Despite the biomedical relevance, very little is known about understanding this mechanism. The quest for ruling principles behind this curious phenomenon that could enable early prediction provided an impetus for our bioinformatics studies.3D domain swapping is an oligomerization process in which structural elements get exchanged between subunits. This mechanism grasped interest of many researchers due to its association with neurodegenerative diseases like Alzheimer's disease, spongiform encephalopathy A novel method, HIDE, has been developed to find non-domain-swapped homologues and to identify hinge from domain-swapped oligomers. Non-domain-swapped homologues were identified from the protein structural databank for majority of the domain-swapped entries and hinge boundaries could be recognised automatically by means of successive superposition techniques. Different sequence and structural features in domain-swapped proteins and related proteins have also been analysed.The HIDE algorithm was able to identify hinge region in 83% cases. Sequence and structural analyses of hinge and interfaces reveal amino acid preferences and specific conformations of residues at hinge regions, while comparing the domain-swapped and non-domain-swapped states. Interactions differ significantly between regular dimeric interfaces and interface formed at the site of domain-swapped examples. Such preferences of residues, conformations and interactions could be of predictive value. This mechanism was first named by Eisenberg and coworkers after their structure of diphtheria toxin in 1994 bonafide domain swapping’ in which a given protein molecule is known to exist in monomeric as well as in domain-swapped form e.g.: cyanovirin-N monomer e.g.: phosphoenol pyruvate mutase dimer There are different categories of domain-swapped oligomers present, depending on the existence of the monomeric form described by Eisenberg and coworkers Domain swapping is also associated with amyloid formation, which ultimately leads to neurodegenerative diseases like Alzheimer's disease, spongiform encephalopathy and human cystatin-C Domain-swapped oligomers have few structural characteristics which differentiate them from other side-by-side oligomers. First obvious characteristic is the swapped region or domain. ‘Swapped domain’ (SD) is a structural element which gets exchanged in 3D-domain-swapped oligomerization Figure 1Swapped domain interface and non-swapped domain interfaces Figure 13DSwap Hinge IDEntification), which begins by searching for protein structure/s of the same molecule and its homologous proteins present in non-domain-swapped form in PDB automatically. To check the domain-swapped or non-domain-swapped status of these homologous protein structures, we implemented a new strategy, which employs the least square plane. The non-domain-swapped homologues, thus identified, were further used to identify hinge region using successive rigid-body superposition and structural alignment approach. This algorithm not only identifies hinge boundaries, but also marks residues critical for the domain-swapping event as evident from high root mean square deviation (r.m.s.d.) values.In this work, we have developed an algorithm, HIDE .In the case of protein structural entries of dimers or higher order oligomers, all interchain residue distances were calculated and pairs with distance less than 7 Å were considered as interface residues. Plane of interface was obtained using least square plane fitting α coordinates of each residue to calculate the least-square fit plane. Suppose Here, we assumed that if a given protein complex is non-domain-swapped then the two protomers will lie completely on opposite sides of the plane. For this purpose, our algorithm uses CIf Then,th residue from domain-swapped protomeri = ii, yi, zi) = X,Y and Z coordinates respectively of ith residue Cα atom.“+” space and “−” space are two hypothetical 3D-spaces lying on both sides of least-square plane .α coordinates of one chain gives all positive or all negative output when substituted in the least square plane equation, then given protein is considered as ‘non-domain-swapped’ structure.If all CWe found that in rare instances, some residues crossed this plane for NDSI. In such cases, we found that the penetrating parts were discontinuous. Therefore, before assigning domain-swapped status to all entries, continuity of swapped domain is checked. In case of complexes with more than two chains, all possible pairs of chains are checked individually. If any pair of chains is found to be domain swapped, then the whole complex will be considered as domain-swapped oligomer.The presence and boundaries of the hinge region can be recognised easily on the graphics for most protein dimers that undergo swapping if structure of non-domain-swapped form is available. However, owing to the large number of domain-swapped entries and to avoid subjectivity for difficult examples, multiple superposition strategies were used as an approach to decide on hinge boundaries. To identify hinge region with good accuracy, first best non-domain-swapped homologue is recognised. Non-domain-swapped homologue of a domain-swapped structure is a closely related protein whose structure is available in Protein Data Bank and is observed not to undergo swapping. The best such homologue is decided based on percent sequence identity with query domain-swapped protein. This algorithm uses two consecutive structure-based superposition and structural alignments between domain-swapped structures and its best non-domain-swapped homologue to identify the hinge region j = Structurally equivalent amino acid residue in non-domain-swapped best homologueth and jth residuesThe region with continuous conformational change was considered as hinge in the domain-swapped protein and its equivalent region in the non-domain-swapped homologue.Sequence of hinge regions were extracted for protein entries in 3DSwap database. Their non-domain-swapped forms were searched using the HIDE algorithm (as mentioned below in detail). The equivalent region of hinge from non-domain-swapped homologues was extracted and amino acid propensities were calculated for α-helix, β-sheets, loops and hinge regions using following statistical parameters.The amino acid propensity at the hinge regions were compared using correlation coefficient against those in other secondary structures.The ratio of fraction of hinge and loop contributed were further used to calculate normalized amino acid propensities of the hinge residues. conformational values of the residues at the hinge region were examined for both domain-swapped oligomer and non-domain-swapped homologues. Srinivasan, et al. 1991 studied conformations of short loops 2 upon oligomerization.Residues were considered as being in the interface only if their solvent accessibility decreases by 1 ÅFollowing structural features were calculated for each entry at the interface:Disulphide bonds. It is a covalent bond between two cysteine residues, if any i.e. if their Cα atoms are within 6.5 Å.Hydrophobic interactions. These are interactions between hydrophobic amino acids  if their Cβ atoms are within 7 Å.Short contacts . These are contacts between residues for whose atoms D (function)<0.Where, r = distance between two atoms, R = Sum of Van der Waals radii of those atoms.Structural Segmentation . Interface residues separated by more than five residues were allocated to different segments.Salt bridge. A salt bridge is formed if the side-chain nitrogen and oxygen atoms of two oppositely charged residues are within 4 Å distance.Protrusion Index . Protrusion index for each interface residue was calculated using algorithm proposed by Pintar and coworkers.Hydrogen bonds . Hydrogen bonds were identified using HBOND program implemented in JOY Van der Waals interactions .R = Van der Waals radius for an atomE = Total Van der Waals energyith atom and jth atom.r = distance between the p.Planarity . Planarity of each interface surface was measured using least square plane. This plane was calculated as explained in first step of HIDE algorithm. Then RMSD of each interface atom from this plane was calculated. Minor surfaces, having number of interface residues less than four, were ignored.Interface surface areas of domain swapped interface (DSI) and non-domain swapped interface (NDSI) were calculated using PSA First, all the values for each structural feature were normalized by respective interface surface area. Then paired t-test was performed using R-package to check whether there is any significant difference between the means of the two datasets to be compared.The performance of the HIDE algorithm , S2, S3 We observed amino acid propensities for the hinge region in all cases and compared it with propensities of secondary structures . We founAmino acids were sorted by their normalized amino acid propensity values and all amino acids obtained the same rank as in loops and in the hinge region. However, correlation between them was not high. We next assumed hinge regions could be special kind of loops and certain amino acids could favour hinge regions over the other. Therefore, we examined the difference in propensities of amino acids in hinge regions and in loops  distribution of residues in the hinge region of domain-swapped oligomer and its structural equivalent portion from monomeric form was observed (as detailed in Methods) for all amino acid residues  and for Most of the residues lie in the first and fourth quadrants and few in other two quadrants in domain-swapped examples, whereas a fair portion of monomeric hinge residues, 7.7% and 6.7% lie in the second and third quadrant for monomeric hinge residues, respectively; only observations of 2.2% and 1.1% of residues from oligomeric hinges are observed in second and third quadrants each. Interestingly, most of the hinge regions of oligomeric domain-swapped form were in extended S-shaped form , e.g. B1 domain in protein L.  plots of oligomeric and monomeric hinge suggest the same. Interestingly, such S-shaped loops were also observed earlier by Ramakrishnan et al. α-C′ angle was 104°). Same residue in the domain-swapped dimer (PDB ID: 1G96) is observed in the first quadrant  in the allowed region and with standard 110° as N-Cα-C′ angle. Such observations suggest that the domain-swapped form retains lesser stereochemical strain at residues in the hinge region. distribution for non-glycine residues of hinge regions from domain-swapped and non-domain-swapped regions remained similar. However, none of the non-glycine residues from oligomeric hinge residues were found in second and third quadrant. But small portion of non-glycine residues i.e. 3.9% and 1.9% from non-domain-swapped form were lying in second and third quadrant, respectively. From  distribution of non-glycine residues in hinge region in both states, it is clear that higher number of residues in the loop region, equivalent to the hinge region but in the non-domain swapped form, occupy partially allowed region . Indeed,We performed analysis of  conformations of adjacent residues in the hinge region and checked their preferences for each quadrant. In domain-swapped proteins, approximately 63% of  conformations of two consecutive hinge residues lie in the first quadrant, while 31% of that of non-domain-swapped forms lie in the first quadrant  and 7. Sposition .For the study of interfaces in domain-swapped structures, all residues involved in the formation of domain-swapped interface  and domain non-swapped interfaces (NDSI) were chosen for the analysis. Most of the structural features used in this study were shown to be important by Thornton and coworkers 2 surface area are placed, along with p-value obtained from paired t-test.All structural features significantly differ in both the interfaces see , except Out of 171 proteins, disulfide bonds were present in only ten proteins at DSI, and in five proteins at NDSI. More than 90% of proteins do not possess any disulfide bond in either of the interfaces.2 in DSI, while one salt-bridge per 1000 Å2 in NDSI. Approximately, four times more salt-bridges are observed in DSI than in NDSI. In 36% and 70% of proteins, salt bridges were absent in DSI and NDSI, respectively. Number of salt bridges correlated well with interface surface area in case of DSI, but no clear correlation found for NDSI. DSI is composed of 24% of charged amino acids, while 21% of NDSI are constituted by charged residues.In the dataset of 171 proteins, number of salt bridges varies from 0–8 in both DSI and NDSI. On an average, there were four salt bridges per 1000 Å2 in DSI, while the average hydrophobic interactions are only 0.7 per 100 Å2 in NDSI. It was quite surprising that DSI composed of 51% hydrophobic residues and NDSI composed of 54% hydrophobic residues.Number of hydrophobic interactions varied from 0–48 in DSI and 0–37 in NDSI. On an average, there were three hydrophobic interactions per 100 Å2 and 6.67 structural segments per 1000 Å2 for NDSIs.We observed that DSIs were more segmented compared to NDSIs. There were on average 8.81 segments in DSI per 1000 ÅIt was found dramatic differences in the protrusion index for DSI and NDSI. The average protrusion index per residue was 1.30 for residues in DSI and 0.27 for residues in NDSI. This suggests side-chains of residues in DSI were approximately five times more protruded outside compared to residues in NDSI.2 in DSI, while approximately 1 per 1000 Å2 in NDSI.There were 3.5 times more short contacts in DSI compared to NDSI. On an average, there were 3.5 short contacts per 1000 Å2 in NDSI.In DSI, Van der Waals interactions were more than NDSI. On an average, there were 6.71 interactions per 1 A sq in DSI and 2.16 per 1 Å2 while one per 100 Å2 sq was observed in NDSI.DSI contains three times more hydrogen bonds than that of NDSI. There were three hydrogen bonds per 100 ÅSurprisingly, we found that NDSI was more planar compared to DSI. But we found that numbers of residues in NDSI were very less compared to DSI. Around 45.6% cases were having less than or equal to three interface residues. While only three cases were found to contain less than three residues in DSI. On an average, there were 12 residues in NDSI, while 43 residues in DSI. This might be one reason of having very less RMSD i.e. more planarity for NDSI.We have developed a structure-based algorithm that uses plane analytical geometry, to identify domain-swapped oligomers and non-domain-swapped proteins automatically with high sensitivity and good specificity. Using the best non-domain-swapped homologue, residues with large conformational variations can be identified easily. Based on this criterion, it is possible to prioritize residues critical for domain swapping for further studies. This algorithm can also suggest pairs of domain-swapped and non-domain-swapped homologous structures which can be used for further comparative structural and sequence analysis which, in turn, may provide further insight into the mechanism of domain swapping.A clear understanding of amino acid composition of swapped interface and preferred amino acids is important for domain swapping mechanism Clear difference between amino acid propensities were observed between their preference to be in the loop and hinge regions. Interestingly, such trends were found to be in agreement with experimental data derived from independent approaches. The higher rank amino acids were very favourable to be in hinge region and several mutational studies (such as random mutations) showed that if lower ranked amino acid were substituted by higher rank amino acids, it leads to domain swapping except for proline. Proline favours extended form of loop which is favourable to form domain-swapped oligomers A  conformational analysis clearly indicated that oligomeric hinge regions were sterically more optimized than that monomeric hinge. Though glycine residues were also present in oligomeric hinge, they preferred to stay in first and second quadrant, unlike glycine residues in the monomeric hinge .Around 86% of non-glycine residues were lying in first quadrant confirming  that oliFrom experimental studies Intramolecular interactions were significantly different in DSI compared to NDSI, except for the disulfide bonds. Both the interfaces had very few disulfide bonds and most of the entries lacked disulfide bonds in both interfaces. One probable reason for this might be the partial unfolding step. In domain swapping mechanism, the first partial unfolding is required in order to form domain-swapped structure. Partial unfolding to form open monomer starts at DSI. If DSI contains any disulfide bonds, then this partial unfolding will require high energy to break disulfide bonds and subsequently increasing energy barrier between monomeric and oligomeric form.DSI is present in monomeric form as well as in oligomeric form. Hence, this interface is evolved to optimize energy of the structure by selecting favourable interactions. Hence all favourable interactions viz. Electrostatic interactions, salt bridges, hydrogen bonds, hydrophobic interactions and Van der Waals interactions, short contacts were significantly more in DSI than that in NDSI.One more possible reason for hydrophobic interactions to be more in DSI might be size of DSI. As average surface area of DSI is approximately two times more than that of NDSI, it contains larger core-interface region, where hydrophobic interactions are more prevalent.It is quite obvious to have high protrusion index values for residues in DSI compared to NDSI after finding that inter-chain hydrophobic interactions and salt bridges were high in number in DSI. To form salt bridges or to make hydrophobic interactions, side chains have to adopt rotameric form which permits side chain atoms to protrude out towards the interacting chain. These structural features analysis might help us to predict DSI and possible NDSI from given monomeric form alone.Sequence and structural analyses have been performed on the hinge and interface of domain-swapped entries. In the process, we have developed a structural algorithm, HIDE, which can identify non-domain swapped homologues of given protein and the boundaries of hinge region. This tool can be further useful to study evolutionary relationships as well. This algorithm also tells about most important residue for domain-swapping event which can be a potential drug target in proteins involved in amyloid formation and related to diseases like Alzheimer's disease.The propensity and relative order of preference of amino acids to be in hinge regions during domain swapping may be very helpful in predicting hinge from protein sequence. This order can also provide clues as to which substitution mutation in hinge region of given protein possibly induces domain swapping in it.Structural characterization of DSI and NDSI can provide clues to understand quasi-domain swapping i.e. why this mechanism is observed in some proteins and not in the others. The thorough analysis of hinge residues will be useful for researchers to understand domain swapping mechanism. All these observations can be used further to find potential candidates for domain swapping from monomeric structure alone.Figure S1Hinge length distribution in our dataset.(TIF)Click here for additional data file.Figure S2Structural alignment of oligomeric hinge regions with three flanking residues.(TIF)Click here for additional data file.Figure S3Structural alignment of monomeric hinge regions with three flanking residues.(TIF)Click here for additional data file.Table S1Dataset used to query HIDE algorithm.(XLS)Click here for additional data file.Table S2Performance of HIDE server for each query.(XLS)Click here for additional data file.Table S3Number of interactions in DSI and NDSI per 1 Å2 for non-redundant 3DSwap database.(XLS)Click here for additional data file.Table S4Dataset used for structural analysis.(XLS)Click here for additional data file.Table S5Additional cases supporting derived order of amino-acid preference in hinge region.(DOC)Click here for additional data file.


Phasing of this 11-protein subunit complex was carried out  Saccharomyces cerevisiae 420 kDa exosome complex bound to an RNA substrate in the pre-cleavage state of a hydrolytic reaction has been determined. Here, the crystallographic steps towards the structural elucidation, which was carried out by molecular replacement, are presented.The RNA exosome is an evolutionarily conserved multi-protein complex involved in the 3′ degradation of a variety of RNA transcripts. In the nucleus, the exosome participates in the maturation of structured RNAs, in the surveillance of pre-mRNAs and in the decay of a variety of noncoding transcripts. In the cytoplasm, the exosome degrades mRNAs in constitutive and regulated turnover pathways. Several structures of subcomplexes of eukaryotic exosomes or related prokaryotic exosome-like complexes are known, but how the complete assembly is organized to fulfil processive RNA degradation has been unclear. An atomic snapshot of a  We then reconstituted the complex in vitro using protocols similar to those previously reported  from a peak that eluted at higher salt concentrations  and contained RNA.To crystallize a nuclease complexed to an RNA substrate, the enzyme has to be inactivated by mutations that abolish catalytic activity without impairing substrate binding. These nuclease mutants often carry endogenous nucleic acids from the expression in bacteria throughout purification .Screening Exo-10 sample preparations with different RNA substrates failed to yield crystals. There are various reasons why a chemically homogeneous sample may not crystallize. Firstly, the sample might dissociate or become unstable. For example, 4.C-term–RNA grew with a needle-like morphology in an optimized condition consisting of 11.4–12.2%(w/v) PEG 3350, 0.27 M NaBr, 0.10–0.15 M MES pH 6.5 . To obtain reflections to 2.8 Å resolution, it was crucial to identify a suitable cryoprotectant for this crystal. A screen of several cryogenic conditions identified a mixture of a higher PEG concentration [25%(w/v)] and small amounts of glycerol [10%(v/v)] as the best cryoprotectant. As has previously been suggested, it is possible that high concentrations of glycerol create disorder upon diffusion through solvent channels and that a better solution is a mixture of small cryogenic molecules (which can immediately diffuse through solvent channels) and a large cryogenic molecule (which cannot easily enter solvent channels) . All data were processed using XDS and were merged and scaled in XSCALE  and the yeast C-terminal Rrp40 . Rrp44 has an RNase II-like exoribonuclease region  and a separate N-terminal PIN domain . Solution searches using these complexes as a whole failed, including a search for the Exo-9 barrel in the absence of the cap proteins . We subsequently divided the complexes into RNase PH pairs  and three separate cap proteins  and subdivided Rrp44 into the PIN domain and the RNase II-like region .The entire molecular-replacement process was performed with in Fig. 3b. Solutns Fig. 3a. We suon Fig. 3b.Phaser is cumulative, the overall LLG is largely negative owing to the contribution of the last two search models, Rrp4 and Csl4. When dealing with multi-subunit complexes, a negative total LLG does not unequivocally imply an incorrect solution. The statistical values output for each subunit search, as shown in Table 1The search order in the molecular replacement of this multi-protein complex proved to be important for successful phase determination by MR. We observed that subunits that are more divergent or have a small size relative to the overall complex are more easily found if some fraction of the complex has already been properly placed. Accordingly, the search order was devised to start with the evolutionarily less divergent subunits and to end with the more variable domains. Table 1Sulfolobus solfataricus Rrp41 and Rrp42 subunits share 17.2 and 18.4% sequence identity with yeast Mtr3 and Rrp42, respectively, which are lower values than the corresponding human Mtr3 and Rrp42 proteins  and, unlike with the human search model, the electron-density maps improved upon refinement. At this point, the refined model comprised about 60% of the total number of atoms present in the asymmetric unit, with an Rfree of 48.6% at 3.5 Å resolution  depending on the quality of the density fit that was achieved with the model used. In the first round of refinement, all secondary structures  were divided into separate rigid groups and refined at low resolution to allow correct angular positioning of the helices relative to other secondary structures in the domain and to more accurately place β-barrels in their correct orientation. However, this approach works if the model is already rather close to the correct position and orientation. A domain of the Rrp44 RNase II-like region, CSD1, illustrates this situation. The RNase II-like region of Rrp44 as a whole was placed by Phaser, mainly via positioning of the large catalytic domain. Inspection of the electron-density map showed that the CSD1 domain, a small β-barrel domain in the RNase II-like region, was incorrectly positioned. The map showed a nearby patch of positive density with recognizable features  about 12 Å away, suggesting that the CSD1 domain might assume a different conformation in the whole complex to that previously reported. Low-resolution rigid-body refinement alone could not position this domain in the density. Manual placement of the CSD1 domain into the unaccounted-for density was necessary in order for rigid-body refinement to converge and therefore to improve the density at CSD1. This step decreased the Rfree value by 0.3%.Since MR solution searches with the S1/KH domains were not successful, we started to manually position and build the missing subunits into the positive densities that became apparent in the map at this stage. For each round of model building, we restricted the refinement only to rigid body, group es Fig. 4a about b). Upon manual positioning and a round of refinement, the electron density for this domain improved considerably and the overall Rfree value decreased by 1.1%. Other domains of the cap proteins were more difficult to identify. In particular, the human N-terminal domain of Csl4 did not match the electron density in terms of size and β-sheet conformation. In this case, manual building of the backbone and sequence assignment was only possible at later stages of the model-building and refinement cycles, when most of the complex had been modelled with the correct sequences from yeast and several loops had been built. The final Csl4 N-terminal model is shown in Fig. 4c). The root-mean-square deviation (r.m.s.d.) values  between the human and the final yeast N-terminal model of Csl4 is 9.84 Å over 214 atoms, rationalizing why molecular replacement with this domain had not been successful.In the case of the cap proteins, we could discern electron-density features on top of the existing six RNase PH subunits where the cap proteins are expected to reside. Molecular-replacement searches had correctly found only the C-terminal S1/KH region of Rrp40; its N-terminal β-barrel and the entire Csl4 and Rrp4 chains were still missing. To guide the identification and positioning of the cap proteins, we superposed the known human Exo-9 structure onto our current model. This procedure allowed us, for example, to identify the density for a β-barrel on top of three RNase PH subunits, Rrp43-Rrp46-Mtr3, as the probable C-terminal domain of Csl4 Fig. 4b. Upon 7.d). Firstly, we built the backbone of two helices and a β-hairpin. After a round of positional and individual B-factor refinements, the side-chain densities became more prominent. To unambiguously assign the sequence register, we made use of information from secondary-structure predictions as well as chemical considerations based on the interacting residues on Exo-9. Eventually, when the model reached an Rfree of 29.3% (2.8 Å resolution), additional electron density became apparent near the cap proteins. This density had strong peaks at regular distances typical of phosphate moieties in a nucleic acid backbone. We placed some phosphate ions into the density and, after a round of positional refinement, positive densities corresponding to riboses and bases became apparent. The final RNA duplex model and its density are shown in Fig. 4e). The completed structure is presented in Fig. 5C-term–RNA has an Rfree of 22.4% and an R factor of 18.3% at 2.8 Å resolution, with good stereochemistry. Overall, the model includes 26 565 non-H atoms of both protein and nucleic acid molecules refined against 99 101 unique reflections. The PDB code for this structure is 4ifd.The crystals that we obtained also contained the C-terminal region of Rrp6 and an extended RNA molecule. Secondary-structure prediction of the Rrp6 C-terminal region suggested the presence of two α-helices, but the overall fold of this region was unclear. This was therefore the last protein density to be built and assigned Fig. 4d. First8.a). It has been noted that MR search models with r.m.s.d.s on Cα atoms higher than 2.5 Å can cause problems in successfully obtaining a solution . This corroborated the largely negative LLG contribution in the molecular-replacement solution shown in Table 1Obtaining a molecular-replacement solution of this complex assembly depended not only on the quality of the processed data and resolution, but also on the quality and the tertiary-structure similarity between the search and the final models. In the case of the Mtr3-Rrp42 search model, for example, refinement and electron-density map improvement was possible when using archaeal proteins as search models, even though they are evolutionarily more distant from the yeast than the human proteins. However, the archaeal structure was at significantly higher resolution and was therefore more accurate and, with hindsight, was also more similar at the tertiary-structure level. The r.m.s.d. value between the yeast and human Mtr3-Rrp42 was 2.76 Å over 1580 atoms, whereas the r.m.s.d. with the archaeal proteins was 2.18 Å over 1494 atoms Fig. 6a. It haC-term–RNA complex with subcomplexes. The cap proteins, for example, differ in their relative domain positioning as well as in their fold . On the other side of the complex, the nuclease shows the most striking conformational differences. Comparison of Rrp44 in the apo form of the Rrp41–Rrp45–Rrp44 subcomplex (search model) and in the RNA-bound form of the exosome structure that we have determined results in an r.m.s.d. value of 9.20 Å over 4759 atoms. When comparing the individual Rrp44 parts, the r.m.s.d. for the N-terminal PIN domain is 0.51 Å and that for the RNase II-like region is 2.04 Å . Using these separate protein regions as two independent search models (low r.m.s.d.) yielded immediate and correct MR solutions whereas the full-length protein (much higher r.m.s.d.) did not.Changes in conformation can also hamper MR searches, as this would also result in an overall increase in r.m.s.d. values. Several exosome proteins undergo significant conformational changes when comparing the Exo-10–Rrp6ld Fig. 6b. On th Å Fig. 6c. UsingB-factor refinements were performed using much lower resolution data to 3.5 Å. Higher resolution reflections were achieved later, after further extensive optimization of the crystallization and cryogen conditions, and with the availability of large number of crystals for data screening. A carefully processed data set satisfying both the completeness and resolution criteria was necessary to identify the predicted unstructured C-terminal tail of Rrp6 and to build the RNA. Finding good search models and accounting for the unknown conformational variability are also important factors to take into consideration, which, in combination with all the above factors, helped to find the path to successful molecular replacement.The structure determination of this complex posed challenging steps from reconstitution through data collection to structure determination by molecular replacement. Once stability/homogeneity of the sample and crystallization had been achieved, several issues arose: screening for a wide range of cryoprotectants, overcoming the parallax effect during crystal beam centring and dealing with very rapid resolution decay owing to radiation damage were some of the challenges that were encountered. A considerable amount of time was spent collecting and analyzing images, and high-resolution data as reported were only available at the latter stages of model building. All initial molecular-replacement trials, rigid-body and group 11-subunit exosome in complex with RNA, 4ifdPDB reference:


Neutrophils represent the body′s primary line of defense against invading pathogens. They most rapidly reach the site of injury or infection, liberate antimicrobial proteins, proteases and produce reactive oxygen species. Prolonged or excessive liberation of these very effective and toxic substances could intensify the inflammatory process and enhance tissue damage in many diseases, such as allergies, infections and rheumatoid arthritis. Pterostilbene belongs to stilbenoids, structural analogues of resveratrol, which act as natural protective agents in defending the plant against viral and microbial attack. It possesses anticancerous, antidiabetic and anti-inflammatory properties.The study provides new information on the effect of pterostilbene [0.01–100 µmol/l] on superoxide generation in and myeloperoxidase (MPO) release from azurophil granules of isolated human neutrophils. PMA [1µmol/l], which activates NADPH-oxidase via protein kinase C, was used for stimulation of neutrophils Unstimulated cells showed neither superoxide generation nor myelopereoxidase release after preincubation with the drug studied. Pterostilbene dose dependently decreased superoxide generation in and MPO release from stimulated human neutrophils, however a significant decrease was recorded only in the concentration 100 µmol/l. The effect of pterostilbene was more pronounced on superoxide generation in comparison to MPO release. Our results suggest that the effect of pterostilbene may prove beneficial in controlling inflammation. In the process of oxidative burst, neutrophils produce a variety of cytotoxic products, e.g. superoxide, hydrogen peroxide and hypochloric acid .i.e. the hydroxyl group required in the 4′position, as well as a trans-configuration of the ethylene functional group in the stilbene skeleton  yet it also interferes with ROS already generated. In this way, pterostilbene may increase the protection of host tissues and prove beneficial in controlling inflammation.


R) genes cloned from different crops. We analyzed protein sequences of seventy R genes of various crops in which twenty six proteins were found to have zinc finger domains along with nucleotide binding sites - leucine rice repeats (NBS-LRR) domains. We identified thirty four zinc finger domains in the R proteins of nine crops and were grouped into 19 types of zinc fingers. The size of individual zinc finger domain within the R genes varied from 11 to 84 amino acids, whereas the size of proteins containing these domains varied from 263 to 1305 amino acids. The biophysical analysis revealed that molecular weight of Pi54 zinc finger was lowest whereas the highest one was found in rice Pib zinc finger named as Transposes Transcription Factor (TTF). The instability (R2 = 0.95) and the aliphatic (R2 = 0.94) indices profile of zinc finger domains follows the polynomial distribution pattern. The pairwise identity analysis showed that the Lin11, Isl-1 & Mec-3 (LIM) zinc finger domain of rice blast resistance protein pi21 have 12.3% similarity with the nuclear transcription factor, X-box binding-like 1 (NFX) type zinc finger domain of Pi54 protein. For the first time, we reported that Pi54 (Pi-kh-Tetep), a rice blast resistance (R) protein have a small zinc finger domain of NFX type located on the C-terminal in between NBS and LRR domains of the R-protein. Compositional analysis depicted by the helical wheel diagram revealed the presence of a hydrophobic region within this domain which might help in exposing the LRR region for a possible R-Avr interaction. This domain is unique among all other cloned plant disease resistance genes and might play an important role in broad-spectrum nature of rice blast resistance gene Pi54.A meta-analysis was performed to understand the role of zinc finger domains in proteins of resistance ( The zinc finger proteins are a super family of proteins involved in numerous activities of plant growth and development and are also known to regulate resistance mechanism for various biotic and abiotic stresses The NBS-LRRs are most prevalent class of R- proteins. These are basically three types such as TIR-NBS-LRR (TNL) and CC-NBS-LRR (CNL) and mixed type having either Toll/interleukin-1 receptor-like domain (TIR) or Coiled coil (CC ) domains or both in one protein Magnaporthe oryzae is one of the most important biotic stresses of rice resulting into huge yield loss every year hPi-k from indica rice cultivar, Tetep showing resistance to different strains of M. oryzaePi-bPi-ta genes Pi54, after it was fine mapped at a slightly different location from the Pi-k cluster of the genes M. oryzae in India Pi54 single functional blast resistant gene show activation of a complex defense mechanism after pathogen inoculation Pi54 gene has also been achieved from wild species of rice O. rhizomatisPi54 protein contains an NBS-LRR domain in addition to a small zinc finger domain Rice blast caused by R genes are also categorized into separate categories on the basis of status and position of zinc finger domains. These domains are present either at N– terminal or C– terminal of the proteins encoded by R genes and along with NBS-LRR domain, play a crucial role in regulating expression of the genes involved in plant resistance The R genes and (iv) comparative analysis of zinc finger proteins in relation to Pi54 protein.The objectives of present study were (i) to analyze the presence of zinc finger domains in all the cloned plant disease resistance genes, (ii) to determine the probable structure of zinc finger domain of blast resistance protein Pi54 (iii) computational analysis of biophysical properties of zinc finger domains in the proteins of cloned Pi54 was downloaded and analyzed using various bioinformatics tools along with a careful manual inspection. A small (11AA) zinc finger motif of nuclear transcription factor, X-box binding-like 1 (NFX) type was identified between the positions 253–263 amino acids in this protein. This domain is C– terminal in nature and integrated within LRR. Earlier hPi-k (Pi54) gene responsible for the expression of this protein was found to be pathogen inducible in nature The amino acid sequence of protein encoded by rice blast resistance gene The nonpolar amino acids were characterized for having non polar atoms (only carbon and hydrogen) in their side chains. They include Glycine , Ala , Val , Leu , Ile , Pro , and Met . Presence of such residues makes domains more hydrophobic in nature. The hydrophobic amino acid residues can increase the rigidity and hydrophobicity of proteins M. oryzaeOne Pro residue was also identified in the zinc finger domain of Pi54 protein. This residue contains a non-polar, uncharged R group . The ProM. oryzae pathogen under varied climatic conditions. The amino acid residue Serine is known as the best residues for interacting with the water molecules surrounding protein due to its hydrophilic nature The polar nature of the side chain means that these amino acids are ready to interact with water (hydrophilic) and can form hydrogen bonds E. coli, Psuedomonas aeruginosa, and Rhodobactor sphaeroides contained 1.03%–1.13% cysteine. The cysteine contents of proteins of different species may be as low as 0.4%–0.5% in Archea. The extreme halophil Haloarcula marismortui and the thermophil Thermus aquaticus were reported to have the lowest 0.49% and 0.41%, respectively cysteine among the investigated species. The C-(X)2-C motifs present at position 253–260 of Pi54 protein are important domains of metal binding proteins The side group of another polar, uncharged residue Cysteine present in zinc finger domain of Pi54 protein contains a sulfur atom . The sulM. oryzae is necessary for R gene interactions M. oryzae pathosystem. The charged amino acid residues are less labile and also retain the hydrogen bonding capacity. The charged residues may be involved in location of ion pairs within molecular structures which also appears to be important in determining protein stability The negatively charged, acidic R group containing Glutamic acid and positively charged, basic amino acid, Histidine were also been identified in zinc finger domain of the Pi54 protein . The polThe Helical wheel diagram gives a view of a helix from a protein sequence looking down the axis of the helix. It is useful for highlighting the amphipathicity and other properties of residues around a helix. The hydrophobic region of Pi54-ZnF domain which constitutes of two Leucine residues, one Proline and one Glycine has been represented by a helical wheel diagram . The helR genes cloned from different crops to delineate their zinc finger domain  similar to PMZ of Tomato R protein Cf-9. This zinc finger domain is also present in RCY1 protein of barley. The LIM zinc finger deduced in Pi21 protein of rice is smallest protein amongst all the proteins of cloned genes in plants, whereas the zinc finger domain deduced in Pi54 protein was found to be the smallest amongst all plant proteins cloned till date. The Pib gene encoded protein of rice was found to have largest zinc finger domain TTF (83AA) among all the cloned R genes of rice though TTF of barley R gene Rpg1 is largest (84 AA) among all the crops  which was deduced in Cf-9 protein and is similar to the rice PMZ domain of blast resistance gene f tomato , S3. SimR genes  to 10.66 (PMZ of RCY1). The isoelectric point (pI) is the pH at which the surface of protein is covered with charge but net charge of protein is zero. The pI of Pi54 zinc finger domain was found to be 5.24   . The insPi54 zinc finger domain ZnF_NFX was recorded as 70.91. This value indicates that the NFX domain of Pi54 is thermo-stable as well as flexible in nature. The high aliphatic index 122.58 was recorded for CDGSH zinc finger domain of Gro 1-4 gene of potato. The lower thermal stability of ZZ zinc finger domain of Lr10 gene of wheat indicates that this domain is very flexible than others. The aliphatic index profile follows the polynomial distribution pattern (R2 = 0.94)  . The aliR genes ranged from −1.072 to 0.421 . The majority of zinc finger domains were hydrophilic in nature and some were found to hydrophobic.The Grand Average of Hydropathy (GRAVY) indices of zinc finger domain of cloned to 0.421 . GRAVY vas 0.191  which inR genes. Fourteen possible groups of taxa having sequence similarities between each other were obtained (R genes Rx2 and Gpa2/Rx1. Both these genes contained BED zinc finger domains. The size of zinc finger domains in both the proteins is similar (47AA) but the size of both the NBS-LRR proteins is 912 and 937 AA, respectively (Piz-t) and tomato (Cf-9). These are having sequence similarities between PMZ zinc fingers. The PMZ zinc finger size is 20 AA in both the proteins though the size of the protein in both the genes was 1033 AA (NBS-LRR) and 863 AA (LRR), respectively  and its homologs in rice OsLSD1 and OsLOL1 that negatively regulates programmed cell death (PCD) and plant defense signaling pathways Pi54 was found under biotic stress conditions R gene N having C2H2 zinc finger domain showed 6.6% similarity with NFX of Pi54. The C2H2 zinc finger proteins were mainly related to the plant development regulation or involved in various types of stress responses This analysis encompasses the identity between the zinc finger domains of blast resistance gene er crops . The blaR gene of sunflower pI8 was found to be having three zinc finger domains viz C2C2, CHCC and GATA. Two zinc finger domains C2C2 and CHCC showed 6.4 and 4.4% similarities with NFX domain of Pi54 protein, respectively. These domains in pI8 protein were also present at -C terminal like that of the zinc finger domain of Pi54 protein. The CHCC zinc finger domains of pI8 protein are the variants of the classical zinc fingers like C2C2 and C2H2 ArcheaPi54. There were no significant similarity between Pi54 and other zinc finger domains of barley proteins. The zinc finger domains of Tomato disease resistance proteins, viz., Hero, I2C, Sw5-e and Mi-1 have 5.6% and 3.4%, 2.7% and 1.7% similarity with Pi54 protein, respectively. The zinc finger domains of Arabidopsis and Potato R proteins showed 5.7 and 2.1% sequence similarities with NFX zinc finger domain of Pi54 protein, respectively. However, no similarity was found between Rad18 zinc finger domain of Flax M protein and NFX of Pi54  whereas 8.73% of the combinations shows an identity between 10–25% and the highest pairwise sequence identity (80.8%) exist between Gpa2Rx1_BED-Potato & Rx2_BED-Potato followed by Cf-9_PMZ-Tomato & Piz-t_PMZ-Rice (40.0%), Mla1_BED-Barley & Rx2_BED-Potato (38.7%) and Mla1_BED-Barley & Gpa2Rx1_BED-Potato (36.7%).pi21gene of rice showed maximum identity (10%) with the zinc finger domain (NFX) of Pi54 protein , Pib (Rice), Lr10 (Wheat), Gro1-4 (Potato), RCY1 (Arabidopsis) and Rpg1 (Barley) contains multiple number of zinc finger domains. The Pi54 protein contains the smallest zinc finger domain, despite the fact that smallest protein among these plant disease resistance proteins is pi21, another blast resistance gene. Instability, aliphatic and hydropathicity profile of these zinc finger domains gave a representation of the biochemical features of these proteins. We identified thirty four zinc finger domains in twenty six plant disease resistance proteins. These were found to be of nineteen different types of zinc fingers belonging to nine crops of five different families. Resistance proteins are known to play a crucial role in pathogen resistance by utilizing NBS and LRR domains for receiving signals from the pathogen. However, the presence of zinc finger domains, in combination with NBS-LRR domains in resistance proteins may reflect a major role of these domains in host- pathogen interaction.This study presents an analysis of plant disease resistance protein sequences in which zinc finger domains were found to be present along with other previously described domains like NBS and LRRs. The in-silico examination was performed to deduce the zinc finger domain in cloned R genes. The protein sequences of more than 70 genes present in National Centre for Biotechnology Information (NCBI) database (www.ncbi.nlm.nih.gov) were downloaded and analyzed (www.smart.embl-heidelberg.de) was used for the identification of zinc finger motif www.clustal.org) and Bio-edit 2.0 (http://www.mbio.ncsu.edu/bioedit) using default parameters The analyzed . The ExphPi-k (Pi54) and other ZnF domains were studied using Expasy proteomic tool Protparam (http://us.expasy.org/tools/protparam.html) The structural and functional prediction of The predicted zinc finger domains of R-proteins were further undertaken for phylogenetic analysis. The aligned sequences were inspected and adjusted manually to minimize the number of gaps and insertions. The manual adjustments were based on the sequence similarities. The phylogenetic tree was constructed according to the Neighbor-Joining method and visualized by MEGA program version 4.0 For the pairwise identity between the zinc finger domain of blast resistance gene Pi54 and the genes cloned from other crops, 2-D pairwise identity data matrix was generated using Bio-Edit version 5.0.6 Supplementary ReferencesList of References for Supplementary files.(DOCX)Click here for additional data file.Table S1The cloned plant disease resistance genes and their specific features.(DOCX)Click here for additional data file.Table S2Distribution of Zinc finger R-proteins across different crops.(DOCX)Click here for additional data file.Table S3R genes.Details of different Zinc finger domains across various crops cloned (DOCX)Click here for additional data file.Table S4R genes.Biophysical parameters of identified Zinc finger domains across various cloned (DOCX)Click here for additional data file.


Antibodies to the V1/V2 domain of gp120 have recently been identified as a correlate of protection in the RV144 clinical trial. To better understand the specificity of broadly neutralizing antibodies) to the V1/V2 domain of Thai CRF01_AE viruses, we analyzed the specificity of antibodies in HIV+ elite neutralizer (EN) sera by swarm analysis.Swarm analysis makes use of the swarm of closely envelope variants that evolve in each HIV-1 infected individual, as a source of naturally occurring and biologically relevant mutations that confer neutralization sensitivity/resistance. Envelopes from clade B and CRF01_AE viruses were tested for neutralization sensitivity/resistance with sera from ENs infected with clade B and CRF01_AE viruses.We found five mutations in the V1 domain that affected neutralization sensitivity/ resistance of CRF01_AE viruses. This differed from clade B viruses in which mutations altering neutralization sensitivity/resistance clustered in the V2 domain. Structural studies have shown that the V1/V2 domain of gp120 consists of a four-stranded β-sheet structure. We found that mutations affecting neutralization sensitivity/resistance in Thai CRF01_AE viruses clustered around the exposed turn at the junction of the A-B strands. In contrast, the mutations that altered neutralization sensitivity/resistance in clade B viruses clustered around exposed turns at the junction of the B-C and the C-D strands.The present studies suggest that there is a major difference in the antigenic structure of the V1V2 domain between clade B and CRF01_AE envelope proteins. These results suggest that antibodies to the V1 domain of CRF01_AE envelope proteins should be evaluated as a correlate of protection in the RV144 trial. For this purpose, studies using novel proteins and scaffolds, that replicate the structure of conformation- and glycoform- dependent epitopes in the V1/V2 domain, are under investigation.


As orthologous proteins are expected to retain function more often than other homologs, they are often used for functional annotation transfer between species. However, ortholog identification methods do not take into account changes in domain architecture, which are likely to modify a protein's function. By domain architecture we refer to the sequential arrangement of domains along a protein sequence.To assess the level of domain architecture conservation among orthologs, we carried out a large-scale study of such events between human and 40 other species spanning the entire evolutionary range. We designed a score to measure domain architecture similarity and used it to analyze differences in domain architecture conservation between orthologs and paralogs relative to the conservation of primary sequence. We also statistically characterized the extents of different types of domain swapping events across pairs of orthologs and paralogs.The analysis shows that orthologs exhibit greater domain architecture conservation than paralogous homologs, even when differences in average sequence divergence are compensated for, for homologs that have diverged beyond a certain threshold. We interpret this as an indication of a stronger selective pressure on orthologs than paralogs to retain the domain architecture required for the proteins to perform a specific function. In general, orthologs as well as the closest paralogous homologs have very similar domain architectures, even at large evolutionary separation.The most common domain architecture changes observed in both ortholog and paralog pairs involved insertion/deletion of new domains, while domain shuffling and segment duplication/deletion were very infrequent.On the whole, our results support the hypothesis that function conservation between orthologs demands higher domain architecture conservation than other types of homologs, relative to primary sequence conservation. This supports the notion that orthologs are functionally more similar than other types of homologs at the same evolutionary distance. Genes are homologous if they share a common evolutionary origin. Homologous genes in different species are defined as orthologs if they descend from a single gene in the last common ancestor , and outThe more genomes are sequenced, the more important orthology identification becomes. This is because orthologs often have the same or closely related functions in the extant species and therefore can be used for the transfer of functional information. For example, the functions of human genes can be identified by studying their orthologs in other organisms. Likewise, transfer of functional information between orthologs is important for annotation of newly sequenced genomes ,5. HowevProtein domains are modules of protein architecture, typically made of independently folding subsequences that can be found in different arrangements in different proteins. The Pfam database ,9 contaiProteins evolve by point mutations, insertions, and deletions. The latter two can occur locally, involving a few residues, or affect one or more entire domains -13. DomaPrevious work suggests that orthologs tend to have the same domain architecture. Lin et al.  investigVarious measures of domain conservation have been suggested ,18,20. LIn the present paper, pairs of orthologs from InParanoid  were comhttp://InParanoid.sbc.su.se×[N - 1] for M rows and N columns).To test whether the two homology types  differed with regards to how common the different domain architecture similarity classes and domain swapping event types were, we performed a variant of Pearson's χ2 test . For eac2 is not recommended if any cell has an expected count lower than 5. Because of this, the difference category NotSame and the architecture change categories domain shuffling and segment duplication/deletion were excluded from the respective analyses, making f = 2 in both cases. Even so, in the architecture change comparison, expected counts went below 5 for the prokaryote comparisons, meaning that the test cannot be considered reliable for those species.The χHomo sapiens and 40 other species. From these clusters, we extracted protein pairs falling into four types depending on their orthology status. See Figure Homo sapiens and one orthologous protein from another species. Inparalog (iP) pairs are pairs of proteins within either Homo sapiens or another species, which are part of the same ortholog cluster, and which thus have arisen through a gene duplication following the divergence of the two species. Outparalog pairs of two types were considered: Closest cross-species (oPx) and same-species (oPs) outparalogs. The former pairs consist of a protein from one species and the protein in the other species to which it is the most similar outside of the ortholog cluster. The latter, analogously, consist of a protein in one species and the most similar protein in the same species outside the cluster.We computed the levels of domain architecture and primary sequence conservation for pairs of orthologous proteins, and compared these with corresponding figures for paralogous proteins at the same evolutionary divergence. We considered sets of ortholog clusters as defined by InParanoid, between We would expect function to be more often conserved in O pairs than in other pair types because in other pair types, gene duplications would have relaxed pressure on one of the copies to retain the ancestral function. We further subdivided the O pair category into pairs from clusters with only a single member in each species (1-1 orthologs), and pairs where gene duplication has occurred in either species. In the non-duplicated case, function conservation should be more frequent than in the case with duplication, as the latter offers more opportunity for undergoing and retaining functional shifts  for the different pair types. Inparalog pairs, having diverged later than any of the other pair types, are in all cases the most conserved, and this difference becomes more pronounced the further back the speciation event happened. The graph shows a gradual drop in similarity across all pair types, except a striking jump for outparalog pairs at the vertebrate/invertebrate border. The point of this accelerated outparalog divergence supports the theory of whole-genome duplications at the vertebrate root [Figure ate root .As seen in Figure The analysis was also done for orthologs split into 1-1 orthologs and duplicated orthologs, as shown in Figure Figure Figure While the overall trends in Figures Given that InParanoid assigns orthology status using relative BLAST  scores, We therefore analysed how DA-score is affected by sequence divergence, and whether this effect is the same for protein pairs of different orthology status. Data from all species comparisons were pooled, and the clusters were divided into bins based on average sequence divergence for each pair type. This can be seen as way to normalize the DA-score with sequence divergence, to determine whether there are differences between orthologs and non-orthologs with respect to how well architecture is conserved as primary sequence diverges. Average DA-score was first calculated for all pair types within each cluster, and then these values were averaged across all clusters. The first step was done to avoid biasing the results towards large clusters.Are differences between categories significant in a bin analysis of this type? Sparsely populated bins might yield spuriously large differences between the mean values for the two categories just by chance. To avoid this we performed a randomization test for a significant (Bonferroni corrected p < 0.05) difference between the category means within each bin.Figure Conceivably, an analysis of this type may yield errors if the categories have a significantly different distribution of data points (clusters in this case) within each bin with respect to the variable which is binned on. False positive trends resulting from such conditions should increase in proportion if bins are made broader, and diminish or disappear if bins are made narrower. To investigate this we repeated the analysis using 10, 20, and 100 bins, which all showed the same trend  belong to class SameContentNotAligned. Protein pairs with overlapping but not identical domain sets belong to class DiffContent, and protein pairs with disjoint domain sets belong to class NoShared.We compared how the ortholog (O) and cross-species outparalog (oPx) pairs fell into these classes. The overall trend across all species is shown in Figure 2) to assess the whether the distribution across the three classes Identical, SameContentNotAligned and DiffContent  was significantly different between O and oPx pairs. For 34 of the 40 species, the distributions were found to be significantly different (P < 0.05). All of the non-significant results were in prokaryotes.The numbers for individual species comparisons are listed in Supplementary Table 5A. We did a statistical test , we further analysed the presence of the five different types of domain swapping events described in Figure 2 test. . The non-significant species were all prokaryotes. Additionally, for two prokaryotes where the difference was significant, the applicability of the χ2 test is questionable because at least one expected cell count was less than 5. Mainly, this difference seems to occur in oPx pairs having fewer repetition differences than O pairs (true in 35 of 40 species) and more often undergoing insertion/deletion of new domains (true in 34 of 40 species).The overall averaged results are shown in Figure For both pair types, it is striking how rarely segment duplication/deletion, domain shuffling, and insertion/deletion of existing domains is observed. Almost all architectural differences can be explained by repetition differences or insertion/deletion of new domains. The fact that oPx pairs have a higher degree of insertion/deletion of new domains supports their generally more relaxed functional constraints.As previous work ,13 has sWe defined a measure for domain architecture conservation, the DA-score, which together with our use of Pfam clans for increased sensitivity and collapsing of repeat/motif-type families, allowed us to compare degrees of architecture conservation in a functionally and evolutionarily sensible way. By taking the DA-score for InParanoid-defined orthologs, we were able to study in detail the degree of domain architecture conservation and variance exhibited by orthologous and other sequence pairs across a wide variety of species.The central observation we make is that orthologs diverge more slowly in domain architecture than paralogs, relative to their divergence in primary sequence. This is consistent with higher selective pressure on orthologs to retain their ancestral domain architecture, and that orthologs are relatively more conserved with respect to function given that the particular domain architecture of a protein is required for its function to be carried out.These conclusions are clear from the data for protein pair comparisons involving more distantly separated homologs, but not for close homologs. Similar observations were made by Peterson et al.  who repoKondrashov et al.  suggesteWe observe that orthologous sequences detected using InParanoid share most if not all of their domains. What are the functional implications of a domain architecture alteration between orthologs? InParanoid, just like all other ortholog predictors, uses evolutionary criteria to assign orthology, and no functional information. The definition of orthology by Fitch  is also Some insight into this issue can be provided by looking at the distributions of DA-scores for different pair types. In Figure One concern about the results presented here, is that InParanoid, like other schemes for systematically finding orthology relationships, is based on degrees of similarity between sequences, which might change as the domain architectures do. As long as InParanoid assignments are correctly made, there should be no problem. However, errors in cluster assignments, to the extent that they occur, are more likely to produce ortholog pairs with similar architectures and paralog pairs with dissimilar architectures, than the reverse. This risk for bias applies equally to any sequence- or structure-based method for orthology assignment, which includes all methods that could conceivably be used for a large-scale analysis of domain architecture evolution under orthology versus paralogy. In the absence of ways to evaluate the extent to which this factor impacts the result, it should be noted as a potential error source. However, to the extent that InParanoid itself is reliable, it should not strongly affect the outcome of this study.We noted that both average sequence divergence and domain architecture conservation change noticeably as we cross the vertebrate-invertebrate border. This might be interpreted either as a consequence of high evolutionary rates early in the vertebrate lineage, a bottleneck effect where most sibling species of the early invertebrates did not survive, or an artifactual bias in the set of fully sequenced genomes available at this time. Another interesting option might be rapid evolution following two consecutive whole-genome duplication events, which have been inferred to have taken place early in the vertebrate lineage . Given tThis study has employed large scale data analysis to investigate the hypothesis that orthologs have a higher level of domain architecture conservation relative to their evolutionary divergence than other homologs. Strong support for this proposition was found, at least for protein pairs that have diverged beyond a certain point. We conclude that it is possible to infer that orthology is accompanied by a selective pressure to retain domain architecture, and that the specific order of domains seems to be important to the function carried out by a set of orthologous proteins. Further work should address the lack of this trend for recently diverged homologs.ES conceived the study and developed the DA-score together with IP. IP wrote most of the underlying software and performed some initial analysis. KF introduced the sequence divergence compensation procedure, carried out the main analysis, and performed all statistical tests. KF and ES wrote the manuscript. All authors read and approved the final manuscript.Supplementary tables. This file contains the following tables: Table S1: This table shows the full selection of species, as well as how many clusters and proteins were included in each species comparison analysis. Table S2: This table shows the fraction of ortholog pairs from clusters with and without duplications, respectively, that have identical domain architectures. Table S3: Bin borders, averages, number of clusters falling into each bin, and permutation test P values underlying Figure Table S4: Spread of distinct architectures across clusters. Table S5: A. Distribution of protein pairs across domain architecture similarity classes in the various species comparisons. B. Distribution of protein pairs where architectures differed across domain architecture change event classes in the various species comparisons. Table S6: This table shows the distribution of domain architecture change events across the N-terminal, middle, and C-terminal domain position categories.Click here for fileSupplementary Figure S1A-C. This file contains the following figure: Figure S1. A. Domain architecture conservation across all species averaged within ranges (bins) of sequence divergence, for pairs of orthologs versus closest cross-species outparalogs. The scores for each pair category were first averaged within each cluster so that each cluster contributes equally to the average scores regardless of size. Error bars show the standard error of the means for each pair category. The triangle markers indicate significant difference between the category means within each bin. In these plots, the data was divided into 10 bins. B. The same analysis for inparalogs versus same-species outparalogs. C. The same analysis for orthologs versus inparalogs.Click here for fileSupplementary Figure S2A-C. This file contains the following figure: Figure S2A-C. Same as S Additional file 2, Figure S1A-C but with 20 bins.Click here for fileSupplementary Figure S3A-C. This file contains the following figure: Figure S3A-C. Same as Additional file Click here for fileSupplementary Figure S4A-C. This file contains the following figure: Figure S4A-C. Same as Figure Click here for file


FMD) were used in order to compare the models’ predictions in term of disease spread, consequence, and the ranking of the applied control strategies, and to discuss the effect of the way disease spread is modeled on the predicted outcomes of each model. The DTU-DADS (version 0.100), and ISP (version 2.001.11) were used to simulate a hypothetical spread of FMD in Denmark. Actual herd type, movements, and location data in the period 1st October 2006 and 30th September 2007 was used. The models simulated the spread of FMD using 3 different control scenarios: 1) A basic scenario representing EU and Danish control strategies, 2) pre-emptive depopulation of susceptible herds within a 500 meters radius around the detected herds, and 3) suppressive vaccination of susceptible herds within a 1,000 meters radius around the detected herds. Depopulation and vaccination started 14 days following the detection of the first infected herd. Five thousand index herds were selected randomly, of which there were 1,000 cattle herds located in high density cattle areas and 1,000 in low density cattle areas, 1,000 swine herds located in high density swine areas and 1,000 in low density swine areas, and 1,000 sheep herds. Generally, DTU-DADS predicted larger, longer duration and costlier epidemics than ISP, except when epidemics started in cattle herds located in high density cattle areas. ISP supported suppressive vaccination rather than pre-emptive depopulation, while DTU-DADS was indifferent to the alternative control strategies. Nonetheless, the absolute differences between control strategies were small making the choice of control strategy during an outbreak to be most likely based on practical reasons.Two widely used simulation models of foot-and-mouth disease ( FMD) is a highly contagious disease of ruminants and pigs that can cause large economic damage Foot-and-mouth disease (ISP) DADS) that has been further developed at the Technical University of Denmark to DTU-DADSSimulation models are widely used to support veterinary authorities to setup contingency plans for FMD awareness and preparedness Because of the absence of outbreak data in some countries, and hence the difficulty to validate outcomes of an FMD simulation model, relative validity has been proposed The objective of this paper is to simulate a hypothetical spread of FMD in Denmark using two widely used simulation models of FMD spread (DADS and ISP), in order to compare the models’ predictions in term of disease spread, consequence, the ranking of the applied control strategies, and the effect of the way disease spread is modeled on the predicted outcomes of each model.st October 2006 until 30th September 2007. For each herd, the herd data included the Danish Herd Identification System, referred to as CHR number, herd type, UTM geo-coordinates, number of animals, and number of off-farm animal movements per day. Herds were categorized into 3 categories; cattle, swine, and small ruminants (in this paper referred to as “sheep”). Cattle herds were categorized as dairy or non-dairy herds. Swine herds were categorized into 19 different types based on their production type and SPF (specific pathogen-free herd) status Both simulation models used the same herd data, which contained information on all Danish cattle, swine, sheep and goats herds in the period from 1The input parameters of the models were based on Danish data, the literature and personal communication to experts A hypothetical spread of FMD between herds in Denmark was simulated using two spatial simulation models; namely DTU-DADS (version 0.100), and ISP (version 2.001.11). The DADS model (version 0.05) was upgraded to DTU-DADS Spread of infection between herds was simulated through 7 spread mechanisms: 1) direct animal movement between herds; 2) abattoir trucks; 3) milk tankers; 4) veterinarians, artificial inseminators, and/or milk controllers (referred to as medium risk contacts); 5) visitors, feedstuff and/or rendering trucks (referred to as low risk contacts); 6) markets; and 7) local spread.Based on actual animal movement data, a rate of animal movements per day was calculated for each herd. The individual daily movement rate was used as lambda in a Poisson distribution to represent the number of movements per day. Similarly, a rate of abattoir deliveries per day was calculated based on herds’ actual data and used in a Poisson distribution to simulate the number of movements to the abattoir per day from the infectious herd. Thereafter, the number of herds visited by an abattoir truck on the way to the abattoir following visit to an infected herd was estimated from a Poisson distribution with a lambda depending on the herd type. For all milking herds, the average probability of having milk picked up was used as lambda in a Poisson distribution describing contacts between herds by milk tankers. Likewise, medium and low risk contacts were simulated, but with different lambdas and risks of infection The disease was modelled to always start in one herd (the index case) and develop until the disease was detected, and hence the herd was depopulated. The period from a herd starts showing clinical signs and until detection was dependent on the herd type, e.g. cattle herds were detected faster than sheep herds, because some sheep do not show clinical signs. Moreover, herds within the protection and surveillance zones would have higher probability of detection, because of surveillance. Detection of the first infected farm was assumed to always be at day 21 following the start of the epidemic. This was based on experience from the UK After detection of the first infected herd, a set of default control strategies were applied representing the basic scenario. These included: 1) depopulation, cleaning and disinfection of detected herds; 2) a 3 days national stand still on animal movements in the country; 3) a 10 km radius zone (surveillance zone) around the detected herds; in which movements between herds and out of the zone were restricted and herds were surveyed one time before lifting the zone; 4) a 3 km radius zone (protection zone) around the detected herd, in which movements between herds and out of the zone were restricted, and herds are surveyed during the first week and a second time, 21 days later; 5) backward and forward tracing of contacts from and to detected herds. When a herd had received animals from a detected herd, the receiving herd was also depopulated and disinfected, while in case of other kind of contacts, the herd was surveyed. When a herd was subject to surveillance, the animals were inspected for clinical signs of FMD. In case of sheep herds, the animals were also sampled for serological analysis Three spread scenarios were run in the models. The scenarios were: 1) the basic scenario, in which the EU and Danish control strategies were implemented as explained bellow, 2) pre-emptive depopulation, including the basic scenario plus depopulation of herds within 500 meters around detected herds, and 3) suppressive vaccination, including the basic scenario plus emergency vaccination of herds within 1,000 meters around detected herds. Vaccination and depopulation were initiated 14 days following the detection of the first infected herd.When a susceptible herd was vaccinated, the herd was assumed to be susceptible for 4 days before the immunity would start to build up and reach its maximum potential at day 9 following vaccination. Vaccinated herds that became infected would be fully infectious, if they had been vaccinated ≤4 days before exposure to the virus, otherwise, the infectiousness reduced until day 9 following vaccination, where it was reduced by 90% The daily animal vaccination capacity was assumed to be 60,000 ruminants and 50,000 pigs Vaccinated herds were assumed to be depopulated after the end of the outbreak Despite that the models were setup to simulate the same spread scenarios, there are several dissimilarities between the models and it was not possible to make all simulations identical in the two models. These differences are:When direct and indirect contact is modelled, a susceptible herd can become infected, based on a distance-based probability, a probability of contact between the different herd types, and a probability of disease transmission. In the DTU-DADS, these risks are multiplied and then a herd is selected from all herds within the country. In the ISP, the model will first select a distance band and then a herd will be selected within the band. Herds of the same type within a distance band will have similar probability of selection.Disease spread within a herd in DTU-DADS is modeled stochastically. This means that disease spread within herds of the same type can be different. In ISP, herds of the same type would have similar patterns of within herd disease spread. This means that the infectiousness of a herd can be different between DTU-DADS and ISP, despite of similar herd characteristics and time of infection.Several parameters are stochastic in the models. However, the way the stochasticity of these parameters is implemented can be different for some of the parameters. In DTU-DADS, risk of infection following low, medium or high risk contacts are stochastic across iterations. This means the risk for a specific herd would be the same during the iteration. Such parameters are stochastic per day in ISP, which means that the values differ between days within iteration for the same herd.When modeling the number of contacts the truck makes on its way to the abattoir to deliver pigs and sheep, ISP uses the herds-specific abattoir lambda to determine whether a movement to the abattoir will occur at that day. Thereafter, it determines the number of herds that will be contacted on the way to the abattoir, based on a probability distribution function. In DTU-DADS, the herd-specific abattoir lambda is used in a Poisson distribution to determine the number of contacts a truck makes picking up pigs or sheep on its way to the abattoir.In DTU-DADS all infected herds will eventually be detected. However, small herds might be infected and then recover without being detected in ISP.Other differences do exist, but are not presented here, because of their minor impact on models’ predictions, following investigation through sensitivity analysis The costs and losses due to the epidemics per control scenario were calculated as explained previously The predicted epidemiologic, total costs and epidemic area outputs of each scenario were compared between the two models. Epidemiologic predictors consisted of number of infected premises and the epidemic duration. The spatial spread (or epidemic area) was calculated by plotting the locations of the detected herds per iterations and constructing a minimum convex hull around the herds and then measuring the area of the resulting convex hull polygon. Predictions of each scenario of the two models were compared using the Wilcoxon Signed Rank Sum test in R 2.14.0 th, 25th, 50th, 75th, 95th percentiles and maximum values of the predicted distances by DTU-DATS were, respectively, 0.00, 0.55, 1.92, 13.61, 72.44 and 163 km, while the predicted values by ISP were, respectively, 0.00, 0.53, 1.47, 3.49, 43.99 and 300 km.When epidemics were initiated in cattle herds located in high density cattle area, the proportion of disease spread through animal movements, indirect contacts and local spread predicted by DTU-DADS were, consecutively, 0.003%, 0.547% and 0.45%, while ISP prediction of disease spread through these mechanisms were, consecutively, 0.01%, 0.33% and 0.66%. Similar trends were observed when epidemics were initiated using the other index herd types. The distributions of distance between the infected herd and the source herd from both models are shown in DTU-DADS predicted larger epidemics of longer duration than the ISP, except from epidemics starting in cattle herds located in high density cattle areas. In these epidemics, ISP predicted larger and longer duration epidemics . There ith and 95th percentiles of the predicted outcomes . Using DTU-DADS, there was no significant difference in the costs of epidemics between depopulation in 500 meters and suppressive vaccination in 1 km control scenarios , regardless the index-herd type. However, using ISP, suppressive vaccination in 1 km was generally a cheaper choice than depopulation in 500 meters . Nonetheless, given the large variation, the difference in the absolute values is rather small.Generally, DTU-DADS showed significantly larger and longer duration of outbreaks than the ISP, when the epidemic started in swine herds located in high density swine areas and in sheep herds. The opposite was true when the epidemic started in cattle herds located in high density cattle areas. When epidemics started in cattle herds located in low density cattle areas, there was no significant difference in the predicted number of infected herds and epidemic duration between the 2 models, but DTU-DADS predicted larger epidemic area and costs than ISP. The tendency that DTU-DADS predicts larger epidemics and epidemic area than ISP could be explained by the way the newly infected herds are selected. In DTU-DADS, when an infected herd would infect other herds, the newly infected herds would be selected from all herds in the country based on distance and contact probabilities. In ISP, a distance band is drawn around the infected herd and then newly infected herds are selected from within the band. Herds of the same type would have similar probability of infection. In case no herds with positive probability of infection were found in the band, a new band will be selected with a maximum retries sat to 100 times in the current project. This means that in DTU-DADS, herds located close to and herds located far away from the infectious herd would be subjected to selection directly in one step. These herds would of course have different probabilities of selection, in which herds located far away would have lower probabilities than herds located close by. Nonetheless, the number of susceptible herds located far away is very large, which means that some might be selected more often compared to the way selection of new infected herds is carried out in ISP. This is because, in ISP such herds would have extremely small chance of selection, because the closer bands would most likely be selected. This means that DTU-DADS tend to spread the disease over longer distances and thus generally larger epidemics and epidemic area than ISP. This can be seen from Furthermore, local spread dominated the different types of spread mechanisms in ISP (66%), while lower percentage (45%) of infection through local spread was predicted by DTU-DADS. This indicates as well that ISP tends to restrict outbreaks to a small area, while DTU-DADS would spread them out over longer distances, and hence larger areas. We speculate that the higher percentage of disease spread through local spread, and the short distance jumps of new infections through indirect contacts (e.g. low risk contact), combined with the presence of large number of susceptible herds in the area have resulted in larger epidemics size in ISP than DTU-DADS, when the index herd was cattle located in high density cattle area.ISP tended to show larger variation and more extreme situations than the DTU-DADS . It is aFrom this study, it was not possible to judge which way of modelling disease spread is the correct one. A way to get closer to the answer, would most likely be to compare models’ output to actual outbreak data, and then use the method that best explain the data. Recent outbreak data is not available in Denmark, given that the last outbreak was in 1982 The models agreed that zone depopulation and suppressive vaccination are cheaper than the basic scenario. When depopulation and vaccination were compared, DTU-DADS was indifferent to the choices, while ISP estimated the costs, size and duration of suppressive vaccination to be smaller. This would indicate that the choice of control strategy might differ depending on the chosen model. However, from a practical point of view, the absolute differences were small, and given the large variation in the results, the final decision on strategy will most likely be based on other issues as well, such as practical, political, ethical and social effects of the epidemic. In this exercise, pre-emptive depopulation and suppressive vaccination were chosen to be implemented 14 day following the detection of the first infected herd. Following consultation with the National Veterinary Authorities, this timeframe seems reasonable before suppressive vaccination can be started. Despite that the two models did not fully agree on the chosen control strategy using this scenario, they have actually agreed that depopulation following the detection of 10 infected herds was the optimal scenario to control FMD spread in Denmark From a practical point of view, the advantage of DTU-DADS is that the structure of the model can be changed easily as soon as new data or knowledge arises, e.g. by adding new modules, because the source code is available. An important advantage, DTU-DADS runs on free software, but it demands personnel trained in programming. On the other hand, ISP is not free and cannot be extended to include changes to the structure of the model by the user. However, the model demands much less programming skills and training than DTU-DADS. ISP ran in few hours on personal computers, which is faster than the DTU-DADS that required one day for some scenarios in this study. Nonetheless, DTU-DADS can be run on a server, and hence can practically be very fast, because many scenarios can run at the same time. The cost-benefit analysis has been integrated within the DTU-DADS, which means that after the end of the model run, all necessary outputs can be obtained and only statistical analysis is still to be carried out. On the other hand, cost-benefit analysis on ISP outputs was carried out separately following the model run. Finally, DTU-DADS (in its current version) does not include elements of airborne spread, while ISP does. Important to mention, for the current exercise, spread of infection through airborne was not modelled, in order to keep the models as close to each other as possible. In a country where detailed herd, movement and contact data is available, ISP and DTU-DADS can both be useful, as they can represent the spread mechanism in details. This allows identifying risky contacts, which can be helpful to the veterinary authorities, while they are setting the preparedness and contingency plans.The spread of FMD was compared using hypothetical data in 3 simulation models: ISP, North American Animal Disease Spread Model (NAADSM) and the Australian model (AusSpread) The current study provided insight into the differences between the models and discussed how those differences could have influenced models’ predictions. Moreover, the current study estimated the financial impact of the epidemics, which is important, because significant epidemiological differences between the models could be financially indifferent or vice-versa. It is important to mention that efforts have been made to include the NAADSM in the comparison. Nonetheless, it was not possible to run the scenarios in the setup defined in the study using the available version of NAADSM when the study was performed, and thus the model was excluded. The restricted access to the source code of ISP has limited our capacity of investigating the effect of differences between the models on their predictions. Thus future research should have unlimited access to models’ code, and should focus on investigating, which method of modeling disease spread between herds would best represent reality. This can be done either by comparing the predicted outputs to outbreak data, or to kernel models that are estimated based on outbreak data