Random forest, an ensemble based supervised machine learning algorithm, is used to predict the SCOP structural classification for a target structure, based on the similarity of its structural descriptors to those of a template structure with an equal number of secondary structure elements (SSEs). An initial assessment of random forest is carried out for domains consisting of three SSEs. The usability of random forest in classifying larger domains is demonstrated by applying it to domains consisting of four, five and six SSEs.Class, Fold, Super-family or Family levels, the predictive quality of the model in terms of Matthew's correlation coefficient (MCC) ranged from 0.61 to 0.83. As the number of constituent SSEs increases the MCC for classification to different structural levels decreases.Random forest, trained on SCOP version 1.69, achieves a predictive accuracy of up to 94% on an independent and non-overlapping test set derived from SCOP version 1.73. For classification to the SCOP Class, Fold, Super-family or Family levels is demonstrated. Issues such as introduction of a new structural level in SCOP and the merger of singleton levels can also be addressed using random forest. A real-world scenario is mimicked by predicting the classification for those protein structures from the PDB, which are yet to be assigned to the SCOP classification hierarchy.The utility of random forest in classifying domains from the place-holder classes of SCOP to the true  In the foreseeable future, current practices of cataloging protein structures will need to cope with a rapid increase in the number of unclassified structures. As of February 2010, approximately 64% of the 58,824 proteins deposited in the Protein Data Bank (PDB)  and funcet al. have applied ensemble learning to protein structure classification [et al. [Efforts to automate structural classification have been ongoing for over a decade using supervised machine learning (ML) algorithms, such as neural networks -12 and sfication . Such apfication . A decis [et al. , assigni [et al. , protein [et al. -26, DNA- [et al. , disorde [et al.  and enzy [et al. . These iA and B, each comprising three SSEs: A1A2A3 and B1B2B3, where A1 is the SSE located at the N-terminus of A and so on. The structurally equivalent pairs are {A1A2, B1B2}, {A1A3, B1B3} and {A2A3, B2B3}. The domains being compared should have the same number of SSEs. Using such a representation, we exploited the similarity among the structurally equivalent pairs of SSEs of the two domains and assessed the predictive performance of the 15 algorithms. The random forest [Class, Fold, Super-Family and Family level, respectively.Previously  we examim forest  outperfom forest , such asm descriptors, where m is much smaller than the total number of descriptors. Decision nodes are added to each of the trees without pruning, until classification for every instance is obtained. This divide and conquer strategy [Random forest is an ensemble of decision trees, where every tree learns the classification on a random subset of the domain pairs. For every tree, at each splitting or decision node, the most discriminating descriptor is chosen from a randomly selected subset of strategy ,33 combiClass (CL), Fold (FO), Super-family (SF) and Family (FA). These definitions are given elsewhere [template (with known SCOP classification) and the other as the target (for which classification is sought). A high similarity between the pairs of SSEs of the target and template, in terms of descriptors, implies they share a deep level in the classification hierarchy. A pair is termed a CL-, FO-, SF- or FA-pair, if the domains belong to the same Class, Fold, Super-family or Family, respectively. Pairs not sharing any of these four structure levels are termed NA-pairs.Here, we treat structure classification as a typical data mining problem and investigate a real-world scenario where random forest can be used to automate SCOP structure classification. As described in an earlier study , we conslsewhere ,6,34. Welsewhere . One of NA-pairs, one of the domains in the pair belongs to a place-holder class of SCOP. Therefore, we focus on classification of domains from place-holder classes to true structural levels. We comprehensively assess the random forest for classifying small domains consisting of three SSEs, and we show that larger domains consisting of four, five and six SSEs are also classified successfully. In addition, we make blind predictions for the structures deposited in the PDB after the release of SCOP version 1.73, which obviously are not yet assigned to the SCOP hierarchy. The quality of the predicted structural levels is demonstrated by the structural similarity among the target and the template domains.Extending our previous work , in thisCL-pairs are higher; for the other types of pairs the difference is not significant. The confusion matrices for the two strategies are given in Table NA-pairs were predicted to share a class. The remaining 2% of the NA-pairs were predicted to share a fold or a family. The NA-pairs predicted to share a super-family were negligible in proportion. The pairs predicted to share a particular structural level were divided into four categories based on their prediction probabilities (p)  as well as to the recently released current version 1.75. However, no change in the status of the NA-pairs was found.The performance of random forest has been evaluated using four different strategies to carry out blind predictions Table . Strategp) Table . In bothtarget domain following the proposed approach, a template domain with an equal number of SSEs should exist in SCOP. The approach has been validated in detail using domains consisting of three SSEs. Its application to classifying domains consisting of four, five and six SSEs shows its extensibility . Nevertheless, random forest seemed to be efficient at classifying such an imbalanced set.In order to classify a template's structural level, determined by the predicted pair type. For example, for a predicted FO-pair, the target will be assigned to the same fold as that of the template. In general, a target is predicted to have the same structure level as all of the templates in that classification lineage(s) if they are in the test set . However, sometimes the target pair with some other template(s) from the same lineage is predicted to a deeper, more specific level in the hierarchy. In such cases, the deeper level is selected to classify the target. In an alternative scenario, although the same structure level is predicted for the target and the template from a lineage, for some target-template pairs the prediction probabilities for the shared level are less than 0.8. If this is the case for multiple target-template pairs and the prediction probability for the level deeper in the hierarchy is greater than 0.2, the deeper level is used to suggest the classification of the target. Applying these methods, multiple folds  and independent lineages (2ZM6 and 3BPJ) are identified as the potential classification for the target. Prediction of multiple folds is mainly due to the multi-domain nature of some of the target proteins.For every target-template test pair, the random forest assigns a probability that it is a particular type of pair. The pair type associated with the highest probability is predicted for the pair. For three out of 21 domains, representing nine proteins containing three SSEs, family level predictions were made with a probability of 0.5 or above. A few of the predicted classifications for the nine target proteins are given in Table FO- or SF-pairs is shown in Figure In the case of proteins with larger domains, predicted classifications can be verified on the basis of structural overlap and similarity. For nine of the 32 proteins containing domains consisting of four SSEs, the random forest predicted a structural family with a probability of 0.5 or above. For 11 out of 51 proteins containing domains consisting of five SSEs, and for six proteins out of 62 proteins with six SSEs a structural family was predicted with a probability of 0.5 or above. We believe that these predictions are correct, as suggested by the near perfect overlap of the target and template structures . MCC reflects the balance in correct and incorrect classification when the classes are of unequal size and therefore is an indicator of a predictor's quality [CL and FA-pairs than predicting FO and SF-pairs. Following strategy 3, some interesting predictions have been made. For example, the domain d1hcia does not share any structural level with d1lj2a and d1lj2b in SCOP version 1.73, but these pairs were predicted to be FO-pairs . The former is a low resolution ribosomal complex and all of its 20 chains have been classified in SCOP's Low resolution protein structures class, "Not a true class". The random forest, however, predicted (p = 0.9) these domains to be members of the Ribosomal protein S18 (sunid = 46912) family. Although in the latest SCOP version 1.75 the classification for d1ibkr is same, our method predicts that in a future version it will move to a true structural class and perhaps to the same lineage to which d1j5er and d1n32r belong (a.7.6.1). Other examples are shown in Figure The overall accuracies reported in strategies 1 and 2 are encouraging  and ruberodoxin-like (g.41.5.3) copper-and zinc-binding folds, respectively. These folds are characteristic of cytochrome c oxidases. 2JZ6 possesses two different folds which we assume (this protein is still unclassified) to be predicted correctly. Further, these two folds (sunid 46688 and 46965) have dedicated super-families to classify only those domains, which belong to the ribosomal complexes and perhaps provide a DNA/RNA binding site on ribosomes. The identification of folds in such cases could guide manual assignment of a super-family or family. The prediction of multiple folds having a related super-family, as in the case of 2JZ6 and 2ZM6, may reflect the capability of the random forest to identify classification networks. Similarly, 3BPJ, a human translation initiation factor is classified correctly to the singleton fold Tetracyclin repressor-like, C-terminal domain (sunid = 48497) and thereby to the family identified with the same name (sunid = 48499). In this case, the template domains used by the predictor were the Transcriptional regulator protein (1ZK8) from bacteria Bacillus cereus and also the other domains constituting the ribosomal complexes in different organisms . In addition, d3h3ma is predicted to share the same fold  as d1fewa and d1g73a , the predicted classification can minimise the number of probable levels for further consideration. Subsequent manual inspection of fewer levels would require less time.We have analysed mis-classified structures instead of making a detailed comparison with other strategies for the automation of SCOP classification. This is mainly due to the difference in the dataset that we have used, which is not biased to proteins or domains from the most populated folds or families or from the true SCOP classes only. In contrast to the four-level hierarchical classification addressed by our work, other methods are restricted to classification to one or two structural levels. Our intention was also to identify hidden patterns of mis-prediction, which may be suggestive of new structural levels and/or rearrangement in the existing SCOP hierarchy. We have found such patterns and proposed classification of structures from place-holder classes to the true structural classes, although further work may be needed to prove that the mis-classification could guide the merger of a few singleton families, super-families and even folds. These observations are important in light of the foreseeable refinements in the SCOP hierarchy  and the We, therefore, conclude that utilisation of a supervised ML algorithm such as random forest in the structure classification pipeline would accelerate the process. In future work, we will include a sub-structure search algorithm to compare domains consisting of unequal number of SSEs. Such an algorithm will be able to identify common cores, as well as the structural drift  among suClasses, seven Folds, eight Super-families and 24 Families from the SCOP hierarchy. Every domain was represented by structural descriptors characterising the constituent SSEs. For every possible pair of the SSEs, the distance between their geometric centre of mass and the relative orientation in terms of the angle between the axes that pass through the terminal Cα atoms were calculated using Cα coordinates. For every SSE, the solvent accessibility, length and secondary structure state  were also used as structural descriptors. Finally, the representation of the two paired domains also includes the root mean square difference (RMSD) for the distance, orientation, solvent accessibility and length descriptors along with the sequence identity of the paired domains [N SSEs is Non-overlapping pairs of domains from two different versions of SCOP were used as the training and test sets. ASTRAL compendium  mappings domains . Thus, tRandom forest was also tested for classification of larger domains consisting of four, five and six SSEs from SCOP version 1.69. These domains are listed in Additional files UnClassified Domains (UCD) datasets followed by the number of SSEs in domains constituting the set, that is, UCD3, UCD4, UCD5 and UCD6, respectively. UCD datasets were subjected to a BLAST search [Single and multi-domain proteins deposited after the release of SCOP version 1.73 were obtained. Since these proteins are not yet classified by SCOP, no domain boundaries are available and we considered every chain as a test domain. In the case of homomeric proteins, only the first chain was considered. Such domains or chains, consisting of three, four, five and six SSEs were filtered as individual sets. We name these sets as T search   features. M is the number of features defining each of the pairs. Four evaluation strategies (Table Uniq). To accommodate new targets in the latest release of SCOP, new structural levels can be introduced with or without rearrangements in the classification hierarchy. This may also alter the classification of previously classified structures. Therefore, using an inter-version test set, it is possible to check if random forest can detect novel relationships that it was not explicitly trained on.The random forest algorithm, as implemented in Weka  develope2M +  featuresNA-pairs  from training, as in strategy 2, would be more realistic. We expect some improvement in the classification accuracy, as most probably some of the NA-pairs share some structural features with other types of pairs i.e., CL-pairs or FO-pairs. In such cases, classification of an NA-pair to another type of pair will appear to be an incorrect prediction. To classify domains from place-holder classes, we evaluated the random forest, using DS1.69 with the NA-pairs removed as the training set and the NA-pairs removed from DS1.69 as the test set (strategy 3). With the same aim, in strategy 4, predictions were obtained for NA-pairs that only appear in DS1.73.In SCOP, four out of 11 classes are regarded as "Not a true class" and are used as place-holders for domains until further information is available. However, in practice, predicting a structure to any of the "Not a true" class, fold, super-family or family level should be avoided. A predictor should be trained only on those structural levels to which domains have been classified in accordance with experimental, structural, sequence or knowledge-based evidence. Such levels are the "True" SCOP levels. Thus, removing Pre = TP/(TP + FP)), recall (Rec = TP/(TP + FN)) and Matthew's correlation coefficient (MCC) (Equ. 1), where TP is the total number of true positives (instances correctly classified as belonging to a type of pairs), TN is the total number of true negatives, FP is the total number of false positives (instances incorrectly classified as belonging to a type of pairs) and FN is the total number of false negatives (instances incorrectly classified as not belonging to a type of pairs). MCC ranges from -1 to +1, and is a good measure of the quality of the predictor for a given a class. A perfect predictor will have an MCC of 1, whereas for a random predictor, the MCC will be zero.For strategies 1 and 2, the overall classification performance of the random forest is given in terms of percent accuracy, representing the fraction of correctly predicted domain pairs. This overall accuracy is a limited statistic, especially when classification is sought for four  or five (strategy 1) types of pairs. The classification performance, therefore, has been measured independently for every type of pair. Thus, various measures of the multi-class classification performance are used, including precision  in the forest. In every tree, the classification for every domain pair is given by the leaf node specific to a structural level. The leaf node assigns a probability, which is the ratio of instances belonging to a level to all of the instances that reach the leaf. In strategies 3 and 4, the NA-pairs which were predicted to one of the four levels with a prediction probability above 0.5 were considered as being predicted correctly. The correctness of the predicted level is checked by visual inspection. We have also checked the triangular equality of the predicted levels. Where possible, either a merger of the two SCOP levels or the addition of a new structural level to accommodate the NA-pairs is suggested based on a probability of 0.9 or above, which is indicative of a greater confidence in the prediction.Measures used in strategies 1 and 2 are not applicable to strategies 3 and 4, which predict one of the top four structural levels for the classification of the JDH proposed the structural descriptors and PJ conceived their use for structural classification. PJ drafted the manuscript and JDH provided advice on presenting results and assisted in drafting the manuscript. Both the authors have read and approved the final version of the manuscript.Unclassified domains consisting of four, five and six SSEs. This file lists the PDB identifiers for the unclassified proteins deposited in PDB after the release of SCOP 1.73 and the confusion matrices used to classify such proteins.Click here for filePredicted classification for unclassified protein 2ZM6. This file lists all of the classification hierarchies to which the protein 2HGP is classified in the SCOP version 1.73. These classification hierarchies can be manually checked to classify 2ZM6 that is predicted to share the same Family as 2HGP.Click here for filePredicted classification for unclassified protein 3BPJ. This file lists all of the classification hierarchies to which the protein 2OTJ is classified in the SCOP version 1.73. These classification hierarchies can be manually checked to classify 3BPJ that is predicted to share the same Fold as 2OTJ.Click here for fileDomains consisting of 3SSEs from SCOP version 1.69. This file lists the identifiers for the 3SSEs containing domains from SCOP version 1.69.Click here for fileDomains consisting of 3SSEs from SCOP version 1.73. This file lists the identifiers for the 3SSEs containing domains from SCOP version 1.73.Click here for fileDomains consisting of 4SSEs from SCOP version 1.69. This file lists the identifiers for the 4SSEs containing domains from SCOP version 1.69.Click here for fileDomains consisting of 5SSEs from SCOP version 1.69. This file lists the identifiers for the 5SSEs containing domains from SCOP version 1.69.Click here for fileDomains consisting of 6SSEs from SCOP version 1.69. This file lists the identifiers for the 6SSEs containing domains from SCOP version 1.69.Click here for file


Large-scale genome sequencing gained general importance for life science because functional annotation of otherwise experimentally uncharacterized sequences is made possible by the theory of biomolecular sequence homology. Historically, the paradigm of similarity of protein sequences implying common structure, function and ancestry was generalized based on studies of globular domains. Having the same fold imposes strict conditions over the packing in the hydrophobic core requiring similarity of hydrophobic patterns. The implications of sequence similarity among non-globular protein segments have not been studied to the same extent; nevertheless, homology considerations are silently extended for them. This appears especially detrimental in the case of transmembrane helices (TMs) and signal peptides (SPs) where sequence similarity is necessarily a consequence of physical requirements rather than common ancestry. Thus, matching of SPs/TMs creates the illusion of matching hydrophobic cores. Therefore, inclusion of SPs/TMs into domain models can give rise to wrong annotations. More than 1001 domains among the 10,340 models of Pfam release 23 and 18 domains of SMART version 6 (out of 809) contain SP/TM regions. As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins. More worryingly, we show explicit examples that the scores of clearly false-positive hits, even in global-mode searches, can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74 using conservative criteria, we find that at least between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. Thus, false-positive domain hits enforced by SP/TM regions can lead to dramatic annotation errors where the hit has nothing in common with the problematic domain model except the SP/TM region itself. We suggest a workflow of flagging problematic hits arising from SP/TM-containing models for critical reconsideration by annotation users. Sequence homology is a fundamental principle of biology. It implies common phylogenetic ancestry of genes and, subsequently, similarity of their protein products with regard to amino acid sequence, three-dimensional structure and molecular and cellular function. Originally an esoteric concept, homology with the proxy of sequence similarity is used to justify the transfer of functional annotation from well-studied protein examples to new sequences. Yet, functional annotation via sequence similarity seems to have hit a plateau in recent years since relentless annotation transfer led to error propagation across sequence databases; thus, leading experimental follow-up work astray. It must be emphasized that the trinity of sequence, 3D structural and functional similarity has only been proven for globular segments of proteins. For non-globular regions, similarity of sequence is not necessarily a result of divergent evolution from a common ancestor but the consequence of amino acid sequence bias. In our investigation, we found that protein domain databases contain many domain models with transmembrane regions and signal peptides, non-globular segments of proteins having hydrophobic bias. Many proteins have inherited completely wrong function assignments from these domain models. We fear that future function predictions will turn out futile if this issue is not immediately addressed. Alternaria alternata (AAB40400). The same approach revealed an apparently significant hit to the Pfam domain PF00497  or convergent evolution (common selective pressure). The criterion of sequence similarity for inferring homology is actually applicable only to globular segments and non-globular parts should be excluded from starting sequences in homology searches. The special case with amino acid compositional bias was recognized early and it was always advised to exclude those segments from similarity searches when hunting after distantly related proteins. For the BLAST/PSI-BLAST suite, the SEG program was advised to suppress at least the most obvious low complexity regions The unsupervised inclusion of transmembrane helices and signal peptide segments in homology searches is especially prone to erroneous addition of unrelated sequences to the sequence family under study since the systematic coincidence of hydrophobic positions creates the appearance of similarity in the hydrophobic pattern, otherwise the key to sequence homology among globular sequence segments Similar precautions are generally out of scope when protein domain model libraries are applied for function prediction over query sequences, especially in a genome-wide mode. It is desirable to have systematic factors that might cause spurious annotations such as isolated similarities to signal peptides or some types of transmembrane helices be suppressed during the annotation workflow.http://mendel.bii.a-star.edu.sg/SEQUENCES/ProblemDomains-TMSP/.When checking domain databases for the inclusion of transmembrane helices and signal peptides into the domain model, we found more than thousand domain instances in Pfam and a couple of examples even in SMART. These hidden Markov models (HMMs) can be a systematic cause of spurious similarity hits especially if the HMM-based sequence scan is applied in the local search mode. In this work, we wish to emphasize that these domain models can also give rise to wrong hits even in the global search mode where the high score from the membrane-helical part can mask the absence of match for the associated globular domains. For support of the reader, database search results, alignments, domain library entry lists and files with “cleanup” domain models as referred to in the following text are provided as supplementary material at the associated WWW site Since the SMART database In brief, we recovered the full length protein sequences that contained the segments in a given alignment of SMART version 6, applied 5 TM and 2 SP predictors published in the literature and we checked overlap of predicted SP/TM regions with the alignment segments. For an alignment position to be considered part of a predicted TM or SP region, the respective residue must be included into the predicted range in a critical number of sequences and by a certain number of prediction tools determined by a statistical criterion based on the binomial distribution .Structural Classification of Proteins For each predicted TM or SP region, we derive a score as the arithmetic mean of the logarithmic probabilities of SP/TM prediction over all alignment columns involved  and SP-score cutoff of ≥−1 (FP rate of 4.02%), the number of predicted TM helices and signal peptides are 40 and 5 respectively. At the domain level, this translates to 13 problematic domains with TMs and 5 with SPs, respectively . Thus, tThese 18 predictions were manually validated: (i) If the respective predicted segments were indeed structural helices and not SPs/TMs, they should be part of one of the nearest globular domains in the sequence. The alignment sequences were searched against the sequences with known 3D structure from the Protein Data Bank (PDB) for any significant hits  and we checked whether the predicted SP/TM region overlaps with the segment covered by the structure. If the predicted SP/TM region was missing in the structure or if it was described as a TM helix in the structural report, we considered the prediction as validated. (ii) Without structural hits, we searched the scientific literature for topological information about membrane embedding of reference sequence segments.As the information collated in Given that our SP/TM detection procedure provides statistical error measures for the prediction, it can be reasonably applied on the body of Pfam domain models. When this work was started, the available Pfam version was release 23 constructed with the HMMER2 package. About 19% (1937 out of 10340) of Pfam-A domains in release 23 et al.At the domain level, this implies 1079 (10.4%) and 164 (1.6%) out of 10340 Pfam-A domains having TM or SP regions included into the domain alignment . The extAmong our 164 domains with SP predictions, we might expect 6.6 (∼7) wrong predictions. On average, each domain with predicted TM regions contains about 3.6 (3849/1079) TM helices, out of which 0.17 (4.67% of 3.6) represent false-positive TM helices. We might expect that about 50 domains out of the 1079 domains are wrongly included into this list. Even if we remove those values from the total number of 1214 problematic domain models , Pfam-A release 23 still contains more than 1001 critical cases as claimed in the title of this article.The domain alignments in Pfam and SMART are used for the derivation of hidden Markov models (HMMs) that, in turn, are applied for searching matches in query sequences with programs of the HMMER packages With SP/TM regions as part of the domain alignment, the respective HMMs are no longer useful for local mode searches since a match in the TM or SP region alone without any other sequence similarity to the query sequence can be sufficient to cause a false-positive fragmentary domain hit as in the introductory case of “Alt a 1”. Further illustrative examples are provided in One of the referees brought up the argument that some of the sequences in The model Herpes_glycop_D (PF01537.9) has a membrane-helix region that, together with its linkers on both side, are the sole part of a match in the fragmented search mode for a large variety of taxonomically and functionally diverse proteins out of which eight architectures are presented here. Similarly, the TM region (plus surrounding polar linkers) of model CDC50 (PF03381.7) significantly hits proteins with at least three different architectures in the fragmented HMM search.For another 4 domain models Cation_ATPase_N (PF00690.1), GSPII_F (PF00482.11), PAP2 (PF01569.13) and HCV_NS4b (PF01001.11) provided as further illustration examples, the respective TM region hit a single TM helix segment of several seemingly unrelated proteins. In all cases, their alignment scores were above their family-wise gathering score thresholds.Not surprisingly, the global search mode that forces a complete match of the domain model over a subsegment of the query sequence is the standard regime for running hmmsearch and hmmpfam of the HMMER2 package. Typically, a positive hit is recognized either by a score above a so-called gathering threshold  or an E-value below a trusted limit . It is particularly worrying that a number of domain models with SP/TM regions included generate quite convincing E-values for unrelated sequences even in the global search mode. In all these cases, matches of a hydrophobic region in the query with the hydrophobic segments of these validated SP/TM regions is the reason for the elevated score that frequently surpasses even the gathering score threshold.To investigate the effects of SP/TM regions in homology searches, two separate HMM searches against the NR database were performed for each domain under study. The first run relied on an HMM using the original alignment. For the second run, we constructed a “cleanup” alignment via the removal of the predicted TM or SP segments. The two HMMs for the hmmls style of search  were built from the alignments using the commands ‘hmmbuild –F –amino model-file alignment-file’ and ‘hmmcalibrate –seed 0 –num 5000’. When contrasting the results of the two HMM runs at E-value≤0.1, we assume all hits of the cleanup model as true-positives and scrutinize all additional hits of the original model as potential false-positive hits. We screened them for potentially contradictory annotation using sequence-analytic tools The model PIG-P (PF08510.4) includes a segment with TM helices (positions 1–91) and hydrophilic region (positions 92–208). In the global-mode search against the non-redundant database, the first 100 alignment positions of the model  hit a pair of C-terminal TM helices in the four protein targets listed in The PAP2 (type 2 phosphatidic acid phosphatase) domain model (PF 01569.13) hits the sequence XP_418136.2  in an inThe members of the EMP24_GP25L family , the respective HMM hits numerous sequences of γ-subunits of voltage-dependent Ca-ion channels with E-values in the order of e-7. Closer inspection of the seed alignment showed that just a single channel sequence (CCG2_mouse) was included although they are not related to the family The decrease in specificity of domain models harboring SP/TM regions is also accompanied by a decrease in sensitivity. In general, the need to have additional good alignment scores for the SP/TM pieces can become a burden for any true-positive sequences that are incompletely sequenced or missing the SP/TM-region pieces naturally.By contrasting the HMM runs between the original and cleanup models, potential false-negatives were identified as hits that were found only by the cleanup models. Then see , we re-cIn It was already suggested in the literature that unsupervised annotation transfer based on spurious sequence similarities has created a myriad of false function annotations for sequences from genome projects Protein Identification Resource) iProClass v3.74 We explored this issue for PIR  with the feature keys “signal” and “transmem”. Among the single-transmembrane proteins, we selected those characterized as “anchor” in a special group. For multi-TM region proteins, we selected those who have 5–9 annotated TM segments. Additionally, we got the experimentally verified α-helical TM regions as provided by TMPDB (release 6.3) In our calculations, we find that only 3% of residues in α-helices in globular domains are covered by hits of the quite stringent low complexity tool SEG  Interestingly, the values for the Uni-Prot sets are 30% for single transmembrane proteins, 33% for single transmembrane proteins with the region annotated as “anchor” but only 12% for multi-transmembrane proteins. Thus, the problems with non-relevant matches in hydrophobic regions are more likely to occur, as a trend, in proteins having signal peptides or only a few transmembrane segments compared with cases of multi-membrane-spanning proteins.There is no substitute for computational methods in large-scale functional annotation of sequence data and sequence similarity as surrogate for homology has to remain a decisive factor for function assignment et.al.The fundamental consideration in this article, namely the difficulty to interpret sequence similarity as a result of similarity of non-globular segments,  within the current theory of sequence homology, the basis of annotation transfer, goes beyond the specific criticism for a few domain models. In this context, it appears necessary to recall what the notion of a protein domain implies. In the introduction of their article, Veretnik In the special case of globular domains that have tertiary structure, sequence similarities imply sequence homology as well as fold and function similarity. If 3D structures are known, domains as compact (having an own hydrophobic core) and spatially distinct units of protein structures that share significant structural similarity can be grouped together  that are similar to each other with statistical significance, (ii) that provide for a specific biological function at the molecular level  or convergent evolution . We wish to emphasize that generally applied sequence-statistical criteria for deducing homology have been derived from studies of globular domains. In these cases, conservation of an intricate, only apparently random hydrophobic pattern is necessary for composing the hydrophobic core and, thus, for fold conservation de novo appearance of a phosphorylation site in a generally serine-rich stretch seems quite high in evolutionary time scales. This issue would deserve a more explicit study on its own.This condition is generally not fulfilled for non-globular segments ; thus, their functional annotation requires other methods than just annotation transfer based on position-wise sequence similarity. It appears likely that many types of non-globular segments re-occurring in evolutionary very distant proteins are rather the result of convergent evolution than common ancestry; for example, the likelihood of a In a generalized theme, SP/TM segments are usually the results of physico-chemical constraints and do not confer the specific biological function of the protein. Therefore, missing alignments in the SP/TM regions is less detrimental than that of the non-SP/TM regions if the membrane-embedded region is just used as translocation signal.To further the argument, in the framework of HMM, there is no clear demarcation of SP/TM and non-SP/TM regions towards the computation of the alignment scores. Hence, this questions the correctness of inclusion of SP/TM regions into the HMM or, at least, makes a separate consideration for them a matter of necessity in the context of the homology argument.Our arguments raise the question whether position-specific scoring matrices (PSSM), HMMs or profiles are indeed the appropriate tool to classify all kinds of non-globular segments with regard to sequence homology. Matching the hydrophobic pattern alone is recognized insufficient for inferring homology among proteins with transmembrane helices. In previous reports The case of SPs/TMs is of special importance since their hydrophobic stretches can create the false appearance of similarity to the respective hydrophobic core of the target template based on a hydrophobic pattern match. Alignments with many hydrophobic residues in the same columns generate high scores; thus, a SP/TM match can elevate an otherwise mediocre HMM score into the range of significance. The inclusion of a SP/TM into the domain model can compromise the selectivity of HMMs towards specific families and create hits not only to neighboring sequence families within the superfamily but also beyond. Whereas errors of the first kind might be considered not dramatic, we show with examples in Thus, the reliability in homology inference is greatly influenced by the amount of non-globular content in such domain library entries. We find that, even in the very well curated SMART domain collection (version 6), there are 18 domain models (out of 809) that include TMs or SPs. Based on our conservative approach, we find that clearly more than 1000 domains harbor SP/TM segments in Pfam release 23 (out of 10340 entries). To make matters worse, we observe a growing trend of addition of SP/TM region-containing domain models in Pfam and especially in SMART during the recent years .In the Therefore, our finding might suggest the mandatory removal of SPs/TMs from domain models. We do not recommend this at this stage. Such a strategy is not easy to implement due to several reasons. The required editing of domain libraries given their current status would be quite laborious and appears impractical in the short term. Then, there is also the issue with some multi-TM region protein domain models where there is little or no soluble globular component. Further, the biological significance of sequence similarity of proteins with TM regions and its relationship to homology has been studied only in a few cases Notably with regard to signal peptides, the Pfam team has conveyed to us the removal of signal peptides in most domain models for future releases . Similarly, it appears reasonable to remove TM regions from models where they are not integral parts of the globular domain and, especially, where the domain occurs also outside the TM region context. An excellent match between SP/TM regions of non-relevant proteins is possible just because of their uniform hydrophobicity and this match will elevate scores in alignments. Often, this might be insufficient to overcome thresholds of significance but, as we see in our experience, it can happen and it happens systematically for some types of models. Most likely, the problems arise with domains having one or very few TM regions which are the majority of cases in Pfam . As we have seen, the trend to low sequence complexity is especially strong for proteins segments representing a signal peptide or a single-TM anchor. Both the exclusion of signal peptides and of transmembrane helix anchors from domain models would remove the bulk  of the problems described in this article. Among all SP/TM regions, signal peptides, signal anchors and single TM regions have a trend to considerably more pronounced sequence complexity than TM regions in multi-TM proteins see .In addition, we propose two other possible workarounds: First, one might process each query sequence with tools recognizing non-globular segments including those for SP/TM regions and mask them with X-runs before comparing the query with domain libraries. Yet, this would not exclude cases such as SPs in HMMs hitting structural helices  brings in an arbitrary component into the prediction process.Firstly, the determination of a gathering score is not guided by a fundamental consideration but, instead, depends on the data and literature situation at the time of seed alignment collection. Regardless, how carefully a gathering score is selected by the expert, it remains a subjective decision. The sequence with a true model hit with lowest score  critically depends on the size of the non-redundant protein database, the variety of sequences therein and the quality of the seed alignment at the time of model construction. Sequence databases have a strong growth due to increasingly cheaper sequencing. With time, our biological knowledge grows and we know more about previously uncharacterized sequences. Not surprisingly, gathering thresholds have an inherent trend to be increased with time even if the underlying seed alignments do not change.Drosophila melanogaster sequence AE003559 would make it over the gathering score threshold in 2000 and later whereas the Pfam5 gathering score would clearly support many homologues. Thus, the experimentally verified discovery of the Eco1 acetyltransferase might have been overseen after 2000 based on a gathering score criterion but it would never disappear from the radar in an E-value guided search at any time point. As for the other introductory example, the PF00497 (SBP_bac_3) model, the fragment-mode gathering thresholds have also been heavily changed over time: For Pfam5 (1999) and Pfam6 (2000) GA = −20 (fm), whereas, for Pfam7 (2001), GA = 49.9 (fm); thus, the sequence “Alt a 1” would have been a hit based on the gathering threshold criterion until the year 2000 but it would be suppressed with the more recent versions of Pfam. At the same time, the E-value generated by the example did not change.For example in the case of PF00583 (Acetyltransferase) in the introductory Eco1 example, the gathering scores have evolved the following way: Pfam5 (1999) with 6.5  and 6.5 (fragment- mode/fm), Pfam6 (2000) with 15 (gm) and 15 (fm) , Pfam7 (2001) with 18.2 (gm) and 16.3(fm). The reader is invited to return to Secondly, gathering scores hide the problem of balance between true-negative and false-positive hits. Although increasing gathering scores (as there is a trend in Pfam releases) reduce false-positive hit rates, this approach excludes a growing number of true hits and, thus, also limits the extrapolation power of domain models into the space of uncharacterized sequences. On the contrary, an E-value gives insights into the orders of magnitude of error rates when assuming the annotation transfer to be correct. The user of a gathering threshold guided assignment does have the illusion of dealing with ultimately correct hits; in contrast, an E-value provides a quantitative and typically non-zero statistical measure for annotation error.Typically, I trust the results of HMMER searches at about E = 0.1 and below, and I examine the hits manually down to E = 10 or so.”Thirdly, gathering thresholds do not relate well with the statistics of hit distribution in the non-redundant database. In the HMMER2 manual, Sean Eddy says on page 22 “Calibrated HMMER E-values tend to be relatively accurate. E-values of 0.1 or less are, in general, significant hits”. Further on page 43, he writes “The best criterion of statistical significance is the E-value. The E-value is calculated from the bit score. It tells you how many false positives you would have expected to see at or above this bit score. Therefore a low E-value is best; an E-value of 0.1, for instance, means that there's only a 10% chance that you would've seen a hit this good in a search of non-homologous sequences. −35 to 105). Nevertheless, E-value thresholds close to the empirical value of 0.1 are most frequent in Pfam , we find actually no general relationship between domain-specific gathering scores and E-value thresholds for Pfam release 23 . In factLastly, E-values are comparable since they are a statistical measure but gathering score thresholds are not and, therefore, scores calculated from different domain models or prediction tools cannot be compared. This makes decisions among domain models and other prediction tools hitting the same segment in the query difficult. For example, the sequence XP_001939830.1  are not relevant for the conclusions in this paper. We have to live with some error rate. In contrast, it is important that the theoretical fundamentals are reliable, that systematic causes for possibly questionable annotations are increasingly suppressed and that, together with the Pfam team, the community develops the theory.It is difficult to assess the total amount of wrong annotations currently persisting in public sequence databases since most of the protein sequences have never been a target of experimental study. With regard to theoretically derived function descriptions, the individual teams contributing to sequence databases, apparently, apply criteria with differing stringency and rigor. It appears that unrestrained annotation transfer justified by spurious sequence similarities is a major cause for annotation errors et al.In their analysis of database annotations for 37 enzyme families, Schnoes Thus, the criteria for sequence homology in their present form appear not directly applicable to non-globular segments. SPs/TMs as part of domain models lead to pollution of database annotations as our PIR iProClass v3.74 analysis demonstrates. As a matter of fact, it is very difficult to prove wrong annotation for experimentally uncharacterized sequences otherwise than by detecting logical contradictions. Whereas the examples in To conclude, sequence similarity among non-globular protein segments does not necessarily imply homology. Since matching of SPs/TMs creates the illusion of alignable hydrophobic cores, the inclusion of SPs/TMs into domain models without precautions can give rise to wrong annotations. We find that clearly more than 1001 domains among the 10340 models of Pfam release 23 suffer from this problem, whereas the issue is of relatively low importance for domains of SMART version 6 (18 out of 809). As expected, fragment-mode HMM searches generate promiscuous hits limited to solely the SP/TM part among clearly unrelated proteins for these models. More worryingly, we show explicit examples that the scores of clearly false-positive hits even in global-mode searches can be elevated into the significance range just by matching the hydrophobic runs. In the PIR iProClass database v3.74, we find that between 2.1% and 13.6% of its annotated Pfam hits appear unjustified for a set of validated domain models. We suggest a workflow of flagging problematic hits arising from SPs/TMs-containing models for critical reconsideration by annotation users. On the other hand, we have also seen that the inclusion of SP/TM regions into domain models can give rise to false negatives by imposing the need to have good scores over these regions in the query sequences when the actual domain occurs without the SP/TM context.It is well known that the problem of transmembrane helix prediction is not so much the detection of true hits as the suppression of false-positives In the general case, domain models are characterized by both seed and full alignments. We think that, in our context, operating with seed alignments is preferable since they are manually validated and are supposed to have lower levels of inclusions of unrelated sequences.For a given domain model alignment, each sequence was subjected to sets of transmembrane (TM) and signal peptide (SP) segment predictors. We have used the following TM predictor tools – DASTM For each predictor a priori unknown) probability of the residue To ensure that columns of domain alignments with an unequal number of sequences and/or gap instances are treated comparably, a hypothesis testing step is introduced We assume the null hypothesis is rejected at a significance level of In practice, some of the predicted transmembrane helices and signal peptides can be fragmented due to small gaps in the alignment. In the case of signal peptide fragments, it is reasonable to assume that all the fragments come from a single signal peptide. Consequently, the average logarithm probability of SP prediction per domain is simply calculated using (5) summing over the smallest region that contains both the N-terminal alignment position and the C-terminal boundary of the most C-terminal predicted segment.th TM helix fragment. Only predicted segments with a TMcutoff or SPcutoff respectively; see below) are considered in the further analysis; others are discarded and the respective positions in However, for the case of the fragmented TM helices, the situation can be complicated by occurrences of multiple transmembrane segments within the alignment. As indicator which fragments to unite into one segment, we use the raw TM predictions. The indicator function We have used our algorithm also to find SP/TM regions in α- and membrane proteins classified by SCOP For the TM prediction problem, only the individual TM helix has been defined so far. To define a TM region that composes of one or more TM helices, adjacent TM helices separated by less than 40 amino acid residues are concatenated to form a region. The choice of 40 amino acids is based on the current knowledge that the smallest known globular domains such as Zinc fingers For the SP prediction problem, it is relevant that the actual N-terminus might be missing in the domain alignment. Thus, two rounds of SP predictions are necessary. After the initial round, the domain sequences with positive SP predictions are subjected to blastp runs (with parameters ‘-M BLOSUM62 -G 11 -E 1 -F F -I T’) against NR database to retrieve their full sequence data. Only the full sequence data with percent identity ≥95% and Blast E-value ≤0.01 are then subjected to SP predictions. Finally, only overlapped SP predictions that are confirmed in both rounds are retained for further processing.The appropriate cutoff for predicted TM and SP segments in domain alignments have been determined with the help of the SCOP v1.75 TM prediction hits among SCOP α class proteins are false-positives since the database contains predominantly structural helices. On the other hand, the membrane class contains mostly TM helices that made up the true-positive hits for these predictors. In the case of the signal peptide prediction, both α- and membrane SCOP classes will deliver false-positive hits while the domain models from SMART with signal peptide are true positive hits. In the following, the reader is assumed to be familiar with chapter three of Here, equation (12) that denotes the total score For a set of sequences with a common problematic domain annotation, each sequence score can be represented by We find that our derivation for Figure S1PF00583 hits leading to the Eco1 function discovery.(0.03 MB PDF)Click here for additional data file.Figure S2False-positive hit of PF00497 in Alt a 1.(0.01 MB PDF)Click here for additional data file.Protocol S1http://www.rarlab.com/download.htm). Besides HMMER outputs, alignments, etc. for http://mendel.bii.a-star.edu.sg/SEQUENCES/ProblemDomains-TMSP/.Mini-site with supplementary information, archive created with WinRAR (to be downloaded from (32.83 MB WinRAR)Click here for additional data file.Table S1Summary of selected sequence hits with problematic domain annotations (fragment-mode search).(0.04 MB PDF)Click here for additional data file.Table S2Summary of selected sequence hits with problematic domain annotations .(0.05 MB PDF)Click here for additional data file.Table S3Summary of selected false-negative sequence hits with problematic domain annotations .(0.05 MB PDF)Click here for additional data file.


Pig herds that provide pork free from zoonotic agents may be possible. Yersinia enterocolitica. Thus, the ability to eliminate this agent from pig herds would be an important step in producing human pathogen–free pork. Pig herds free from Y. enterocolitica O:3/biovar 4 have been established and maintained. According to serologic and cultural testing results, 15 of 16 specific pathogen–free herds were free from Y. enterocolitica O:3/biovar 4; this closed breeding pyramid has remained free from this organism since 1996. Pig herds free from human pathogenic Y. enterocolitica suggest that human pathogen–free herds could be attained to provide pork free from zoonotic agents.Pork products are a substantial source of human yersiniosis, a foodborne disease caused by  Yersinia enterocolitica is a major cause of foodborne disease in the industrialized world   of 287 herds to be positive for Y. enterocolitica O:3. Among the seropositive herds in this study, significantly fewer were mixed herds of piglets and fatteners (53.1%) than fattening herds (86%). Mixed herds represent a significant protective factor against infection with Y. enterocolitica O:3/biovar 4 because the herd is not supplemented by animals brought in from outside sources. Thus, reducing the herd prevalence of Y. enterocolitica O:3/biovar 4 may be possible by minimizing contact between infected and noninfected herds.Most Norwegian pig production is organized in a closed breeding system in which primary nucleus-herd farms sell breeding animals to secondary multiplying-herd farms. These multiplying-herd farms sell breeding animals to conventional-herd farms (farrowing to finishing herds or young pig production). In turn, animals from young pig–production farms are sold to fattening-herd farms. These breeding pyramids are kept free from animal diseases such as sarcoptic mange, swine dysentery, and enzootic pneumonia. If successful elimination of human pathogenic Y. enterocolitica has been evaluated. We report that a specific pathogen–free (SPF) breeding pyramid with focus on animal disease can be established and maintained free from Y. enterocolitica O:3/biovar 4.The ability to create pig herds free of human pathogenic In 1996, the first SPF nucleus herd  was established by hysterectomy, and the piglets were reared without contact with other pigs. In 1999, a second nucleus SPF herd  was established with gilts from herd 1. These 2 herds have been totally isolated from other herds, except for artificial insemination. Since 1997, 14 new SPF herds have been established with gilts from 1 or both of the above-mentioned SPF nucleus herds; each has been maintained as a closed herd (or supplemented with replacement gilts from 1 of the 2 SPF nucleus herds). Each of these 14 new SPF herds had an average of 60 animals (range 20–150). All SPF herds are housed, the water supply is potable, and pest control systems are established. Pets and wild animals cannot enter the pig house. The owner, herdsmen, veterinarians, and technicians must shower and change clothes before entering the pig housing. Many pig herds organized in the general closed breeding system have also implemented many of these preventive measures.Testing of pigsY. enterocolitica O:3/biovar 4 can be detected in different age groups of pigs by 1) serologic testing of pigs at all ages from ≈100 days, including at slaughter when the pigs are 150–180 days old; and 2) bacteriologic examination of feces from pigs of all ages from 85 days until ≈135 days. In most instances, the testing of pigs in our study has been in accordance with the conclusions of Nesbakken et al  by using the revised biogrouping scheme for Y. enterocolitica  are consistent with past exposure to the organism or nonspecific cross-reaction rather than active infection. Bowman et al.  was classified as serologically positive for antibodies against Y. enterocolitica O:3. Among the 30 animals tested, 15 were positive . This herd was also the only one that was positive for Y. enterocolitica O:3/biovar 4 according to culture result. The isolation method used in our study has proven to be sensitive for isolation of Y. enterocolitica O:3/biovar 4 even when the fecal samples are small  by Pilon et al.  pork on the market. The following facts should be considered in discussions of the possibility of establishing HPF herds: 1) <0.1% of the pigs in Norway harbor Salmonella  ≈100% of the pigs harbor Campylobacter spp. (The low prevalence of human pathogenic Salmonella, Trichinella,T. gondii, and, according to our findings, even human pathogenic Y. enterocolitica. Freedom from Campylobacter spp. in pigs is probably impossible. However, blast chilling after the slaughtering process seems to reduce the number of Campylobacter spp. ≈100% (Closed SPF pig herds are probably nearly free from Y. enterocolitica. This contamination has a human health aspect because one of the risk factors for human yersiniosis might be drinking water that has not been disinfected (Y. enterocolitica–free herds might have a positive environmental effect.Another aspect to consider is the environment. Usually manure from pig farms is spread in fields and may contaminate wild animals, lakes, and rivers. Drinking water may thereby be contaminated with pathogenic


Mycobacterium tuberculosis, remains a leading infectious disease despite the availability of chemotherapy and BCG vaccine. The commonly used avirulent M. tuberculosis strain H37Ra was derived from virulent strain H37 in 1935 but the basis of virulence attenuation has remained obscure despite numerous studies. We determined the complete genomic sequence of H37Ra ATCC25177 and compared that with its virulent counterpart H37Rv and a clinical isolate CDC1551. The H37Ra genome is highly similar to that of H37Rv with respect to gene content and order but is 8,445 bp larger as a result of 53 insertions and 21 deletions in H37Ra relative to H37Rv. Variations in repetitive sequences such as IS6110 and PE/PPE/PE-PGRS family genes are responsible for most of the gross genetic changes. A total of 198 single nucleotide variations (SNVs) that are different between H37Ra and H37Rv were identified, yet 119 of them are identical between H37Ra and CDC1551 and 3 are due to H37Rv strain variation, leaving only 76 H37Ra-specific SNVs that affect only 32 genes. The biological impact of missense mutations in protein coding sequences was analyzed in silico while nucleotide variations in potential promoter regions of several important genes were verified by quantitative RT-PCR. Mutations affecting transcription factors and/or global metabolic regulations related to in vitro survival under aging stress, and mutations affecting cell envelope, primary metabolism, in vivo growth as well as variations in the PE/PPE/PE-PGRS family genes, may underlie the basis of virulence attenuation. These findings have implications not only for improved understanding of pathogenesis of M. tuberculosis but also for development of new vaccines and new therapeutic agents.Tuberculosis, caused by  Mycobacterium tuberculosis, the causative agent of TB, is a highly successful pathogen that has latently infected one third of the world population (2 billion people) and causes 9 million new cases and about 2 million deaths each year worldwide (http://www.who.int/gtb/). The mechanisms by which M. tuberculosis causes disease have remained largely unknown until the improvement made recently via the application of modern molecular genetic tools, including genomic sequencing of the common lab virulent reference strain H37Rv M. bovisM. bovis BCG M. tuberculosis H37Rv strain versus the avirulent H37Ra strain has been lacking.Tuberculosis (TB) remains a leading infectious disease despite the availability of chemotherapy and the BCG vaccine. M. tuberculosis H37Ra is the avirulent counterpart of virulent strain H37Rv and both strains are derived from their virulent parent strain H37, which was originally isolated from a 19 year-old male patient with chronic pulmonary tuberculosis by Edward R. Baldwin in 1905 i.e., H37 but with rough colony morphology) M. tuberculosis worldwide since 1940s and H37Ra is also used as an adjuvant to boost immunogenicity during immunization.Historically, H37Ra has several characteristics that are different from its virulent sister strain H37Rv, including a raised colony morphology M. tuberculosis H37Ra strain from the American Type Culture Collection (ATCC25177). Comparative genomic analysis of H37Ra with its recently sequenced virulent counterpart H37Rv not only provides important insights into the basis of attenuation of virulence in H37Ra but also improves our understanding of virulence and pathogenesis of M. tuberculosis.In this study, we determined the whole genome sequence of the M. tuberculosis H37Ra ATCC25177 contains a single circular chromosome of 4,419,977 bp with an average G+C content of 65.61% (GenBank accession number CP000611). A total of 4034 protein-coding sequences (CDS) are identified in the H37Ra genome with an average length of 1000 bp, representing 90.1% of the genome  in H37Ra relative to H37Rv  were compared with their corresponding loci in the clinical isolate CDC1551, it is interesting to note that the similarity between H37Ra and CDC1551 is higher than that between H37Ra and H37Rv . BetweenSince H37Ra and H37Rv both evolved from the same parental strain H37, it is unlikely that H37Ra could accumulate so many variations identical to CDC1551. Thus we re-sequenced the corresponding H37Rv genomic regions covering the 138 “H37Rv-specific” variation loci. Surprisingly, among the 85 successfully sequenced sites , only 3 SNV loci, 2 insertion loci and 1 deletion loci are the same as the published H37Rv genomic sequence, while the remaining 79 sites are identical to those of H37Ra and CDC1551 .i.e., those that are different from both H37Ra and CDC1551) are likely acquired during long-term repeated in vitro cultivation of H37Rv over the years but unrelated to the virulence property. Therefore, when all the 138 “H37Rv-specific” variations are excluded from the 272 genetic variations in H37Ra, we have only 134 total genetic variations that are “H37Ra-specific”, including 79 SNVs, 39 insertions and 16 deletions that are different from H37Rv or CDC1551. To ensure that these 134 “H37Ra-specific” variations are indeed unique to H37Ra and not due to possible sequencing errors of H37Rv sequence, we again re-sequenced these 134 “H37Ra-specific” variation loci using our strain of H37Rv , and 58 affect the CDSs of 20 genes. Among the 39 “H37Ra-specific” insertions, 14 are located in the intergenic regions that might have no effect on gene promoters. For the remaining 25 insertions, 23 affect 19 CDSs while the other 2 might affect probable promoters of 2 genes. A total of 15 “H37Ra-specific” deletions might affect 12 genes, of which, 5 might affect probable gene promoters and 10 affect the CDSs. In sum, a total of 57 genes in H37Ra are likely affected by genetic variations and will be the focus of our functional analysis  in H37Ra vs H37Rv both in culture and in macrophages. Interestingly, their expression levels were all increased in vitro but decreased in macrophages in H37Ra compared with H37Rv (p<0.05)  to compare the levels of expression for several important candidate genes ((p<0.05) .6110. In addition, only a limited number of Long Sequence Polymorphisms  6110 related or in the PE/PPE/PE-PGRS family. There are 17 complete copies of IS6110 in H37Ra, but 16 copies in H37Rv. The difference in IS6110 copy number between H37Ra and H37Rv is due to two complete “H37Ra-specific” IS6110 insertions, located at 14 kb oriC, respectively  in H37Ra located in a region which seems to be a hot spot for IS6110 insertion, where H37Ra has only one but H37Rv has two copies of IS6110  found flanking the IS6110 at this location in H37Ra do not support the possibility of a recombination between two neighboring IS elements causing deletion of one copy of IS6110 in H37Ra The H37Ra genome contain 32 complete and 10 disrupted IS elements , almost ectively , in addif IS6110 . The absM. tuberculosis H37Rv compared to M. bovis and M. bovis BCG 6110plcD gene and the adjacent IS6110  is inserted between the 476th and the 477th bp of the plcD gene thus causing a 5′ truncation in the plcD gene of H37Ra . However, in H37Rv, the IS6110 is inserted between the 696th and the 697th bp of the plcD gene and a subsequent homologous recombination between this IS6110 and the adjacent IS6110 located at the 5′ end of the cut1 gene caused a deletion of an 8 kb fragment in H37Rv. This proposition is supported by analyzing the DRs flanking these IS6110 elements. The ‘GAG’ adjacent to the right end of IS6110 in H37Rv is similar to the DRs of IS6110 next to the cut1 gene in H37Ra and CDC1551, while the 3 bases adjacent to the left end of H37Rv IS6110 is ‘AAG’, similar to the IS6110 insertion site of plcD gene. Therefore, the two pieces of the once co-existing IS6110 elements with DRs of ‘AAG’ and ‘GAG’ likely had undergone a recombination that generated one IS6110 element carrying one DR of each parent in H37Rv.There are five H37Rv related deletion (RvD) loci defined based on the genomic deletions identified in t IS6110 . This frbination . Assumin6110 remnant of 247 bp is found adjacent to this region in H37Ra, joining with a complete IS6110 , is located at 1 bp upstream of the start codon of MRA_2373 (Rv2352c). The MRA_2376 is identical to MRA_2373, and the same gene duplication is observed in CDC1551 (MT2419 and MT2422). However, only one homologous gene (Rv2352c) is found in H37Rv. It is most likely that a recombination occurred between the two precursors of Rv2352c and induced the 2064 bp deletion in H37Rv.A 2064 bp insertion present in both H37Ra and CDC1551 but deleted from H37Rv is designated as RvD6 . This frThe high degree of similarity in the genomic content and the apparently disparate phenotypes between H37Ra and H37Rv indicate that mutations in transcription factors and genes related to global regulations might account for its unique characteristic of growth and/or attenuated virulence of H37Ra. In fact, only limited mutations are found in this category.sigC (MRA_2083/Rv2069), which encodes the extracellular function (ECF) subfamily sigma factor sigC in H37Ra were up-regulated compared to H37Rv in log phase cultures, but the transcription level of sigC in H37Ra was nearly 4 times lower than H37Rv in macrophages  is identified in H37Ra, in the predicted promoter region, 51 bp upstream of the start codon of rophages . SigC isM. tuberculosis under starvation and in vivo persistence E. coli, MazG regulates the MazEF-mediated stringent response by depletion of ppGpp 218PAL- motif of the MazG (Hyperphosphorylated guanine [(p)ppGpp], an important cellular second message controlling gene expression in stringent response, is required for long-term survival of the MazG . This mophoP (MRA_0767/Rv0757) gene is a well known virulence factor in M. tuberculosisThe in vitro and/or in vivo. Both pabB (MRA_1014/Rv1005c) and nrdH (MRA_3085/Rv3053c) genes were poorly expressed in H37Ra at 24h post-infection inside macrophages, but their in vitro expression in culture was up-regulated (pabB identified in H37Ra (pabB gene encodes para-aminobenzoate synthetase component-I involved in the biosynthesis of p-aminobenzoate (PABA), a precursor of folate biosynthesis. Mutation in pabB was shown to cause attenuation of virulence in Burkholderia pseudomalleinrdH gene encoding glutaredoxin had a 14 bp deletion in its potential promoter region compared to that of Rv3053c in H37Rv, where it forms a duplicated tandem repeat (nrdH gene is likely located in an operon with nrdI (MRA_3084), nrdE (MRA_3083) and perhaps, with nrdF2 (MRA_3080) as well. Due to the important role of the NrdEF2 complex in chromosome duplication and DNA repair nrdHIEF2 operon might impact exit from dormancy or survival in vivo.Several variations, mostly in the probable promoter regions of genes related to primary metabolism , might iegulated . A T-A tin H37Ra  might afm repeat . The nrdilvD  gene encoding dihydroxy-acid dehydratase, an essential enzyme for branch chain amino acid and pantothenate (coenzyme A) biosynthesis, has a highly conserved Val284 (GTA) being substituted by Gly284 (GGA) in H37Ra. Since this variation is not located in any specific domains and there is no branch chain amino acid related phenotype for H37Ra The lpdA (Rv3303c/MRA_3344) and Rv3304 (MRA_3345), a 58 bp tandem repeat unit is repeated twice in H37Rv and CDC1551, but three times in H37Ra (lpdA and its downstream gene glpD2 as we found a hairpin structure (CACGCCGCGCGGCGTG TGC) within this sequence, which implies a potential binding site for regulatory proteins. Downstream of the lpdA-glpD2 operon, there is a cluster of genes, phoY1, MRA_3341 (Rv3300c), atsB and lpqC, which might be affected by the 58 bp repeat if they were co-transcribed with the lpdA-glpD2 operon. Within the potential promoter regions of lpdA-glpD2 operon and the downstream phoY1, each has a SigF binding site, GGATTG-N16-GGGTAT for phoY1 and GGTTC-N16-GGGTGC for lpdA . This gene encodes a putative ATPase involved in phospholipid metabolism and RNA modification phoH2 in vitro but lower expression in macrophages in H37Ra than H37Rv  might affect the expression of this gene in H37Ra.FtsH is an ATP-dependent membrane bound Zn metalloprotease involved in degradation of short-lived regulatory proteins such as heat shock M. tuberculosis contains a significant number of genes (pks) devoted to polyketide synthesis Cell envelope proteins, cell wall associated polyketide lipids and secreted proteins are involved in virulence and host cell interaction and immune responses pks12 (MRA_2063/Rv2048c) is involved in dimycocerosyl phthiocerol synthesis and the disruption of this gene in H37Rv caused high degree of attenuation of virulence in mice CA-TTA and CCA-GCA in MRA_2063 compared to Rv2048c cause S3004L and P1056A substitutions, respectively. The latter is in the conserved motif -1055WPP- within the linker peptide connecting two domains of acyl transferase and dehydratase, and is very likely to be deleterious.The nrp (MRA_0105/Rv0101) gene encodes a non-ribosomal peptide synthetase involved in polyketide synthesis nrp gene has a nonsense mutation at its N-terminus, where the 93Trp codon TGG is replaced by a stop codon TAG(*), causing a large truncation of 2420 amino acids at the C-terminus, which is expected to disrupt the protein function and affect the synthesis of PDIM.The In H37Ra, the MRA_0040 has a G177D mutation in the putative transmembrane region leading to loss of two transmembrane-helices in Rv0037 of H37Rv. Sequence analysis revealed that this protein belongs to the major facilitator superfamily (MFS) involved in transporting small solutes in response to chemiosmotic ion gradients. Furthermore, a 55 bp deletion is identified at the −66 bp from the start codon of H37Ra MRA_0391 relative to H37Rv Rv0383c . AlthougWe found 35 PE/PPE/PE-PGRS family members that differ between H37Ra and H37Rv resulting from SNVs and indels . MRA_177th base, causing frameshift and premature termination at the C-terminus in H37Ra relative to its ortholog Rv1196 (PPE18), which has previously been shown to induce gamma interferon production in infected or BCG-vaccinated calves MRA_0754 has five amino acid alterations in the PGRS domain compared to its ortholog Rv0746, which was induced during infection M. tuberculosis strains.Several PE/PPE/PE-PGRS genes seem to be preferential “hot spots” for mutations, including MRA_0288 (Rv0279), MRA_0395 (Rv0388c), MRA_0754 (Rv0746), MRA_3548 (Rv3508), MRA_3553 (Rv3514) . MRA_355M. tuberculosis strain H37Ra, performed comparative genomic analysis of H37Ra against its virulent counterpart H37Rv, and studied their genetic variations in reference to the clinical isolate CDC1551 genome sequence. These analyses provide accurate genomic information regarding the genetic differences between H37Ra and H37Rv, which are useful for better understanding of the pathogenesis of M. tuberculosis and basis for virulence attenuation in H37Ra.Despite numerous studies in the past several decades aimed at elucidating the mechanisms of virulence attenuation in H37Ra, the genetic basis for the attenuation has remained largely unknown. In this study, we determined the whole genome sequence of the attenuated 6110 related indels and associated LSPs  among H37Ra, H37Rv and CDC1551, may serve as effective molecular markers to trace their phylogenetic relationship  constructed at different times over several years from different batches of H37Rv in the original H37Rv sequencing project To exclude the mutations accumulated after the original isolation of H37Ra that are not involved in virulence attenuation, we freshly ordered the ATCC H37Ra strain ATCC21577 specifically for the genome sequencing in this study. H37Ra ATCC21577 was deposited in 1960s Through a process of aging for 3–4 months, William Steenken caused the dissociation of avirulent strain from H37 and other fresh clinical isolates in vitro survival under the conditions of aging and cell lysis  in H37Ra is yet to be determined, it may be inferred from the regulatory role of MazG upon the MazEF mediated bacteriostasis via depletion of ppGpp as established in E. colimazF genes in M. tuberculosis, 4 of which caused cell growth arrest when expressed in E. coliM. tuberculosis has a similar function as in E. coli and the A219E mutation of H37Ra MazG does alter its function, the cellular ppGpp level in H37Ra and the MazF (or other toxin/antitoxin systems) mediated persistence might be altered as well. Therefore, the MazG mutation might be critical for the original emergence of H37Ra after aging. Further studies are needed to determine if this mutated MazG provides an advantage for H37Ra to survive during the aging-mediated cell lysis and to allow subsequent accumulation of multiple mutations for better in vitro growth.Although the impact of the pks2 and the msl3 gene clusters involved in the biosynthesis of complex cell wall lipids such as SL, PAT and diacyltrehalose M. tuberculosisphoP has failed to confer these phenotypes It has been reported that H37Ra lost the ability to synthesize sulpholipids (SL), polyacyltrehalose (PAT) and phthiodiolone dimycocerosate ester (PDIM) nrp gene  and the two mutations in pks12 identified in this study  is the last gene of an operon Mb0099-Mb0104 (ppe1-nrp) involved in the synthesis of phthiocerol and PDIM M. bovis caused a polar effect on the expression of the downstream genes including Mb0104, which led to altered morphology and attenuation of virulence in guinea pigs nrp is an important virulence factor.Our study found other mutations besides PhoP that could be responsible for the loss of virulence in H37Ra. The PDIM synthesis defect in H37Ra and the loss of neutral red binding could be due to a severe truncation in the is study , which ain vivo or under anaerobic conditions lpdA-glpD2 operon, which could affect the expression of both LpdA and GlpD2. The differential expression phenotype of lpdA transcription revealed by our qRT-PCR experiment indicated that the lpdA was probably an early response gene involved in adaption to macrophage environment. The M. tuberculosis LpdA (Rv3303c), despite its significant homology to other LpdA enzymes, does not have lipoamide dehydrogenase activity but instead has transhydrogenase activity and quinone reductase activity that enables transfer of reducing power from the reduced pyridine nucleotide pool to the electron transport chain in vivo virulence lpdA-glpD2 could affect the expression of lpdA and glpD2 and may cause reduced survival of H37Ra under reduced oxygen tension in host tissues.It is well known since 1950s that avirulent strain H37Ra has a defect in anaerobic metabolism and survives much less well than virulent H37Rv lpdA, other genes with promoter region mutations may also be involved in virulence attenuation since those genes in H37Ra had different expression patterns compared to H37Rv , transcription activation of lipid biosynthesis (phoP), amino acid biosynthesis (ilvD), polyketide synthesis (pks12 and nrp) may also contribute to loss of virulence in H37Ra. Our qRT-PCR experiments validated that most genes with promoter mutations in H37Ra were up-regulated in vitro but down-regulated in macrophages relative to H37Rv. These promoter mutations may affect transcription of operons encoding genes related to energy metabolism (lpdA-glpD2), cofactor biosynthesis (pabB), nucleotide metabolism (nrdH/nrdE/nrdF2), lipid metabolism (phoH2), protein degradation (ftsH), and sigma factors (sigC), which may all contribute to the loss of virulence in H37Ra  was freshly obtained from American Type Culture specifically for the purpose of genome sequencing in this study. The H37Ra was grown in Middlebrook 7H9 liquid medium with ADC  supplement to late log phase for preparation of high-molecular weight genomic DNA as described www.phrap.org) were used for sequence assembly. Sequence gaps were filled in by first determining the order of supercontigs using multiplex PCR via primer walking or shotgun sequencing. The H37Rv strain used in this study for resequencing to resolve the discrepancies in the published H37Rv genome sequence www.sanger.ac.uk_Software). Promoter region was analyzed by Neural Network Promoter Prediction (http://www.fruitfly.org/seq_tools/ promoter.html). The atlas of genome is drawn using GenomeViz1.1 CDSs were identified by ZCURVE 1.0 M. tuberculosis H37Rv and H37Ra were grown in Middlebrook 7H9 broth with albumin-dextrose-catalase supplement (Becton Dickinson) to log phase. The THP-1 macrophage cell culture was performed as previously described sigA expression in the same sample. Changes in target gene's mRNA levels between H37Ra and H37Rv were illustrated by the ratio of relative transcription levels of H37Ra compared to H37Rv.t-test analysis of ΔCt values of the selected genes between H37Ra and H37Rv. p-value<0.05 was considered to have statistical significance.The RT-PCR data of the transcription levels of selected genes with promoter mutations were subjected to statistical analysis. Significance was defined according to p-values calculated from Student Figure S1(0.19 MB DOC)Click here for additional data file.Table S1(0.18 MB DOC)Click here for additional data file.Table S2(0.09 MB DOC)Click here for additional data file.Table S3(0.60 MB DOC)Click here for additional data file.Table S4(0.34 MB DOC)Click here for additional data file.Table S5(0.05 MB DOC)Click here for additional data file.Table S6(0.08 MB DOC)Click here for additional data file.Table S7(0.07 MB DOC)Click here for additional data file.


Related protein domains of a superfamily can be specified by proteins of diverse lengths. The structural and functional implications of indels in a domain scaffold have been examined.In this study, domain superfamilies with large length variations  were analyzed for the functional impact of such structural differences. Our delineated dataset, derived from an objective algorithm, enables us to address indel roles in the presence of peculiar structural repeats, functional variation, protein-protein interactions and to examine ‘domain contexts’ of proteins tolerant to large length variations. Amongst the top-10 length-deviant superfamilies analyzed, we found that 80% of length-deviant superfamilies possess distant internal structural repeats and nearly half of them acquired diverse biological functions. In general, length-deviant superfamilies have higher chance, than length-rigid superfamilies, to be engaged in internal structural repeats. We also found that ∼40% of length-deviant domains exist as multi-domain proteins involving interactions with domains from the same or other superfamilies. Indels, in diverse domain superfamilies, were found to participate in the accretion of structural and functional features amongst related domains. With specific examples, we discuss how indels are involved directly or indirectly in the generation of oligomerization interfaces, introduction of substrate specificity, regulation of protein function and stability.Our data suggests a multitude of roles for indels that are specialized for domain members of different domain superfamilies. These specialist roles that we observe and trends in the extent of length variation could influence decision making in modeling of new superfamily members. Likewise, the observed limits of length variation, specific for each domain superfamily would be particularly relevant in the choice of alignment length search filters commonly applied in protein sequence analysis. Diverse factors, such as the accumulation of sequence changes, gene duplications, gene combinations Length differences between domains are introduced through insertions and deletions (indels) into pre-existing domains. It has been shown that protein length expansions are 40–60% greater in eukaryotes than in prokaryotes and that such expansions correlate with the presence of introns and accretion of functional motifs that are involved in sophisticated regulatory networks albeit, discontinuous insertions In an analysis of length differences in 353 multi-membered PASS2 domain superfamily alignments Length variation in proteins has been the object of several analyses and many groups have performed independent studies on domain and protein length variations. Pascarella and Argos Here, we assess the functional and structural advantages of length variations amongst homologous members of 64 length-deviant domain superfamilies. The role of indels in mediating novel interaction interfaces through the formation of structural repeats, multi-domain combinations and higher order oligomers has been examined. The presence of distant internal repeats in length-deviant superfamilies has been carried out using computer algorithms, both using sequence and structural information. In addition to a manual comparison of the giant and dwarf representative domains in each length-rigid and length-deviant domain superfamily, literature has been consulted, where relevant, to support the structural observations and inferences on functional impact made here. Likewise, SCOP domain definitions and domain assignments have been consulted to understand the social contexts of domains. Further, the analysis has been extended to protein-protein interaction databases to examine if length-deviant domains are indeed social in functional contexts and associate with a high number of interacting partners. We have also reasoned whether additional lengths assist domains to interact with multiple copies of domains-either homologous or other. This would address if the ability to accommodate extra length reflects on the ‘social’ skills of a domain to interact with more neighboring domains.We have investigated the functional and structural implications of indels amongst related members of a domain superfamily. We applied the CUSP algorithm to identify indels in domain members of 353 multi-membered domain superfamilies etc., It is observed that several domain superfolds that are repeatedly re-used in protein evolution in diverse domain architectures Gene duplication is a method that facilitates evolution since it leads to the formation of phenotypically redundant genome portions that can be experimented for the generation of novel structural and functional products Interestingly, eight of the top-10 length-deviant domains occur as structural repeats . Full-leWe observe that domain repeats involving the entire structural domain can occur in single or multiple chains . The dometc. (data not shown). In these domain superfamilies, differences in the number of structural repeats can generate varied interaction interfaces that confer additional functional properties amongst the different members. This is also observed in some larger domains such as the pectin lyase, cupins and domain superfamilies such as the four-helical cytokines that harbor diverse copies of the Ig-like fold.The number and lengths of repeats across different members varies across related members as seen in proteins that contain repeating copies of the TPR, ARM, Ankyrin repeats, EF-hand domains etc. (data not shown).Duplications of entire domains result in tandem arrangements of the self-domain along the length of the protein in a beads-on-a-string arrangement as seen in cupredoxins and phospholipase D or result in discontinuous arrangements of the domain as in squalene hopene cyclase. In the former type of structural repeat, dwarf members function as homodimers that associate to generate an active site. Giant domains meet such functional requirements by possessing multiple copies of the same domain on a single chain and most likely involve gene duplication events. Such tandem arrangements of domains involve longer loops that serve as linkers in bringing domains together and are seen in length-deviant superfamilies such as the phospholipase D, trypsin-like serine proteases, actin-like ATPases In length-rigid domain superfamilies, only 33% (8 out of 24) are engaged in structural repeats suggesting that internal repeats are more common in length-deviant domain superfamilies  and S3. An important contribution to new structural interfaces/functional units is domain combination and shuffling resulting in new multi-domain architectures Domain assignments were tabulated for 1189 protein domains from length-deviant superfamilies and 268 domains from length-rigid superfamilies. The recurrent domains have more domain partners. Of the 1189 protein domains that we have examined in the 64 length-deviant superfamilies , 31.4% oDrosophila, yeast and other organisms and the number of known and predicted interactions was determined. We find that length- deviant domain superfamilies are highly interacting (1307), notably the domain superfamilies of SAM, cytochrome C and PRTase-like  specify a characteristic fold  that conn length   as the methyl donor are widespread and participate in a multitude of cellular processes through the methylation of a variety of substrates such as proteins, nucleic acids, phospholipids and small molecules. The domain superfamily includes ‘giant’ members such as the PRMT3 (321 residues) and VP39 (291 residues) and other ‘dwarf’ domains such as the ftsj and COMT that are only 180 and 213 residues in length, respectively , Table 4The lysozyme-like domain superfamily is a large multi-membered superfamily with at least seven different families in the SCOP database, all of them unified by the characteristic lysozyme-like fold. The ‘giant’ domain differs from other ‘dwarf’ domains of the lysozyme-like superfamily members in its acquisition of additional α-domain and β-domain extensions at its N- and C- terminal ends . AdditioThe protein members of the actin-like ATPase domain superfamily include a varied set such as sugar kinases, heat shock proteins and actins that perform distinct functional roles involving phosphoryltransfer . The ranThe collection of structure-derived domain superfamily alignments from PASS2 provides an opportunity to examine such alignments on a large scale in order to study domain length variations. Firstly, it has enabled a qualitative assessment of the extent of length variation in domain superfamilies and aided description of domains tolerant to length variation from a structural perspective. Secondly, by applying CUSP, we have examined the range of variations in diverse domain superfamilies by distinguishing structurally conserved blocks (of similar nature and lengths) from indels (regions susceptible to undergo length differences). We have found that the extent of length variation is not uniform across all classes. Thirdly, we have examined the role of such additional lengths in modifying/altering the general functions associated with a domain.Our investigations on the nature and typical lengths of indels showed that not all domains are uniformly tolerant to large variations in length and that certain domains are more susceptible. Indeed, of the 353 domain superfamilies considered, 64 domain superfamilies showed over 30% variation in length from their mean domain size.In the present analysis, we have addressed the functional and structural advantages conferred on a domain due to indels by considering the extreme cases, namely the giant and dwarf in the length-deviant domain superfamilies. It is possible that large insertions, such as whole-domain insertions, have arisen in the proteins considered in our dataset and are actually due to large gene insertions and not pointing to subtle functional changes brought about by small length variations. However, our current dataset is not biased by such occurrences since we perform our analysis at the domain level and do not consider whole domain insertions. In every case, the functional roles were considered in the light of increasing domain sizes and the effect of loss of indels or decrease in average domain length were excluded since the direction of indel evolution is not the focus of the analysis. Earlier findings have already projected that long insertions predominate over long deletions that are also less likely to occur in protein domain evolution We have also consulted GO annotations for each domain member of the length-deviant domain superfamilies and find that 40 of the 64 length-deviant domain superfamilies include proteins that are involved in catalytic activity, where additional lengths are perhaps required to confer varying themes in substrate specificity (data not shown). At least 14 length-deviant domains are involved in regulatory processes and others are involved in structural roles where protein-protein interactions would be the main functional theme.etc. By an examination of the functional and structural advantages in these most length-deviant domain datasets, we determine, at least in outline, the different contributions that additional lengths confer on a length-deviant domain although more may emerge with the determination of new structures. The descriptions given here attempt to discuss the salient roles of extra lengths in the most length-deviant superfamilies but do not undermine the important contributions of shorter length changes in variant domains. Indeed, in domain superfamilies such as the lipocalins and DNA polymerases, incremental additions in lengths are associated with substrate specificity. We have also briefly examined those length-rigid domain proteins that are functionally versatile. The strategies employed here are refreshingly different and include changes in the orientation of structures, modifications local to the active site to attain functional diversity despite such high structural integrity , we believe that this is a consequence of the high variability in the number of proteins in each dataset. Length-rigid protein domains are not as well-populated (270) as the number of length-deviant domains (1130) and this could affect the numbers projected for length-rigid domains. Here again, although the data is not discussed, we have observed that the number of interacting partners in length-deviant domains is far more than the length-rigid proteins and a more in-depth analysis is required to understand why this may be the case.The interesting trends that we have obtained on the nature and type of indels in protein superfamilies from different classes could affect the area of comparative modeling in structurally unconserved regions in newer superfamily members. Our analysis has shown that in a majority of the superfamilies that we have examined, the core structural scaffold is rarely affected, despite length differences. However, even within a superfamily, the extra lengths impacted differently on function for different members, and therefore, it may be difficult to generalize the exact role of additional lengths in newer members. Depending on their locations and lengths in the structure, we may be able to suggest an involvement in introducing substrate specificity, or in presenting newer protein interfaces for interaction with other proteins or promoters.What is the wealth component that dictates such vivid length variations observed in some protein superfamilies? We find that the ‘currency’ for versatility in length of domain superfamilies is not differential amino acid composition since both length-rigid and length-deviant domain superfamilies exhibit similar amino acid propensities. Could the complexities of domain architecture, nature of co-existing domains, need for internal symmetry, repeating structural themes and diverse quaternary arrangements dictate length variations amongst related protein members of a superfamily? Our analysis suggests that a multitude of these parameters operate to influence the structural revolts of length-deviant domains, imposing still a daunting exercise to predict such variations.In the current study, we have employed SCOP We have considered 353 multi-member superfamily alignments (with at least 3 distantly related members) from PASS2 We have also applied the CUSP algorithm To examine the occurrence of structural repeats in domain superfamilies, giant and dwarf domains were identified in each length-deviant and length-rigid domain superfamily. Full-length protein sequences for each of the giant and dwarf domains were retrieved from the SWISS-PROT database We define ‘domain context’ as the preferred mode of occurrence of a domain. Domain superfamily members differ in their associations. We attempted a correlation of the observed length variations with domain contexts and nature of domain associations for each length-deviant and length-rigid domain superfamily. For each length-rigid and length-deviant domain superfamilies in our dataset, all domain members were pooled together. If a domain member is available from multiple species, a representative sequence with the best resolution from any one species was selected. Full-length protein sequences were obtained from the SWISS-PROT database for representative domains from any one species. SCOP domain assignments were made for each sequence. Full-length proteins of known crystal structure were considered for domain assignments and structural databases alone were consulted for this preliminary analysis. In addition, the occurrence of domains singly (single domain in a single chain or single domain in multiple chains), repeating domains  and in their domain associations (single/multiple copies of a domain in association with neighboring/partnering domains in a single or multiple chains) was also noted.Functional interactions of length-deviant and length-rigid domain superfamilies were studied by examining the known and predicted protein-protein interactions for each domain member in searches in the STRING database We have analyzed the functional roles of indels for the 64 length-deviant domains by examining if indels are involved directly or indirectly in domain function in the giant (longest) and dwarf (shortest) domains of each length-deviant domain superfamily. Indels were identified by the CUSP method and alignments were projected through a graphical viewer, StructView, described elsewhere Text S1Functional variety in deviant and rigid domain superfamilies. This text file provides detailed description of eight types of functional attributes to length-deviant superfamilies and five superfamilies of length-rigid superfamilies by giving relevant examples.(0.14 MB DOC)Click here for additional data file.Figure S1DNA-glycosylase domain superfamily. The two domain scaffold of the DNA-glycosylase domain superfamily in Adenine glycosylase and Endonuclease III harbors a HhH motif (in pink) with active site residues (in red) to bind their respective substrates. Composition of residues in the active site is distinct for each member.(0.51 MB TIF)Click here for additional data file.Figure S2Interleukin-8-like superfamily. Interleukin-8 like chemokine superfamily shows high conservation of the core structure. Lymphotactin (a) and stromal cell derived factor 1 alpha (b) differ primarily in the N and C termini. Lower panel (c) shows a graphical projection of the alignments. The core structure involving the well conserved 310 helix and the three stranded sheet is well conserved across different members and structurally equivalent regions in the alignment are extensive.(0.15 MB TIF)Click here for additional data file.Table S1Structural repeats in length-deviant and length-rigid protein domain superfamilies(1.27 MB DOC)Click here for additional data file.Table S2The number of occurrences of domains singly, multiply and as repeats (tandem repeats of domain) in a single or multiple chain are provided.(0.31 MB DOC)Click here for additional data file.Table S3The number of occurrences of domains in single(SD), multiple(MD) and as R (tandem repeats of domain) in a single or multiple chain are provided. R+MD includes domain occurrences that show repeating copies of self domain in multidomain contexts.(0.07 MB DOC)Click here for additional data file.Table S4Number of Protein-protein interactions (known and predicted) in length-deviant and length rigid domain superfamilies after searching in STRING databaseS30.(0.05 MB DOC)Click here for additional data file.Table S5Structural and functional role of indels in the 64 length-deviant domain superfamilies(0.05 MB XLS)Click here for additional data file.