They typically feature an N-terminal effector domain, a central nucleotide-binding domain (NACHT) and a C-terminal ligand-binding region that is composed of several leucine-rich repeats (LRRs). NLRs are believed to initiate or regulate host defense pathways through formation of signaling platforms that subsequently trigger the activation of inflammatory caspases and NF-kB. Despite their fundamental role in orchestrating key pathways in innate immunity, their mode of action in molecular terms remains largely unknown. Here we present the first comprehensive sequence and structure modeling analysis of NLR proteins, revealing that NLRs posses a domain architecture similar to the apoptotic initiator protein Apaf-1. Apaf-1 performs its cellular function by the formation of a heptameric platform, dubbed apoptosome, ultimately triggering the controlled demise of the affected cell. The mechanism of apoptosome formation by Apaf-1 potentially offers insight into the activation mechanisms of NLR proteins. Multiple sequence alignment analysis and homology modeling revealed Apaf-1-like structural features in most members of the NLR family, suggesting a similar biochemical behaviour in catalytic activity and oligomerization. Evolutionary tree comparisons substantiate the conservation of characteristic functional regions within the NLR family and are in good agreement with domain distributions found in distinct NLRs. Importantly, the analysis of LRR domains reveals surprisingly low conservation levels among putative ligand-binding motifs. The same is true for the effector domains exhibiting distinct interfaces ensuring specific interactions with downstream target proteins. All together these factors suggest specific biological functions for individual NLRs.Innate immunity represents an important system with a variety of vital processes at the core of many diseases. In recent years, the central role of the Nod-like receptor (NLR) protein family became increasingly appreciated in innate immune responses. NLRs are classified as part of the  Eukaryotes have evolved complex systems to detect microbial infection and other potential threats to the host. Recognition of microbes relies on the sensing of microbe associated molecular patterns (MAMPs) by germline-encoded host pattern recognition molecules (PRMs), which include various families of leucine-rich repeat (LRR) bearing proteins in plants and animals. While Toll-like receptors (TLRs) constitute the main sensors for detection of extracellular microbes, recent findings suggest that two distinct protein families, the RIG-like helicases (RLHs) and the Nod-like receptors (NLRs), act as intracellular surveillance molecules Like TLRs, NLRs are thought to recognize microbial products, as well as other intracellular danger signals, thereby initiating host defense pathways through the activation of the NF-kB response and inflammatory caspases Structurally, NLRs are large multi-domain proteins with a tripartite architecture. NLR proteins typically contain a central nucleotide-binding domain termed NACHT domain , N-terminal effector domains  for binding downstream signaling molecules, while the C-terminal part consists of a receptor domain, which is characterized by a series of leucine-rich repeats (LRRs). It is hypothesized that the crucial step in NLR activation lies in the oligomerization of the NACHT-domain, thereby forming an active signaling platform  In respect to the C-terminal LRR region NALP10 is the only NLR member that has no or only a very short occurrence of LRR repeats. NALP1 on the other hand shows a typical LRR region, but contains two additional domains C-terminally of those LRRs; a FIIND domain of yet unknown function and a C-terminal CARD domain  that disDetailed sequence comparisons of 22 human NLR members reveal an overall sequence identity in the range of 10–30% amongst pairs. Since domain shuffling is a eukaryotic hallmark and has created a large complexity of functions in proteins, it hampers evolutionary analysis of full length proteins. In particular effector domains are subject to domain accretion, and/or domain shuffling or duplication for the acquisition of new domain architecture. Taking this into account, we have chosen the NACHT domain to conduct a phylogenetic analysis, addressing the possible evolutionary history of NLR proteins.By comparing the NACHT region of the NLR family members, we observed that the phylogenetic distribution clearly correlates with their respective effector domain composition . For insMembers of the AAA+ superfamily feature a so called ATPase, P-loop or Rossman-fold which adopts a three-layered α-β sandwich configuration. This fold contains recurring regulatory units with the β-strands forming a central, parallel β-sheet, which is embedded between α-helices on both sides (scop id C.37.1.20). The parallel β-sheet forming the core of the ATPase domain assumes a 51432 topology To decipher the structure and mechanism of the NLR protein family we performed a multiple sequence alignment of NLR proteins and Apaf-1  focusingThe Walker A motif is composed of the characteristic consensus pattern GxxxxGKT/S (x represents any amino acid), where the lysine residue directly interacts with a phosphate moiety of ATP The Walker B motif of ATPases, located in the nucleotide-binding site, is characterized by the conserved sequence pattern hhhhDD/E (h represents a hydrophobic amino acid). The proximal aspartate residue is crucial for coordinating binding of the Mg2+ cation, which has been shown to be required for nucleotide hydrolysis. The second acidic residue, usually glutamate, primes a water molecule for the hydrolysis of ATP et al reports that NALP12, which also contains a modified Walker B box, is capable of both, ATP hydrolysis and oligomerization Generally, the Walker A/P-loop and Walker B motifs are well conserved amongst ATPases (e.g. Apaf-1 and CED-4). However, our multiple sequence alignment revealed that all NLRs, with the exception of NAIP and NALP11, contain a modified Walker B motif, where the second acidic residue is missing. Thus, it remains elusive if NLRs harbouring these substituting amino acids  within the Walker B motif, hhhhD[GAS]hDE, are still capable of nucleotide hydrolysis . InteresThe Sensor 1 motif is typically found adjacent to the Walker A and B motifs and interacts with or “senses” the γ-phosphate of ATP  Additional NLR sequence motifs are the cysteine rich region in the NACHT domain, containing a VCWxVCT motif located adjacent to the nucleotide-binding site , which pSince our detailed sequence analyses revealed that most NLRs and Apaf-1 share the same domain architecture and many secondary structure features, the availability of structural and mechanistic data for Apaf-1 provides the opportunity to link conserved sequence features of NLRs to functional aspects of NLR signaling. Cytochrome c activated Apaf-1 has been shown to undergo an ATP-hydrolysis-dependent conformational rearrangement in order to form heptamers through an interaction of its NACHT domains. Interestingly, the heptamers were proposed to arrange in a ring-like structure, which is usually found in AAA+ ATPases such as RuvB or NtrC1 Based on the fact that all structural features required for oligomerization are present , we hypoStructural and mutational studies of the CARD domains of Apaf-1 and procaspase-9 have identified the essential motifs for procaspase-9 activation by Apaf-1 Based on these findings, we examined whether these residues, which are necessary for homophilic CARD-CARD interactions, are conserved among NLR CARDs and the CARDs of their effector proteins, respectively, by means of multiple sequence alignment . AlthougTo date, neither information from crystal structures nor mutational analysis of PYRIN domains or PYRIN-PYRIN interactions have been reported By utilizing multiple sequence alignments  of both,The proposed model of CARD-CARD and PYD-PYD interactions is that the acidic patch of one domain interacts with the basic patch of the other protein. Hydrophobic residues of adjacent regions are also suggested to be important in this interaction. Nevertheless, it is not clear so far, if there is a limited repertoire of structurally conserved motifs that may mediate interactions among death domain superfamily members. Therefore, more structural studies and mutational analysis of complexes built of those domains are necessary to define the motifs and interacting residues involved.Similarly to the WD-40 repeats in Apaf-1, leucine-rich repeats are the ligand sensing motif of NLR proteins, a property they share with members of the TLR and RLR (RIG-I-like receptors) families. LRRs in general consist of 2–45 motifs of 20–30 amino acids in length and exhibit a typical curved horseshoe-like structure with a parallel beta sheet on the concave side and helical elements on the convex side In NLRs the C-terminal LRR domain is thought to act as a sensor of bacterial products. Yet, little is known about how the PAMP is interacting with the LRR or even how the LRR region interacts with the remainder of the NLR, since no structural data is available on these questions. Recently, some insight into the possible mechanism of ligand-receptor binding was provided by the two LRR-ligand complex structures of TLR1:TLR2 In order to augment our understanding of the molecular mechanism of ligand recognition we generated a homology model of the NOD2 LRR domains based on the structure of the ribonuclease inhibitor  as a template. Additionally, we utilized the Consurf Server for the identification of functional regions in NLRs by surface mapping of phylogenetic information. Our findings are in accordance with recently published work by Tanabe and colleagues, which showed loss of function mutations in the LRR domain to be located on the convex surface with additional residues on the concave region Our homology model suggests a putative ligand-binding pocket situated in the concave surface and supports earlier observations, where the predicted loss-of function-mutations W907L, V935M, E959K, C961Y, K989E, S991F as well as the Crohn's disease related mutation G908 have been mapped to the same area Several diseases were found to arise from aberrant NLR function In summary, our study clearly shows that the overall architecture and secondary structure features of most NLRs resemble those of Apaf-1. From the structural point of view, most of the NLR family members are therefore Apaf-1-like, with deviations including NOD2 (2 CARD domains), NALP1 , NALP10 (missing LRR region), NOD5 and CIITA . Analyses of multiple sequence alignments revealed that all NLRs contain the crucial features for ATP-binding. In comparison to Apaf-1, most NLRs display a modified Walker B box. Since all NLRs, except for NAIP and NALP11, do not contain the crucial Walker B glutamate or aspartate required to activate the water molecule, they seem to have developed a new motif, the extended Walker B box to retain ATP hydrolysis activity. This is supported by the observation that NALP12 is able to bind and hydrolyze ATP Additionally, we identified one of the most intriguing features, which is the conserved histidine in the WH-domain, to be conserved among members of the NLR family. NLRs displaying this feature most likely assemble similar to Apaf-1 and activate their targets by oligomerization. Interestingly, NOD5, CIITA, NALP6, and NALP8 do not contain the conserved histidine in their WH domain. Whether their oligomerization mechanism and ATP hydrolysis capacity differ remains an open issue.Our analyses of the effector domains of NLRs as well as those of their adaptors and target caspases, or kinases reveal a common interface, which is composed of charged surface patches. The presence of acidic and basic surface patches theoretically renders all CARD and PYD domains compatible for interaction with each other. Yet their distinct profile and that of surrounding residues found in the described interfaces ensure the specificity for each interaction. This selectivity allows a well-balanced fine-tuning of the elicited immune response.Finally, sequence comparison of LRRs in human NLRs does not reveal one particular region that serves as the general ligand-binding site. This suggests that individual NLRs evolved highly specialized modes to recognize specific ligands. However, conserved residues found within this domain may contribute to the intramolecular interaction or backfolding of the LRR region in order to regulate NLR activation. Our results serve as a basis for further mutational and functional analyses required to more precisely define the role of LRRs in ligand recognition and NLR activation.http://ffas.ljcrf.edu) http://www.predictprotein.org) NLR protein sequences see  were subThe alignments were used to run phylogenetic probabilistic analyses using the parallel implementation of MrBayes http://www1.jcsg.org/scripts/prod/scwrl) https://prosa.services.came.sbg.ac.at/prosa.php) manually inspected, analyzed, and figures were prepared using Pymol (http://pymol.sourceforge.net). The degree of conservation was calculated using the ConSurf Server (http://consurf.tau.ac.il).The protein structure with the highest scoring alignment from the FFAS-search was used as a template for modeling the structures with the SCWRL-Server (


The crystal structure of the first representative of DUF364 family reveals a combination of enolase N-terminal-like and C-terminal Rossmann-like folds. Analysis of the interdomain cleft combined with sequence and genome context conservation among homologs, suggests a unique catalytic site likely involved in the synthesis of a flavin or pterin derivative. Desulfitobacterium hafniense DCB-2 was determined by single-wavelength anomalous diffraction (SAD) to a resolution of 2.01 Å using the semi-automated high-throughput pipeline of the Joint Center for Structural Genomics (JCSG) as part of the NIGMS Protein Structure Initiative (PSI). This protein structure is the first representative of the PF04016 (DUF364) Pfam family and reveals a novel combination of two well known domains . Structural and bioinformatic analyses reveal partial similarities to Rossmann-like methyltransferases, with residues from the enolase-like fold combining to form a unique active site that is likely to be involved in the condensation or hydrolysis of molecules implicated in the synthesis of flavins, pterins or other siderophores. The genome context of Dhaf4260 and homologs additionally supports a role in heavy-metal chelation.The crystal structure of Dhaf4260 from  Desulfitobacterium spp. are anaerobic bacteria that are capable of dehalogenating organic compounds and have been studied for their potential in bioremediation processes  and I-PIPE (Insert) primers  that included sequences for the predicted 5′ and 3′ ends. The expression vector pSpeedET, which encodes an amino-terminal tobacco etch virus (TEV) protease-cleavable expression and purification tag (MGSDKIHHHHHHENLYFQ/G), was PCR-amplified with V-PIPE (Vector) primers . V-PIPE and I-PIPE PCR products were mixed to anneal the amplified DNA fragments together. Escherichia coli GeneHogs (Invitrogen) competent cells were transformed with the V-PIPE/I-PIPE mixture and dispensed onto selective LB-agar plates. The cloning junctions were confirmed by DNA sequencing. Expression was performed in a selenomethionine-containing medium at 310 K with suppression of normal methionine synthesis. At the end of fermentation, lysozyme was added to the culture to a final concentration of 250 µg ml−1 and the cells were harvested and frozen. After one freeze–thaw cycle, the cells were sonicated in lysis buffer  and the lysate was clarified by centrifugation at 32 500g for 30 min. The soluble fraction was passed over nickel-chelating resin  pre-equilibrated with lysis buffer, the resin was washed with wash buffer  and the protein was eluted with elution buffer . Since prior testing had revealed that the designed protease site in the expression and purification tag did not cleave with TEV protease, protease was not added to the protein preparation. The eluate was buffer-exchanged with crystallization buffer  using a PD-10 column  and concentrated to 5 mg ml−1 by centrifugal ultrafiltration (Millipore). Dhaf4260 was crystallized at 277 K by mixing 200 nl protein solution with 200 nl crystallization solution and equilibrating against 50 µl reservoir volume using the nanodroplet vapor-diffusion method  was added to the crystal as a cryoprotectant to a final concentration of 20%(v/v). A diamond-shaped crystal of approximate dimensions 100 × 100 × 100 µm was harvested at room temperature after 46 d at 277 K and cryocooled in liquid nitrogen. Initial screening for diffraction was carried out using the Stanford Automated Mounting system  cloning method  sodium azide.The oligomeric state of Dhaf4260 in solution was determined using a 1 × 30 cm Superdex 200 column  coupled with miniDAWN static light-scattering (SEC/SLS) and Optilab differential refractive-index detectors (Wyatt Technology). The mobile phase consisted of 20 m2.2.Blu-Ice data-collection environment  and TLS refinement with one TLS group per chain. Data-reduction and refinement statistics are summarized in Table 1Single-wavelength anomalous diffraction (SAD) data were collected on beamline BL9-2 at the SSRL at a wavelength corresponding to the peak of a selenium SAD experiment. The data set was collected at 100 K with a MAR 325 CCD detector using the 2.3.AutoDepInputTool  was adapted from an analysis using PDBsum . Atomic coordinates and experimental structure factors for Dhaf4260 at 2.01 Å have been deposited in the PDB and are accessible under code 3l5o.Analysis of the stereochemical quality of the model was accomplished using 3.3.1.a) was determined to 2.01 Å resolution using the single-wavelength anomalous dispersion (SAD) method. Data-collection, model and refinement statistics are summarized in Table 1A contains residues 1–102 and 110–251 in addition to three residues from the N-terminal expression and purification tag (residues −2A to 0A), and molecule B contains residues 1–102 and 115–251 and five residues from the N-terminal expression and purification tag (residues −4B to 0B)], six ethylene glycol molecules, four imidazole molecules, two chloride ions and 239 water molecules in the asymmetric unit. The electron density was insufficient to model the loop connecting the N- and C-terminal domains (residues 103–109 in molecule A and residues 103–114 in molecule B) and the remainder of the N-terminal expression and purification tags (residues −18 to −3 in molecule A and −18 to −5 in molecule B). Side-chain atoms from Phe(−2), Phe44, Glu45, Thr46, Arg47, Gln53, Gln90, Asp101, Glu135, Leu137, Arg194, Lys223, Lys237 and Lys239 in chain A and Leu(−4), Tyr(−3), Gln(−1), Ser100, Asp101, SeMet115, Ser116, Gln117, Asn118, Lys121, Lys123, Lys137, Glu153, Lys237, Lys239 and Lys250 in chain B were omitted owing to weak electron density. The Matthews coefficient  characterized by three helices (H1–H3) with up–down–up topology and a three-stranded antiparallel β-sheet (β1–β3). The C-terminal domain (residues 110–251) adopts a Rossmann-like fold that is described in SCOP as PLP-dependent transferase-like (http://scop.mrc-lmb.cam.ac.uk/scop/data/scop.b.d.jg.html). The typical NAD(P)-binding Rossmann fold is characterized by a three-layer α/β/α sandwich structure with a parallel sheet and a 321456 topology, but does not contain the additional antiparallel strand observed in Dhaf4260 (strand order 3214567). The PLP-dependent transferase-like fold is characterized by a similar sandwich that contains a seven-stranded mixed β-sheet (β4–β10 in Dhaf4260) with the seventh strand (β10 in Dhaf4260) antiparallel to the rest. However, there is only partial congruence between the sheet topology of the PLP-dependent transferase-like fold (strand order 3245671) and that observed in Dhaf4260. Further, the lysine to which the co-factor is linked in the PLP-dependent transferase-like fold is absent in Dhaf4260-like proteins.Dhaf4260 is a two-domain α+β protein Fig. 1. SCOP deFATCAT ], with Cα r.m.s.d.s of 3.1 and 3.2 Å over 170 and 173 residues, respectively (sequence identity of 9%) for these Rossmann-like methyltransferases involved in the anaerobic pathway of cobalamin (vitamin B12) biosynthesis . In addition, a similar mode of tetramerization is not possible in Dhaf4260 because the corresponding interface is involved in interactions with the N-terminal domain.A search of intact Dhaf4260 with 4enl; Lebioda et al., 1989α r.m.s.d. of 1.9 Å over 60 residues, but a sequence identity of only 8%. Structural differences involve an extra N-terminal helix (H1) in Dhaf4260 and different orientations of helices H3 and H5 . A weak similarity of this domain to several RNA-binding proteins was also observed, including ribosomal protein L22  and a double-stranded RNA-specific editase . Although in both of these latter cases the β-sheet and long central helix (H4) systematically superimpose well, along with one or two of the outer helices , the connectivity is different, limiting the scope for functional inference.The N-terminal domain of Dhaf4260 shows strong structural similarity to the N-terminal domain of enolase (PDB code b). The corresponding loop in Dhaf4260 (β3–H2) is much shorter. Many enolases are dimers and the dimerization interface is conserved among prokaryotes and eukaryotes, where dimerization is proposed to play a role in promoting subunit stability , muconate-lactonizing enzyme (MLE) and enolases, is a group of functionally related enzymes each of which is organized into two domains: a substrate-specificity-determining capping N-terminal domain followed by a TIM barrel that contains the metal-ion ligands and acid/base catalysts at the C-terminal ends of the β-strands  and a family of RNA polymerase II-associated proteins . The C-terminal domain showed significant homology with PF03446 , PF02826  and PF00670 . All three families contain NAD-binding domains, with PF00670 being a member of a family of S-adenosyl-l-homocysteine (SAH) hydrolases, which are B12-dependent enzymes of the activated methyl cycle. Residues that are conserved among all three families and DUF364 are Gly129, Glu148, Thr174 and Asp180 (the numbering is for Dhaf4260). Residues that additionally show high conservation among Dhaf4260 homologs include Gly37, Gly39, Arg42, Asn83, Thr133 and Thr182. Mapping of these residues that are conserved in DUF364 homologs onto the structure of Dhaf4260 shows that they cluster inside a deep pocket (∼660 Å3) in the interface between the enolase-like and Rossmann-like domains , suggesting that this region serves as an active site and that DUF364 homologs function as enzymes.A search of the N-terminal domain of Dhaf4260 against the Pfam database using the remote protein homology-detection server e.g. ATP, NAD and S-adenosyl-l-methionine) binding sites . Other similarities to Rossmann-like folds .An aspartate or glutamate residue that interacts with the hydroxyl groups of the ribose is the most highly conserved feature of adenosyl (S-adenosyl-l-methionine (SAM) through an induced-fit mechanism is absent from Dhaf4260, suggesting a different ligand and a different reaction mechanism. A GXG-type signature is observed in a different loop  bordering one side of the adenine base, with Arg42  possibly engaged in a similar hydrophobic packing interaction as Arg63 in MT0146. In addition, the Dhaf4260 pocket is both narrower and longer than in Rossmann-like methyltransferases such as MT0146, suggesting that it catalyzes the modification of a longer substrate or the condensation of two molecules.The GGSGG loop that completes the precorrin binding site and is implicated in binding M), this interaction is not likely to be functionally relevant.A chloride ion is present in this cleft in both molecules in the asymmetric unit and is coordinated by the backbone amide of Trp149 and solvent. This chloride-binding site is in a similar location to that of the adenosyl ring of the SAH bound in the MT0146 structure. Since the chloride only makes a single protein contact within the pocket and given the high chloride concentration in the crystallization reagent  that comprises the core of vitamin B3.3.http://string.embl.de) of DUF364 homologs shows a high degree of confidence in a predicted functional association with a number of proteins involved in the transport and chelation of rare metals such as iron (WS1133), tungstate (MTH926), vanadium (RPA1384 and RPA1385) and molybdate , as well as transcriptional regulators  involved in the chemical stress response. Gene neighborhood association with ABC transporters (including both ATPase and membrane-spanning permease subunits) are found with a wide phylogenetic distribution in prokaryotic homologs, suggesting that DUF364 enzymes predominantly act on a soluble substrate, which is likely to be a heavy metal that is transported by these systems. In this context, DUF364 homologs could function in the condensation or hydrolysis of specific side chains in the synthesis of derivatives of flavins, pterins or similar compounds (e.g. siderophores) that might serve to chelate these metals.The genome context (http://www1.jcsg.org/cgi-bin/models/get_mor.pl?key=3l5oA.The Dhaf4260 protein family DUF364 (PF04016) contains around 165 homologs that are mostly found in cyanobacteria, actinobacteria, thermotogae and proteobacteria, but are also found in firmicutes and a range of archaea; all of these proteins are approximately 230 residues in length. The availability of further DUF364 member sequences and structures might shed light on the evolutionary history of this intriguing protein family. The information presented here, in combination with further biochemical and biophysical studies, should yield valuable insights into the functional role of Dhaf4260. Models for Dhaf4260 homologs can be accessed at TOPSAN (Krishna et al., 2010http://www.topsan.org/explore?PDBid=3l5o.Additional information about Dhaf4260 is available from 4.The first structural representative of the DUF364 family reveals a novel two-domain organization in which an enolase N-terminal-like fold combines with a C-terminal Rossmann-fold-like domain to form a unique catalytic site at the domain interface. Analysis of the genetic context and interdomain cleft suggest a role in heavy-metal uptake, possibly involving the synthesis of a flavin or pterin derivative.D. hafniense DCB-2, 3l5oDhaf4260 from             PDB reference: 10.1107/S1744309110007517/wd5120sup1.pdf            Supplementary material file. DOI:


The inhibition of specific SH2 domain mediated protein-protein interactions as an effective chemotherapeutic approach in the treatment of diseases remains a challenge. That different conformations of peptide-ligands are preferred by different SH2 domains is an underappreciated observation from the structural analysis of phosphotyrosine peptide binding to SH2 domains that may aid in future drug design. To explore the nature of ligand binding, we use simulated annealing (SA) to sample the conformational space of phosphotyrosine-containing peptides complexed with the Src SH2 domain. While in good agreement with the crystallographic and NMR studies of high-affinity phosphopeptide-SH2 domain complexes, the results suggest that the structural basis for phopsphopeptide- Src SH2 interactions is more complex than the “two-pronged plug two-hole socket” model. A systematic study of peptides of type pYEEX, where pY is phosphotyrosine and X is a hydrophobic residue, indicates that these peptides can assume two conformations, one extended and one helical, representing the balance between the interaction of residue X with the hydrophobic hole on the surface of the Src SH2 domain, and its contribution to the inherent tendency of the two glutamic acids to form an α-helix. In contrast, a β-turn conformation, almost identical to that observed in the crystal structure of pYVNV bound to the Grb2 SH2 domain, predominates for pYXNX peptides, even in the presence of isoleucine at the third position. While peptide binding affinities, as measured by fluorescence polarization, correlate with the relative proportion of extended peptide conformation, these results suggest a model where all three residues C-terminal to the phosphotyrosine determine the conformation of the bound phosphopeptide. The information obtained in this work can be used in the design of specific SH2 domain inhibitors. SH2 (Src homology 2) domains are found as modules in many proteins involved in cell signal transduction pathways. They bind to short stretches of amino acids that contain phosphorylated tyrosine residues (pTyr) and thereby mediate the interactions between proteins during cell signaling. Since these processes control cell growth and differentiation, abnormal alterations of these signaling pathways result in malignancies. For example, the Src family of tyrosine kinases, whose members contain SH2 domains, play a role in both breast cancer The Src SH2 domain binds with high affinity pTyr-Glu-Glu-Ile (pYEEI) Since neither single-crystal X-ray diffraction methods, nor NMR methods have yielded a complete picture of the conformational diversity of phosphopeptides complexed with SH2 domains, we have turned to computational methods to explore the conformational trends of the bound phosphopeptides. Molecular simulations are well suited for this purpose; however, exhaustive sampling of the conformational space of even small peptides using fully solvated models, whether by Monte-Carlo methods or by molecular dynamics, is computationally expensive. A widely used computationally-efficient approach to this problem is simulated annealing, which is based on high-temperature sampling of selected degrees of freedom in implicit solvent Using this simulated annealing method in combination with fluorescence polarization techniques, we explore the conformational space of various phosphopeptides bound to the Src SH2 domain. We characterize the conformational landscape of the bound peptides by systematically examining the effect of different cooling rates, and we identify their conformational trends. We show that the nature of the residue at pTyr+3 plays an important, but not the key role in determining the binding affinity and the conformation of the bound peptide.IP of fluorescent probe binding to the Src SH2 domain by the sample was calculated by the following equation:0FP is the fluorescent polarization value of the blank control, FP is the fluorescent polarization value of the sample (peptide), and bFP is the fluorescent polarization value of the background control. The inhibition percentages of the various concentrations of the assayed peptides were plotted, and the IC50 value (the concentration that inhibits the binding of the fluorescent probe to the Src SH2 domain by 50%) was calculated.The relative binding affinity of phosphopeptides to the Src SH2 domain was tested by competition against a fluorescent probe using a fluorescence polarization (FP) binding assay Sampling of the conformational space of the phosphopeptides complexed with the Src SH2 domain was carried out using a simulated annealing procedure in which a number of conformationally random replicas of the bound peptide are generated at high temperature, and then slowly cooled down. The conformation of the SH2 domain is assumed to remain unchanged; this assumption is based on single-crystal X-ray diffraction studies of both free and peptide-bound SH2 domains, which show that their structure is not affected by binding ff96 force field together with Verlet dynamics.  2) were applied to phosphotyrosine atoms, while the SH2 domain atoms were constrained to their positions in the starting models.The simulations were performed in a finite size, non-periodic system. Since exhaustive sampling of the full conformational space is beyond the reach of explicit-solvent simulations, the solvent effect is represented by a distance-dependent dielectric constant of the form ε = RPartial charges for the phosphotyrosine were derived using the restrained electrostatic potential method (RESP) i, the temperature T was chosen such that:x set to 0.8.Before starting the SA protocol, the geometries of the starting models were optimized and bad interatomic contacts relieved by energy minimization consisting of 250 steps of steepest descent, followed by 750 steps of conjugate gradient minimization. Following the initial energy minimization, the SH2 domain complexed with the phosphotyrosine containing peptide was heated up to 3000K over three 10 ps long temperature steps, after which 50 samples were collected at this temperature at regular time intervals, varying between 2.5 and 10 ps. The time intervals were chosen so as to maximize both the conformational spread of the 50 copies, and their average CA-RMSD with respect to the starting model. Each structure was then cooled down using a logarithmic cooling protocol: at each cooling step In order to characterize the energy landscape of the bound peptide, we tested various cooling rates, ranging from 10ps to 500ps per temperature step. This allowed us to identify structural trends of the peptide bound to the SH2 domain, as well as the rate at which these trends evolve, yielding a qualitative picture of the energy landscape.The copies were cooled down to 300K, after which they underwent another round of energy minimization consisting of 200 steepest descent steps and 600 conjugate gradient steps. After all copies were cooled down and energy minimized, their CA-RMSDs from the initial structure were calculated and cluster analysis was performed by examining the joint rmsd-energy distribution, as well as using NMRCLUST (based on pair-wise RMSD) 50 of various phosphopeptides are presented in 50 effected by mutating these two residues, with the changes resulting from mutations of the residue at position pTyr+3. Substitution of Glu(pTyr+2) with Asn in pYEEI and pYEEV , or of Ile(pTyr+3) with Val in pYEEI and pYENI causes a decrease in binding affinity by approximately 1.8 kJ/mol. Furthermore, replacing Glu(pTyr+1) with Val in pYENV causes a 3.3 kJ/mol decrease.The IPThe starting model for this simulation was the crystal structure of the high-affinity (0.08–0.2 µM) SA dynamics of this complex with various cooling schedules, which differ from each other by the time spent at each temperature level, shows that, even at relatively fast cooling schedules, most of the structures cluster close to the starting model.Cooling with 50 ps per temperature step results in an average CA-RMSD of 4±2 Å relative to the starting model and the average energy is −335±7 kcal/mol. At 500 ps per cooling step, the average CA-RMSD is 3±2 Å and the average energy is −342±3 kcal/mol. When considering only the three residues that form the specificity determining segment: Glu(pTyr+1)-Glu(pTyr+2)-Ile(pTyr+3), the average RMSD goes from 5±2 Å at 3000K  and 3A td of 5.5 µM This complex is less specific, with a reported KAfter cooling at a rate of 500 ps/step, the average CA-RMSD of the 50 copies of pYVPML relative to the crystal structure decreases from 6±2 Å to 3±2 Å, with 56% of the population in a cluster with a CA-RMSD of 1.5±0.5 Å. The CA-RMSD of the specificity determining segment Val(pTyr+1)-Pro(pTyr+2)-Met(pTyr+3) goes from 4.0±1 Å at 3000K, to 3±1 Å with a cooling rate of 50 ps/step, to 2±1 Å at 500 ps/step; 94% of the population lie in a broad cluster with an average CA-RMSD of 2±1 Å, and 28% of the population in a tight sub-cluster with a CA-RMSD of 0.6±0.1 Å  and 3D–FFor the complexes of Src SH2 with peptides of type pYEEX  the binding affinities correlated with the size of the residue at position pTyr+3 . Since wFor all five phosphopeptides, SA with 500ps/step results in 80% of the population located in two clusters, indicating two main tendencies for the phosphopeptide conformation . One clu50 of 1.0 µM for pYENI and 7.9 µM for pYVNV  adopt two main conformations: one fully or partially extended, the other one partially helical. The presence of a helical conformation for the pYEEX fragment is entirely consistent with the strong helical propensity of Glu residues The fact that when pYVNV is bound to the Src SH2 domain, it adopts mainly a β-turn conformation with Asn(pTyr+2) forming non-specific polar contacts with the protein, confirms the prediction that in addition to pYEEI the Src SH2 domain is also capable of binding peptides of the type pYXNX In conclusion, we show that all three residues C-terminal to the phosphotyrosine play an important role in determining both the binding affinity of phosphopeptides complexed with the Src SH2 domain, as well as their conformation. Significantly, the conformational trends of the bound phosphopeptides are a result not only of the phosphopeptide-SH2 domain interactions, but also of the intra-phosphopeptide interactions. These observations can support the design of better, more specific inhibitors of the Src SH2 domain.Text S1Phosphotyrosine force field parameters.(0.03 MB DOC)Click here for additional data file.


Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically modeling a protein dissection protocol in terms of a production system. Here, we report a mathematical model for empirically optimizing the cost of large-scale domain production in proteomics research.The model computes the expected number of successfully producing soluble domains, using a conditional probability between domain and boundary identification. Typical values for the model's parameters were estimated using the experimental results for identifying soluble domains from the 2,032 Kazusa HUGE protein sequences. Among the 215 fragments corresponding to the 24 domains that were expressed correctly, 111, corresponding to 18 domains, were soluble. Our model indicates that, under the conditions used in our pilot experiment, the probability of correctly predicting the existence of a domain was 81% (175/215) and that of predicting its boundary was 63% (111/175). Under these conditions, the most cost/effort-effective production of soluble domains was to prepare one to seven fragments per predicted domain.a priori. The application range of our model is not limited to protein dissection, and it can be utilized for designing various large-scale mutational analyses or screening libraries.Our mathematical modeling of protein dissection protocols indicates that the optimum number of fragments tested per domain is actually much smaller than expected  Comprehensive elucidation of the functional and structural units present in the proteome is the ultimate goal in proteomics research, and it is expected to provide basic data for a rational understanding of complex biological systems. As proteomics studies are being pursued -5, the dExperimental approaches for dissecting proteins are usually based on limited proteolysis, which has been used to explore protein domain boundaries . AlthougComputer-aided protein dissection approaches are relatively inexpensive, and thus represent promising methodologies that have practical values in high throughput proteomics research. The strategies for predicting novel domain regions, without sequence similarity to domain databases, can be categorized into two classes. The first strategy aims at directly predicting domain regions by analyzing various sequence properties of the foldable region . TheProteomics projects require the identification of soluble and well behaved proteins enabling a fast structural/functional analysis . SolubilSo far, reports of high throughput protein domain production protocols have mainly focused on their development and on optimizing individual experimental steps of the protocols. No study has mathematically modelled a protein domain production protocol in terms of a production system, and thus substantial room for cost-optimization through mathematical modeling remains available. In this report, we present a mathematical model for empirically optimizing large-scale protein domain production. Our model conceptually divides domain predictions into the prediction of the domain region and its boundary, and it computes the expected number of successfully produced soluble domains, using a conditional probability between these two events. We estimated the model parameters using the experimental results from a computer-aided identification of novel soluble protein domains from Kazusa HUGE protein sequences, in which 436 fragments, encoding 36 novel putative domains with slightly different domain boundaries, were expressed by using an E-coli-based cell-free system, and their solubilities were assessed with SDS-PAGE gels.c), respectively . The above classification yields four fragment categories , S ∩ Dc , and Sc ∩ Dc .In our mathematical model, the prediction of a protein domain is conceptually divided into two steps: A first step that predicts the existence of a domain, and a second step that predicts its boundaries (or termini). "Fragments" are domain fragments with specific termini residues, and each fragment is either soluble or insoluble. Soluble fragments and insoluble fragments belong to the soluble (S) and insoluble set , is expressed asThe probability of successfully predicting a soluble fragment, c is empty and all sets are exclusive. p(D) is thus related to p(S) assince S ∩ Dp(S|D) is the conditional probability of obtaining a soluble fragment of a soluble domain. When p(S) and p(S|D) are given, the probability of successfully predicting the existence of a soluble domain is calculated aswhere Note that Eqs. 1-3 are direct consequences from probability rules for independent sets, without any approximations or assumptions.Edomain, by assuming average probability values over all of the protein domains examined in the experiment. Our model examines M protein domains and generates N fragments per domain. According to this model, the expected number of soluble domains, Edomain, is given by:In a large-scale experiment aimed at obtaining as many soluble domains as possible, cost-optimization is achieved by maximizing the number of soluble domains for a fixed number of tests. As an approximation, our model computes the expected number of producing soluble domains, M and N are, respectively, the number of domains and the number of fragments per domain that are assessed. When the total number of fragments (MN) is held constant, the probability of obtaining one or more soluble fragments, PN, is a function of N and p(S|D). The explicit form of PN depends on the experimental setting, and is derived in the next section for two specific cases.where MN + Mr, where r is the ratio between the supplemental cost of analyzing a fragment from a new domain and that of analyzing a new fragment from the current domain. Keeping the total cost constant [M (N + r) = constant] yields:We can further modify Eq. 4 to add a set-up cost related to the analysis of a new domain. The set-up cost is taken into account by expressing the total cost as PN for two basic types of experimental settings. In the first one, the generation of N fragments occurs by independent events (multiple copy case). This situation occurs in genetic screening experiments, where N fragments per domain are randomly selected and tested, allowing multiple copies of the same fragment to be tested. In this case, the mathematical expression for PN is simply calculated as:Let us derive PN is the probability of obtaining one or more soluble fragments of a soluble domain when N fragments are simultaneously tested. F is the (average) number of all of the testable (assessable) fragments, and f is the (average) number of soluble fragments associated to a domain.where N fragments are generated, but each fragment is selected only once (single copy case). This situation occurs when the fragments are identifiable, such as in our pilot experiment with the Kazusa sequences. PN is derived by using a hypergeometric distribution (PNC), which describes the probability of obtaining no soluble elements when N elements are drawn without replacement from a finite population of FCN elements . PN is given byThe second situation occurs when PNC is the probability of obtaining no soluble fragments when N fragments are tested  and p(SC) = 0.48; Table p(S|D), which is the conditional probability of correctly predicting a soluble fragment of a soluble domain, is 0.63 (111 fragments/175 fragments). The calculation of p(D) from Eq. 3, using the obtained values of p(S) and p(S|D), generates 0.81. Substituting 0.81 and 9 for p(D) and N, respectively, in Eq. 4, yields Edomain/M as 0.81, which is equal to p(D), within rounding error, since PN is almost 1 when N = 9. The discrepancy from the above experimental Edomain/M value of 0.75 , and 52%  and p(S), respectively. As a result, 240 fragments are produced, corresponding to 24 domains, of which 6 do not yield any soluble fragment, since p(D) = 0.75. Since p(S) = 0.52, 124.8 fragments are soluble. These soluble fragments are uniformly distributed among the 18 domains, and thus 6.93 (7) fragments per domain out of 10 fragments are soluble, which yields 0.693 for p(S|D). Using Eq. 3, p(D) = 0.52/0.693 = 0.75, and thus p(D) PN = 0.75 * {1 - (1 - 0.693)10 = 0.75, which is equal, within rounding error, to the experimentally determined value of 0.75 (18 domains out of 24).Let us perform a simple mathematical simulation to assess the discrepancy between the above experimental and calculated values of 0.75 and 0.81. The discrepancy can be resolved by setting the number of tested fragments per domain to, for example, 10, and by uniformly using for all domains the above experimentally determined average values of 0.75 and 0.52 for In the present experiment, 12 domains (221 fragments) could not be analyzed because of failure to express a fragment with a correct molecular weight, or to assess the solubility, which obviously indicates that the efficiency of our automated protein expression system has room for improvement. These 12 domains can be included in the analysis and will give lower (or upper) limits by considering them as insoluble (or soluble) domains. The inclusion yields 50% (18/36) and 25% (111/436) for, respectively, the lower probability limit of predicting the domain existence and that of predicting a soluble fragment for 12 tested fragments, on average. Similarly, some or all of the 6 domains that were considered insoluble in the above discussion might turn out to be soluble if more fragments were tested.The effect of the cleavage site on the solubility of the dissected domain was also examined  is independent of N, we can simplify Eq. 5, and examine the normalized expected number of soluble domains, edomain, instead of Edomain:Let us use our model to analyze protein dissection experiments with different settings. Since PN can be computed with either Eq. 6 or 7, since no noticeable difference will occur for practical purposes , edomain is a monotone decreasing function of N . This is analytically demonstrated by showing that Noptimum is the solution of a transcendental equation with a unique solution  = 0.63 (= f/F)] and assuming that r = 10, we find that testing three fragments per predicted domain would yield more soluble domains than just one, for the same total cost , becomes smaller , edomain depends on the ratio of f and F, which is the probability of finding the correct cleavage sites, but it does not depend directly on F alone, which is the total number of possible cleavage sites. The direct dependency on F alone is also minimal when the single copy model (Eq. 7) is used  and r , which is intuitively sensible, since a smaller p(S|D) requires a larger number of trials for finding the termini residues that yield a soluble domain fragment. Noptimum is between 1 and 4 for r = 1 and a broad range of p(S|D) > 0.1, which covers most experimental settings including our pilot experiment using Kazusa sequences. A p(S|D) > 0.1 would also cover typical domain prediction tools such as Armadillo prediction, which has a p(S|D) value estimated by cross validation method between 0.3 and 0.5 [r = 10 and p(S|D) > 0.5, Noptimum is between 1 and 3; and for p(S|D) = 0.63, Noptimum is 7 fragments, even for r = 1,000 .Insight into a wide range of experimental settings can be obtained by analyzing the behavior of r Figure . For exa and 0.5 . FinallyWe have presented a novel mathematical formulation for optimizing large-scale protein domain production experiments. Our model demonstrated that the testing of one to seven fragments per domain will fit most high throughput protein domain production experiments. The probabilistic approach presented here is not limited to protein domain production, and it can be readily modified and applied for designing various types of large-scale mutational analyses or screening libraries.Domains were computationally predicted from 2,032 Kazusa HUGE  protein The cDNA clones for the selected 36 protein domains (436 fragments) were kindly provided by Kazusa DNA Research Institute . The corresponding protein fragments were expressed using an E-coli based cell-free system and were purified as described . The fraEC, AK, TY, SY, and YK designed the research. YK and EC derived the mathematical forms. TT, SM, AK, and EC developed the analysis programs. TY performed the experiments. EC and AK analyzed the data. EC and YK wrote the paper.Additional methods. An extension for handling the distribution of p(S|D) with the multiple copy model; Basic properties of PN; Intuitive Derivation of PN for the single copy per fragment case; Direct Derivation of PN for the single copy per fragment case using the hypergeometric distribution.Click here for fileCalculator of the expected number of soluble domains. A JavaScript program, implementing Eqs. 5-7.Click here for fileFigure S1: List of expressed domains. List of expressed domains in our pilot example. Computationally dissected protein domains and their experimentally assessed solubilities.Click here for file


This work presents a novel detection method for three-dimensional domain swapping (DS), a mechanism for forming protein quaternary structures that can be visualized as if monomers had “opened” their “closed” structures and exchanged the opened portion to form intertwined oligomers. Since the first report of DS in the mid 1990s, an increasing number of identified cases has led to the postulation that DS might occur in a protein with an unconstrained terminus under appropriate conditions. DS may play important roles in the molecular evolution and functional regulation of proteins and the formation of depositions in Alzheimer's and prion diseases. Moreover, it is promising for designing auto-assembling biomaterials. Despite the increasing interest in DS, related bioinformatics methods are rarely available. Owing to a dramatic conformational difference between the monomeric/closed and oligomeric/open forms, conventional structural comparison methods are inadequate for detecting DS. Hence, there is also a lack of comprehensive datasets for studying DS. Based on angle-distance (A-D) image transformations of secondary structural elements (SSEs), specific patterns within A-D images can be recognized and classified for structural similarities. In this work, a matching algorithm to extract corresponding SSE pairs from A-D images and a novel DS score have been designed and demonstrated to be applicable to the detection of DS relationships. The Matthews correlation coefficient (MCC) and sensitivity of the proposed DS-detecting method were higher than 0.81 even when the sequence identities of the proteins examined were lower than 10%. On average, the alignment percentage and root-mean-square distance (RMSD) computed by the proposed method were 90% and 1.8Å for a set of 1,211 DS-related pairs of proteins. The performances of structural alignments remain high and stable for DS-related homologs with less than 10% sequence identities. In addition, the quality of its hinge loop determination is comparable to that of manual inspection. This method has been implemented as a web-based tool, which requires two protein structures as the input and then the type and/or existence of DS relationships between the input structures are determined according to the A-D image-based structural alignments and the DS score. The proposed method is expected to trigger large-scale studies of this interesting structural phenomenon and facilitate related applications. Involved in the formation of quaternary structures from monomers, three-dimensional (3D) domain swapping refers to two or more identical proteins exchanging equivalent parts of their structures to form intertwined oligomers, inclusive of dimers A subunit of a 3D domain-swapped oligomer appears to have two conformational states, a monomeric closed-form and an oligomeric open-form. 3D domain swapping (abbreviated as DS) has been accordingly classified into three types DS can originate from environmental changes such as variations in pH values and protein concentrations CO pair”), several methods, such as FAST et al., the well-known domain motion detection method DynDom Although DS is an interesting and important structural phenomenon, related bioinformatics resources are rarely available. Previous studies have shown that 3D domain-swapping homologs may share minor sequence similarity CO pairs, including the largest literature-based dataset by Perhaps because of the unavailability of suitable detection and analytical methods, currently the datasets for DSWe previously developed a protein structural comparison technique based on angle-distance (A-D) image transformation CO pair can be matched simultaneously because the matching process does not depend on structural superposition. This feature markedly differs from that of most conventional structural alignment methods, which can only align one region at a time. Exploiting this significant difference allows us to compare the results of the SSE matching with those of a typical protein structural alignment and thus locate the boundary between the protein body and the swapped domain, i.e., the approximate location of the hinge loop. Next, superimposing the two structures based on their protein bodies enables us to measure and normalize the conformational difference between these two structures with respect to the swapped domains into the DS score defined here. Two proteins with a DS score higher than a specific cutoff trained on known data are identified as a DSCO pair. Finally, the superpositions of the protein bodies and swapped domains can be output to the users simultaneously, separately or in a fused form. Meanwhile, the location and range of the hinge loop is refined according to the results of structure superpositions based on an improved version of the hinge loop determination algorithm by Eisenberg et al.Each point in an A-D image records the angular difference between representative vectors and the distance between the centers of two SSEs in a protein Given the lack of a DS database, we collected many DS cases either reported in the literature or annotated in the Protein Data Bank (PDB) i.e., partial structural homologs and DS-related homologs, remaining in the test set by calculating the average MCC, sensitivity and specificity over the five-fold cross-validation. As an extensively adopted measure in machine learning for evaluating the quality of binary classifications, the MCC ranges from −1 (inverse prediction) to 0 (random prediction) to +1 (perfect prediction) and is generally considered to be a balanced measure even if the size of classes varies remarkably.Although it has been noticed that conventional protein structural comparison (PSC) methods may not be adequate for detecting DS relationships among proteins According to i.e., the main domains or swapped domains, of DS-related proteins ”, respectively. Moreover, most well-defined protein structural similarity measures, e.g., the Q-score We supposed that the weakness of most conventional PSC methods at detecting DS relationship lies in the fact that they can only identify the structural similarities of a part, pair see . The outThis experiment attempts to evaluate this DS-detecting procedure and examine the capabilities of various virtual structural similarity measures as suitable discriminators for 3D domain-swapping proteins and common homologous proteins. As shown in CO pair  to 1 (high similarity); pf represents an exponential penalty function that reduces S0 for common structural homologs while maintaining the high value of S0 for domain-swapped homologs; η denotes the binary function determined by whether a suspected hinge loop was detected or not. Function pf consists of three DS-specific factors, i.e., the angular difference factor θγ, displacement factor dγ and minimal structural diversity sdμ for the swapped domains, the last of which is defined as,Ne,sd represents the number of equivalent residues between two swapped domains, while No,sd and Nc,sd refer to the size of the swapped domain of the open oligomer and closed monomer, respectively. A greater similarity between the two domains implies a smaller sdμ. Two “identical domains” result in a zero sdμ.Despite the feasibility of the A-D image-based PSC method to detect DS, appropriate scoring functions must be identified or designed to assist the determination of the likelihood that the proteins under examination are related by DS. Because conventional protein  structural similarity measures are relatively weak at distinguishing DS-related proteins from other structural homolog types, we hypothesized that a practical scoring system for DS should be defined in a more complex manner in which the different properties of the DS and non-DS homologs revealed by the A-D image-based PSC method could be fully exploited. We observed that the A-D image transformation and matching allowed us to match SSEs from corresponding domains of a DSCO pair . HoweverCO pair , implyinloop see . The exiS0 value selected in this work was the vQ-score. As long as a candidate hinge loop is identified, two proteins can be aligned by the main domains in general  and average displacement of residues (dγ) of the swapped domains are thus, respectively, determined and normalized within a range of 0 to 1. The eral see . In thisθγ and dγ cannot be large, in addition to the fact that η can be zero when no hinge loop is detected. By using this scoring scheme, only proteins with solid DS relationships yield a high DS score, which greatly facilitates the development of the DS detection method. Indeed, a repeat of the experiment stated in the previous subsection demonstrated that the DS score performed better than most conventional structural similarity measures at separating DS-related homologs from non-homologs  analyses indicate that, regardless of whether trained or tested by literature-derived DS cases or manually identified DS candidates, the effectiveness of the proposed method remained high. The area under the ROC curve (AUC) in each experiment was greater than 0.95. Moreover, the MCC, sensitivity and specificity values all exceeded 0.80  alignment ratio and RMSD of the main domains and swapped domains and the average virtual alignment ratio and vRMSD of whole proteins were calculated and are listed in 3.X can be the main domain, swapped domain or the whole protein; eN denotes the number of equivalent residue pairs, i.e., the alignment size; cN and oN refer to the number of residues in the closed-form and open-form homologs, respectively.Because the proposed method is the first DS-specific detection and alignment approach, the quality of its structural alignments may serve as a standard for the evaluation of related future works. There are 1,211 DSCO pair could be aligned with a vRMSD of 1.8 Å. The average alignment ratio calculated by the proposed method for main domains were 7% smaller than that for swapped domains. Detailed analyses revealed that the alignment ratios calculated by the proposed method for the two domains of DS-related proteins differed more as the sequence identity decreased, although the differences were not very large , psi (ψ) differences until two consecutive residues have a torsion angular difference smaller than a cutoff θ0 (see Formula 10), which was empirically set as 20° for bona fide and 30° for quasi domain swapping in their report The hinge loop is the region linking the main domain and the swapped domain, and it is barely aligned in the structural alignment of a DSCO pairs available in Datasets L and M. According to 10) by 51% and 35% on average when θ0 was set as 20° and 30°, respectively. A close examination of the determined hinge loops revealed that by using either 20° or 30° as the cutoff, Formula 10 is likely to over-extend the boundary of hinge loops. Calculated hinge loops with obviously excessive or insufficient ranges judged by manual verifications are highlighted in this table. See the While attempting to develop a fully automated DS-detecting procedure, this work designed a novel method for identifying the location and range of hinge loops by improving Eisenberg's method http://ADiDoS.cs.nthu.edu.tw/]. The basic output on the web interface includes graphical and interactive Jmol CO pairs identified in this work performed using the web interface are shown in Agkistrodon acutus, and SUB-MPB-A, a subtilisin fragment of mannose binding protein A from the rat, their DS relationships were well identified by the proposed method even though their overall sequence identity is only 12%.The proposed DS-detecting method has been implemented as a web-based tool [CO pair due to a significant orientational difference between the swapped domains. A situation in which a method highly prioritizes RMSD implies its feasibility in detecting local structural similarities, but it is subsequently less applicable to the detection of DS relationships. Among the PSC methods assessed in the experiments of CO pairs. CO pairs TM-align could only align one domain and thus could not detect their global structural similarities. On the other hand, despite the availability of several more flexible PSC methods that do not completely treat protein structures as rigid bodies and may detect the global structural similarity of 3D domain-swapping proteins by determining more aligned residues, their scoring systems prevent them from distinguishing between DS-related homologs and common structural homologs. Take SARST for example. It is a PSC method working based on a structural linear encoding methodology CO pairs, in many cases the hinge loop will only cause some gaps but not prevent it from simultaneously aligning the main and swapped domains  CO pair usually has a DS score around 1 while a pair of common global homologs possesses a DS score close to 0 (note that the range of DS score is between 0 and 1 by definition).The fact that conventional protein structural comparison (PSC) methods are weak at specifically identifying domain-swapped homologs implies that detecting DS relationships is a very different problem from detecting common structural similarities between proteins. As shown in ains see . For a DCO pairs, unfortunately it makes no difference to BLAST whether a high alignment score is achieved by a DSCO pair or by a globally superimposable pair of common homologs. In the experiment of CO pairs with small swapped domains and common homologous pairs with a small number of non-superimposable residues were eliminated. Thus, there were still many highly globally superimposable homologs, which were not distinguishable by BLAST from the DSCO pairs, remaining in the testing set. As a result, the MCC of BLAST was only ∼0.3. However, as the alignment ratio cutoff decreased, the number of common global homologs in the testing set decreased much more rapidly than that of DSCO pairs , spatial displacement (dγ) and structural similarity (sdμ) of the swapped domains (see Formulas 1.1 to 1.6), the DS score has a theoretical minimum value of zero (no DS relationship) and maximum value of one (definite DS relationship). In addition to simplifying the development of an automated procedure, this normalized score also offers users an easy way to recognize the DS relationship between proteins.The DS-detecting ability of the proposed method has three critical factors. (1) The A-D image-based approach functions through SSE matching rather than structure superposition. This approach recognizes protein structural similarities without considering RMSD. As long as the relative geometric relationship of the corresponding SSEs is retained, a novel global structural relationship like 3D domain swapping can be detected. (2) The A·D profile generated by combining the results of the structural comparisons from the A-D image-based SSE matching and a conventional structural alignment greatly facilitates efforts to locate hinge loops. Possibly the origin Both the current two evolutionary mechanisms proposed for DS involve the hinge loop. Deletions may shorten the hinge loop and turn a closed monomer into an open oligomer CO pair can be easily identified is very improbable. The proposed method bypasses this difficulty by using the A·D profile along with a morphological smoothing technique . According to their algorithm, a higher θ0 results in a more restricted extension, allowing us to infer that they reasonably set a higher cutoff for quasi-DSCO pairs (θ0 = 30°), which are evolutionarily more distant than bona fide cases (θ0 = 20°). Such an extension step is assumed here to be an excellent design and is thus applied as the fundamental hinge loop determination procedure in our DS-detecting system. However, as the discrimination of bona fide and quasi-domain swapping is somewhat empirical as well, manual inspections may be unavoidable to handle ambiguous cases. Although Eisenberg's algorithm was shown to be feasible based on a dataset of 33 DSCO pairs, according to the large-scale test results shown in the θ0 as either 20° or 30°, the algorithm tended to over-extend the hinge loop of DSCO pairs, especially for those with low sequence identities. This work also attempts to simplify the requirement for manual examinations and increase the precision of hinge loop identification by, first simply unifying the cutoff for bona fide and quasi-DS cases as 25° multiplied by hln, the number of hinge loops detected in the same swapped domain (see Formula 11). Then, the extension is restricted by a distance constraint of aligned residues in the front of the extending region. Given that the estimated range of hinge loops agrees well with the semi-manually verified ones reported in After the approximate location of a hinge loop is identified, the boundary must be determined. Eisenberg hln was introduced to help define the boundary condition (θ0) of a hinge loop. Because a middle-swapped domain has two hinge loops, its θ0 is twice as large as that of N-terminal- or C-terminal-swapped domains, resulting in a situation in which a higher restriction is imposed upon middle-DS than upon other DS types. Without the parameter hln, the hinge loop(s) of a candidate middle-swapped domain may be determined to be invalid because of its over-extension  between the swapped domains. The swapping of domains of an N-terminal- or C-terminal-DSCO pair can be visualized as a swinging movement, during which the single hinge loop might bend and slightly twist, but the overall conformation of the swapped domain is preserved. Differently, the swapping movement of a middle-DSCO pair could lead to some torsion of the swapped domain, especially when the extents or directions of bending of the two hinge loops differed. In this case, the way we transform the swapped domains into representative vectors  cannot be determined, its candidacy is inevitably rejected based on our methodology. This is why hln was introduced into Formula 11 to make the extension step more restricted in the determination process of hinge loops for middle-DS cases.Second, structural dissimilarities between small equivalent swapped domains can blur the boundary of hinge loops. Many of the swapped domains of known middle-DSSSEs see , meaningab initio predictor for DS but determines the existence and type of DS relationship between two given protein structures. The A-D image-based algorithm proposed and the DS score defined here achieved a satisfactory performance, i.e., an average MCC>0.80 in every experiment  theperiment , and,  from August 2008. Specifically, the 90% sequence identity non-redundant subset of this PDB snapshot (abbreviated as nrPDB-90) was used. Programs were written in the Java, Asp.net and PHP languages. The structures shown in the figures were rendered using PyMol This work established two main datasets for the 3D domain swapping, one based on literature-derived information (Dataset L) and the other based on manual inspections (Dataset M).CO pairs summarized in The “bona fide” and “quasi-domain swapping” DSQ, a rapid protein structural similarity search service, iSARST, was applied to search against nrPDB-90 for its structural neighbors with an E-value cutoff of 10 For each protein Q and any protein S retrieved by iSARST in the hit list were defined as a neighboring structural pair (NS pair).Protein DaliLite v.3 According to the suggestion of Q is an open-form dimer; when S was found to have a similar structure to the known closed monomer of Q, Q and S would be considered a DSCO pair.Each NS pair belonging to the ten groups was then carefully examined by manual inspection. A DS relationship was identified when structural complementarities were observed. For instance, assume that CO pair was identified, homologs of the closed and open forms were paired and examined to identify additional DSCO pairs.After a DSCO pairs were selected into a dataset Lds, those homologous structural pairs without DS relationships were randomly selected into another dataset Lch, where the subscript ch refers to “common homologs”.While all identified DSnh.NS pairs with a Z-score <2 were generally considered as non-homologous (nh) structures, from which some were randomly selected into dataset Lds (737 pairs), Lch (499 pairs) and Lnh (720 pairs). Finally, Dataset L consisted of datasets Lds (474 pairs), Mch , and Mnh , were generated by following the same procedure as used for Dataset L. See the Due to the small number of experimentally and theoretically identified DS cases available at present, this work constructed Dataset M for a more detailed development and evaluation of the proposed method by screening nrPDB-90 for candidate cases of 3D domain swapping. According to the definition of DS, regardless of whether in the “bona fide”, “quasi” or “candidate” categories, an important prerequisite is the existence of homo-oligomers. The Protein Quaternary Structure (PQS) database maintained by the European Bioinformatics Institute contains predicted and experimentally-confirmed oligomeric proteins for PDB entries determined by X-ray crystallography G, in which vertices (V) denote possible pairings between points from the two A-D images, and edges (E) denote the compatibility between such pairings. Pair graphs were applied in a residue-based protein structural alignment method FAST, in which two residues from two proteins can be paired because of their similar local backbone conformation i.e., two pairs of SSEs, from two proteins can be paired due to their similar geometric relationships. The SSE matching algorithm is illustrated in A-D image is a novel alignment-free PSC technique based on the angle-distance image transformation of SSEs Q and S, are transformed into two A-D images. If Q and S have QE and SE SSEs, respectively, there are QI and SI. If all combinations of these points are allowed to form vertices in the pair graph, there are QI is allowed to be paired only with the points residing within i.e., block SI. Next, a geometric similarity score is assigned to each resulting pair of points i in QI and j in SI according to the following formula:i,jd represents the Euclidean distance between i and j in the virtually superimposed A-D images  and q in the pair graph cannot be assigned under the following conditions: (1) it is redundant with an existing edge, for instance, p,) and q,) are redundant with p,) and q,); and (2) p and q are contradictory, e.g., p,) and q,). Because d cannot be equivalent to b′ and e′ simultaneously, the edge between p and q leads to a contradictory outcome. Except for the above “bad edges”, each possible edge between two vertices p and q in pair graph is assigned a weight calculated as follows:Let i  and the center of u in protein Q, while j and v in protein S. The exponential decay envelope and the distance term dt are adopted from the elastic score defined by Holm and Sander αt, βt and γt are used to assess the similarity in the SSE directionality by using six angles, α1, β1, γ1, α−1, β−1, and γ−1. The threshold td and other scaling factors, αk, βk, γk, and dk, were empirically determined as 25 Å, 10 Å, 4/π (rad), 4/π (rad), and 3/π (rad), respectively.As shown in p are summed up according to Formula 6 and assigned to p as its total weight i,jW suggests that i,jW is high if p is contained in the optimal set of equivalent pairs of A-D points. However, this work does not attempt to determine this optimal set. As long as the relative probability of equivalence of each pair of A-D points is approximately figured out, the equivalence of SSEs between the two proteins can be determined efficiently through an empirical voting process.A high positive weight of an edge suggests a high likelihood that the two pairs of A-D points are both equivalent pairs. A situation in which all positive weights of edges associated with vertex i,jW. Any A-D point i has two component SSEs a and b, with each possibly appearing in more than one A-D point. If SSE a of protein Q is matched with SSE a′ of protein S, a and a′ are supposed to co-exist in one or more vertices. A weighted matching score , and xn represents the number of residues that the SSE contains. The summation is a voting procedure in essence. A higher The equivalence of SSEs is extracted from the equivalence of A-D points, which is estimated by SSE pairs with a zero The first criterion guarantees a sequential or order-dependent matching because 3D domain swapping itself does not affect the sequential order of SSEs in a protein structure. Without this restriction, the algorithm can perform order-independent SSE matching and, thus, detect non-sequential structural similarities that can be observed in proteins with circular or crumbled permutations CO pair throughout the main domains and swapped domains. Conversely, most conventional structural alignment methods, which are superposition-dependent, only report the equivalence of SSEs (and residues) for one of the two domains. Comparing the results of SSE matching and those of a superposition-dependent protein structural alignment reveals that the boundary between the region well aligned by both methods, and the region only aligned by matching can be recognized as the approximate location of a hinge loop. The profile of the A-D product is thus designed for quantifying and analyzing the differences between the results of the two methods.The fact that matching processes do not depend on structure superposition allows us to identify the equivalence of the SSEs of a DSTo develop a well-integrated system, a superposition-dependent structural alignment method based on SSE matching has been designed and utilized here. However, this design is not required because most conventional structural alignment algorithms can actually be applied in this step.CO pair.The equivalent SSEs of two proteins determined by SSE matching are used as the seed anchors for the two protein structures. Using dynamic programming, the equivalent residues for each pair of seed anchors are determined in a manner such that the total number of equivalent residue pairs is as large as possible. These equivalent residues are then initially superimposed, and the RMSD is calculated by a classical singular value decomposition method developed by Kabsch adP) is thus formulated as follows:uθ and ud are set as 180° and 25 Å, respectively, as scaling factors, and ijθ and ijd denote the A and D factors of a pair of aligned/matched SSEs i and j, respectively.Following the above structural alignment, a pair of aligned SSEs should have a similar position and orientation in the superposition. An SSE is represented by a transformed vector in the N- to C-terminus direction and its centroid point. The angular difference between two SSE vectors (A) and the distance between their centroids (D) can describe the similarities and differences between the aligned and matched-only SSEs. A measure called angle-distance product (A-D product or adP (or A·D profile) is extremely useful for rapidly locating possible hinge loop(s) in a DSCO pair. Aligned SSE pairs normally possess low adP, while matched-only SSEs possess high adP values. Consequently, a low-valued region in the A·D profile is normally formed by two well-aligned domains (the candidate main domains). A high-valued region reflects similar and matched substructures with different orientations (the candidate swapped domains), while the transition zone in between can be considered as a candidate hinge loop. In other words, a hinge loop can be recognized as a sudden increase or decrease in the A·D profile. According to the example in The profile of adP between every two adjacent points is transformed into a sequence, the elements of which are subjected to a t-test to identify the most significant difference. For instance, the smoothened A·D profile of the above-mentioned crystallins consists of ten points: {p1, p2, p3, p4, p5, p6, p7, p8, p9, p10} = {1.10, 1.10, 1.10, 1.10, 4.50, 4.50, 4.50, 4.37, 4.37, 4.37}. Thus, the sequence of differences contains nine elements: {Abs(d2-1), Abs(d3-2), Abs(d4-3), Abs(d5-4), Abs(d6-5), Abs(d7-6), Abs(d8-7), Abs(d9-8), Abs(d10-9)} = {0.00, 0.00, 0.00, 3.40, 0.00, 0.00, 0.13, 0.00, 0.00}. A t-test is then performed at the 80% confidence interval on this sequence, with a t-value of 0.92 subsequently obtained. Because Abs(d5-4) = 3.40 was the only significant (>0.92) difference, a candidate for the hinge loop was identified between the SSE pairs constituting p4 and p5.It is not necessary that every increase or decrease corresponds to a hinge loop. A short peak or valley is normally attributed to a local dissimilarity between two structures. In this study, an attempt is made to identify a transition (a significant increase or decrease) in an A·D profile by applying a morphological smoothing algorithm, which is commonly used in image processing to remove isolated dark and bright spots, to reduce the noise of the A·D profile see . After mCO pair typically result in a high-valued region in an A·D profile, when a high-valued region is on the N-/C-terminal side or in the middle of an A·D profile, the two proteins are identified as N-/C-domain-swapped or middle-domain-swapped cases, respectively.Because swapped domains of a DSThe “opening point” of hinge loops is defined here as the boundary between two well-superimposable main domains and the non-superimposable regions that cover the swapped domains. After a candidate hinge loop in a protein has been located between two consecutive SSEs by analyzing the A·D profile, its opening point can be determined as follows.NSSE and CSSE denote these two consecutive SSEs, where N and C specify that the SSE is close to the N- or C-terminus. (1) In an N-domain-swapped case, the opening point of the hinge loop is determined as the first residue of CSSE. (2) In a C-domain-swapped case, the opening point is determined as the last residue of NSSE. (3) For a middle-domain-swapped protein, the opening points of its N- and C-terminal hinge loops are determined in the same manner as in (2) and (1), respectively.Let In theory, the above procedures can identify the approximate opening point of a candidate hinge loop for DS cases with large swapped domains possessing at least two SSEs. However, the term “domain” swapping is sometimes not well defined because the swapped “domain” may only be a small structural fragment with a few or even no regular SSEs. A situation in which the swapped domain is small, or its SSE(s) is distorted almost into a loop form, implies no significant increase or decrease in the A·D profile. Therefore, whether a small swapped “domain” exists in a flat region of an A·D profile must be further analyzed  of the known middle-swapped domains contains only one or even no SSE. The main feature of middle-DS is that both the N-terminal region prior to and the C-terminal region after the swapped domains of two proteins can be well-superimposed, leaving the swapped domains bifurcated. An unaligned fragment located in the flat region of an A·D profile is thus considered as a candidate swapped domain. The opening point of each hinge loop of the candidate middle-swapped domain is determined approximately as the first unaligned residue in the bifurcation region of the two superimposed protein structures.After the above procedures, each candidate hinge loop is determined with an approximate opening point. A refining procedure is then applied to identify its precise location through the following steps,o) and moving a probe (p) along the backbone of a protein (Q), two directions are applied including towards the peak (the well-superimposed region) and towards the bifurcation of the structural superposition. The principle of the scanning processes is peak first and bifurcation last (PF and BL).Starting from the initially assigned opening point (S).At the PF stage, the stop condition works when more than five consecutive residues are aligned with the other protein  is thus determined. If p has reached the terminus, and uN still does not exceed NT, the feasibility of this over-extended candidate hinge loop is rejected.At the BL stage, starting from the stop point identified in step (2), the number of unaligned residues is counted by LT is set to be 0.5.The feasibility of a candidate hinge loop is quickly rejected if this criterion is not satisfied:o′, the non-superimposed fragments of both proteins, which should contain the swapped domains in a DS case, are subjected to a typical superposition-dependent structural alignment. Because swapped domains are supposed to resemble each other, a situation in which either their alignment ratio is too small  or the RMSD is too large (≥4 Å) suggests that these two fragments will not receive approval as a pair of swapped domains, subsequently leading to the rejection of the feasibility of this candidate hinge loop. The feasibilities of a candidate hinge loop and the swapped domain that it links are both validated if the candidate hinge loop is not rejected by these criteria.In addition, by cutting at o′. Next, the main domains and swapped domains of the two proteins are, respectively, subjected to the above-mentioned typical structural alignment to obtain two superpositions and two sets of equivalent residues, ultimately forming the basis for determining the range of the hinge loop and the computation of the DS score.Once a hinge loop is validated, the protein structure can be cut into a main/large domain and a swapped/small domain at et alϕ and ψ refer to the torsion angles of the protein backbone conformation. In their work, the cutoff θ0 was empirically set as 20° and 30° for bona fide and quasi domain swapping cases, respectively.This step is a modified version of the hinge loop determination method of Eisenberg θ0 was modified in this study as follows:hln stands for the number of hinge loops identified for a swapped domain. For an N- or C-terminal-swapped domain, hln = 1, while a middle-swapped domain has hln = 2.To avoid the manual determination of the category of 3D domain swapping, the setting of In addition, there are two extra conditions that will terminate the extension: (1) Each of the last three pairs of aligned  residues included has a Cα-Cα distance ≤2.6 Å. (2) The total number of residues remaining in the candidate swapped domain ≤10.sdμ) was defined to determine the structural similarity of the swapped domains. Interestingly, as implied by the angle-distance (A-D) image technique, this work designed two A-D factors to determine the conformational difference of a candidate DSCO pair with respect to the swapped domains and hinge loops, i.e., the angular difference factor θγ and displacement factor dγ.The 0C) of the central residue and the terminal residue of the swapped domain. (3) Make a vector pointing from the refined opening point (o′) of the hinge loop to 0C.Following the identification of the hinge loops and swapped domains as stated above, these are transformed into representative vectors using two methods. The first is a conventional regression model for solving the minimum distance problems. This is the same method that we utilized to vectorize SSEs, except that here we use a whole swapped domain, excluding its hinge loop(s), as an input. See the Supporting Information of xθ and yθ, then the angular difference factor θγ is calculated as dγ is calculated as the normalized average displacement of equivalent residues in the swapped domains as the two proteins are superimposed by their main domains  alignment size is a very important parameter in this evaluation mechanism. To rule out the effects of various protein sizes, in implementation the alignment size was normalized by the protein size with Formula 3 and was thus called the alignment ratio (unit: percentage). In the experiments of In this work, the first evaluation mechanism of DS-detection ability for PSC methods is proposed. Since common global structural homologs usually possess a large structural alignment size, e.g., vRMSD, vQ-score and vS-div, and their conventional versions, e.g., RMSD, Q-score and S-Div, is that the calculation of virtual measures depends on the virtual structural superposition of proteins. In the proposed methodology, a virtual superposition of a DSCO pair is done by flexibly allowing the main and swapped domains to be independently transformed in the Cartesian coordinate system; hence, the superpositions of main and swapped domains are optimal at the same time in the virtual superposition. Conventional PSC methods are more rigid than the proposed method. They usually align (superimpose) only one domain for a given DSCO pair, or separately align the two domains and output two sets of similarity values. In either case, the structural similarity of the DSCO pair as a whole is poorly described and cannot be directly conceived. It is supposed that the virtual structural similarity measures and the virtual structural superposition are currently the best ways to describe and visualize the structural similarities of DS-related proteins.The only difference between the virtual structural similarity measures proposed in this study, 2) is conceptionally the simplest one and the Q-score The original rigid versions of the virtual structural similarity measures utilized in the evaluation tests  were allBLAST The electronic version of this document does not represent a published work according to the International Code of Zoological Nomenclature (ICZN), and hence the nomenclatural acts contained in the electronic version are not available under that Code from the electronic edition. Therefore, a separate edition of this document was produced by a method that assures numerous identical and durable copies, and those copies were simultaneously obtainable (from the publication date noted on the first page of this article) for the purpose of providing a public and permanent scientific record, in accordance with Article 8.1 of the Code. The separate print-only edition is available on request from PLoS by sending a request to PLoS ONE, 185 Berry Street, Suite 3100, San Francisco, CA 94107, USA along with a check for $10 (to cover printing and postage) payable to “Public Library of Science”.http://zoobank.org/”. The LSID for this publication is: (to be determined by PLoS ONE)In addition, this published work and the nomenclatural acts it contains have been registered in ZooBank, the proposed online registration system for the ICZN. The ZooBank LSIDs (Life Science Identifiers) can be resolved and the associated information viewed through any standard web browser by appending the LSID to the prefix “Figure S1The number of DS-related homologs, common homologs and non-homologs remaining in the test set from the experiments presented in (0.46 MB PDF)Click here for additional data file.Figure S2k-fold cross-validations. The stability of the discriminatory model applied in the proposed DS-scoring scheme was evaluated based on two datasets. (a) Evaluations based on Dataset L. (b) Evaluations based on Dataset M.Stability evaluations of the discriminatory model of the proposed method by (1.34 MB PDF)Click here for additional data file.Figure S3Performances of several protein structure/sequence comparison methods for the detection of global structural similarities between DS-related homologs with various sequence identities. An experiment that determines the simultaneous alignment qualities of the hinge loops, main domains and swapped domains for several protein structure/sequence comparison methods.(0.53 MB PDF)Click here for additional data file.Figure S4(a) Crystallins with PDB identifiers 4gcrA and 1blbA, a quasi-domain swapping case (b) Crystallins with PDB identifiers 4gcrA and 2a5mA, a pair of common global homologs. (c) Acetyltransferases with PDB identifiers 1s60A and 1b6bA, a pair of quasi-domain swapping homologs with a small C-terminal-swapped “domain”.Examples of the A⋅D profile and related hinge loop detection procedure. (1.56 MB PDF)Click here for additional data file.Table S1et al. in DS-detecting performance of DynDom assessed based on Eisenberg's DS dataset. Among the 39 query proteins, 12 are detected to posses hinge loops by DynDom (0.11 MB PDF)Click here for additional data file.Table S28 in Sensitivity and specificity of various alignment methods and structural similarity measures for the identification of common structural homologs and/or DS-related homologs. Sensitivity and specificity values of all alignment methods were determined based on S-div (0.07 MB XLS)Click here for additional data file.Table S3Results of inter-dataset training and testing of the proposed method for the identification of DS-related homologs. Only DS-related homologs were used as positive data in this experiment, in which common homologs and non-homologs were both regarded as negative data. Performance measures listed in this table include AUC, MCC, sensitivity and specificity.(0.06 MB PDF)Click here for additional data file.Table S4CO pairs in Datasets L and M. The 1,093 DSCO pairs successfully identified by the proposed method are listed here each with detailed information of the ranges of hinge loops determined by Eisenberg's and our methods, several structural similarity measures as well as the DS score defined in this work, and the virtual superimposition computed by our method. Structural superimpositions shown in this table were drawn using Jmol.Results of the structural alignments and hinge loop determinations for DS(9.67 MB PDF)Click here for additional data file.Table S5CO pairs in Datasets L and M performed by several protein structural comparison methods. The structure-based sequence alignments performed by TM-align CO pairs shown in Structure-based sequence alignments for DS(9.84 MB PDF)Click here for additional data file.Table S6Number of SSEs in the swapped domains. Here an SSE means an α-helix or a β-strand. The number of SSEs that a swapped domain contains roughly reflects the size of the domain. The ranges of SSEs were extracted from the PDB files according to the HELIX and SHEET records.(0.07 MB PDF)Click here for additional data file.Dataset S1ds, Lch and Lnh.PDB entry list for the Dataset L. A list of the PDB entries of the protein pairs constituting sub-datasets L(0.13 MB XLS)Click here for additional data file.Dataset S2ds, Mch and Mnh.PDB entry list for the Dataset M. A list of the PDB entries of the protein pairs constituting sub-datasets M(0.24 MB XLS)Click here for additional data file.