Aeromonas species. However, a comprehensive description of architecture and structure of the aerolysin-like superfamily has not been determined. In this study, we define a more compact aerolysin-like domain – or aerolysin fold – and show that this domain is far more widely spread than anticipated since it can be found throughout kingdoms. The aerolysin-fold could be found in very diverse domain and functional contexts, although a toxic function could often be assigned. Due to this diversity, the borders of the superfamily could not be set on a sequence level. As a border-defining member, we therefore chose pXO2-60 – a protein from the pathogenic pXO2 plasmid of Bacillus anthracis. This fascinating protein, which harbors a unique ubiquitin-like fold domain at the C-terminus of the aerolysin-domain, nicely illustrates the diversity of the superfamily. Its putative role in the virulence of B. anthracis and its three dimensional model are discussed.A number of bacterial virulence factors have been observed to adopt structures similar to that of aerolysin, the principal toxin of  Bacillus, Clostridium, Streptococcus, Listeria and Arcanobacterium genera. Interestingly, structural, but not sequence, similarity have recently been found with proteins produced by the vertebrate immune system, more specifically with the C8 and C9 components of the complement cascade and with perforin Toxoplasma gondii and Plasmodium falciparumAmong bacterial toxins, the most widely distributed and largest family is of pore-forming toxins (PFTs). Within the PFT family, the largest sub-family is currently composed of the Cholesterol Dependent Cytolysins (CDCs), which are produced by Gram-positive bacteria of Aeromonas species, but related proteins are present in both Gram-positive and Gram-negative bacteria, plants and eukaryotes. It shares high sequence identity with alpha-toxin from Clostridium septicumEnterolobium contorliquumCnidaria, ε-toxin form Clostridium perfringensLaetiporus sulphureusBacillus thuringiensisA second family has started to emerge as new structures of PFTs are being discovered: the aerolysin family. Aerolysin is produced by C. septicum alpha-toxin, but likely to differ for other members of the family. Once receptor bound and proteolytically activated, the toxin undergoes circular polymerization, generating ring like structures that subsequently insert into the membrane and form a pore. While aerolysin and e-toxin form heptamers, the stochiometry might differ between members. The membrane-inserting portion represents only a small fraction of the entire protein. It is thought to cross the membrane in ß-barrel conformation, as shown for the leukocidin-like fold alpha hemolysin of Staphylococcus aureus. The sequence of the transmembrane domain of aerolysin family pore-forming toxins is thus characterized by the alternating pattern of polar and hydrophobic residues, rather than any distinct sequence conservation These various toxins are thought to share the same overall mode of action. The toxin is produced by the bacterium as a soluble protein that can either be a precursor, as is the case for aerolysin itself. The aerolysin precursor is called proaerolysin and activation consists in the proteolytic processing of a C-terminal peptide Here we analyzed the similarities between various aerolysin like structures and defined the „aerolysin domain.” Searching through multiple genomes for proteins containing this domain, we reveal that members of the superfamily can be found in all kingdoms. Sequence variability and fusion events with other domains suggest aerolysin core is widely used and may serve for diverse functions.Following the procedure described in the method section, we identified 338 sequences in the NCBI non-redundant database that have a detectable similarity to the aerolysin domain. When performing the alignment of these sequences, a subset of which is shown in L. sulphureus, to multiple secondary structure elements such as in aerolysin  ended at the β4; further similarities along the sequence (between sequences of different β5s) were only due to biased, beta-strand-like, residue composition of that segment. This is probably due to different evolutionary pressure on that element: presence of a variable loop suggests that this strand is susceptible to phase shifts in the sequence, i.e. to shifts of amino acids along the β-strand as it needs to adapt to the presence of different insertions in the loop . Additionally, conservation of this element might not be needed due to stable structural scaffold provided by hairpins β1∶β4 and β2∶β3. Thus, the middle strand of the fold is the necessary part of the topological unit, but it does not belong to highly conserved sequence core. We use the historical convention (domains 3 and 4) and our convention interchangeably. When talking about aerolysin-like structures as a whole, we use historical convention as it refers to two distinct structural units. However, when we refer to sequence-structure relationship in aerolysin superfamily, we prefer the β1–β5 convention, as it better corresponds to relations between various structural elements.Proteins containing the aerolysin domain defined in here were found in all kingdoms of life . ApproxiPseudomonas phage phiCTX, which has an experimentally confirmed toxic function Less than 30% of the proteins were annotated as hypothetical of putative proteins. The other 70% had a function confirmed by an experiment or assigned by similarity, and these were almost exclusively from the three groups mentioned before: Proteobacteria, Firmicutes and Fungi A notable exception is a cytotoxin from Aeromonas salmonicida and Vibrio splendidus. In Eukaryotes, except for fungi, cnidarians and the Brazilian fish Thalassophryne nattereri, there are no other predatory or pathogenic species. The archaeal Methanosarcinaceae, that possess aerolysin-like genes, are anaerobic methanogens.About half of the eukaryotic and one third of the prokaryotic species harboring aerolysin domain containing proteins are considered non-pathogenic. These bacterial species are aquatic and some pathogenic ones are of the same provenience, e.g. In our analysis we identified several domain topologies, both in Prokaryotes and Eukaryotes . In bactAerolysin turns out to be amongst the most complex members. The aerolysin domain is extended N-terminally into a so-called domain 2, involved in binding to the glycan core of Glycosylphosphatidyl inositol anchored proteins Bacteroides uniformis ATCC 8492 protein (gi: 160892167) from a recently sequenced B. uniformis genome obtained from the human gut (data obtained from the Human Microbiome Project). The unique feature of this protein is the presence of a prokaryotic membrane lipoprotein lipid attachment site at the N terminus. In Prokaryotes, membrane lipoproteins are synthesized with a precursor signal peptide, which is cleaved by a specific lipoprotein signal peptidase . The peptidase recognizes a conserved sequence and cuts upstream of a cysteine residue to which a glyceride-fatty acid lipid is attached Bacteroides protein is specifically bound to the plasma membrane, which is proven by the PSORT cell location program that predicts this aerolysin-like protein to be extracellular. The other case is the Bacillus anthracis protein pXO2-60  located on the pXO2 plasmid, known to be involved in virulence Clostridium perfringens epsilon-toxin. While it harbors no N-terminal fusion, it carries a β-grasp fold at the C-terminus. Importantly, the B. anthracis gene has been shown to be highly regulated by the general virulence regulator, AtxA B. anthracis cytotoxicity.We also identified two novel bacterial fusions. The first is the Basidiomycota and three Ascomycota. The common theme for all of them is arboreal/plant pathogenesis . The only exceptions from the rule are members of the Coprinus genus (C. cinerea and C. comatus) that are saprophytes. The presence of a toxin in this genus suggests that these fungi may in fact be pathogenic; another hypothesis is that the aerolysin domain is used for defense against other pathogens or when hunting. For instance, C. comatus is known to attack nematodes Basidiomycota were identified in a ‘fungal pathogens of plants’ sequencing project .Eukaryotic proteins containing aerolysin domains can also be found in fungi. They are present in several species of pathogenic Vitis vinifera and the wheat Triticum aestivum two N-terminal agglutinin and a C-terminal aerolysin-like domains constitute a protein that in the latter organism was shown to be involved in defense against insects and maybe other pathogens Amaranthus caudatus (amaranthin) is a lectin from the ancient South American crop, amaranth grain. Although its biological function is unknown, it can agglutinate A, B and O red blood cells, and has a carbohydrate-binding site that is specific for the methyl-glycoside of the T-antigen found linked to serine or threonine residues of cell surface glycoproteins Danio rerio and Salmo salar . Most probably the same topology would be found in other fish species when sequenced. Amazingly, D. rerio possesses as many as 16 copies of the gene encoding the same aerolysin-like protein. The last lectin-aerolysin topology is quite unusual and may be a virtual translation of a false CDS (gi: 148223884). We discovered it in the African clawed frog Xenopus laevis, but it is not present in its close relative, Xenopus tropicalis. This protein contains a tandem of immunoglobulin-like domains at the N terminus, followed by a transmembrane motif and a duplication of large fragments located at the C terminus. The latter is composed of a tachylectin-2-like domain and an aerolysin-like domain (gi: 165970884). In X. tropicalis, immunoglobulin domains are in a different ORF than the lectin-toxin tandem . The Japanese horseshoe crab Tachypleus tridentatus tachylectin-2 binds specifically to N-acetylglucosamine and N-acetylgalactosamine and is a part of the innate immunity host defense system of this crab The eukaryotic aerolysin family members that seem to be most similar in structure (and function) to canonical bacterial toxins are composed of an N-terminal lectin domain followed by the pore-forming domain. We detected three new combinations of this type. In the grapevine Cynops pyrrhogaster epidermis-specific protein EP37. This organism is armed with several homologues that are present in skin, gastric epithelium and fundic glands of an adult newt and in the swimming larva In the eukaryotic family of aerolysins we identified two exceptional fusions. First, it is a crystallin domain tandem followed by an aerolysin domain. Such topology is found in the Japanese newt Thalassophryne nattereri proteins named natterins [e.g. gi: 75571591]. There are 5 known paralogues of this toxin with 4 full-length proteins (natterins 1–4). These are known to cause nociception and oedema DrosophilaAscaris suum. MFP2 increases the rate of movement in vitro in Ascaris sperm cells and appears to function by increasing the rate of the major sperm protein (MSP) polymerization, possibly in a manner analogous to formins in actin-based systems Gallus gallus)(gi: 118105776), with no function assigned to date. Interestingly, gene coding for the chicken protein overlaps head-to-tail with a chromatin modifying protein 2A (CHMP2A). This DNA region is not assigned to any G. gallus chromosomes.The second exceptional topology can be found in Brazilian venomous fish http://hmmer.janelia.org/)  see . Major grg/) see . Other grg/) see . Distincnections .Interesting small groups are formed at the boundaries of the core. These distant clusters are formed by hemolytic lectins, known to have structure similar to aerolysins and ETX toxins; lysenins, pXO2-60 and anthrax protective antigen. The last one has a clearly different structure from aerolysins despite sharing a very similar mode of pore-formation Clostridium perfringens epsilon toxin) and 1ubi (ubiquitin protein).pXO2-60 is composed of three elements: signal peptide, aerolysin-domain and a ß-grasp fold, also found in ubiquitin. The signal peptide is predicted to be cleaved at position 29 (AAA-BBB). We have modeled the 3D structure of the exported part using two template structures: 1uyj : are these toxins used for defense or are important in other functions (e.g. mycorrhiza)? Most puzzling case is of the honeybee anarchy 1 aerolysin-like protein, ‘a genetic locus for worker sterility in a social insect’ (cited after annotation in gi: 67848428). Its function seems to be unrelated to either attack or defense. Does it play a role in a physiological process? Is it an element in sterility regulation?Our analysis suggests that both prokaryotic and eukaryotic family members can be used either for attack or for defense. It is obvious in such species as bacterial pathogens Bacillus anthracis and Bacteroides uniformis. The latter has an N-terminal membrane lipoprotein lipid attachment site that suggests a unique mechanism of cell attachment. B. anthracis pXO2-60 protein is the only one to possess a C-terminal ubiquitin fold domain. It will be of interest to determine whether this ubiquitin fold has a role on the outside of the target cell, or whether it is at some point translocated, possibly through the channel formed by the aerolysin-domain in the cytoplasm of the host cell. The determination of a precise structure, or function, of this domain would help to qualify it to anything in between a classical ubiquitin, a mechanical cap to control the aerolysin pore, an immunoglobulin-binding (Ig-binding) domain or a ligand binding domain Our analysis also allowed the identification of previously unknown domain topologies. In Eukaryotes unexpected fusions include crystallin and DM9 domains. We hypothesize their functions to be similar to lectin domains, which is the facilitation of cell membrane binding. In bacteria, novel fusions were identified for proteins expressed in B.anthracis virulence in the mouse model An interesting far-fetching hypothesis may be driven from experiments by Welkos et al. and Chand et al. that show that the lack of protective antigen does not significantly diminish the http://hmmer.janelia.org/). We have employed an E-value of 10 in order to identify as many hits as possible and, then, we have manually analyzed the distribution to assess which threshold to apply to the results. This was necessary, as the software was still in alpha development stage. The twilight-zone hits were analyzed with HHpred www.ncbi.nlm.nih.gov/Entrez/). A sequence alignment was generated on the basis of pairwise alignments of the sequences and PFAM aerolysin domain using HMM-HMM comparison tool HHpred To identify aerolysin-like sequences, we made queries at the National Center for Bioinformatics Information (NCBI) using BLAST and PSI-BLAST For cluster map we have extracted all sequences from NCBI non-redundant database at an E-value of 100 using the hmmsearch program from HMMER3 package; that set included false positives. All vs. all comparisons were done with the jackhammer application from the same package with default of 5 iterations.Domain annotation for selected proteins was obtained using combined effort of Interpro Both full Aerub sequence as well as single N- and C-terminal domain regions were analyzed with 3D-Jury server Figures of structures were prepared using Chimera Table S1List of proteins identified as members of the aerolysin superfamily containing Uniprot ID, protein name and species.(XLS)Click here for additional data file.


P). They are predominantly found in proteins involved in various trafficking pathways. Although FYVE domains may function as individual modules, dimers or in partnership with other proteins, structurally, all FYVE domains share a fold comprising two small characteristic double-stranded β-sheets, and a C-terminal α-helix, which houses eight conserved Zn2+ ion-binding cysteines. To date, the structural, biochemical, and biophysical mechanisms for subcellular targeting of FYVE domains for proteins from various model organisms have been worked out but plant FYVE domains remain noticeably under-investigated.FYVE domains have emerged as membrane-targeting domains highly specific for phosphatidylinositol 3-phosphate (PtdIns(3)Arabidopsis FYVE domains, including their identification, classification, molecular modeling and biophysical characterization using computational approaches. Our classification of fifteen Arabidopsis FYVE proteins at the outset reveals unique domain architectures for FYVE containing proteins, which are not paralleled in other organisms. Detailed sequence analysis and biophysical characterization of the structural models are used to predict membrane interaction mechanisms previously described for other FYVE domains and their subtle variations as well as novel mechanisms that seem to be specific to plants.We carried out an extensive examination of all Our study contributes to the understanding of the molecular basis of FYVE-based membrane targeting in plants on a genomic scale. The results show that FYVE domain containing proteins in plants have evolved to incorporate significant differences from those in other organisms implying that they play a unique role in plant signaling pathways and/or play similar/parallel roles in signaling to other organisms but use different protein players/signaling mechanisms. The FYVatom [P) -18. Thisependent  and stemependent . MutagendIns(3)P ,19-21.P-binding signature contains three classic conserved regions: the N-terminal WxxD, the central R(R/K)HHCR and the C-terminal R(V/I)C motifs [P specific membrane recruitment of FYVE domains. However, there are several factors in addition to PtdIns(3)P-binding that are thought to contribute to the membrane affinity of FYVE domains: nonspecific electrostatic interactions between the basic face of the domain and the anionic membrane surface [P binding pocket and the membrane bilayer [P to the ligand-binding pocket of FYVE domains neutralizes nearby basic residues to reduce the local positive potential and allow conserved hydrophobic residues to penetrate the membrane interface enhancing membrane attachment [P ligand and a phospholipid bilayer [The PtdIns(3)C motifs . Combine surface -24, hydr bilayer ,23-26, d bilayer ,27 and p bilayer . In additachment ,24,25. R bilayer .P-binding FYVE domains are well conserved in various organisms and have been studied extensively in different model organisms except plants. Plants possess several FYVE domain-containing proteins and PtdIns(3)P has been shown to be present in various compartments [P, are present in plant cells [P may not be the only known phosphoinositide ligand recognized by plant FYVE domains, for example, the FYVE of EEA1 has been shown to be capable of binding to PtdIns(5)P [The PtdIns(3)artments  as well artments  of plantartments  for FYVEnt cells . HoweverdIns(5)P ,33.Arabidopsis thaliana (At) to understand the structural basis for the mechanism of their function and to explore their similarities and differences with respect to other organisms. We describe the 15 different FYVE domain-containing proteins that are expressed in Arabidopsis, all of which are largely unexplored. Our detailed sequence analysis and biophysical characterization of the structural models of the FYVE domains in Arabidopsis suggest membrane interaction mechanisms and their subtleties. Moreover, the study also reveals unique biophysical properties of plant FYVE domains, a new binding motif specific only to the variant class of plant FYVE domains and novel domain architectures unique to plant FYVE proteins.We have undertaken a comprehensive examination of all FYVE domains of the model plant H. sapiens, 13 in a total of 18,000 in C. elegans and 5 in a total of 6,000 in S. cerevisiae [AtFYVE proteins in the Arabidopsis protein sequence database i.e. TAIR first genome release . Later genome releases built upon the gene structures of TAIR6 release as well as community input regarding missing and incorrectly annotated genes and they do not contain any new genes encoding FYVE proteins. Our finding of 15 FYVE proteins encoded within predicted 25,500 genes [Arabidopsis genome falls in line with the above observation. The initial identification was done using an automated pipeline [AtFYVE proteins and their individual accession numbers were verified through manual searches performed in various databases. The 15 FYVE domains present in various Arabidopsis proteins (representing the entire family of AtFYVE proteins) aligned with human EEA1 FYVE domain  are shoe 2 Fig. . The disAtFYVE proteins , Fab1p (S. cerevisiae), and phosphatidylinositol-3-phosphate 5-kinase type III (H. sapiens) . Ppk-3 and Fab1p proteins share domain architecture identical to class I members and phosphatidylinositol-3-phosphate 5-kinase type III protein has an additional DEP domain . Class II is represented by two sequences, AT3G43230 and AT1G29800, which possess two domains: a FYVE and a Domain of Unknown Function (DUF500). Class III comprises the AT1G61690 protein and class IV comprises the AT1G20110 protein. Both classes are unique in that they contain only a FYVE domain but they differ in the placement of the FYVE domain (N-terminus versus C-terminus) and also their biophysical properties (this study). UniProtKB/TrEMBL annotates function for class II-IV as putative uncharacterized. The representation of class II-IV members in the literature is full of contradictions. They are not mentioned in the classification by Drobak and Heras [AtFYVE proteins, which share similar domain architecture, i.e. Pleckstrin Homology of Phospholipase C (PH_PLC), followed by Regulator of Chromosome Condensation 1 (RCC1) regions/blades (overlapping with Alpha Tubulin Suppressor 1 (ATS1)) and FYVE domains. In addition, seven out of nine class V proteins are characterized by the presence of a DZC motif found near the C-terminus DZC. UniProtKB/TrEMBL annotates function for class V members as either disease resistance protein-like, e.g. AT5G42140 and AT4G14370, Ran GTPase binding/chromatin binding/zinc ion binding, e.g. AT1G65920, AT1G69710, AT3G23270 and AT5G12350, or putative uncharacterized, e.g. AT3G47660, AT1G76950, and AT5G19420.On the basis of domain architecture of the proteins, we propose five classes of ins Fig. . Class IT4G33240 . The othT4G33240 . Class Ind Heras  and AT1Gnd Heras . Class VAtFYVE proteins, whereas the CD-search identifies additionally yeast domain with similarity to human RCC1 domain, ATS1 domain, overlapping the RCC1 blades  [AtFYVE proteins. In human RCC1, one half of the first sequence repeat, the C and D repeats, is made from the N-terminal end of the protein, and the other half, the A and B repeats, is made from the C-terminal end [AtFYVE proteins align well with six of human seven RCC1 blades. In fact, the seven highly conserved residues, i.e. four glycines, a tyrosine, a leucine and a cis-proline, identified in human RCC1 repeats are also mostly conserved among putative AtRCC1 blades (boxed residues). However, it appears that the first blade of human RCC1 shares little or no primary and/or secondary sequence similarity with most putative AtRCC1 blades. The first blade of putative AtRCC1 may not even be a potential repeat for at least seven out of nine class V AtFYVE proteins because they share a low sequence similarity with human RCC1 in the corresponding region as compared to other regions.The SMART database recognizes between three and five RCC1 regions within class V des Fig. . In some (Alsin)  and Golg (Alsin) . Fig. 3  (Alsin)  aligned inal end . It has inal end . Our datAtFYVE proteins listed in Fig. AtFYVE domain , bears a resemblance to the electrostatic profile of D. melanogaster Hrs-FYVE (PDB: 1DVP). The model of the other class I member, AT4G33240-FYVE, exhibits weaker positive potential. Both members of class II AtFYVE domains share a potential profile that is similar to the profile observed for H. sapiens EEA1-FYVE (PDB: 1HYI). Class II AT3G43230-FYVE has a net charge of +6 and AT1G29880-FYVE has a net charge of +8. Intriguingly, the model of class III AT1G61690-FYVE has a very strong positive potential similar to that observed also for all models of class V AtFYVE domains, M. musculus 19 protein-FYVE (PDB: 1WFK) and for H. sapiens 27 isoform β-FYVE (PDB: 1X4U). Their overall net charge is highly positive, but varies from +9 to +16. The electrostatic profile of class IV AtFYVE model shows the weakest positive potential observed among AtFYVE domain models. Additional electrostatic profiles for the alternative models, their PDB coordinate files and verification profiles are available online .We built homology models of all FYVE domains present in 15 ain Fig. . In Fig.AtFYVE domains can be divided into two distinct groups based on different consensus sequences identified via CLUSTALW multiple sequence alignment HHCR and the C-terminal R(V/I)C motifs [P. Class I-IV AtFYVE proteins have a classic FYVE domain  and a conserved arginine in the last position within the turret loop. As in the case of class I-IV AtFYVE domains, the putative dimerization interface region of class V AtFYVE domains is highly hydrophobic. Unlike class I-IV AtFYVE domains, however, class V AtFYVE domains dimerization interface regions seems highly conserved with at least three absolutely conserved residues, i.e. AxxAP.We observe that the variable turret loop prior to the R(R/K)HHCR motif, which is associated with membrane penetration of the FYVE domain, and the putative dimerization interface region are made up of residues, which are quite diverse in the various class I-IV FYVE domains. Despite the observed differences in residues, however, all class I-IV AtFYVE domains have a potential to bind headgroups of both PtdIns(3)P and PtdIns(5)P using the same set of residues previously identified to bind the headgroup of PtdIns(3)P in other FYVE domains, i.e. the RHHxR motif and the arginine residue of RVC motif YR motif, which overlaps the classic RVC motif, to potentially bind headgroups of PtdIns(3)P and PtdIns(5)P P Fig. . In addioup Fig. .P playing a major role in endocytic and vesicular trafficking [P is a phosphoinositide that is present at very low levels in plant cells [P and PI3K are essential for normal plant growth [Rhizobium infection in M. truncatula [P, they could play important roles in various physiological processes. In this study we have modeled the structure of all AtFYVE domains and predicted their membrane targeting behavior based on the biophysical profiles of the modeled structures.Proteins that contain FYVE zinc finger domains have so far been known as effectors of PtdIns(3)fficking -48. PtdInt cells -52. It it growth  and havet growth , auxin-it growth , root hauncatula , maintenuncatula , increasuncatula , stomatauncatula ,58, and uncatula . If we eAtFYVE proteins into five distinct classes  domain observed in mammals and worms [AtFYVE proteins class II-V are unique and exhibit completely different domains suggesting that FYVE domains in plants play a unique role in plant signaling pathways and/or play similar/parallel roles in signaling as other organisms but use different protein players or signaling mechanisms.Based on the domain architecture and homology to proteins of known function, we have classified ses Fig. . Similar genomes ,45 sugge genomes . Howeverications ,40,45. End worms ,40,45,59P to make PtdIns(5)P and PtdInsP2, respectively [P with high specificity through their FYVE domains [AtFYVE, PIKfyve proteins, were found to participate in vacuolar rearrangement essential for successful pollen development [P-binding and the conserved hydrophobic motif suggesting that they likely bind membranes using the general mechanism of non-specific electrostatic interactions, followed by membrane penetration of hydrophobic residues close to the PtdIns(3)P-binding pocket facilitated by an electrostatic switch coupled with specific interactions with PtdIns(3)P as proposed by previous computational modeling studies [AtFYVE domains and their S. cerevisiae and C. elegans homologues  and PtdIns3)ectively . PIKfyve domains ,60 and a domains , transdu domains  and othe domains  as well  domains -65. Receelopment  and our  studies . These sectively ues Fig. . AdditioP-binding motif and a large basic binding pocket except for class IV AT1G20110-FYVE, which has a significantly reduced basic surface patch in the potential ligand-binding pocket P-containing membranes using the classic mechanism of action of previously studied FYVE domains with a strong contribution from non-specific electrostatic interactions.Class II-IV proteins have untouched sequences in terms of functional assignment, which remain annotated as "putative uncharacterized proteins" in various sequence databases. All of them share the complete/nearly complete conserved PtdIns(3)AtFYVE domain has the most reduced basic surface patch and the lowest net charge of +2 among AtFYVE domains. Hydrophobic contribution through membrane insertion will likely be an important component of membrane binding for this class, similar to FENS-FYVE [Class IV ENS-FYVE , which lENS-FYVE  even thoENS-FYVE .AtFYVE proteins [AtFYVE proteins (this study). Since β-propellers (including RCC1 repeats) could be made of a variable number of blades and are thought to evolve by blade duplication and deletion [AtFYVE proteins: 1) the second half of blade 1 and the first half of blade 7 engage with one another to form a symmetrical 6-bladed β-propeller; 2) an "open" ring-propeller forms as known for the C-terminal domain of ParC subunit [AtFYVE adapt a β-propeller structure similar to β-propeller structures found in proteins from other organisms.Class V proteins are the most interesting class of the FYVE domain-containing proteins although much remains to be understood about their function. Out of the 18 human RCC1 superfamily proteins, none corresponds, in their domain architecture to class V FYVE proteins . The cloproteins . In contproteins  and somedeletion , there c subunit  and sugg subunit ; or 3) t subunit . TherefoP2 while its FYVE domain binds to PtdIns(3)P as well as PtdIns(5)P [AtFYVE domains show a high degree of sequence conservation within an enrichment of basic residues throughout the length of the FYVE domain , Q1SIN6 (M. truncatula), Q84RS2 (M. sativa), Q5JL00 (O. sativa) Q5N8I7 (O. sativa) Q6AV10 (O. sativa) Q6L5B2 (O. sativa) Q259N3 (O. sativa), Q5XWP1 (S. tuberosum), Q60CZ5 (S. tuberosum), Q60CZ5 (S. demissum) or Q5EWZ4 (T. turgidum).Previously, it has been suggested that association with membrane(s) may be crucial for the functioning of this class of AtFYVE proteins given the presence of two phosphoinositide-binding domains, i.e. PH and FYVE domains . ExperimdIns5)P . The varP . The vain Fig. , which sain Fig. . When thP over other phosphoinositides [P as well, perhaps because PtdIns(3)P and PtdIns(5)P are similar in all aspects except having the phosphomonoester in a different position [P has been shown to induce small but important chemical shift changes similar to those induced by PtdIns(3)P in the binding motif residues with the exception of one arginine, which remains practically unaltered by PtdIns(5)P [P specific recognition by the FYVE domain seems to involve indirect recognition of this specific ligand by exclusion of alternatively phosphorylated phosphoinositides: the two residues implicated in this are the aspartic acid of the N-terminal WxxD motif and the second histidine of the central HHCR motif [AtFYVE domains P and PtdIns(5)P headgroups using practically the same set of residues P and/or PtdIns(5)P using the variant binding residues and an additional conserved motif specific to this class of FYVE domains.The obvious question that comes to mind is whether this variant signature is responsible for an altered binding specificity in this class of FYVE proteins and therefore associated with a novel pattern of membrane/sub-cellular targeting. Within mammalian cells, FYVE domains are highly conserved and seem to select PtdIns(3)ositides . Despiteposition . ConsequdIns5)P . PtdInsP-induced hydrophobic interactions for membrane localization, the contribution differing for individual domains as described earlier. Additionally, dimerization may play an important role in the membrane recruitment of FYVE domains [AtFYVE domains are longer and more hydrophobic  as dimers.Based on experimental studies, it has been suggested that the strength of the positive potential and the identity of the hydrophobic residues near the binding site may be two key factors, which are critical in determining which FYVE domains act alone, undergo dimerization or require additional partners before anchoring to the membrane . For exa domains ,74 and i domains . The dimbic Fig.  than theAtFYVE proteins are quite distinct from other organisms, exhibiting unique domain architectures, biophysical properties as well as altered binding motifs. The biophysical profiles of the modeled FYVE domains in Arabidopsis suggest membrane-targeting mechanisms ranging from the previously described classic modes to the novel binding mode of the class V FYVE domains, which seem to be found only in plants. Our predictions provide a foundation for designing directed mutational studies to confirm these behaviors, which is crucial to the understanding of the role of these domains in important plant signaling pathways, something that has so far not been explored.Overall, AtFYVE proteins were identified using a computational pipeline for automated high-throughput modeling [Arabidopsis protein sequence database (TAIR6_pep_20051108). The AtFYVE protein sequences corresponding to the identified accession numbers were retrieved from KEGG GENES [The accession numbers of the modeling , which rGG GENES ,77 and vGG GENES -80.AtFYVE proteins obtained with pipeline, three additional methods were employed: 1) search of publicly available sequence databases: Swiss-Prot/TrEMBL [Arabidopsis Information Resource (TAIR) for BLASTn 2.2.14 [AtFYVE domains in PROSITE format offered by GenomeNet service [To verify the total number and individual accession numbers of t/TrEMBL ,82, NCBIt/TrEMBL ,84 and Ut/TrEMBL -87; 2) qn 2.2.14 ; and 3) n 2.2.14  using a  service .AtFYVE protein sequence was analyzed by searching against Pfam [Each nst Pfam , SMART [nst Pfam -80, Consnst Pfam -94 and Cnst Pfam ,96. All AtFYVE domains, we implemented a number of programs to create many different alternative alignments and models followed by a quality assessment and a selection process. We used two separate approaches: automated and manual. The automated approach involved the use of a high-throughput computational pipeline, which uses its own built in alignment, modeling and evaluation methods [There is no single homology modeling program/routine that has been singled out as the best method for comparative modeling . To gene methods  as well  methods . The man methods . The sch methods -102, Mod methods ,104, NES methods , LOOPP [ methods -108, HOM methods , CPH [11 methods , PHYRE [ methods , manual  methods , guided  methods ,114 and  methods . Loop re methods  and the  methods  and SCAP methods -120.AtFYVE domains as well as total charges were computed using the automated system H++ [The models were analyzed for their sequence, structural and biophysical properties. The analyses of biophysical properties including the electrostatics, hydrophobicity and shape of each model were conducted using the surface property analysis tools in the program GRASP . The pKastem H++ -124, whistem H++ .P2, InsP2, InsP3, InsP3, InsP3, and InsP4 ligands were extracted from their corresponding PDB files. InsP2 ligands were created from InsP3 ligands in Chimera [Ins Chimera  and ener Chimera . GasteigP2 of known FYVE domains into their corresponding solved structures. Although FYVE domains are suggested to bind only InsP2 and InsP2, we also docked InsP2, InsP3, InsP3, InsP3, and InsP4 as controls. Following the initial validation we used our approach to dock three InsP2 and three InsP2 ligands using rigid and flexible docking scenarios with the predictive models of AtFYVE domains. In the end, each predictive model was subjected to twelve docking runs, six for each headgroup. A given residue is reported to interact with the headgroup only if it does so 50% or more of the time (i.e. 3 or more times) as evaluated by the Ligand-Protein Contacts (LPC) server [Rigid and flexible docking was performed using DOCK 6.1  and DOCK) server .AtFYVE domains as well as other supplementary information  are available at the following website: http://userhome.brooklyn.cuny.edu/ssingh/arabidopsis/FYVE/fyve.html.The sequences and coordinate files representing our models for all AT: Arabidopsis thaliana; ATS1: Alpha Tubulin Suppressor 1; DUF500: Domain of Unknown Function 500; EEA1: Early Endosomal Antigen 1; FAB1P: Formation of aploid and binucleate cells; FCP: Fucoxanthin Chlorophyll a/c-Binding; FYVE: Fab1, YOTB, Vac1, and EEA1; HRS: Hepatocyte growth factor-regulated tyrosine kinase substrate; MVB: MultiVesicular Bodies; PH_PLC: Pleckstrin Homology of Phospholipase C; PIKFYVE: PhosphatidylInositol 3-phosphate 5-Kinase; PI 3-kinase: PhosphoInositide 3-Kinase; PRAF: PH, RCC1 and FYVE; PTDINS: PhosphatIdylinositol; PTDINS(3)P: PhosphatIdylinositol 3 Phosphate; PtdIns(5)P: PhosphatIdylinositol 5 Phosphate; RCC1: Regulator of Chromosome Condensation 1; SARA: Smad Anchor for Receptor Activation; VPS27P: Vacuolar protein sorting mutant 27 phenotype; VPS34P: Vacuolar protein sorting mutant 34; ROS: Reactive Oxygen Species.EW carried out the modeling and sequence/structure analyses and drafted the manuscript. SMS conceived of the study, participated in its design and coordination, guided the analyses and refined the manuscript. All authors read and approved the final manuscript.


PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.http://webservice.baderlab.org/domains/POW.We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at PSD95/DlgA/Zo-1 (PDZ) domains are modular peptide recognition domains that are generally found in eukaryotic signalling pathways, often in scaffolding proteins that are responsible for regulating protein complex assembly and localization to specialized sites in the cell, especially at membranes[embranes-4. PDZ dembranes,7.Recent high throughput experiments have resulted in the availability of large data sets of PDZ domain-peptide interactions,8. As a Structure-based predictors have been developed to more generally predict protein-protein interactions. For instance, Hue et al., used a support vector machine (SVM) to predict PPIs using a structure kernel. MethodsIn this paper, we present a structure-based predictor for PDZ domain-peptide interactions that can be used for proteome scanning. Our predictor uses a variety of different structure features that are known to play roles in protein structure stability and facilitating PPIs. Through leave 12% of domain out cross validation, we showed that the structure-based predictor depends less on training–testing domain sequence similarity compared to our previous sequence-based predictor. Based on human proteome scanning results, we also show that the structure-based predictions correspond to known experimentally determined PDZ domain-peptide interactions and known PPIs involving PDZ domain containing proteins. A substantial number of the structure-based predictions correspond to known PPIs not previously predicted by the sequence-based predictor (48% increase), confirming that the structure-based predictor finds different interactions than the sequence-based predictor. Using predictions from both methods, we created a functional map using all predicted human PDZ mediated PPIs and identify xenobiotic metabolism as a novel biological process enriched in PDZ interactors.http://webservice.baderlab.org/domains/POW), which enables users to run our sequence-based and structure-based predictors online in human, mouse, fly and worm.Finally, we developed a website called POW! PDZ domain-peptide interaction prediction website , these positions were identified from only a single PDZ domain-peptide complex structure,10 and mThe initial set of PDZ domain structures consists of one NMR and 17 X-ray structures for human collected from the Protein Data Bank (PDB) with corThe quality of the homology models was estimated by computing the number of identical residues between the target and template sequence (i.e. template sequence identity). It has been shown that target-template sequence identity is positively correlated with model quality. In particular, state-of-the-art algorithms can always build high quality models RMSD < 2 Å) if the target-template sequence identity is higher than 35-40%. Furthermore, there is no significant variation in model quality for targets with sequence similarity between 40-70%. If the similarity is 35%, there is no correlation,26. All  Å if thePDZ domain-peptide interactions were collected from published high throughput phage display and protein microarray experiments for human and mouse, respectively,8. SinceStructure features across the entire PDZ domain structure were computed and values corresponding to the ten core binding site positions were extracted from the larger list of features computed for all domain positions. Four types of structure features (detailed below) involved in protein folding and stability were computed to describe the PDZ domain structure Figure . Three-dThe first feature type consists of five values describing protein structure and were computed using the JOY web server. SolventThe second feature type is a single value indicating how much surface (i.e. area) for a core residue is available for binding to a ligand residue. This feature was computed using the SURFV software for eachProtein-protein interactions are facilitated by the electrostatic and hydrophobic complementarity of molecular surfaces. Therefore, the third and fourth feature types describe the electrostatic potential and hydrophobicity along the surface of the domain. At each core residue position, nine values were sampled from the surface resulting in a total of 90 electrostatic and 90 hydrophobicity values (9 features x 10 core positions). These features were generated by the VASCo software.Peptides were encoded using a sparse binary vector encoding, as described in previous work. Briefly1y1),…,(xmym) where m is the number of samples, xi is a feature vector for domain di and peptide pi and y is a class label such that yi = {−1, +1}[We used the support vector machine (SVM) binary machine learning method for our predictor,32. Give{−1, +1}, the SVMb describe a maximum margin hyperplane  that separates positive and negative training examples. For such a hyperplane:where sgn(0) = +1, otherwise −1. The weight vector w and bias term i’s are positive real numbers that maximize the following objective function:where the αK can be thought of as describing the similarity between two feature vectors, and C is a cost parameter that penalizes training errors. We used the radial basis function (RBF) kernel, defined as:where C[C+ = (n+/n-) C-, where n+ is the number of positive training interactions and n- is the number of negative training interactions. The LibSVM software library was used to build the SVM[A grid search was used to find locally optimal values for γ and C. Instead the SVM.An initial predictor was built using the data for 88 PDZ domains described above. A preliminary assessment of the predictor’s proteome scanning performance was performed by scanning the human proteome (defined by genome assembly Ensembl:37.64) for each domain in the training set. This initial predictor returned a large number of hits (1000 or more) for over half of the domains with an average number of predictions returned per domain of over 2000 , 8% of the peptides (to estimate predictor performance dependence on specific sets of peptides) and both 12% of the domains and 8% of the peptides (to estimate predictor performance dependence on specific sets of domains and peptides) and tested on the rest, again repeating this ten times. In geneWe computed the following statistics to measure predictor performance:• Sensitivity or Recall: TP/(TP + FN)• Specificity: TN/(TN + FP)• Precision: TP/(TP + FP)where TP is the number of true positives, FP is the number of false positives, TN is the number of true negatives, FP is the number of false positives. The overall performance was summarized by computing the area under the receiver operating characteristic (ROC) curves and Precision/Recall (PR) curves,38.p-value assessing the GO term enrichment for a given set of predicted genes. Multiple testing correction was performed using the Benjamini and Hochberg False Discovery Rate (FDR) correction. GO v1.2  and human GO annotations  were used. Only gene-sets with between five and 300 genes were used from the GO ontology . A list of enriched terms  with more than one gene interactor and associated with more than two domains were retained. To better interpret the structure-based and sequence-based enrichment results, we created an enrichment map, a network-based visual representation of enriched terms that groups similar terms and eases identification of functional themes. We used the Enrichment Map Cytoscape plugin software to create the enrichment map[p-value < 0.05, FDR Q value < 0.1 and “Jaccard + overlap similarity” cutoff = 0.517.A gene function enrichment analysis was performed on the predicted sequence-based and structure-based gene targets using Gene Ontology (GO) biological process terms. The BiNment map,42, usinTo estimate the generality of the predictor, we ran multiple cross validation tests and plotted the ROC and PR curves to summarize the performance. The predictor achieves high ROC and PR area under the curve (AUC) scores compared to random performance AUCs over all cross validation strategies. In particular the ten fold cross validation ROC and PR AUCs were 0.96 and 0.936, respectively . The leave 8% of peptides out cross validation ROC and PR AUCs were 0.935 and 0.909 respectively . The leave 12% of domains and 8% of peptides out cross validation out ROC and PR AUCs were 0.927 and 0.886 respectively . Finally, slightly lower AUCs were obtained for the leave 12% of domains out cross validations, which achieved 0.872 and 0.785 respectively  . These results show that on average, the structure-based predictor is less dependent on training–testing domain sequence similarity compared to the sequence-based predictor at lower similarity thresholds.In previous work, we showed that the performance of the sequence-based predictor depends on how similar in binding site sequence a given testing domain is to its nearest training domain. In particular, as the domain binding site sequence similarity decreases so does the predictor’s average performance until it is comparable to that of a naïve nearest neighbour sequence predictor. To moreWe used the predictor to scan the human C-terminal proteome (defined by genome assembly Ensembl:GRCh37.64) for bindThe structure-based predictor has a true positive rate (TPR) of 0.36 and precision of 0.0033 and correctly predicted interactions for 22 of the 45 domains. For these domains approximately 73% of known PDZ domain-peptide interactions in PDZBase, an independent data source not used for training, were predicted  of the predictor using two real negative data sets for human, which were used in a recent study to benchTo determine how many structure-based predicted interactions correspond to known PPIs, we scanned the human proteome to predict interactions for 218 human PDZ domains with known PPIs (that we could obtain structures and compute structure features for). Known PPIs were retrieved from iRefIndex, which ip-value < 0.05, Fisher’s exact test) for all but ten domains. A caveat of this result is that PDZ domain containing proteins may contain multiple PDZ domains and other domains, so it is not possible to uniquely assign a PPI to a PDZ domain. This could result in erroneous false negative or true positive statistics for the above tests. However, the results still serve as an estimate of predictor performance and show that the predictor is able to predict many known human PPIs.In total, 88 domains had predicted interactions that corresponded to known PPIs, with an average of greater than 21% of known PPIs being correctly predicted per domain. The number of PPIs successfully predicted per domain was significant  are more challenging for the sequence-based predictor, which returns a low number of hits per domain (ten or less) with none corresponding to known PPIs , covering known PDZ domain binding classes I and II . To examine if this is caused by specific structural features used by the structure-based predictor, we searched the literature to find known structure determinants influencing these specific amino acid preferences and compared them to our results. For MLLT4-1, the structure-based predictions indicate a preference for a hydrophilic Thr residue at position −2. The preference for a hydrophilic Thr residue at position −2 is explained by the findings of Chen et al.. Their wTo identify gene functions better predicted by sequence or structure-based methods, we performed GO-based gene function enrichment analysis on all predicted hits. The results were visualized using an enrichment map, which groups related gene function terms to ease identification of functional themes Figure . EnrichmWe also compared the themes from our predictions to those from 1249 known PDZ mediated PPIs in the iRefIndex database. Some thIn some cases, although the themes were also enriched in the iRefIndex map, only limited information about PDZ domain involvement in the associated process was found in the literature. These themes represent opportunities for our predictions to shed light on the role of PDZ domains where little is currently known. One example is ‘wound healing’, where both predictors predicted PDZ domains to interact with proteins involved in different stages of wound healing. These included platelet activators and aggregators License: NoneFor proteome scanning software:Project name: PDZ Structure-based Proteome Scanninghttp://baderlab.org/Data/StructurePDZProteomeScanningProject home page:Operating systems: Platform independentProgramming language: Java 1.5License: Source code is freely available under the GNU Lesser Public General License (LPGL)The authors declare that they have no competing interests.SH collected the data, developed and implemented the algorithms, drafted and revised this manuscript, and helped to develop the website. XX developed the website. GDB substantially contributed to all aspects of this project including conception and design, critically revised the manuscript and provided feedback about the website. All authors read and approved the final manuscript.Supplementary Information.Click here for fileTraining domain structure information. In total, 83 PDZ domains were used for training. Domain structures were obtained from the PDB or homology modelled through the Protein Model Portal. For NMR structures, only the first model was used. All homology models were generated by SWISS-MODEL and have greater than 50% sequence similarity to their template structure (average 90%). Model quality is estimated using template sequence ID  and QMEAN score . Table S2. Blind test domain structure information. Blind testing was performed using interaction data from mouse, worm and fly protein microarray experiments. In total, 13 mouse orphan, 7 worm and 6 fly PDZ domains were used. Homology models were generated by SWISS-MODEL. All models have at least 40% sequence identity to their template structures. An NMR structure was available for one fly domain and the first model was used. The average template sequence similarity was 0.92, 0.61 and 0.61 for mouse, worm and fly domains, respectively. One mouse domain (CHAPSYN-110-1) was removed from the test set because its performance was consistently poor for both predictors. Model quality is estimated using template sequence ID  and QMEAN score . Table S3. Human proteome scanning domain structure information. Proteome scanning was performed for 218 human PDZ domains, which have known interactions in iRefIndex. In total, 61 X-ray and nine NMR structures (only the first models used) were obtained from the PDB and 148 homology models were created . Model quality is estimated using template sequence ID  and QMEAN score . Table S4. Validation of structure-based predictions against known human PDZ domain-peptide interactions. Proteome scanning predictions for 45 human PDZ domains were validated against known PDZ domain-peptide interactions in PDZBase. Several statistics were calculated including: # Positives, # TP , # Predicted Structure (number of predictions predicted only by the structure-based predictor). # Predicted Sequence (number of predictions predicted only by the sequence-based predictor), # Predicted Both (number of predictions predicted by both), # TP Structure (number of true positives predicted by the structure-based predictor only), # TP Sequence (number of true positives predicted by the sequence-based predictor only), # TP Both (number of true positives predicted by both). Table S5. Validation of structure-based predictions against known negative PDZ domain-peptide interactions for human. a. Negatives involving peptides with PDZ binding motifs. Proteome scanning predictions for 74 human PDZ domains were validated against experimentally determined negative interactions involving peptides with PDZ binding motifs (found from the literature) for a total of 410 interactions. b. Negatives involving peptides with non binding PDZ motifs. Proteome scanning predictions for 24 human PDZ domains were validated against known negative interactions involving mutated peptides with non-binding PDZ motifs (found from the literature) for a total of 126 interactions. Table S6. Validation of structure-based predictions against known experimentally determined PDZ domain-peptide interactions for worm. Proteome scanning was performed for six worm PDZ domains with interactions from protein microarray experiments. Several statistics were calculated including the ones from Table S4 as well as the following: # Negatives, # FP Structure , # FP Sequence , # FP Both . Table S7. Validation of structure-based predictions against known experimentally determined PDZ domain-peptide interactions for fly. Proteome scanning was performed for seven fly PDZ domains with interactions from protein microarray experiments. Several statistics were calculated (see Table S6 caption). Table S8. Validation of structure-based predictions against known protein-protein interactions. Proteome scanning results for 221 human PDZ domains with both structure-based and sequence-based predictions were validated against known human PPIs in iRefIndex. A prediction is considered to be a true positive if the domain involved is found in a known PPI where one of the proteins contains the domain. See Table S4 caption for details about the calculated statistics. Table S9. Structure-based predicted PDZ domain interactors for according to functional theme. These tables contain domains, their sequence-based predicted interactors and the enriched functional theme (i.e. clusters in the Enrichment Map). Table S10. Sequence-based predicted PDZ domain interactors according to functional theme. These tables contain domains, their structure-based predicted interactors and the enriched functional theme (i.e. clusters in the Enrichment Map).Click here for file


The isolated phosphoproteins were eluted and isotopically labeled for relative and absolute quantitation (iTRAQ) and mass spectrometry identification. We found 40 proteins that showed decreased phosphorylation at Cdk5−/− brains. In addition, out of these 40 hypophosphorylated proteins we characterized two proteins, :MARCKS (Myristoylated Alanine-Rich protein Kinase C substrate) and Grin1 (G protein regulated inducer of neurite outgrowth 1). MARCKS is known to be phosphorylated by Cdk5 in chick neural cells while Grin1 has not been reported to be phosphorylated by Cdk5. When these proteins were overexpressed in N2A neuroblastoma cell line along with p35, serine phosphorylation in their Cdk5 motifs was found to be increased. In contrast, treatments with roscovitine, the Cdk5 inhibitor, resulted in an opposite effect on serine phosphorylation in N2A cells and primary hippocampal neurons transfected with MARCKS. In summary, the results presented here identify Grin 1 as novel Cdk5 substrate and confirm previously identified MARCKS as a a bona fide Cdk5 substrate.Protein phosphorylation is the most common post-translational modification that regulates several pivotal functions in cells. Cyclin-dependent kinase 5 (Cdk5) is a proline-directed serine/threonine kinase which is mostly active in the nervous system. It regulates several biological processes such as neuronal migration, cytoskeletal dynamics, axonal guidance and synaptic plasticity among others. In search for novel substrates of Cdk5 in the brain we performed quantitative phosphoproteomics analysis, isolating phosphoproteins from whole brain derived from E18.5 Cdk5 The most common post-translational modification of proteins is the phosphorylation of serine, threonine, and tyrosine residues. This is particularly important to cellular mechanisms that integrate extracellular and intracellular signals through selective phosphorylation of proteins leading to specific functional outcomes. Protein phosphorylation plays a key role in many cellular functions such as cell signaling, apoptosis, cell migration, cytoskeletal dynamics and brain development. Cyclin-dependent kinase 5 (Cdk5) is an atypical member of the cyclin-dependent kinase family of which most of the members are key regulators of the cell cycle 32P) orthophosphate labeling after the administration of a Cdk5 inhibitor. They found that Indolinone A treatment regulated protein phosphorylation and gene expression of candidates involved in neuronal survival, neurite outgrowth, and synaptic functions Given that a large number of the key cellular processes involve Cdk5 activity, suggesting that with the advent of new proteomic techniques many more Cdk5 substrates will be discovered. Interestingly, in the last few years many reports have indicated that Cdk5 also has extra-neuronal functions, such as regulating gene transcription, vesicular transport, apoptosis, cell adhesion, and migration in many cell types and tissues +/+ and Cdk5−/− mice brains. We analyzed E18.5 mouse brain lysates using IMAC followed by iTRAQ and mass spectrometry. We discovered forty proteins with decreased phosphorylation at Cdk5 motifs in the Cdk5−/− brain. These proteins contained one or more phosphopeptides containing the canonical consensus phosphorylation motif for Cdk5 in vivo  In this study, we carried out systematic phosphoproteomic analysis of the Cdk5−/−) were generated as previously described +/− mice pregnant were euthanized at E18.5 with a lethal injection of Ketamine/Xilacine mix. The genotype of each mouse was determined by PCR from DNA obtained from tail biopsies. For primary culture of rat hippocampal neurons, female rats Sprague-Dawley pregnant were euthanized at E18.5 with a lethal injection of Ketamine/Xilacine mix. These studies were performed in compliance with the National Institutes of Health's Guidelines on the Care and Use of Laboratory and Experimental Animals. All experimental procedures were approved by the Animal Care and Use Committee of the National Institute of Dental and Craniofacial Research, NIH, and the Bioethical Committee of the Faculty of Sciences, University of Chile, according to the ethical rules of the Biosafety Policy Manual of the National Council for Scientific and Technological Development (FONDECYT).Cdk5 knockout mice , in accordance with the manufacturer's protocol. Briefly, protein extracts were loaded in columns, washed, eluted, and quantified by the Bradford method Ten whole brains of E18.5 from Cdk54HCO3 for reduction by dithiothreitol and alkylation by indole acetic acid. After dilution into 2 M urea, 0.1 M NH4HCO3, tryptic digestion was performed as described before One mg of phosphoprotein obtained from brain lysates was dried in the SpeedVac  and then dissolved in 8 M urea, 0.4 M NH+/+ and Cdk5−/− mice were performed as previously described http://fields.scripps.edu/sequest/) to quantify the low molecular mass reporter ion generated by the fragmentation of the attached tag and consequently the peptides and proteins from which they originated.The identification and quantitation of phosphoproteins obtained from Cdk5linear trap quadraplole) with 2 Surveyor MS Pump plus HPLC pumps and Micro AS  and they were equipped with an Advance ESI (electrospray ionization) source . The equipment was used with an instrument configuration, columns, gradient, and source conditions as previously described m/z 400–1400 followed by a PQD MS/MS spectrum on each of the top 10 most abundant ions in the survey scan. Source conditions were as previously described and are listed here as follows: capillary temperature, 165°C; sheath gas flow, 2 U; spray voltage, 1.6 kV. Key optimized PQD instrument parameters (32) were as follows: CE, 35%; isolation width, 3 m/z; activation Q, 0.700; activation time, 0.100 ms; minimum signal threshold, 10,000 cts; dynamic exclusion, repeat count 2, repeat duration 30 s, exclusion duration 60 s; MS/MS target, 4.0 X e4; maximum fill time, 100 ms; 4 microscans.Samples were analyzed by LC/MS/MS on LTQ XL  of 0.1. MS/MS spectra were manually reviewed for spectral quality and the assignment of most major ions.PQD MS/MS spectra were searched in the mouse database utilizing BioWorks 3.3.1 SP1 (Thermo Scientific) for site-specific phosphopeptide identification and iTRAQ quantification. The search parameters were set and data analysis was done as previously described Mouse neuroblastoma N2A cells (ATCC#CCL-131) were transiently co-transfected with pBC12/pCMV-60k plasmid (chicken MARCKS) from Dr. Perry Blackshear 5 cells were plated in coverslips previously coated with poly-D-lysine 1 mg/ml (Sigma-Aldrich) in Neurobasal medium (Gibco) including 10% horse serum and Glutamine (Gibco). Then, neurons were transiently transfected with 0.6 µg of plasmid DNA (0.3 µg of CMV-GFP vector and 0.3 µg of chicken MARKCS vector) for 2 h and then the medium was replaced with Neurobasal medium supplemented with B27 and Glutamax (Gibco) in the absence of serum. Cells were kept in a humidified atmosphere of 5% CO2 at 37°C during 48 h and half of the covers were treated with roscovitine 10 µM. After 24 h neurons were fixed for posterior immunofluorescence against phosphor Ser25 MARCKS and DAPI.Rat primary hippocampal neurons were performed as described before Protein concentration was determined by the Bradford protein assay 500 µg of protein from the whole mouse brain or the transfected N2A cells were immunoprecipitated in 500 µl of TPER buffer (Pierce) with 1–2 µg of Grin1, MARCKS, or Cdk5 antibodies overnight at 4°C in a shaker. Then, 30 µl of Protein A/G (Sigma) was added and incubated for 4 h at 4°C in a shaker. The immunocomplexes were centrifuged at 4°C during 5 min at 1000×g and washed 4 times in cold PBS1X. The loading buffer 5×  was added into the beads and samples, and they were boiled at 95°C for 5 min and ran in SDS-PAGE gels.The PANTHER  Classification System was used to classify proteins. The proteins were classified according to family and subfamily of molecular function, biological process and signaling pathway All experiments were performed at a minimum of three times. A statistical evaluation was performed with GraphPad Prism software, version 5.0 . The significant differences between experiments were assessed by an unpaired Student test where α was set to 0.05.−/− brain. The majority of these phosphosites  present in 40 phosphoproteins have a minimal consensus Cdk5 motif of (S/T)P P . However−/− brains, we classified all of the phosphorylated proteins with the canonical Cdk5 motif which we identified in our experimental analysis in three different groups, namely signaling pathways , Ser138 (32.9%) and Thr143 (44.1%) (MARCKS has at least 7 sites that can be phosphorylated by MAPK and/or Cdks  (44.1%) . A recen (44.1%) . This re (44.1%) . However (44.1%) . Additio (44.1%) . However (44.1%) . Our res (44.1%) . MARCKS  (44.1%) . In summ−/− brain. Ser369 corresponded to a classical consensus sequence for Cdk5, which is conserved in mice and rats but not in humans  could be phosphorylated by Cdk5 MARCKS and Grin1 were two phosphoproteins among the total of 40 identified in our HTS phosphoproteomic analysis of Cdk5−/− brain. Ser369 corresponds to Cdk5 SPXK motif i/o protein are part of a G-couple receptor signaling pathway that regulates neurite growth in neural cells z/Gi and Goo in N2A cells induces neurite elongation, suggesting that Grin1 is an effector of Goo and Grin1 are related to increase Cdc42 activity We also found that Grin1 displayed decreased phosphorylation in two residues Ser369 and Ser691 in Cdk5Amongst the candidates identified to be potential Cdk5 substrates there is a group of proteins involved in the regulation of microtubule dynamics. This group encompasses Collapsin response mediator protein 1 (crmp1), Collapsin response mediator protein 2 (crmp2), Collapsin response mediator protein 4 (crmp4), microtubule-associated protein 1B (MAP1B), microtubule associated protein 2 (MAP2), tau, doublecortin (DCX) and stathmin. It has been previously shown that crmp1, crmp2 and crmp4 are phosphorylated by Cdk5 In summary, our phosphoproteomics analysis of Cdk5 null brain identified decreased phosphorylation in several potential Cdk5 proteins that are involved in neuronal morphology, metabolism and signal transduction. These phosphoproteome data may provide a basis for identifying new Cdk5 substrates; however, further work is required to determine how the novel substrates or sites identified in our study regulates process such as cytoskeleton dynamics, neuronal migration and synapses formation and stability.Table S1(PDF)Click here for additional data file.


We show here for 18 diverse representatives from all eukaryotic kingdoms that a pooled analysis of the tendencies for co-occurrence or avoidance of protein domains is indeed feasible. This type of analysis can reveal general large-scale patterns in the domain co-occurrence and helps to identify lineage-specific variations in the evolution of protein domains. Somewhat surprisingly, we do not find strong ubiquitous patterns governing the evolutionary behavior of specific functional classes. Instead, there are strong variations between the major groups of Eukaryotes, pointing at systematic differences in their evolutionary constraints.The metabolic and regulatory capabilities of an organism are implicit in its protein content. This is often hard to estimate, however, due to ascertainment biases inherent in the available genome annotations. Its complement of recognizable functional protein domains and their combinations convey essentially the same information and at the same time are much more readily accessible, although protein domain models trained for one phylogenetic group frequently fail on distantly related sequences. Pooling related domain models based on their GO-annotation in combination with  It should be possible at least in principle to identify large-scale trends in evolution such as the increased complexity of transcriptional regulation, chromatin-related mechanisms, or post-transcriptional silencing, by comparing the proteomes among species. This approach is indeed widely used. The comparison of predicted protein contents among related species is, for instance, an integral part of most genome papers.The identification of gene families and the determination of orthologs, or at least homologs, is an extremely difficult and time-consuming task in comparisons across kingdoms or even across the three domains of life. The obstacles are not only of a technical nature. Proteins are composed of recognizable protein domains that can be recombined in a combinatorial fashion to produce new functionalities over large time-scales. Individual proteins often have multiple ancestors that contributed with different domains to an extant protein ,2. From The distribution of domains within proteins is not completely uniform. For instance, about fifty specific domains are preferentially found in alternatively spliced exons and hence systematically lacking in some protein variants . As mostMore global trends can be uncovered by considering aggregate statistics of domains and domain combinations. The average number of domains in a protein, for instance, increases systematically along the human lineage . NetworkKEGG or ENSEMBL. Protein domains from Pfam , comprising a combination of family and superfamily level.rC* regulation of binding: GO:0051098 at superfamily level.rB regulators of enzymatic activity: GO:0050790 at superfamily level.rE metabolism of saccharides: GO:0005976 at superfamily level.mS Version 1.75 of the SUPERFAMILY database offers a “Structural Domain Functional Ontology” providing functional and phenotypic annotations of protein domains at the y levels . Since aThe five functional groups bN, bP, rC, rC* and rB were chosen because of their expected preferential co-occurrence with zinc finger genes. Both bN and bP play an important role for gene regulation by transcription factors and are among the most abundant GO classes. The choice of the two variants of chromatin-associated domains rC and rC* is motivated by our previous work on the co-occurrence of protein domains that can act as readers, writers, and erasers of histone modification , which rC and D we correct for the fact that a domain x can be a member of both C and D by counting these cases with a weight of 1/2.We then annotated the membership of a domain in the functional groups. 3.3.x and y co-occur. Let xn be the total number of annotated domains belonging to group x. The simplest estimate for the expected number of domain co-occurrences is E = xny/ngn, where gn is the number genes in the genome under consideration. This estimate does not account for biases arising from the non-uniform distribution of domains over genes. Let dn(i) be the number of domains predicted for protein i, and let dn= Σidn(i) be the total number of domains. Then the number of domains of group x that occur in genes that also contain a y domain can be estimated byy. These expectations are then compared with the number of empirically observed co-occurrences n. We speak of co-occurrence of domain families or groups if n ≫ E(x|y), E(y|x), E and of avoidance if n ≪ E(x|y), E(y|x), E. The statistical significance of an observed difference between n and E(x|y), E(y|x), or E, respectively, is determined under the assumption that n is drawn from a Poisson distribution.For each of the 18 species, we separately evaluated the number of domain co-occurrences and the number of genes in which two domains 4.Despite obvious shortcoming of the gene finding procedure in organisms with unusual genome structure or extreme sequence composition and the unavoidable limitations of the domain annotation, some global patterns nevertheless become visible in this pilot study. The classes of protein domains investigated here are all involved in binding and/or regulation. There does not seem to be an intrinsic tendency of these domains to segregate into different proteins or protein families. In the multi-cellular organisms with large genomes and large gene families, however, we observe a strong signal of avoidance between several functional groups of protein domains,


The tripartite DENN module, comprised of a N-terminal longin domain, followed by DENN, and d-DENN domains, is a GDP-GTP exchange factor (GEFs) for Rab GTPases, which are regulators of practically all membrane trafficking events in eukaryotes. Using sequence and structure analysis we identify multiple novel homologs of the DENN module, many of which can be traced back to the ancestral eukaryote. These findings provide unexpected leads regarding key cellular processes such as autophagy, vesicle-vacuole interactions, chromosome segregation, and human disease. Of these, SMCR8, the folliculin interacting protein-1 and 2 (FNIP1 and FNIP2), nitrogen permease regulator 2 (NPR2), and NPR3 are proposed to function in recruiting Rab GTPases during different steps of autophagy, fusion of autophagosomes with the vacuole and regulation of cellular metabolism. Another novel DENN protein identified in this study is C9ORF72; expansions of the hexanucleotide GGGGCC in its first intron have been recently implicated in amyotrophic lateral sclerosis (ALS) and fronto-temporal dementia (FTD). While this mutation is proposed to cause a RNA-level defect, the identification of C9ORF72 as a potential DENN-type GEF raises the possibility that at least part of the pathology might relate to a specific Rab-dependent vesicular trafficking process, as has been observed in the case of some other neurological conditions with similar phenotypes. We present evidence that the longin domain, such as those found in the DENN module, are likely to have been ultimately derived from the related domains found in prokaryotic GTPase-activating proteins of MglA-like GTPases. Thus, the origin of the longin domains from this ancient GTPase-interacting domain, concomitant with the radiation of GTPases, especially of the Rab clade, played an important role in the dynamics of eukaryotic intracellular membrane systems. The origin of eukaryotes was marked by the emergence of several sub-cellular structures that are either infrequent or entirely absent in the two prokaryotic superkingdoms  and GTPase-activating proteins (GAPs) that are mostly unique to eukaryotes . Further, these searches also recovered proteins from basal eukaryotes such as Giardia  and Trichomonas , which are related to both FLCN and SMCR8. To extend these relationships we used a HMM derived from the multiple sequence alignment of the homologous region shared by FLCN and SMCR8 to initiate a profile-profile search with the HHpred program against a library of profiles derived from the models in the PFAM database. This search produced significant hits  to the DENN domain profiles  as well as to the nitrogen permease regulator 2  and Npr3 (PF03666) proteins which have been implicated in autophagy . The expansion of a GGGGCC hexanucleotide repeat in the first intron of the C9ORF72 gene in humans has been the basis for two apparently distinct human neurological diseases, namely as ALS and FTD , which are believed to function as a metabolic checkpoint during B-cell proliferation in vertebrates , a tumor suppressor protein disrupted in various cancers and the Birt–Hogg–Dubé syndrome, had shown that it contains a divergent DENN module that was previously undetected by sequence analysis  domain Figures  and 3. TThe precedence of the divergent DENN module in FLCN acting as a Rab GEF suggests that the novel DENN homologs, which preserve all the three domains in the DENN module  are also likely to function as GEFs for Rab GTPases. The fungal FLCN orthologs, Npr2, and Npr3 might bind a Rab GTPase like other longin domain proteins, but it remains uncertain as to whether they function as GEFs. The SEA complex, which includes a number of other proteins, is a coatomer-like protein complex that dynamically associates with the vacuole and has been implicated in biomolecule trafficking during autophagy  and diplomonads (Giardia). Thus, at least three distinct versions of the module were already present by the time of the LECA  and heteroloboseans (Naegleria), whereas Npr3 is found in heteroloboseans. Both these taxa are considered to be “excavate” taxa, like the diplomonads and parabasalids, and early branching eukaryotic lineages. Hence, by a more relaxed estimate a protein resembling Npr2/Npr3 and even the FNIP clade could have also been present in the LECA. Thus, the potential number of distinct types of DENN homologs in the LECA could have been between five and seven  and plants, which are distinguished by the lack of amoeboid motility, cilia, and phagotrophic ability. On the other hand, while C9ORF72 is present only in a single copy in most eukaryotes that possess it, it displays independent lineage-specific expansions in Entameba and Trichomonas. Hence, it is conceivable that the C9ORF72 clade originally arose in the context of a vesicular trafficking process associated with sub-cellular patterning in conjunction with the cytoskeleton. For example, the Rab GTPase Sec4 has been implicated in the formation of de novo membrane structures by docking of incoming vesicles to the spindle pole body  as they are present in the basal eukaryotic clades of the parabasalids (e.g., A Figure . Of the n Figure , suggestThe identification of a specific relationship between the longin domain and the MglB-like Roadblock domain GAPs of MglA-like small GTPases provides clues regarding the origin of the DENN module. The mobile MglAB operons are present in most archaeal lineages and are also widely distributed across several bacterial lineages (Koonin and Aravind, The identification of hitherto unknown versions of the DENN module provides new directions for understanding the role of Rab GTPases in autophagy and vacuolar-vesicle interactions. The identification of a DENN module in C9ORF72, which is mutated in ALS and FTD suggests that the pathology of these neurological disorders might, at least in certain cases involve defects in protein trafficking. These new DENN modules also suggest that there was a major diversification of the DENN module prior to the LECA. Furthermore, given the relationship of the longin domain of the DENN module with the Roadblock domain GAPs of MglA-like GTPases, it is possible that the DENN modules emerged by extension of an ancient interaction between longin-like domains with Rab GTPases. We hope the results presented here might aid in laboratory studies on these proteins.e-value of 0.01 was used to assess significance. In each iteration, the newly detected sequences that had e-values lower than the cut-off were examined for being false positives. Similarity-based clustering was performed using the BLASTCLUST programIterative profile searches with the PSI-BLAST (Altschul et al., The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.http://www.frontiersin.org/Bioinformatics_and_Computational_Biology/10.3389/fgene.2012.00283/abstractThe Supplementary Material for this article can be found online at


Although the two integrins are known to have distinct functions, the corresponding micro-structural bases remain unclear. Here (steered-)molecular dynamics simulations were employed to elucidate the conformational stability of α subunit I domains of LFA-1 and Mac-1 in different affinity states and relevant I domain-ICAM-1 interaction features. Compared with low affinity (LA) Mac-1, the LA LFA-1 I domain was unstable in the presence or absence of ICAM-1 ligand, stemming from diverse orientations of its α7-helix with different motifs of zipper-like hydrophobic junction between α1- and α7-helices. Meanwhile, spontaneous transition of LFA-1 I domain from LA state to intermediate affinity (IA) state was first visualized. All the LA, IA, and high affinity (HA) states of LFA-1 I domain and HA Mac-1 I domain were able to bind to ICAM-1 ligand effectively, while LA Mac-1 I domain was unfavorable for binding ligand presumably due to the specific orientation of S144 side-chain that capped the MIDAS ion. These results furthered our understanding in correlating the structural bases with their functions of LFA-1 and Mac-1 integrins from the viewpoint of I domain conformational stability and of the characteristics of I domain-ICAM-1 interactions.β An I (inserted) domain of about 180–190 residues is located on the top of each α subunit. Both the α subunit I domains contain a metal ion-dependent adhesion site (MIDAS) for ligand binding, adopt Rossmann fold with 7 α-helices surrounding 6 central β-strands 7-helix downward movement, which facilitates the capture by I-like domain pocket and transfers the conformational change to β subunit 7-helix should be, at least partially, responsible for the differences in their biological functions.Even with the distinct functionality of the two βIn the current work, we performed molecular dynamic (MD) simulations to test the structural evolution of LFA-1 and Mac-1 I domains upon existing crystal structures. Conformational stability was compared between two molecules as well as among three affinity states. Impact of ICAM-1 ligation on conformational stability and I domain-ICAM-1 interactions were also analyzed. Mechanical features of the complexes were tested using steered MD (SMD) simulations. Our results provided a new insight in the structure-function relationship for both LFA-1 and Mac-1.HA)/low affinity (LA) Mac-1 I domain (PDB codes of 1IDO HA/intermediate affinity (IA)/LA LFA-1 I domain  domains HA/LA Mac-1-D3 complexes were constructed by aligning HA/LA Mac-1 I domain and D3 domain  and the periodic boundary conditions were applied in the simulations. A smooth (10–12 Å) cutoff and the Particle Mesh Ewald (PME) method were employed to calculate van der Waals forces and full electrostatics, respectively. 300 K heat bath was manipulated under Langevin thermostat, and 1 atm pressure was controlled by Nosé-Hoover Langevin piston method. Prior to the equilibration process, energy minimization with 10000 steps of fixed backbone atoms followed by additional 10000 steps with all atoms free, and system heating from 0 to 300 K at 30 K increment every 5 picosecond (ps) were performed. 10 nanosecond (ns) or longer equilibration was performed for each system.Each simulation system was built up by solvating the target molecule(s) into a rectangular water box and neutralized with ∼100 mM Naα atoms of I domain were fixed independently and constant force of 200, 800, 1200 or 1600 piconewton (pN) was applied on C-terminal Cα atom of ICAM-1 D1 or D3 along the vector from the fixed atom or geometry center to the pulled end. All the simulation set-up was summarized in SMD simulations were also conducted to unbind the LFA-1/Mac-1-ICAM-1 complex HA, LA (for both LFA-1 and Mac-1), or IA (only for LFA-1) I domain respectively, the RMSD of α7-helix was used to probe if the trajectory tends to switch among different affinity states. MIDAS residues Geometry parameters were defined to represent the characteristics of conformational change. Root mean squared deviation of entire I domain  was used to quantify I domain stability, and average RMSD of each residue over time was employed to identify the unstable amino acids within I domain. Aligning the stable core residues of trajectory conformations to that of crystal structure of ns MD and 783-ns SMD simulations were performed in this work, and spent 5,795 h on 64 compute nodes with two 3.0 GHz quad-core Intel Xeon processors.Simulations were conducted on the DeepComp 7000 supercomputer at the Computer Network Information Center, Chinese Academy of Science. Altogether, 690-HA/LA Mac-1 I domains and HA/IA/LA LFA-1 I domains were superposed in HA and LA Mac-1 and one state of LA LFA-1 structures are defined for wild-type molecules while two states of HA and IA LFA-1 structures stem from the mutated molecules which are stabilized by a disulfide in β6-α7 loop and a disulfide locking C-terminals of α1 and α7-helix, respectively. It is noted that both the disulfide bonds distort the α7-helix conformation. Two common features were observed for the structure of IA and HA states by comparing with LA state: MIDAS re-orientation with α1-helix inward movement and the distinct α7-helix axially downward shift LA LFA-1 I domain. To confirm this interesting observation, the simulation was repeated ten times for LA LFA-1 and three times for each of the other four. Global RMSDs relative to their respective crystal structures were calculated and averaged for each system. RMSD of LA Mac-1 I domain yielded as small value as ∼1 Å (purple) and that of HA Mac-1 and HA/IA LFA-1 I domains retained <2 Å , while it fluctuated from 2 to 4 Å for LA LFA-1 (red)  C.LA LFA-1 I domain, global RMSD of each trajectory and average RMSD of each residue were compared between LA LFA-1 and LA Mac-1. It was found that LA Mac-1 state remained stable with a small RMSD of 0.9–1.5 Å in three repeats (LA LFA-1 state was much higher (1.2–5.5 Å) and dramatically diverse from one to another simulation , β4-α5 loop (D242–G251), and β5-α6 loop (V271–S277)  and remarkably fluctuating in repeated simulations, implying that there exists the diversified conformations. Together, the conformational stability is distinct between two β2 integrin I domains and from one state to another, of which LA LFA-1 state is most likely unstable.To further understand the intramolecular bases of structural instability of  repeats A while mulation B. AveraD values C and 2Eunstable D and 2FLA LFA-1 I domain equilibrations. Here the core residues of equilibrated snapshots were aligned to that of crystal structure of HA/IA/LA LFA-1 I domain, respectively, and RMSD of α7-helix was calculated with time which is used to define the movement of α7-helix. Ten repeated simulations were classified into four categories: 1) α7-helix stabilized in LA state (as observed from two of ten trajectories). Here RMSD was low when aligning equilibrated snapshot to LA LFA-1 crystal structure (dark red and dark blue lines), high when aligning to HA LFA-1 structure (red and blue lines) , which implied that the equilibrated α7-helix (red newcartoon in opaque) retained similar conformation as that of LA crystal structure (silver newcartoon in opaque) (7-helix swung in and moved downward slightly (LA (dark-colored lines) or to HA (colored lines) reference exhibited a transition phase with time to approach each other followed by a stable phase at >6 ns  α7-helix moved downward significantly (from two of ten trajectories). This movement resembles much to the state transition allostery of I domain with a descending phase of RMSDs to HA reference (colored lines) and an ascending phase to LA reference (dark-colored lines) . Here RMSDs were significantly high and fluctuated from one to another run of simulation  A, and i opaque) A′. 2) αslightly B′ (fromat >6 ns B. Thesed lines) C. One ss stable S1. Unlik residue C′. 4) αmulation D. Definα7-helix D′, whic7-helix presented multiple patterns of conformational change, corresponding MIDAS conformation re-orientated similarly. It was found from the ten simulations that the ion in MIDAS  moved inwards about 2.1 to 2.8 Å (licorice).While αto 2.8 Å A′–D′. Tto 2.8 Å A″–D″. ILA LFA-1 I domain. The first reserved its original LA state  in α7-helix orientated downwards and inserted between I150 and F153 and between V157 and L161 (yellow in licorice) of α1-helix, respectively  interacting strongly with their counterpart residues of α1-helix and closed the hydrophobic core perfectly  and LA  states yielded large distances for both Mac-1  and LFA-1  with respective over-zippered and inadequately-zippered hydrophobic junctions, while the IA LFA-1 (D) state had small distances with properly-zippered hydrophobic junction. Four categories of LA LFA-1 yielded different hydrophobic junctions with different evolving of key residue distance (E–H)  S3. TogetLA LFA-1 presented the conformational instability and the spontaneous allostery of state transition but LA Mac-1 retained its original state stable. Meanwhile it has also been reported that the ligand binding could induce the conformational change of αvβ3 headpiece LA LFA-1-ICAM-1 D1 and LA Mac-1-ICAM-1 D3 complex for comparison. Again, LA Mac-1 I domain remained low global RMSDs to its crystal reference in four simulations whereas ten runs of LA LFA-1 exhibited the unstable features with diverse RMSDs  and DL161Cβ-F299Cβ (red arrow) also supported the same hydrophobic junction as that of IA LFA-1  pointed inwards to enable S141 residue coordinating the ion in the primary coordination sphere and the re-orientation of β4-α5 loop (purple in newcartoon) induced E241 substitution for D239 residue in the primary sphere  to LA Mac-1 I domain (0.11±0.04 ns) (st1 and nd bars2), which is consistent to that for the shielding of S144 to abrogate ion-D229 binding (HA (36.53±11.16 ns) or IA (35.89±14.70 ns) LFA-1 I domain to D1 domain, which was dramatically higher than those for Mac-1 I domain (rd3 and th bars4), presumably due to the introduced disulfide bonds. Three cases of LA LFA-1 I domain, corresponding to the first three categories shown in A), 18.33±7.01 (B), and 11.59±2.69 ns (C), respectively (th5–7th bars) (LA LFA-1 I domain (D), the fourth category presented in 7-helix may not be practical. These results indicated that all states of LFA-1 I domain were able to bind to D1 domain effectively but LA Mac-1 I domain was unfavorable in binding to D3 domain with poor mechanical resistance. Such the nature was further confirmed by varying the fixed end from N-terminal to C-terminal and to jointed N- and C-terminals and altering the external forces from 200 to 1600 pN. In fact, even though the favorable unfolding of α7-helix was visualized when only C-terminal Cα atoms was fixed, both the fixation of C-terminal and of jointed N- and C-terminals yielded similar feature with that from N-terminal fixation (data not shown), implying the fixation strategy used here is appropriate. Since applying the force of 200 pN is unable to dissociate HA/LA LFA-1-ICAM-1 and HA Mac-1-ICAM-1 complexes in 36-ns simulation but applying a force of 1200 or 1600 pN dissociates the complex too fast, considering the balance between simulation time and conformational details, most of SMD simulations employed a typical force of 800 pN. Total fifty-one SMD tests were done in different cases, as summarized in We further tested the mechanical strength of the complex to illustrate if and how the conformational change of I domain would affect the binding function of β binding I. And cth bars) I. The lLA LFA-1 I domain is found to be more flexible than that of LA Mac-1 . LA state I domain consistently prevents its MIDAS ion from binding to D229 of D3 domain in four 10 to 30-ns simulations (S5B), which resembles to the mutation of D229 that completely abrogated the binding of Mac-1 to ICAM-1 In the current work, five complex systems were constructed upon high similarity in structure by aligning the target molecules to those crystallized complex available in PDB database, which worked quite well for both LFA-1 and Mac-1. All states of LFA-1 I domain binds to ICAM-1 with the vital coordination of MIDAS ion to E34 of D1 finely formed S5C–G. Os ligand I, which2+ by Mg2+ retained the similar global RMSDs of I domain and similar I domain-ICAM-1 interaction patterns Click here for additional data file.Figure S2Global RMSD of wide type IA LFA-1 I domain (A1 and α7-helices of the end-point snapshot  and zipper-like hydrophobic junction between its αB and C). Same presentations are shown as in (TIF)Click here for additional data file.Figure S3Stability of the zipper-like hydrophobic junction. Distance evolving between typical residues was calculated for HA/LA Mac-1 , HA/IA LFA-1  and four cases of LA LFA-1 (E–H). For Mac-1, black and red lines denote DF156Cβ-F302Cβ and DL164Cβ-Q309Cβ, respectively. For LFA-1, same presentations are shown as in red line of IA LFA-1 denotes DC161Cβ-C299Cβ. Here four cases of LA LFA-1 (E–H) correspond to the typical equilibrations of four categories of LA LFA-1 I domain shown in (TIF)Click here for additional data file.Figure S4Multiple patterns of LA LFA-1 I domain conformation from ten equilibrations of LA LFA-1-ICAM-1 D1 complex. Same presentations are shown as in (TIF)Click here for additional data file.Figure S5LFA-1/Mac-1 - ICAM-1 interactions for all repeated complex simulations. Same presentations are shown as in (TIF)Click here for additional data file.Figure S6Global RMSDs of I domain equilibrated alone  or ICAM-1 ligated , and I domain-ICAM-1 interactions  for LA Mac-1  and LA LFA-1 , for the equilibration simulations with ion substitution of Ca Same presentations are shown as in E) and (F).(TIF)Click here for additional data file.