of the X-ray crystal structures (color-coded) were superimposed against the MD model.Click here for file


PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners.The focus of the current study is two folds: 1) predicting to which peptides a PDZ domain will bind and 2) classification of PDZ domains, as Class I, II or I-II, given the primary sequences of the PDZ domains. Trigram and bigram amino acid frequencies are used as features in machine learning methods. Using 85 PDZ domains and 181 peptides, our model reaches high prediction accuracy (91.4%) for binary interaction prediction which outperforms previously investigated similar methods. Also, we can predict classes of PDZ domains with an accuracy of 90.7%. We propose three critical amino acid sequence motifs that could have important roles on specificity pattern of PDZ domains.Our model on PDZ interaction dataset shows that our approach produces encouraging results. The method can be further used as a virtual screening technique to reduce the search space for putative candidate target proteins and drug-like molecules of PDZ domains. Protein-protein interactions play fundamental roles in signal transduction, formation of functional protein complexes and protein modification . One of et al. investigated the binding specificities of nine PDZ domains by using an oriented peptide library and concluded that additional selection specificities, depending on up to -8 position of the peptide ligand, were observed beside the 0 and -2 positions [C-terminus of a peptide recognizes and binds to a pocket between carboxylate-binding loop (βA - βB loop) that contains the conserved GLGF motif, and αB helix of the PDZ domain -11, thisositions .et al. [et al. [et al. [et al. [et al. [et al. [Although PDZ domains show selectivity toward their target ligands, they also display promiscuity, binding to more than one ligand, and degenerate specificity -21, so iet al.  predicteet al.  used a c [et al.  tried to [et al.  used a n [et al.  construc [et al.  used an In this study, we propose a method to predict PDZ domain-peptide interactions by using only the sequence information of PDZ domains and ligands. In order to construct a numerical feature vector for each interaction, trigram and bigram frequencies of each primary sequence of PDZ domains and peptides are calculated. We obtain a high prediction performance (accuracy of 91.4% and AUC of 0.97 for trigram model) distinguishing between binding and non-binding peptides of PDZ domains. We make use of the most commonly used classifiers  and find Random Forest classifier with the best prediction accuracy. Moreover, we show that our method can be efficiently used to distinguish between Class I, Class II and Class I-II PDZs (both binding to Class I and Class II peptides) with an accuracy of 90.7% and AUC of 0.90 for trigram model.et al., which is composed of interaction data of 85 mouse PDZ domains with respect to 217 mouse genome-encoded peptides [For interaction prediction part, a positive (binding) and a negative (non-binding) dataset are needed in our machine learning model. The PDZ interaction dataset is retrieved from the study of Stiffler peptides ,24. Theyet al. and it is composed of 27 binding and 62 non-binding interactions of 16 PDZ domains and 20 peptides [An independent validation dataset is also used in interaction prediction part in order to test the predictive performance of our model. The validation dataset is extracted from the previous study of Stiffler For class prediction part, 86 PDZ domains are categorized, resulting in 45 Class I, 20 Class II, 21 Class I-II. These are retrieved from our interaction dataset and PDZBase  by lookiIn order to be consistent in our interaction prediction model, we took the last 10 residues of each peptide sequence due to the selection specificities of PDZ domains up to -10 positions of peptides. The sequence data of PDZ domains and peptides can be seen in additional file Frequencies of consecutive three amino acids (trigram) and two amino acids (bigram) in the primary sequences are used as features. For instance, a sequence of "ABCDE" results in a trigram set of "ABC", "BCD", "CDE" and a bigram set of "AB", "BC", "CD" and "DE". In order to reduce the dimension of the features, 20 amino acids are clustered into 7 different classes Table  accordinTo calculate trigram frequency of the PDZ and corresponding peptide sequences, the number of occurrence of each subsequent trigram in the sequence is counted, and this number is divided by the total number of trigrams in the sequence which is (n-2), where n is the sequence length. At the end, we obtain 343 (7 × 7 × 7) features for each sequence because amino acids are clustered into seven classes resulting in 7 × 7 × 7 different combination of trigrams. For the interaction prediction part, feature vector space is constructed by combining trigram frequency sets of both PDZ domain and corresponding peptide which gave 686 features for each interaction . For bigram frequency calculation, the same procedure is applied and we obtained 49 (7 × 7) features for each sequence and a total of 98 features  are constructed for each interaction. Therefore, we constructed a feature vector space  to represent an interaction:i represents the frequency of each trigram where i = 1, 2,...., 343 or each bigram where i = 1, 2,..., 49, Y is the feature vector space of peptide sequence, each feature yi represents the frequency of each trigram or bigram, and W is the corresponding label that contains binary data . Thus, a 686 dimensional vector for trigram part and a 98 dimensional vector for bigram part are constructed to represent each binding/non-binding interaction.Here, X is the feature vector space of the PDZ sequence, and each feature x1: ClassI, w2: ClassII, w3: ClassI-II) are built to represent each class of PDZ domains.For the class prediction part, the peptide sequences are discarded and only the sequences of PDZ domains are used to construct the feature vector space, because peptide sequences are used as the label of the dataset. Therefore, a 343 dimensional vector space for trigram part and 49 for bigram part with three labels  to be used as the test set during the training process. The exclusion is repeated until every instance in the dataset is once among those left-outs. In comparison to using an independent test set, cross-validation provides less bias and a better predictive performance. Parameter selection for each classifier is done by varying their parameters step-by-step and their accuracy and AUC (Area Under the ROC Curve) values are compared to obtain the best parameters with the highest performance , true negatives (TN), false positives (FP) and false negatives (FN) are used to calculate true positive rate , TPR = TP/(TP + FN), false positive rate, FPR = FP/(FP + TN) and precision, P = TP/(TP + FP). We measure the performance of each classifier by using a ROC curve which is drawn as TPR (Sensitivity) versus FPR (1-Specificity). The area under the ROC curve, referred as AUC, represents the predictive power: while a random predictive model has an AUC = 0.5, a perfect one has an AUC = 1.0 so that a larger AUC shows a better predictive power. However, ROC curves can sometimes be misleading while dealing with highly unbalanced datasets. Therefore, Precision versus Recall (PR) curves are also constructed to interpret the performance of models in a more informative manner . PR curvRandom forest is chosen to build our model due to its highest AUC and accuracy values  is slightly higher than the bigram part 91.2%)  and binary classes  in order to have an insight about their pair wise classifications. As seen in Table i) as well as the level of intercorrelation among all features. Successful feature subsets include features highly correlated with the label, but uncorrelated with each other [In order to make the resulting model faster and extract important features, dimensionality of our dataset is reduced by using feature selection methods . Selectich other . In the Feature selection (dimension reduction) is applied to both trigram and bigram models because we want to observe important common features of both models. For the trigram model, we obtained 23 features for PDZ domain and 23 features for peptides to be used in interaction prediction part. Also, 53 trigram features, for the classification part, are obtained (Data is not shown).For the bigram model, there are 11 features for PDZ domain and 12 features for peptides for the interaction prediction part and 10 features for the classification part. The accuracy values of our model did not increase after feature reduction for both interaction prediction and classification parts except trigram classification model Table . HoweverWe analyzed the selected features to understand why they are important to distinguish different PDZ interactions and classes. These extracted features might correspond to some critical amino acid motifs which may be important for PDZ domain interaction specificity. Indeed, one of the features points out the GLGF (Gly-Leu-Gly-Phe) repeat of PDZ domains which is an important conserved region for peptide interaction . This coAs seen in Figure et al. investigated the importance of αB helix on peptide selectivity of PDZ domains by showing high correlation between first residue in the αB helix and peptide position -2 [Extracted motif on αB helix could function in specificity of PDZ domains. Songyang ition -2 . Below, et al. and they found that Leu 14, Gly 15 and Ile 16 showed a large chemical shift upon binding of ligand [The specific interaction property of α1-syntrophin PDZ domain was investigated by Schultz f ligand . PDZ domFirst PDZ domain of NHERF1 plays important role in cellular localization by binding to the cystic fibrosis transmembrane conductance regulator (CFTR) . Leu 0 oet al. tried to elucidate structural basis of binding pattern of Harmonin(2/3) and found that carboxyl group of cad 23 ligand forms hydrogen bonds with Leu 222, Glu 223, Cys 224 (GLGF motif) and is stabilized by Lys 279 [Pan  Lys 279 . These iarmonin2/ and founThe carboxyl group of ligand forms hydrogen bonds with Ile 33, Gly 34 and Ile 35 of Pick1 PDZ domain . While Get al. investigated allosteric property of PTP-BL(2/5) domain by using structural and dynamical methods and found that binding is regulated by long range interactions which showed correlation with ligand-induced structural rearrangements [Gianni ngements . There iOur results show that our model can be used as a stable interaction prediction model of PDZ domains with higher accuracy than other similar methods ,24. We aet al., it was shown that the dimeric interface of GRIP1 PDZ6 dimer include a βA strand and αA-βD loop from each domain, and motif "16" is located on this αA-βD loop of GRIP1 PDZ6 domain. Also, it could have an allosteric effect regulating the binding specificity of PDZ domains [Some important characteristic features of PDZ domains were extracted. After selection of most occurring features along the same secondary structure region of PDZ domains, we obtained three critical sequence motifs. Two of them ("12" and "25") were previously shown to have an important role in ligand interaction. Motif "12" is on conserved GLGF repeat and located between βA-βB loop and motif "25" is located on αB which is one of the parts of binding pocket. There is not any previous study investigating the importance of motif "16" which is positioned on αA-βD loop. After multiple alignment of PDZ domain sequences, it was observed that motif "16" on αA-βD loop is conserved as shown in another study . Althoug domains . HoweverThis study has two intercorrelated aims: prediction of PDZ domain-peptide interactions, and classification of PDZ domains as Class I, II and I-II. A statistical learning model was constructed by using interaction dataset of PDZ domains (consist of 85 PDZ domains and corresponding 181 peptides). To convert primary sequence information into numerical feature input, trigram and bigram amino acid frequencies were calculated for each instance. We predicted binary interactions and classes of PDZ domains with accuracies of 91.4% and 90.7%, respectively. After feature extraction, three critical amino acid sequence motifs were proposed to have significant roles on PDZ domain specificity. With these highly encouraging results, this study could be an important step in the automated prediction of PDZ domain interactions.The discovery of features within primary sequences of known protein interaction pairs could be subsequently developed by using other features  in the learning model. Further improvements on these lines may generate a powerful computational virtual screening technique that significantly reduces the search space for putative candidate target proteins of PDZ domains.SK, OK and AG designed the study. SK implemented the algorithms, performed the experiments. SK, OK and AG analyzed the data. All authors have read and approved the final manuscript.Table S1 - PDZ domain interactions.Click here for fileTable S2 - Unseen validation dataset interactions. Table S3 - Classes of PDZ domains according to their binding/non-binding target peptide sequences. Table S4 - PDZ domain sequence IDs. Table S5 - Peptide sequences (up to -10 amino acid position). Table S6 - Search methods that are used to reduce dimensionality. Table S7 - Parameter values for each classifier used in trigram interaction prediction model. Figure S1 - Comparison of all classifiers used in interaction prediction trigram model. Figure S2 - Comparison of all classifiers used in interaction prediction bigram model. Figure S3 - Comparison of all classifiers used in classification trigram model. Figure S4 - Comparison of all classifiers used in classification bigram model. Figure S5 - Parameter selection of Random Forest algorithm for interaction prediction trigram model. Figure S6. Amino acid frequency distribution of Class I/II/I-II PDZ domains.Click here for file


Many important cellular protein interactions are mediated by peptide recognition domains. The ability to predict a domain's binding specificity directly from its primary sequence is essential to understanding the complexity of protein-protein interaction networks. One such recognition domain is the PDZ domain, functioning in scaffold proteins that facilitate formation of signaling networks. Predicting the PDZ domain's binding specificity was a part of the DREAM4 Peptide Recognition Domain challenge, the goal of which was to describe, as position weight matrices, the specificity profiles of five multi-mutant ERBB2IP-1 domains. We developed a method that derives multi-mutant binding preferences by generalizing the effects of single point mutations on the wild type domain's binding specificities. Our approach, trained on publicly available ERBB2IP-1 single-mutant phage display data, combined linear regression-based prediction for ligand positions whose specificity is determined by few PDZ positions, and single-mutant position weight matrix averaging for all other ligand columns. The success of our method as the winning entry of the DREAM4 competition, as well as its superior performance over a general PDZ-ligand binding model, demonstrates the advantages of training a model on a well-selected domain-specific data set. Many vital cellular functions are mediated by protein complex formation One important recognition domain is the PDZ domain, commonly found in organisms from bacteria to humans, and functioning in scaffold proteins to assemble large molecular complexes that facilitate formation of signaling networks et al.Naturally, a question of whether binding specificity can be predicted from the PDZ domain's primary sequence, arises. The analysis by Tonikian Addressing this problem, the DREAM  et al.Tonikian et al., we generated a position weight matrix (PWM) for every single-mutant PDZ variant based on its set of binding peptides, adding no pseudo-counts and correcting for codon bias by dividing observed amino acid frequencies by their expected frequencies in the NNK codon set Following Tonikian et al.The DREAM4 PDZ-peptide recognition challenge was comprised of five multi-mutant ERBB2IP-1 sequences , each coet al., who have shown that binding preferences at these positions are determined, in large part, by mutations at PDZ positions In defining the prediction model, we needed to identify the PDZ positions that affect binding specificity of every ligand position. Importantly, limiting sets of interactions between a ligand position and PDZ domain residues necessarily reduces the complexity of any potential predictor. We were able to restrict the set of interacting PDZ positions for ligand positions For a particular ligand position, we represented each amino acid among the set of interacting PDZ positions  as a fiv , right.  . Given aWe speculated that, in some cases, the regression approach might lead to over-fitting and, therefore, considered a supplementary, more “conservative” approach denoted ant PWMs . Finallyt PWMs ( .The similarity between a computationally predicted PWM and its gold standard, experimentally-determined counterpart was judged using the Frobenius norm, computed as follows:et al.et al.et al. is much sparser, with as few as one or two interactions for some ligand positions.Chen et al.For each of the et al. model is applicable only to the five C-terminal ligand positions and, therefore, uniform amino acid preferences were assumed for the remaining positions.To predict binding specificities for a given ERBB2IP-1 multi-mutant sequence and some ligand position, we had to convert model scores to PWM probabilities. First, we summed the


Understanding how avian influenza viruses adapt to human hosts is critical for the monitoring and prevention of future pandemics. Host specificity is determined by multiple sites in different viral proteins, and mutation of only a limited number of these sites can lead to inter-species transmission. Several of these sites have been identified in the viral polymerase, the best characterised being position 627 in the PB2 subunit. Efficient viral replication at the relatively low temperature of the human respiratory tract requires lysine 627 rather than the glutamic acid variant found systematically in avian viruses. However, the molecular mechanism by which any of these host specific sites determine host range are unknown, although adaptation to host factors is frequently evoked. We used ESPRIT, a library screening method, to identify a new PB2 domain that contains a high density of putative host specific sites, including residue 627. The X-ray structure of this domain (denoted the 627-domain) exhibits a novel fold with the side-chain of Lys627 solvent exposed. The structure of the K627E mutated domain shows no structural differences but the charge reversal disrupts a striking basic patch on the domain surface. Five other recently proposed host determining sites of PB2 are also located on the 627-domain surface. The structure of the complete C-terminal region of PB2 comprising the 627-domain and the previously identified NLS-domain, which binds the host nuclear import factor importin alpha, was also determined. The two domains are found to pack together with a largely hydrophilic interface. These data enable a three-dimensional mapping of approximately half of PB2 sites implicated in cross-species transfer onto a single structural unit. Their surface location is consistent with roles in interactions with other viral proteins or host factors. The identification and structural characterization of these well-defined PB2 domains will help design experiments to elucidate the effects of mutations on polymerase–host factor interactions. There is worldwide concern that currently circulating avian influenza viruses will cross the species barrier and become highly pathogenic, human transmissible strains with pandemic potential. This could result from residue changes in several influenza proteins, either by point mutations, or through shuffling of the segmented avian and mammalian viral genomes. Numerous studies have highlighted potentially important residues for inter-species transmission, and several are found in the influenza polymerase that replicates and transcribes the viral genome. The polymerase PB2 subunit contains a number of such positions, notably residue 627, which is glutamic acid in avian viruses but lysine in human-adapted strains. Experiments have shown that the polymerase mutations affect the efficiency of viral replication in different host species, but the molecular mechanisms are unknown. As a first step towards resolving this enigma, we have identified a novel domain of PB2, containing many host determinant sites, and determined its atomic structure by X-ray crystallography. The species-specific residues are all located on the domain surface, suggesting they could be involved in interactions with viral proteins or host factors. The 627 position is solvent-exposed in both the lysine and glutamic acid variants, respectively either reinforcing or disrupting a striking positively charged surface patch. The identification and structural characterisation of biochemically well-behaved domains of PB2 provides new tools for understanding the phenomenon of inter-species transmission that is of global health importance. It is therefore of great importance to understand the molecular mechanism of avian to human host adaptation, as well as the factors leading to high virulence, as this will contribute to effective monitoring of the likelihood of a pandemic, the development of new diagnostic tools and therapeutic strategies, and global counter-pandemic planning. Studies have identified specific features of the receptor binding glycoprotein, HA, the non-structural protein 1 (NS1) and the polymerase as being critical for both inter-species transmission and virulence Influenza A viruses are orthomyxoviruses possessing an eight segment RNA genome of negative polarity. Each segment is packaged in a ribonucleoprotein complex (RNP) together with the nucleoprotein (NP) and the three subunits  of the trimeric RNA-dependent RNA polymerase, which mediates viral transcription and replication in the host cell nucleus The PB2 residue at position 627, which in nearly all human and avian influenza strains is either a lysine or glutamate respectively, is the best characterised polymerase determinant of host range and virulence. It was first identified using single gene reassortment viruses showing that the avian glutamic acid variant had restricted replication in mammalian cells and that a change to lysine restored viability E. coliIn addition to residue 627, PB2 residues 701 and 714 are also implicated in host specific differences in polymerase efficiency, as revealed by laboratory studies of the adaptation of pathogenic avian strains to mice More generally, statistical analysis of multiple sequence alignments based on large-scale influenza virus genome sequencing of avian and human isolates Unfortunately, with the exception of the NLS-domain pb2 gene fragments using the ESPRIT method We identified two soluble protein constructs in the C-terminal region of PB2 from strain A/Victoria/3/1975(H3N2), via expression testing of random PB2 residues 538–676 form a compact, highly ordered domain with a novel fold as indicated by the lack of structural homologues found by DALI http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html), the interface buries respectively 820 and 925 Å2 of solvent accessible surface of the 627- and NLS-domains. Given the flexible nature of the linkage between the two domains, it remains to be seen whether this moderately strong interface is of biological significance. The complete 627-NLS double domain (residues 538–759) was observed to form a stable complex in vitro with human importin α1 by size exclusion chromatography (The 1.95 Å resolution structure of the double 627-NLS-domain (residues 539–753) shows that the two domains pack side by side forming a single module . The weltography  as shown7GTP 5′ extremity of host pre-mRNAs E. coli expressible domain from PB2 (538–693), termed the 627-domain after the most well-characterised host determinant site contained within it at position 627. The 156 amino acid 627-domain is located between the cap-binding and NLS-domains and contains six of the seventeen host species determining sites described within the PB2 subunit: N567D/E, I588A/V, T613V/A, K627E, T661A, T674A/S  was codon-optimised for expression in E. coli (Geneart) and cloned into a modified pET9a vector (Novagen) with N-terminal hexahistidine tag and TEV protease cleavage sequence (MGHHHHHHDYDIPTTENLYFQG) and C-terminal biotin acceptor peptide with linker (SNNGSGGGLNDIFEAQKIEWHE). Restriction site pairs 5′ AatII/AscI and 3′ NsiI/NotI sites flanked the pb2 gene enabling generation of internal gene fragments by sequential exonuclease III truncation reactions pb2 inserts were excised from 1% agarose gel prior to the second ligation step, then recovered by transforming Mach1 cells (Invitrogen). Purified DNA was prepared from about 35,000 pooled colonies and electroporated into E. coli strain BL21 AI (Invitrogen) containing the RIL plasmid (Stratagene). Colony blots of approximately 27,000 clones were hybridized with Alexa 488 streptavidin (Invitrogen) and screened for expression constructs by fluorimager The E. coli strain BL21 AI RIL in TB medium. Partially selenomethionine labelled 538–693 protein was produced using M9 medium supplemented with 50 mg/l of selenomethionine and 5 mg/l of methionine. Protein expression was induced by the addition of 0.2% w/v arabinose for 20 h at 25°C. Cells were resuspended and sonicated in lysis buffer . Proteins were purified on Ni2+ chelating sepharose column . Columns were intensively washed with 4 different buffers  and the proteins were eluted with elution buffer . The hexahistidine tag was removed with TEV protease overnight at 15°C leaving an additional N-terminal glycine residue. Proteins were dialyzed against 10 mM Tris-HCl pH 7.0, 200 mM NaCl and a second Ni2+ chelating sepharose column was used to remove unwanted material. Proteins were then purified by gel filtration on Superdex 75 column .Native proteins were expressed in Hexahistidine-tagged human importin α1  and the 627-NLS-domain  were purified and the affinity tags removed by TEV digestion. They were then mixed at a 1∶2 molar ratio (importin:PB2) overnight at 4°C. Proteins were concentrated to 5.5 mg/ml and purified using a Superdex S200 size exclusion column  in 10 mM Tris-HCl pH 7.0, 200 mM NaCl.Hanging drop vapour diffusion trials were performed at 20°C. Native, mutated and partially selenomethionine labelled 538–693 PB2 protein crystals were grown by mixing 1 µl of 2.4 mg/ml protein solution in 10 mM Tris-HCl pH 7.0 and 200 mM NaCl with 1 µl of 100 mM citric acid pH 4.0–7.0 and 1.4–1.6 M ammonium sulfate solution. Native 538–753 PB2 protein crystals were grown by mixing 1 µl of 5.5 mg/ml protein solution in 10 mM Tris-HCl pH 7.0 and 200 mM NaCl with equal volume of 100 mM Hepes pH 7.5 and K/Na Tartrate 1.2 M. Crystals were frozen in liquid nitrogen after soaking in crystallization solution supplemented with 30% glycerol.http://molprobity.biochem.duke.edu/).Crystals of the 627-domain (residues 538–693) with Lys627 (native and selenomethionine labelled), the 627-domain with Glu627 (native) and the 627-NLS-domain  were measured at the European Synchrotron Radiation Facility (ESRF). The co-ordinates and structure factors of the PB2 domains are available from the PDB with codes 2vy6 for the 627-NLS double domain (native data), 2vy7 for the 627-domain with Lys627 (selenomethionine labelled protein) and 2vy8 for the 627-domain with Glu627 (native data).Figure S1Comparison of the structure of the isolated 627-domain (pink) with that in the double 627-NLS-domain (red). The domain is in the same orientation as that of (4.30 MB TIF)Click here for additional data file.Figure S2Comparison of the structures of the NLS-domain. The NLS-domain (blue) from the double 627-NLS-domain has been superimposed on that in the complex with human importin α5 . Visible secondary structural elements are labelled as in the double 627-NLS-domain. Significant differences are observed at the two extremities of the domain. In particular residues 686-692 are helical in the importin complex but extended in the double domain.(2.34 MB TIF)Click here for additional data file.Figure S3Diagram showing hydrogen bonds at the interface between the 627- (red) and NLS- (cyan) domains in the 627-NLS-domain structure. The interface comprises 11 hydrogen bonds (dotted green with distances between acceptor and donor marked) including one salt bridge (Arg650 to Glu687). Several hydrophobic residues on helix α5 of the 627-domain  and from the NLS-domain (e.g. Ile710 and Ile726) are buried or partially buried at the interface. For clarity, these residues are not shown.(2.94 MB TIF)Click here for additional data file.Figure S4Sequence alignment of the 627-domain from human (H3N2) and avian (H5N1) strains of influenza A, influenza B and influenza C with superposed secondary structure. Residues with a red background are conserved in all strains; these are primariliy in the helices α5 and α6 in proximity to the residue 627, which is a lysine in all strains except avian. The purple boxes indicate differences between the human and avian influenza A strains. All the differences highlighted by Miotto et al. (0.52 MB TIF)Click here for additional data file.Figure S5Interaction assay between large C-terminal PB2 domain and human importin α1 by size exclusion chromatography. Fractions were analyzed by SDS-PAGE revealing a major peak comprising a complex of importin α1 and 627-NLS-domain (fractions 25 to 29) and a minor peak containing excess unbound 627-NLS-domain (fractions 31 to 36). The 627-NLS domain alone eluted in fractions 31 to 36 (not shown).(3.81 MB TIF)Click here for additional data file.


The Src homology 2 (SH2) domain is a sequence-specific phosphotyrosine-binding module present in many signaling molecules. In cytoplasmic tyrosine kinases, the SH2 domain is located N-terminally to the catalytic kinase domain (SH1) where it mediates cellular localization, substrate recruitment, and regulation of kinase activity. Initially, structural studies established a role of the SH2 domain stabilizing the inactive state of Src family members. However, biochemical characterization showed that the presence of the SH2 domain is frequently required for catalytic activity, suggesting a crucial function stabilizing the active state of many nonreceptor tyrosine kinases. Recently, the structure of the SH2–kinase domain of Fes revealed that the SH2 domain stabilizes the active kinase conformation by direct interactions with the regulatory helix αC. Stabilizing interactions between the SH2 and the kinase domains have also been observed in the structures of active Csk and Abl. Interestingly, mutations in the SH2 domain found in human disease can be explained by SH2 domain destabilization or incorrect positioning of the SH2. Here we summarize our understanding of mechanisms that lead to tyrosine kinase activation by direct interactions mediated by the SH2 domain and discuss how mutations in the SH2 domain trigger kinase inactivation. Protein kinase activity is tightly controlled in eukaryotic cells. To guarantee quick and specific propagation of cellular signals most kinases are locked in an inactive state that can be rapidly activated by interaction with regulatory elements such as interacting proteins or domains located outside the catalytic domain as well as post-translational modifications. In addition, selectivity of signaling is dramatically enhanced by localizing kinases to signaling complexes and through selective targeting of substrates via interactions with secondary substrate recruitment sites.Monosiga brevicollis, a primitive common ancestor of multicellular organisms [M. brevicollis the Csk ortholog (MbCSK) phosphorylates the Src ortholog MBSrc but the autoinhibitory role of this phosphorylation event, which is a hallmark of Src kinases inactivation, had not yet developed in Monosiga and evolved most likely much later within the metazoan lineage [SH2 domains represent the largest class of pTyr-selective recognition domains in the human proteome , 2. In crganisms •. It is rganisms . With thrganisms . Interes lineage •.The selectivity of SH2 domains for their pTyr substrates has been investigated using directed phosphopeptide library screening and a large number of biophysical and biochemical studies , 9, 10. In nonreceptor tyrosine kinases the conserved SH2–kinase unit is flanked by a number of additional domains which may comprise the N-terminal flanking region, a Src homology 3 (SH3) domain, a second SH2 domain, a sequence of PH–BTK–SH3 domains, a ‘Four-point-one, Ezrin, Radixin, Moesin’ (FERM) domain, or an F-BAR domain. In Abl kinases the C-terminus is extended containing an F-actin binding domain (FABD) .Figure 1The mechanism of autoinhibition of Src family members has been elucidated by a number of high-resolution X-ray crystallography studies , 14. TheThe crystal structure of the open active kinase conformation of Src confirmed that the SH2/SH3 domain is rotated away from the kinase domain but it retains its ‘clamp’ structure in which the SH3 domain remains bound to the SH2 linker •. The opMany aspects of Src inactivation are conserved in Abl kinases, which are also autoinhibited by intramolecular interactions of the SH2/SH3 domains. However, autoinhibition in Abl is phosphotyrosine independent , 21. TheZap70 and the closely related kinase Syk contain two N-terminal SH2 domains instead of the Src SH3–SH2 domain arrangement . In the In the structure of active Src the SH2 domain does not interact with the kinase domain and has no influence on the active state of this kinase. Indeed, deletion of the entire N-terminal region comprising both the SH3 and SH2 domains has little effect on Src kinase activity .The situation is very different in Csk where the deletion of the SH3 and SH2 domains results in a drastic reduction of enzymatic activity , 27, 28.SAXS analysis of active Brutons tyrosine kinase (Btk), also revealed close proximity of the SH2 domain to the N lobe of the kinase domain •. HoweveAlso the active state of Abl is stabilized by a tight contact of the SH2 domain with the upper kinase lobe . MutatioIn Fes/Fps insertion mutagenesis identified the SH2 domain as a strongly positive regulator of kinase activity. Dipeptide insertions in the N-terminal region of the v-Fps SH2 domain cause a severe loss of both kinase and transforming functions, suggesting that the conformation of the SH2 domain is important for the catalytic properties of the adjacent kinase domain , 39. TheThe SH2 domain is a key regulator of allosteric kinase regulation and may play a role in both the inactive as well as the active states of tyrosine kinases. It is therefore no surprise that mutations have been identified in patients that compromise SH2 function and as a consequence kinase activity. A recent survey found that the majority of the disease-causing missense mutations in SH2 domains affect functionally important amino acids (71%) either directly involved in ligand binding, or residues that mediate interactions with the catalytic domain. The remaining missense mutations are located distantly from the SH2 domain and presumably affect its stability . In Fes,in vitro and in vivo studies [One of the best studied examples of disease-causing mutations in SH2 domains is the Tec family member Btk. Mutations in the SH2 domain of this kinase cause X-linked agammaglobulinemia (XLA), a prototypical humoral immunodeficiency characterized by low levels of circulating B cells and a drastic reduction in serum concentrations of immunoglobulins •. So far studies , 47, 48. studies •. Since  studies , 32. A sStructural data and mutations found in human disease that lead to SH2 domain destabilization suggest a pivotal role of the SH2 domain in structural stability and in the stabilization of the active kinase conformation for a large number of nonreceptor tyrosine kinases. We hope that further structural and functional studies on multidomain constructs of tyrosine kinases will shed further light on the diverse mechanisms of kinase activation for this important group of enzymes.• of special interest•• of outstanding interestPapers of particular interest, published within the period of review, have been highlighted as:


Bovine respiratory syncytial virus (BRSV) and bovine corona virus (BCV) affects cattle worldwide. Our objective was to evaluate the effects of these infections on general health and reproduction parameters measurable on herd level and to explore the association between antibody status and some herd characteristics.We collected a pooled milk sample from five primiparous cows from 79 Swedish dairy herds in September 2006. The samples were analysed for immunoglobulin G antibodies to BCV and BRSV with indirect enzyme-linked immunosorbent assays. Herd level data from 1 September 2005 to 30 August 2006 were accessed retrospectively. The location of the herds was mapped using a geographical information system.Ten herds were antibody negative to both viruses and were compared with 69 herds positive to BCV or BRSV or both. Positive herds had a higher (P = 0.001) bulk tank milk somatic cell count (BMSCC) compared with negative herds. The medians for all other analyzed health and reproductive parameters were consistently in favour of the herds negative to both viruses although the differences were not statistically significant. A higher proportion (P = 0.01) of herds used professional technicians for artificial insemination, rather than farm personnel, amongst the 33 herds negative to BCV compared with the 46 positive herds.Our result shows that herds that were antibody positive to BCV and/or BRSV had a higher BMSCC compared with herds negative to BCV and BRSV. There was also tendency that negative herds had a better general herd health compared with positive. A higher proportion amongst the BCV negative herds used external technicians for AI instead of farm personnel, indicating that it is possible to avoid infection although having regular visits. Negative herds were located in close proximity to positive herds, indicating that local spread and airborne transmission between herds might not be of great importance and that herds can stay free from these infection transmission although virus is circulating in the area. Bovine corona virus (BCV) and bovine respiratory syncytial virus (BRSV) are two worldwide distributed viruses ,2. BCV cPrevious studies have shown that BRSV and BCV infections are effectively spread within the herd ,6,13. ItDespite the importance of these viruses and the fact that they are widely spread, little is known about transmission routes and management risk factors. Introduction of new animals and indirect spread via people and equipment are believed to be important and airborne transmission has been shown to occur for BRSV, at least under experimental conditions . StudiesWe used dairy herds from two neighbouring areas in central-eastern Sweden as study population. The areas approximately correspond to two veterinary districts. The herds were members of the local livestock association (Svenska Husdjur) and enrolled in the National Animal Disease Recording System (NADRS)  and the st to October 31st 2006. The geographical locations of the herds were mapped using a geographic information system .A convenience sample of 44 herds was taken from area 1 and of 35 herds from area 2, corresponding to 85% and 71% of the existing dairy herds in area 1 and 2, respectively. Sampling was performed by personnel from Svenska Husdjur. Herds were included if the farmer agreed to participate in the study, in the order they were visited by the personnel. The sampling period was from September 1A pooled milk sample from five home-bred primiparous cows was collected from each herd . We usedThe milk samples were analysed for presence of immunoglobulin G antibodies to BCV  and BRSVst 2005 to August 31st 2006 were obtained from NADRS and SOMRS. Disease incidences included treated cases of mastitis, any treatment for fertility problems, diseases in young stock and udder disease score (UDS) ≥6. UDS describes the udder health based on individual SCC corrected for milk yield, breed, number of parity and time in lactation [Herd level data on number of cows, AI-strategy, milk production, reproductive performance and health status from September 1actation . The scaThe main predictor was antibody status, defined as NEG for herds that were antibody negative to both BRSV and BCV, and POS for herds positive to either BRSV or BCV or both, based on the result from the pooled milk samples. Herd characteristics were also compared separately for BCV and BRSV. The models also included the effects of herd size, milk production, breed and AI-strategy according to table We used t-test to compare herd size and Fisher's exact test to compare breed and AI strategy between NEG and POS herds. Fisher's exact test was also used to compare the proportion of antibody positive herds between the areas.The outcome variables mastitis, treatment for fertility problems, number of calves dead between one day and three months and diseases in young stock had more than 10% missing values and were not analysed because the remaining sample of NEG herds was considered too small. The continuous outcome variables were analysed with a linear regression model whereas logistic regression models were used for the binomial outcome variables. The eight models are shown in Table The predictor variables were tested for collinearity by pair-wise Spearman rank correlation test; a test result of <0.6 was considered as negative i.e. no strong correlation. There were no collinearities found between the explanatory variables. No interactions were tested in the statistical model because of the small sample size. To evaluate the fit of the models with significant outcome we plotted the studentised residuals against the predicted observation. The statistical analyses were done using Stata Software .Number of herds that were antibody positive in the pooled milk samples was for BCV 46 58.2%) and for BRSV 62 (78.5%). Ten herds were classified as NEG and 69 herds as POS The proportion of antibody positive herds did not differ significantly between the two areas under study for both BCV and BRSV (table % and forThe NEG group had a lower mean BMSCC (P < 0.001) compared with the POS group. No other significant (P > 0.05) differences were found between NEG and POS herds in the remaining outcome variables. The median and interquartile range of each variable is presented in table Herd size, milk production, breed and AI strategy did not differ significantly between the POS and NEG herds. Of all sampled herds 72% used AIT; 80% in NEG group and 71% in the POS group. There was a significantly (P < 0.01) higher proportion using AIT (29/33) amongst the herds negative to BCV, compared with the proportion amongst BCV positive herds (28/46). For BRSV, 12 out of 17 negative herds used AIT; corresponding numbers for the positive herds was 45 out of 62. For the distribution of breed, 56% of all herds had mainly SRB, 4% SHB and 40% had mixed/other breeds.The only herd health parameter with statistically significant differences between POS and NEG herds was BMSCC for which POS herds had a higher BMSCC than NEG herds. Despite the difficulties to evaluate causality in cross-sectional studies it is notable that, although not statistically significant, the medians for all analysed variables were consistently in favour of the NEG herds. One explanation for the lower BMSCC in NEG herds may be that an outbreak of BCV or BRSV increases the susceptibility to clinical or subclinical udder infections. Another explanation can be that these herds are better managed with better hygienic routines also at cow level. The information on mastitis incidence had too many missing values to be analyzed. The incidence of high UDS pointed in the same direction as the BMSCC, although the difference was not statistically significant. A high BMSCC is of economic importance for the farmers because the dairy (Arla foods in this area) pays 1% extra for milk with a cell count of ≤300 000 cells/ml and 2% extra when SCC is ≤200 000/ml whereas a deduction of 4% is made for milk with a cell count of ≥401 000 cells/ml and 10% for ≥501 000 cells/ml. Studies have also shown that a high SCC is related to a loss in milk yield -30 and tA surprising result was that a higher proportion of herds used professional AI technicians rather than AI performed by farm personnel amongst the herds negative to BCV compared with the herds positive to BCV. This finding confirms the results the study by Bidokhti et al  where AISeveral negative herds were located in close proximity to positive herds, e.g. one of the NEG herds was located between two herds positive to both viruses, at a distance of 1.3 km and 1.9 km. This indicates that local spread and airborne transmission between herds are not of great importance for these infections and that a herd can stay negative although virus is circulating in the area.The cows included in the pooled milk sample were primiparous, which means that positive herds have experienced an infection during the last two years, approximately. Some herds in the POS group could have had the infection in the year before the investigated year, which may lead to smaller differences between NEG and POS herds. Herds in the NEG group, however, have been free from infection during the year under investigation.There is a risk of selection bias because the herds were selected by personnel from the local livestock association and only farmers willing to participate were included in the study. The antibody status of the herds was, however, not known at the time of sampling. The disease and reproduction data in the NADRS and SOMRS are reported by farmers, AI technicians and veterinarians. The reports from veterinarian and technicians should not differ between the study groups, but there could be differences in the willingness to report amongst the farmers causing information bias. It is possible that well managed herds with high biosecurity are also better in reporting, which may lead to higher incidences of diseases in NEG herds and thus an underestimate of the differences between the groups. BMSCC, UDS and milk yield are objective measurements from the dairies and from the milk-recording schemes and are not influenced by information bias.It would be of interest to further investigate the association between BCV/BRSV infections and herd health parameters on a larger scale. It would be beneficial to follow herds over time and compare uninfected herds with herds that have shown primary infection during the study period.In this study we found that herds that were antibody positive to BCV and/or BRSV had a higher BMSCC compared with herds negative to BCV and BRSV. There was a tendency that negative herds had a better general herd health compared with positive. A higher proportion amongst the BCV negative herds used external technicians for AI instead of farm personnel, indicating that it is possible to avoid infection although having regular visits. Negative herds were located geographically close to positive, indicating that local spread and airborne transmission between herds is not the major transmission routes for BCV and BRSV infections.The authors declare that they have no competing interests.AO carried out the statistical analysis and drafted the manuscript. UE assisted the statistical analysis. UE, MT and SA conceived the study. All authors participated in its design and coordination, were involved in revising the manuscript and read and approved the final manuscript.


The global IgC-like fold is maintained, but a piston-like rearrangement between BC and DE β-turns results in β-strand slippage. This substantial conformational change may represent a signaling intermediate. Our structure is the first example for the Ig fold of the increasingly recognized concept of “metamorphic proteins.”αβ T-cell receptors (TcRs) play a central role in cellular immune response. They are members of the Ig superfamily, with extracellular regions of the α and β chains each comprising a V-type domain and a C-type domain. We have determined the ectodomain structure of an αβ TcR, which recognizes the autoantigen myelin basic protein. The 2.0-Å-resolution structure reveals canonical main-chain conformations for the V The extracellular region of αβ TcRs comprises an α chain and a β chain, each consisting of Ig-like variable and constant domains. The TcR Cα domain is notable in differing from the classical IgC1 domain.α and Cβ domains, respectively, contribute to the stability and function of the TcR–CD3 signaling complex.et al. suggests that TcR triggering requires the TcR–CD3 interface to accommodate large changes in the relative positions of the TcR and CD3 components.CD8α domain, which may be of functional relevance.Our studies of TcRs specific for a variety of peptide–MHC complexes (pMHCs) have allowed us to sample TcR constant domain structures in a range of crystal lattice environments.The 1F1E8 TcR, cloned from a humanized transgenic mouse,R-value of 24% (Rfree = 20%) using data up to 2.0 Å resolution  and typifies foreign antigen-reactive TcRs in its pMHC-binding characteristics. Structural superpositions of 1F1E8hu on B7 and Ob1A12, respectively, give RMSDs between Cα equivalents of 1.2 Å and 0.9 Å for the Vα domain, and Cα equivalents of 1.0 Å and 0.9 Å for the Vβ domain. The buried surface between the 1F1E8hu Vα domain and the 1F1E8hu Vβ domain is 1486 Å2, which is very similar to those of other TcRs (1450 Å2 in B7 and 1498 Å2 in Ob1A12).Both the α variable domain and the β variable domain are similar to those in previously reported TcR structures, with the α and β complementarity-determining region (CDR) 1 and CDR2 loops adopting canonical main-chain conformations.Since Ob1A12 and 1F1E8hu recognize the same pMHC, we carried out a detailed comparison of the six CDR loops, which determine the binding characteristics of each TcR b. The nuβ) are superimposable on those of JM22, which has a 100% sequence homology for the constant domains [Protein Data Bank (PDB) code 1OGA],α) shows a large structural deviation from all previously reported structures, including the identical sequence in JM22  of the Cα domain, as in a conventional IgC domain, but strands “C,” “F,” and “G” (top sheet), although linked to the bottom sheet by a disulfide bond, pack only loosely against it, leading to surface exposure of some hydrophobic core residues10 helical turn, followed by an extended chain. The CD loop is different in shape, length, and, most interestingly, charge distribution (there is an extensive hydrophobic patch on the 1F1E8hu surface) due to the shift in residue usage , comparison using the program GAP  reveals an RMSD of 8.7 Å for equivalent residues. A very different set of residues now contributes to the interface with the Cβ domain, yet the hydrophobic character required for this interactionβ domain must be able to interface with at least two different surfaces: the pre-TcR α subunitα interface changes are accommodated by relatively small structural adjustments in the D strand of the Cβ domain (B-factors) in previous structures .The Cmobility . This flmobility a. In bot crystal a and is α domain may facilitate the “β-strand slippage” that we observed in the 1F1E8hu structure. Firstly, the number of amino acids between anchoring cysteine residues is only 50, as opposed to 60–65 in the classical IgC domain. Secondly, the otherwise conserved Trp15 residue upstream of the first cysteine is omitted in the TcR Cα domain. These deviations from the normal IgC domain produce a more loosely packed core, allowing for structural rearrangement within the domain.Several distinctive features of the TcR Caryl hydrocarbon receptor nuclear translocator) PAS-B (Per–Arnt–Sim B) can adopt an alternative β-sheet register.α domain is intrinsically able to adopt two conformational states raises the question as to what role this structural plasticity plays in TcR function. One very appealing theory is that flexibility and/or conformational changes in the TcR Cα domain could provide a mechanism to allow it to adapt to binding CD3 and hence play its role in TcR signaling.In line with our findings, other groups have reported similarly large conformational changes in a range of proteins: firstly, the crystal structure of a metastable dimeric form of the variable domain from the cell surface adhesion molecule CD2.α domain could play a vital role in TcR signal transduction. Kuhns and Davis have shown, by mutagenesis of different Cα loops, that the CD3 contact region is likely to be located within the DE loop.α domain residues 150–177) prevents TcR–CD3 interaction,α domain.et al. showed that, upon antigen recognition, the CD3 complex undergoes a conformational change to initiate T-cell signaling,α domain may be transmitted to CD3 and vice versa. Thus, our data on the structural plasticity of the Cα domain are of direct relevance to a number of hypotheses that have proposed that a structural rearrangement in the TcR–CD3 complex is necessary in early T-cell signaling.Experiments by other groups support the hypothesis that the Cα domain is also able to adopt two very different stable conformations, a fundamental property that is potentially central to TcR function.The ability of certain proteins to adopt multiple conformations is central to many biological processes, and the increasing number of examples of this phenomenon has recently been characterized by the term “metamorphic proteins.”α and Vβ regions were PCR amplified from cDNA derived from the mouse 1F1E8 T-cell hybridoma. These regions were then spliced by overlap extension to human TcR Cα and Cβ regions, including C-terminal extensions designed to assist the formation of an interchain disulfide bridge (as previously reported for the structure of JM22).Escherichia coli cells . The heterodimeric functional TcR (termed 1F1E8hu) was produced using previously described protocols.The TcR VPrior to crystallization, the refolded protein sample was subjected to quality control by means of SDS-PAGE, mass spectrometry, and dynamic light scattering. Because the correctly refolded protein contains an interchain disulfide bond, all these techniques were performed under reducing and nonreducing conditions by the addition or omission of β-mercaptoethanol from the sample. The predicted molecular mass of the correctly refolded protein is 54,423 Da. SDS-PAGE analysis of the purified sample clearly showed the presence of a single 53-kDa band, which, upon the addition of reducing agent, ran as two distinctly separate bands of ∼ 26 kDa and ∼ 29 kDa (with similar intensities), concurrent with the 1F1E8hu α and β chains, respectively. Mass spectrometry further verified the presence of correctly refolded heterodimeric TcR; the nonreduced sample had a molecular mass of 54,416 Da and, under reducing conditions, the presence of equimolar amounts of α chain  and β chain  with the expected molecular masses was confirmed. Dynamic light-scattering experiments were performed on the nonreduced sample to ensure that no aggregation of the protein sample had occurred; this technique, although not as sensitive, confirmed a homogeneous solution containing nonaggregated protein with a calculated mass very similar to that of 1F1E8hu.4)2 hydrogen citrate and 20% wt/vol polyethylene glycol 3000. The resulting crystals were plate-like with dimensions of 100 μm × 50 μm × 10 μm. The crystals were cryoprotected in mother liquor containing 20% glycerol and cooled to ∼ 100 K. X-ray data were collected at the European Synchrotron Radiation Facility  on beamline id14-EH1 and processed using the program package HKL2000.1BD2) as initial search modelCrystallization trials were carried out using previously reported robotic technologies and protocols.3MFF.Coordinates and structure factors have been deposited in the PDB under accession number


The SROs (SIMILAR TO RCD-ONE) are a group of plant-specific proteins which have important functions in stress adaptation and development. They contain the catalytic core of the poly(ADP-ribose) polymerase (PARP) domain and a C-terminal RST (RCD-SRO-TAF4) domain. In addition to these domains, several, but not all, SROs contain an N-terminal WWE domain.SROs are present in all analyzed land plants and sequence analysis differentiates between two structurally distinct groups; cryptogams and monocots possess only group I SROs whereas eudicots also contain group II. Group I SROs possess an N-terminal WWE domain (PS50918) but the WWE domain is lacking in group II SROs. Group I domain structure is widely represented in organisms as distant as humans . We propose a unified nomenclature for the SRO family. The SROs are able to interact with transcription factors through the C-terminal RST domain but themselves are generally not regulated at the transcriptional level. The most conserved feature of the SROs is the catalytic core of the poly(ADP-ribose) polymerase (PS51059) domain. However, bioinformatic analysis of the SRO PARP domain fold-structure and biochemical assays of AtRCD1 suggested that SROs do not possess ADP-ribosyl transferase activity.The SROs are a highly conserved family of plant specific proteins. Sequence analysis of the RST domain implicates a highly preserved protein structure in that region. This might have implications for functional conservation. We suggest that, despite the presence of the catalytic core of the PARP domain, the SROs do not possess ADP-ribosyl transferase activity. Nevertheless, the function of SROs is critical for plants and might be related to transcription factor regulation and complex formation. Arabidopsis thaliana it is a member of a small protein family consisting of RCD1 and five SROs (SIMILAR-TO-RCD-ONE). RCD1 was first identified as a plant gene able to complement the oxidative stress sensitive phenotype of a yeast strain deficient in the YAP1 transcription factor  was monitored by autoradiography. To verify the specificity of NAD binding, competition experiments were performed with excess of unlabeled NAD.NAD binding was investigated by covalent cross-linking of bound NAD by ultraviolet irradiation ,47. AfteE. coli. DnaK protein contains a nucleotide-binding domain explaining its ability to bind NAD.The NAD binding of the positive control, SAD-A, was visible as two bands in an autoradiogram Figure . The majThese results demonstrated that AtRCD1 does not bind NAD and thus should not have ART activity. To verify this, we tested possible poly(ADP-ribosyl) transferase activity of RCD1-GST and PCT-GST directly in a standard ART activity assay using recombinant HsPARP1 as a positive control. HsPARP1 exhibited automodification Figure  and 12D.β4 and β5  (0.8 mCi mmol-1)  were incubated in a 96-well plate on an ice bath. Unlabeled NAD in concentration 0.6 mM was added to the mixtures in competition experiment. The UV irradiation was performed for 15 min as described in  (0.8 mCi mmol-1). Recombinant HsPARP1 (Sigma) was used as a positive control. 3 μg of total histones  or DREB2A-GST were added as acceptor proteins. 1 mM unlabeled NAD was added in competition experiment. The reaction was stopped by addition of ice-cold trichloroacetic acid as described above. 5 μg BSA were added to the reaction mixture just before protein precipitation as a carrier.Samples corresponding to 200 nM of proteins were incubated for 20 min at 22°C in assay buffer (50 The proteins were separated on SDS-PAGE (12% or 4-15%) according to the protocol of . After pIn-gel digestion and sample preparation for mass spectrometry was performed as described . MALDI TPJ, KO, MW, JPV and JK designed research. PJ, KO, MW, JPV, TB, RAR and JS carried out research. PJ, KO, MW, JPV, TB, JS and JK analyzed the data. PJ, KO, MW and JK wrote the paper. All authors have read and approved the final manuscript.Neighbour-joining phylogenetic tree of Arabidopsis thaliana and Brassica SROs. The gene duplication leading to the AtRCD1/AtSRO1, AtSRO2/AtSRO3 and AtSRO4/AtSRO5 gene pairs in Arabidopsis is also present in Brassica. Individual protein-coding ESTs from Brassica can be assigned to AtRCD1 or AtSRO1, AtSRO2 or AtSRO3 or AtSRO5. No EST was identified for AtSRO4. This indicates that the gene duplication event leading to the gene pairs occurred early during the evolution of the Brassicaceae family before the split between the Arabidopsis and Brassica genera. Representative ESTs from Brassica napus, Brassica rapa and Brassica oleracea coding for SRO proteins were extracted via NCBI blast and the PARP domain was identified using Prosite. The PARP domains of the Brassica SROs were aligned with the PARP domains of the members of the A. thaliana and A. lyrata SRO protein families an unrooted Neighbour-joining tree was constructed using MEGA4.Click here for fileActive site alignments for all plant SROs. Alignments of the region around the active site catalytic triad of all SROs analyzed here. Alignments were hand-adjusted according to the positions of conserved folds in AtRCD1, AtPARP1 and AtPARP2 as in figure Click here for filePrimers used for analysis. All primer sequences used for qPCR anaylsis in the manuscript are listed in this file.Click here for file