Here we present a new structural prediction approach for modelling two-domain proteins based on rigid-body domain-domain docking.Modelling proteins with multiple domains is one of the central challenges in Structural Biology. Although homology modelling has successfully been applied for prediction of protein structures, very often domain-domain interactions cannot be inferred from the structures of homologues and their prediction requires tethered-docking), which uses rigid-body docking to generate domain-domain poses that are further scored by binding energy and a pseudo-energy term based on restraints derived from linker end-to-end distances. The method has been benchmarked on a set of 77 non-redundant pairs of domains with available X-ray structure. We have evaluated the docking method ZDOCK, which is able to generate acceptable domain-domain orientations in 51 out of the 77 cases. Among them, our method pyDockTET finds the correct assembly within the top 10 solutions in over 60% of the cases. As a further test, on a subset of 20 pairs where domains were built by homology modelling, ZDOCK generates acceptable orientations in 13 out of the 20 cases, among which the correct assembly is ranked lower than 10 in around 70% of the cases by our pyDockTET method.Here we focus on interacting domain pairs that are part of the same peptide chain and thus have an inter-domain peptide region . We have developed a method called pyDockTET  domains. It is estimated that two thirds of proteins in prokaryotes and four fifths of those in eukaryotes are multi-domain proteins ,2, many ab initio approaches. For instance, Wollacott and co-workers [For multi-domain proteins with no structural information, their domain orientations may be predicted through homology modelling. However, homologous multi-domain templates are not always available. Furthermore, even if a homologous template exists, its domains might not interact in the same way as the protein to model (see the review of Aloy and Russell ). To min-workers  modelledab initio modelling of multi-domain proteins is docking. Rigid-body docking approaches have already shown success in predicting interactions between relatively rigid globular protomers in protein complexes, as seen in the recent CAPRI  blind tests. However, although protein-protein docking could be directly applied to model domain-domain interactions, only a few specific cases have been reported . As an example, vitronectin was reportedly modelled by docking two of its domains [Another promising tool for  domains , but it  domains , using p domains  have ext domains  applied In this paper, we describe a new approach, pyDockTET, for pair-wise assembly of domains that are connected by an inter-domain linker. In addition to the electrostatics and desolvation energy in the original pyDock scoring function , which gWe begin our analysis by examining the inter-domain peptide region , whose information is applied in the scoring function of pyDockTET. The goal is to study the relationship between the linker sequence length (in number of residues) and the distance between the linker ends (defined as the distance between the Cα atoms of the two ends of a linker). We began by compiling inter-domain linkers from multi-domain structures in PDB (Protein Data Bank), with the following requisites: i) The linkers were identified as those polypeptide parts placed between domains defined by Pfam  althoug.L*N0.5  [The average linker end-to-end distances increased almost linearly with sequence length for linkers less than 18 residues long Figure . This deresidue) . This isresidue) . HoweverFrom these average end-to-end distance values and their standard deviations, we have derived a scoring function pyDockTET for docking of domain pairs (see Methods). Given the low frequency  of linkers with length larger than 17, the docking sets we used to benchmark pyDockTET included only domain pairs that have inter-domain linkers with length between 2 – 17 residues.We have used docking to rebuild a data set of 77 non-redundant proteins formed by two interacting domains (see Methods). First, we extracted the coordinates of the domains from their X-ray structures, then we separated them and modified all side-chains of the isolated domains with SCWRL 3.0  in orderThe use of pyDock to identify domain assemblies from docking sets clearly gives well over random scoring, and the introduction of linker-based restraints as in pyDockTET further improves the results. Of course, for a realistic case, one has to rely on the docking procedure to generate near-native orientations. We have used here the known FFT-based docking method ZDOCK, but it is expected that the increasing success of rigid-body docking methods will also improve the predictive rates of pyDockTET.L  as a restraint. It is expected that, as the linker length increases, it will provide a less useful restraint on the selection of docking poses, and therefore the performance of the method will likely depend on linker length. Here we analyse the success rates of pyDockTET for different linker lengths, considering only those cases of our domain-domain set that have at least one acceptable solution.The scoring function of pyDockTET uses the average end-to-end distance for every linker length We classified linker lengths into five groups: 2–4, 5–7, 8–10, 11–13 and 14–17 amino acids. As shown in Figure The scoring function of pyDockTET consists of a pseudo-energy term derived from linker end-to-end distances, in addition to the original pyDock function that is formed by electrostatics and desolvation energies. We have already shown that pyDockTET function performs in general better than that of pyDock, so now we will analyze in which cases this improvement is more apparent.First, we will check whether the pyDockTET improvement over pyDock depends on the average pyDock energy obtained for the pool of docking poses, which is different for each case. For each one of the cases that have at least an acceptable docking solution, we sorted the docking solutions by pyDock energy (defined as only electrostatics and desolvation) and computed the average of the best 100 energy values. Figure As for the size of the domain-domain interface, we showed above (the first section of Results) that the existence of cases with low number of inter-domain contact residues did not affect the average linker distances and corresponding standard deviations derived from our data set. However, we observed in Figure So far we have shown that pyDockTET gives excellent performance in assembling domain pairs where the structures of the two domains have been obtained from crystal structures, after separating them and remodelling the side-chains of the isolated domains. However, in real situations the individual domains will have been obtained from independent crystal structures or from homology models. Thus we have also tested pyDockTET over a docking sub-set of 20 cases, in which subunits were modelled from the structure of homologues (see Methods). As can be seen in Table In Figure  we can observe a significant number of cases that, in spite of having fraction of native contacts above 0.1, are incorrect predictions by the global CAPRI criteria . On the contrary, virtually all cases with ligand RMSD below 10 Å are correct predictions , and there are even some solutions with ligand RMSD above 10 Å that are still acceptable .We have evaluated the performance of pyDockTET with respect to other computational methods that have been recently reported for domain-domain assembly. Lise et al.  tested tInbar et al.  recentlyFinally, Wollacott et al.  recentlyWe also performed an additional test on the difficult cases of Wollacott et al. , by indeWe have described here a procedure to build multi-domain proteins from the structure  of their individual domains, using a combination of rigid-body docking, binding energy scoring, and linker-length based distance restraints. The inclusion of linker-based distance restraints largely improves the structural predictions, especially for those cases where binding energy alone is not sufficient to discriminate the near-native conformations. Provided that the rigid-body generation method is able to produce acceptable domain-domain orientations, our scoring function (based on docking energy plus restraints) finds the correct assembly within the top 10 solutions in about 60–70 % of the cases.m, as a restraint to select the correct docking poses. The Xm value is calculated as the average of the end-to-end distance values of linkers that have same length (i.e. same number of residues) in the 542 linker structures collected from multi-domain proteins in Protein Data Bank (PDB). The Xm value and its corresponding standard deviation, SD, are then used to develop a function, Elinker  is superimposed onto that of the X-ray structure .For a pair of domain structures we generated 2,000 rigid-body docking orientations by ZDOCK2.1 . The scoA benchmark of 77 non-redundant domain pair structures was compiled by selecting all crystal structures of multi-domain proteins in PDB that satisfied the following criteria: i) since domain pairs that are not in direct contact cannot be predicted by our domain-domain docking, the benchmark cases were required to have at least one pair of residues that had side chain atoms within a distance ≤ 5Å in their crystal structures  and used Baton  to do mlgorithm  was usedlgorithm  was usedTLB and JFR devised the concept and directed the research. JFR designed the procedure. TMKC design the implementation of the program and performed the calculations. TMKC and JFR analysed the data. TMKC drafted the paper. TLB and JFR finalized the draft. All authors read and approved the final manuscript.The 20 unbound (modelled) structures. Each structure is shown as (PDB ID)_(chain)_(the first residue of the linker)_(the last residue of the linker).Click here for fileThe 77 non-redundant bound structures. Each domain complex is shown as (PDB ID)_(chain)_(the first residue of the linker)_(the last residue of the linker).Click here for file


Sir,In 1988, Sybert described a rare form of transgradient keratoderma which resembled mal-de-melada but had autosomal dominant inheritance.[In a family of five siblings, two sisters aged 14 and 12 and their brother aged 4 years presented to the OPD with a history of thickening and scaling over the palms and soles since their birth. History of progression with deformity and a similar family history for the mother. There was no history of consanguinity and the other two siblings were normal.Examination revealed the presence of hyperkeratosis of the palms and soles which extended on to the dorsum of the hands and the feet . There wA diagnosis of Sybert’s keratoderma was made based on the clinical findings and the patients were treated with topical keratolytics and moisturizers. Clinical improvement was seen with respect to the extent of palmar hyperkeratosis on follow-up after four weeks.Hereditary palmoplantar keratodermas (PPK) are a heterogeneous group of diseases characterized by hyperkeratosis of the palms and soles with a thickening of the stratum corneum, usually distinguishable by the mode of inheritance and by associated clinical findings.The inheritance may be autosomal dominance, autosomal recessive, or X-linked.Palmoplantar keratodermas has been historically based on the spread to the wrist, dorsa of the hand, knuckles, elbow, and knee (transgradiens), and progression with age (progradiens).Clinically, three distinct patterns of palmoplantar keratodermas are seen:Diffuse palmoplantar keratodermas characterized by even, thick, symmetrical hyperkeratosis all over the palms or soles.Focal palmoplantar keratodermas in which large compact masses of keratin develop at the site of recent friction.Punctuate palmoplantar keratodermas in which multiple, tiny, ‘raindrop’ keratoses involve the palmoplantar surface.Keratodermas are additionally classified into three subgroups:Simple keratodermas with only palmoplantar keratodermasComplex keratodermas: palmoplantar keratodermas with lesions on nonvolar skin, hair, teeth, nails, and sweat glands.Syndromic keratodermas: palmoplantar keratodermas associated with abnormalities of other organs including deafness and cancer.Based on the above classification, all three patients were diagnosed with a diffuse complex palmoplantar keratoderma with transgradiens and progradiens and an autosomal dominant inheritance. The differential diagnosis to this subset of palmoplantar keratodermas was a milder Grither’s syndrome and a severe Sybert’s keratoderma. Sybert’s keratoderma differs from Grither’s keratoderma with respect to its early onset, progression, and greater severity characterised by pseudoainhum formation as well as glove and stocking hyperkeratosis, which were present in our patients. Histopat


The DExD/H domain containing RNA helicases such as retinoic acid-inducible gene I (RIG-I) and melanoma differentiation-associated gene 5 (MDA5) are key cytosolic pattern recognition receptors (PRRs) for detecting nucleotide pathogen associated molecular patterns (PAMPs) of invading viruses. The RIG-I and MDA5 proteins differentially recognise conserved PAMPs in double stranded or single stranded viral RNA molecules, leading to activation of the interferon system in vertebrates. They share three core protein domains including a RNA helicase domain near the C terminus (HELICc), one or more caspase activation and recruitment domains (CARDs) and an ATP dependent DExD/H domain. The RIG-I/MDA5 directed interferon response is negatively regulated by laboratory of genetics and physiology 2 (LGP2) and is believed to be controlled by the mitochondria antiviral signalling protein (MAVS), a CARD containing protein associated with mitochondria.in silico in a wide spectrum of invertebrate and vertebrate genomes. The gene synteny of MDA5 and LGP2 is well conserved among vertebrates whilst conservation of the gene synteny of RIG-I is less apparent. Invertebrate homologues had a closer phylogenetic relationship with the vertebrate RIG-Is than the MDA5/LGP2 molecules, suggesting the RIG-I homologues may have emerged earlier in evolution, possibly prior to the appearance of vertebrates. Our data suggest that the RIG-I like helicases possibly originated from three distinct genes coding for the core domains including the HELICc, CARD and ATP dependent DExD/H domains through gene fusion and gene/domain duplication. Furthermore, presence of domains similar to a prokaryotic DNA restriction enzyme III domain (Res III), and a zinc finger domain of transcription factor (TF) IIS have been detected by bioinformatic analysis.The DExD/H containing RNA helicases including RIG-I, MDA5 and LGP2 were analysed The RIG-I/MDA5 viral surveillance system is conserved in vertebrates. The RIG-I like helicase family appears to have evolved from a common ancestor that originated from genes encoding different core functional domains. Diversification of core functional domains might be fundamental to their functional divergence in terms of recognition of different viral PAMPs. Pattern recognition receptors (PRRs) are crucial to animal surveillance of pathogen invasion. The PRRs recognise conserved pathogen-associated molecular pattern (PAMP) motifs, including proteins, lipids and nucleotides, resulting in activation of host innate defences . The PRRThe RIG-I like receptors are crucial to the RNA virus triggered interferon response. They consist of three members, retinoic acid-inducible gene I  box polypeptide 58 (DDX58)) and melanoma differentiation-associated gene 5 ), and laboratory of genetics and physiology 2  box polypeptide 58 (DHX58)), which share a common functional RNA helicase domain near the C terminus (HELICc) specifically binding to the RNA molecules with viral origin . Two tanvesicular stomatitis virus whilst MDA5 reacts to picornaviruses [Both RIG-I and MDA5 appear to have overlapping binding properties with viral PAMPs and share similar signalling pathways leading to activation of the interferon system. However, evidence of differential recognition of viral PAMPs by RIG-I has begun to emerge recently. It seems that MDA5 preferentially binds long, capped di- or mono-5' phosphate double stranded (ds) RNAs whilst RIG-I has high binding affinity with short dsRNAs or 5' ppp uncapped single stranded (ss) RNAs -11. Inteaviruses . Some viWhilst most studies are focused on the RIG-I like PRRs in mammals, little is known about such molecules in other living organisms. A recent study surveying the purple sea urchin genome has revealed multiple putative RIG-I like homologues that appear to be present in invertebrates . More reExtensive BLAST analysis of vertebrate genomes or expressed sequence tag databases using known RIG-I protein sequences identified a putative full length RIG-I homologue in Western Clawed Xenopus but not in chicken Table . The putUnlike RIG-I, whose presence in chicken and some fish species is uncertain, the MDA5 homologues can be found throughout vertebrate species including fish, amphibians, birds and mammals Table . MDA5 isLGP2 is an adaptor protein lacking CARD domains but containing a DExD/H domain and a HELICc domain homologous to their corresponding motifs in the RIG-I and MDA5 protein. It competes with RIG-I and MDA5 for the ligands, viral derived RNA PAMPs, but is unable to interact with down stream signalling proteins due to the absence of CARD domains. Thus it acts as a negative regulator of the RIG-I/MDA5 directed antiviral response. LGP2 appears to co-exist with MDA5 in vertebrates as a single copy gene. It is located in a different chromosome to MDA5 in every species analysed. The putative LGP2 proteins from non-mammalian species contain 588–682 aa, much shorter than the RIG-I and MDA5 proteins. The DExD/H domain and the HELICc domain in the LGP2 protein share higher homology with the corresponding regions in MDA5 than those in RIG-I. The LGP2 DExD/H domains are 33.4–55.3% identical to the MDA5 counterparts compared to 22.3–39.8% for the RIG-I proteins. Similarly, 47.6–66.7% identity is seen between the LGP2 HELICc domains and the MDA5 HELICc domains, in contrast to 31.7–48.7% between LGP2 HELICc domains and the RIG-I helicase domains.RIG-I/MDA5/LGP2 have been reported in a recent survey of the sea urchin genome draft [Nematostella vectensis genome database ,  This partial sequence was then used to search the NCBI database and two contigs (NEMVEDRAFT_v1g95706 and NEMVEDRAFT_v1g87071) were retrieved, which encoded two putative RIG-I/MDA5/LGP2 homologues. The putative proteins are 672 aa and 689 aa in length, similar to that of LGP2. Further prediction of functional motifs revealed the presence of a DExD/H domain and a HELICc domain but not the N terminal CARD domain. The proteins share 17.4–26.3 identity with RIG-I, 21.7–32.4% with MDA5 and 25.3–36.3% with LGP2.Twelve genes coding for RNA helicase proteins homologous to me draft . Some ofRIG-I, MDA5 and LGP2 are evolutionary conserved, we analysed draft genome sequences of invertebrates, fish, amphibians, birds and humans. The RIG-I locus was identifiable in Xenopus where the genes neighbouring RIG-I were different to those in humans except for the ACO1 gene , and predicted or known RIG-I molecules from zebrafish, Xenopus, and mammals, suggesting RIG-I may have emerged in invertebrates. The fact that RIG-I homologues were absent in most fish species suggests that the RIG-I gene may have been lost in some fish lineages. The tree also indicates that LGP2 and MDA5 proteins from vertebrate species form two clear groups closely neighbouring each other, suggesting they possibly diverged from a common ancestor that originated from a RIG-I like molecule in invertebrates or early vertebrates. In addition, two phylogenetic trees were constructed using helicase C domains and DExD/H domains and a similar grouping of the major branches was observed (data not shown).RIG-I, MDA5 and LGP2 are believed to bind RNA molecules through helicase-C domains. Closely related proteins containing a RNA binding helicase-C domain and a DExD/H helicase domain are the double-stranded RNA-specific endoribonuclease (DICER) and elongation initiation factor (eIF) 4A. DICER belongs to the RNase III family that cleaves double-stranded RNA (dsRNA) into short double-stranded RNA fragments, called small interfering RNA, that is required for specific cleavage of complementary viral RNAs, whilst eIF4A mainly participates in translation and other processes such as pre-mRNA splicing and ribosome biogenesis. Since homology analysis of the RIG-I and MDA5 proteins in the databases using the BLASTP programme gave top scores to DICER and eIF4A in addition to the RIG-I, MDA5 and LGP2 proteins, we included DICER and eIF4A in the phylogenetic tree analysis using the neighbour joining method within the Mega4 programme. As shown in Fig. The putative domains were analysed in the Pfam database using a cut-off E-value of 10.0 and selected domains listed in Table The domain sequences were further analysed by multiple alignment and their 3 dimensional structures modelled. It is apparent that the DExD/H and Res III domains comprised a well conserved DECH motif Fig. . The ovein silico in the nucleotide databases of invertebrates and vertebrates and their evolutionary origin discussed.The RIG-I like helicase family members have recently been reported to play pivotal roles in recognising viral nucleotides in mammals. In this report, the RIG-like homologues have been identified Double stranded RNA is the genetic component of viruses with double stranded genomes and part of a single stranded RNA with secondary structures. It can be generated during viral replication and RNA metabolism. This nature of dsRNA makes it the prime target for host PRRs. Classical double stranded RNA binding domains are often used by some cytosolic PRRs, such as PKRs, as the detectors to sense viral presence. DICER proteins also contain two dsRNA binding domains (dsRBDs) for capturing dsRNA molecules. In the present study, a zinc finger domain similar to that of transcription factor (TF) IIS has been found in MDA5 but not in LGP2, with moderate homology to the RIG-I C terminal region. Furthermore, a well conserved type III restriction enzyme domain responsible for restriction in prokaryotic organisms is identified in the middle of both MDA5 and the N terminal region of LGP2. This domain was not detected in RIG-I molecules by the Pfam HMM programme although it shared some degree of homology. We speculate that these two domains may serve as potential binding domains to interact with viral PAMPs.One striking finding is that a well conserved restriction enzyme III (Res III) domain is predicted in all MDA5 and LGP2 proteins (except human LGP2). The Res III domain is structurally similar to the DExD/H domain. Restriction enzymes are important components of prokaryotic DNA restriction-modification mechanisms in defence against foreign DNA . They fuAnother putative important domain, a zinc finger motif similar to that of transcription factor (TF) IIS, was identified by homology analysis in the Pfam database. The zinc finger motif can bind a range of targets including DNAs, RNAs, proteins and even lipids. It is known that the zinc finger motif at the C-terminus of the TFIIS is essential for RNA binding and processing . IntegraRIG-I, MDA5 and LGP2 were analysed in this study. Our data suggest they evolved from common invertebrate ancestors encoding distinct core domains , pufferfish (Takifugu rubripes and Tetraodon nigroviridis), medaka (Oryzias latipes), stickleback (Gasterosteus aculeatus), Western Clawed Xenopus  and Chicken  in the Ensembl database . The obtained sequences were reciprocally searched against the other genomes to further verify their identity. The translated proteins from predicted transcripts were verified by BLASTP in the NCBI non-redundant protein sequence database and the SWISSPROT protein database . In addition, known MDA5, LGP2 and RIG-I genes were retrieved from the NCBI database for analysis.To identify MDA-5, LGP2 and RIG-I were used as anchor sites. Orthologous comparisons of the genes in the regions of approximately 1 to 10 mb (million base pairs) flanking the human (NCBI 36) anchor site with medaka (HdrR), zebrafish (Zv7), stickleback (BROAD S1), pufferfish , Western Clawed Xenopus (JGI 4.1) or chicken (WASHUC2) genome were done within the Ensembl genome browser using the GeneView and MultiContigView options. Manual annotation of orthologous genes was also performed using FGENESH+ to predict structures based on homology with human genes: "fish" specific parameters were applied in this program.For gene synteny analyis, human . Caspase recruitment domain, DExD/H box helicase, Type III restriction enzyme and helicase conserved C-terminal domains were predicted by a Pfam HMM search with a cutoff value of 10.0. The full-length amino acid sequences and the conserved functional domains were used in phylogenetic tree analysis. Multiple protein sequence alignments were performed using the ClustalW programme (version 1.83) [. The 3D structural images were displayed by the Cn3D programme (version 4.1).The conserved domains were predicted using software at the ExPASy Molecular Biology Server on 1.83) . A phyloon 1.83) . Data weon 1.83)  and compJZ initiated the study, performed bioinformatic and phylogenetic analysis, and drafted the manuscript. MXC made substantial contributions to bioinformatic analysis. PN and CJS advised on data analysis and interpretation. CJS contributed significantly in editing the manuscript. All authors read and approved the final manuscript.


Protein interactions are essential for most cellular functions. Interactions mediated by domains that appear in a large number of proteins are of particular interest since they are expected to have an impact on diversities of cellular processes such as signal transduction and immune response. Many well represented domains recognize and bind to primary sequences less than 10 amino acids in length called Short Linear Motifs (SLiMs).In this study, we systematically studied the evolutionary conservation of SLiMs recognized by SH2, SH3 and Ser/Thr Kinase domains in both ordered and disordered protein regions. Disordered protein regions are protein sequences that lack a fixed three-dimensional structure under putatively native conditions. We find that, in all these domains examined, SLiMs are more conserved in disordered regions. This trend is more evident in those protein functional groups that are frequently reported to interact with specific domains.The correlation between SLiM conservation with disorder prediction demonstrates that functional SLiMs recognized by each domain occur more often in disordered as compared to structured regions of proteins. A score smaller than 1 indicates 1/CR times greater variability between species.A CThis relative conservation approach is originally developed to study domain recognized motifs within protein sequences in different functional groups . The method may not be suitable for SLiMs longer than 10 amino acids, since it assumes that most residues in the SLiM could influence the interaction. This may not be the case in longer sequences where only a small subset of the residues is critical to binding. Although not all residues in a SLiM shorter than 10 amino acids are essential for interaction, their relative conservation is usually strong enough to be detected.Please see Additional file 1 for information on additional materials and methods.Traditional methods measure sequence conservation without considering the conservation background of the protein. Here, we took background conservation into consideration by measuring the relative conservation score. Our central hypothesis was that SLiMs should be subject to two kinds of evolutionary selection. The first is background selection, which is imposed upon the entire length of the protein sequence, due to the integral function of the protein. The second is SLiM-specific selection superimposed on the background, due to the special function mediated by the SLiM.Therefore, a well-conserved SLiM in an overall highly conserved protein does not guarantee independent importance. In this case, the high sequence matching probably results because the SLiM is an integral part of the conserved protein structure. For example, although the putative SH2 binding Tyr-SLiM in Histone H3.1 is conserved among sequences from all selected species, their relative conservation was low because of the highly conserved background Figure . ConversIn this section and the sections that follow, we use "SLiM conservation" to indicate relative conservation unless specified otherwise.R) scores than those SLiMs that do not bind to SH2 domains in both disordered and ordered sequences , including EGFR, IR, KIT, PDGFRB, IGF-IR, VEGFR2, ERBB2, FGFR1, HGFR, RET and TKR-A . We manually extracted the interactions between these 11 RTKs and 21 SH2 domains from literature. This resulted in a total of 76 interactions involving 56 unique Tyr-SLiMs (see Table S2 for details). Using our SLiM conservation calculation, we found that SLiMs reported to bind to SH2 domains have significantly higher ln(CR) values between SLiMs with lower medium, upper medium and high selectivity values as compared to those SLiMs with low selectivity values. The conservation profiles were first averaged within each protein functional group, and then over the different functional groups within frequent, occasional and rare domain binding categories to avoid over-representation of any particular functional groups.To investigate the functional importance of domain-recognized SLiMs in ordered and disordered regions of proteins, we performed a systematic analysis on the evolutionary conservation of SLiMs in predicted ordered and disordered protein sequences from different protein functional groups. As shown in Figure The frequent, occasional or rare interaction groups for each domain were defined by setting thresholds of the percentage of proteins in the functional group that interact with (or are phosphorylated by) proteins containing that domain according to Hprd (or PhosphoELM) database . As expected, in those functional groups that are frequently reported to interact with respective domains, the conservation signal is highest in the motif region that mediates the interaction Figure . FurtherAlthough the conservation of the SLiMs is more manifest in disordered than ordered protein regions in all three domains examined, there are still some differences among the three domains. Tyr-SLiMs recognized by SH2 domains are conserved in disordered but not in ordered protein regions. Ser-SLiMs  recognized by Ser/Thr kinases are conserved in both ordered and disordered protein regions but are more conserved in disordered regions. PXXP containing SLiMs recognized by SH3 domains are conserved in disordered but not ordered protein regions. Interestingly, the sequences nearby the PXXP motifs recognized by SH3 have high conservation score. One possible explanation is that the proline residue is strongly disorder-promoting ,47, and Protein disorder is believed to play an important role in protein-protein interactions. In this study, we show that the SH2, SH3 and Ser/Thr Kinase domain-recognizable short linear motifs in disordered regions of proteins are more conserved than those in ordered protein regions. This difference is most significant in those molecular functional classes that are frequently reported to interact with their respective domains, but weak in functional groups that are rarely reported to interact with their respective domains.From an evolutionary perspective, ordered or structural regions are generally more conserved than disordered regions . In thisThe enrichment of relatively conserved SLiMs in disordered protein regions is highly related to their function. Location of SLiMs in intrinsically disordered regions provides several important functional benefits for interactions with domains. First, SLiMs in disordered regions are more accessible to domains since they are necessarily fully exposed. Second, SLiM domain interaction are usually very weak due to small recognition surface involved. Localization within intrinsically disordered proteins allows the SLiM to adapt to recognition surface and thus improve the stability of the interaction. Third, being located within disordered regions enables overlapping SLiMs to change their conformations to bind to different partners and thus increase signalling complexity. For example, the SH2 domain binds to Tyr-SLiMs previously phosphorylated by Tyr-kinases, so the same region has overlapping motifs, one for the kinase and one for the SH2. The structure of this region changes when it binds to the different partners, and this structural change is facilitated by the flexibility of intrinsic disorder.vice versa – the previous work supports our finding.Phosphorylation is an important post-translational modification that merits closer attention. Phosphorylation occurs in ~30–50% of the proteins in eukaryotes . Sites oFurthermore, several computational methods have been developed for identifying protein phosphorylation sites according to their surrounding peptide sequences. Some of these methods On the other hand, it would be expected that DisPhos would give a false positive when the Ser/Thr or Tyr in a disordered region is not within a kinase recognition motif. These observations suggest that combining a motif-based prediction method with a disorder-based prediction method should give a large increase in phosphorylation prediction accuracy because each method would reduce the false positives from the other method.et al. [This hypothesis was recently confirmed by an elegant study where a new method named PhoScan was elaborated to predict phosphorylation sites for specific protein kinases without using non-phosphorylated training data . The autet al. . PhoScanet al. . This reAlthough the SLiM conservation signal is more evident in disordered than ordered protein regions in all the three domains examined, some SLiMs in ordered regions can also interact with domains under physiological condition. For example, serine residues in the structured activation loop of several kinases can be phosphorylated and change the kinase activities. However, these loops undergo large-scale conformational shifts following phosphorylation, and so it is likely that the loops become disordered during the phosphorylation event. This observation suggests that each example in which a motif is apparently in a structured region should be checked for the possibility of transient disorder during binding. Use of transient disorder for signalling presents a number of opportunities for regulation and control . This stThis study provides evolutionary evidence for the importance of intrinsic disorder in the context of functional protein interactions. Specifically, SLiMs within disordered protein regions are more conserved than equivalent sites within ordered regions. Study of manually extracted SH2 interaction sites in 11 most studied receptor tyrosine kinases provided experimental evidence that Tyr-SLiMs within disordered regions are more likely to be involved in interaction. Although there is currently no direct evidence to show that this is the general rule for SLiMs recognized by domains studied here or other domains in vivo, we hope our current observations will contribute to discussion of the role of intrinsically disordered protein regions.The authors declare that they have no competing interests.SR was involved in design and planning of the experiments has done the computational analysis, designed figures and contributed to the manuscript writing. ZC was involved in planning of experiments, contributed to the manuscript writing and revised the final version. VNU, AKD, and ZO were involved in design and planning of all the experiments, drafted the manuscript and headed the project. All authors have read and approved the final manuscript.Click here for file


Alternative splicing (AS) is a widespread phenomenon in higher eukaryotes but the extent to which it leads to functional protein isoforms and to proteome expansion at large is still a matter of debate. In contrast to animal species, for which AS has been studied extensively at the protein and functional level, protein-centered studies of AS in plant species are scarce. Here we investigate the functional impact of AS in dicot and monocot plant species using a comparative approach.Arabidopsis thaliana and the monocot Oryza sativa (rice) revealed that the vast majority of AS events in both species do not result from functional conservation. Transcript isoforms that are putative targets for the nonsense-mediated decay (NMD) pathway are as likely to contain conserved AS events as isoforms that are translated into proteins. Similar results were obtained when the same comparison was performed between the two more closely related monocot species rice and Zea mays (maize).Detailed comparison of AS events in alternative spliced orthologs from the dicot Genome-wide computational analysis of functional protein domains encoded in alternatively and constitutively spliced genes revealed that only the RNA recognition motif (RRM) is overrepresented in alternatively spliced genes in all species analyzed. In contrast, three domain types were overrepresented in constitutively spliced genes. AS events were found to be less frequent within than outside predicted protein domains and no domain type was found to be enriched with AS introns. Analysis of AS events that result in the removal of complete protein domains revealed that only a small number of domain types is spliced-out in all species analyzed. Finally, in a substantial fraction of cases where a domain is completely removed, this domain appeared to be a unit of a tandem repeat.The results from the ortholog comparisons suggest that the ability of a gene to produce more than one functional protein through AS does not persist during evolution. Cross-species comparison of the results of the protein-domain oriented analyses indicates little correspondence between the analyzed species. Based on the premise that functional genetic features are most likely to be conserved during evolution, we conclude that AS has only a limited role in functional expansion of the proteome in plants. Eukarotyes can produce different mRNAs from a single gene transcript through the process of alternative splicing (AS). Large-scale EST sequencing efforts have revealed that AS is widespread among higher eukaryotes and that it greatly affects their transcriptome complexity, with, for instance, more than 60% of human genes and around 20–30% of plant genes undergoing AS -3. The oin silico approaches. A number of studies, in which AS isoforms were structurally modeled, have yielded insight into the impact of AS on the stability and function of proteins . StruAt the gene-structural level, conserved AS events are identified as similar or distinct event types on orthologous introns and/or exons of at least two species (see for instance -12). HowArabidopsis thaliana and rice but also the frequencies of the individual AS events are similar in both species [A number of general mechanistic features of AS have been identified in diverse species over the past few years. For instance, not only are the fractions of all genes that undergo AS similar in  species ,3. Anoth species . The pre species . A recen species . In line species .Other biases involving the mechanisms by which AS modulates the function of proteins have also been identified in animal species. For instance, AS within the protein coding region of animal genes has been shown to occur more frequently outside than inside the boundaries of predicted functional domains . The autArabidopsis thaliana, Oryza sativa (rice), Zea mays (maize) and Populus thrichocarpa. To investigate whether the production of different functional isoforms from a single gene through AS persists during evolution, we analyzed the extent to which polymorphism induced by AS is conserved in orthologous genes. The comparative analysis was performed between the monocot rice and dicot Arabidopsis and between the two monocots rice and maize. Following this ortholog-based comparison, we analyzed conservation of more general mechanistic aspects of AS. These analyses were performed separately on each species and the results were compared afterwards. Finally, we address the role of AS in proteome diversification and discuss the evolutionary events that might have led to the patterns that we observed in the plant species.Although it has been suggested that AS in plants has a less prominent role in the expansion of proteome diversity than in animals , proteinArabidopsis, rice and Populus, for which a significant part of the genome is represented by few large contigs corresponding to the chromosomes, the genomic data for maize was represented by over 16,000 BAC contigs. For this reason, additional steps were taken for processing the raw maize predictions . From all the initially predicted maize loci, only 9360 met our criteria and were considered further.An overview of the gene structure and AS prediction results prior to the construction of hypothetical isoforms is given in Table Arabidopsis and rice, respectively. The frequencies of the individual AS event types in these two species are comparable to those that have been reported in a previous genome-wide study [Arabidopsis and rice, the fraction of maize genes that are predicted to undergo AS is relatively high (~40%). However, the distribution of the individual AS events within this species is similar to those found in Arabidopsis and rice . Of the 15,951 initial orthologous groups that were predicted between maize and rice, 3,053 main ortholog pairs were kept. From these, 481 pairs  were predicted to undergo AS in both species.Out of 10,859 orthologous groups predicted by Inparanoid between i) ortholog pairs with AS variants that were predicted to be likely targets for the NMD-pathway in both species (NMD-set); and (ii) ortholog pairs with AS variants that were predicted to be translated into proteins in both species (translated-set). These two sets were stepwise dissected into sets containing ortholog pairs with  AS events on different positions; (ii) AS events on the same position; (iii) different type of events on the same position; (iv) the same type of event on the same position; (v) homologous modification sites; and (vi) similar modifications  AS Arabidopsis and rice was low in both the translated and the NMD sets .In total 24 (~4.9%) and 17 (~3.5%) maize-rice ortholog pairs had similar modifications in the translated and NMD set, respectively Figure . AlthougArabidopsis, 2,487 in rice and 1,791 in maize were analyzed. A domain was considered to be disproportionately distributed when the ratio AS- to CS-genes encoding the domain was significantly different from the same ratio for all genes that lack that particular domain. The fraction of domains that are disproportionately distributed in the studied species (Table We investigated whether particular protein domains were disproportionately distributed towards either constitutively (CS) or alternatively (AS) spliced genes in all three plants. Disproportionately distributed domains are indicative for the under- or overrepresentation of AS in genes with a particular function. To this end, the distributions of 2,626 domain types in Arabidopsis 2,486 (~6%) of the 41,107 intron clusters located within the boundaries of predicted Pfam domains and 1,941 (~7.6%) of the 25,657 clusters outside Pfam domains were found to be AS clusters. Similarly, in rice, 2,487 (~7%) of the 35,595 intron clusters within domains and 2,088 (~9%) of the 23,093 clusters outside predicted domains were AS clusters. The same trend was also observed in maize where 2,292 (~14%) of the 16,466 intron-clusters within predicted domains were AS-clusters, while 1,634 (~19%) of the 8,677 intron clusters outside predicted domains were AS clusters. The difference between the AS-cluster frequencies within and outside predicted domains is significant in all three species .To determine whether introns located within predicted Pfam domains are less or more frequently subjected to AS compared to other introns within the CDS regions of genes, the ratio between AS intron clusters and CS intron clusters within the predicted domains was compared to the same ratio for intron clusters located outside predicted domains. In Arabidopsis, rice and maize, respectively, were excluded from the test as no introns were found within their boundaries. The number of domain types that were enriched with either AS or CS introns ranged from six in maize to twelve in Arabidopsis . No domain type was found that was spliced out with significantly elevated rates compared to other domains in all three species.In total, 218 (117 types), 244 131 types) and 148 (88 types) spliced-out domains were identified in 1 types aArabidopsis and rice, were: nucleic acid binding (GO:0003676), zinc ion-binding (GO:0008270), calcium ion-binding (GO:0005509), protein-binding (GO:0005515) and ATP binding (GO:0005524)  were analyzed and compared between the species. A total of 152 unique splicing patterns resulting in 129 unique products were identified in Arabidopsis, 66 (~37.9%) of the rice and 43 (41.7%) of the maize splicing patterns involved the removal of a domain which was a unit of a tandem-repeat  of the , 66 ~37.% of the Arabidopsis and the monocot rice and orthologous genes of the two monocots maize and rice. The comparison was focused on the functional outcome of the AS events rather than on the AS events themselves. The number of cases in which orthologs in Arabidopsis and rice contained AS variants that are likely targets for NMD was roughly half that of ortholog pairs with variants that can be translated into proteins. Only a very small number of cases were found in which AS events in both orthologs resulted in similar modifications to the principle protein product. Interestingly, the fractions of ortholog pairs that have AS events on different positions and those that have similar or different event types on the same position were quite similar in both the NMD and translated subsets of the Arabidopsis-rice orthologs. Because the isoforms in the NMD subset would never function as protein, this similarity suggests that a considerable fraction of the putatively conserved AS events are not the result of functional conservation. Similar values for the distributions mentioned above were observed in the translated subset of the maize-rice orthologs. This similarity further strengthens the notion that conservation of AS events is not the result of the preservation of functional AS induced protein polymorphism. Although the fraction of orthologs that have similar AS-event types on orthologous intron-positions leading to modifications at similar sites is higher in the maize-rice comparison than that observed for the Arabidopsis-rice orthologs in the translated as well as the NMD set, these numbers are still remarkably low. In the NMD subset of the maize-rice orthologs the fractions of different- and similar event types on orthologous intron-positions are very similar. In summary, the results suggest that conservation of AS events as the result of the selective advantage of retaining the ability to produce multiple protein isoforms through AS is not frequent, even at short evolutionary distances.In this study we addressed the functional impact of alternative splicing in plants using a comparative analysis approach. According to the ascribed role of AS as a mechanism for expanding proteome diversity, a gene can become polymorph in its expression through an additional splice variant that encodes a different, yet functional protein. When the principle and alternative protein isoforms each confer a selective advantage, both are likely to be retained during evolution. This can be achieved through either retention of the AS-induced polymorphism or through gene-duplication followed by sub-functionalization of the duplicates . Not onlPrevious studies on animal data (see introduction) have revealed similarities between genes undergoing AS in various species. The observed patterns are not the result of ortholog-level conservation, which is evident from the low numbers of conserved AS events between, for instance, human and mouse . HoweverIn contrast to animals, protein function-oriented studies of genome-wide AS patterns in plants are scarce. Motivated by the results obtained from computational analysis of the impact of AS on protein function in animals, we have applied a number of those analyses on plant data. The first analysis involved the distribution of Pfam domains over constitutively and alternatively spliced genes using a similar method as previously performed on human data . Three dArabidopsis and rice [However, such an inverse correlation has not been found in and rice . These cand rice .Only one domain, the RRM domain, was significantly overrepresented in alternatively spliced genes in all species. This RNA recognition domain, like the domains mentioned above, is found in a large number of genes, many of which are involved in the regulation of the splicing process . HoweverZooming in on the sequence level, it has been shown in four animal species that AS occurs less frequently within than outside the boundaries of predicted protein domains . We obseIt has been reported that domains with particular functions are more frequently targeted by AS than expected by chance (reviewed in ). Not onArabidopsis and maize. The top five molecular function-related GO terms of the spliced-out domains, comprising around 50% of all GO-term assignments, were the same in Arabidopsis and rice while in maize only two of these five GO-terms comprised 45% of GO term assignments. Genes having these GO terms in Arabidopsis have been shown to have elevated levels of AS [The final analysis involved AS events that remove entire protein domains. The specific AS events that lead to the removal of the domains are not required to be located within the boundaries of the spliced-out domain. In human genes, various domains have been shown to be more often spliced-out than average . Here, wls of AS . HoweverInterestingly, in a large fraction of the unique splicing patterns in all species, the spliced-out domain was a unit of a tandem repeat. A similar observation has been reported in a study that was conducted on data derived from the Swiss-Prot and PDB- databases . The autArabidopsis and rice. The same has been shown for ortholog genes of the more closely related species maize and rice. The persistence of gene-specific function expansion has thus been very limited over the divergence time between not only the monocot rice and the dicot Arabidopsis but also between the two monocots rice and maize. Functional diversification through AS within protein domains appeared to be independent on the nature of the domain itself. The identification of domains that are frequently spliced out in both species is likely to be the result of their abundance rather than their specific function. Taken together, these results, in the context of comparative analysis, suggest that the role of AS as a mechanism for expansion of function proteome diversity in plants is very limited. It has previously been suggested that the majority of AS variants are unlikely to be functional [Although a considerable number of functional AS events in plants have been described in literature , we havenctional ,39. Howenctional . In concArabidopsis, rice and maize and around 90,000 transcript sequences for poplar were downloaded from the PlantGDB database [Arabidopsis, respectively. For maize, BAC contigs and annotation data were download from the maize sequence ftp site [Populus version 1.1 were downloaded from the JGI poplar download-site [Populus were expanded by adding proteins from these organisms that were extracted from NCBI Nr database [-10). Only those transcripts for which ≥90% of their bases could be matched to the genome with a mean identity ≥97% were considered further. Clusters of transcripts that mapped to overlapping genomic regions were realigned to the genome using the program GeneSeqer [Over 1,000,000 transcript sequences for database  and inspdatabase . The  the two isoforms shared at least one intron or exon; or (ii) fifty percent of the exonic nucleotides were shared between both isoforms. The following types of AS events were classified (using terms different from those in [The majority of the available transcript data for d method  with thethose in ): alterni) the longest ORF from start to stop that overlaps with the ORF of the reference isoforms and; (ii) if possible the ORF starting at the same genomic position as the ORF of the reference isoform. All isoforms containing a termination codon more than 55 nt upstream of the last exon/exon junction were considered as putative targets for the NMD-pathway [Two open reading frames (ORFs) were assigned to those isoforms that did not fully match to an annotated protein:  that were predicted to be alternatively spliced in both species. To achieve this, the introns located within the CDS underlying the aligned orthologous proteins were projected onto their global alignment. Conserved introns were subsequently identified as "aligned" introns with the same phase or as "unaligned" introns with the same phase within a distance of six amino acids that could unambiguously be matched.i) similar additions were identified by searching for identical insertion sites and comparing the stretches of amino acids; (ii) similar substitutions caused by frame shifts were identified by searching for aligned amino acids that were substituted in AS isoforms in both species and comparing the stretches of amino acids resulting from the frame shift(s); (iii) similar deletions were detected by searching for stretches of aligned amino acids that were deleted as the results of AS in both species. These changes at identical positions were designated "homologous modification sites". Both (i) and (ii) were designated "similar modification" if the similarity of the aligned stretches was over 40%.The second level of comparison involved the identification of similar changes to the orthologous protein sequences resulting from AS events. Three types of similar changes were investigated:  or alternatively spliced (AS) genes was performed as described elsewhere , with thn method  was usedPrior to testing whether particular domains were significantly enriched with either constitutively or alternatively spliced introns, clusters of overlapping introns were constructed. All clusters containing more than one unique intron and singleton introns that were retained in one or more isoforms were labeled as alternative splicing (AS) clusters. All other singleton introns were labeled as constitutive (CS) clusters. This clustering step prevents a bias in the results caused by over-counting of individual AS introns . Fisher exact tests (with Holm's correction method) were performed for each domain type to test whether the ratio between its number of associated AS and CS introns deviated significantly from the ratio between all AS and CS introns located within the boundaries of all domains other than the one being tested.A spliced-out domain  was idenES designed the study, performed the analysis and drafted the manuscript. AvD participated in the analysis and preparation of the manuscript. WS supervised the study. RvH initiated and supervised the study and revised the manuscript. All authors read and approved the final manuscript.Detection of AS events and construction of full-length isoforms. The figure is a schematic representation of the methods used to detect alternative splicing events and to construct full-length isoforms.Click here for fileDissection of AS events in orthologous genes. The figure is a schematic representation of the method used to dissect AS events in orthologous genes.Click here for fileArabidopsisSpliced out domains in . The table provides an overview of the domain types that were found to be spliced out in Arabidopsis.Click here for fileSpliced out domains in rice. The table provides an overview of the domain types that were found to be spliced out in rice.Click here for fileSpliced out domains in maize. The table provides an overview of the domain types that were found to be spliced out in maize.Click here for fileArabidopsisMolecular function related GO term assignments in . The table provides an overview of the molecular function related GO terms belonging to the spliced out domains in Arabidopsis.Click here for fileMolecular function related GO term assignments in rice. The table provides an overview of molecular the function related GO terms belonging to the spliced out domains in rice.Click here for fileMolecular function related GO term assignment in maize. The table provides an overview of the molecular function related GO terms belonging to the spliced out domains in maize.Click here for fileArabidopsisSpliced-out domains from tandem repeat units in . The table provides an overview of the spliced out domains in Arabidopsis that are a unit of a tandem repeat.Click here for fileSpliced-out domains from tandem repeat units in rice. The table provides an overview of the spliced out domains in rice that are a unit of a tandem repeat.Click here for fileSpliced-out domains from tandem repeat units in maize. The table provides an overview of the spliced out domains in maize that are a unit of a tandem repeat.Click here for file


Our work represents a departure from the prevailing view that the concept of homology cannot be applied to genes that have undergone domain shuffling. In contrast to current approaches that either focus on the homology of individual domains or consider only families with identical domain architectures, we show that homology can be rationally defined for multidomain families with diverse architectures by considering the genomic context of the genes that encode them. Our study demonstrates the utility of mining network structure for evolutionary information, suggesting this is a fertile approach for investigating evolutionary processes in the post-genomic era.We address the problem of homology identification in complex multidomain families with varied domain architectures. The challenge is to distinguish sequence pairs that share common ancestry from pairs that share an inserted domain but are otherwise unrelated. This distinction is essential for accuracy in gene annotation, function prediction, and comparative genomics. There are two major obstacles to multidomain homology identification: lack of a formal definition and lack of curated benchmarks for evaluating the performance of new methods. We offer preliminary solutions to both problems: 1) an extension of the traditional model of homology to include domain insertions; and 2) a manually curated benchmark of well-studied families in mouse and human. We further present Neighborhood Correlation, a novel method that exploits the local structure of the sequence similarity network to identify homologs with great accuracy based on the observation that gene duplication and domain shuffling leave distinct patterns in the sequence similarity network. In a rigorous, empirical comparison using our curated data, Neighborhood Correlation outperforms sequence similarity, alignment length, and domain architecture comparison. Neighborhood Correlation is well suited for automated, genome-scale analyses. It is easy to compute, does not require explicit knowledge of domain architecture, and classifies both single and multidomain homologs with high accuracy. Homolog predictions obtained with our method, as well as our manually curated benchmark and a web-based visualization tool for exploratory analysis of the network neighborhood structure, are available at  New genes evolve through the duplication and modification of existing genes. As a result, genes that share common ancestry tend to have similar structure and function. Computational methods that use common ancestry have been extraordinarily successful in inferring function. The practice of discerning evolutionary relationships is stymied, however, by modular sequences made up of two or more domains. When two genes share some domains but not others, it is difficult to distinguish a case of common ancestry from insertion of the same domain into both genes. We present a formal framework to define how multidomain genes are related, and propose a novel method for rapid, robust characterization of evolutionary relationships. In an empirical comparison with the current state of the art, we demonstrate superior performance of our method using a large hand-curated set of sequences known to share common ancestry. The success of our method derives from its unique ability to infer evolutionary history from local topology in the sequence similarity network. This represents a departure from the view that protein family classification must be restricted to families with conserved architecture. By exploiting the structure of the sequence similarity network, our approach surmounts this limitation and opens the door to studies of the role of modularity in protein evolution. Homology identification is integral to the annotation of novel genes Accurate identification of All of these applications exploit one or both of the following properties of homologous sequences: genes that share common ancestry tend (1) to have similar structure and function, and (2) be located in homologous chromosomal regions, making them suitable markers for comparative genomics. Because of their prevalence and importance, it is desirable to incorporate multidomain sequences in such analyses: Multidomain proteins represent 40% of the metazoan proteome, with functional roles in signal transduction, cellular adhesion, tissue repair, and immune response a and b, which share not only a homologous domain but also a common ancestral gene. In contrast, b and c are a domain-only match, a pair of sequences that share similarity due to insertion of the same domain into both sequences but are otherwise unrelated.The original definition of molecular homology PDGFRB) and cGMP-dependent protein kinase 1, beta (PRKG1B), in e−8 that covers 13% of the average of their lengths. While they share a common domain (Pkinase), the Ig domains are unique to PDGFRB and the cNMP-binding domains are unique to PRKG1B. An example of a domain-only match is shown in NCAM2) and PDGFRB share two Ig domains, resulting in a significant alignment, also with an E-value of 2.4e−8, and alignment coverage of 24%. However, the genes that encode them are not homologous and they perform different functions: NCAM2 is involved in cell-cell adhesion with no enzymatic function.Beta platelet-derived growth factor receptor  have been proposed to screen out domain insertions. Recently, approaches based on domain architecture comparison have also been proposed NCAM2 would also eliminate roughly 240 sequences that are related to PDGFRB, since more than a quarter of the Kinases that match PDGFRB have E-values less significant than 2.4e−8. Alignment coverage would not help distinguish these two cases: the homologous pair has a shorter alignment than the unrelated pair. Nor could we separate this case by comparing domain content, since PDGFRB and PRKG1B share one domain, while PDGFRB and NCAM2 share two. For this example, sequence similarity, the length of the shared region, and domain architecture comparison all fail to distinguish the homologous pair from the domain-only match.The ability to distinguish multidomain homologs from unrelated pairs that share a domain is essential to genomic analysis. The evolutionary relationship between To determine the extent of this problem, here we evaluate sequence similarity, alignment coverage, and domain architecture comparison on a hand-curated benchmark of 853,465 known homologous pairs. Our results show that these heuristics are all insufficient for consistent, reliable identification of multidomain homologs. Surprisingly, given its widespread use, even a modest alignment coverage requirement dramatically increased the number of mis-assigned homologs in our study. These results challenge two unstated, but widely accepted hypotheses: (1) homologous sequences share similarity along the bulk of their length and (2) the local alignment between homologous sequences usually covers a greater fraction of their mean length than the local alignments of sequences that only share a domain.Neighborhood Correlation, that leverages additional information contained in the weighted sequence similarity network to distinguish homologs from domain-only matches. In this network, each vertex corresponds to a sequence. Vertices whose corresponding sequences have significant similarity are connected by an edge with weight proportional to that similarity. The neighborhood of a sequence is the set of vertices adjacent to it; that is, the set of all sequences that match it above a predefined significance threshold.  Our analysis demonstrates that the neighborhood structure of gene pairs related through shared domain insertions is characteristically different from that of pairs related through duplication or speciation. These differences in neighborhood organization are detectable and can be exploited to distinguish homology from domain sharing.These observations suggest to us that sequences alone may not consistently contain enough information to differentiate homology from domain-only matches. We introduce a novel method, called A homology detection method for genomic analysis must meet the following criteria: It should correctly predict homologous pairs and reject unrelated pairs, including those that share domains. With a single set of parameter values, it should perform reliably on sequences with a broad range of attributes, including single domain families, multidomain families, families with short regions of conservation, and families with weak sequence homology. Finally, it should be easy to use and fast enough for datasets comprising hundreds of millions of sequence pairs.individual domains In an empirical evaluation, we demonstrate that Neighborhood Correlation meets these criteria. It is highly effective in classifying multidomain homologs and achieves superior performance in comparisons with sequence similarity (BLAST and PSI-BLAST), alignment coverage, and domain architecture comparison. To evaluate performance, we hand-curated a benchmark of 853,465 known homologous pairs of mouse and human sequences, drawn from twenty well-studied families. Our test set includes single-domain families, as well as multidomain families with promiscuous domains that are at risk for domain-only matches. Although comprehensive datasets are available for testing methods for predicting homology of gene homology rather than domain homology.As a validation of our approach, we applied Neighborhood Correlation to all complete, mouse and human sequences in SwissProt 50.9 to predict homologs. A comparison of our predictions with the euKaryotic Clusters of Orthologous Groups (KOGs) database and acquistion of domains from outside the family et cetera, apply.” It has been proposed that sub-genic sequence fragments should be the units of interest Homology has traditionally been defined in terms of families that evolve by vertical descent locus. The rationale for this definition is illustrated in g2 and g2′ are homologous, despite the fact that g2 contains a domain not present in g2′ and vice-versa. In contrast, genes g2 and g3′ are not homologous, despite the fact that they share a homologous domain, since 2g and g3′ are not located in chromosomal regions that share common ancestry. For comparative mapping applications, where homologous genes are used as markers for identifying chromosomal regions, this distinction is crucial. For example, phylogenetic footprinting g2 and g2′ have an elevated probability of sharing conserved motifs since they share common ancestry. However, there is no reason to expect an enrichment of motifs shared between the flanking regions of 2g and g3′.Here, we propose a model of multidomain homology based on vertical descent and insertion of a sequence fragment into an existing gene. In our model, two sequences are homologous if they are encoded by genes that share an ancestral Our model is applicable to families that evolved through acquisition of a new domain by an existing gene. This can occur through insertion of sequence fragments into the gene or by recruitment of adjacent exons. Formation of a new gene architecture by domain loss is also consistent with our model. Several lines of evidence suggest that acquisition of an auxiliary domain by an existing gene is a relatively common mode of domain shuffling. First, a substantial number of metazoan, chordate, and vertebrate families have been identified that evolved through a pattern of duplication, insertion of domains, and further duplication, a pattern consistent with this model de novo from several unrelated building blocks and subsequently acquires a regulatory region. We consider such a novel architecture to be the progenitor of a new family, since it is not clear that the ancestry of any one constituent is preferred. Similarly, our model does not capture formation of new architectures through fragmentation of more complex ones. However, recent evidence suggests that both of these scenarios occur rarely Our model is not applicable to the case where a new domain architecture is assembled Homology detection is the problem of distinguishing between sequence pairs with different types of evolutionary histories: evolution via gene duplication or via domain insertion. Sequence similarity, alignment coverage, and domain architecture comparison have all been considered for this purpose. However, none of these distinguish the homologous pair from the domain-only match given in S is the normalized bit score x and database sequence i, N is the number of sequences in the database, and S over all sequences  increases with the number, weight, and correlation of edges in the shared neighborhoods of x and y and decreases with the number and weight of edges in their unique neighborhoods.The structure of the sequence similarity network provides a basis for distinguishing pairs related through vertical descent from other pairs. The local network neighborhoods of homologs and domain-only matches differ in both topology and edge weights. In particular, for homologous pairs, the shared neighborhood  tends to have more vertices and stronger edge weights than their unique neighborhoods . This is not true for domain-only matches. We express this distinction quantitatively by the Neighborhood Correlation score of two sequences, defined to be the correlation coefficient of their respective neighborhoods:nces see . Note thp = 2.08e−30, Spearman test). This can be understood by observing that when a domain is inserted into a new context, it is likely to experience new selective pressures leading to rapid mutational change.The Neighborhood Correlation score captures properties of the sequence similarity network that are strongly influenced by the evolutionary processes of interest. The number of edges in the shared and unique neighborhoods is influenced by the rates of gene duplication and domain insertion, while edge weights depend on sequence divergence. Immediately following a gene duplication, the two resulting paralogs have identical neighborhoods. The Neighborhood Correlation score of this new pair is initially one and decreases as the sequences diverge. Additional gene duplications in the same family further increase the size of the shared neighborhood and, hence, the Neighborhood Correlation score. In contrast, if a domain is inserted into a single member of the pair, the number of edges in its unique neighborhood increases and the Neighborhood Correlation score decreases. The increase in the number of unique edges is directly related to the promiscuity of the inserted domain, while the weights of these new edges are proportional to the degree of sequence conservation in the domain superfamily. In practice, the impact of insertion of a domain into a single member on the Neighborhood Correlation score is typically small because promiscuity and sequence conservation within domain superfamilies are inversely related. For example, Pfam domains exhibit a highly significant, negative correlation between domain promiscuity see  and sequPDGFRB, PRKG1B, and NCAM2 in the sequence similarity network derived from our test dataset , their neighborhoods in the weighted sequence similarity network are very different. The shared neighborhood of the Kinase homologs PDGFRB and PRKG1B is substantially larger (779 sequences) than their unique neighborhoods . The shared neighborhood consists almost entirely of Kinases. The unique neighborhoods are dominated by domain-only matches, due to Ig in the case of PDGFRB and the cNMP-binding domain in the case of PRKG1B. Sequence similarities within these unique neighborhoods are weak; the Pfam models for the Ig and cNMP-binding domains have average sequence identities of 20% and 18%, respectively. Thus, the edge weights (not shown) in the shared neighborhood are strong and well correlated, while the edge weights in the unique neighborhoods are weak, yielding a Neighborhood Correlation score of NC = 0.65.To see how these principles play out in practice, we consider the neighborhoods of  dataset  and 4. APDGFRB and NCAM2 are related through domain insertion and have significant sequence similarity due to a shared Ig domain. Their shared neighborhood is relatively small (242 sequences) and comprised primarily of Ig-based matches. These contribute little to the Neighborhood Correlation score of this pair due to low sequence conservation within the Ig superfamily. In contrast, the unique neighborhood of PDGFRB is large (630 sequences), with strong edge weights. For these reasons, PDGFRB and NCAM2 have a Neighborhood Correlation score of 0.29, distinctly smaller than the score for PDGFRB and PRKG1B. Unlike sequence comparison, this clear difference in neighborhood structure can be used to recognize multidomain homology.Conversely, Evaluation of classification performance requires a trusted set of positive examples (known homologous pairs) and negative examples (pairs known not to share common ancestry). Although benchmarks are available for detection of remote homology  and k(N−k) FO pairs (the negative examples). Individual families, which cover a range of functional properties and domain architecture complexity, can be used for family specific tests. In addition, we constructed a test set (ALL) for general performance evaluation by merging all sets of FF and, respectively, FO pairs, yielding 853,465 positive and 40,459,204 negative examples. Performance measurements obtained with this set could be biased by the Kinase family, which is much larger than the other families. We therefore also considered the set of all sequences excluding the Kinases (ALL-Kin), resulting in 32,629 positive and 17,545,558 negative examples.For each family, we identified two sets of sequence pairs: family (FF) pairs, where both members of the pair are in the family, and non-family (FO) pairs, where only one of the two sequences is in the family. Given a family of size Our goal is a method that can correctly identify homologs in multidomain families without degrading performance in other types of families. We therefore devised a benchmark to test a range of homology detection challenges, involving single domain as well as multidomain families. Families with complex and varied domain architectures represent the primary challenge undertaken in this study. Such families result from duplication, domain accretion, and further duplication. Some of these families are defined by a single domain that is unique to the family , while others are characterized by a particular combination of domains  or by a conserved set of domains with variations in domain copy number (e.g. Laminin). Modularity in both single and multidomain families can also arise through the presence of sequence motifs, such as subcellular localization signals, transactivation sequences , and functional components that confer substrate specificity (e.g. USP). These motifs can result in matches to unrelated sequences. In addition, promiscuous domains challenge homology identification because they can result in significant sequence similarity but carry little information about gene homology. Promiscuity can confound reliable detection of homologs even in families with conserved domain architectures.Remote homology detection is a serious challenge that has received widespread attention. In our dataset, this challenge is represented set by FGF, TNF, TNFR, and USP, families that exhibit low sequence conservation. Finally, we considered homologous pairs with short conserved regions. A minimum alignment coverage criterion is frequently imposed to eliminate domain-only matches, reflecting a widely held, but untested belief that homologous pairs have regions of similarity that cover a substantial fraction of their length. To test the robustness of homology detection methods with respect to alignment length, we included single domain families with short conserved regions such as the Tbox family.Our selection of test families was limited to those for which it was possible to obtain evidence concerning their evolutionary history. Evolutionary evidence was obtained from published articles and/or curation by a nomenclature committee. In the best cases, direct syntenic evidence of vertical descent can be found. In other cases, indirect evidence such as conserved intron/exon structure is used. Phylogenetic evidence can confirm vertical descent, for example, if all domains in a family have consistent phylogenies. However, phylogenetic disagreement between core and auxiliary domains does not rule out homology according to our model. For each, the evidence used is described in We evaluated Neighborhood Correlation using our benchmark, and compared its performance with other methods currently in use. We considered performance on multidomain homology identification, as well as overall performance on diverse, heterogeneous datasets. We also used Neighborhood Correlation to predict novel homologous relationships.We compared the performance of Neighborhood Correlation with BLAST BLAST gives a measure of sequence similarity based on the optimal local alignment between two sequences. BLAST does not capture gene structure , nor does it reflect additional information that might be derived from suboptimal local alignments. BLAST is widely used, its behavior is well understood, and its scores are easily compared with those from other studies. A great deal of attention has been devoted to tuning BLAST performance and to developing accurate statistical tests. It represents an attractive balance between rigor and speed.A significant BLAST score is evidence of similarity greater than that expected by chance, but cannot distinguish whether that similarity stems from vertical descent or domain insertion. In order to eliminate domain-only matches, many analyses combine sequence similarity with alignment coverage to identify homologs In the presence of high sequence divergence, BLAST is limited by the amount of information that can be derived from pairwise comparison. To address this problem, approaches based on multiple sequence alignments (MSAs) have been used to increase sensitivity. PSI-BLAST, one of the most widely used examples of this approach, constructs a Position Specific Scoring Matrix (PSSM) through iterative search and has been shown to dramatically improve sensitivity In addition to sequence based methods, we considered direct comparison of domain architectures for multidomain homology detection. Each sequence was represented by a linear sequence of Pfam domains. Linker sequences between domains were ignored, as was sequence variation between instances of a given Pfam domain family. The resulting domain architectures were compared based on their domain composition. In a previous study, we proposed and evaluated 21 different methods for comparing domain architectures ROC-n score (ROC-n is the area under the Receiver Operating Characteristic (ROC) curve comprised of the top ranking pairs up to the first n false positives. We used n = 100k, where k is family size, corresponding to 100 false positives per query.The performance of each method was assessed via the -n score , which r-n score . ROC-n iIn evaluating homology identification methods, we consider two user models. Genome-scale analyses require all-against-all comparison of a large and heterogeneous set of sequences. In order to be suitable for automated, genomic analyses, a method must be robust enough for use without human intervention, deliver consistent behavior on different types of domain architectures, and be fast and easy to use. In this case, the goal is to maximize the total number of homolog pairs that are correctly predicted. A second application is analysis of individual families, where the goal is to obtain good per-family prediction scores over a wide range of families.ROC-100k scores for all pairs (ALL and ALL-Kin), as well as ROC-100k scores for each family. To show how the methods tested behave on proteins with various attributes, we also report the average ROC-100k score per family for single domain families, multidomain families with conserved architectures, and multidomain families with variable architectures.To evaluate performance for both user models, we report rank plots, which show the scores of all matches to a given query sequence in rank order. Rank plots provide a visual representation of the organizational structure of the network neighborhood of the query sequence, as well as organizational substructure within the family. For example, PDGFRB, a protein tyrosine kinase. The break in the curve in NC≈0.8 corresponds to the first match to a Serine/Threonine Kinase, the inflection point at NC≈0.75 corresponds to the first match to a Dual-Specificity Kinase, and the downward plunge at NC≈0.59 corresponds to the first Casein Kinase. Rank plots for each of the 26,197 sequences in our dataset are provided at http://www.neighborhoodcorrelation.org.As a visualization tool, we generated ALL), Neighborhood Correlation dramatically outperforms the other three methods . How typical are these examples? As shown in To demonstrate why Neighborhood Correlation is more effective for complex families, we consider its performance on the Kinase family. Sn) and specificity (Sp) are considered independently. For example, when matches to the query sequence PDGFRB are ranked by Neighborhood Correlation score  is used. As a result, unrelated sequences that contain Ig domains match the resulting profile with better scores than Kinases without Ig. PSI-BLAST performs better on the Kinase family as a whole than it does on PDGFRB  indicates that PSI-BLAST scores tend to vary widely from family to family. PSI-BLAST introduces a clear tradeoff between sensitivity and generality, to the particular detriment of large-scale studies. Moreover, PSI-BLAST is characterized by greater instability and running time than BLAST or Neighborhood Correlation.When classification of heterogeneous data is considered, PSI-BLAST's performance is inferior to Neighborhood Correlation on the Domain architecture comparison performs well on single domain families and on multidomain families with conserved domain architectures . Like PSI-BLAST, DAC can recognize distant homology because domain architectures are recognized by MSA-based models. The performance of DAC on other families is mixed, however, because it faces a number of challenges that do not arise with the other classification methods.ROC-100k scores for many families. This explains why single domain families, such as Tbox, which have identical domain architectures, do not achieve perfect ROC-100k scores, contrary to expectations. An additional shortcoming is that domain architecture comparison methods do not capture information in linker sequences or sequence variation within a domain family. Therefore, domain architecture comparison tends to assign the same score to pairs that actually differ in sequence divergence. This explains the long plateaus in the ROC curve for DAC in First, all domain architecture comparison methods are substantially restricted by the limitations of domain detection. In our dataset, 12.7% of sequences do not have domain annotations, resulting in low A that sustains repeated duplication, followed by insertion of different domains into the resulting copies, yielding AB, AC, AD, and so on. Domain A will have a high promiscuity score, although it is never inserted into new contexts. As a concrete example, the Pkinase domain partners with more than 100 different domains. However, the resulting high promiscuity score may be inappropriate since Pkinase lacks many of the other characteristics of promiscuous domains, such as small size and 1-1 phase A particularly challenging problem for domain architecture comparison is how to effectively distinguish domains that proliferated through gene duplication from promiscuous domains that proliferated through domain shuffling. The number of domain partners, used here, is a typical measure of promiscuity, based on the assumption that this measure reflects the frequency of domain insertion ROC-100k scores for sequence similarity alone and combined with alignment coverage .To assess the effectiveness of alignment coverage in eliminating domain-only matches, we compared e α, see . We consure e.g.. The resure e.g. show thaα≥0.3, α≥0.6, and α≥0.8. The results, shown in ALL-Kin dataset, a cutoff of α≥0.3 eliminates 40% of true positives, specifying α≥0.6 eliminates 70% of true positives, and α≥0.8 eliminates 83% true positives. The loss in Recall is even more extreme in the ALL dataset.Alignment coverage is based on the assumption that non-homologous pairs have shorter regions of similarity than homologous pairs, yet α for individual families . These lists were used to generate a preliminary roster for each family, then confirmed by referring to recent analyses of gene family evolution in the literature. A detailed account of the curation procedure for each family with specific identification criteria and references is given in For each family, we derived a list of designated gene symbols, Pfam (11+k) for a gap of length k, and low complexity filtering. For both searches, the size of the search space was set to Y = n2 and the significance threshold to E = 10N, where n is the size of the database in residues and N is the number of sequences in the dataset.We conducted all-against-all BLAST (Version 2.2.15) N = 26,197 sequences, 11,553 mouse and 14,644 human sequences, corresponding to a total of n = 14,073,417 residues. For PSI-BLAST, four passes were executed with an inclusion threshold of E<10−13 for inclusion in the multiple alignment used to search in the next pass. Although this cutoff is much more stringent than the default, we found it essential to obtain correct results with sequences containing low complexity regions. Less stringent thresholds resulted in the inclusion of unrelated sequences in the intermediate PSSM. Asymmetries ≠E) that occur due to low complexity filtering The combined dataset has N single query BLAST searches with E = 10 and x nY = m, where xm is the length of query sequence x. Treating the all-against-all BLAST comparison as a single experiment results in symmetric E-values in the absence of low complexity filtering. We define θ = E/10N to be the expected number of chance hits per sequence in the dataset with a score equivalent to, or better than, that of the alignment of query sequence x with matching sequence y. The significance threshold of E = 10N corresponds to θ = 10 chance hits per sequence, in expectation.The parameter values used in this study embody the view that an all-against-all BLAST search is a single experiment. This approach is roughly equivalent to conducting x,i) is the normalized bit score x and i and ςmin = log2(n2/10N)*0.95 = 28.019, which is 5% less than the bit score corresponding to θ = 10 for a dataset of the size used in this study.We calculated the Neighborhood Correlation scores for all sequence pairs in our dataset from Equation 1 using the similarity score,S, is defined. We considered three measures of similarity: S = log ς, S = ς and an unweighted comparison of neighborhood membership defined as S = 1 if there is a significant match between x and i, and zero otherwise. Although the other two measures performed well on some families, only S = log ς gave consistent, good performance on a wide range of families. This suggests two factors that may be important to Neighborhood Correlation performance. First, the relatively poor performance of the unweighted score indicates that it is necessary to capture differences in the degree of similarity to sequences in the neighborhood to capture complete evolutionary information. Second, the improved performance obtained with S = log ς can be understood by recalling that the correlation coefficient captures only linear associations. The use of the logarithm compresses the range of ς, resulting in scores that more closely approximate linearity.The effectiveness of Neighborhood Correlation depends strongly on how the similarity score, min, the score assigned to pairs without significant similarity, may influence Neighborhood Correlation performance in homology identification. We experimented with values of ςmin corresponding to significance thresholds ranging over two orders of magnitude. The results (data not shown) suggest that varying ςmin has little impact on Neighborhood Correlation.The choice of ςpartners if they co-occur in at least one sequence Promiscuity refers to the tendency of domains to be inserted into many different contexts. Typically, promiscuity of a domain is defined as the number of distinct partners associated with it, where two domains are We further obtained percent sequence identity for each Pfam identifier from the Pfam website. The Spearman ranked correlation coefficient of domain promiscuity and sequence identity was calculated to evaluate whether promiscuity and sequence identity were related.x and y were calculated as follows:w is the weight of domain id in sequence x. Domains are assigned weights inversely proportional to their promiscuity. Promiscuous domains may occur in many unrelated sequences, and so are less useful than relatively rare domains in determining homology. The weight of a domain not contained in a given sequence is zero. As a result, pairs of sequences which share no domains are assigned a similarity of zero. This domain architecture comparison function corrects for the bias of proteins with many domains. Proteins with numerous domains have an elevated probability of sharing a domain with other proteins. Of the 21 domain architecture comparison methods we evaluated in a previous study We conducted an all-against-all domain architecture comparison using the Pfam identifiers provided by SwissProt. Similarity of each pair of sequences, x and y, with significant similarity, we calculated the alignment coverage, defined as α = 2al/(xl+yl), where xl and yl are the length of sequences x and y, and al is the length of the optimal local alignment, define to be the number of columns needed to represent it; that is, it includes gapped positions. The length of the optimal alignment between query x and match y will not, in general, be the same as the length of the optimal alignment between query y and match x. We forced the alignment coverage to be symmetric by setting both α and α to the maximum of the two values.For every pair of sequences, By considering only the optimal alignment, we risk underestimating the extent of similarity between homologous sequences. To take suboptimal alignments into account, we used a simple heuristic method for selecting a set of high-scoring local alignments that do not conflict. Two alignments conflict if they overlap or do not appear in the same order in both sequences see .ROC), which captures the tradeoff between sensitivity (nS) and specificity (pS) as a function of the classifier threshold. A ROC curve is a plot of nS as a function of 1−pS, where nS = TP/(TP+FN) and pS = TN/(TN+FP). TP, FP, TN, and FN refer to the number of True Positives, False Positives, True Negatives, and False Negatives, respectively. In the context of our test, TP is the number of sequence pairs that have common ancestry and have been correctly identified by the classifier. FP represents the number of pairs that are classified as homologs, but are not family pairs. TN and FN refer to the number of non-homologous pairs that are correctly ruled out and incorrectly included, respectively.Classifier performance was evaluated using Receiver Operating Characteristic .Novel predictions of mouse and human homologs using our method (http://www.ebi.ac.uk/swissprot): human PDGFRG (P09619), human PRKG1B (P14619), and mouse NCAM2 (O35136). Accession numbers for all 1577 sequences in the twenty families in our benchmark are given in The accession numbers used in this paper are from Swiss Prot , α≥0.3 (green), α≥0.6 (yellow), and α≥0.8 (orange) for all families.ROC-100k curves for all families. ROC-100k curves of Neighborhood Correlation (blue), PSI-BLAST (magenta), DAC (purple), and BLAST sequence similarity with alignment coverage thresholds of (0.15 MB PDF)Click here for additional data file.Figure S2Distributions of BLAST and NC scores for all families. .(0.04 MB PDF)Click here for additional data file.Figure S3Distribution of Neighborhood Correlation scores for all sequence pairs.(0.00 MB PDF)Click here for additional data file.Figure S4Distributions of alignment coverage for all families. Distributions of alignment coverage calculated with the optimal alignment length only  and with combined non-conflicting alignments  for all families.(0.03 MB PDF)Click here for additional data file.Table S1Precision and Recall for predictions using simple alignment coverage thresholds of 0.3, 0.6, and 0.8 for all families.(0.07 MB DOC)Click here for additional data file.Table S2Precision and recall for predictions using combined alignment coverage thresholds of 0.3, 0.6, and 0.8 for all families.(0.07 MB DOC)Click here for additional data file.Dataset S1Curated Benchmark.(0.02 MB TDS)Click here for additional data file.Text S1Supporting Text.(0.09 MB DOC)Click here for additional data file.